![](\"https://media.istockphoto.com/photos/brass-trombone-picture-id490455809?k=20&m=490455809&s=612x612&w=0&h=l9KJvH_25z0QTLggHrcH_MsR4gPLH7uXwDPUAZ_C5zk=\")
![](\"https://images.unsplash.com/photo-1574613362884-f79513a5128c?fit=crop&w=500&q=80\"/)
',\n", " ],\n", " [\n", " \"## hello again again\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"### hello thrice\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " ]\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column():\n", " txt = gr.Textbox(label=\"Small Textbox\", lines=1, show_label=False)\n", " txt = gr.Textbox(label=\"Large Textbox\", lines=5, show_label=False)\n", " num = gr.Number(label=\"Number\", show_label=False)\n", " check = gr.Checkbox(label=\"Checkbox\", show_label=False)\n", " check_g = gr.CheckboxGroup(\n", " label=\"Checkbox Group\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " radio = gr.Radio(\n", " label=\"Radio\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " drop = gr.Dropdown(\n", " label=\"Dropdown\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " slider = gr.Slider(label=\"Slider\", show_label=False)\n", " audio = gr.Audio(show_label=False)\n", " file = gr.File(show_label=False)\n", " video = gr.Video(show_label=False)\n", " image = gr.Image(show_label=False)\n", " df = gr.Dataframe(show_label=False)\n", " html = gr.HTML(show_label=False)\n", " json = gr.JSON(show_label=False)\n", " md = gr.Markdown(show_label=False)\n", " label = gr.Label(show_label=False)\n", " highlight = gr.HighlightedText(show_label=False)\n", " gr.Dataframe(interactive=True, col_count=(3, \"fixed\"), label=\"Dataframe\")\n", " gr.Dataframe(interactive=True, col_count=4, label=\"Dataframe\")\n", " gr.Dataframe(\n", " interactive=True, headers=[\"One\", \"Two\", \"Three\", \"Four\"], label=\"Dataframe\"\n", " )\n", " gr.Dataframe(\n", " interactive=True,\n", " headers=[\"One\", \"Two\", \"Three\", \"Four\"],\n", " col_count=(4, \"fixed\"),\n", " row_count=(7, \"fixed\"),\n", " value=[[0, 0, 0, 0]],\n", " label=\"Dataframe\",\n", " )\n", " gr.Dataframe(\n", " interactive=True, headers=[\"One\", \"Two\", \"Three\", \"Four\"], col_count=4\n", " )\n", " df = gr.DataFrame(\n", " [\n", " [\n", " \"# hello\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"## hello\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"### hello\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " ],\n", " headers=[\"One\", \"Two\", \"Three\"],\n", " wrap=True,\n", " datatype=[\"markdown\", \"markdown\", \"html\"],\n", " interactive=True,\n", " )\n", " btn = gr.Button(\"Run\")\n", " btn.click(fn=make_markdown, inputs=None, outputs=df)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+<<<<<<< HEAD
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_outputs"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def make_markdown():\n", " return [\n", " [\n", " \"# hello again\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"## hello again again\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"### hello thrice\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " ]\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column():\n", " txt = gr.Textbox(label=\"Small Textbox\", lines=1, show_label=False)\n", " txt = gr.Textbox(label=\"Large Textbox\", lines=5, show_label=False)\n", " num = gr.Number(label=\"Number\", show_label=False)\n", " check = gr.Checkbox(label=\"Checkbox\", show_label=False)\n", " check_g = gr.CheckboxGroup(\n", " label=\"Checkbox Group\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " radio = gr.Radio(\n", " label=\"Radio\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " drop = gr.Dropdown(\n", " label=\"Dropdown\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " slider = gr.Slider(label=\"Slider\", show_label=False)\n", " audio = gr.Audio(show_label=False)\n", " file = gr.File(show_label=False)\n", " video = gr.Video(show_label=False)\n", " image = gr.Image(show_label=False)\n", " df = gr.Dataframe(show_label=False)\n", " html = gr.HTML(show_label=False)\n", " json = gr.JSON(show_label=False)\n", " md = gr.Markdown(show_label=False)\n", " label = gr.Label(show_label=False)\n", " highlight = gr.HighlightedText(show_label=False)\n", " gr.Dataframe(interactive=True, col_count=(3, \"fixed\"), label=\"Dataframe\")\n", " gr.Dataframe(interactive=True, col_count=4, label=\"Dataframe\")\n", " gr.Dataframe(\n", " interactive=True, headers=[\"One\", \"Two\", \"Three\", \"Four\"], label=\"Dataframe\"\n", " )\n", " gr.Dataframe(\n", " interactive=True,\n", " headers=[\"One\", \"Two\", \"Three\", \"Four\"],\n", " col_count=(4, \"fixed\"),\n", " row_count=(7, \"fixed\"),\n", " value=[[0, 0, 0, 0]],\n", " label=\"Dataframe\",\n", " )\n", " gr.Dataframe(\n", " interactive=True, headers=[\"One\", \"Two\", \"Three\", \"Four\"], col_count=4\n", " )\n", " df = gr.DataFrame(\n", " [\n", " [\n", " \"# hello\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"## hello\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"### hello\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " ],\n", " headers=[\"One\", \"Two\", \"Three\"],\n", " wrap=True,\n", " datatype=[\"markdown\", \"markdown\", \"html\"],\n", " interactive=True,\n", " )\n", " btn = gr.Button(\"Run\")\n", " btn.click(fn=make_markdown, inputs=None, outputs=df)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+=======
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_outputs"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def make_markdown():\n", " return [\n", " [\n", " \"# hello again\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"## hello again again\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"### hello thrice\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " ]\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column():\n", " txt = gr.Textbox(label=\"Small Textbox\", lines=1, show_label=False)\n", " txt = gr.Textbox(label=\"Large Textbox\", lines=5, show_label=False)\n", " num = gr.Number(label=\"Number\", show_label=False)\n", " check = gr.Checkbox(label=\"Checkbox\", show_label=False)\n", " check_g = gr.CheckboxGroup(\n", " label=\"Checkbox Group\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " radio = gr.Radio(\n", " label=\"Radio\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " drop = gr.Dropdown(\n", " label=\"Dropdown\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " slider = gr.Slider(label=\"Slider\", show_label=False)\n", " audio = gr.Audio(show_label=False)\n", " file = gr.File(show_label=False)\n", " video = gr.Video(show_label=False)\n", " image = gr.Image(show_label=False)\n", " ts = gr.Timeseries(show_label=False)\n", " df = gr.Dataframe(show_label=False)\n", " html = gr.HTML(show_label=False)\n", " json = gr.JSON(show_label=False)\n", " md = gr.Markdown(show_label=False)\n", " label = gr.Label(show_label=False)\n", " highlight = gr.HighlightedText(show_label=False)\n", " gr.Dataframe(interactive=True, col_count=(3, \"fixed\"), label=\"Dataframe\")\n", " gr.Dataframe(interactive=True, col_count=4, label=\"Dataframe\")\n", " gr.Dataframe(\n", " interactive=True, headers=[\"One\", \"Two\", \"Three\", \"Four\"], label=\"Dataframe\"\n", " )\n", " gr.Dataframe(\n", " interactive=True,\n", " headers=[\"One\", \"Two\", \"Three\", \"Four\"],\n", " col_count=(4, \"fixed\"),\n", " row_count=(7, \"fixed\"),\n", " value=[[0, 0, 0, 0]],\n", " label=\"Dataframe\",\n", " )\n", " gr.Dataframe(\n", " interactive=True, headers=[\"One\", \"Two\", \"Three\", \"Four\"], col_count=4\n", " )\n", " df = gr.DataFrame(\n", " [\n", " [\n", " \"# hello\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"## hello\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " [\n", " \"### hello\",\n", " \"Hello my name is frank, I am liking the small turtle you have there. It would be a shame if it went missing.\",\n", " '',\n", " ],\n", " ],\n", " headers=[\"One\", \"Two\", \"Three\"],\n", " wrap=True,\n", " datatype=[\"markdown\", \"markdown\", \"html\"],\n", " interactive=True,\n", " )\n", " btn = gr.Button(\"Run\")\n", " btn.click(fn=make_markdown, inputs=None, outputs=df)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+>>>>>>> main
diff --git a/demo/blocks_page_load/run.ipynb b/demo/blocks_page_load/run.ipynb
index 25c31e7ad315b..dfd1ad0ed795e 100644
--- a/demo/blocks_page_load/run.ipynb
+++ b/demo/blocks_page_load/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_page_load"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def print_message(n):\n", " return \"Welcome! This page has loaded for \" + n\n", "\n", "\n", "with gr.Blocks() as demo:\n", " t = gr.Textbox(\"Frank\", label=\"Name\")\n", " t2 = gr.Textbox(label=\"Output\")\n", " demo.load(print_message, t, t2)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_page_load"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def print_message(n):\n", " return \"Welcome! This page has loaded for \" + n\n", "\n", "\n", "with gr.Blocks() as demo:\n", " t = gr.Textbox(\"Frank\", label=\"Name\")\n", " t2 = gr.Textbox(label=\"Output\")\n", " demo.load(print_message, t, t2)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/blocks_plug/run.ipynb b/demo/blocks_plug/run.ipynb
index c005fbae9abb9..8a037f584a6a5 100644
--- a/demo/blocks_plug/run.ipynb
+++ b/demo/blocks_plug/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_plug"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def change_tab():\n", " return gr.Tabs(selected=2)\n", "\n", "\n", "identity_demo, input_demo, output_demo = gr.Blocks(), gr.Blocks(), gr.Blocks()\n", "\n", "with identity_demo:\n", " gr.Interface(lambda x: x, \"text\", \"text\")\n", "\n", "with input_demo:\n", " t = gr.Textbox(label=\"Enter your text here\")\n", " with gr.Row():\n", " btn = gr.Button(\"Submit\")\n", " clr = gr.ClearButton(t)\n", "\n", "with output_demo:\n", " gr.Textbox(\"This is a static output\")\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"Three demos in one!\")\n", " with gr.Tabs(selected=1) as tabs:\n", " with gr.TabItem(\"Text Identity\", id=0) as tab0:\n", " tab0.select(lambda: gr.Tabs(selected=0), None, tabs)\n", " identity_demo.render()\n", " with gr.TabItem(\"Text Input\", id=1) as tab1:\n", " tab1.select(lambda: gr.Tabs(selected=1), None, tabs)\n", " input_demo.render()\n", " with gr.TabItem(\"Text Static\", id=2) as tab2:\n", " tab2.select(lambda: gr.Tabs(selected=2), None, tabs)\n", " output_demo.render()\n", " btn = gr.Button(\"Change tab\")\n", " btn.click(inputs=None, outputs=tabs, fn=change_tab)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_plug"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def change_tab():\n", " return gr.Tabs(selected=2)\n", "\n", "\n", "identity_demo, input_demo, output_demo = gr.Blocks(), gr.Blocks(), gr.Blocks()\n", "\n", "with identity_demo:\n", " gr.Interface(lambda x: x, \"text\", \"text\")\n", "\n", "with input_demo:\n", " t = gr.Textbox(label=\"Enter your text here\")\n", " with gr.Row():\n", " btn = gr.Button(\"Submit\")\n", " clr = gr.ClearButton(t)\n", "\n", "with output_demo:\n", " gr.Textbox(\"This is a static output\")\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"Three demos in one!\")\n", " with gr.Tabs(selected=1) as tabs:\n", " with gr.TabItem(\"Text Identity\", id=0) as tab0:\n", " tab0.select(lambda: gr.Tabs(selected=0), None, tabs)\n", " identity_demo.render()\n", " with gr.TabItem(\"Text Input\", id=1) as tab1:\n", " tab1.select(lambda: gr.Tabs(selected=1), None, tabs)\n", " input_demo.render()\n", " with gr.TabItem(\"Text Static\", id=2) as tab2:\n", " tab2.select(lambda: gr.Tabs(selected=2), None, tabs)\n", " output_demo.render()\n", " btn = gr.Button(\"Change tab\")\n", " btn.click(inputs=None, outputs=tabs, fn=change_tab)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/blocks_random_slider/run.ipynb b/demo/blocks_random_slider/run.ipynb
index 857e8e7ef3c83..52b32cb819a67 100644
--- a/demo/blocks_random_slider/run.ipynb
+++ b/demo/blocks_random_slider/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_random_slider"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["\n", "import gradio as gr\n", "\n", "\n", "def func(slider_1, slider_2):\n", " return slider_1 * 5 + slider_2\n", "\n", "\n", "with gr.Blocks() as demo:\n", " slider = gr.Slider(minimum=-10.2, maximum=15, label=\"Random Slider (Static)\", randomize=True)\n", " slider_1 = gr.Slider(minimum=100, maximum=200, label=\"Random Slider (Input 1)\", randomize=True)\n", " slider_2 = gr.Slider(minimum=10, maximum=23.2, label=\"Random Slider (Input 2)\", randomize=True)\n", " slider_3 = gr.Slider(value=3, label=\"Non random slider\")\n", " btn = gr.Button(\"Run\")\n", " btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number())\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_random_slider"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["\n", "import gradio as gr\n", "\n", "\n", "def func(slider_1, slider_2):\n", " return slider_1 * 5 + slider_2\n", "\n", "\n", "with gr.Blocks() as demo:\n", " slider = gr.Slider(minimum=-10.2, maximum=15, label=\"Random Slider (Static)\", randomize=True)\n", " slider_1 = gr.Slider(minimum=100, maximum=200, label=\"Random Slider (Input 1)\", randomize=True)\n", " slider_2 = gr.Slider(minimum=10, maximum=23.2, label=\"Random Slider (Input 2)\", randomize=True)\n", " slider_3 = gr.Slider(value=3, label=\"Non random slider\")\n", " btn = gr.Button(\"Run\")\n", " btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number())\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/blocks_scroll/run.ipynb b/demo/blocks_scroll/run.ipynb
index 6bcf0dcdeecaf..075ff8c943334 100644
--- a/demo/blocks_scroll/run.ipynb
+++ b/demo/blocks_scroll/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_scroll"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", " inp = gr.Textbox(placeholder=\"Enter text.\")\n", " scroll_btn = gr.Button(\"Scroll\")\n", " no_scroll_btn = gr.Button(\"No Scroll\")\n", " big_block = gr.HTML(\"\"\"\n", " \n", " \"\"\")\n", " out = gr.Textbox()\n", " \n", " scroll_btn.click(lambda x: x, \n", " inputs=inp, \n", " outputs=out,\n", " scroll_to_output=True)\n", " no_scroll_btn.click(lambda x: x, \n", " inputs=inp, \n", " outputs=out)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_scroll"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", " inp = gr.Textbox(placeholder=\"Enter text.\")\n", " scroll_btn = gr.Button(\"Scroll\")\n", " no_scroll_btn = gr.Button(\"No Scroll\")\n", " big_block = gr.HTML(\"\"\"\n", " \n", " \"\"\")\n", " out = gr.Textbox()\n", " \n", " scroll_btn.click(lambda x: x, \n", " inputs=inp, \n", " outputs=out,\n", " scroll_to_output=True)\n", " no_scroll_btn.click(lambda x: x, \n", " inputs=inp, \n", " outputs=out)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/blocks_simple_squares/run.ipynb b/demo/blocks_simple_squares/run.ipynb
index 37ce209a22eb3..0d4b6d5f41732 100644
--- a/demo/blocks_simple_squares/run.ipynb
+++ b/demo/blocks_simple_squares/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_simple_squares"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Blocks(css=\"\"\"#btn {color: red} .abc {font-family: \"Comic Sans MS\", \"Comic Sans\", cursive !important}\"\"\")\n", "\n", "with demo:\n", " default_json = {\"a\": \"a\"}\n", "\n", " num = gr.State(value=0)\n", " squared = gr.Number(value=0)\n", " btn = gr.Button(\"Next Square\", elem_id=\"btn\", elem_classes=[\"abc\", \"def\"])\n", "\n", " stats = gr.State(value=default_json)\n", " table = gr.JSON()\n", "\n", " def increase(var, stats_history):\n", " var += 1\n", " stats_history[str(var)] = var**2\n", " return var, var**2, stats_history, stats_history\n", "\n", " btn.click(increase, [num, stats], [num, squared, stats, table])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_simple_squares"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Blocks(css=\"\"\"#btn {color: red} .abc {font-family: \"Comic Sans MS\", \"Comic Sans\", cursive !important}\"\"\")\n", "\n", "with demo:\n", " default_json = {\"a\": \"a\"}\n", "\n", " num = gr.State(value=0)\n", " squared = gr.Number(value=0)\n", " btn = gr.Button(\"Next Square\", elem_id=\"btn\", elem_classes=[\"abc\", \"def\"])\n", "\n", " stats = gr.State(value=default_json)\n", " table = gr.JSON()\n", "\n", " def increase(var, stats_history):\n", " var += 1\n", " stats_history[str(var)] = var**2\n", " return var, var**2, stats_history, stats_history\n", "\n", " btn.click(increase, [num, stats], [num, squared, stats, table])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/blocks_speech_text_sentiment/run.ipynb b/demo/blocks_speech_text_sentiment/run.ipynb
index 3a656fa92522e..c864e6fe66190 100644
--- a/demo/blocks_speech_text_sentiment/run.ipynb
+++ b/demo/blocks_speech_text_sentiment/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_speech_text_sentiment"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "\n", "import gradio as gr\n", "\n", "asr = pipeline(\"automatic-speech-recognition\", \"facebook/wav2vec2-base-960h\")\n", "classifier = pipeline(\"text-classification\")\n", "\n", "\n", "def speech_to_text(speech):\n", " text = asr(speech)[\"text\"]\n", " return text\n", "\n", "\n", "def text_to_sentiment(text):\n", " return classifier(text)[0][\"label\"]\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", " audio_file = gr.Audio(type=\"filepath\")\n", " text = gr.Textbox()\n", " label = gr.Label()\n", "\n", " b1 = gr.Button(\"Recognize Speech\")\n", " b2 = gr.Button(\"Classify Sentiment\")\n", "\n", " b1.click(speech_to_text, inputs=audio_file, outputs=text)\n", " b2.click(text_to_sentiment, inputs=text, outputs=label)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_speech_text_sentiment"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "\n", "import gradio as gr\n", "\n", "asr = pipeline(\"automatic-speech-recognition\", \"facebook/wav2vec2-base-960h\")\n", "classifier = pipeline(\"text-classification\")\n", "\n", "\n", "def speech_to_text(speech):\n", " text = asr(speech)[\"text\"]\n", " return text\n", "\n", "\n", "def text_to_sentiment(text):\n", " return classifier(text)[0][\"label\"]\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", " audio_file = gr.Audio(type=\"filepath\")\n", " text = gr.Textbox()\n", " label = gr.Label()\n", "\n", " b1 = gr.Button(\"Recognize Speech\")\n", " b2 = gr.Button(\"Classify Sentiment\")\n", "\n", " b1.click(speech_to_text, inputs=audio_file, outputs=text)\n", " b2.click(text_to_sentiment, inputs=text, outputs=label)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/blocks_static/run.ipynb b/demo/blocks_static/run.ipynb
index ad17acc436d82..2010f8432016d 100644
--- a/demo/blocks_static/run.ipynb
+++ b/demo/blocks_static/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_static"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", " gr.Image(\n", " \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\"\n", " )\n", " gr.Textbox(\"hi\")\n", " gr.Number(3)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_static"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", " gr.Image(\n", " \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\"\n", " )\n", " gr.Textbox(\"hi\")\n", " gr.Number(3)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/blocks_style/run.ipynb b/demo/blocks_style/run.ipynb
index fcff0d7d706a1..65b1b200f7a72 100644
--- a/demo/blocks_style/run.ipynb
+++ b/demo/blocks_style/run.ipynb
@@ -1 +1,5 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_style"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks(title=\"Styling Examples\") as demo:\n", " with gr.Column(variant=\"box\"):\n", " txt = gr.Textbox(label=\"Small Textbox\", lines=1)\n", " num = gr.Number(label=\"Number\", show_label=False)\n", " slider = gr.Slider(label=\"Slider\", show_label=False)\n", " check = gr.Checkbox(label=\"Checkbox\", show_label=False)\n", " check_g = gr.CheckboxGroup(\n", " label=\"Checkbox Group\",\n", " choices=[\"One\", \"Two\", \"Three\"],\n", " show_label=False,\n", " )\n", " radio = gr.Radio(\n", " label=\"Radio\",\n", " choices=[\"One\", \"Two\", \"Three\"],\n", " show_label=False,\n", " )\n", " drop = gr.Dropdown(\n", " label=\"Dropdown\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " image = gr.Image(show_label=False)\n", " video = gr.Video(show_label=False)\n", " audio = gr.Audio(show_label=False)\n", " file = gr.File(show_label=False)\n", " df = gr.Dataframe(show_label=False)\n", " label = gr.Label(container=False)\n", " highlight = gr.HighlightedText(\n", " [(\"hello\", None), (\"goodbye\", \"-\")],\n", " color_map={\"+\": \"green\", \"-\": \"red\"},\n", " container=False,\n", " )\n", " json = gr.JSON(container=False)\n", " html = gr.HTML(show_label=False)\n", " gallery = gr.Gallery(\n", " columns=(3, 3, 1),\n", " height=\"auto\",\n", " container=False,\n", " )\n", " chat = gr.Chatbot([(\"hi\", \"good bye\")])\n", "\n", " model = gr.Model3D()\n", "\n", " md = gr.Markdown(show_label=False)\n", "\n", " highlight = gr.HighlightedText()\n", "\n", " btn = gr.Button(\"Run\")\n", "\n", " gr.Dataset(components=[txt, num])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+<<<<<<< HEAD
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_style"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks(title=\"Styling Examples\") as demo:\n", " with gr.Column(variant=\"box\"):\n", " txt = gr.Textbox(label=\"Small Textbox\", lines=1)\n", " num = gr.Number(label=\"Number\", show_label=False)\n", " slider = gr.Slider(label=\"Slider\", show_label=False)\n", " check = gr.Checkbox(label=\"Checkbox\", show_label=False)\n", " check_g = gr.CheckboxGroup(\n", " label=\"Checkbox Group\",\n", " choices=[\"One\", \"Two\", \"Three\"],\n", " show_label=False,\n", " )\n", " radio = gr.Radio(\n", " label=\"Radio\",\n", " choices=[\"One\", \"Two\", \"Three\"],\n", " show_label=False,\n", " )\n", " drop = gr.Dropdown(\n", " label=\"Dropdown\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " image = gr.Image(show_label=False)\n", " video = gr.Video(show_label=False)\n", " audio = gr.Audio(show_label=False)\n", " file = gr.File(show_label=False)\n", " df = gr.Dataframe(show_label=False)\n", " label = gr.Label(container=False)\n", " highlight = gr.HighlightedText(\n", " [(\"hello\", None), (\"goodbye\", \"-\")],\n", " color_map={\"+\": \"green\", \"-\": \"red\"},\n", " container=False,\n", " )\n", " json = gr.JSON(container=False)\n", " html = gr.HTML(show_label=False)\n", " gallery = gr.Gallery(\n", " columns=(3, 3, 1),\n", " height=\"auto\",\n", " container=False,\n", " )\n", " chat = gr.Chatbot([(\"hi\", \"good bye\")])\n", "\n", " model = gr.Model3D()\n", "\n", " md = gr.Markdown(show_label=False)\n", "\n", " highlight = gr.HighlightedText()\n", "\n", " btn = gr.Button(\"Run\")\n", "\n", " gr.Dataset(components=[txt, num])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+=======
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_style"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks(title=\"Styling Examples\") as demo:\n", " with gr.Column(variant=\"box\"):\n", " txt = gr.Textbox(label=\"Small Textbox\", lines=1)\n", " num = gr.Number(label=\"Number\", show_label=False)\n", " slider = gr.Slider(label=\"Slider\", show_label=False)\n", " check = gr.Checkbox(label=\"Checkbox\", show_label=False)\n", " check_g = gr.CheckboxGroup(\n", " label=\"Checkbox Group\",\n", " choices=[\"One\", \"Two\", \"Three\"],\n", " show_label=False,\n", " )\n", " radio = gr.Radio(\n", " label=\"Radio\",\n", " choices=[\"One\", \"Two\", \"Three\"],\n", " show_label=False,\n", " )\n", " drop = gr.Dropdown(\n", " label=\"Dropdown\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", " )\n", " image = gr.Image(show_label=False)\n", " video = gr.Video(show_label=False)\n", " audio = gr.Audio(show_label=False)\n", " file = gr.File(show_label=False)\n", " df = gr.Dataframe(show_label=False)\n", " ts = gr.Timeseries(show_label=False)\n", " label = gr.Label(container=False)\n", " highlight = gr.HighlightedText(\n", " [(\"hello\", None), (\"goodbye\", \"-\")],\n", " color_map={\"+\": \"green\", \"-\": \"red\"},\n", " container=False,\n", " )\n", " json = gr.JSON(container=False)\n", " html = gr.HTML(show_label=False)\n", " gallery = gr.Gallery(\n", " columns=(3, 3, 1),\n", " height=\"auto\",\n", " container=False,\n", " )\n", " chat = gr.Chatbot([(\"hi\", \"good bye\")])\n", "\n", " model = gr.Model3D()\n", "\n", " md = gr.Markdown(show_label=False)\n", "\n", " highlight = gr.HighlightedText()\n", "\n", " btn = gr.Button(\"Run\")\n", "\n", " gr.Dataset(components=[txt, num])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+>>>>>>> main
diff --git a/demo/blocks_textbox_max_lines/run.ipynb b/demo/blocks_textbox_max_lines/run.ipynb
index 8cf2d4855b7f8..00c909378f062 100644
--- a/demo/blocks_textbox_max_lines/run.ipynb
+++ b/demo/blocks_textbox_max_lines/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_textbox_max_lines"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def greet(name: str, repeat: float):\n", " return \"Hello \" + name * int(repeat) + \"!!\"\n", "\n", "\n", "demo = gr.Interface(\n", " fn=greet, inputs=[gr.Textbox(lines=2, max_lines=4), gr.Number()], outputs=\"textarea\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_textbox_max_lines"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def greet(name: str, repeat: float):\n", " return \"Hello \" + name * int(repeat) + \"!!\"\n", "\n", "\n", "demo = gr.Interface(\n", " fn=greet, inputs=[gr.Textbox(lines=2, max_lines=4), gr.Number()], outputs=\"textarea\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/blocks_update/run.ipynb b/demo/blocks_update/run.ipynb
index 285db671e5497..31eb77c2f3c4d 100644
--- a/demo/blocks_update/run.ipynb
+++ b/demo/blocks_update/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_update"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", " # Animal Generator\n", " Once you select a species, the detail panel should be visible.\n", " \"\"\"\n", " )\n", "\n", " species = gr.Radio(label=\"Animal Class\", choices=[\"Mammal\", \"Fish\", \"Bird\"])\n", " animal = gr.Dropdown(label=\"Animal\", choices=[])\n", "\n", " with gr.Column(visible=False) as details_col:\n", " weight = gr.Slider(0, 20)\n", " details = gr.Textbox(label=\"Extra Details\")\n", " generate_btn = gr.Button(\"Generate\")\n", " output = gr.Textbox(label=\"Output\")\n", "\n", " species_map = {\n", " \"Mammal\": [\"Elephant\", \"Giraffe\", \"Hamster\"],\n", " \"Fish\": [\"Shark\", \"Salmon\", \"Tuna\"],\n", " \"Bird\": [\"Chicken\", \"Eagle\", \"Hawk\"],\n", " }\n", "\n", " def filter_species(species):\n", " return gr.Dropdown(\n", " choices=species_map[species], value=species_map[species][1]\n", " ), gr.Column(visible=True)\n", "\n", " species.change(filter_species, species, [animal, details_col])\n", "\n", " def filter_weight(animal):\n", " if animal in (\"Elephant\", \"Shark\", \"Giraffe\"):\n", " return gr.Slider(maximum=100)\n", " else:\n", " return gr.Slider(maximum=20)\n", "\n", " animal.change(filter_weight, animal, weight)\n", " weight.change(lambda w: gr.Textbox(lines=int(w / 10) + 1), weight, details)\n", "\n", " generate_btn.click(lambda x: x, details, output)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_update"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", " # Animal Generator\n", " Once you select a species, the detail panel should be visible.\n", " \"\"\"\n", " )\n", "\n", " species = gr.Radio(label=\"Animal Class\", choices=[\"Mammal\", \"Fish\", \"Bird\"])\n", " animal = gr.Dropdown(label=\"Animal\", choices=[])\n", "\n", " with gr.Column(visible=False) as details_col:\n", " weight = gr.Slider(0, 20)\n", " details = gr.Textbox(label=\"Extra Details\")\n", " generate_btn = gr.Button(\"Generate\")\n", " output = gr.Textbox(label=\"Output\")\n", "\n", " species_map = {\n", " \"Mammal\": [\"Elephant\", \"Giraffe\", \"Hamster\"],\n", " \"Fish\": [\"Shark\", \"Salmon\", \"Tuna\"],\n", " \"Bird\": [\"Chicken\", \"Eagle\", \"Hawk\"],\n", " }\n", "\n", " def filter_species(species):\n", " return gr.Dropdown(\n", " choices=species_map[species], value=species_map[species][1]\n", " ), gr.Column(visible=True)\n", "\n", " species.change(filter_species, species, [animal, details_col])\n", "\n", " def filter_weight(animal):\n", " if animal in (\"Elephant\", \"Shark\", \"Giraffe\"):\n", " return gr.Slider(maximum=100)\n", " else:\n", " return gr.Slider(maximum=20)\n", "\n", " animal.change(filter_weight, animal, weight)\n", " weight.change(lambda w: gr.Textbox(lines=int(w / 10) + 1), weight, details)\n", "\n", " generate_btn.click(lambda x: x, details, output)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/blocks_webcam/run.ipynb b/demo/blocks_webcam/run.ipynb
index 91a8c5f8ee53c..730332f69314a 100644
--- a/demo/blocks_webcam/run.ipynb
+++ b/demo/blocks_webcam/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_webcam"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "\n", "import gradio as gr\n", "\n", "\n", "def snap(image):\n", " return np.flipud(image)\n", "\n", "\n", "demo = gr.Interface(snap, \"webcam\", \"image\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_webcam"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "\n", "import gradio as gr\n", "\n", "\n", "def snap(image):\n", " return np.flipud(image)\n", "\n", "\n", "demo = gr.Interface(snap, \"webcam\", \"image\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/blocks_xray/run.ipynb b/demo/blocks_xray/run.ipynb
index e287312a15063..6754b73faff91 100644
--- a/demo/blocks_xray/run.ipynb
+++ b/demo/blocks_xray/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_xray"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "disease_values = [0.25, 0.5, 0.75]\n", "\n", "def xray_model(diseases, img):\n", " return [{disease: disease_values[idx] for idx,disease in enumerate(diseases)}]\n", "\n", "\n", "def ct_model(diseases, img):\n", " return [{disease: 0.1 for disease in diseases}]\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", "# Detect Disease From Scan\n", "With this model you can lorem ipsum\n", "- ipsum 1\n", "- ipsum 2\n", "\"\"\"\n", " )\n", " gr.DuplicateButton()\n", " disease = gr.CheckboxGroup(\n", " info=\"Select the diseases you want to scan for.\",\n", " choices=[\"Covid\", \"Malaria\", \"Lung Cancer\"], label=\"Disease to Scan For\"\n", " )\n", " slider = gr.Slider(0, 100)\n", "\n", " with gr.Tab(\"X-ray\") as x_tab:\n", " with gr.Row():\n", " xray_scan = gr.Image()\n", " xray_results = gr.JSON()\n", " xray_run = gr.Button(\"Run\")\n", " xray_run.click(\n", " xray_model,\n", " inputs=[disease, xray_scan],\n", " outputs=xray_results,\n", " api_name=\"xray_model\"\n", " )\n", "\n", " with gr.Tab(\"CT Scan\"):\n", " with gr.Row():\n", " ct_scan = gr.Image()\n", " ct_results = gr.JSON()\n", " ct_run = gr.Button(\"Run\")\n", " ct_run.click(\n", " ct_model,\n", " inputs=[disease, ct_scan],\n", " outputs=ct_results,\n", " api_name=\"ct_model\"\n", " )\n", "\n", " upload_btn = gr.Button(\"Upload Results\", variant=\"primary\")\n", " upload_btn.click(\n", " lambda ct, xr: None,\n", " inputs=[ct_results, xray_results],\n", " outputs=[],\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_xray"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "disease_values = [0.25, 0.5, 0.75]\n", "\n", "def xray_model(diseases, img):\n", " return [{disease: disease_values[idx] for idx,disease in enumerate(diseases)}]\n", "\n", "\n", "def ct_model(diseases, img):\n", " return [{disease: 0.1 for disease in diseases}]\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", "# Detect Disease From Scan\n", "With this model you can lorem ipsum\n", "- ipsum 1\n", "- ipsum 2\n", "\"\"\"\n", " )\n", " gr.DuplicateButton()\n", " disease = gr.CheckboxGroup(\n", " info=\"Select the diseases you want to scan for.\",\n", " choices=[\"Covid\", \"Malaria\", \"Lung Cancer\"], label=\"Disease to Scan For\"\n", " )\n", " slider = gr.Slider(0, 100)\n", "\n", " with gr.Tab(\"X-ray\") as x_tab:\n", " with gr.Row():\n", " xray_scan = gr.Image()\n", " xray_results = gr.JSON()\n", " xray_run = gr.Button(\"Run\")\n", " xray_run.click(\n", " xray_model,\n", " inputs=[disease, xray_scan],\n", " outputs=xray_results,\n", " api_name=\"xray_model\"\n", " )\n", "\n", " with gr.Tab(\"CT Scan\"):\n", " with gr.Row():\n", " ct_scan = gr.Image()\n", " ct_results = gr.JSON()\n", " ct_run = gr.Button(\"Run\")\n", " ct_run.click(\n", " ct_model,\n", " inputs=[disease, ct_scan],\n", " outputs=ct_results,\n", " api_name=\"ct_model\"\n", " )\n", "\n", " upload_btn = gr.Button(\"Upload Results\", variant=\"primary\")\n", " upload_btn.click(\n", " lambda ct, xr: None,\n", " inputs=[ct_results, xray_results],\n", " outputs=[],\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/bokeh_plot/run.ipynb b/demo/bokeh_plot/run.ipynb
index 2949824df0d52..43cddee02193f 100644
--- a/demo/bokeh_plot/run.ipynb
+++ b/demo/bokeh_plot/run.ipynb
@@ -1 +1,5 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: bokeh_plot"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio bokeh>=3.0 xyzservices"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import xyzservices.providers as xyz\n", "from bokeh.models import ColumnDataSource, Whisker\n", "from bokeh.plotting import figure\n", "from bokeh.sampledata.autompg2 import autompg2 as df\n", "from bokeh.sampledata.penguins import data\n", "from bokeh.transform import factor_cmap, jitter, factor_mark\n", "\n", "\n", "def get_plot(plot_type):\n", " if plot_type == \"map\":\n", " plot = figure(\n", " x_range=(-2000000, 6000000),\n", " y_range=(-1000000, 7000000),\n", " x_axis_type=\"mercator\",\n", " y_axis_type=\"mercator\",\n", " )\n", " plot.add_tile(xyz.OpenStreetMap.Mapnik)\n", " return plot\n", " elif plot_type == \"whisker\":\n", " classes = list(sorted(df[\"class\"].unique()))\n", "\n", " p = figure(\n", " height=400,\n", " x_range=classes,\n", " background_fill_color=\"#efefef\",\n", " title=\"Car class vs HWY mpg with quintile ranges\",\n", " )\n", " p.xgrid.grid_line_color = None\n", "\n", " g = df.groupby(\"class\")\n", " upper = g.hwy.quantile(0.80)\n", " lower = g.hwy.quantile(0.20)\n", " source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n", "\n", " error = Whisker(\n", " base=\"base\",\n", " upper=\"upper\",\n", " lower=\"lower\",\n", " source=source,\n", " level=\"annotation\",\n", " line_width=2,\n", " )\n", " error.upper_head.size = 20\n", " error.lower_head.size = 20\n", " p.add_layout(error)\n", "\n", " p.circle(\n", " jitter(\"class\", 0.3, range=p.x_range),\n", " \"hwy\",\n", " source=df,\n", " alpha=0.5,\n", " size=13,\n", " line_color=\"white\",\n", " color=factor_cmap(\"class\", \"Light6\", classes),\n", " )\n", " return p\n", " elif plot_type == \"scatter\":\n", "\n", " SPECIES = sorted(data.species.unique())\n", " MARKERS = [\"hex\", \"circle_x\", \"triangle\"]\n", "\n", " p = figure(title=\"Penguin size\", background_fill_color=\"#fafafa\")\n", " p.xaxis.axis_label = \"Flipper Length (mm)\"\n", " p.yaxis.axis_label = \"Body Mass (g)\"\n", "\n", " p.scatter(\n", " \"flipper_length_mm\",\n", " \"body_mass_g\",\n", " source=data,\n", " legend_group=\"species\",\n", " fill_alpha=0.4,\n", " size=12,\n", " marker=factor_mark(\"species\", MARKERS, SPECIES),\n", " color=factor_cmap(\"species\", \"Category10_3\", SPECIES),\n", " )\n", "\n", " p.legend.location = \"top_left\"\n", " p.legend.title = \"Species\"\n", " return p\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " plot_type = gr.Radio(value=\"scatter\", choices=[\"scatter\", \"whisker\", \"map\"])\n", " plot = gr.Plot()\n", " plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])\n", " demo.load(get_plot, inputs=[plot_type], outputs=[plot])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+<<<<<<< HEAD
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: bokeh_plot"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio bokeh>=3.0 xyzservices"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import xyzservices.providers as xyz\n", "from bokeh.models import ColumnDataSource, Whisker\n", "from bokeh.plotting import figure\n", "from bokeh.sampledata.autompg2 import autompg2 as df\n", "from bokeh.sampledata.penguins import data\n", "from bokeh.transform import factor_cmap, jitter, factor_mark\n", "\n", "\n", "def get_plot(plot_type):\n", " if plot_type == \"map\":\n", " plot = figure(\n", " x_range=(-2000000, 6000000),\n", " y_range=(-1000000, 7000000),\n", " x_axis_type=\"mercator\",\n", " y_axis_type=\"mercator\",\n", " )\n", " plot.add_tile(xyz.OpenStreetMap.Mapnik)\n", " return plot\n", " elif plot_type == \"whisker\":\n", " classes = list(sorted(df[\"class\"].unique()))\n", "\n", " p = figure(\n", " height=400,\n", " x_range=classes,\n", " background_fill_color=\"#efefef\",\n", " title=\"Car class vs HWY mpg with quintile ranges\",\n", " )\n", " p.xgrid.grid_line_color = None\n", "\n", " g = df.groupby(\"class\")\n", " upper = g.hwy.quantile(0.80)\n", " lower = g.hwy.quantile(0.20)\n", " source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n", "\n", " error = Whisker(\n", " base=\"base\",\n", " upper=\"upper\",\n", " lower=\"lower\",\n", " source=source,\n", " level=\"annotation\",\n", " line_width=2,\n", " )\n", " error.upper_head.size = 20\n", " error.lower_head.size = 20\n", " p.add_layout(error)\n", "\n", " p.circle(\n", " jitter(\"class\", 0.3, range=p.x_range),\n", " \"hwy\",\n", " source=df,\n", " alpha=0.5,\n", " size=13,\n", " line_color=\"white\",\n", " color=factor_cmap(\"class\", \"Light6\", classes),\n", " )\n", " return p\n", " elif plot_type == \"scatter\":\n", "\n", " SPECIES = sorted(data.species.unique())\n", " MARKERS = [\"hex\", \"circle_x\", \"triangle\"]\n", "\n", " p = figure(title=\"Penguin size\", background_fill_color=\"#fafafa\")\n", " p.xaxis.axis_label = \"Flipper Length (mm)\"\n", " p.yaxis.axis_label = \"Body Mass (g)\"\n", "\n", " p.scatter(\n", " \"flipper_length_mm\",\n", " \"body_mass_g\",\n", " source=data,\n", " legend_group=\"species\",\n", " fill_alpha=0.4,\n", " size=12,\n", " marker=factor_mark(\"species\", MARKERS, SPECIES),\n", " color=factor_cmap(\"species\", \"Category10_3\", SPECIES),\n", " )\n", "\n", " p.legend.location = \"top_left\"\n", " p.legend.title = \"Species\"\n", " return p\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " plot_type = gr.Radio(value=\"scatter\", choices=[\"scatter\", \"whisker\", \"map\"])\n", " plot = gr.Plot()\n", " plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])\n", " demo.load(get_plot, inputs=[plot_type], outputs=[plot])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+=======
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: bokeh_plot"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio bokeh>=3.0 xyzservices"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import xyzservices.providers as xyz\n", "from bokeh.models import ColumnDataSource, Whisker\n", "from bokeh.plotting import figure\n", "from bokeh.sampledata.autompg2 import autompg2 as df\n", "from bokeh.sampledata.penguins import data\n", "from bokeh.transform import factor_cmap, jitter, factor_mark\n", "\n", "\n", "def get_plot(plot_type):\n", " if plot_type == \"map\":\n", " plot = figure(\n", " x_range=(-2000000, 6000000),\n", " y_range=(-1000000, 7000000),\n", " x_axis_type=\"mercator\",\n", " y_axis_type=\"mercator\",\n", " )\n", " plot.add_tile(xyz.OpenStreetMap.Mapnik)\n", " return plot\n", " elif plot_type == \"whisker\":\n", " classes = list(sorted(df[\"class\"].unique()))\n", "\n", " p = figure(\n", " height=400,\n", " x_range=classes,\n", " background_fill_color=\"#efefef\",\n", " title=\"Car class vs HWY mpg with quintile ranges\",\n", " )\n", " p.xgrid.grid_line_color = None\n", "\n", " g = df.groupby(\"class\")\n", " upper = g.hwy.quantile(0.80)\n", " lower = g.hwy.quantile(0.20)\n", " source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n", "\n", " error = Whisker(\n", " base=\"base\",\n", " upper=\"upper\",\n", " lower=\"lower\",\n", " source=source,\n", " level=\"annotation\",\n", " line_width=2,\n", " )\n", " error.upper_head.size = 20\n", " error.lower_head.size = 20\n", " p.add_layout(error)\n", "\n", " p.circle(\n", " jitter(\"class\", 0.3, range=p.x_range),\n", " \"hwy\",\n", " source=df,\n", " alpha=0.5,\n", " size=13,\n", " line_color=\"white\",\n", " color=factor_cmap(\"class\", \"Light6\", classes),\n", " )\n", " return p\n", " elif plot_type == \"scatter\":\n", "\n", " SPECIES = sorted(data.species.unique())\n", " MARKERS = [\"hex\", \"circle_x\", \"triangle\"]\n", "\n", " p = figure(title=\"Penguin size\", background_fill_color=\"#fafafa\")\n", " p.xaxis.axis_label = \"Flipper Length (mm)\"\n", " p.yaxis.axis_label = \"Body Mass (g)\"\n", "\n", " p.scatter(\n", " \"flipper_length_mm\",\n", " \"body_mass_g\",\n", " source=data,\n", " legend_group=\"species\",\n", " fill_alpha=0.4,\n", " size=12,\n", " marker=factor_mark(\"species\", MARKERS, SPECIES),\n", " color=factor_cmap(\"species\", \"Category10_3\", SPECIES),\n", " )\n", "\n", " p.legend.location = \"top_left\"\n", " p.legend.title = \"Species\"\n", " return p\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " plot_type = gr.Radio(value=\"scatter\", choices=[\"scatter\", \"whisker\", \"map\"])\n", " plot = gr.Plot()\n", " plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])\n", " demo.load(get_plot, inputs=[plot_type], outputs=[plot])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+>>>>>>> main
diff --git a/demo/button_component/run.ipynb b/demo/button_component/run.ipynb
index 00e4c891c2603..641a17bf891cc 100644
--- a/demo/button_component/run.ipynb
+++ b/demo/button_component/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: button_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Button()\n", " \n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: button_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Button()\n", " \n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/calculator/run.ipynb b/demo/calculator/run.ipynb
index 3fbf935446837..8b4e1433c39c4 100644
--- a/demo/calculator/run.ipynb
+++ b/demo/calculator/run.ipynb
@@ -1 +1,5 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('examples')\n", "!wget -q -O examples/log.csv https://github.com/gradio-app/gradio/raw/main/demo/calculator/examples/log.csv"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "#from foo import BAR\n", "#\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " if num2 == 0:\n", " raise gr.Error(\"Cannot divide by zero!\")\n", " return num1 / num2\n", "\n", "demo = gr.Interface(\n", " calculator,\n", " [\n", " \"number\", \n", " gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", " \"number\"\n", " ],\n", " \"number\",\n", " examples=[\n", " [45, \"add\", 3],\n", " [3.14, \"divide\", 2],\n", " [144, \"multiply\", 2.5],\n", " [0, \"subtract\", 1.2],\n", " ],\n", " title=\"Toy Calculator\",\n", " description=\"Here's a sample toy calculator. Allows you to calculate things like $2+2=4$\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+<<<<<<< HEAD
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('examples')\n", "!wget -q -O examples/log.csv https://github.com/gradio-app/gradio/raw/main/demo/calculator/examples/log.csv"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "#from foo import BAR\n", "#\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " if num2 == 0:\n", " raise gr.Error(\"Cannot divide by zero!\")\n", " return num1 / num2\n", "\n", "demo = gr.Interface(\n", " calculator,\n", " [\n", " \"number\", \n", " gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", " \"number\"\n", " ],\n", " \"number\",\n", " examples=[\n", " [45, \"add\", 3],\n", " [3.14, \"divide\", 2],\n", " [144, \"multiply\", 2.5],\n", " [0, \"subtract\", 1.2],\n", " ],\n", " title=\"Toy Calculator\",\n", " description=\"Here's a sample toy calculator. Allows you to calculate things like $2+2=4$\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+=======
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('examples')\n", "!wget -q -O examples/log.csv https://github.com/gradio-app/gradio/raw/main/demo/calculator/examples/log.csv"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " if num2 == 0:\n", " raise gr.Error(\"Cannot divide by zero!\")\n", " return num1 / num2\n", "\n", "demo = gr.Interface(\n", " calculator,\n", " [\n", " \"number\", \n", " gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", " \"number\"\n", " ],\n", " \"number\",\n", " examples=[\n", " [5, \"add\", 3],\n", " [4, \"divide\", 2],\n", " [-4, \"multiply\", 2.5],\n", " [0, \"subtract\", 1.2],\n", " ],\n", " title=\"Toy Calculator\",\n", " description=\"Here's a sample toy calculator. Allows you to calculate things like $2+2=4$\",\n", ")\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+>>>>>>> main
diff --git a/demo/calculator_blocks/run.ipynb b/demo/calculator_blocks/run.ipynb
index b7523f3e87dcf..bad9e149406bc 100644
--- a/demo/calculator_blocks/run.ipynb
+++ b/demo/calculator_blocks/run.ipynb
@@ -1 +1,5 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator_blocks"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " return num1 / num2\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " num_1 = gr.Number(value=4)\n", " operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n", " num_2 = gr.Number(value=0)\n", " submit_btn = gr.Button(value=\"Calculate\")\n", " with gr.Column():\n", " result = gr.Number()\n", "\n", " submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result], api_name=False)\n", " examples = gr.Examples(examples=[[5, \"add\", 3],\n", " [4, \"divide\", 2],\n", " [-4, \"multiply\", 2.5],\n", " [0, \"subtract\", 1.2]],\n", " inputs=[num_1, operation, num_2])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch(show_api=False)"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+<<<<<<< HEAD
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator_blocks"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " return num1 / num2\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " num_1 = gr.Number(value=4)\n", " operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n", " num_2 = gr.Number(value=0)\n", " submit_btn = gr.Button(value=\"Calculate\")\n", " with gr.Column():\n", " result = gr.Number()\n", "\n", " submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result], api_name=False)\n", " examples = gr.Examples(examples=[[5, \"add\", 3],\n", " [4, \"divide\", 2],\n", " [-4, \"multiply\", 2.5],\n", " [0, \"subtract\", 1.2]],\n", " inputs=[num_1, operation, num_2])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch(show_api=False)"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+=======
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator_blocks"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " return num1 / num2\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " num_1 = gr.Number(value=4)\n", " operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n", " num_2 = gr.Number(value=0)\n", " submit_btn = gr.Button(value=\"Calculate\")\n", " with gr.Column():\n", " result = gr.Number()\n", "\n", " submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result])\n", " examples = gr.Examples(examples=[[5, \"add\", 3],\n", " [4, \"divide\", 2],\n", " [-4, \"multiply\", 2.5],\n", " [0, \"subtract\", 1.2]],\n", " inputs=[num_1, operation, num_2])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+>>>>>>> main
diff --git a/demo/calculator_blocks_cached/run.ipynb b/demo/calculator_blocks_cached/run.ipynb
index b1d4482ff8b35..5ca1b4d3af8f5 100644
--- a/demo/calculator_blocks_cached/run.ipynb
+++ b/demo/calculator_blocks_cached/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator_blocks_cached"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " return num1 / num2\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " num_1 = gr.Number()\n", " operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n", " num_2 = gr.Number()\n", " submit_btn = gr.Button(value=\"Calculate\")\n", " with gr.Column():\n", " result = gr.Number()\n", "\n", " submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result])\n", " examples = gr.Examples(examples=[[5, \"add\", 3],\n", " [4, \"divide\", 2],\n", " [-4, \"multiply\", 2.5],\n", " [0, \"subtract\", 1.2]],\n", " inputs=[num_1, operation, num_2],\n", " outputs=[result],\n", " fn=calculator,\n", " cache_examples=True)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator_blocks_cached"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " return num1 / num2\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " num_1 = gr.Number()\n", " operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n", " num_2 = gr.Number()\n", " submit_btn = gr.Button(value=\"Calculate\")\n", " with gr.Column():\n", " result = gr.Number()\n", "\n", " submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result])\n", " examples = gr.Examples(examples=[[5, \"add\", 3],\n", " [4, \"divide\", 2],\n", " [-4, \"multiply\", 2.5],\n", " [0, \"subtract\", 1.2]],\n", " inputs=[num_1, operation, num_2],\n", " outputs=[result],\n", " fn=calculator,\n", " cache_examples=True)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/calculator_list_and_dict/run.ipynb b/demo/calculator_list_and_dict/run.ipynb
index 8336b3b9c13c5..c967498f3f82c 100644
--- a/demo/calculator_list_and_dict/run.ipynb
+++ b/demo/calculator_list_and_dict/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator_list_and_dict"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " a = gr.Number(label=\"a\")\n", " b = gr.Number(label=\"b\")\n", " with gr.Row():\n", " add_btn = gr.Button(\"Add\")\n", " sub_btn = gr.Button(\"Subtract\")\n", " c = gr.Number(label=\"sum\")\n", "\n", " def add(num1, num2):\n", " return num1 + num2\n", " add_btn.click(add, inputs=[a, b], outputs=c)\n", "\n", " def sub(data):\n", " return data[a] - data[b]\n", " sub_btn.click(sub, inputs={a, b}, outputs=c)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator_list_and_dict"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " a = gr.Number(label=\"a\")\n", " b = gr.Number(label=\"b\")\n", " with gr.Row():\n", " add_btn = gr.Button(\"Add\")\n", " sub_btn = gr.Button(\"Subtract\")\n", " c = gr.Number(label=\"sum\")\n", "\n", " def add(num1, num2):\n", " return num1 + num2\n", " add_btn.click(add, inputs=[a, b], outputs=c)\n", "\n", " def sub(data):\n", " return data[a] - data[b]\n", " sub_btn.click(sub, inputs={a, b}, outputs=c)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/calculator_live/run.ipynb b/demo/calculator_live/run.ipynb
index 4e150f1858133..a5c2131ad1b4b 100644
--- a/demo/calculator_live/run.ipynb
+++ b/demo/calculator_live/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator_live"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " return num1 / num2\n", "\n", "demo = gr.Interface(\n", " calculator,\n", " [\n", " \"number\",\n", " gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", " \"number\"\n", " ],\n", " \"number\",\n", " live=True,\n", ")\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator_live"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def calculator(num1, operation, num2):\n", " if operation == \"add\":\n", " return num1 + num2\n", " elif operation == \"subtract\":\n", " return num1 - num2\n", " elif operation == \"multiply\":\n", " return num1 * num2\n", " elif operation == \"divide\":\n", " return num1 / num2\n", "\n", "demo = gr.Interface(\n", " calculator,\n", " [\n", " \"number\",\n", " gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", " \"number\"\n", " ],\n", " \"number\",\n", " live=True,\n", ")\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/cancel_events/run.ipynb b/demo/cancel_events/run.ipynb
index a00f54fb1e769..449220364b136 100644
--- a/demo/cancel_events/run.ipynb
+++ b/demo/cancel_events/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: cancel_events"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "\n", "def fake_diffusion(steps):\n", " for i in range(steps):\n", " print(f\"Current step: {i}\")\n", " time.sleep(0.2)\n", " yield str(i)\n", "\n", "\n", "def long_prediction(*args, **kwargs):\n", " time.sleep(10)\n", " return 42\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " n = gr.Slider(1, 10, value=9, step=1, label=\"Number Steps\")\n", " run = gr.Button(value=\"Start Iterating\")\n", " output = gr.Textbox(label=\"Iterative Output\")\n", " stop = gr.Button(value=\"Stop Iterating\")\n", " with gr.Column():\n", " textbox = gr.Textbox(label=\"Prompt\")\n", " prediction = gr.Number(label=\"Expensive Calculation\")\n", " run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", " with gr.Column():\n", " cancel_on_change = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Change\")\n", " cancel_on_submit = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Submit\")\n", " echo = gr.Textbox(label=\"Echo\")\n", " with gr.Row():\n", " with gr.Column():\n", " image = gr.Image(source=\"webcam\", tool=\"editor\", label=\"Cancel on edit\", interactive=True)\n", " with gr.Column():\n", " video = gr.Video(source=\"webcam\", label=\"Cancel on play\", interactive=True)\n", "\n", " click_event = run.click(fake_diffusion, n, output)\n", " stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])\n", " pred_event = run_pred.click(fn=long_prediction, inputs=[textbox], outputs=prediction)\n", "\n", " cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", " cancel_on_submit.submit(lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event])\n", " image.edit(None, None, None, cancels=[click_event, pred_event])\n", " video.play(None, None, None, cancels=[click_event, pred_event])\n", "\n", " demo.queue(concurrency_count=2, max_size=20)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: cancel_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "\n", "def fake_diffusion(steps):\n", " for i in range(steps):\n", " print(f\"Current step: {i}\")\n", " time.sleep(0.2)\n", " yield str(i)\n", "\n", "\n", "def long_prediction(*args, **kwargs):\n", " time.sleep(10)\n", " return 42\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " n = gr.Slider(1, 10, value=9, step=1, label=\"Number Steps\")\n", " run = gr.Button(value=\"Start Iterating\")\n", " output = gr.Textbox(label=\"Iterative Output\")\n", " stop = gr.Button(value=\"Stop Iterating\")\n", " with gr.Column():\n", " textbox = gr.Textbox(label=\"Prompt\")\n", " prediction = gr.Number(label=\"Expensive Calculation\")\n", " run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", " with gr.Column():\n", " cancel_on_change = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Change\")\n", " cancel_on_submit = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Submit\")\n", " echo = gr.Textbox(label=\"Echo\")\n", " with gr.Row():\n", " with gr.Column():\n", " image = gr.Image(source=\"webcam\", tool=\"editor\", label=\"Cancel on edit\", interactive=True)\n", " with gr.Column():\n", " video = gr.Video(source=\"webcam\", label=\"Cancel on play\", interactive=True)\n", "\n", " click_event = run.click(fake_diffusion, n, output)\n", " stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])\n", " pred_event = run_pred.click(fn=long_prediction, inputs=[textbox], outputs=prediction)\n", "\n", " cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", " cancel_on_submit.submit(lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event])\n", " image.edit(None, None, None, cancels=[click_event, pred_event])\n", " video.play(None, None, None, cancels=[click_event, pred_event])\n", "\n", " demo.queue(concurrency_count=2, max_size=20)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/change_vs_input/run.ipynb b/demo/change_vs_input/run.ipynb
index 4fccd7df65cb5..49df4f8a00a17 100644
--- a/demo/change_vs_input/run.ipynb
+++ b/demo/change_vs_input/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: change_vs_input"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/change_vs_input/files/cantina.wav\n", "!wget -q -O files/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/change_vs_input/files/lion.jpg\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/change_vs_input/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " set_button = gr.Button(\"Set Values\")\n", " with gr.Row():\n", " with gr.Column(min_width=200):\n", " gr.Markdown(\"# Enter Here\")\n", " text = gr.Textbox()\n", " num = gr.Number()\n", " slider = gr.Slider()\n", " checkbox = gr.Checkbox()\n", " checkbox_group = gr.CheckboxGroup([\"a\", \"b\", \"c\"])\n", " radio = gr.Radio([\"a\", \"b\", \"c\"])\n", " dropdown = gr.Dropdown([\"a\", \"b\", \"c\"])\n", " colorpicker = gr.ColorPicker()\n", " code = gr.Code()\n", " dataframe = gr.Dataframe()\n", " image = gr.Image(elem_id=\"image-original\")\n", " audio = gr.Audio(elem_id=\"audio-original\")\n", " video = gr.Video(elem_id=\"video-original\")\n", "\n", " with gr.Column(min_width=200):\n", " gr.Markdown(\"# ON:INPUT/UPLOAD\")\n", " text_in = gr.Textbox()\n", " num_in = gr.Number()\n", " slider_in = gr.Slider()\n", " checkbox_in = gr.Checkbox()\n", " checkbox_group_in = gr.CheckboxGroup([\"a\", \"b\", \"c\"])\n", " radio_in = gr.Radio([\"a\", \"b\", \"c\"])\n", " dropdown_in = gr.Dropdown([\"a\", \"b\", \"c\"])\n", " colorpicker_in = gr.ColorPicker()\n", " code_in = gr.Code()\n", " dataframe_in = gr.Dataframe()\n", " image_up = gr.Image(elem_id=\"image-upload\")\n", " audio_up = gr.Audio(elem_id=\"audio-upload\")\n", " video_up = gr.Video(elem_id=\"video-upload\")\n", "\n", " with gr.Column(min_width=200):\n", " gr.Markdown(\"# ON:CHANGE\")\n", " text_ch = gr.Textbox()\n", " num_ch = gr.Number()\n", " slider_ch = gr.Slider()\n", " checkbox_ch = gr.Checkbox()\n", " checkbox_group_ch = gr.CheckboxGroup([\"a\", \"b\", \"c\"])\n", " radio_ch = gr.Radio([\"a\", \"b\", \"c\"])\n", " dropdown_ch = gr.Dropdown([\"a\", \"b\", \"c\"])\n", " colorpicker_ch = gr.ColorPicker()\n", " code_ch = gr.Code()\n", " dataframe_ch = gr.Dataframe()\n", " image_ch = gr.Image(elem_id=\"image-change\")\n", " audio_ch = gr.Audio(elem_id=\"audio-change\")\n", " video_ch = gr.Video(elem_id=\"video-change\")\n", "\n", " with gr.Column(min_width=200):\n", " gr.Markdown(\"# ON:CHANGE x2\")\n", " text_ch2 = gr.Textbox()\n", " num_ch2 = gr.Number()\n", " slider_ch2 = gr.Slider()\n", " checkbox_ch2 = gr.Checkbox()\n", " checkbox_group_ch2 = gr.CheckboxGroup([\"a\", \"b\", \"c\"])\n", " radio_ch2 = gr.Radio([\"a\", \"b\", \"c\"])\n", " dropdown_ch2 = gr.Dropdown([\"a\", \"b\", \"c\"])\n", " colorpicker_ch2 = gr.ColorPicker()\n", " code_ch2 = gr.Code()\n", " dataframe_ch2 = gr.Dataframe()\n", " image_ch2 = gr.Image(elem_id=\"image-change-2\")\n", " audio_ch2 = gr.Audio(elem_id=\"audio-change-2\")\n", " video_ch2 = gr.Video(elem_id=\"video-change-2\")\n", "\n", " counter = gr.Number(label=\"Change counter\")\n", "\n", " lion = os.path.join(os.path.abspath(''), \"files/lion.jpg\")\n", " cantina = os.path.join(os.path.abspath(''), \"files/cantina.wav\")\n", " world = os.path.join(os.path.abspath(''), \"files/world.mp4\")\n", "\n", " set_button.click(\n", " lambda: [\"asdf\", 555, 12, True, [\"a\", \"c\"], \"b\", \"b\", \"#FF0000\", \"import gradio as gr\", [[\"a\", \"b\", \"c\", \"d\"], [\"1\", \"2\", \"3\", \"4\"]], lion, cantina, world], \n", " None, \n", " [text, num, slider, checkbox, checkbox_group, radio, dropdown, colorpicker, code, dataframe, image, audio, video])\n", "\n", " text.input(lambda x:x, text, text_in)\n", " num.input(lambda x:x, num, num_in)\n", " slider.input(lambda x:x, slider, slider_in)\n", " checkbox.input(lambda x:x, checkbox, checkbox_in)\n", " checkbox_group.input(lambda x:x, checkbox_group, checkbox_group_in)\n", " radio.input(lambda x:x, radio, radio_in)\n", " dropdown.input(lambda x:x, dropdown, dropdown_in)\n", " colorpicker.input(lambda x:x, colorpicker, colorpicker_in)\n", " code.input(lambda x:x, code, code_in)\n", " dataframe.input(lambda x:x, dataframe, dataframe_in)\n", " image.upload(lambda x:x, image, image_up)\n", " audio.upload(lambda x:x, audio, audio_up)\n", " video.upload(lambda x:x, video, video_up)\n", "\n", " text.change(lambda x,y:(x,y+1), [text, counter], [text_ch, counter])\n", " num.change(lambda x,y:(x, y+1), [num, counter], [num_ch, counter])\n", " slider.change(lambda x,y:(x, y+1), [slider, counter], [slider_ch, counter])\n", " checkbox.change(lambda x,y:(x, y+1), [checkbox, counter], [checkbox_ch, counter])\n", " checkbox_group.change(lambda x,y:(x, y+1), [checkbox_group, counter], [checkbox_group_ch, counter])\n", " radio.change(lambda x,y:(x, y+1), [radio, counter], [radio_ch, counter])\n", " dropdown.change(lambda x,y:(x, y+1), [dropdown, counter], [dropdown_ch, counter])\n", " colorpicker.change(lambda x,y:(x, y+1), [colorpicker, counter], [colorpicker_ch, counter])\n", " code.change(lambda x,y:(x, y+1), [code, counter], [code_ch, counter])\n", " dataframe.change(lambda x,y:(x, y+1), [dataframe, counter], [dataframe_ch, counter])\n", " image.change(lambda x,y:(x, y+1), [image, counter], [image_ch, counter])\n", " audio.change(lambda x,y:(x, y+1), [audio, counter], [audio_ch, counter])\n", " video.change(lambda x,y:(x, y+1), [video, counter], [video_ch, counter])\n", "\n", " text_ch.change(lambda x:x, text_ch, text_ch2)\n", " num_ch.change(lambda x:x, num_ch, num_ch2)\n", " slider_ch.change(lambda x:x, slider_ch, slider_ch2)\n", " checkbox_ch.change(lambda x:x, checkbox_ch, checkbox_ch2)\n", " checkbox_group_ch.change(lambda x:x, checkbox_group_ch, checkbox_group_ch2)\n", " radio_ch.change(lambda x:x, radio_ch, radio_ch2)\n", " dropdown_ch.change(lambda x:x, dropdown_ch, dropdown_ch2)\n", " colorpicker_ch.change(lambda x:x, colorpicker_ch, colorpicker_ch2)\n", " code_ch.change(lambda x:x, code_ch, code_ch2)\n", " dataframe_ch.change(lambda x:x, dataframe_ch, dataframe_ch2)\n", " image_ch.change(lambda x:x, image_ch, image_ch2)\n", " audio_ch.change(lambda x:x, audio_ch, audio_ch2)\n", " video_ch.change(lambda x:x, video_ch, video_ch2)\n", " \n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: change_vs_input"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/change_vs_input/files/cantina.wav\n", "!wget -q -O files/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/change_vs_input/files/lion.jpg\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/change_vs_input/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " set_button = gr.Button(\"Set Values\")\n", " with gr.Row():\n", " with gr.Column(min_width=200):\n", " gr.Markdown(\"# Enter Here\")\n", " text = gr.Textbox()\n", " num = gr.Number()\n", " slider = gr.Slider()\n", " checkbox = gr.Checkbox()\n", " checkbox_group = gr.CheckboxGroup([\"a\", \"b\", \"c\"])\n", " radio = gr.Radio([\"a\", \"b\", \"c\"])\n", " dropdown = gr.Dropdown([\"a\", \"b\", \"c\"])\n", " colorpicker = gr.ColorPicker()\n", " code = gr.Code()\n", " dataframe = gr.Dataframe()\n", " image = gr.Image(elem_id=\"image-original\")\n", " audio = gr.Audio(elem_id=\"audio-original\")\n", " video = gr.Video(elem_id=\"video-original\")\n", "\n", " with gr.Column(min_width=200):\n", " gr.Markdown(\"# ON:INPUT/UPLOAD\")\n", " text_in = gr.Textbox()\n", " num_in = gr.Number()\n", " slider_in = gr.Slider()\n", " checkbox_in = gr.Checkbox()\n", " checkbox_group_in = gr.CheckboxGroup([\"a\", \"b\", \"c\"])\n", " radio_in = gr.Radio([\"a\", \"b\", \"c\"])\n", " dropdown_in = gr.Dropdown([\"a\", \"b\", \"c\"])\n", " colorpicker_in = gr.ColorPicker()\n", " code_in = gr.Code()\n", " dataframe_in = gr.Dataframe()\n", " image_up = gr.Image(elem_id=\"image-upload\")\n", " audio_up = gr.Audio(elem_id=\"audio-upload\")\n", " video_up = gr.Video(elem_id=\"video-upload\")\n", "\n", " with gr.Column(min_width=200):\n", " gr.Markdown(\"# ON:CHANGE\")\n", " text_ch = gr.Textbox()\n", " num_ch = gr.Number()\n", " slider_ch = gr.Slider()\n", " checkbox_ch = gr.Checkbox()\n", " checkbox_group_ch = gr.CheckboxGroup([\"a\", \"b\", \"c\"])\n", " radio_ch = gr.Radio([\"a\", \"b\", \"c\"])\n", " dropdown_ch = gr.Dropdown([\"a\", \"b\", \"c\"])\n", " colorpicker_ch = gr.ColorPicker()\n", " code_ch = gr.Code()\n", " dataframe_ch = gr.Dataframe()\n", " image_ch = gr.Image(elem_id=\"image-change\")\n", " audio_ch = gr.Audio(elem_id=\"audio-change\")\n", " video_ch = gr.Video(elem_id=\"video-change\")\n", "\n", " with gr.Column(min_width=200):\n", " gr.Markdown(\"# ON:CHANGE x2\")\n", " text_ch2 = gr.Textbox()\n", " num_ch2 = gr.Number()\n", " slider_ch2 = gr.Slider()\n", " checkbox_ch2 = gr.Checkbox()\n", " checkbox_group_ch2 = gr.CheckboxGroup([\"a\", \"b\", \"c\"])\n", " radio_ch2 = gr.Radio([\"a\", \"b\", \"c\"])\n", " dropdown_ch2 = gr.Dropdown([\"a\", \"b\", \"c\"])\n", " colorpicker_ch2 = gr.ColorPicker()\n", " code_ch2 = gr.Code()\n", " dataframe_ch2 = gr.Dataframe()\n", " image_ch2 = gr.Image(elem_id=\"image-change-2\")\n", " audio_ch2 = gr.Audio(elem_id=\"audio-change-2\")\n", " video_ch2 = gr.Video(elem_id=\"video-change-2\")\n", "\n", " counter = gr.Number(label=\"Change counter\")\n", "\n", " lion = os.path.join(os.path.abspath(''), \"files/lion.jpg\")\n", " cantina = os.path.join(os.path.abspath(''), \"files/cantina.wav\")\n", " world = os.path.join(os.path.abspath(''), \"files/world.mp4\")\n", "\n", " set_button.click(\n", " lambda: [\"asdf\", 555, 12, True, [\"a\", \"c\"], \"b\", \"b\", \"#FF0000\", \"import gradio as gr\", [[\"a\", \"b\", \"c\", \"d\"], [\"1\", \"2\", \"3\", \"4\"]], lion, cantina, world], \n", " None, \n", " [text, num, slider, checkbox, checkbox_group, radio, dropdown, colorpicker, code, dataframe, image, audio, video])\n", "\n", " text.input(lambda x:x, text, text_in)\n", " num.input(lambda x:x, num, num_in)\n", " slider.input(lambda x:x, slider, slider_in)\n", " checkbox.input(lambda x:x, checkbox, checkbox_in)\n", " checkbox_group.input(lambda x:x, checkbox_group, checkbox_group_in)\n", " radio.input(lambda x:x, radio, radio_in)\n", " dropdown.input(lambda x:x, dropdown, dropdown_in)\n", " colorpicker.input(lambda x:x, colorpicker, colorpicker_in)\n", " code.input(lambda x:x, code, code_in)\n", " dataframe.input(lambda x:x, dataframe, dataframe_in)\n", " image.upload(lambda x:x, image, image_up)\n", " audio.upload(lambda x:x, audio, audio_up)\n", " video.upload(lambda x:x, video, video_up)\n", "\n", " text.change(lambda x,y:(x,y+1), [text, counter], [text_ch, counter])\n", " num.change(lambda x,y:(x, y+1), [num, counter], [num_ch, counter])\n", " slider.change(lambda x,y:(x, y+1), [slider, counter], [slider_ch, counter])\n", " checkbox.change(lambda x,y:(x, y+1), [checkbox, counter], [checkbox_ch, counter])\n", " checkbox_group.change(lambda x,y:(x, y+1), [checkbox_group, counter], [checkbox_group_ch, counter])\n", " radio.change(lambda x,y:(x, y+1), [radio, counter], [radio_ch, counter])\n", " dropdown.change(lambda x,y:(x, y+1), [dropdown, counter], [dropdown_ch, counter])\n", " colorpicker.change(lambda x,y:(x, y+1), [colorpicker, counter], [colorpicker_ch, counter])\n", " code.change(lambda x,y:(x, y+1), [code, counter], [code_ch, counter])\n", " dataframe.change(lambda x,y:(x, y+1), [dataframe, counter], [dataframe_ch, counter])\n", " image.change(lambda x,y:(x, y+1), [image, counter], [image_ch, counter])\n", " audio.change(lambda x,y:(x, y+1), [audio, counter], [audio_ch, counter])\n", " video.change(lambda x,y:(x, y+1), [video, counter], [video_ch, counter])\n", "\n", " text_ch.change(lambda x:x, text_ch, text_ch2)\n", " num_ch.change(lambda x:x, num_ch, num_ch2)\n", " slider_ch.change(lambda x:x, slider_ch, slider_ch2)\n", " checkbox_ch.change(lambda x:x, checkbox_ch, checkbox_ch2)\n", " checkbox_group_ch.change(lambda x:x, checkbox_group_ch, checkbox_group_ch2)\n", " radio_ch.change(lambda x:x, radio_ch, radio_ch2)\n", " dropdown_ch.change(lambda x:x, dropdown_ch, dropdown_ch2)\n", " colorpicker_ch.change(lambda x:x, colorpicker_ch, colorpicker_ch2)\n", " code_ch.change(lambda x:x, code_ch, code_ch2)\n", " dataframe_ch.change(lambda x:x, dataframe_ch, dataframe_ch2)\n", " image_ch.change(lambda x:x, image_ch, image_ch2)\n", " audio_ch.change(lambda x:x, audio_ch, audio_ch2)\n", " video_ch.change(lambda x:x, video_ch, video_ch2)\n", " \n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/chatbot_component/run.ipynb b/demo/chatbot_component/run.ipynb
index 82f59d809f75b..d95d2c5f4f473 100644
--- a/demo/chatbot_component/run.ipynb
+++ b/demo/chatbot_component/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Chatbot(value=[[\"Hello World\",\"Hey Gradio!\"],[\"\u2764\ufe0f\",\"\ud83d\ude0d\"],[\"\ud83d\udd25\",\"\ud83e\udd17\"]])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Chatbot(value=[[\"Hello World\",\"Hey Gradio!\"],[\"\u2764\ufe0f\",\"\ud83d\ude0d\"],[\"\ud83d\udd25\",\"\ud83e\udd17\"]])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/chatbot_consecutive/run.ipynb b/demo/chatbot_consecutive/run.ipynb
index 93c2fcf8e9f85..ff489e011a9a6 100644
--- a/demo/chatbot_consecutive/run.ipynb
+++ b/demo/chatbot_consecutive/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_consecutive"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot()\n", " msg = gr.Textbox()\n", " clear = gr.Button(\"Clear\")\n", "\n", " def user(user_message, history):\n", " return \"\", history + [[user_message, None]]\n", "\n", " def bot(history):\n", " bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", " time.sleep(2)\n", " history[-1][1] = bot_message\n", " return history\n", "\n", " msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", " bot, chatbot, chatbot\n", " )\n", " clear.click(lambda: None, None, chatbot, queue=False)\n", " \n", "demo.queue()\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_consecutive"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot()\n", " msg = gr.Textbox()\n", " clear = gr.Button(\"Clear\")\n", "\n", " def user(user_message, history):\n", " return \"\", history + [[user_message, None]]\n", "\n", " def bot(history):\n", " bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", " time.sleep(2)\n", " history[-1][1] = bot_message\n", " return history\n", "\n", " msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", " bot, chatbot, chatbot\n", " )\n", " clear.click(lambda: None, None, chatbot, queue=False)\n", " \n", "demo.queue()\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/chatbot_dialogpt/run.ipynb b/demo/chatbot_dialogpt/run.ipynb
index 879b1d5c7b775..650f7abcb9041 100644
--- a/demo/chatbot_dialogpt/run.ipynb
+++ b/demo/chatbot_dialogpt/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_dialogpt"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "import torch\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-medium\")\n", "model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-medium\")\n", "\n", "\n", "def user(message, history):\n", " return \"\", history + [[message, None]]\n", "\n", "\n", "def bot(history):\n", " user_message = history[-1][0]\n", " new_user_input_ids = tokenizer.encode(\n", " user_message + tokenizer.eos_token, return_tensors=\"pt\"\n", " )\n", "\n", " # append the new user input tokens to the chat history\n", " bot_input_ids = torch.cat([torch.LongTensor([]), new_user_input_ids], dim=-1)\n", "\n", " # generate a response\n", " response = model.generate(\n", " bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id\n", " ).tolist()\n", "\n", " # convert the tokens to text, and then split the responses into lines\n", " response = tokenizer.decode(response[0]).split(\"<|endoftext|>\")\n", " response = [\n", " (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)\n", " ] # convert to tuples of list\n", " history[-1] = response[0]\n", " return history\n", "\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot()\n", " msg = gr.Textbox()\n", " clear = gr.Button(\"Clear\")\n", "\n", " msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", " bot, chatbot, chatbot\n", " )\n", " clear.click(lambda: None, None, chatbot, queue=False)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_dialogpt"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "import torch\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-medium\")\n", "model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-medium\")\n", "\n", "\n", "def user(message, history):\n", " return \"\", history + [[message, None]]\n", "\n", "\n", "def bot(history):\n", " user_message = history[-1][0]\n", " new_user_input_ids = tokenizer.encode(\n", " user_message + tokenizer.eos_token, return_tensors=\"pt\"\n", " )\n", "\n", " # append the new user input tokens to the chat history\n", " bot_input_ids = torch.cat([torch.LongTensor([]), new_user_input_ids], dim=-1)\n", "\n", " # generate a response\n", " response = model.generate(\n", " bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id\n", " ).tolist()\n", "\n", " # convert the tokens to text, and then split the responses into lines\n", " response = tokenizer.decode(response[0]).split(\"<|endoftext|>\")\n", " response = [\n", " (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)\n", " ] # convert to tuples of list\n", " history[-1] = response[0]\n", " return history\n", "\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot()\n", " msg = gr.Textbox()\n", " clear = gr.Button(\"Clear\")\n", "\n", " msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", " bot, chatbot, chatbot\n", " )\n", " clear.click(lambda: None, None, chatbot, queue=False)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/chatbot_multimodal/run.ipynb b/demo/chatbot_multimodal/run.ipynb
index 5b20ca0c24f9f..dfe33c0335e58 100644
--- a/demo/chatbot_multimodal/run.ipynb
+++ b/demo/chatbot_multimodal/run.ipynb
@@ -1 +1,5 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_multimodal"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/chatbot_multimodal/avatar.png"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "import time\n", "\n", "# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n", "\n", "\n", "def add_text(history, text):\n", " history = history + [(text, None)]\n", " return history, gr.Textbox(value=\"\", interactive=False)\n", "\n", "\n", "def add_file(history, file):\n", " history = history + [((file.name,), None)]\n", " return history\n", "\n", "\n", "def bot(history):\n", " response = \"**That's cool!**\"\n", " history[-1][1] = \"\"\n", " for character in response:\n", " history[-1][1] += character\n", " time.sleep(0.05)\n", " yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot(\n", " [],\n", " elem_id=\"chatbot\",\n", " bubble_full_width=False,\n", " avatar_images=(None, (os.path.join(os.path.abspath(''), \"avatar.png\"))),\n", " )\n", "\n", " with gr.Row():\n", " txt = gr.Textbox(\n", " scale=4,\n", " show_label=False,\n", " placeholder=\"Enter text and press enter, or upload an image\",\n", " container=False,\n", " )\n", " btn = gr.UploadButton(\"\ud83d\udcc1\", file_types=[\"image\", \"video\", \"audio\"])\n", "\n", " txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(\n", " bot, chatbot, chatbot, api_name=\"bot_response\"\n", " )\n", " txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)\n", " file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(\n", " bot, chatbot, chatbot\n", " )\n", "\n", "demo.queue()\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+<<<<<<< HEAD
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_multimodal"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/chatbot_multimodal/avatar.png"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "import time\n", "\n", "# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n", "\n", "\n", "def add_text(history, text):\n", " history = history + [(text, None)]\n", " return history, gr.Textbox(value=\"\", interactive=False)\n", "\n", "\n", "def add_file(history, file):\n", " history = history + [((file.name,), None)]\n", " return history\n", "\n", "\n", "def bot(history):\n", " response = \"**That's cool!**\"\n", " history[-1][1] = \"\"\n", " for character in response:\n", " history[-1][1] += character\n", " time.sleep(0.05)\n", " yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot(\n", " [],\n", " elem_id=\"chatbot\",\n", " bubble_full_width=False,\n", " avatar_images=(None, (os.path.join(os.path.abspath(''), \"avatar.png\"))),\n", " )\n", "\n", " with gr.Row():\n", " txt = gr.Textbox(\n", " scale=4,\n", " show_label=False,\n", " placeholder=\"Enter text and press enter, or upload an image\",\n", " container=False,\n", " )\n", " btn = gr.UploadButton(\"\ud83d\udcc1\", file_types=[\"image\", \"video\", \"audio\"])\n", "\n", " txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(\n", " bot, chatbot, chatbot, api_name=\"bot_response\"\n", " )\n", " txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)\n", " file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(\n", " bot, chatbot, chatbot\n", " )\n", "\n", "demo.queue()\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+=======
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_multimodal"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/chatbot_multimodal/avatar.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "import time\n", "\n", "# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n", "\n", "\n", "def add_text(history, text):\n", " history = history + [(text, None)]\n", " return history, gr.Textbox(value=\"\", interactive=False)\n", "\n", "\n", "def add_file(history, file):\n", " history = history + [((file.name,), None)]\n", " return history\n", "\n", "\n", "def bot(history):\n", " response = \"**That's cool!**\"\n", " history[-1][1] = \"\"\n", " for character in response:\n", " history[-1][1] += character\n", " time.sleep(0.05)\n", " yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot(\n", " [],\n", " elem_id=\"chatbot\",\n", " bubble_full_width=False,\n", " avatar_images=(None, (os.path.join(os.path.abspath(''), \"avatar.png\"))),\n", " )\n", "\n", " with gr.Row():\n", " txt = gr.Textbox(\n", " scale=4,\n", " show_label=False,\n", " placeholder=\"Enter text and press enter, or upload an image\",\n", " container=False,\n", " )\n", " btn = gr.UploadButton(\"\ud83d\udcc1\", file_types=[\"image\", \"video\", \"audio\"])\n", "\n", " txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(\n", " bot, chatbot, chatbot\n", " )\n", " txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)\n", " file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(\n", " bot, chatbot, chatbot\n", " )\n", "\n", "demo.queue()\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+>>>>>>> main
diff --git a/demo/chatbot_simple/run.ipynb b/demo/chatbot_simple/run.ipynb
index 472690ebc1383..9085788db1004 100644
--- a/demo/chatbot_simple/run.ipynb
+++ b/demo/chatbot_simple/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_simple"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot()\n", " msg = gr.Textbox()\n", " clear = gr.ClearButton([msg, chatbot])\n", "\n", " def respond(message, chat_history):\n", " bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", " chat_history.append((message, bot_message))\n", " time.sleep(2)\n", " return \"\", chat_history\n", "\n", " msg.submit(respond, [msg, chatbot], [msg, chatbot])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_simple"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot()\n", " msg = gr.Textbox()\n", " clear = gr.ClearButton([msg, chatbot])\n", "\n", " def respond(message, chat_history):\n", " bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", " chat_history.append((message, bot_message))\n", " time.sleep(2)\n", " return \"\", chat_history\n", "\n", " msg.submit(respond, [msg, chatbot], [msg, chatbot])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/chatbot_streaming/run.ipynb b/demo/chatbot_streaming/run.ipynb
index 72a22daacc75d..f93bf5b3ec328 100644
--- a/demo/chatbot_streaming/run.ipynb
+++ b/demo/chatbot_streaming/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_streaming"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot()\n", " msg = gr.Textbox()\n", " clear = gr.Button(\"Clear\")\n", "\n", " def user(user_message, history):\n", " return \"\", history + [[user_message, None]]\n", "\n", " def bot(history):\n", " bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", " history[-1][1] = \"\"\n", " for character in bot_message:\n", " history[-1][1] += character\n", " time.sleep(0.05)\n", " yield history\n", "\n", " msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", " bot, chatbot, chatbot\n", " )\n", " clear.click(lambda: None, None, chatbot, queue=False)\n", " \n", "demo.queue()\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_streaming"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", " chatbot = gr.Chatbot()\n", " msg = gr.Textbox()\n", " clear = gr.Button(\"Clear\")\n", "\n", " def user(user_message, history):\n", " return \"\", history + [[user_message, None]]\n", "\n", " def bot(history):\n", " bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", " history[-1][1] = \"\"\n", " for character in bot_message:\n", " history[-1][1] += character\n", " time.sleep(0.05)\n", " yield history\n", "\n", " msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", " bot, chatbot, chatbot\n", " )\n", " clear.click(lambda: None, None, chatbot, queue=False)\n", " \n", "demo.queue()\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/chatinterface_random_response/run.ipynb b/demo/chatinterface_random_response/run.ipynb
index 7e8fbdd684fcc..dd95ecee051e2 100644
--- a/demo/chatinterface_random_response/run.ipynb
+++ b/demo/chatinterface_random_response/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatinterface_random_response"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import random\n", "import gradio as gr\n", "\n", "def random_response(message, history):\n", " return random.choice([\"Yes\", \"No\"])\n", "\n", "demo = gr.ChatInterface(random_response)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatinterface_random_response"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import random\n", "import gradio as gr\n", "\n", "def random_response(message, history):\n", " return random.choice([\"Yes\", \"No\"])\n", "\n", "demo = gr.ChatInterface(random_response)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/chatinterface_streaming_echo/run.ipynb b/demo/chatinterface_streaming_echo/run.ipynb
index c19db84df761b..7b56e7526db6f 100644
--- a/demo/chatinterface_streaming_echo/run.ipynb
+++ b/demo/chatinterface_streaming_echo/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatinterface_streaming_echo"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "def slow_echo(message, history):\n", " for i in range(len(message)):\n", " time.sleep(0.05)\n", " yield \"You typed: \" + message[: i+1]\n", "\n", "demo = gr.ChatInterface(slow_echo).queue()\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatinterface_streaming_echo"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "def slow_echo(message, history):\n", " for i in range(len(message)):\n", " time.sleep(0.05)\n", " yield \"You typed: \" + message[: i+1]\n", "\n", "demo = gr.ChatInterface(slow_echo).queue()\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/chatinterface_system_prompt/run.ipynb b/demo/chatinterface_system_prompt/run.ipynb
index c92e7ea5d9082..7ad525d719a5a 100644
--- a/demo/chatinterface_system_prompt/run.ipynb
+++ b/demo/chatinterface_system_prompt/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatinterface_system_prompt"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "def echo(message, history, system_prompt, tokens):\n", " response = f\"System prompt: {system_prompt}\\n Message: {message}.\"\n", " for i in range(min(len(response), int(tokens))):\n", " time.sleep(0.05)\n", " yield response[: i+1]\n", "\n", "demo = gr.ChatInterface(echo, \n", " additional_inputs=[\n", " gr.Textbox(\"You are helpful AI.\", label=\"System Prompt\"), \n", " gr.Slider(10, 100)\n", " ]\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue().launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatinterface_system_prompt"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "def echo(message, history, system_prompt, tokens):\n", " response = f\"System prompt: {system_prompt}\\n Message: {message}.\"\n", " for i in range(min(len(response), int(tokens))):\n", " time.sleep(0.05)\n", " yield response[: i+1]\n", "\n", "demo = gr.ChatInterface(echo, \n", " additional_inputs=[\n", " gr.Textbox(\"You are helpful AI.\", label=\"System Prompt\"), \n", " gr.Slider(10, 100)\n", " ]\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue().launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/checkbox_component/run.ipynb b/demo/checkbox_component/run.ipynb
index 02a1469c2665e..2fa85f1986b02 100644
--- a/demo/checkbox_component/run.ipynb
+++ b/demo/checkbox_component/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: checkbox_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.Checkbox()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: checkbox_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.Checkbox()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/checkboxgroup_component/run.ipynb b/demo/checkboxgroup_component/run.ipynb
index 91e85fc2dc6a2..bac9ffa2c07a2 100644
--- a/demo/checkboxgroup_component/run.ipynb
+++ b/demo/checkboxgroup_component/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: checkboxgroup_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.CheckboxGroup(choices=[\"First Choice\", \"Second Choice\", \"Third Choice\"])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: checkboxgroup_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.CheckboxGroup(choices=[\"First Choice\", \"Second Choice\", \"Third Choice\"])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/chicago-bikeshare-dashboard/run.ipynb b/demo/chicago-bikeshare-dashboard/run.ipynb
index f936d3be1f983..a8023631fa47e 100644
--- a/demo/chicago-bikeshare-dashboard/run.ipynb
+++ b/demo/chicago-bikeshare-dashboard/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chicago-bikeshare-dashboard"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio psycopg2 matplotlib SQLAlchemy "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "import pandas as pd\n", "\n", "DB_USER = os.getenv(\"DB_USER\")\n", "DB_PASSWORD = os.getenv(\"DB_PASSWORD\")\n", "DB_HOST = os.getenv(\"DB_HOST\")\n", "PORT = 8080\n", "DB_NAME = \"bikeshare\"\n", "\n", "connection_string = (\n", " f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}\"\n", ")\n", "\n", "\n", "def get_count_ride_type():\n", " df = pd.read_sql(\n", " \"\"\"\n", " SELECT COUNT(ride_id) as n, rideable_type\n", " FROM rides\n", " GROUP BY rideable_type\n", " ORDER BY n DESC\n", " \"\"\",\n", " con=connection_string,\n", " )\n", " return df\n", "\n", "\n", "def get_most_popular_stations():\n", "\n", " df = pd.read_sql(\n", " \"\"\"\n", " SELECT COUNT(ride_id) as n, MAX(start_station_name) as station\n", " FROM RIDES\n", " WHERE start_station_name is NOT NULL\n", " GROUP BY start_station_id\n", " ORDER BY n DESC\n", " LIMIT 5\n", " \"\"\",\n", " con=connection_string,\n", " )\n", " return df\n", "\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", " # Chicago Bike Share Dashboard\n", " \n", " This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.\n", " This demo uses psycopg2 but any postgresql client library (SQLAlchemy)\n", " is compatible with gradio.\n", " \n", " Connection credentials are handled by environment variables\n", " defined as secrets in the Space.\n", "\n", " If data were added to the database, the plots in this demo would update\n", " whenever the webpage is reloaded.\n", " \n", " This demo serves as a starting point for your database-connected apps!\n", " \"\"\"\n", " )\n", " with gr.Row():\n", " bike_type = gr.BarPlot(\n", " x=\"rideable_type\",\n", " y='n',\n", " title=\"Number of rides per bicycle type\",\n", " y_title=\"Number of Rides\",\n", " x_title=\"Bicycle Type\",\n", " vertical=False,\n", " tooltip=['rideable_type', \"n\"],\n", " height=300,\n", " width=300,\n", " )\n", " station = gr.BarPlot(\n", " x='station',\n", " y='n',\n", " title=\"Most Popular Stations\",\n", " y_title=\"Number of Rides\",\n", " x_title=\"Station Name\",\n", " vertical=False,\n", " tooltip=['station', 'n'],\n", " height=300,\n", " width=300\n", " )\n", "\n", " demo.load(get_count_ride_type, inputs=None, outputs=bike_type)\n", " demo.load(get_most_popular_stations, inputs=None, outputs=station)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chicago-bikeshare-dashboard"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio psycopg2 matplotlib SQLAlchemy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "import pandas as pd\n", "\n", "DB_USER = os.getenv(\"DB_USER\")\n", "DB_PASSWORD = os.getenv(\"DB_PASSWORD\")\n", "DB_HOST = os.getenv(\"DB_HOST\")\n", "PORT = 8080\n", "DB_NAME = \"bikeshare\"\n", "\n", "connection_string = (\n", " f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}\"\n", ")\n", "\n", "\n", "def get_count_ride_type():\n", " df = pd.read_sql(\n", " \"\"\"\n", " SELECT COUNT(ride_id) as n, rideable_type\n", " FROM rides\n", " GROUP BY rideable_type\n", " ORDER BY n DESC\n", " \"\"\",\n", " con=connection_string,\n", " )\n", " return df\n", "\n", "\n", "def get_most_popular_stations():\n", "\n", " df = pd.read_sql(\n", " \"\"\"\n", " SELECT COUNT(ride_id) as n, MAX(start_station_name) as station\n", " FROM RIDES\n", " WHERE start_station_name is NOT NULL\n", " GROUP BY start_station_id\n", " ORDER BY n DESC\n", " LIMIT 5\n", " \"\"\",\n", " con=connection_string,\n", " )\n", " return df\n", "\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " \"\"\"\n", " # Chicago Bike Share Dashboard\n", " \n", " This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.\n", " This demo uses psycopg2 but any postgresql client library (SQLAlchemy)\n", " is compatible with gradio.\n", " \n", " Connection credentials are handled by environment variables\n", " defined as secrets in the Space.\n", "\n", " If data were added to the database, the plots in this demo would update\n", " whenever the webpage is reloaded.\n", " \n", " This demo serves as a starting point for your database-connected apps!\n", " \"\"\"\n", " )\n", " with gr.Row():\n", " bike_type = gr.BarPlot(\n", " x=\"rideable_type\",\n", " y='n',\n", " title=\"Number of rides per bicycle type\",\n", " y_title=\"Number of Rides\",\n", " x_title=\"Bicycle Type\",\n", " vertical=False,\n", " tooltip=['rideable_type', \"n\"],\n", " height=300,\n", " width=300,\n", " )\n", " station = gr.BarPlot(\n", " x='station',\n", " y='n',\n", " title=\"Most Popular Stations\",\n", " y_title=\"Number of Rides\",\n", " x_title=\"Station Name\",\n", " vertical=False,\n", " tooltip=['station', 'n'],\n", " height=300,\n", " width=300\n", " )\n", "\n", " demo.load(get_count_ride_type, inputs=None, outputs=bike_type)\n", " demo.load(get_most_popular_stations, inputs=None, outputs=station)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
\ No newline at end of file
diff --git a/demo/clear_components/run.ipynb b/demo/clear_components/run.ipynb
index 4316a9a7717b1..a5b0c358523c2 100644
--- a/demo/clear_components/run.ipynb
+++ b/demo/clear_components/run.ipynb
@@ -1 +1,5 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: clear_components"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/clear_components/__init__.py"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from datetime import datetime\n", "import os\n", "import random\n", "import string\n", "import pandas as pd\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "\n", "def random_plot():\n", " start_year = 2020\n", " x = np.arange(start_year, start_year + 5)\n", " year_count = x.shape[0]\n", " plt_format = \"-\"\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", " series = np.arange(0, year_count, dtype=float)\n", " series = series**2\n", " series += np.random.rand(year_count)\n", " ax.plot(x, series, plt_format)\n", " return fig\n", "\n", "\n", "images = [\n", " \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", " \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", "]\n", "file_dir = os.path.join(os.path.abspath(''), \"..\", \"kitchen_sink\", \"files\")\n", "model3d_dir = os.path.join(os.path.abspath(''), \"..\", \"model3D\", \"files\")\n", "highlighted_text_output_1 = [\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9988978,\n", " \"index\": 2,\n", " \"word\": \"Chicago\",\n", " \"start\": 5,\n", " \"end\": 12,\n", " },\n", " {\n", " \"entity\": \"I-MISC\",\n", " \"score\": 0.9958592,\n", " \"index\": 5,\n", " \"word\": \"Pakistani\",\n", " \"start\": 22,\n", " \"end\": 31,\n", " },\n", "]\n", "highlighted_text_output_2 = [\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9988978,\n", " \"index\": 2,\n", " \"word\": \"Chicago\",\n", " \"start\": 5,\n", " \"end\": 12,\n", " },\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9958592,\n", " \"index\": 5,\n", " \"word\": \"Pakistan\",\n", " \"start\": 22,\n", " \"end\": 30,\n", " },\n", "]\n", "\n", "highlighted_text = \"Does Chicago have any Pakistani restaurants\"\n", "\n", "\n", "def random_model3d():\n", " model_3d = random.choice(\n", " [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != \"source.txt\"]\n", " )\n", " return model_3d\n", "\n", "\n", "\n", "components = [\n", " gr.Textbox(value=lambda: datetime.now(), label=\"Current Time\"),\n", " gr.Number(value=lambda: random.random(), label=\"Random Percentage\"),\n", " gr.Slider(minimum=0, maximum=100, randomize=True, label=\"Slider with randomize\"),\n", " gr.Slider(\n", " minimum=0,\n", " maximum=1,\n", " value=lambda: random.random(),\n", " label=\"Slider with value func\",\n", " ),\n", " gr.Checkbox(value=lambda: random.random() > 0.5, label=\"Random Checkbox\"),\n", " gr.CheckboxGroup(\n", " choices=[\"a\", \"b\", \"c\", \"d\"],\n", " value=lambda: random.choice([\"a\", \"b\", \"c\", \"d\"]),\n", " label=\"Random CheckboxGroup\",\n", " ),\n", " gr.Radio(\n", " choices=list(string.ascii_lowercase),\n", " value=lambda: random.choice(string.ascii_lowercase),\n", " ),\n", " gr.Dropdown(\n", " choices=[\"a\", \"b\", \"c\", \"d\", \"e\"],\n", " value=lambda: random.choice([\"a\", \"b\", \"c\"]),\n", " ),\n", " gr.Image(\n", " value=lambda: random.choice(images)\n", " ),\n", " gr.Video(value=lambda: os.path.join(file_dir, \"world.mp4\")),\n", " gr.Audio(value=lambda: os.path.join(file_dir, \"cantina.wav\")),\n", " gr.File(\n", " value=lambda: random.choice(\n", " [os.path.join(file_dir, img) for img in os.listdir(file_dir)]\n", " )\n", " ),\n", " gr.Dataframe(\n", " value=lambda: pd.DataFrame({\"random_number_rows\": range(5)}, columns=[\"one\", \"two\", \"three\"])\n", " ),\n", " gr.ColorPicker(value=lambda: random.choice([\"#000000\", \"#ff0000\", \"#0000FF\"])),\n", " gr.Label(value=lambda: random.choice([\"Pedestrian\", \"Car\", \"Cyclist\"])),\n", " gr.HighlightedText(\n", " value=lambda: random.choice(\n", " [\n", " {\"text\": highlighted_text, \"entities\": highlighted_text_output_1},\n", " {\"text\": highlighted_text, \"entities\": highlighted_text_output_2},\n", " ]\n", " ),\n", " ),\n", " gr.JSON(value=lambda: random.choice([{\"a\": 1}, {\"b\": 2}])),\n", " gr.HTML(\n", " value=lambda: random.choice(\n", " [\n", " 'I am red
',\n", " 'I am blue
',\n", " ]\n", " )\n", " ),\n", " gr.Gallery(\n", " value=lambda: images\n", " ),\n", " gr.Model3D(value=random_model3d),\n", " gr.Plot(value=random_plot),\n", " gr.Markdown(value=lambda: f\"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}\"),\n", "]\n", "\n", "\n", "def evaluate_values(*args):\n", " are_false = []\n", " for a in args:\n", " if isinstance(a, (pd.DataFrame, np.ndarray)):\n", " are_false.append(not a.any().any())\n", " elif isinstance(a, str) and a.startswith(\"#\"):\n", " are_false.append(a == \"#000000\")\n", " else:\n", " are_false.append(not a)\n", " return all(are_false)\n", "\n", "\n", "with gr.Blocks() as demo:\n", " for i, component in enumerate(components):\n", " component.label = f\"component_{str(i).zfill(2)}\"\n", " component.render()\n", " clear = gr.ClearButton(value=\"Clear\", components=components)\n", " result = gr.Textbox(label=\"Are all cleared?\")\n", " hide = gr.Button(value=\"Hide\")\n", " reveal = gr.Button(value=\"Reveal\")\n", " hide.click(\n", " lambda: [c.__class__(visible=False) for c in components],\n", " inputs=[],\n", " outputs=components\n", " )\n", " reveal.click(\n", " lambda: [c.__class__(visible=True) for c in components],\n", " inputs=[],\n", " outputs=components\n", " )\n", " get_value = gr.Button(value=\"Get Values\")\n", " get_value.click(evaluate_values, components, result)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +<<<<<<< HEAD +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: clear_components"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/clear_components/__init__.py"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from datetime import datetime\n", "import os\n", "import random\n", "import string\n", "import pandas as pd\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "\n", "def random_plot():\n", " start_year = 2020\n", " x = np.arange(start_year, start_year + 5)\n", " year_count = x.shape[0]\n", " plt_format = \"-\"\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", " series = np.arange(0, year_count, dtype=float)\n", " series = series**2\n", " series += np.random.rand(year_count)\n", " ax.plot(x, series, plt_format)\n", " return fig\n", "\n", "\n", "images = [\n", " \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", " \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", "]\n", "file_dir = os.path.join(os.path.abspath(''), \"..\", \"kitchen_sink\", \"files\")\n", "model3d_dir = os.path.join(os.path.abspath(''), \"..\", \"model3D\", \"files\")\n", "highlighted_text_output_1 = [\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9988978,\n", " \"index\": 2,\n", " \"word\": \"Chicago\",\n", " \"start\": 5,\n", " \"end\": 12,\n", " },\n", " {\n", " \"entity\": \"I-MISC\",\n", " \"score\": 0.9958592,\n", " \"index\": 5,\n", " \"word\": \"Pakistani\",\n", " \"start\": 22,\n", " \"end\": 31,\n", " },\n", "]\n", "highlighted_text_output_2 = [\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9988978,\n", " \"index\": 2,\n", " \"word\": \"Chicago\",\n", " \"start\": 5,\n", " \"end\": 12,\n", " },\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9958592,\n", " \"index\": 5,\n", " \"word\": \"Pakistan\",\n", " \"start\": 22,\n", " \"end\": 30,\n", " },\n", "]\n", "\n", "highlighted_text = \"Does Chicago have any Pakistani restaurants\"\n", "\n", "\n", "def random_model3d():\n", " model_3d = random.choice(\n", " [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != \"source.txt\"]\n", " )\n", " return model_3d\n", "\n", "\n", "\n", "components = [\n", " gr.Textbox(value=lambda: datetime.now(), label=\"Current Time\"),\n", " gr.Number(value=lambda: random.random(), label=\"Random Percentage\"),\n", " gr.Slider(minimum=0, maximum=100, randomize=True, label=\"Slider with randomize\"),\n", " gr.Slider(\n", " minimum=0,\n", " maximum=1,\n", " value=lambda: random.random(),\n", " label=\"Slider with value func\",\n", " ),\n", " gr.Checkbox(value=lambda: random.random() > 0.5, label=\"Random Checkbox\"),\n", " gr.CheckboxGroup(\n", " choices=[\"a\", \"b\", \"c\", \"d\"],\n", " value=lambda: random.choice([\"a\", \"b\", \"c\", \"d\"]),\n", " label=\"Random CheckboxGroup\",\n", " ),\n", " gr.Radio(\n", " choices=list(string.ascii_lowercase),\n", " value=lambda: random.choice(string.ascii_lowercase),\n", " ),\n", " gr.Dropdown(\n", " choices=[\"a\", \"b\", \"c\", \"d\", \"e\"],\n", " value=lambda: random.choice([\"a\", \"b\", \"c\"]),\n", " ),\n", " gr.Image(\n", " value=lambda: random.choice(images)\n", " ),\n", " gr.Video(value=lambda: os.path.join(file_dir, \"world.mp4\")),\n", " gr.Audio(value=lambda: os.path.join(file_dir, \"cantina.wav\")),\n", " gr.File(\n", " value=lambda: random.choice(\n", " [os.path.join(file_dir, img) for img in os.listdir(file_dir)]\n", " )\n", " ),\n", " gr.Dataframe(\n", " value=lambda: pd.DataFrame({\"random_number_rows\": range(5)}, columns=[\"one\", \"two\", \"three\"])\n", " ),\n", " gr.ColorPicker(value=lambda: random.choice([\"#000000\", \"#ff0000\", \"#0000FF\"])),\n", " gr.Label(value=lambda: random.choice([\"Pedestrian\", \"Car\", \"Cyclist\"])),\n", " gr.HighlightedText(\n", " value=lambda: random.choice(\n", " [\n", " {\"text\": highlighted_text, \"entities\": highlighted_text_output_1},\n", " {\"text\": highlighted_text, \"entities\": highlighted_text_output_2},\n", " ]\n", " ),\n", " ),\n", " gr.JSON(value=lambda: random.choice([{\"a\": 1}, {\"b\": 2}])),\n", " gr.HTML(\n", " value=lambda: random.choice(\n", " [\n", " 'I am red
',\n", " 'I am blue
',\n", " ]\n", " )\n", " ),\n", " gr.Gallery(\n", " value=lambda: images\n", " ),\n", " gr.Model3D(value=random_model3d),\n", " gr.Plot(value=random_plot),\n", " gr.Markdown(value=lambda: f\"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}\"),\n", "]\n", "\n", "\n", "def evaluate_values(*args):\n", " are_false = []\n", " for a in args:\n", " if isinstance(a, (pd.DataFrame, np.ndarray)):\n", " are_false.append(not a.any().any())\n", " elif isinstance(a, str) and a.startswith(\"#\"):\n", " are_false.append(a == \"#000000\")\n", " else:\n", " are_false.append(not a)\n", " return all(are_false)\n", "\n", "\n", "with gr.Blocks() as demo:\n", " for i, component in enumerate(components):\n", " component.label = f\"component_{str(i).zfill(2)}\"\n", " component.render()\n", " clear = gr.ClearButton(value=\"Clear\", components=components)\n", " result = gr.Textbox(label=\"Are all cleared?\")\n", " hide = gr.Button(value=\"Hide\")\n", " reveal = gr.Button(value=\"Reveal\")\n", " hide.click(\n", " lambda: [c.__class__(visible=False) for c in components],\n", " inputs=[],\n", " outputs=components\n", " )\n", " reveal.click(\n", " lambda: [c.__class__(visible=True) for c in components],\n", " inputs=[],\n", " outputs=components\n", " )\n", " get_value = gr.Button(value=\"Get Values\")\n", " get_value.click(evaluate_values, components, result)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +======= +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: clear_components"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/clear_components/__init__.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from datetime import datetime\n", "import os\n", "import random\n", "import string\n", "import pandas as pd\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "\n", "def random_plot():\n", " start_year = 2020\n", " x = np.arange(start_year, start_year + 5)\n", " year_count = x.shape[0]\n", " plt_format = \"-\"\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", " series = np.arange(0, year_count, dtype=float)\n", " series = series**2\n", " series += np.random.rand(year_count)\n", " ax.plot(x, series, plt_format)\n", " return fig\n", "\n", "\n", "images = [\n", " \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", " \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", "]\n", "file_dir = os.path.join(os.path.abspath(''), \"..\", \"kitchen_sink\", \"files\")\n", "model3d_dir = os.path.join(os.path.abspath(''), \"..\", \"model3D\", \"files\")\n", "highlighted_text_output_1 = [\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9988978,\n", " \"index\": 2,\n", " \"word\": \"Chicago\",\n", " \"start\": 5,\n", " \"end\": 12,\n", " },\n", " {\n", " \"entity\": \"I-MISC\",\n", " \"score\": 0.9958592,\n", " \"index\": 5,\n", " \"word\": \"Pakistani\",\n", " \"start\": 22,\n", " \"end\": 31,\n", " },\n", "]\n", "highlighted_text_output_2 = [\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9988978,\n", " \"index\": 2,\n", " \"word\": \"Chicago\",\n", " \"start\": 5,\n", " \"end\": 12,\n", " },\n", " {\n", " \"entity\": \"I-LOC\",\n", " \"score\": 0.9958592,\n", " \"index\": 5,\n", " \"word\": \"Pakistan\",\n", " \"start\": 22,\n", " \"end\": 30,\n", " },\n", "]\n", "\n", "highlighted_text = \"Does Chicago have any Pakistani restaurants\"\n", "\n", "\n", "def random_model3d():\n", " model_3d = random.choice(\n", " [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != \"source.txt\"]\n", " )\n", " return model_3d\n", "\n", "\n", "\n", "components = [\n", " gr.Textbox(value=lambda: datetime.now(), label=\"Current Time\"),\n", " gr.Number(value=lambda: random.random(), label=\"Random Percentage\"),\n", " gr.Slider(minimum=0, maximum=100, randomize=True, label=\"Slider with randomize\"),\n", " gr.Slider(\n", " minimum=0,\n", " maximum=1,\n", " value=lambda: random.random(),\n", " label=\"Slider with value func\",\n", " ),\n", " gr.Checkbox(value=lambda: random.random() > 0.5, label=\"Random Checkbox\"),\n", " gr.CheckboxGroup(\n", " choices=[\"a\", \"b\", \"c\", \"d\"],\n", " value=lambda: random.choice([\"a\", \"b\", \"c\", \"d\"]),\n", " label=\"Random CheckboxGroup\",\n", " ),\n", " gr.Radio(\n", " choices=list(string.ascii_lowercase),\n", " value=lambda: random.choice(string.ascii_lowercase),\n", " ),\n", " gr.Dropdown(\n", " choices=[\"a\", \"b\", \"c\", \"d\", \"e\"],\n", " value=lambda: random.choice([\"a\", \"b\", \"c\"]),\n", " ),\n", " gr.Image(\n", " value=lambda: random.choice(images)\n", " ),\n", " gr.Video(value=lambda: os.path.join(file_dir, \"world.mp4\")),\n", " gr.Audio(value=lambda: os.path.join(file_dir, \"cantina.wav\")),\n", " gr.File(\n", " value=lambda: random.choice(\n", " [os.path.join(file_dir, img) for img in os.listdir(file_dir)]\n", " )\n", " ),\n", " gr.Dataframe(\n", " value=lambda: pd.DataFrame({\"random_number_rows\": range(5)}, columns=[\"one\", \"two\", \"three\"])\n", " ),\n", " gr.Timeseries(value=lambda: os.path.join(file_dir, \"time.csv\")),\n", " gr.ColorPicker(value=lambda: random.choice([\"#000000\", \"#ff0000\", \"#0000FF\"])),\n", " gr.Label(value=lambda: random.choice([\"Pedestrian\", \"Car\", \"Cyclist\"])),\n", " gr.HighlightedText(\n", " value=lambda: random.choice(\n", " [\n", " {\"text\": highlighted_text, \"entities\": highlighted_text_output_1},\n", " {\"text\": highlighted_text, \"entities\": highlighted_text_output_2},\n", " ]\n", " ),\n", " ),\n", " gr.JSON(value=lambda: random.choice([{\"a\": 1}, {\"b\": 2}])),\n", " gr.HTML(\n", " value=lambda: random.choice(\n", " [\n", " 'I am red
',\n", " 'I am blue
',\n", " ]\n", " )\n", " ),\n", " gr.Gallery(\n", " value=lambda: images\n", " ),\n", " gr.Model3D(value=random_model3d),\n", " gr.Plot(value=random_plot),\n", " gr.Markdown(value=lambda: f\"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}\"),\n", "]\n", "\n", "\n", "def evaluate_values(*args):\n", " are_false = []\n", " for a in args:\n", " if isinstance(a, (pd.DataFrame, np.ndarray)):\n", " are_false.append(not a.any().any())\n", " elif isinstance(a, str) and a.startswith(\"#\"):\n", " are_false.append(a == \"#000000\")\n", " else:\n", " are_false.append(not a)\n", " return all(are_false)\n", "\n", "\n", "with gr.Blocks() as demo:\n", " for i, component in enumerate(components):\n", " component.label = f\"component_{str(i).zfill(2)}\"\n", " component.render()\n", " clear = gr.ClearButton(value=\"Clear\", components=components)\n", " result = gr.Textbox(label=\"Are all cleared?\")\n", " hide = gr.Button(value=\"Hide\")\n", " reveal = gr.Button(value=\"Reveal\")\n", " hide.click(\n", " lambda: [c.__class__(visible=False) for c in components],\n", " inputs=[],\n", " outputs=components\n", " )\n", " reveal.click(\n", " lambda: [c.__class__(visible=True) for c in components],\n", " inputs=[],\n", " outputs=components\n", " )\n", " get_value = gr.Button(value=\"Get Values\")\n", " get_value.click(evaluate_values, components, result)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +>>>>>>> main diff --git a/demo/clearbutton_component/run.ipynb b/demo/clearbutton_component/run.ipynb index cf3b373bdf469..ddcf635a234ac 100644 --- a/demo/clearbutton_component/run.ipynb +++ b/demo/clearbutton_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: clearbutton_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "with gr.Blocks() as demo:\n", " textbox = gr.Textbox(value=\"This is some text\")\n", " gr.ClearButton(textbox)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: clearbutton_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "with gr.Blocks() as demo:\n", " textbox = gr.Textbox(value=\"This is some text\")\n", " gr.ClearButton(textbox)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/clustering/run.ipynb b/demo/clustering/run.ipynb index adfb40bc63556..b21a4c5d6ff99 100644 --- a/demo/clustering/run.ipynb +++ b/demo/clustering/run.ipynb @@ -1 +1,5 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: clustering\n", "### This demo built with Blocks generates 9 plots based on the input.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio matplotlib>=3.5.2 scikit-learn>=1.0.1 "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import math\n", "from functools import partial\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.cluster import (\n", " AgglomerativeClustering, Birch, DBSCAN, KMeans, MeanShift, OPTICS, SpectralClustering, estimate_bandwidth\n", ")\n", "from sklearn.datasets import make_blobs, make_circles, make_moons\n", "from sklearn.mixture import GaussianMixture\n", "from sklearn.neighbors import kneighbors_graph\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "plt.style.use('seaborn-v0_8')\n", "SEED = 0\n", "MAX_CLUSTERS = 10\n", "N_SAMPLES = 1000\n", "N_COLS = 3\n", "FIGSIZE = 7, 7 # does not affect size in webpage\n", "COLORS = [\n", " 'blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'gray', 'olive', 'cyan'\n", "]\n", "if len(COLORS) <= MAX_CLUSTERS:\n", " raise ValueError(\"Not enough different colors for all clusters\")\n", "np.random.seed(SEED)\n", "\n", "\n", "def normalize(X):\n", " return StandardScaler().fit_transform(X)\n", "\n", "def get_regular(n_clusters):\n", " # spiral pattern\n", " centers = [\n", " [0, 0],\n", " [1, 0],\n", " [1, 1],\n", " [0, 1],\n", " [-1, 1],\n", " [-1, 0],\n", " [-1, -1],\n", " [0, -1],\n", " [1, -1],\n", " [2, -1],\n", " ][:n_clusters]\n", " assert len(centers) == n_clusters\n", " X, labels = make_blobs(n_samples=N_SAMPLES, centers=centers, cluster_std=0.25, random_state=SEED)\n", " return normalize(X), labels\n", "\n", "\n", "def get_circles(n_clusters):\n", " X, labels = make_circles(n_samples=N_SAMPLES, factor=0.5, noise=0.05, random_state=SEED)\n", " return normalize(X), labels\n", "\n", "\n", "def get_moons(n_clusters):\n", " X, labels = make_moons(n_samples=N_SAMPLES, noise=0.05, random_state=SEED)\n", " return normalize(X), labels\n", "\n", "\n", "def get_noise(n_clusters):\n", " np.random.seed(SEED)\n", " X, labels = np.random.rand(N_SAMPLES, 2), np.random.randint(0, n_clusters, size=(N_SAMPLES,))\n", " return normalize(X), labels\n", "\n", "\n", "def get_anisotropic(n_clusters):\n", " X, labels = make_blobs(n_samples=N_SAMPLES, centers=n_clusters, random_state=170)\n", " transformation = [[0.6, -0.6], [-0.4, 0.8]]\n", " X = np.dot(X, transformation)\n", " return X, labels\n", "\n", "\n", "def get_varied(n_clusters):\n", " cluster_std = [1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0][:n_clusters]\n", " assert len(cluster_std) == n_clusters\n", " X, labels = make_blobs(\n", " n_samples=N_SAMPLES, centers=n_clusters, cluster_std=cluster_std, random_state=SEED\n", " )\n", " return normalize(X), labels\n", "\n", "\n", "def get_spiral(n_clusters):\n", " # from https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html\n", " np.random.seed(SEED)\n", " t = 1.5 * np.pi * (1 + 3 * np.random.rand(1, N_SAMPLES))\n", " x = t * np.cos(t)\n", " y = t * np.sin(t)\n", " X = np.concatenate((x, y))\n", " X += 0.7 * np.random.randn(2, N_SAMPLES)\n", " X = np.ascontiguousarray(X.T)\n", "\n", " labels = np.zeros(N_SAMPLES, dtype=int)\n", " return normalize(X), labels\n", "\n", "\n", "DATA_MAPPING = {\n", " 'regular': get_regular,\n", " 'circles': get_circles,\n", " 'moons': get_moons,\n", " 'spiral': get_spiral,\n", " 'noise': get_noise,\n", " 'anisotropic': get_anisotropic,\n", " 'varied': get_varied,\n", "}\n", "\n", "\n", "def get_groundtruth_model(X, labels, n_clusters, **kwargs):\n", " # dummy model to show true label distribution\n", " class Dummy:\n", " def __init__(self, y):\n", " self.labels_ = labels\n", "\n", " return Dummy(labels)\n", "\n", "\n", "def get_kmeans(X, labels, n_clusters, **kwargs):\n", " model = KMeans(init=\"k-means++\", n_clusters=n_clusters, n_init=10, random_state=SEED)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_dbscan(X, labels, n_clusters, **kwargs):\n", " model = DBSCAN(eps=0.3)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_agglomerative(X, labels, n_clusters, **kwargs):\n", " connectivity = kneighbors_graph(\n", " X, n_neighbors=n_clusters, include_self=False\n", " )\n", " # make connectivity symmetric\n", " connectivity = 0.5 * (connectivity + connectivity.T)\n", " model = AgglomerativeClustering(\n", " n_clusters=n_clusters, linkage=\"ward\", connectivity=connectivity\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_meanshift(X, labels, n_clusters, **kwargs):\n", " bandwidth = estimate_bandwidth(X, quantile=0.25)\n", " model = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_spectral(X, labels, n_clusters, **kwargs):\n", " model = SpectralClustering(\n", " n_clusters=n_clusters,\n", " eigen_solver=\"arpack\",\n", " affinity=\"nearest_neighbors\",\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_optics(X, labels, n_clusters, **kwargs):\n", " model = OPTICS(\n", " min_samples=7,\n", " xi=0.05,\n", " min_cluster_size=0.1,\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_birch(X, labels, n_clusters, **kwargs):\n", " model = Birch(n_clusters=n_clusters)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_gaussianmixture(X, labels, n_clusters, **kwargs):\n", " model = GaussianMixture(\n", " n_components=n_clusters, covariance_type=\"full\", random_state=SEED,\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "MODEL_MAPPING = {\n", " 'True labels': get_groundtruth_model,\n", " 'KMeans': get_kmeans,\n", " 'DBSCAN': get_dbscan,\n", " 'MeanShift': get_meanshift,\n", " 'SpectralClustering': get_spectral,\n", " 'OPTICS': get_optics,\n", " 'Birch': get_birch,\n", " 'GaussianMixture': get_gaussianmixture,\n", " 'AgglomerativeClustering': get_agglomerative,\n", "}\n", "\n", "\n", "def plot_clusters(ax, X, labels):\n", " set_clusters = set(labels)\n", " set_clusters.discard(-1) # -1 signifiies outliers, which we plot separately\n", " for label, color in zip(sorted(set_clusters), COLORS):\n", " idx = labels == label\n", " if not sum(idx):\n", " continue\n", " ax.scatter(X[idx, 0], X[idx, 1], color=color)\n", "\n", " # show outliers (if any)\n", " idx = labels == -1\n", " if sum(idx):\n", " ax.scatter(X[idx, 0], X[idx, 1], c='k', marker='x')\n", "\n", " ax.grid(None)\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " return ax\n", "\n", "\n", "def cluster(dataset: str, n_clusters: int, clustering_algorithm: str):\n", " if isinstance(n_clusters, dict):\n", " n_clusters = n_clusters['value']\n", " else:\n", " n_clusters = int(n_clusters)\n", "\n", " X, labels = DATA_MAPPING[dataset](n_clusters)\n", " model = MODEL_MAPPING[clustering_algorithm](X, labels, n_clusters=n_clusters)\n", " if hasattr(model, \"labels_\"):\n", " y_pred = model.labels_.astype(int)\n", " else:\n", " y_pred = model.predict(X)\n", "\n", " fig, ax = plt.subplots(figsize=FIGSIZE)\n", "\n", " plot_clusters(ax, X, y_pred)\n", " ax.set_title(clustering_algorithm, fontsize=16)\n", "\n", " return fig\n", "\n", "\n", "title = \"Clustering with Scikit-learn\"\n", "description = (\n", " \"This example shows how different clustering algorithms work. Simply pick \"\n", " \"the dataset and the number of clusters to see how the clustering algorithms work. \"\n", " \"Colored circles are (predicted) labels and black x are outliers.\"\n", ")\n", "\n", "\n", "def iter_grid(n_rows, n_cols):\n", " # create a grid using gradio Block\n", " for _ in range(n_rows):\n", " with gr.Row():\n", " for _ in range(n_cols):\n", " with gr.Column():\n", " yield\n", "\n", "with gr.Blocks(title=title) as demo:\n", " gr.HTML(f\"{title}\")\n", " gr.Markdown(description)\n", "\n", " input_models = list(MODEL_MAPPING)\n", " input_data = gr.Radio(\n", " list(DATA_MAPPING),\n", " value=\"regular\",\n", " label=\"dataset\"\n", " )\n", " input_n_clusters = gr.Slider(\n", " minimum=1,\n", " maximum=MAX_CLUSTERS,\n", " value=4,\n", " step=1,\n", " label='Number of clusters'\n", " )\n", " n_rows = int(math.ceil(len(input_models) / N_COLS))\n", " counter = 0\n", " for _ in iter_grid(n_rows, N_COLS):\n", " if counter >= len(input_models):\n", " break\n", "\n", " input_model = input_models[counter]\n", " plot = gr.Plot(label=input_model)\n", " fn = partial(cluster, clustering_algorithm=input_model)\n", " input_data.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)\n", " input_n_clusters.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)\n", " counter += 1\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +<<<<<<< HEAD +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: clustering\n", "### This demo built with Blocks generates 9 plots based on the input.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio matplotlib>=3.5.2 scikit-learn>=1.0.1 "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import math\n", "from functools import partial\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.cluster import (\n", " AgglomerativeClustering, Birch, DBSCAN, KMeans, MeanShift, OPTICS, SpectralClustering, estimate_bandwidth\n", ")\n", "from sklearn.datasets import make_blobs, make_circles, make_moons\n", "from sklearn.mixture import GaussianMixture\n", "from sklearn.neighbors import kneighbors_graph\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "plt.style.use('seaborn-v0_8')\n", "SEED = 0\n", "MAX_CLUSTERS = 10\n", "N_SAMPLES = 1000\n", "N_COLS = 3\n", "FIGSIZE = 7, 7 # does not affect size in webpage\n", "COLORS = [\n", " 'blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'gray', 'olive', 'cyan'\n", "]\n", "if len(COLORS) <= MAX_CLUSTERS:\n", " raise ValueError(\"Not enough different colors for all clusters\")\n", "np.random.seed(SEED)\n", "\n", "\n", "def normalize(X):\n", " return StandardScaler().fit_transform(X)\n", "\n", "def get_regular(n_clusters):\n", " # spiral pattern\n", " centers = [\n", " [0, 0],\n", " [1, 0],\n", " [1, 1],\n", " [0, 1],\n", " [-1, 1],\n", " [-1, 0],\n", " [-1, -1],\n", " [0, -1],\n", " [1, -1],\n", " [2, -1],\n", " ][:n_clusters]\n", " assert len(centers) == n_clusters\n", " X, labels = make_blobs(n_samples=N_SAMPLES, centers=centers, cluster_std=0.25, random_state=SEED)\n", " return normalize(X), labels\n", "\n", "\n", "def get_circles(n_clusters):\n", " X, labels = make_circles(n_samples=N_SAMPLES, factor=0.5, noise=0.05, random_state=SEED)\n", " return normalize(X), labels\n", "\n", "\n", "def get_moons(n_clusters):\n", " X, labels = make_moons(n_samples=N_SAMPLES, noise=0.05, random_state=SEED)\n", " return normalize(X), labels\n", "\n", "\n", "def get_noise(n_clusters):\n", " np.random.seed(SEED)\n", " X, labels = np.random.rand(N_SAMPLES, 2), np.random.randint(0, n_clusters, size=(N_SAMPLES,))\n", " return normalize(X), labels\n", "\n", "\n", "def get_anisotropic(n_clusters):\n", " X, labels = make_blobs(n_samples=N_SAMPLES, centers=n_clusters, random_state=170)\n", " transformation = [[0.6, -0.6], [-0.4, 0.8]]\n", " X = np.dot(X, transformation)\n", " return X, labels\n", "\n", "\n", "def get_varied(n_clusters):\n", " cluster_std = [1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0][:n_clusters]\n", " assert len(cluster_std) == n_clusters\n", " X, labels = make_blobs(\n", " n_samples=N_SAMPLES, centers=n_clusters, cluster_std=cluster_std, random_state=SEED\n", " )\n", " return normalize(X), labels\n", "\n", "\n", "def get_spiral(n_clusters):\n", " # from https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html\n", " np.random.seed(SEED)\n", " t = 1.5 * np.pi * (1 + 3 * np.random.rand(1, N_SAMPLES))\n", " x = t * np.cos(t)\n", " y = t * np.sin(t)\n", " X = np.concatenate((x, y))\n", " X += 0.7 * np.random.randn(2, N_SAMPLES)\n", " X = np.ascontiguousarray(X.T)\n", "\n", " labels = np.zeros(N_SAMPLES, dtype=int)\n", " return normalize(X), labels\n", "\n", "\n", "DATA_MAPPING = {\n", " 'regular': get_regular,\n", " 'circles': get_circles,\n", " 'moons': get_moons,\n", " 'spiral': get_spiral,\n", " 'noise': get_noise,\n", " 'anisotropic': get_anisotropic,\n", " 'varied': get_varied,\n", "}\n", "\n", "\n", "def get_groundtruth_model(X, labels, n_clusters, **kwargs):\n", " # dummy model to show true label distribution\n", " class Dummy:\n", " def __init__(self, y):\n", " self.labels_ = labels\n", "\n", " return Dummy(labels)\n", "\n", "\n", "def get_kmeans(X, labels, n_clusters, **kwargs):\n", " model = KMeans(init=\"k-means++\", n_clusters=n_clusters, n_init=10, random_state=SEED)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_dbscan(X, labels, n_clusters, **kwargs):\n", " model = DBSCAN(eps=0.3)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_agglomerative(X, labels, n_clusters, **kwargs):\n", " connectivity = kneighbors_graph(\n", " X, n_neighbors=n_clusters, include_self=False\n", " )\n", " # make connectivity symmetric\n", " connectivity = 0.5 * (connectivity + connectivity.T)\n", " model = AgglomerativeClustering(\n", " n_clusters=n_clusters, linkage=\"ward\", connectivity=connectivity\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_meanshift(X, labels, n_clusters, **kwargs):\n", " bandwidth = estimate_bandwidth(X, quantile=0.25)\n", " model = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_spectral(X, labels, n_clusters, **kwargs):\n", " model = SpectralClustering(\n", " n_clusters=n_clusters,\n", " eigen_solver=\"arpack\",\n", " affinity=\"nearest_neighbors\",\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_optics(X, labels, n_clusters, **kwargs):\n", " model = OPTICS(\n", " min_samples=7,\n", " xi=0.05,\n", " min_cluster_size=0.1,\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_birch(X, labels, n_clusters, **kwargs):\n", " model = Birch(n_clusters=n_clusters)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_gaussianmixture(X, labels, n_clusters, **kwargs):\n", " model = GaussianMixture(\n", " n_components=n_clusters, covariance_type=\"full\", random_state=SEED,\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "MODEL_MAPPING = {\n", " 'True labels': get_groundtruth_model,\n", " 'KMeans': get_kmeans,\n", " 'DBSCAN': get_dbscan,\n", " 'MeanShift': get_meanshift,\n", " 'SpectralClustering': get_spectral,\n", " 'OPTICS': get_optics,\n", " 'Birch': get_birch,\n", " 'GaussianMixture': get_gaussianmixture,\n", " 'AgglomerativeClustering': get_agglomerative,\n", "}\n", "\n", "\n", "def plot_clusters(ax, X, labels):\n", " set_clusters = set(labels)\n", " set_clusters.discard(-1) # -1 signifiies outliers, which we plot separately\n", " for label, color in zip(sorted(set_clusters), COLORS):\n", " idx = labels == label\n", " if not sum(idx):\n", " continue\n", " ax.scatter(X[idx, 0], X[idx, 1], color=color)\n", "\n", " # show outliers (if any)\n", " idx = labels == -1\n", " if sum(idx):\n", " ax.scatter(X[idx, 0], X[idx, 1], c='k', marker='x')\n", "\n", " ax.grid(None)\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " return ax\n", "\n", "\n", "def cluster(dataset: str, n_clusters: int, clustering_algorithm: str):\n", " if isinstance(n_clusters, dict):\n", " n_clusters = n_clusters['value']\n", " else:\n", " n_clusters = int(n_clusters)\n", "\n", " X, labels = DATA_MAPPING[dataset](n_clusters)\n", " model = MODEL_MAPPING[clustering_algorithm](X, labels, n_clusters=n_clusters)\n", " if hasattr(model, \"labels_\"):\n", " y_pred = model.labels_.astype(int)\n", " else:\n", " y_pred = model.predict(X)\n", "\n", " fig, ax = plt.subplots(figsize=FIGSIZE)\n", "\n", " plot_clusters(ax, X, y_pred)\n", " ax.set_title(clustering_algorithm, fontsize=16)\n", "\n", " return fig\n", "\n", "\n", "title = \"Clustering with Scikit-learn\"\n", "description = (\n", " \"This example shows how different clustering algorithms work. Simply pick \"\n", " \"the dataset and the number of clusters to see how the clustering algorithms work. \"\n", " \"Colored circles are (predicted) labels and black x are outliers.\"\n", ")\n", "\n", "\n", "def iter_grid(n_rows, n_cols):\n", " # create a grid using gradio Block\n", " for _ in range(n_rows):\n", " with gr.Row():\n", " for _ in range(n_cols):\n", " with gr.Column():\n", " yield\n", "\n", "with gr.Blocks(title=title) as demo:\n", " gr.HTML(f\"{title}\")\n", " gr.Markdown(description)\n", "\n", " input_models = list(MODEL_MAPPING)\n", " input_data = gr.Radio(\n", " list(DATA_MAPPING),\n", " value=\"regular\",\n", " label=\"dataset\"\n", " )\n", " input_n_clusters = gr.Slider(\n", " minimum=1,\n", " maximum=MAX_CLUSTERS,\n", " value=4,\n", " step=1,\n", " label='Number of clusters'\n", " )\n", " n_rows = int(math.ceil(len(input_models) / N_COLS))\n", " counter = 0\n", " for _ in iter_grid(n_rows, N_COLS):\n", " if counter >= len(input_models):\n", " break\n", "\n", " input_model = input_models[counter]\n", " plot = gr.Plot(label=input_model)\n", " fn = partial(cluster, clustering_algorithm=input_model)\n", " input_data.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)\n", " input_n_clusters.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)\n", " counter += 1\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +======= +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: clustering\n", "### This demo built with Blocks generates 9 plots based on the input.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio matplotlib>=3.5.2 scikit-learn>=1.0.1 "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import math\n", "from functools import partial\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.cluster import (\n", " AgglomerativeClustering, Birch, DBSCAN, KMeans, MeanShift, OPTICS, SpectralClustering, estimate_bandwidth\n", ")\n", "from sklearn.datasets import make_blobs, make_circles, make_moons\n", "from sklearn.mixture import GaussianMixture\n", "from sklearn.neighbors import kneighbors_graph\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "plt.style.use('seaborn-v0_8')\n", "SEED = 0\n", "MAX_CLUSTERS = 10\n", "N_SAMPLES = 1000\n", "N_COLS = 3\n", "FIGSIZE = 7, 7 # does not affect size in webpage\n", "COLORS = [\n", " 'blue', 'orange', 'green', 'red', 'purple', 'brown', 'pink', 'gray', 'olive', 'cyan'\n", "]\n", "if len(COLORS) <= MAX_CLUSTERS:\n", " raise ValueError(\"Not enough different colors for all clusters\")\n", "np.random.seed(SEED)\n", "\n", "\n", "def normalize(X):\n", " return StandardScaler().fit_transform(X)\n", "\n", "def get_regular(n_clusters):\n", " # spiral pattern\n", " centers = [\n", " [0, 0],\n", " [1, 0],\n", " [1, 1],\n", " [0, 1],\n", " [-1, 1],\n", " [-1, 0],\n", " [-1, -1],\n", " [0, -1],\n", " [1, -1],\n", " [2, -1],\n", " ][:n_clusters]\n", " assert len(centers) == n_clusters\n", " X, labels = make_blobs(n_samples=N_SAMPLES, centers=centers, cluster_std=0.25, random_state=SEED)\n", " return normalize(X), labels\n", "\n", "\n", "def get_circles(n_clusters):\n", " X, labels = make_circles(n_samples=N_SAMPLES, factor=0.5, noise=0.05, random_state=SEED)\n", " return normalize(X), labels\n", "\n", "\n", "def get_moons(n_clusters):\n", " X, labels = make_moons(n_samples=N_SAMPLES, noise=0.05, random_state=SEED)\n", " return normalize(X), labels\n", "\n", "\n", "def get_noise(n_clusters):\n", " np.random.seed(SEED)\n", " X, labels = np.random.rand(N_SAMPLES, 2), np.random.randint(0, n_clusters, size=(N_SAMPLES,))\n", " return normalize(X), labels\n", "\n", "\n", "def get_anisotropic(n_clusters):\n", " X, labels = make_blobs(n_samples=N_SAMPLES, centers=n_clusters, random_state=170)\n", " transformation = [[0.6, -0.6], [-0.4, 0.8]]\n", " X = np.dot(X, transformation)\n", " return X, labels\n", "\n", "\n", "def get_varied(n_clusters):\n", " cluster_std = [1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0, 2.5, 0.5, 1.0][:n_clusters]\n", " assert len(cluster_std) == n_clusters\n", " X, labels = make_blobs(\n", " n_samples=N_SAMPLES, centers=n_clusters, cluster_std=cluster_std, random_state=SEED\n", " )\n", " return normalize(X), labels\n", "\n", "\n", "def get_spiral(n_clusters):\n", " # from https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html\n", " np.random.seed(SEED)\n", " t = 1.5 * np.pi * (1 + 3 * np.random.rand(1, N_SAMPLES))\n", " x = t * np.cos(t)\n", " y = t * np.sin(t)\n", " X = np.concatenate((x, y))\n", " X += 0.7 * np.random.randn(2, N_SAMPLES)\n", " X = np.ascontiguousarray(X.T)\n", "\n", " labels = np.zeros(N_SAMPLES, dtype=int)\n", " return normalize(X), labels\n", "\n", "\n", "DATA_MAPPING = {\n", " 'regular': get_regular,\n", " 'circles': get_circles,\n", " 'moons': get_moons,\n", " 'spiral': get_spiral,\n", " 'noise': get_noise,\n", " 'anisotropic': get_anisotropic,\n", " 'varied': get_varied,\n", "}\n", "\n", "\n", "def get_groundtruth_model(X, labels, n_clusters, **kwargs):\n", " # dummy model to show true label distribution\n", " class Dummy:\n", " def __init__(self, y):\n", " self.labels_ = labels\n", "\n", " return Dummy(labels)\n", "\n", "\n", "def get_kmeans(X, labels, n_clusters, **kwargs):\n", " model = KMeans(init=\"k-means++\", n_clusters=n_clusters, n_init=10, random_state=SEED)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_dbscan(X, labels, n_clusters, **kwargs):\n", " model = DBSCAN(eps=0.3)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_agglomerative(X, labels, n_clusters, **kwargs):\n", " connectivity = kneighbors_graph(\n", " X, n_neighbors=n_clusters, include_self=False\n", " )\n", " # make connectivity symmetric\n", " connectivity = 0.5 * (connectivity + connectivity.T)\n", " model = AgglomerativeClustering(\n", " n_clusters=n_clusters, linkage=\"ward\", connectivity=connectivity\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_meanshift(X, labels, n_clusters, **kwargs):\n", " bandwidth = estimate_bandwidth(X, quantile=0.25)\n", " model = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_spectral(X, labels, n_clusters, **kwargs):\n", " model = SpectralClustering(\n", " n_clusters=n_clusters,\n", " eigen_solver=\"arpack\",\n", " affinity=\"nearest_neighbors\",\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_optics(X, labels, n_clusters, **kwargs):\n", " model = OPTICS(\n", " min_samples=7,\n", " xi=0.05,\n", " min_cluster_size=0.1,\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_birch(X, labels, n_clusters, **kwargs):\n", " model = Birch(n_clusters=n_clusters)\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "def get_gaussianmixture(X, labels, n_clusters, **kwargs):\n", " model = GaussianMixture(\n", " n_components=n_clusters, covariance_type=\"full\", random_state=SEED,\n", " )\n", " model.set_params(**kwargs)\n", " return model.fit(X)\n", "\n", "\n", "MODEL_MAPPING = {\n", " 'True labels': get_groundtruth_model,\n", " 'KMeans': get_kmeans,\n", " 'DBSCAN': get_dbscan,\n", " 'MeanShift': get_meanshift,\n", " 'SpectralClustering': get_spectral,\n", " 'OPTICS': get_optics,\n", " 'Birch': get_birch,\n", " 'GaussianMixture': get_gaussianmixture,\n", " 'AgglomerativeClustering': get_agglomerative,\n", "}\n", "\n", "\n", "def plot_clusters(ax, X, labels):\n", " set_clusters = set(labels)\n", " set_clusters.discard(-1) # -1 signifiies outliers, which we plot separately\n", " for label, color in zip(sorted(set_clusters), COLORS):\n", " idx = labels == label\n", " if not sum(idx):\n", " continue\n", " ax.scatter(X[idx, 0], X[idx, 1], color=color)\n", "\n", " # show outliers (if any)\n", " idx = labels == -1\n", " if sum(idx):\n", " ax.scatter(X[idx, 0], X[idx, 1], c='k', marker='x')\n", "\n", " ax.grid(None)\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " return ax\n", "\n", "\n", "def cluster(dataset: str, n_clusters: int, clustering_algorithm: str):\n", " if isinstance(n_clusters, dict):\n", " n_clusters = n_clusters['value']\n", " else:\n", " n_clusters = int(n_clusters)\n", "\n", " X, labels = DATA_MAPPING[dataset](n_clusters)\n", " model = MODEL_MAPPING[clustering_algorithm](X, labels, n_clusters=n_clusters)\n", " if hasattr(model, \"labels_\"):\n", " y_pred = model.labels_.astype(int)\n", " else:\n", " y_pred = model.predict(X)\n", "\n", " fig, ax = plt.subplots(figsize=FIGSIZE)\n", "\n", " plot_clusters(ax, X, y_pred)\n", " ax.set_title(clustering_algorithm, fontsize=16)\n", "\n", " return fig\n", "\n", "\n", "title = \"Clustering with Scikit-learn\"\n", "description = (\n", " \"This example shows how different clustering algorithms work. Simply pick \"\n", " \"the dataset and the number of clusters to see how the clustering algorithms work. \"\n", " \"Colored circles are (predicted) labels and black x are outliers.\"\n", ")\n", "\n", "\n", "def iter_grid(n_rows, n_cols):\n", " # create a grid using gradio Block\n", " for _ in range(n_rows):\n", " with gr.Row():\n", " for _ in range(n_cols):\n", " with gr.Column():\n", " yield\n", "\n", "with gr.Blocks(title=title) as demo:\n", " gr.HTML(f\"{title}\")\n", " gr.Markdown(description)\n", "\n", " input_models = list(MODEL_MAPPING)\n", " input_data = gr.Radio(\n", " list(DATA_MAPPING),\n", " value=\"regular\",\n", " label=\"dataset\"\n", " )\n", " input_n_clusters = gr.Slider(\n", " minimum=1,\n", " maximum=MAX_CLUSTERS,\n", " value=4,\n", " step=1,\n", " label='Number of clusters'\n", " )\n", " n_rows = int(math.ceil(len(input_models) / N_COLS))\n", " counter = 0\n", " for _ in iter_grid(n_rows, N_COLS):\n", " if counter >= len(input_models):\n", " break\n", "\n", " input_model = input_models[counter]\n", " plot = gr.Plot(label=input_model)\n", " fn = partial(cluster, clustering_algorithm=input_model)\n", " input_data.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)\n", " input_n_clusters.change(fn=fn, inputs=[input_data, input_n_clusters], outputs=plot)\n", " counter += 1\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +>>>>>>> main diff --git a/demo/code/run.ipynb b/demo/code/run.ipynb index bd0bb5efe2fe4..06f147924fcc1 100644 --- a/demo/code/run.ipynb +++ b/demo/code/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: code"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/code/file.css"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "from time import sleep\n", "\n", "\n", "css_file = os.path.join(os.path.abspath(''), \"file.css\")\n", "\n", "\n", "def set_lang(language):\n", " print(language)\n", " return gr.Code(language=language)\n", "\n", "\n", "def set_lang_from_path():\n", " sleep(1)\n", " return gr.Code((css_file,), language=\"css\")\n", "\n", "\n", "def code(language, code):\n", " return gr.Code(code, language=language)\n", "\n", "\n", "io = gr.Interface(lambda x: x, \"code\", \"code\")\n", "\n", "with gr.Blocks() as demo:\n", " lang = gr.Dropdown(value=\"python\", choices=gr.Code.languages)\n", " with gr.Row():\n", " code_in = gr.Code(\n", " language=\"python\",\n", " label=\"Input\",\n", " value='def all_odd_elements(sequence):\\n \"\"\"Returns every odd element of the sequence.\"\"\"',\n", " )\n", " code_out = gr.Code(label=\"Output\")\n", " btn = gr.Button(\"Run\")\n", " btn_two = gr.Button(\"Load File\")\n", "\n", " lang.change(set_lang, inputs=lang, outputs=code_in)\n", " btn.click(code, inputs=[lang, code_in], outputs=code_out)\n", " btn_two.click(set_lang_from_path, inputs=None, outputs=code_out)\n", " io.render()\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: code"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/code/file.css"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "from time import sleep\n", "\n", "\n", "css_file = os.path.join(os.path.abspath(''), \"file.css\")\n", "\n", "\n", "def set_lang(language):\n", " print(language)\n", " return gr.Code(language=language)\n", "\n", "\n", "def set_lang_from_path():\n", " sleep(1)\n", " return gr.Code((css_file,), language=\"css\")\n", "\n", "\n", "def code(language, code):\n", " return gr.Code(code, language=language)\n", "\n", "\n", "io = gr.Interface(lambda x: x, \"code\", \"code\")\n", "\n", "with gr.Blocks() as demo:\n", " lang = gr.Dropdown(value=\"python\", choices=gr.Code.languages)\n", " with gr.Row():\n", " code_in = gr.Code(\n", " language=\"python\",\n", " label=\"Input\",\n", " value='def all_odd_elements(sequence):\\n \"\"\"Returns every odd element of the sequence.\"\"\"',\n", " )\n", " code_out = gr.Code(label=\"Output\")\n", " btn = gr.Button(\"Run\")\n", " btn_two = gr.Button(\"Load File\")\n", "\n", " lang.change(set_lang, inputs=lang, outputs=code_in)\n", " btn.click(code, inputs=[lang, code_in], outputs=code_out)\n", " btn_two.click(set_lang_from_path, inputs=None, outputs=code_out)\n", " io.render()\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/code_component/run.ipynb b/demo/code_component/run.ipynb index 97c871f025da3..22ac4a3a17db8 100644 --- a/demo/code_component/run.ipynb +++ b/demo/code_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: code_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Code(\n", " value=\"\"\"def hello_world():\n", " return \"Hello, world!\"\n", " \n", "print(hello_world())\"\"\",\n", " language=\"python\",\n", " interactive=True,\n", " show_label=False,\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: code_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Code(\n", " value=\"\"\"def hello_world():\n", " return \"Hello, world!\"\n", " \n", "print(hello_world())\"\"\",\n", " language=\"python\",\n", " interactive=True,\n", " show_label=False,\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/color_generator/run.ipynb b/demo/color_generator/run.ipynb index 03faf48d55cb2..f36577da5d485 100644 --- a/demo/color_generator/run.ipynb +++ b/demo/color_generator/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: color_generator"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio opencv-python numpy"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import cv2\n", "import numpy as np\n", "import random\n", "\n", "\n", "# Convert decimal color to hexadecimal color\n", "def RGB_to_Hex(rgb):\n", " color = \"#\"\n", " for i in rgb:\n", " num = int(i)\n", " color += str(hex(num))[-2:].replace(\"x\", \"0\").upper()\n", " return color\n", "\n", "\n", "# Randomly generate light or dark colors\n", "def random_color(is_light=True):\n", " return (\n", " random.randint(0, 127) + int(is_light) * 128,\n", " random.randint(0, 127) + int(is_light) * 128,\n", " random.randint(0, 127) + int(is_light) * 128,\n", " )\n", "\n", "\n", "def switch_color(color_style):\n", " if color_style == \"light\":\n", " is_light = True\n", " elif color_style == \"dark\":\n", " is_light = False\n", " back_color_ = random_color(is_light) # Randomly generate colors\n", " back_color = RGB_to_Hex(back_color_) # Convert to hexadecimal\n", "\n", " # Draw color pictures.\n", " w, h = 50, 50\n", " img = np.zeros((h, w, 3), np.uint8)\n", " cv2.rectangle(img, (0, 0), (w, h), back_color_, thickness=-1)\n", "\n", " return back_color, back_color, img\n", "\n", "\n", "inputs = [gr.Radio([\"light\", \"dark\"], value=\"light\")]\n", "\n", "outputs = [\n", " gr.ColorPicker(label=\"color\"),\n", " gr.Textbox(label=\"hexadecimal color\"),\n", " gr.Image(type=\"numpy\", label=\"color picture\"),\n", "]\n", "\n", "title = \"Color Generator\"\n", "description = (\n", " \"Click the Submit button, and a dark or light color will be randomly generated.\"\n", ")\n", "\n", "demo = gr.Interface(\n", " fn=switch_color,\n", " inputs=inputs,\n", " outputs=outputs,\n", " title=title,\n", " description=description,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: color_generator"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio opencv-python numpy"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import cv2\n", "import numpy as np\n", "import random\n", "\n", "\n", "# Convert decimal color to hexadecimal color\n", "def RGB_to_Hex(rgb):\n", " color = \"#\"\n", " for i in rgb:\n", " num = int(i)\n", " color += str(hex(num))[-2:].replace(\"x\", \"0\").upper()\n", " return color\n", "\n", "\n", "# Randomly generate light or dark colors\n", "def random_color(is_light=True):\n", " return (\n", " random.randint(0, 127) + int(is_light) * 128,\n", " random.randint(0, 127) + int(is_light) * 128,\n", " random.randint(0, 127) + int(is_light) * 128,\n", " )\n", "\n", "\n", "def switch_color(color_style):\n", " if color_style == \"light\":\n", " is_light = True\n", " elif color_style == \"dark\":\n", " is_light = False\n", " back_color_ = random_color(is_light) # Randomly generate colors\n", " back_color = RGB_to_Hex(back_color_) # Convert to hexadecimal\n", "\n", " # Draw color pictures.\n", " w, h = 50, 50\n", " img = np.zeros((h, w, 3), np.uint8)\n", " cv2.rectangle(img, (0, 0), (w, h), back_color_, thickness=-1)\n", "\n", " return back_color, back_color, img\n", "\n", "\n", "inputs = [gr.Radio([\"light\", \"dark\"], value=\"light\")]\n", "\n", "outputs = [\n", " gr.ColorPicker(label=\"color\"),\n", " gr.Textbox(label=\"hexadecimal color\"),\n", " gr.Image(type=\"numpy\", label=\"color picture\"),\n", "]\n", "\n", "title = \"Color Generator\"\n", "description = (\n", " \"Click the Submit button, and a dark or light color will be randomly generated.\"\n", ")\n", "\n", "demo = gr.Interface(\n", " fn=switch_color,\n", " inputs=inputs,\n", " outputs=outputs,\n", " title=title,\n", " description=description,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/color_picker/run.ipynb b/demo/color_picker/run.ipynb index eb0c1ebcc9692..83fed85097bad 100644 --- a/demo/color_picker/run.ipynb +++ b/demo/color_picker/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: color_picker"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio Pillow"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/color_picker/rabbit.png"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import os\n", "from PIL import Image, ImageColor\n", "\n", "\n", "def change_color(icon, color):\n", "\n", " \"\"\"\n", " Function that given an icon in .png format changes its color\n", " Args:\n", " icon: Icon whose color needs to be changed.\n", " color: Chosen color with which to edit the input icon.\n", " Returns:\n", " edited_image: Edited icon.\n", " \"\"\"\n", " img = icon.convert(\"LA\")\n", " img = img.convert(\"RGBA\")\n", " image_np = np.array(icon)\n", " _, _, _, alpha = image_np.T\n", " mask = alpha > 0\n", " image_np[..., :-1][mask.T] = ImageColor.getcolor(color, \"RGB\")\n", " edited_image = Image.fromarray(image_np)\n", " return edited_image\n", "\n", "\n", "inputs = [\n", " gr.Image(label=\"icon\", type=\"pil\", image_mode=\"RGBA\"),\n", " gr.ColorPicker(label=\"color\"),\n", "]\n", "outputs = gr.Image(label=\"colored icon\")\n", "\n", "demo = gr.Interface(\n", " fn=change_color,\n", " inputs=inputs,\n", " outputs=outputs,\n", " examples=[\n", " [os.path.join(os.path.abspath(''), \"rabbit.png\"), \"#ff0000\"],\n", " [os.path.join(os.path.abspath(''), \"rabbit.png\"), \"#0000FF\"],\n", " ],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: color_picker"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio Pillow"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/color_picker/rabbit.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import os\n", "from PIL import Image, ImageColor\n", "\n", "\n", "def change_color(icon, color):\n", "\n", " \"\"\"\n", " Function that given an icon in .png format changes its color\n", " Args:\n", " icon: Icon whose color needs to be changed.\n", " color: Chosen color with which to edit the input icon.\n", " Returns:\n", " edited_image: Edited icon.\n", " \"\"\"\n", " img = icon.convert(\"LA\")\n", " img = img.convert(\"RGBA\")\n", " image_np = np.array(icon)\n", " _, _, _, alpha = image_np.T\n", " mask = alpha > 0\n", " image_np[..., :-1][mask.T] = ImageColor.getcolor(color, \"RGB\")\n", " edited_image = Image.fromarray(image_np)\n", " return edited_image\n", "\n", "\n", "inputs = [\n", " gr.Image(label=\"icon\", type=\"pil\", image_mode=\"RGBA\"),\n", " gr.ColorPicker(label=\"color\"),\n", "]\n", "outputs = gr.Image(label=\"colored icon\")\n", "\n", "demo = gr.Interface(\n", " fn=change_color,\n", " inputs=inputs,\n", " outputs=outputs,\n", " examples=[\n", " [os.path.join(os.path.abspath(''), \"rabbit.png\"), \"#ff0000\"],\n", " [os.path.join(os.path.abspath(''), \"rabbit.png\"), \"#0000FF\"],\n", " ],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/colorpicker_component/run.ipynb b/demo/colorpicker_component/run.ipynb index 349861e93766c..2e41a5c7ff5f2 100644 --- a/demo/colorpicker_component/run.ipynb +++ b/demo/colorpicker_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: colorpicker_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.ColorPicker()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: colorpicker_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.ColorPicker()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/concurrency_with_queue/run.ipynb b/demo/concurrency_with_queue/run.ipynb index 2b7e0c7aa5eb2..52974299e2877 100644 --- a/demo/concurrency_with_queue/run.ipynb +++ b/demo/concurrency_with_queue/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: concurrency_with_queue"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "\n", "def say_hello(name):\n", " time.sleep(5)\n", " return f\"Hello {name}!\"\n", "\n", "\n", "with gr.Blocks() as demo:\n", " inp = gr.Textbox()\n", " outp = gr.Textbox()\n", " button = gr.Button()\n", " button.click(say_hello, inp, outp)\n", "\n", " demo.queue(concurrency_count=41).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: concurrency_with_queue"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "\n", "def say_hello(name):\n", " time.sleep(5)\n", " return f\"Hello {name}!\"\n", "\n", "\n", "with gr.Blocks() as demo:\n", " inp = gr.Textbox()\n", " outp = gr.Textbox()\n", " button = gr.Button()\n", " button.click(say_hello, inp, outp)\n", "\n", " demo.queue(concurrency_count=41).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/concurrency_without_queue/run.ipynb b/demo/concurrency_without_queue/run.ipynb index 70f6cf54ddd94..5418a82d71c0d 100644 --- a/demo/concurrency_without_queue/run.ipynb +++ b/demo/concurrency_without_queue/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: concurrency_without_queue"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "\n", "def say_hello(name):\n", " time.sleep(5)\n", " return f\"Hello {name}!\"\n", "\n", "\n", "with gr.Blocks() as demo:\n", " inp = gr.Textbox()\n", " outp = gr.Textbox()\n", " button = gr.Button()\n", " button.click(say_hello, inp, outp)\n", "\n", " demo.launch(max_threads=41)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: concurrency_without_queue"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "\n", "def say_hello(name):\n", " time.sleep(5)\n", " return f\"Hello {name}!\"\n", "\n", "\n", "with gr.Blocks() as demo:\n", " inp = gr.Textbox()\n", " outp = gr.Textbox()\n", " button = gr.Button()\n", " button.click(say_hello, inp, outp)\n", "\n", " demo.launch(max_threads=41)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/count_generator/run.ipynb b/demo/count_generator/run.ipynb index d38f56877869b..3a75d90ac0ff7 100644 --- a/demo/count_generator/run.ipynb +++ b/demo/count_generator/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: count_generator"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "def count(n):\n", " for i in range(int(n)):\n", " time.sleep(0.5)\n", " yield i\n", "\n", "def show(n):\n", " return str(list(range(int(n))))\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column():\n", " num = gr.Number(value=10)\n", " with gr.Row():\n", " count_btn = gr.Button(\"Count\")\n", " list_btn = gr.Button(\"List\")\n", " with gr.Column():\n", " out = gr.Textbox()\n", " \n", " count_btn.click(count, num, out)\n", " list_btn.click(show, num, out)\n", " \n", "demo.queue()\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: count_generator"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "def count(n):\n", " for i in range(int(n)):\n", " time.sleep(0.5)\n", " yield i\n", "\n", "def show(n):\n", " return str(list(range(int(n))))\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column():\n", " num = gr.Number(value=10)\n", " with gr.Row():\n", " count_btn = gr.Button(\"Count\")\n", " list_btn = gr.Button(\"List\")\n", " with gr.Column():\n", " out = gr.Textbox()\n", " \n", " count_btn.click(count, num, out)\n", " list_btn.click(show, num, out)\n", " \n", "demo.queue()\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/dashboard/run.ipynb b/demo/dashboard/run.ipynb index 4506d6aee0d0c..453dad0ee04ae 100644 --- a/demo/dashboard/run.ipynb +++ b/demo/dashboard/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dashboard\n", "### This demo shows how you can build an interactive dashboard with gradio. Click on a python library on the left hand side and then on the right hand side click on the metric you'd like to see plot over time. Data is pulled from HuggingFace Hub datasets.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/dashboard/helpers.py"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import plotly.express as px\n", "from helpers import *\n", "\n", "\n", "LIBRARIES = [\"accelerate\", \"datasets\", \"diffusers\", \"evaluate\", \"gradio\", \"hub_docs\",\n", " \"huggingface_hub\", \"optimum\", \"pytorch_image_models\", \"tokenizers\", \"transformers\"]\n", "\n", "\n", "def create_pip_plot(libraries, pip_choices):\n", " if \"Pip\" not in pip_choices:\n", " return gr.Plot(visible=False)\n", " output = retrieve_pip_installs(libraries, \"Cumulated\" in pip_choices)\n", " df = pd.DataFrame(output).melt(id_vars=\"day\")\n", " plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", " title=\"Pip installs\")\n", " plot.update_layout(legend=dict(x=0.5, y=0.99), title_x=0.5, legend_title_text=\"\")\n", " return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "def create_star_plot(libraries, star_choices):\n", " if \"Stars\" not in star_choices:\n", " return gr.Plot(visible=False)\n", " output = retrieve_stars(libraries, \"Week over Week\" in star_choices)\n", " df = pd.DataFrame(output).melt(id_vars=\"day\")\n", " plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", " title=\"Number of stargazers\")\n", " plot.update_layout(legend=dict(x=0.5, y=0.99), title_x=0.5, legend_title_text=\"\")\n", " return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "def create_issue_plot(libraries, issue_choices):\n", " if \"Issue\" not in issue_choices:\n", " return gr.Plot(visible=False)\n", " output = retrieve_issues(libraries,\n", " exclude_org_members=\"Exclude org members\" in issue_choices,\n", " week_over_week=\"Week over Week\" in issue_choices)\n", " df = pd.DataFrame(output).melt(id_vars=\"day\")\n", " plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", " title=\"Cumulated number of issues, PRs, and comments\",\n", " )\n", " plot.update_layout(legend=dict(x=0.5, y=0.99), title_x=0.5, legend_title_text=\"\")\n", " return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " gr.Markdown(\"## Select libraries to display\")\n", " libraries = gr.CheckboxGroup(choices=LIBRARIES, show_label=False)\n", " with gr.Column():\n", " gr.Markdown(\"## Select graphs to display\")\n", " pip = gr.CheckboxGroup(choices=[\"Pip\", \"Cumulated\"], show_label=False)\n", " stars = gr.CheckboxGroup(choices=[\"Stars\", \"Week over Week\"], show_label=False)\n", " issues = gr.CheckboxGroup(choices=[\"Issue\", \"Exclude org members\", \"week over week\"], show_label=False)\n", " with gr.Row():\n", " fetch = gr.Button(value=\"Fetch\")\n", " with gr.Row():\n", " with gr.Column():\n", " pip_plot = gr.Plot(visible=False)\n", " star_plot = gr.Plot(visible=False)\n", " issue_plot = gr.Plot(visible=False)\n", "\n", " fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=pip_plot)\n", " fetch.click(create_star_plot, inputs=[libraries, stars], outputs=star_plot)\n", " fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dashboard\n", "### This demo shows how you can build an interactive dashboard with gradio. Click on a python library on the left hand side and then on the right hand side click on the metric you'd like to see plot over time. Data is pulled from HuggingFace Hub datasets.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/dashboard/helpers.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import plotly.express as px\n", "from helpers import *\n", "\n", "\n", "LIBRARIES = [\"accelerate\", \"datasets\", \"diffusers\", \"evaluate\", \"gradio\", \"hub_docs\",\n", " \"huggingface_hub\", \"optimum\", \"pytorch_image_models\", \"tokenizers\", \"transformers\"]\n", "\n", "\n", "def create_pip_plot(libraries, pip_choices):\n", " if \"Pip\" not in pip_choices:\n", " return gr.Plot(visible=False)\n", " output = retrieve_pip_installs(libraries, \"Cumulated\" in pip_choices)\n", " df = pd.DataFrame(output).melt(id_vars=\"day\")\n", " plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", " title=\"Pip installs\")\n", " plot.update_layout(legend=dict(x=0.5, y=0.99), title_x=0.5, legend_title_text=\"\")\n", " return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "def create_star_plot(libraries, star_choices):\n", " if \"Stars\" not in star_choices:\n", " return gr.Plot(visible=False)\n", " output = retrieve_stars(libraries, \"Week over Week\" in star_choices)\n", " df = pd.DataFrame(output).melt(id_vars=\"day\")\n", " plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", " title=\"Number of stargazers\")\n", " plot.update_layout(legend=dict(x=0.5, y=0.99), title_x=0.5, legend_title_text=\"\")\n", " return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "def create_issue_plot(libraries, issue_choices):\n", " if \"Issue\" not in issue_choices:\n", " return gr.Plot(visible=False)\n", " output = retrieve_issues(libraries,\n", " exclude_org_members=\"Exclude org members\" in issue_choices,\n", " week_over_week=\"Week over Week\" in issue_choices)\n", " df = pd.DataFrame(output).melt(id_vars=\"day\")\n", " plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", " title=\"Cumulated number of issues, PRs, and comments\",\n", " )\n", " plot.update_layout(legend=dict(x=0.5, y=0.99), title_x=0.5, legend_title_text=\"\")\n", " return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " gr.Markdown(\"## Select libraries to display\")\n", " libraries = gr.CheckboxGroup(choices=LIBRARIES, show_label=False)\n", " with gr.Column():\n", " gr.Markdown(\"## Select graphs to display\")\n", " pip = gr.CheckboxGroup(choices=[\"Pip\", \"Cumulated\"], show_label=False)\n", " stars = gr.CheckboxGroup(choices=[\"Stars\", \"Week over Week\"], show_label=False)\n", " issues = gr.CheckboxGroup(choices=[\"Issue\", \"Exclude org members\", \"week over week\"], show_label=False)\n", " with gr.Row():\n", " fetch = gr.Button(value=\"Fetch\")\n", " with gr.Row():\n", " with gr.Column():\n", " pip_plot = gr.Plot(visible=False)\n", " star_plot = gr.Plot(visible=False)\n", " issue_plot = gr.Plot(visible=False)\n", "\n", " fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=pip_plot)\n", " fetch.click(create_star_plot, inputs=[libraries, stars], outputs=star_plot)\n", " fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} diff --git a/demo/dataframe_block-ui-test/run.ipynb b/demo/dataframe_block-ui-test/run.ipynb index 9c7dced1a1644..62b2ea6c3aafe 100644 --- a/demo/dataframe_block-ui-test/run.ipynb +++ b/demo/dataframe_block-ui-test/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataframe_block-ui-test"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " count = gr.Slider(minimum=1, maximum=10, step=1, label=\"count\")\n", " data = gr.DataFrame(\n", " headers=[\"A\", \"B\"], col_count=(2, \"fixed\"), type=\"array\", interactive=True\n", " )\n", " btn = gr.Button(value=\"click\")\n", " btn.click(\n", " fn=lambda cnt: [[str(2 * i), str(2 * i + 1)] for i in range(int(cnt))],\n", " inputs=[count],\n", " outputs=[data],\n", " )\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_block-ui-test"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " count = gr.Slider(minimum=1, maximum=10, step=1, label=\"count\")\n", " data = gr.DataFrame(\n", " headers=[\"A\", \"B\"], col_count=(2, \"fixed\"), type=\"array\", interactive=True\n", " )\n", " btn = gr.Button(value=\"click\")\n", " btn.click(\n", " fn=lambda cnt: [[str(2 * i), str(2 * i + 1)] for i in range(int(cnt))],\n", " inputs=[count],\n", " outputs=[data],\n", " )\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/dataframe_colorful/run.ipynb b/demo/dataframe_colorful/run.ipynb index cf144477eaf6a..e7df93c5aa22b 100644 --- a/demo/dataframe_colorful/run.ipynb +++ b/demo/dataframe_colorful/run.ipynb @@ -1 +1,5 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataframe_colorful"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import pandas as pd \n", "import gradio as gr\n", "\n", "df = pd.DataFrame({\"A\" : [14, 4, 5, 4, 1], \n", "\t\t\t\t\"B\" : [5, 2, 54, 3, 2], \n", "\t\t\t\t\"C\" : [20, 20, 7, 3, 8], \n", "\t\t\t\t\"D\" : [14, 3, 6, 2, 6], \n", "\t\t\t\t\"E\" : [23, 45, 64, 32, 23]}) \n", "\n", "t = df.style.highlight_max(color = 'lightgreen', axis = 0)\n", "\n", "with gr.Blocks() as demo:\n", " gr.Dataframe(t)\n", " \n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +<<<<<<< HEAD +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataframe_colorful"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import pandas as pd \n", "import gradio as gr\n", "\n", "df = pd.DataFrame({\"A\" : [14, 4, 5, 4, 1], \n", "\t\t\t\t\"B\" : [5, 2, 54, 3, 2], \n", "\t\t\t\t\"C\" : [20, 20, 7, 3, 8], \n", "\t\t\t\t\"D\" : [14, 3, 6, 2, 6], \n", "\t\t\t\t\"E\" : [23, 45, 64, 32, 23]}) \n", "\n", "t = df.style.highlight_max(color = 'lightgreen', axis = 0)\n", "\n", "with gr.Blocks() as demo:\n", " gr.Dataframe(t)\n", " \n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +======= +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_colorful"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import pandas as pd \n", "import gradio as gr\n", "\n", "df = pd.DataFrame({\"A\" : [14, 4, 5, 4, 1], \n", "\t\t\t\t\"B\" : [5, 2, 54, 3, 2], \n", "\t\t\t\t\"C\" : [20, 20, 7, 3, 8], \n", "\t\t\t\t\"D\" : [14, 3, 6, 2, 6], \n", "\t\t\t\t\"E\" : [23, 45, 64, 32, 23]}) \n", "\n", "t = df.style.highlight_max(color = 'lightgreen', axis = 0)\n", "\n", "with gr.Blocks() as demo:\n", " gr.Dataframe(t)\n", " \n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +>>>>>>> main diff --git a/demo/dataframe_component/run.ipynb b/demo/dataframe_component/run.ipynb index f0a4d750f33ed..8efac85a93a4f 100644 --- a/demo/dataframe_component/run.ipynb +++ b/demo/dataframe_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataframe_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.Dataframe(interactive=True)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.Dataframe(interactive=True)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/dataframe_datatype/run.ipynb b/demo/dataframe_datatype/run.ipynb index 6612fda6b6350..42654a0b5a6c4 100644 --- a/demo/dataframe_datatype/run.ipynb +++ b/demo/dataframe_datatype/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataframe_datatype"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import numpy as np\n", "\n", "\n", "def make_dataframe(n_periods):\n", " return pd.DataFrame({\"date_1\": pd.date_range(\"2021-01-01\", periods=n_periods),\n", " \"date_2\": pd.date_range(\"2022-02-15\", periods=n_periods).strftime('%B %d, %Y, %r'),\n", " \"number\": np.random.random(n_periods).astype(np.float64),\n", " \"number_2\": np.random.randint(0, 100, n_periods).astype(np.int32),\n", " \"bool\": [True] * n_periods,\n", " \"markdown\": [\"# Hello\"] * n_periods})\n", "\n", "\n", "demo = gr.Interface(make_dataframe,\n", " gr.Number(precision=0),\n", " gr.Dataframe(datatype=[\"date\", \"date\", \"number\", \"number\", \"bool\", \"markdown\"]))\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_datatype"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import numpy as np\n", "\n", "\n", "def make_dataframe(n_periods):\n", " return pd.DataFrame({\"date_1\": pd.date_range(\"2021-01-01\", periods=n_periods),\n", " \"date_2\": pd.date_range(\"2022-02-15\", periods=n_periods).strftime('%B %d, %Y, %r'),\n", " \"number\": np.random.random(n_periods).astype(np.float64),\n", " \"number_2\": np.random.randint(0, 100, n_periods).astype(np.int32),\n", " \"bool\": [True] * n_periods,\n", " \"markdown\": [\"# Hello\"] * n_periods})\n", "\n", "\n", "demo = gr.Interface(make_dataframe,\n", " gr.Number(precision=0),\n", " gr.Dataframe(datatype=[\"date\", \"date\", \"number\", \"number\", \"bool\", \"markdown\"]))\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/dataset/run.ipynb b/demo/dataset/run.ipynb index e4f8677e27409..ed500d57d2928 100644 --- a/demo/dataset/run.ipynb +++ b/demo/dataset/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataset"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/Bunny.obj https://github.com/gradio-app/gradio/raw/main/demo/dataset/files/Bunny.obj\n", "!wget -q -O files/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/dataset/files/cantina.wav\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/dataset/files/cheetah1.jpg\n", "!wget -q -O files/time.csv https://github.com/gradio-app/gradio/raw/main/demo/dataset/files/time.csv\n", "!wget -q -O files/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/dataset/files/titanic.csv\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/dataset/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "import numpy as np\n", "\n", "\n", "txt = \"the quick brown fox\"\n", "num = 10\n", "\n", "img = os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\")\n", "vid = os.path.join(os.path.abspath(''), \"files/world.mp4\")\n", "audio = os.path.join(os.path.abspath(''), \"files/cantina.wav\")\n", "csv = os.path.join(os.path.abspath(''), \"files/time.csv\")\n", "model = os.path.join(os.path.abspath(''), \"files/Bunny.obj\")\n", "\n", "dataframe = [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]]\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"# Dataset previews\")\n", " a = gr.Audio(visible=False)\n", " gr.Dataset(\n", " components=[a],\n", " label=\"Audio\",\n", " samples=[\n", " [audio],\n", " [audio],\n", " [audio],\n", " [audio],\n", " [audio],\n", " [audio],\n", " ],\n", " )\n", " c = gr.Checkbox(visible=False)\n", " gr.Dataset(\n", " label=\"Checkbox\",\n", " components=[c],\n", " samples=[[True], [True], [False], [True], [False], [False]],\n", " )\n", "\n", " c_2 = gr.CheckboxGroup(visible=False, choices=['a', 'b', 'c'])\n", " gr.Dataset(\n", " label=\"CheckboxGroup\",\n", " components=[c_2],\n", " samples=[\n", " [[\"a\"]],\n", " [[\"a\", \"b\"]],\n", " [[\"a\", \"b\", \"c\"]],\n", " [[\"b\"]],\n", " [[\"c\"]],\n", " [[\"a\", \"c\"]],\n", " ],\n", " )\n", " c_3 = gr.ColorPicker(visible=False)\n", " gr.Dataset(\n", " label=\"ColorPicker\",\n", " components=[c_3],\n", " samples=[\n", " [\"#FFFFFF\"],\n", " [\"#000000\"],\n", " [\"#FFFFFF\"],\n", " [\"#000000\"],\n", " [\"#FFFFFF\"],\n", " [\"#000000\"],\n", " ],\n", " )\n", " d = gr.DataFrame(visible=False)\n", " gr.Dataset(\n", " components=[d],\n", " label=\"Dataframe\",\n", " samples=[\n", " [np.zeros((3, 3)).tolist()],\n", " [np.ones((2, 2)).tolist()],\n", " [np.random.randint(0, 10, (3, 10)).tolist()],\n", " [np.random.randint(0, 10, (10, 3)).tolist()],\n", " [np.random.randint(0, 10, (10, 10)).tolist()],\n", " ],\n", " )\n", " d_2 = gr.Dropdown(visible=False, choices=[\"one\", \"two\", \"three\"])\n", " gr.Dataset(\n", " components=[d_2],\n", " label=\"Dropdown\",\n", " samples=[[\"one\"], [\"two\"], [\"three\"], [\"one\"], [\"two\"], [\"three\"]],\n", " )\n", " f = gr.File(visible=False)\n", " gr.Dataset(\n", " components=[f],\n", " label=\"File\",\n", " samples=[\n", " [csv],\n", " [csv],\n", " [csv],\n", " [csv],\n", " [csv],\n", " [csv],\n", " ],\n", " )\n", " h = gr.HTML(visible=False)\n", " gr.Dataset(\n", " components=[h],\n", " label=\"HTML\",\n", " samples=[\n", " [\"This example was written in HTML
\")\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: html_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.HTML(value=\"This example was written in HTML
\")\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/image_classification/run.ipynb b/demo/image_classification/run.ipynb index 792ee745d137a..434fbbf4ae7e2 100644 --- a/demo/image_classification/run.ipynb +++ b/demo/image_classification/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_classification\n", "### Simple image classification in Pytorch with Gradio's Image input and Label output.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchvision"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classification/cheetah.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "import requests\n", "from torchvision import transforms\n", "\n", "model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "def predict(inp):\n", " inp = transforms.ToTensor()(inp).unsqueeze(0)\n", " with torch.no_grad():\n", " prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n", " confidences = {labels[i]: float(prediction[i]) for i in range(1000)} \n", " return confidences\n", "\n", "demo = gr.Interface(fn=predict, \n", " inputs=gr.inputs.Image(type=\"pil\"),\n", " outputs=gr.outputs.Label(num_top_classes=3),\n", " examples=[[\"cheetah.jpg\"]],\n", " )\n", " \n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classification\n", "### Simple image classification in Pytorch with Gradio's Image input and Label output.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchvision"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classification/cheetah.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "import requests\n", "from torchvision import transforms\n", "\n", "model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "def predict(inp):\n", " inp = transforms.ToTensor()(inp).unsqueeze(0)\n", " with torch.no_grad():\n", " prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n", " confidences = {labels[i]: float(prediction[i]) for i in range(1000)} \n", " return confidences\n", "\n", "demo = gr.Interface(fn=predict, \n", " inputs=gr.inputs.Image(type=\"pil\"),\n", " outputs=gr.outputs.Label(num_top_classes=3),\n", " examples=[[\"cheetah.jpg\"]],\n", " )\n", " \n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/image_classifier/run.ipynb b/demo/image_classifier/run.ipynb index 6d489271b6d9b..020f71da17a9a 100644 --- a/demo/image_classifier/run.ipynb +++ b/demo/image_classifier/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_classifier"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy tensorflow"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/imagenet_labels.json https://github.com/gradio-app/gradio/raw/main/demo/image_classifier/files/imagenet_labels.json\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_classifier/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_classifier/images/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import os\n", "import requests\n", "import tensorflow as tf\n", "\n", "import gradio as gr\n", "\n", "inception_net = tf.keras.applications.MobileNetV2() # load the model\n", "\n", "# Download human-readable labels for ImageNet.\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "\n", "def classify_image(inp):\n", " inp = inp.reshape((-1, 224, 224, 3))\n", " inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)\n", " prediction = inception_net.predict(inp).flatten()\n", " return {labels[i]: float(prediction[i]) for i in range(1000)}\n", "\n", "\n", "image = gr.Image(shape=(224, 224))\n", "label = gr.Label(num_top_classes=3)\n", "\n", "demo = gr.Interface(\n", " fn=classify_image,\n", " inputs=image,\n", " outputs=label,\n", " examples=[\n", " os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"images/lion.jpg\")\n", " ]\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n", "\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classifier"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy tensorflow"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/imagenet_labels.json https://github.com/gradio-app/gradio/raw/main/demo/image_classifier/files/imagenet_labels.json\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_classifier/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_classifier/images/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "import requests\n", "import tensorflow as tf\n", "\n", "import gradio as gr\n", "\n", "inception_net = tf.keras.applications.MobileNetV2() # load the model\n", "\n", "# Download human-readable labels for ImageNet.\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "\n", "def classify_image(inp):\n", " inp = inp.reshape((-1, 224, 224, 3))\n", " inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)\n", " prediction = inception_net.predict(inp).flatten()\n", " return {labels[i]: float(prediction[i]) for i in range(1000)}\n", "\n", "\n", "image = gr.Image(shape=(224, 224))\n", "label = gr.Label(num_top_classes=3)\n", "\n", "demo = gr.Interface(\n", " fn=classify_image,\n", " inputs=image,\n", " outputs=label,\n", " examples=[\n", " os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"images/lion.jpg\")\n", " ]\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n", "\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/image_classifier_2/run.ipynb b/demo/image_classifier_2/run.ipynb index 7598db8e31cf2..907f57b3b75a7 100644 --- a/demo/image_classifier_2/run.ipynb +++ b/demo/image_classifier_2/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_classifier_2"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio pillow torch torchvision"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/imagenet_labels.json https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_2/files/imagenet_labels.json"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import requests\n", "import torch\n", "from PIL import Image\n", "from torchvision import transforms\n", "\n", "import gradio as gr\n", "\n", "model = torch.hub.load(\"pytorch/vision:v0.6.0\", \"resnet18\", pretrained=True).eval()\n", "\n", "# Download human-readable labels for ImageNet.\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "\n", "def predict(inp):\n", " inp = Image.fromarray(inp.astype(\"uint8\"), \"RGB\")\n", " inp = transforms.ToTensor()(inp).unsqueeze(0)\n", " with torch.no_grad():\n", " prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n", " return {labels[i]: float(prediction[i]) for i in range(1000)}\n", "\n", "\n", "inputs = gr.Image()\n", "outputs = gr.Label(num_top_classes=3)\n", "\n", "demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classifier_2"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio pillow torch torchvision"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/imagenet_labels.json https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_2/files/imagenet_labels.json"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import requests\n", "import torch\n", "from PIL import Image\n", "from torchvision import transforms\n", "\n", "import gradio as gr\n", "\n", "model = torch.hub.load(\"pytorch/vision:v0.6.0\", \"resnet18\", pretrained=True).eval()\n", "\n", "# Download human-readable labels for ImageNet.\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "\n", "def predict(inp):\n", " inp = Image.fromarray(inp.astype(\"uint8\"), \"RGB\")\n", " inp = transforms.ToTensor()(inp).unsqueeze(0)\n", " with torch.no_grad():\n", " prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n", " return {labels[i]: float(prediction[i]) for i in range(1000)}\n", "\n", "\n", "inputs = gr.Image()\n", "outputs = gr.Label(num_top_classes=3)\n", "\n", "demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/image_classifier_interface_load/run.ipynb b/demo/image_classifier_interface_load/run.ipynb index 122dfa407fde1..d2aa59dacc228 100644 --- a/demo/image_classifier_interface_load/run.ipynb +++ b/demo/image_classifier_interface_load/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_classifier_interface_load"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interface_load/cheetah1.jpeg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interface_load/cheetah1.jpg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interface_load/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pathlib\n", "\n", "current_dir = pathlib.Path(__file__).parent\n", "\n", "images = [str(current_dir / \"cheetah1.jpeg\"), str(current_dir / \"cheetah1.jpg\"), str(current_dir / \"lion.jpg\")]\n", "\n", "\n", "img_classifier = gr.load(\n", " \"models/google/vit-base-patch16-224\", examples=images, cache_examples=False\n", ")\n", "\n", "\n", "def func(img, text):\n", " return img_classifier(img), text\n", "\n", "\n", "using_img_classifier_as_function = gr.Interface(\n", " func,\n", " [gr.Image(type=\"filepath\"), \"text\"],\n", " [\"label\", \"text\"],\n", " examples=[\n", " [str(current_dir / \"cheetah1.jpeg\"), None],\n", " [str(current_dir / \"cheetah1.jpg\"), \"cheetah\"],\n", " [str(current_dir / \"lion.jpg\"), \"lion\"],\n", " ],\n", " cache_examples=False,\n", ")\n", "demo = gr.TabbedInterface([using_img_classifier_as_function, img_classifier])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classifier_interface_load"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interface_load/cheetah1.jpeg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interface_load/cheetah1.jpg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interface_load/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pathlib\n", "\n", "current_dir = pathlib.Path(__file__).parent\n", "\n", "images = [str(current_dir / \"cheetah1.jpeg\"), str(current_dir / \"cheetah1.jpg\"), str(current_dir / \"lion.jpg\")]\n", "\n", "\n", "img_classifier = gr.load(\n", " \"models/google/vit-base-patch16-224\", examples=images, cache_examples=False\n", ")\n", "\n", "\n", "def func(img, text):\n", " return img_classifier(img), text\n", "\n", "\n", "using_img_classifier_as_function = gr.Interface(\n", " func,\n", " [gr.Image(type=\"filepath\"), \"text\"],\n", " [\"label\", \"text\"],\n", " examples=[\n", " [str(current_dir / \"cheetah1.jpeg\"), None],\n", " [str(current_dir / \"cheetah1.jpg\"), \"cheetah\"],\n", " [str(current_dir / \"lion.jpg\"), \"lion\"],\n", " ],\n", " cache_examples=False,\n", ")\n", "demo = gr.TabbedInterface([using_img_classifier_as_function, img_classifier])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/image_classifier_interpretation/run.ipynb b/demo/image_classifier_interpretation/run.ipynb index 92fcfcd7142ea..74d94d3e45b3c 100644 --- a/demo/image_classifier_interpretation/run.ipynb +++ b/demo/image_classifier_interpretation/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_classifier_interpretation"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy tensorflow"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/imagenet_labels.json https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interpretation/files/imagenet_labels.json\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interpretation/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interpretation/images/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import requests\n", "import tensorflow as tf\n", "\n", "import gradio as gr\n", "\n", "inception_net = tf.keras.applications.MobileNetV2() # load the model\n", "\n", "# Download human-readable labels for ImageNet.\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "\n", "def classify_image(inp):\n", " inp = inp.reshape((-1, 224, 224, 3))\n", " inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)\n", " prediction = inception_net.predict(inp).flatten()\n", " return {labels[i]: float(prediction[i]) for i in range(1000)}\n", "\n", "\n", "image = gr.Image(shape=(224, 224))\n", "label = gr.Label(num_top_classes=3)\n", "\n", "demo = gr.Interface(\n", " fn=classify_image, inputs=image, outputs=label, interpretation=\"default\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_classifier_interpretation"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy tensorflow"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/imagenet_labels.json https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interpretation/files/imagenet_labels.json\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interpretation/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_classifier_interpretation/images/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import requests\n", "import tensorflow as tf\n", "\n", "import gradio as gr\n", "\n", "inception_net = tf.keras.applications.MobileNetV2() # load the model\n", "\n", "# Download human-readable labels for ImageNet.\n", "response = requests.get(\"https://git.io/JJkYN\")\n", "labels = response.text.split(\"\\n\")\n", "\n", "\n", "def classify_image(inp):\n", " inp = inp.reshape((-1, 224, 224, 3))\n", " inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)\n", " prediction = inception_net.predict(inp).flatten()\n", " return {labels[i]: float(prediction[i]) for i in range(1000)}\n", "\n", "\n", "image = gr.Image(shape=(224, 224))\n", "label = gr.Label(num_top_classes=3)\n", "\n", "demo = gr.Interface(\n", " fn=classify_image, inputs=image, outputs=label, interpretation=\"default\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/image_component/run.ipynb b/demo/image_component/run.ipynb index f27b99ee1084d..ef0fbc57f94dd 100644 --- a/demo/image_component/run.ipynb +++ b/demo/image_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.Image()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.Image()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/image_mod/run.ipynb b/demo/image_mod/run.ipynb index 5cb9b4fbb6e8f..b7b80afe2b600 100644 --- a/demo/image_mod/run.ipynb +++ b/demo/image_mod/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_mod"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod/images/lion.jpg\n", "!wget -q -O images/logo.png https://github.com/gradio-app/gradio/raw/main/demo/image_mod/images/logo.png\n", "!wget -q -O images/tower.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod/images/tower.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "def image_mod(image):\n", " return image.rotate(45)\n", "\n", "\n", "demo = gr.Interface(\n", " image_mod,\n", " gr.Image(type=\"pil\"),\n", " \"image\",\n", " flagging_options=[\"blurry\", \"incorrect\", \"other\"],\n", " examples=[\n", " os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"images/lion.jpg\"),\n", " os.path.join(os.path.abspath(''), \"images/logo.png\"),\n", " os.path.join(os.path.abspath(''), \"images/tower.jpg\"),\n", " ],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_mod"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod/images/lion.jpg\n", "!wget -q -O images/logo.png https://github.com/gradio-app/gradio/raw/main/demo/image_mod/images/logo.png\n", "!wget -q -O images/tower.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod/images/tower.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "def image_mod(image):\n", " return image.rotate(45)\n", "\n", "\n", "demo = gr.Interface(\n", " image_mod,\n", " gr.Image(type=\"pil\"),\n", " \"image\",\n", " flagging_options=[\"blurry\", \"incorrect\", \"other\"],\n", " examples=[\n", " os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"images/lion.jpg\"),\n", " os.path.join(os.path.abspath(''), \"images/logo.png\"),\n", " os.path.join(os.path.abspath(''), \"images/tower.jpg\"),\n", " ],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/image_mod_default_image/run.ipynb b/demo/image_mod_default_image/run.ipynb index 48d8ee868fae0..9e2befb5bcd57 100644 --- a/demo/image_mod_default_image/run.ipynb +++ b/demo/image_mod_default_image/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_mod_default_image"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/lion.jpg\n", "!wget -q -O images/logo.png https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/logo.png"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "def image_mod(image):\n", " return image.rotate(45)\n", "\n", "\n", "cheetah = os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\")\n", "\n", "demo = gr.Interface(image_mod, gr.Image(type=\"pil\", value=cheetah), \"image\",\n", " flagging_options=[\"blurry\", \"incorrect\", \"other\"], examples=[\n", " os.path.join(os.path.abspath(''), \"images/lion.jpg\"),\n", " os.path.join(os.path.abspath(''), \"images/logo.png\")\n", " ])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_mod_default_image"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/lion.jpg\n", "!wget -q -O images/logo.png https://github.com/gradio-app/gradio/raw/main/demo/image_mod_default_image/images/logo.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "def image_mod(image):\n", " return image.rotate(45)\n", "\n", "\n", "cheetah = os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\")\n", "\n", "demo = gr.Interface(image_mod, gr.Image(type=\"pil\", value=cheetah), \"image\",\n", " flagging_options=[\"blurry\", \"incorrect\", \"other\"], examples=[\n", " os.path.join(os.path.abspath(''), \"images/lion.jpg\"),\n", " os.path.join(os.path.abspath(''), \"images/logo.png\")\n", " ])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/image_segmentation/run.ipynb b/demo/image_segmentation/run.ipynb index 8544aedd0213b..ac39c2862e509 100644 --- a/demo/image_segmentation/run.ipynb +++ b/demo/image_segmentation/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_segmentation\n", "### Simple image segmentation using gradio's AnnotatedImage component.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import random\n", "\n", "with gr.Blocks() as demo:\n", " section_labels = [\n", " \"apple\",\n", " \"banana\",\n", " \"carrot\",\n", " \"donut\",\n", " \"eggplant\",\n", " \"fish\",\n", " \"grapes\",\n", " \"hamburger\",\n", " \"ice cream\",\n", " \"juice\",\n", " ]\n", "\n", " with gr.Row():\n", " num_boxes = gr.Slider(0, 5, 2, step=1, label=\"Number of boxes\")\n", " num_segments = gr.Slider(0, 5, 1, step=1, label=\"Number of segments\")\n", "\n", " with gr.Row():\n", " img_input = gr.Image()\n", " img_output = gr.AnnotatedImage(\n", " color_map={\"banana\": \"#a89a00\", \"carrot\": \"#ffae00\"}\n", " )\n", "\n", " section_btn = gr.Button(\"Identify Sections\")\n", " selected_section = gr.Textbox(label=\"Selected Section\")\n", "\n", " def section(img, num_boxes, num_segments):\n", " sections = []\n", " for a in range(num_boxes):\n", " x = random.randint(0, img.shape[1])\n", " y = random.randint(0, img.shape[0])\n", " w = random.randint(0, img.shape[1] - x)\n", " h = random.randint(0, img.shape[0] - y)\n", " sections.append(((x, y, x + w, y + h), section_labels[a]))\n", " for b in range(num_segments):\n", " x = random.randint(0, img.shape[1])\n", " y = random.randint(0, img.shape[0])\n", " r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))\n", " mask = np.zeros(img.shape[:2])\n", " for i in range(img.shape[0]):\n", " for j in range(img.shape[1]):\n", " dist_square = (i - y) ** 2 + (j - x) ** 2\n", " if dist_square < r**2:\n", " mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4\n", " sections.append((mask, section_labels[b + num_boxes]))\n", " return (img, sections)\n", "\n", " section_btn.click(section, [img_input, num_boxes, num_segments], img_output)\n", "\n", " def select_section(evt: gr.SelectData):\n", " return section_labels[evt.index]\n", "\n", " img_output.select(select_section, None, selected_section)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_segmentation\n", "### Simple image segmentation using gradio's AnnotatedImage component.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import random\n", "\n", "with gr.Blocks() as demo:\n", " section_labels = [\n", " \"apple\",\n", " \"banana\",\n", " \"carrot\",\n", " \"donut\",\n", " \"eggplant\",\n", " \"fish\",\n", " \"grapes\",\n", " \"hamburger\",\n", " \"ice cream\",\n", " \"juice\",\n", " ]\n", "\n", " with gr.Row():\n", " num_boxes = gr.Slider(0, 5, 2, step=1, label=\"Number of boxes\")\n", " num_segments = gr.Slider(0, 5, 1, step=1, label=\"Number of segments\")\n", "\n", " with gr.Row():\n", " img_input = gr.Image()\n", " img_output = gr.AnnotatedImage(\n", " color_map={\"banana\": \"#a89a00\", \"carrot\": \"#ffae00\"}\n", " )\n", "\n", " section_btn = gr.Button(\"Identify Sections\")\n", " selected_section = gr.Textbox(label=\"Selected Section\")\n", "\n", " def section(img, num_boxes, num_segments):\n", " sections = []\n", " for a in range(num_boxes):\n", " x = random.randint(0, img.shape[1])\n", " y = random.randint(0, img.shape[0])\n", " w = random.randint(0, img.shape[1] - x)\n", " h = random.randint(0, img.shape[0] - y)\n", " sections.append(((x, y, x + w, y + h), section_labels[a]))\n", " for b in range(num_segments):\n", " x = random.randint(0, img.shape[1])\n", " y = random.randint(0, img.shape[0])\n", " r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))\n", " mask = np.zeros(img.shape[:2])\n", " for i in range(img.shape[0]):\n", " for j in range(img.shape[1]):\n", " dist_square = (i - y) ** 2 + (j - x) ** 2\n", " if dist_square < r**2:\n", " mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4\n", " sections.append((mask, section_labels[b + num_boxes]))\n", " return (img, sections)\n", "\n", " section_btn.click(section, [img_input, num_boxes, num_segments], img_output)\n", "\n", " def select_section(evt: gr.SelectData):\n", " return section_labels[evt.index]\n", "\n", " img_output.select(select_section, None, selected_section)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/image_selections/run.ipynb b/demo/image_selections/run.ipynb index 37f1f12230785..d39daa8b59bdd 100644 --- a/demo/image_selections/run.ipynb +++ b/demo/image_selections/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: image_selections"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "with gr.Blocks() as demo:\n", " tolerance = gr.Slider(label=\"Tolerance\", info=\"How different colors can be in a segment.\", minimum=0, maximum=256*3, value=50)\n", " with gr.Row():\n", " input_img = gr.Image(label=\"Input\")\n", " output_img = gr.Image(label=\"Selected Segment\")\n", "\n", " def get_select_coords(img, tolerance, evt: gr.SelectData):\n", " visited_pixels = set()\n", " pixels_in_queue = set()\n", " pixels_in_segment = set()\n", " start_pixel = img[evt.index[1], evt.index[0]]\n", " pixels_in_queue.add((evt.index[1], evt.index[0]))\n", " while len(pixels_in_queue) > 0:\n", " pixel = pixels_in_queue.pop()\n", " visited_pixels.add(pixel)\n", " neighbors = []\n", " if pixel[0] > 0:\n", " neighbors.append((pixel[0] - 1, pixel[1]))\n", " if pixel[0] < img.shape[0] - 1:\n", " neighbors.append((pixel[0] + 1, pixel[1]))\n", " if pixel[1] > 0:\n", " neighbors.append((pixel[0], pixel[1] - 1))\n", " if pixel[1] < img.shape[1] - 1:\n", " neighbors.append((pixel[0], pixel[1] + 1))\n", " for neighbor in neighbors:\n", " if neighbor in visited_pixels:\n", " continue\n", " neighbor_pixel = img[neighbor[0], neighbor[1]]\n", " if np.abs(neighbor_pixel - start_pixel).sum() < tolerance:\n", " pixels_in_queue.add(neighbor)\n", " pixels_in_segment.add(neighbor)\n", "\n", " out = img.copy() * 0.2\n", " out = out.astype(np.uint8)\n", " for pixel in pixels_in_segment:\n", " out[pixel[0], pixel[1]] = img[pixel[0], pixel[1]]\n", " return out\n", " \n", " input_img.select(get_select_coords, [input_img, tolerance], output_img)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_selections"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "with gr.Blocks() as demo:\n", " tolerance = gr.Slider(label=\"Tolerance\", info=\"How different colors can be in a segment.\", minimum=0, maximum=256*3, value=50)\n", " with gr.Row():\n", " input_img = gr.Image(label=\"Input\")\n", " output_img = gr.Image(label=\"Selected Segment\")\n", "\n", " def get_select_coords(img, tolerance, evt: gr.SelectData):\n", " visited_pixels = set()\n", " pixels_in_queue = set()\n", " pixels_in_segment = set()\n", " start_pixel = img[evt.index[1], evt.index[0]]\n", " pixels_in_queue.add((evt.index[1], evt.index[0]))\n", " while len(pixels_in_queue) > 0:\n", " pixel = pixels_in_queue.pop()\n", " visited_pixels.add(pixel)\n", " neighbors = []\n", " if pixel[0] > 0:\n", " neighbors.append((pixel[0] - 1, pixel[1]))\n", " if pixel[0] < img.shape[0] - 1:\n", " neighbors.append((pixel[0] + 1, pixel[1]))\n", " if pixel[1] > 0:\n", " neighbors.append((pixel[0], pixel[1] - 1))\n", " if pixel[1] < img.shape[1] - 1:\n", " neighbors.append((pixel[0], pixel[1] + 1))\n", " for neighbor in neighbors:\n", " if neighbor in visited_pixels:\n", " continue\n", " neighbor_pixel = img[neighbor[0], neighbor[1]]\n", " if np.abs(neighbor_pixel - start_pixel).sum() < tolerance:\n", " pixels_in_queue.add(neighbor)\n", " pixels_in_segment.add(neighbor)\n", "\n", " out = img.copy() * 0.2\n", " out = out.astype(np.uint8)\n", " for pixel in pixels_in_segment:\n", " out[pixel[0], pixel[1]] = img[pixel[0], pixel[1]]\n", " return out\n", " \n", " input_img.select(get_select_coords, [input_img, tolerance], output_img)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/input_output/run.ipynb b/demo/input_output/run.ipynb index c8e72fcb86c8b..c59f54523bbc0 100644 --- a/demo/input_output/run.ipynb +++ b/demo/input_output/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: input_output"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def image_mod(text):\n", " return text[::-1]\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", " text = gr.Textbox(label=\"Input-Output\")\n", " btn = gr.Button(\"Run\")\n", " btn.click(image_mod, text, text)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: input_output"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def image_mod(text):\n", " return text[::-1]\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", " text = gr.Textbox(label=\"Input-Output\")\n", " btn = gr.Button(\"Run\")\n", " btn.click(image_mod, text, text)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/interface_parallel/run.ipynb b/demo/interface_parallel/run.ipynb index b74d27f11741d..146323684aa79 100644 --- a/demo/interface_parallel/run.ipynb +++ b/demo/interface_parallel/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: interface_parallel"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "greeter_1 = gr.Interface(lambda name: f\"Hello {name}!\", inputs=\"textbox\", outputs=gr.Textbox(label=\"Greeter 1\"))\n", "greeter_2 = gr.Interface(lambda name: f\"Greetings {name}!\", inputs=\"textbox\", outputs=gr.Textbox(label=\"Greeter 2\"))\n", "demo = gr.Parallel(greeter_1, greeter_2)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: interface_parallel"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "greeter_1 = gr.Interface(lambda name: f\"Hello {name}!\", inputs=\"textbox\", outputs=gr.Textbox(label=\"Greeter 1\"))\n", "greeter_2 = gr.Interface(lambda name: f\"Greetings {name}!\", inputs=\"textbox\", outputs=gr.Textbox(label=\"Greeter 2\"))\n", "demo = gr.Parallel(greeter_1, greeter_2)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/interface_parallel_load/run.ipynb b/demo/interface_parallel_load/run.ipynb index 08bd42d7dec55..4ac4acb308335 100644 --- a/demo/interface_parallel_load/run.ipynb +++ b/demo/interface_parallel_load/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: interface_parallel_load"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "generator1 = gr.load(\"huggingface/gpt2\")\n", "generator2 = gr.load(\"huggingface/gpt2-xl\")\n", "\n", "demo = gr.Parallel(generator1, generator2)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: interface_parallel_load"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "generator1 = gr.load(\"huggingface/gpt2\")\n", "generator2 = gr.load(\"huggingface/gpt2-xl\")\n", "\n", "demo = gr.Parallel(generator1, generator2)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/interface_random_slider/run.ipynb b/demo/interface_random_slider/run.ipynb index 22a6f038da7d9..ef67d768a2506 100644 --- a/demo/interface_random_slider/run.ipynb +++ b/demo/interface_random_slider/run.ipynb @@ -1 +1,5 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: interface_random_slider"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def func(slider_1, slider_2, *args):\n", " return slider_1 + slider_2 * 5\n", "\n", "\n", "demo = gr.Interface(\n", " func,\n", " [\n", " gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label=\"Random Big Range\"),\n", " gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only multiple of 0.05 allowed\"),\n", " gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label=\"Random only multiples of 0.25 allowed\"),\n", " gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random between -100 and 100 step 3\"),\n", " gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n", " gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n", " ],\n", " \"number\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +<<<<<<< HEAD +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: interface_random_slider"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def func(slider_1, slider_2, *args):\n", " return slider_1 + slider_2 * 5\n", "\n", "\n", "demo = gr.Interface(\n", " func,\n", " [\n", " gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label=\"Random Big Range\"),\n", " gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only multiple of 0.05 allowed\"),\n", " gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label=\"Random only multiples of 0.25 allowed\"),\n", " gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random between -100 and 100 step 3\"),\n", " gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n", " gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n", " ],\n", " \"number\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +======= +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: interface_random_slider"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def func(slider_1, slider_2, *args):\n", " return slider_1 + slider_2 * 5\n", "\n", "\n", "demo = gr.Interface(\n", " func,\n", " [\n", " gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label=\"Random Big Range\"),\n", " gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only multiple of 0.05 allowed\"),\n", " gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label=\"Random only multiples of 0.25 allowed\"),\n", " gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random between -100 and 100 step 3\"),\n", " gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n", " gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n", " ],\n", " \"number\",\n", " interpretation=\"default\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +>>>>>>> main diff --git a/demo/interface_series/run.ipynb b/demo/interface_series/run.ipynb index 399476329f08a..0ed5fe48fcde8 100644 --- a/demo/interface_series/run.ipynb +++ b/demo/interface_series/run.ipynb @@ -1 +1,5 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: interface_series"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "get_name = gr.Interface(lambda name: name, inputs=\"textbox\", outputs=\"textbox\")\n", "prepend_hello = gr.Interface(lambda name: f\"Hello {name}!\", inputs=\"textbox\", outputs=\"textbox\")\n", "append_nice = gr.Interface(lambda greeting: f\"Nice to meet you!\",\n", " inputs=\"textbox\", outputs=gr.Textbox(label=\"Greeting\"))\n", "translator = gr.Interface(lambda s: \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\", gr.Textbox(), gr.Image())\n", "demo = gr.Series(get_name, translator, append_nice)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +<<<<<<< HEAD +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: interface_series"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "get_name = gr.Interface(lambda name: name, inputs=\"textbox\", outputs=\"textbox\")\n", "prepend_hello = gr.Interface(lambda name: f\"Hello {name}!\", inputs=\"textbox\", outputs=\"textbox\")\n", "append_nice = gr.Interface(lambda greeting: f\"Nice to meet you!\",\n", " inputs=\"textbox\", outputs=gr.Textbox(label=\"Greeting\"))\n", "translator = gr.Interface(lambda s: \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\", gr.Textbox(), gr.Image())\n", "demo = gr.Series(get_name, translator, append_nice)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +======= +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: interface_series"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "get_name = gr.Interface(lambda name: name, inputs=\"textbox\", outputs=\"textbox\")\n", "prepend_hello = gr.Interface(lambda name: f\"Hello {name}!\", inputs=\"textbox\", outputs=\"textbox\")\n", "append_nice = gr.Interface(lambda greeting: f\"{greeting} Nice to meet you!\",\n", " inputs=\"textbox\", outputs=gr.Textbox(label=\"Greeting\"))\n", "demo = gr.Series(get_name, prepend_hello, append_nice)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +>>>>>>> main diff --git a/demo/interface_series_load/run.ipynb b/demo/interface_series_load/run.ipynb index c899c8230c848..9d8f3cd13537e 100644 --- a/demo/interface_series_load/run.ipynb +++ b/demo/interface_series_load/run.ipynb @@ -1 +1,5 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: interface_series_load"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "generator = gr.load(\"huggingface/gpt2\")\n", "\n", "\n", "translator = gr.Interface(lambda s: \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\", gr.Textbox(), gr.Image())\n", "\n", "demo = gr.Series(generator, translator, description=\"This demo combines two Spaces: a text generator (`huggingface/gpt2`) and a text translator (`huggingface/t5-small`). The first Space takes a prompt as input and generates a text. The second Space takes the generated text as input and translates it into another language.\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +<<<<<<< HEAD +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: interface_series_load"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "generator = gr.load(\"huggingface/gpt2\")\n", "\n", "\n", "translator = gr.Interface(lambda s: \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\", gr.Textbox(), gr.Image())\n", "\n", "demo = gr.Series(generator, translator, description=\"This demo combines two Spaces: a text generator (`huggingface/gpt2`) and a text translator (`huggingface/t5-small`). The first Space takes a prompt as input and generates a text. The second Space takes the generated text as input and translates it into another language.\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +======= +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: interface_series_load"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "generator = gr.load(\"huggingface/gpt2\")\n", "translator = gr.load(\"huggingface/t5-small\")\n", "\n", "demo = gr.Series(generator, translator, description=\"This demo combines two Spaces: a text generator (`huggingface/gpt2`) and a text translator (`huggingface/t5-small`). The first Space takes a prompt as input and generates a text. The second Space takes the generated text as input and translates it into another language.\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +>>>>>>> main diff --git a/demo/interpretation_component/run.ipynb b/demo/interpretation_component/run.ipynb index 3ffe8c4a11de8..09c1982d3e2bf 100644 --- a/demo/interpretation_component/run.ipynb +++ b/demo/interpretation_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: interpretation_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio shap transformers torch"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import shap\n", "from transformers import pipeline\n", "\n", "\n", "sentiment_classifier = pipeline(\"text-classification\", return_all_scores=True)\n", "\n", "def interpretation_function(text):\n", " explainer = shap.Explainer(sentiment_classifier)\n", " shap_values = explainer([text])\n", " scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))\n", " return {\"original\": text, \"interpretation\": scores}\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " input_text = gr.Textbox(label=\"Sentiment Analysis\", value=\"Wonderfully terrible\")\n", " with gr.Row():\n", " interpret = gr.Button(\"Interpret\")\n", " with gr.Column():\n", " interpretation = gr.components.Interpretation(input_text)\n", "\n", " interpret.click(interpretation_function, input_text, interpretation)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: interpretation_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio shap transformers torch"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import shap\n", "from transformers import pipeline\n", "\n", "\n", "sentiment_classifier = pipeline(\"text-classification\", return_all_scores=True)\n", "\n", "def interpretation_function(text):\n", " explainer = shap.Explainer(sentiment_classifier)\n", " shap_values = explainer([text])\n", " scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))\n", " return {\"original\": text, \"interpretation\": scores}\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " input_text = gr.Textbox(label=\"Sentiment Analysis\", value=\"Wonderfully terrible\")\n", " with gr.Row():\n", " interpret = gr.Button(\"Interpret\")\n", " with gr.Column():\n", " interpretation = gr.components.Interpretation(input_text)\n", "\n", " interpret.click(interpretation_function, input_text, interpretation)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/json_component/run.ipynb b/demo/json_component/run.ipynb index d465c76d31377..c0959b07eddfb 100644 --- a/demo/json_component/run.ipynb +++ b/demo/json_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: json_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.JSON(value={\"Key 1\": \"Value 1\", \"Key 2\": {\"Key 3\": \"Value 2\", \"Key 4\": \"Value 3\"}, \"Key 5\": [\"Item 1\", \"Item 2\", \"Item 3\"]})\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: json_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.JSON(value={\"Key 1\": \"Value 1\", \"Key 2\": {\"Key 3\": \"Value 2\", \"Key 4\": \"Value 3\"}, \"Key 5\": [\"Item 1\", \"Item 2\", \"Item 3\"]})\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/kitchen_sink/run.ipynb b/demo/kitchen_sink/run.ipynb index 428f1d231e4dc..5e7685ae595d7 100644 --- a/demo/kitchen_sink/run.ipynb +++ b/demo/kitchen_sink/run.ipynb @@ -1 +1,5 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: kitchen_sink"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cantina.wav\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cheetah1.jpg\n", "!wget -q -O files/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/lion.jpg\n", "!wget -q -O files/logo.png https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/logo.png\n", "!wget -q -O files/time.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/time.csv\n", "!wget -q -O files/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/titanic.csv\n", "!wget -q -O files/tower.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/tower.jpg\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import os\n", "import json\n", "\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "CHOICES = [\"foo\", \"bar\", \"baz\"]\n", "JSONOBJ = \"\"\"{\"items\":{\"item\":[{\"id\": \"0001\",\"type\": null,\"is_good\": false,\"ppu\": 0.55,\"batters\":{\"batter\":[{ \"id\": \"1001\", \"type\": \"Regular\" },{ \"id\": \"1002\", \"type\": \"Chocolate\" },{ \"id\": \"1003\", \"type\": \"Blueberry\" },{ \"id\": \"1004\", \"type\": \"Devil's Food\" }]},\"topping\":[{ \"id\": \"5001\", \"type\": \"None\" },{ \"id\": \"5002\", \"type\": \"Glazed\" },{ \"id\": \"5005\", \"type\": \"Sugar\" },{ \"id\": \"5007\", \"type\": \"Powdered Sugar\" },{ \"id\": \"5006\", \"type\": \"Chocolate with Sprinkles\" },{ \"id\": \"5003\", \"type\": \"Chocolate\" },{ \"id\": \"5004\", \"type\": \"Maple\" }]}]}}\"\"\"\n", "\n", "\n", "def fn(\n", " text1,\n", " text2,\n", " num,\n", " slider1,\n", " slider2,\n", " single_checkbox,\n", " checkboxes,\n", " radio,\n", " dropdown,\n", " multi_dropdown,\n", " im1,\n", " im2,\n", " im3,\n", " im4,\n", " video,\n", " audio1,\n", " audio2,\n", " file,\n", " df1,\n", "):\n", " return (\n", " (text1 if single_checkbox else text2)\n", " + \", selected:\"\n", " + \", \".join(checkboxes), # Text\n", " {\n", " \"positive\": num / (num + slider1 + slider2),\n", " \"negative\": slider1 / (num + slider1 + slider2),\n", " \"neutral\": slider2 / (num + slider1 + slider2),\n", " }, # Label\n", " (audio1[0], np.flipud(audio1[1]))\n", " if audio1 is not None\n", " else os.path.join(os.path.abspath(''), \"files/cantina.wav\"), # Audio\n", " np.flipud(im1)\n", " if im1 is not None\n", " else os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"), # Image\n", " video\n", " if video is not None\n", " else os.path.join(os.path.abspath(''), \"files/world.mp4\"), # Video\n", " [\n", " (\"The\", \"art\"),\n", " (\"quick brown\", \"adj\"),\n", " (\"fox\", \"nn\"),\n", " (\"jumped\", \"vrb\"),\n", " (\"testing testing testing\", None),\n", " (\"over\", \"prp\"),\n", " (\"the\", \"art\"),\n", " (\"testing\", None),\n", " (\"lazy\", \"adj\"),\n", " (\"dogs\", \"nn\"),\n", " (\".\", \"punc\"),\n", " ]\n", " + [(f\"test {x}\", f\"test {x}\") for x in range(10)], # HighlightedText\n", " # [(\"The testing testing testing\", None), (\"quick brown\", 0.2), (\"fox\", 1), (\"jumped\", -1), (\"testing testing testing\", 0), (\"over\", 0), (\"the\", 0), (\"testing\", 0), (\"lazy\", 1), (\"dogs\", 0), (\".\", 1)] + [(f\"test {x}\", x/10) for x in range(-10, 10)], # HighlightedText\n", " [\n", " (\"The testing testing testing\", None),\n", " (\"over\", 0.6),\n", " (\"the\", 0.2),\n", " (\"testing\", None),\n", " (\"lazy\", -0.1),\n", " (\"dogs\", 0.4),\n", " (\".\", 0),\n", " ]\n", " + [(f\"test\", x / 10) for x in range(-10, 10)], # HighlightedText\n", " json.loads(JSONOBJ), # JSON\n", " \"\", # HTML\n", " os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", " df1, # Dataframe\n", " np.random.randint(0, 10, (4, 4)), # Dataframe\n", " )\n", "\n", "\n", "demo = gr.Interface(\n", " fn,\n", " inputs=[\n", " gr.Textbox(value=\"Lorem ipsum\", label=\"Textbox\"),\n", " gr.Textbox(lines=3, placeholder=\"Type here..\", label=\"Textbox 2\"),\n", " gr.Number(label=\"Number\", value=42),\n", " gr.Slider(10, 20, value=15, label=\"Slider: 10 - 20\"),\n", " gr.Slider(maximum=20, step=0.04, label=\"Slider: step @ 0.04\"),\n", " gr.Checkbox(label=\"Checkbox\"),\n", " gr.CheckboxGroup(label=\"CheckboxGroup\", choices=CHOICES, value=CHOICES[0:2]),\n", " gr.Radio(label=\"Radio\", choices=CHOICES, value=CHOICES[2]),\n", " gr.Dropdown(label=\"Dropdown\", choices=CHOICES),\n", " gr.Dropdown(\n", " label=\"Multiselect Dropdown (Max choice: 2)\",\n", " choices=CHOICES,\n", " multiselect=True,\n", " max_choices=2,\n", " ),\n", " gr.Image(label=\"Image\"),\n", " gr.Image(label=\"Image w/ Cropper\", tool=\"select\"),\n", " gr.Image(label=\"Sketchpad\", source=\"canvas\"),\n", " gr.Image(label=\"Webcam\", source=\"webcam\"),\n", " gr.Video(label=\"Video\"),\n", " gr.Audio(label=\"Audio\"),\n", " gr.Audio(label=\"Microphone\", source=\"microphone\"),\n", " gr.File(label=\"File\"),\n", " gr.Dataframe(label=\"Dataframe\", headers=[\"Name\", \"Age\", \"Gender\"]),\n", " ],\n", " outputs=[\n", " gr.Textbox(label=\"Textbox\"),\n", " gr.Label(label=\"Label\"),\n", " gr.Audio(label=\"Audio\"),\n", " gr.Image(label=\"Image\"),\n", " gr.Video(label=\"Video\"),\n", " gr.HighlightedText(\n", " label=\"HighlightedText\", color_map={\"punc\": \"pink\", \"test 0\": \"blue\"}\n", " ),\n", " gr.HighlightedText(label=\"HighlightedText\", show_legend=True),\n", " gr.JSON(label=\"JSON\"),\n", " gr.HTML(label=\"HTML\"),\n", " gr.File(label=\"File\"),\n", " gr.Dataframe(label=\"Dataframe\"),\n", " gr.Dataframe(label=\"Numpy\"),\n", " ],\n", " examples=[\n", " [\n", " \"the quick brown fox\",\n", " \"jumps over the lazy dog\",\n", " 10,\n", " 12,\n", " 4,\n", " True,\n", " [\"foo\", \"baz\"],\n", " \"baz\",\n", " \"bar\",\n", " [\"foo\", \"bar\"],\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/world.mp4\"),\n", " os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", " os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", " os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", " [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],\n", " ]\n", " ]\n", " * 3,\n", " title=\"Kitchen Sink\",\n", " description=\"Try out all the components!\",\n", " article=\"Learn more about [Gradio](http://gradio.app)\",\n", " cache_examples=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +<<<<<<< HEAD +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: kitchen_sink"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cantina.wav\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cheetah1.jpg\n", "!wget -q -O files/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/lion.jpg\n", "!wget -q -O files/logo.png https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/logo.png\n", "!wget -q -O files/time.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/time.csv\n", "!wget -q -O files/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/titanic.csv\n", "!wget -q -O files/tower.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/tower.jpg\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import os\n", "import json\n", "\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "CHOICES = [\"foo\", \"bar\", \"baz\"]\n", "JSONOBJ = \"\"\"{\"items\":{\"item\":[{\"id\": \"0001\",\"type\": null,\"is_good\": false,\"ppu\": 0.55,\"batters\":{\"batter\":[{ \"id\": \"1001\", \"type\": \"Regular\" },{ \"id\": \"1002\", \"type\": \"Chocolate\" },{ \"id\": \"1003\", \"type\": \"Blueberry\" },{ \"id\": \"1004\", \"type\": \"Devil's Food\" }]},\"topping\":[{ \"id\": \"5001\", \"type\": \"None\" },{ \"id\": \"5002\", \"type\": \"Glazed\" },{ \"id\": \"5005\", \"type\": \"Sugar\" },{ \"id\": \"5007\", \"type\": \"Powdered Sugar\" },{ \"id\": \"5006\", \"type\": \"Chocolate with Sprinkles\" },{ \"id\": \"5003\", \"type\": \"Chocolate\" },{ \"id\": \"5004\", \"type\": \"Maple\" }]}]}}\"\"\"\n", "\n", "\n", "def fn(\n", " text1,\n", " text2,\n", " num,\n", " slider1,\n", " slider2,\n", " single_checkbox,\n", " checkboxes,\n", " radio,\n", " dropdown,\n", " multi_dropdown,\n", " im1,\n", " im2,\n", " im3,\n", " im4,\n", " video,\n", " audio1,\n", " audio2,\n", " file,\n", " df1,\n", "):\n", " return (\n", " (text1 if single_checkbox else text2)\n", " + \", selected:\"\n", " + \", \".join(checkboxes), # Text\n", " {\n", " \"positive\": num / (num + slider1 + slider2),\n", " \"negative\": slider1 / (num + slider1 + slider2),\n", " \"neutral\": slider2 / (num + slider1 + slider2),\n", " }, # Label\n", " (audio1[0], np.flipud(audio1[1]))\n", " if audio1 is not None\n", " else os.path.join(os.path.abspath(''), \"files/cantina.wav\"), # Audio\n", " np.flipud(im1)\n", " if im1 is not None\n", " else os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"), # Image\n", " video\n", " if video is not None\n", " else os.path.join(os.path.abspath(''), \"files/world.mp4\"), # Video\n", " [\n", " (\"The\", \"art\"),\n", " (\"quick brown\", \"adj\"),\n", " (\"fox\", \"nn\"),\n", " (\"jumped\", \"vrb\"),\n", " (\"testing testing testing\", None),\n", " (\"over\", \"prp\"),\n", " (\"the\", \"art\"),\n", " (\"testing\", None),\n", " (\"lazy\", \"adj\"),\n", " (\"dogs\", \"nn\"),\n", " (\".\", \"punc\"),\n", " ]\n", " + [(f\"test {x}\", f\"test {x}\") for x in range(10)], # HighlightedText\n", " # [(\"The testing testing testing\", None), (\"quick brown\", 0.2), (\"fox\", 1), (\"jumped\", -1), (\"testing testing testing\", 0), (\"over\", 0), (\"the\", 0), (\"testing\", 0), (\"lazy\", 1), (\"dogs\", 0), (\".\", 1)] + [(f\"test {x}\", x/10) for x in range(-10, 10)], # HighlightedText\n", " [\n", " (\"The testing testing testing\", None),\n", " (\"over\", 0.6),\n", " (\"the\", 0.2),\n", " (\"testing\", None),\n", " (\"lazy\", -0.1),\n", " (\"dogs\", 0.4),\n", " (\".\", 0),\n", " ]\n", " + [(f\"test\", x / 10) for x in range(-10, 10)], # HighlightedText\n", " json.loads(JSONOBJ), # JSON\n", " \"\", # HTML\n", " os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", " df1, # Dataframe\n", " np.random.randint(0, 10, (4, 4)), # Dataframe\n", " )\n", "\n", "\n", "demo = gr.Interface(\n", " fn,\n", " inputs=[\n", " gr.Textbox(value=\"Lorem ipsum\", label=\"Textbox\"),\n", " gr.Textbox(lines=3, placeholder=\"Type here..\", label=\"Textbox 2\"),\n", " gr.Number(label=\"Number\", value=42),\n", " gr.Slider(10, 20, value=15, label=\"Slider: 10 - 20\"),\n", " gr.Slider(maximum=20, step=0.04, label=\"Slider: step @ 0.04\"),\n", " gr.Checkbox(label=\"Checkbox\"),\n", " gr.CheckboxGroup(label=\"CheckboxGroup\", choices=CHOICES, value=CHOICES[0:2]),\n", " gr.Radio(label=\"Radio\", choices=CHOICES, value=CHOICES[2]),\n", " gr.Dropdown(label=\"Dropdown\", choices=CHOICES),\n", " gr.Dropdown(\n", " label=\"Multiselect Dropdown (Max choice: 2)\",\n", " choices=CHOICES,\n", " multiselect=True,\n", " max_choices=2,\n", " ),\n", " gr.Image(label=\"Image\"),\n", " gr.Image(label=\"Image w/ Cropper\", tool=\"select\"),\n", " gr.Image(label=\"Sketchpad\", source=\"canvas\"),\n", " gr.Image(label=\"Webcam\", source=\"webcam\"),\n", " gr.Video(label=\"Video\"),\n", " gr.Audio(label=\"Audio\"),\n", " gr.Audio(label=\"Microphone\", source=\"microphone\"),\n", " gr.File(label=\"File\"),\n", " gr.Dataframe(label=\"Dataframe\", headers=[\"Name\", \"Age\", \"Gender\"]),\n", " ],\n", " outputs=[\n", " gr.Textbox(label=\"Textbox\"),\n", " gr.Label(label=\"Label\"),\n", " gr.Audio(label=\"Audio\"),\n", " gr.Image(label=\"Image\"),\n", " gr.Video(label=\"Video\"),\n", " gr.HighlightedText(\n", " label=\"HighlightedText\", color_map={\"punc\": \"pink\", \"test 0\": \"blue\"}\n", " ),\n", " gr.HighlightedText(label=\"HighlightedText\", show_legend=True),\n", " gr.JSON(label=\"JSON\"),\n", " gr.HTML(label=\"HTML\"),\n", " gr.File(label=\"File\"),\n", " gr.Dataframe(label=\"Dataframe\"),\n", " gr.Dataframe(label=\"Numpy\"),\n", " ],\n", " examples=[\n", " [\n", " \"the quick brown fox\",\n", " \"jumps over the lazy dog\",\n", " 10,\n", " 12,\n", " 4,\n", " True,\n", " [\"foo\", \"baz\"],\n", " \"baz\",\n", " \"bar\",\n", " [\"foo\", \"bar\"],\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/world.mp4\"),\n", " os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", " os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", " os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", " [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],\n", " ]\n", " ]\n", " * 3,\n", " title=\"Kitchen Sink\",\n", " description=\"Try out all the components!\",\n", " article=\"Learn more about [Gradio](http://gradio.app)\",\n", " cache_examples=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +======= +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: kitchen_sink"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cantina.wav\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/cheetah1.jpg\n", "!wget -q -O files/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/lion.jpg\n", "!wget -q -O files/logo.png https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/logo.png\n", "!wget -q -O files/time.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/time.csv\n", "!wget -q -O files/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/titanic.csv\n", "!wget -q -O files/tower.jpg https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/tower.jpg\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/kitchen_sink/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "import json\n", "\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "CHOICES = [\"foo\", \"bar\", \"baz\"]\n", "JSONOBJ = \"\"\"{\"items\":{\"item\":[{\"id\": \"0001\",\"type\": null,\"is_good\": false,\"ppu\": 0.55,\"batters\":{\"batter\":[{ \"id\": \"1001\", \"type\": \"Regular\" },{ \"id\": \"1002\", \"type\": \"Chocolate\" },{ \"id\": \"1003\", \"type\": \"Blueberry\" },{ \"id\": \"1004\", \"type\": \"Devil's Food\" }]},\"topping\":[{ \"id\": \"5001\", \"type\": \"None\" },{ \"id\": \"5002\", \"type\": \"Glazed\" },{ \"id\": \"5005\", \"type\": \"Sugar\" },{ \"id\": \"5007\", \"type\": \"Powdered Sugar\" },{ \"id\": \"5006\", \"type\": \"Chocolate with Sprinkles\" },{ \"id\": \"5003\", \"type\": \"Chocolate\" },{ \"id\": \"5004\", \"type\": \"Maple\" }]}]}}\"\"\"\n", "\n", "\n", "def fn(\n", " text1,\n", " text2,\n", " num,\n", " slider1,\n", " slider2,\n", " single_checkbox,\n", " checkboxes,\n", " radio,\n", " dropdown,\n", " multi_dropdown,\n", " im1,\n", " im2,\n", " im3,\n", " im4,\n", " video,\n", " audio1,\n", " audio2,\n", " file,\n", " df1,\n", " df2,\n", "):\n", " return (\n", " (text1 if single_checkbox else text2)\n", " + \", selected:\"\n", " + \", \".join(checkboxes), # Text\n", " {\n", " \"positive\": num / (num + slider1 + slider2),\n", " \"negative\": slider1 / (num + slider1 + slider2),\n", " \"neutral\": slider2 / (num + slider1 + slider2),\n", " }, # Label\n", " (audio1[0], np.flipud(audio1[1]))\n", " if audio1 is not None\n", " else os.path.join(os.path.abspath(''), \"files/cantina.wav\"), # Audio\n", " np.flipud(im1)\n", " if im1 is not None\n", " else os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"), # Image\n", " video\n", " if video is not None\n", " else os.path.join(os.path.abspath(''), \"files/world.mp4\"), # Video\n", " [\n", " (\"The\", \"art\"),\n", " (\"quick brown\", \"adj\"),\n", " (\"fox\", \"nn\"),\n", " (\"jumped\", \"vrb\"),\n", " (\"testing testing testing\", None),\n", " (\"over\", \"prp\"),\n", " (\"the\", \"art\"),\n", " (\"testing\", None),\n", " (\"lazy\", \"adj\"),\n", " (\"dogs\", \"nn\"),\n", " (\".\", \"punc\"),\n", " ]\n", " + [(f\"test {x}\", f\"test {x}\") for x in range(10)], # HighlightedText\n", " # [(\"The testing testing testing\", None), (\"quick brown\", 0.2), (\"fox\", 1), (\"jumped\", -1), (\"testing testing testing\", 0), (\"over\", 0), (\"the\", 0), (\"testing\", 0), (\"lazy\", 1), (\"dogs\", 0), (\".\", 1)] + [(f\"test {x}\", x/10) for x in range(-10, 10)], # HighlightedText\n", " [\n", " (\"The testing testing testing\", None),\n", " (\"over\", 0.6),\n", " (\"the\", 0.2),\n", " (\"testing\", None),\n", " (\"lazy\", -0.1),\n", " (\"dogs\", 0.4),\n", " (\".\", 0),\n", " ]\n", " + [(f\"test\", x / 10) for x in range(-10, 10)], # HighlightedText\n", " json.loads(JSONOBJ), # JSON\n", " \"\", # HTML\n", " os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", " df1, # Dataframe\n", " np.random.randint(0, 10, (4, 4)), # Dataframe\n", " df2, # Timeseries\n", " )\n", "\n", "\n", "demo = gr.Interface(\n", " fn,\n", " inputs=[\n", " gr.Textbox(value=\"Lorem ipsum\", label=\"Textbox\"),\n", " gr.Textbox(lines=3, placeholder=\"Type here..\", label=\"Textbox 2\"),\n", " gr.Number(label=\"Number\", value=42),\n", " gr.Slider(10, 20, value=15, label=\"Slider: 10 - 20\"),\n", " gr.Slider(maximum=20, step=0.04, label=\"Slider: step @ 0.04\"),\n", " gr.Checkbox(label=\"Checkbox\"),\n", " gr.CheckboxGroup(label=\"CheckboxGroup\", choices=CHOICES, value=CHOICES[0:2]),\n", " gr.Radio(label=\"Radio\", choices=CHOICES, value=CHOICES[2]),\n", " gr.Dropdown(label=\"Dropdown\", choices=CHOICES),\n", " gr.Dropdown(label=\"Multiselect Dropdown (Max choice: 2)\", choices=CHOICES, multiselect=True, max_choices=2),\n", " gr.Image(label=\"Image\"),\n", " gr.Image(label=\"Image w/ Cropper\", tool=\"select\"),\n", " gr.Image(label=\"Sketchpad\", source=\"canvas\"),\n", " gr.Image(label=\"Webcam\", source=\"webcam\"),\n", " gr.Video(label=\"Video\"),\n", " gr.Audio(label=\"Audio\"),\n", " gr.Audio(label=\"Microphone\", source=\"microphone\"),\n", " gr.File(label=\"File\"),\n", " gr.Dataframe(label=\"Dataframe\", headers=[\"Name\", \"Age\", \"Gender\"]),\n", " gr.Timeseries(x=\"time\", y=[\"price\", \"value\"], colors=[\"pink\", \"purple\"]),\n", " ],\n", " outputs=[\n", " gr.Textbox(label=\"Textbox\"),\n", " gr.Label(label=\"Label\"),\n", " gr.Audio(label=\"Audio\"),\n", " gr.Image(label=\"Image\"),\n", " gr.Video(label=\"Video\"),\n", " gr.HighlightedText(label=\"HighlightedText\", \n", " color_map={\"punc\": \"pink\", \"test 0\": \"blue\"}\n", " ),\n", " gr.HighlightedText(label=\"HighlightedText\", show_legend=True),\n", " gr.JSON(label=\"JSON\"),\n", " gr.HTML(label=\"HTML\"),\n", " gr.File(label=\"File\"),\n", " gr.Dataframe(label=\"Dataframe\"),\n", " gr.Dataframe(label=\"Numpy\"),\n", " gr.Timeseries(x=\"time\", y=[\"price\", \"value\"], label=\"Timeseries\"),\n", " ],\n", " examples=[\n", " [\n", " \"the quick brown fox\",\n", " \"jumps over the lazy dog\",\n", " 10,\n", " 12,\n", " 4,\n", " True,\n", " [\"foo\", \"baz\"],\n", " \"baz\",\n", " \"bar\",\n", " [\"foo\", \"bar\"],\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\"),\n", " os.path.join(os.path.abspath(''), \"files/world.mp4\"),\n", " os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", " os.path.join(os.path.abspath(''), \"files/cantina.wav\"),\n", " os.path.join(os.path.abspath(''), \"files/titanic.csv\"),\n", " [[1, 2, 3, 4], [4, 5, 6, 7], [8, 9, 1, 2], [3, 4, 5, 6]],\n", " os.path.join(os.path.abspath(''), \"files/time.csv\"),\n", " ]\n", " ]\n", " * 3,\n", " title=\"Kitchen Sink\",\n", " description=\"Try out all the components!\",\n", " article=\"Learn more about [Gradio](http://gradio.app)\",\n", " cache_examples=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +>>>>>>> main diff --git a/demo/label_component/run.ipynb b/demo/label_component/run.ipynb index 0cd6779e9aa52..c0fdf29ca71de 100644 --- a/demo/label_component/run.ipynb +++ b/demo/label_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: label_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.Label(value={\"First Label\": 0.7, \"Second Label\": 0.2, \"Third Label\": 0.1})\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: label_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.Label(value={\"First Label\": 0.7, \"Second Label\": 0.2, \"Third Label\": 0.1})\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/latex/run.ipynb b/demo/latex/run.ipynb index 4d0585bdf0a99..344513773befa 100644 --- a/demo/latex/run.ipynb +++ b/demo/latex/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: latex"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " r\"\"\"\n", " # Hello World! $\\frac{\\sqrt{x + y}}{4}$ is today's lesson\n", "\n", " ## the $\\sqrt{x + y}$ is first\n", "\n", " Start with $\\frac{\\frac{x+1}{x+2}}{x+3}$ then we get $ 2+x $ and $3$.\n", " \n", " There are three formulas to know:\n", " \n", " the first is $\\gamma^2 + \\theta^2 = \\omega^2$\n", " \n", " $\\sqrt{x^2+1}$ is next\n", " \n", " Integral $\\int_{a}^{b} x^2 \\,dx$ is last\n", "\n", " Start typing below to see the output.\n", "\n", " I spent $5 at the grocery store. Then I bought a $2.50 ice cream cone.\n", " \"\"\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: latex"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\n", " r\"\"\"\n", " # Hello World! $\\frac{\\sqrt{x + y}}{4}$ is today's lesson\n", "\n", " ## the $\\sqrt{x + y}$ is first\n", "\n", " Start with $\\frac{\\frac{x+1}{x+2}}{x+3}$ then we get $ 2+x $ and $3$.\n", " \n", " There are three formulas to know:\n", " \n", " the first is $\\gamma^2 + \\theta^2 = \\omega^2$\n", " \n", " $\\sqrt{x^2+1}$ is next\n", " \n", " Integral $\\int_{a}^{b} x^2 \\,dx$ is last\n", "\n", " Start typing below to see the output.\n", "\n", " I spent $5 at the grocery store. Then I bought a $2.50 ice cream cone.\n", " \"\"\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/leaderboard/run.ipynb b/demo/leaderboard/run.ipynb index b368932998ecf..b4c74a920d4e2 100644 --- a/demo/leaderboard/run.ipynb +++ b/demo/leaderboard/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: leaderboard\n", "### A simple dashboard ranking spaces by number of likes.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import requests\n", "import pandas as pd\n", "from huggingface_hub.hf_api import SpaceInfo\n", "path = f\"https://huggingface.co/api/spaces\"\n", "\n", "\n", "def get_blocks_party_spaces():\n", " r = requests.get(path)\n", " d = r.json()\n", " spaces = [SpaceInfo(**x) for x in d]\n", " blocks_spaces = {}\n", " for i in range(0,len(spaces)):\n", " if spaces[i].id.split('/')[0] == 'Gradio-Blocks' and hasattr(spaces[i], 'likes') and spaces[i].id != 'Gradio-Blocks/Leaderboard' and spaces[i].id != 'Gradio-Blocks/README':\n", " blocks_spaces[spaces[i].id]=spaces[i].likes\n", " df = pd.DataFrame(\n", " [{\"Spaces_Name\": Spaces, \"likes\": likes} for Spaces,likes in blocks_spaces.items()])\n", " df = df.sort_values(by=['likes'],ascending=False)\n", " return df\n", "\n", "block = gr.Blocks()\n", "\n", "with block: \n", " gr.Markdown(\"\"\"Leaderboard for the most popular Blocks Event Spaces. To learn more and join, see Blocks Party Event\"\"\")\n", " with gr.Tabs():\n", " with gr.TabItem(\"Blocks Party Leaderboard\"):\n", " with gr.Row():\n", " data = gr.outputs.Dataframe(type=\"pandas\")\n", " with gr.Row():\n", " data_run = gr.Button(\"Refresh\")\n", " data_run.click(get_blocks_party_spaces, inputs=None, outputs=data)\n", " # running the function on page load in addition to when the button is clicked\n", " block.load(get_blocks_party_spaces, inputs=None, outputs=data) \n", "\n", "block.launch()\n", "\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: leaderboard\n", "### A simple dashboard ranking spaces by number of likes.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import requests\n", "import pandas as pd\n", "from huggingface_hub.hf_api import SpaceInfo\n", "path = f\"https://huggingface.co/api/spaces\"\n", "\n", "\n", "def get_blocks_party_spaces():\n", " r = requests.get(path)\n", " d = r.json()\n", " spaces = [SpaceInfo(**x) for x in d]\n", " blocks_spaces = {}\n", " for i in range(0,len(spaces)):\n", " if spaces[i].id.split('/')[0] == 'Gradio-Blocks' and hasattr(spaces[i], 'likes') and spaces[i].id != 'Gradio-Blocks/Leaderboard' and spaces[i].id != 'Gradio-Blocks/README':\n", " blocks_spaces[spaces[i].id]=spaces[i].likes\n", " df = pd.DataFrame(\n", " [{\"Spaces_Name\": Spaces, \"likes\": likes} for Spaces,likes in blocks_spaces.items()])\n", " df = df.sort_values(by=['likes'],ascending=False)\n", " return df\n", "\n", "block = gr.Blocks()\n", "\n", "with block: \n", " gr.Markdown(\"\"\"Leaderboard for the most popular Blocks Event Spaces. To learn more and join, see Blocks Party Event\"\"\")\n", " with gr.Tabs():\n", " with gr.TabItem(\"Blocks Party Leaderboard\"):\n", " with gr.Row():\n", " data = gr.outputs.Dataframe(type=\"pandas\")\n", " with gr.Row():\n", " data_run = gr.Button(\"Refresh\")\n", " data_run.click(get_blocks_party_spaces, inputs=None, outputs=data)\n", " # running the function on page load in addition to when the button is clicked\n", " block.load(get_blocks_party_spaces, inputs=None, outputs=data) \n", "\n", "block.launch()\n", "\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/line_plot/run.ipynb b/demo/line_plot/run.ipynb index 2590b0d18ba72..10b8606740efa 100644 --- a/demo/line_plot/run.ipynb +++ b/demo/line_plot/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: line_plot"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets pandas"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "stocks = data.stocks()\n", "gapminder = data.gapminder()\n", "gapminder = gapminder.loc[\n", " gapminder.country.isin([\"Argentina\", \"Australia\", \"Afghanistan\"])\n", "]\n", "climate = data.climate()\n", "seattle_weather = data.seattle_weather()\n", "\n", "## Or generate your own fake data, here's an example for stocks:\n", "#\n", "# import pandas as pd\n", "# import random\n", "#\n", "# stocks = pd.DataFrame(\n", "# {\n", "# \"symbol\": [\n", "# random.choice(\n", "# [\n", "# \"MSFT\",\n", "# \"AAPL\",\n", "# \"AMZN\",\n", "# \"IBM\",\n", "# \"GOOG\",\n", "# ]\n", "# )\n", "# for _ in range(120)\n", "# ],\n", "# \"date\": [\n", "# pd.Timestamp(year=2000 + i, month=j, day=1)\n", "# for i in range(10)\n", "# for j in range(1, 13)\n", "# ],\n", "# \"price\": [random.randint(10, 200) for _ in range(120)],\n", "# }\n", "# )\n", "\n", "\n", "def line_plot_fn(dataset):\n", " if dataset == \"stocks\":\n", " return gr.LinePlot(\n", " stocks,\n", " x=\"date\",\n", " y=\"price\",\n", " color=\"symbol\",\n", " color_legend_position=\"bottom\",\n", " title=\"Stock Prices\",\n", " tooltip=[\"date\", \"price\", \"symbol\"],\n", " height=300,\n", " width=500,\n", " )\n", " elif dataset == \"climate\":\n", " return gr.LinePlot(\n", " climate,\n", " x=\"DATE\",\n", " y=\"HLY-TEMP-NORMAL\",\n", " y_lim=[250, 500],\n", " title=\"Climate\",\n", " tooltip=[\"DATE\", \"HLY-TEMP-NORMAL\"],\n", " height=300,\n", " width=500,\n", " )\n", " elif dataset == \"seattle_weather\":\n", " return gr.LinePlot(\n", " seattle_weather,\n", " x=\"date\",\n", " y=\"temp_min\",\n", " tooltip=[\"weather\", \"date\"],\n", " overlay_point=True,\n", " title=\"Seattle Weather\",\n", " height=300,\n", " width=500,\n", " )\n", " elif dataset == \"gapminder\":\n", " return gr.LinePlot(\n", " gapminder,\n", " x=\"year\",\n", " y=\"life_expect\",\n", " color=\"country\",\n", " title=\"Life expectancy for countries\",\n", " stroke_dash=\"cluster\",\n", " x_lim=[1950, 2010],\n", " tooltip=[\"country\", \"life_expect\"],\n", " stroke_dash_legend_title=\"Country Cluster\",\n", " height=300,\n", " width=500,\n", " )\n", "\n", "\n", "with gr.Blocks() as line_plot:\n", " with gr.Row():\n", " with gr.Column():\n", " dataset = gr.Dropdown(\n", " choices=[\"stocks\", \"climate\", \"seattle_weather\", \"gapminder\"],\n", " value=\"stocks\",\n", " )\n", " with gr.Column():\n", " plot = gr.LinePlot()\n", " dataset.change(line_plot_fn, inputs=dataset, outputs=plot)\n", " line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " line_plot.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: line_plot"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets pandas"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "stocks = data.stocks()\n", "gapminder = data.gapminder()\n", "gapminder = gapminder.loc[\n", " gapminder.country.isin([\"Argentina\", \"Australia\", \"Afghanistan\"])\n", "]\n", "climate = data.climate()\n", "seattle_weather = data.seattle_weather()\n", "\n", "## Or generate your own fake data, here's an example for stocks:\n", "#\n", "# import pandas as pd\n", "# import random\n", "#\n", "# stocks = pd.DataFrame(\n", "# {\n", "# \"symbol\": [\n", "# random.choice(\n", "# [\n", "# \"MSFT\",\n", "# \"AAPL\",\n", "# \"AMZN\",\n", "# \"IBM\",\n", "# \"GOOG\",\n", "# ]\n", "# )\n", "# for _ in range(120)\n", "# ],\n", "# \"date\": [\n", "# pd.Timestamp(year=2000 + i, month=j, day=1)\n", "# for i in range(10)\n", "# for j in range(1, 13)\n", "# ],\n", "# \"price\": [random.randint(10, 200) for _ in range(120)],\n", "# }\n", "# )\n", "\n", "\n", "def line_plot_fn(dataset):\n", " if dataset == \"stocks\":\n", " return gr.LinePlot(\n", " stocks,\n", " x=\"date\",\n", " y=\"price\",\n", " color=\"symbol\",\n", " color_legend_position=\"bottom\",\n", " title=\"Stock Prices\",\n", " tooltip=[\"date\", \"price\", \"symbol\"],\n", " height=300,\n", " width=500,\n", " )\n", " elif dataset == \"climate\":\n", " return gr.LinePlot(\n", " climate,\n", " x=\"DATE\",\n", " y=\"HLY-TEMP-NORMAL\",\n", " y_lim=[250, 500],\n", " title=\"Climate\",\n", " tooltip=[\"DATE\", \"HLY-TEMP-NORMAL\"],\n", " height=300,\n", " width=500,\n", " )\n", " elif dataset == \"seattle_weather\":\n", " return gr.LinePlot(\n", " seattle_weather,\n", " x=\"date\",\n", " y=\"temp_min\",\n", " tooltip=[\"weather\", \"date\"],\n", " overlay_point=True,\n", " title=\"Seattle Weather\",\n", " height=300,\n", " width=500,\n", " )\n", " elif dataset == \"gapminder\":\n", " return gr.LinePlot(\n", " gapminder,\n", " x=\"year\",\n", " y=\"life_expect\",\n", " color=\"country\",\n", " title=\"Life expectancy for countries\",\n", " stroke_dash=\"cluster\",\n", " x_lim=[1950, 2010],\n", " tooltip=[\"country\", \"life_expect\"],\n", " stroke_dash_legend_title=\"Country Cluster\",\n", " height=300,\n", " width=500,\n", " )\n", "\n", "\n", "with gr.Blocks() as line_plot:\n", " with gr.Row():\n", " with gr.Column():\n", " dataset = gr.Dropdown(\n", " choices=[\"stocks\", \"climate\", \"seattle_weather\", \"gapminder\"],\n", " value=\"stocks\",\n", " )\n", " with gr.Column():\n", " plot = gr.LinePlot()\n", " dataset.change(line_plot_fn, inputs=dataset, outputs=plot)\n", " line_plot.load(fn=line_plot_fn, inputs=dataset, outputs=plot)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " line_plot.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/lineplot_component/run.ipynb b/demo/lineplot_component/run.ipynb index 9a118e0084571..a30e401b71241 100644 --- a/demo/lineplot_component/run.ipynb +++ b/demo/lineplot_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: lineplot_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "with gr.Blocks() as demo:\n", " gr.LinePlot(\n", " data.stocks(),\n", " x=\"date\",\n", " y=\"price\",\n", " color=\"symbol\",\n", " color_legend_position=\"bottom\",\n", " title=\"Stock Prices\",\n", " tooltip=[\"date\", \"price\", \"symbol\"],\n", " height=300,\n", " width=300,\n", " container=False,\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: lineplot_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "with gr.Blocks() as demo:\n", " gr.LinePlot(\n", " data.stocks(),\n", " x=\"date\",\n", " y=\"price\",\n", " color=\"symbol\",\n", " color_legend_position=\"bottom\",\n", " title=\"Stock Prices\",\n", " tooltip=[\"date\", \"price\", \"symbol\"],\n", " height=300,\n", " width=300,\n", " container=False,\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/live_dashboard/run.ipynb b/demo/live_dashboard/run.ipynb index 6fa3e43432590..6983127d7c013 100644 --- a/demo/live_dashboard/run.ipynb +++ b/demo/live_dashboard/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: live_dashboard\n", "### This demo shows how you can build a live interactive dashboard with gradio.\n", "The current time is refreshed every second and the plot every half second by using the 'every' keyword in the event handler.\n", "Changing the value of the slider will control the period of the sine curve (the distance between peaks). \n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import math\n", "\n", "import pandas as pd\n", "\n", "import gradio as gr\n", "import datetime\n", "import numpy as np\n", "\n", "\n", "def get_time():\n", " return datetime.datetime.now()\n", "\n", "\n", "plot_end = 2 * math.pi\n", "\n", "\n", "def get_plot(period=1):\n", " global plot_end\n", " x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02)\n", " y = np.sin(2 * math.pi * period * x)\n", " update = gr.LinePlot(\n", " value=pd.DataFrame({\"x\": x, \"y\": y}),\n", " x=\"x\",\n", " y=\"y\",\n", " title=\"Plot (updates every second)\",\n", " width=600,\n", " height=350,\n", " )\n", " plot_end += 2 * math.pi\n", " if plot_end > 1000:\n", " plot_end = 2 * math.pi\n", " return update\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " c_time2 = gr.Textbox(label=\"Current Time refreshed every second\")\n", " gr.Textbox(\n", " \"Change the value of the slider to automatically update the plot\",\n", " label=\"\",\n", " )\n", " period = gr.Slider(\n", " label=\"Period of plot\", value=1, minimum=0, maximum=10, step=1\n", " )\n", " plot = gr.LinePlot(show_label=False)\n", " with gr.Column():\n", " name = gr.Textbox(label=\"Enter your name\")\n", " greeting = gr.Textbox(label=\"Greeting\")\n", " button = gr.Button(value=\"Greet\")\n", " button.click(lambda s: f\"Hello {s}\", name, greeting)\n", "\n", " demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)\n", " dep = demo.load(get_plot, None, plot, every=1)\n", " period.change(get_plot, period, plot, every=1, cancels=[dep])\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue().launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: live_dashboard\n", "### This demo shows how you can build a live interactive dashboard with gradio.\n", "The current time is refreshed every second and the plot every half second by using the 'every' keyword in the event handler.\n", "Changing the value of the slider will control the period of the sine curve (the distance between peaks). \n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import math\n", "\n", "import pandas as pd\n", "\n", "import gradio as gr\n", "import datetime\n", "import numpy as np\n", "\n", "\n", "def get_time():\n", " return datetime.datetime.now()\n", "\n", "\n", "plot_end = 2 * math.pi\n", "\n", "\n", "def get_plot(period=1):\n", " global plot_end\n", " x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02)\n", " y = np.sin(2 * math.pi * period * x)\n", " update = gr.LinePlot(\n", " value=pd.DataFrame({\"x\": x, \"y\": y}),\n", " x=\"x\",\n", " y=\"y\",\n", " title=\"Plot (updates every second)\",\n", " width=600,\n", " height=350,\n", " )\n", " plot_end += 2 * math.pi\n", " if plot_end > 1000:\n", " plot_end = 2 * math.pi\n", " return update\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " c_time2 = gr.Textbox(label=\"Current Time refreshed every second\")\n", " gr.Textbox(\n", " \"Change the value of the slider to automatically update the plot\",\n", " label=\"\",\n", " )\n", " period = gr.Slider(\n", " label=\"Period of plot\", value=1, minimum=0, maximum=10, step=1\n", " )\n", " plot = gr.LinePlot(show_label=False)\n", " with gr.Column():\n", " name = gr.Textbox(label=\"Enter your name\")\n", " greeting = gr.Textbox(label=\"Greeting\")\n", " button = gr.Button(value=\"Greet\")\n", " button.click(lambda s: f\"Hello {s}\", name, greeting)\n", "\n", " demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)\n", " dep = demo.load(get_plot, None, plot, every=1)\n", " period.change(get_plot, period, plot, every=1, cancels=[dep])\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue().launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/live_with_vars/run.ipynb b/demo/live_with_vars/run.ipynb index 46cafe84f694b..7ed43fbd12134 100644 --- a/demo/live_with_vars/run.ipynb +++ b/demo/live_with_vars/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: live_with_vars"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Interface(\n", " lambda x, y: (x + y if y is not None else x, x + y if y is not None else x), \n", " [\"textbox\", \"state\"], \n", " [\"textbox\", \"state\"], live=True)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: live_with_vars"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Interface(\n", " lambda x, y: (x + y if y is not None else x, x + y if y is not None else x), \n", " [\"textbox\", \"state\"], \n", " [\"textbox\", \"state\"], live=True)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/loginbutton_component/run.ipynb b/demo/loginbutton_component/run.ipynb index 54acb92649428..ee38deb411b2d 100644 --- a/demo/loginbutton_component/run.ipynb +++ b/demo/loginbutton_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: loginbutton_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio gradio[oauth]"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.LoginButton()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: loginbutton_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio gradio[oauth]"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.LoginButton()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/logoutbutton_component/run.ipynb b/demo/logoutbutton_component/run.ipynb index e42b7e828358d..954963165387a 100644 --- a/demo/logoutbutton_component/run.ipynb +++ b/demo/logoutbutton_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: logoutbutton_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio gradio[oauth]"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.LogoutButton()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: logoutbutton_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio gradio[oauth]"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.LogoutButton()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/longest_word/run.ipynb b/demo/longest_word/run.ipynb index 70d70f8aa0b77..e0862d244f53c 100644 --- a/demo/longest_word/run.ipynb +++ b/demo/longest_word/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: longest_word"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def longest_word(text):\n", " words = text.split(\" \")\n", " lengths = [len(word) for word in words]\n", " return max(lengths)\n", "\n", "\n", "ex = \"The quick brown fox jumped over the lazy dog.\"\n", "\n", "demo = gr.Interface(\n", " longest_word, \"textbox\", \"label\", interpretation=\"default\", examples=[[ex]]\n", ")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: longest_word"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def longest_word(text):\n", " words = text.split(\" \")\n", " lengths = [len(word) for word in words]\n", " return max(lengths)\n", "\n", "\n", "ex = \"The quick brown fox jumped over the lazy dog.\"\n", "\n", "demo = gr.Interface(\n", " longest_word, \"textbox\", \"label\", interpretation=\"default\", examples=[[ex]]\n", ")\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/main_note/run.ipynb b/demo/main_note/run.ipynb index 174ea808c1e11..c76024ee90ad2 100644 --- a/demo/main_note/run.ipynb +++ b/demo/main_note/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: main_note"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scipy numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('audio')\n", "!wget -q -O audio/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/main_note/audio/cantina.wav\n", "!wget -q -O audio/recording1.wav https://github.com/gradio-app/gradio/raw/main/demo/main_note/audio/recording1.wav"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["from math import log2, pow\n", "import os\n", "\n", "import numpy as np\n", "from scipy.fftpack import fft\n", "\n", "import gradio as gr\n", "\n", "A4 = 440\n", "C0 = A4 * pow(2, -4.75)\n", "name = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n", "\n", "\n", "def get_pitch(freq):\n", " h = round(12 * log2(freq / C0))\n", " n = h % 12\n", " return name[n]\n", "\n", "\n", "def main_note(audio):\n", " rate, y = audio\n", " if len(y.shape) == 2:\n", " y = y.T[0]\n", " N = len(y)\n", " T = 1.0 / rate\n", " yf = fft(y)\n", " yf2 = 2.0 / N * np.abs(yf[0 : N // 2])\n", " xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)\n", "\n", " volume_per_pitch = {}\n", " total_volume = np.sum(yf2)\n", " for freq, volume in zip(xf, yf2):\n", " if freq == 0:\n", " continue\n", " pitch = get_pitch(freq)\n", " if pitch not in volume_per_pitch:\n", " volume_per_pitch[pitch] = 0\n", " volume_per_pitch[pitch] += 1.0 * volume / total_volume\n", " volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()}\n", " return volume_per_pitch\n", "\n", "\n", "demo = gr.Interface(\n", " main_note,\n", " gr.Audio(source=\"microphone\"),\n", " gr.Label(num_top_classes=4),\n", " examples=[\n", " [os.path.join(os.path.abspath(''),\"audio/recording1.wav\")],\n", " [os.path.join(os.path.abspath(''),\"audio/cantina.wav\")],\n", " ],\n", " interpretation=\"default\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: main_note"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scipy numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('audio')\n", "!wget -q -O audio/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/main_note/audio/cantina.wav\n", "!wget -q -O audio/recording1.wav https://github.com/gradio-app/gradio/raw/main/demo/main_note/audio/recording1.wav"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["from math import log2, pow\n", "import os\n", "\n", "import numpy as np\n", "from scipy.fftpack import fft\n", "\n", "import gradio as gr\n", "\n", "A4 = 440\n", "C0 = A4 * pow(2, -4.75)\n", "name = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n", "\n", "\n", "def get_pitch(freq):\n", " h = round(12 * log2(freq / C0))\n", " n = h % 12\n", " return name[n]\n", "\n", "\n", "def main_note(audio):\n", " rate, y = audio\n", " if len(y.shape) == 2:\n", " y = y.T[0]\n", " N = len(y)\n", " T = 1.0 / rate\n", " yf = fft(y)\n", " yf2 = 2.0 / N * np.abs(yf[0 : N // 2])\n", " xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)\n", "\n", " volume_per_pitch = {}\n", " total_volume = np.sum(yf2)\n", " for freq, volume in zip(xf, yf2):\n", " if freq == 0:\n", " continue\n", " pitch = get_pitch(freq)\n", " if pitch not in volume_per_pitch:\n", " volume_per_pitch[pitch] = 0\n", " volume_per_pitch[pitch] += 1.0 * volume / total_volume\n", " volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()}\n", " return volume_per_pitch\n", "\n", "\n", "demo = gr.Interface(\n", " main_note,\n", " gr.Audio(source=\"microphone\"),\n", " gr.Label(num_top_classes=4),\n", " examples=[\n", " [os.path.join(os.path.abspath(''),\"audio/recording1.wav\")],\n", " [os.path.join(os.path.abspath(''),\"audio/cantina.wav\")],\n", " ],\n", " interpretation=\"default\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/map_airbnb/run.ipynb b/demo/map_airbnb/run.ipynb index d94989ab556cd..952b3cd187550 100644 --- a/demo/map_airbnb/run.ipynb +++ b/demo/map_airbnb/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: map_airbnb\n", "### Display an interactive map of AirBnB locations with Plotly. Data is hosted on HuggingFace Datasets. \n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import plotly.graph_objects as go\n", "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"gradio/NYC-Airbnb-Open-Data\", split=\"train\")\n", "df = dataset.to_pandas()\n", "\n", "def filter_map(min_price, max_price, boroughs):\n", "\n", " filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) & \n", " (df['price'] > min_price) & (df['price'] < max_price)]\n", " names = filtered_df[\"name\"].tolist()\n", " prices = filtered_df[\"price\"].tolist()\n", " text_list = [(names[i], prices[i]) for i in range(0, len(names))]\n", " fig = go.Figure(go.Scattermapbox(\n", " customdata=text_list,\n", " lat=filtered_df['latitude'].tolist(),\n", " lon=filtered_df['longitude'].tolist(),\n", " mode='markers',\n", " marker=go.scattermapbox.Marker(\n", " size=6\n", " ),\n", " hoverinfo=\"text\",\n", " hovertemplate='Name: %{customdata[0]}\"\n", " \"\ud83c\udf99\ufe0f Learn more about UniSpeech-SAT | \"\n", " \"\ud83d\udcda UniSpeech-SAT paper | \"\n", " \"\ud83d\udcda X-Vector paper\"\n", " \"
\"\n", ")\n", "examples = [\n", " [\"samples/cate_blanch.mp3\", \"samples/cate_blanch_2.mp3\"],\n", " [\"samples/cate_blanch.mp3\", \"samples/heath_ledger.mp3\"],\n", "]\n", "\n", "interface = gr.Interface(\n", " fn=similarity_fn,\n", " inputs=inputs,\n", " outputs=output,\n", " layout=\"horizontal\",\n", " allow_flagging=False,\n", " live=False,\n", " examples=examples,\n", " cache_examples=False\n", ")\n", "interface.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: same-person-or-different\n", "### This demo identifies if two speakers are the same person using Gradio's Audio and HTML components.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio git+https://github.com/huggingface/transformers torchaudio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/same-person-or-different/packages.txt\n", "os.mkdir('samples')\n", "!wget -q -O samples/cate_blanch.mp3 https://github.com/gradio-app/gradio/raw/main/demo/same-person-or-different/samples/cate_blanch.mp3\n", "!wget -q -O samples/cate_blanch_2.mp3 https://github.com/gradio-app/gradio/raw/main/demo/same-person-or-different/samples/cate_blanch_2.mp3\n", "!wget -q -O samples/heath_ledger.mp3 https://github.com/gradio-app/gradio/raw/main/demo/same-person-or-different/samples/heath_ledger.mp3"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "from torchaudio.sox_effects import apply_effects_file\n", "from transformers import AutoFeatureExtractor, AutoModelForAudioXVector\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "OUTPUT_OK = (\n", " \"\"\"\n", "\"\n", " \"\ud83c\udf99\ufe0f Learn more about UniSpeech-SAT | \"\n", " \"\ud83d\udcda UniSpeech-SAT paper | \"\n", " \"\ud83d\udcda X-Vector paper\"\n", " \"
\"\n", ")\n", "examples = [\n", " [\"samples/cate_blanch.mp3\", \"samples/cate_blanch_2.mp3\"],\n", " [\"samples/cate_blanch.mp3\", \"samples/heath_ledger.mp3\"],\n", "]\n", "\n", "interface = gr.Interface(\n", " fn=similarity_fn,\n", " inputs=inputs,\n", " outputs=output,\n", " layout=\"horizontal\",\n", " allow_flagging=False,\n", " live=False,\n", " examples=examples,\n", " cache_examples=False\n", ")\n", "interface.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/save_file_no_output/run.ipynb b/demo/save_file_no_output/run.ipynb index b26dccf81b2db..73988d9cafaf0 100644 --- a/demo/save_file_no_output/run.ipynb +++ b/demo/save_file_no_output/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: save_file_no_output"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import random\n", "import string\n", "import gradio as gr \n", "\n", "def save_image_random_name(image):\n", " random_string = ''.join(random.choices(string.ascii_letters, k=20)) + '.png'\n", " image.save(random_string)\n", " print(f\"Saved image to {random_string}!\")\n", "\n", "demo = gr.Interface(\n", " fn=save_image_random_name, \n", " inputs=gr.Image(type=\"pil\"), \n", " outputs=None,\n", ")\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: save_file_no_output"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import random\n", "import string\n", "import gradio as gr \n", "\n", "def save_image_random_name(image):\n", " random_string = ''.join(random.choices(string.ascii_letters, k=20)) + '.png'\n", " image.save(random_string)\n", " print(f\"Saved image to {random_string}!\")\n", "\n", "demo = gr.Interface(\n", " fn=save_image_random_name, \n", " inputs=gr.Image(type=\"pil\"), \n", " outputs=None,\n", ")\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/scatter_plot/run.ipynb b/demo/scatter_plot/run.ipynb index d5d992f1a732f..ca9e7f5a75c01 100644 --- a/demo/scatter_plot/run.ipynb +++ b/demo/scatter_plot/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: scatter_plot"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets pandas"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "cars = data.cars()\n", "iris = data.iris()\n", "\n", "# # Or generate your own fake data\n", "\n", "# import pandas as pd\n", "# import random\n", "\n", "# cars_data = {\n", "# \"Name\": [\"car name \" + f\" {int(i/10)}\" for i in range(400)],\n", "# \"Miles_per_Gallon\": [random.randint(10, 30) for _ in range(400)],\n", "# \"Origin\": [random.choice([\"USA\", \"Europe\", \"Japan\"]) for _ in range(400)],\n", "# \"Horsepower\": [random.randint(50, 250) for _ in range(400)],\n", "# }\n", "\n", "# iris_data = {\n", "# \"petalWidth\": [round(random.uniform(0, 2.5), 2) for _ in range(150)],\n", "# \"petalLength\": [round(random.uniform(0, 7), 2) for _ in range(150)],\n", "# \"species\": [\n", "# random.choice([\"setosa\", \"versicolor\", \"virginica\"]) for _ in range(150)\n", "# ],\n", "# }\n", "\n", "# cars = pd.DataFrame(cars_data)\n", "# iris = pd.DataFrame(iris_data)\n", "\n", "\n", "def scatter_plot_fn(dataset):\n", " if dataset == \"iris\":\n", " return gr.ScatterPlot(\n", " value=iris,\n", " x=\"petalWidth\",\n", " y=\"petalLength\",\n", " color=\"species\",\n", " title=\"Iris Dataset\",\n", " color_legend_title=\"Species\",\n", " x_title=\"Petal Width\",\n", " y_title=\"Petal Length\",\n", " tooltip=[\"petalWidth\", \"petalLength\", \"species\"],\n", " caption=\"\",\n", " )\n", " else:\n", " return gr.ScatterPlot(\n", " value=cars,\n", " x=\"Horsepower\",\n", " y=\"Miles_per_Gallon\",\n", " color=\"Origin\",\n", " tooltip=\"Name\",\n", " title=\"Car Data\",\n", " y_title=\"Miles per Gallon\",\n", " color_legend_title=\"Origin of Car\",\n", " caption=\"MPG vs Horsepower of various cars\",\n", " )\n", "\n", "\n", "with gr.Blocks() as scatter_plot:\n", " with gr.Row():\n", " with gr.Column():\n", " dataset = gr.Dropdown(choices=[\"cars\", \"iris\"], value=\"cars\")\n", " with gr.Column():\n", " plot = gr.ScatterPlot()\n", " dataset.change(scatter_plot_fn, inputs=dataset, outputs=plot)\n", " scatter_plot.load(fn=scatter_plot_fn, inputs=dataset, outputs=plot)\n", "\n", "if __name__ == \"__main__\":\n", " scatter_plot.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: scatter_plot"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets pandas"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "cars = data.cars()\n", "iris = data.iris()\n", "\n", "# # Or generate your own fake data\n", "\n", "# import pandas as pd\n", "# import random\n", "\n", "# cars_data = {\n", "# \"Name\": [\"car name \" + f\" {int(i/10)}\" for i in range(400)],\n", "# \"Miles_per_Gallon\": [random.randint(10, 30) for _ in range(400)],\n", "# \"Origin\": [random.choice([\"USA\", \"Europe\", \"Japan\"]) for _ in range(400)],\n", "# \"Horsepower\": [random.randint(50, 250) for _ in range(400)],\n", "# }\n", "\n", "# iris_data = {\n", "# \"petalWidth\": [round(random.uniform(0, 2.5), 2) for _ in range(150)],\n", "# \"petalLength\": [round(random.uniform(0, 7), 2) for _ in range(150)],\n", "# \"species\": [\n", "# random.choice([\"setosa\", \"versicolor\", \"virginica\"]) for _ in range(150)\n", "# ],\n", "# }\n", "\n", "# cars = pd.DataFrame(cars_data)\n", "# iris = pd.DataFrame(iris_data)\n", "\n", "\n", "def scatter_plot_fn(dataset):\n", " if dataset == \"iris\":\n", " return gr.ScatterPlot(\n", " value=iris,\n", " x=\"petalWidth\",\n", " y=\"petalLength\",\n", " color=\"species\",\n", " title=\"Iris Dataset\",\n", " color_legend_title=\"Species\",\n", " x_title=\"Petal Width\",\n", " y_title=\"Petal Length\",\n", " tooltip=[\"petalWidth\", \"petalLength\", \"species\"],\n", " caption=\"\",\n", " )\n", " else:\n", " return gr.ScatterPlot(\n", " value=cars,\n", " x=\"Horsepower\",\n", " y=\"Miles_per_Gallon\",\n", " color=\"Origin\",\n", " tooltip=\"Name\",\n", " title=\"Car Data\",\n", " y_title=\"Miles per Gallon\",\n", " color_legend_title=\"Origin of Car\",\n", " caption=\"MPG vs Horsepower of various cars\",\n", " )\n", "\n", "\n", "with gr.Blocks() as scatter_plot:\n", " with gr.Row():\n", " with gr.Column():\n", " dataset = gr.Dropdown(choices=[\"cars\", \"iris\"], value=\"cars\")\n", " with gr.Column():\n", " plot = gr.ScatterPlot()\n", " dataset.change(scatter_plot_fn, inputs=dataset, outputs=plot)\n", " scatter_plot.load(fn=scatter_plot_fn, inputs=dataset, outputs=plot)\n", "\n", "if __name__ == \"__main__\":\n", " scatter_plot.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/scatterplot_component/run.ipynb b/demo/scatterplot_component/run.ipynb index 66f60f7be4e96..79ac60139652f 100644 --- a/demo/scatterplot_component/run.ipynb +++ b/demo/scatterplot_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: scatterplot_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "cars = data.cars()\n", "\n", "with gr.Blocks() as demo:\n", " gr.ScatterPlot(\n", " value=cars,\n", " x=\"Horsepower\",\n", " y=\"Miles_per_Gallon\",\n", " color=\"Origin\",\n", " tooltip=\"Name\",\n", " title=\"Car Data\",\n", " y_title=\"Miles per Gallon\",\n", " color_legend_title=\"Origin of Car\",\n", " container=False,\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: scatterplot_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from vega_datasets import data\n", "\n", "cars = data.cars()\n", "\n", "with gr.Blocks() as demo:\n", " gr.ScatterPlot(\n", " value=cars,\n", " x=\"Horsepower\",\n", " y=\"Miles_per_Gallon\",\n", " color=\"Origin\",\n", " tooltip=\"Name\",\n", " title=\"Car Data\",\n", " y_title=\"Miles per Gallon\",\n", " color_legend_title=\"Origin of Car\",\n", " container=False,\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/score_tracker/run.ipynb b/demo/score_tracker/run.ipynb index c089e64c461d9..6030d1e5b6be9 100644 --- a/demo/score_tracker/run.ipynb +++ b/demo/score_tracker/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: score_tracker"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "scores = []\n", "\n", "def track_score(score):\n", " scores.append(score)\n", " top_scores = sorted(scores, reverse=True)[:3]\n", " return top_scores\n", "\n", "demo = gr.Interface(\n", " track_score, \n", " gr.Number(label=\"Score\"), \n", " gr.JSON(label=\"Top Scores\")\n", ")\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: score_tracker"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "scores = []\n", "\n", "def track_score(score):\n", " scores.append(score)\n", " top_scores = sorted(scores, reverse=True)[:3]\n", " return top_scores\n", "\n", "demo = gr.Interface(\n", " track_score, \n", " gr.Number(label=\"Score\"), \n", " gr.JSON(label=\"Top Scores\")\n", ")\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/sentence_builder/run.ipynb b/demo/sentence_builder/run.ipynb index 994c2af1611a7..8b154a371fe80 100644 --- a/demo/sentence_builder/run.ipynb +++ b/demo/sentence_builder/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: sentence_builder"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n", " return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n", "\n", "\n", "demo = gr.Interface(\n", " sentence_builder,\n", " [\n", " gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n", " gr.Dropdown(\n", " [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n", " ),\n", " gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n", " gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n", " gr.Dropdown(\n", " [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n", " ),\n", " gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n", " ],\n", " \"text\",\n", " examples=[\n", " [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n", " [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n", " [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n", " [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n", " ]\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: sentence_builder"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n", " return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n", "\n", "\n", "demo = gr.Interface(\n", " sentence_builder,\n", " [\n", " gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n", " gr.Dropdown(\n", " [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n", " ),\n", " gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n", " gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n", " gr.Dropdown(\n", " [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n", " ),\n", " gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n", " ],\n", " \"text\",\n", " examples=[\n", " [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n", " [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n", " [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n", " [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n", " ]\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/sentiment_analysis/run.ipynb b/demo/sentiment_analysis/run.ipynb index 84ca228e2f4e5..9ef6ef66035d5 100644 --- a/demo/sentiment_analysis/run.ipynb +++ b/demo/sentiment_analysis/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: sentiment_analysis\n", "### This sentiment analaysis demo takes in input text and returns its classification for either positive, negative or neutral using Gradio's Label output. It also uses the default interpretation method so users can click the Interpret button after a submission and see which words had the biggest effect on the output.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio nltk"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import nltk\n", "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", "\n", "nltk.download(\"vader_lexicon\")\n", "sid = SentimentIntensityAnalyzer()\n", "\n", "def sentiment_analysis(text):\n", " scores = sid.polarity_scores(text)\n", " del scores[\"compound\"]\n", " return scores\n", "\n", "demo = gr.Interface(\n", " fn=sentiment_analysis, \n", " inputs=gr.Textbox(placeholder=\"Enter a positive or negative sentence here...\"), \n", " outputs=\"label\", \n", " interpretation=\"default\",\n", " examples=[[\"This is wonderful!\"]])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: sentiment_analysis\n", "### This sentiment analaysis demo takes in input text and returns its classification for either positive, negative or neutral using Gradio's Label output. It also uses the default interpretation method so users can click the Interpret button after a submission and see which words had the biggest effect on the output.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio nltk"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import nltk\n", "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", "\n", "nltk.download(\"vader_lexicon\")\n", "sid = SentimentIntensityAnalyzer()\n", "\n", "def sentiment_analysis(text):\n", " scores = sid.polarity_scores(text)\n", " del scores[\"compound\"]\n", " return scores\n", "\n", "demo = gr.Interface(\n", " fn=sentiment_analysis, \n", " inputs=gr.Textbox(placeholder=\"Enter a positive or negative sentence here...\"), \n", " outputs=\"label\", \n", " interpretation=\"default\",\n", " examples=[[\"This is wonderful!\"]])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/sepia_filter/run.ipynb b/demo/sepia_filter/run.ipynb index 74fc34c2048bf..3f8c9fac800eb 100644 --- a/demo/sepia_filter/run.ipynb +++ b/demo/sepia_filter/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: sepia_filter"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "import gradio as gr\n", "\n", "def sepia(input_img):\n", " sepia_filter = np.array([\n", " [0.393, 0.769, 0.189], \n", " [0.349, 0.686, 0.168], \n", " [0.272, 0.534, 0.131]\n", " ])\n", " sepia_img = input_img.dot(sepia_filter.T)\n", " sepia_img /= sepia_img.max()\n", " return sepia_img\n", "\n", "demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), \"image\")\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: sepia_filter"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "import gradio as gr\n", "\n", "def sepia(input_img):\n", " sepia_filter = np.array([\n", " [0.393, 0.769, 0.189], \n", " [0.349, 0.686, 0.168], \n", " [0.272, 0.534, 0.131]\n", " ])\n", " sepia_img = input_img.dot(sepia_filter.T)\n", " sepia_img /= sepia_img.max()\n", " return sepia_img\n", "\n", "demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), \"image\")\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/sine_curve/run.ipynb b/demo/sine_curve/run.ipynb index 6871a2655ea06..08683bbbf7e7c 100644 --- a/demo/sine_curve/run.ipynb +++ b/demo/sine_curve/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: sine_curve"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import math\n", "import gradio as gr\n", "import plotly.express as px\n", "import numpy as np\n", "\n", "\n", "plot_end = 2 * math.pi\n", "\n", "\n", "def get_plot(period=1):\n", " global plot_end\n", " x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02)\n", " y = np.sin(2*math.pi*period * x)\n", " fig = px.line(x=x, y=y)\n", " plot_end += 2 * math.pi\n", " if plot_end > 1000:\n", " plot_end = 2 * math.pi\n", " return fig\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " gr.Markdown(\"Change the value of the slider to automatically update the plot\")\n", " period = gr.Slider(label=\"Period of plot\", value=1, minimum=0, maximum=10, step=1)\n", " plot = gr.Plot(label=\"Plot (updates every half second)\")\n", "\n", " dep = demo.load(get_plot, None, plot, every=1)\n", " period.change(get_plot, period, plot, every=1, cancels=[dep])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue().launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: sine_curve"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import math\n", "import gradio as gr\n", "import plotly.express as px\n", "import numpy as np\n", "\n", "\n", "plot_end = 2 * math.pi\n", "\n", "\n", "def get_plot(period=1):\n", " global plot_end\n", " x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02)\n", " y = np.sin(2*math.pi*period * x)\n", " fig = px.line(x=x, y=y)\n", " plot_end += 2 * math.pi\n", " if plot_end > 1000:\n", " plot_end = 2 * math.pi\n", " return fig\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " gr.Markdown(\"Change the value of the slider to automatically update the plot\")\n", " period = gr.Slider(label=\"Period of plot\", value=1, minimum=0, maximum=10, step=1)\n", " plot = gr.Plot(label=\"Plot (updates every half second)\")\n", "\n", " dep = demo.load(get_plot, None, plot, every=1)\n", " period.change(get_plot, period, plot, every=1, cancels=[dep])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue().launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/slider_component/run.ipynb b/demo/slider_component/run.ipynb index ea8007b32a5ea..50cf32dadf354 100644 --- a/demo/slider_component/run.ipynb +++ b/demo/slider_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: slider_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.Slider()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: slider_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.Slider()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/slider_release/run.ipynb b/demo/slider_release/run.ipynb index b11864cffccc7..efb64c5194a7b 100644 --- a/demo/slider_release/run.ipynb +++ b/demo/slider_release/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: slider_release"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def identity(x, state):\n", " state += 1\n", " return x, state, state\n", "\n", "\n", "with gr.Blocks() as demo:\n", " slider = gr.Slider(0, 100, step=0.1)\n", " state = gr.State(value=0)\n", " with gr.Row():\n", " number = gr.Number(label=\"On release\")\n", " number2 = gr.Number(label=\"Number of events fired\")\n", " slider.release(identity, inputs=[slider, state], outputs=[number, state, number2], api_name=\"predict\")\n", "\n", "if __name__ == \"__main__\":\n", " print(\"here\")\n", " demo.launch()\n", " print(demo.server_port)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: slider_release"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def identity(x, state):\n", " state += 1\n", " return x, state, state\n", "\n", "\n", "with gr.Blocks() as demo:\n", " slider = gr.Slider(0, 100, step=0.1)\n", " state = gr.State(value=0)\n", " with gr.Row():\n", " number = gr.Number(label=\"On release\")\n", " number2 = gr.Number(label=\"Number of events fired\")\n", " slider.release(identity, inputs=[slider, state], outputs=[number, state, number2], api_name=\"predict\")\n", "\n", "if __name__ == \"__main__\":\n", " print(\"here\")\n", " demo.launch()\n", " print(demo.server_port)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/sound_alert/run.ipynb b/demo/sound_alert/run.ipynb index 15da4349700c4..5237197a80629 100644 --- a/demo/sound_alert/run.ipynb +++ b/demo/sound_alert/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: sound_alert"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/sound_alert/beep.mp3"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "\n", "js_function = \"() => {new Audio('file=beep.mp3').play();}\"\n", "\n", "def task(x):\n", " time.sleep(2)\n", " return \"Hello, \" + x \n", "\n", "with gr.Blocks() as demo:\n", " name = gr.Textbox(label=\"name\")\n", " greeting = gr.Textbox(label=\"greeting\")\n", " name.blur(task, name, greeting)\n", " greeting.change(None, [], [], _js=js_function) # Note that _js is a special argument whose usage may change in the future\n", " \n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: sound_alert"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/sound_alert/beep.mp3"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "\n", "js_function = \"() => {new Audio('file=beep.mp3').play();}\"\n", "\n", "def task(x):\n", " time.sleep(2)\n", " return \"Hello, \" + x \n", "\n", "with gr.Blocks() as demo:\n", " name = gr.Textbox(label=\"name\")\n", " greeting = gr.Textbox(label=\"greeting\")\n", " name.blur(task, name, greeting)\n", " greeting.change(None, [], [], _js=js_function) # Note that _js is a special argument whose usage may change in the future\n", " \n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/spectogram/run.ipynb b/demo/spectogram/run.ipynb index f4b592cc6e827..f5968d14a4b63 100644 --- a/demo/spectogram/run.ipynb +++ b/demo/spectogram/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: spectogram"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scipy numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import signal\n", "\n", "import gradio as gr\n", "\n", "\n", "def spectrogram(audio):\n", " sr, data = audio\n", " if len(data.shape) == 2:\n", " data = np.mean(data, axis=0)\n", " frequencies, times, spectrogram_data = signal.spectrogram(\n", " data, sr, window=\"hamming\"\n", " )\n", " plt.pcolormesh(times, frequencies, np.log10(spectrogram_data))\n", " return plt\n", "\n", "\n", "demo = gr.Interface(spectrogram, \"audio\", \"plot\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: spectogram"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio scipy numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy import signal\n", "\n", "import gradio as gr\n", "\n", "\n", "def spectrogram(audio):\n", " sr, data = audio\n", " if len(data.shape) == 2:\n", " data = np.mean(data, axis=0)\n", " frequencies, times, spectrogram_data = signal.spectrogram(\n", " data, sr, window=\"hamming\"\n", " )\n", " plt.pcolormesh(times, frequencies, np.log10(spectrogram_data))\n", " return plt\n", "\n", "\n", "demo = gr.Interface(spectrogram, \"audio\", \"plot\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/stable-diffusion/run.ipynb b/demo/stable-diffusion/run.ipynb index 42272eec2979e..a059ed9ddf3eb 100644 --- a/demo/stable-diffusion/run.ipynb +++ b/demo/stable-diffusion/run.ipynb @@ -1 +1,5 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stable-diffusion\n", "### Note: This is a simplified version of the code needed to create the Stable Diffusion demo. See full code here: https://hf.co/spaces/stabilityai/stable-diffusion/tree/main\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio diffusers transformers nvidia-ml-py3 ftfy torch"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "from diffusers import StableDiffusionPipeline\n", "from PIL import Image\n", "import os\n", "\n", "auth_token = os.getenv(\"auth_token\")\n", "model_id = \"CompVis/stable-diffusion-v1-4\"\n", "device = \"cpu\"\n", "pipe = StableDiffusionPipeline.from_pretrained(\n", " model_id, use_auth_token=auth_token, revision=\"fp16\", torch_dtype=torch.float16\n", ")\n", "pipe = pipe.to(device)\n", "\n", "\n", "def infer(prompt, samples, steps, scale, seed):\n", " generator = torch.Generator(device=device).manual_seed(seed)\n", " images_list = pipe(\n", " [prompt] * samples,\n", " num_inference_steps=steps,\n", " guidance_scale=scale,\n", " generator=generator,\n", " )\n", " images = []\n", " safe_image = Image.open(r\"unsafe.png\")\n", " for i, image in enumerate(images_list[\"sample\"]):\n", " if images_list[\"nsfw_content_detected\"][i]:\n", " images.append(safe_image)\n", " else:\n", " images.append(image)\n", " return images\n", "\n", "\n", "block = gr.Blocks()\n", "\n", "with block:\n", " with gr.Group():\n", " with gr.Row():\n", " text = gr.Textbox(\n", " label=\"Enter your prompt\",\n", " max_lines=1,\n", " placeholder=\"Enter your prompt\",\n", " container=False,\n", " )\n", " btn = gr.Button(\"Generate image\")\n", " gallery = gr.Gallery(\n", " label=\"Generated images\",\n", " show_label=False,\n", " elem_id=\"gallery\",\n", " columns=[2],\n", " height=\"auto\",\n", " )\n", "\n", " advanced_button = gr.Button(\"Advanced options\", elem_id=\"advanced-btn\")\n", "\n", " with gr.Row(elem_id=\"advanced-options\"):\n", " samples = gr.Slider(label=\"Images\", minimum=1, maximum=4, value=4, step=1)\n", " steps = gr.Slider(label=\"Steps\", minimum=1, maximum=50, value=45, step=1)\n", " scale = gr.Slider(\n", " label=\"Guidance Scale\", minimum=0, maximum=50, value=7.5, step=0.1\n", " )\n", " seed = gr.Slider(\n", " label=\"Seed\",\n", " minimum=0,\n", " maximum=2147483647,\n", " step=1,\n", " randomize=True,\n", " )\n", " gr.on([text.submit, btn.click], infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)\n", " advanced_button.click(\n", " None,\n", " [],\n", " text,\n", " )\n", "\n", "block.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +<<<<<<< HEAD +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stable-diffusion\n", "### Note: This is a simplified version of the code needed to create the Stable Diffusion demo. See full code here: https://hf.co/spaces/stabilityai/stable-diffusion/tree/main\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio diffusers transformers nvidia-ml-py3 ftfy torch"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "from diffusers import StableDiffusionPipeline\n", "from PIL import Image\n", "import os\n", "\n", "auth_token = os.getenv(\"auth_token\")\n", "model_id = \"CompVis/stable-diffusion-v1-4\"\n", "device = \"cpu\"\n", "pipe = StableDiffusionPipeline.from_pretrained(\n", " model_id, use_auth_token=auth_token, revision=\"fp16\", torch_dtype=torch.float16\n", ")\n", "pipe = pipe.to(device)\n", "\n", "\n", "def infer(prompt, samples, steps, scale, seed):\n", " generator = torch.Generator(device=device).manual_seed(seed)\n", " images_list = pipe(\n", " [prompt] * samples,\n", " num_inference_steps=steps,\n", " guidance_scale=scale,\n", " generator=generator,\n", " )\n", " images = []\n", " safe_image = Image.open(r\"unsafe.png\")\n", " for i, image in enumerate(images_list[\"sample\"]):\n", " if images_list[\"nsfw_content_detected\"][i]:\n", " images.append(safe_image)\n", " else:\n", " images.append(image)\n", " return images\n", "\n", "\n", "block = gr.Blocks()\n", "\n", "with block:\n", " with gr.Group():\n", " with gr.Row():\n", " text = gr.Textbox(\n", " label=\"Enter your prompt\",\n", " max_lines=1,\n", " placeholder=\"Enter your prompt\",\n", " container=False,\n", " )\n", " btn = gr.Button(\"Generate image\")\n", " gallery = gr.Gallery(\n", " label=\"Generated images\",\n", " show_label=False,\n", " elem_id=\"gallery\",\n", " columns=[2],\n", " height=\"auto\",\n", " )\n", "\n", " advanced_button = gr.Button(\"Advanced options\", elem_id=\"advanced-btn\")\n", "\n", " with gr.Row(elem_id=\"advanced-options\"):\n", " samples = gr.Slider(label=\"Images\", minimum=1, maximum=4, value=4, step=1)\n", " steps = gr.Slider(label=\"Steps\", minimum=1, maximum=50, value=45, step=1)\n", " scale = gr.Slider(\n", " label=\"Guidance Scale\", minimum=0, maximum=50, value=7.5, step=0.1\n", " )\n", " seed = gr.Slider(\n", " label=\"Seed\",\n", " minimum=0,\n", " maximum=2147483647,\n", " step=1,\n", " randomize=True,\n", " )\n", " gr.on([text.submit, btn.click], infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)\n", " advanced_button.click(\n", " None,\n", " [],\n", " text,\n", " )\n", "\n", "block.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +======= +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stable-diffusion\n", "### Note: This is a simplified version of the code needed to create the Stable Diffusion demo. See full code here: https://hf.co/spaces/stabilityai/stable-diffusion/tree/main\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio diffusers transformers nvidia-ml-py3 ftfy torch"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "from diffusers import StableDiffusionPipeline\n", "from PIL import Image\n", "import os\n", "\n", "auth_token = os.getenv(\"auth_token\")\n", "model_id = \"CompVis/stable-diffusion-v1-4\"\n", "device = \"cpu\"\n", "pipe = StableDiffusionPipeline.from_pretrained(\n", " model_id, use_auth_token=auth_token, revision=\"fp16\", torch_dtype=torch.float16\n", ")\n", "pipe = pipe.to(device)\n", "\n", "\n", "def infer(prompt, samples, steps, scale, seed):\n", " generator = torch.Generator(device=device).manual_seed(seed)\n", " images_list = pipe(\n", " [prompt] * samples,\n", " num_inference_steps=steps,\n", " guidance_scale=scale,\n", " generator=generator,\n", " )\n", " images = []\n", " safe_image = Image.open(r\"unsafe.png\")\n", " for i, image in enumerate(images_list[\"sample\"]):\n", " if images_list[\"nsfw_content_detected\"][i]:\n", " images.append(safe_image)\n", " else:\n", " images.append(image)\n", " return images\n", "\n", "\n", "block = gr.Blocks()\n", "\n", "with block:\n", " with gr.Group():\n", " with gr.Row():\n", " text = gr.Textbox(\n", " label=\"Enter your prompt\",\n", " max_lines=1,\n", " placeholder=\"Enter your prompt\",\n", " container=False,\n", " )\n", " btn = gr.Button(\"Generate image\")\n", " gallery = gr.Gallery(\n", " label=\"Generated images\",\n", " show_label=False,\n", " elem_id=\"gallery\",\n", " columns=[2],\n", " height=\"auto\",\n", " )\n", "\n", " advanced_button = gr.Button(\"Advanced options\", elem_id=\"advanced-btn\")\n", "\n", " with gr.Row(elem_id=\"advanced-options\"):\n", " samples = gr.Slider(label=\"Images\", minimum=1, maximum=4, value=4, step=1)\n", " steps = gr.Slider(label=\"Steps\", minimum=1, maximum=50, value=45, step=1)\n", " scale = gr.Slider(\n", " label=\"Guidance Scale\", minimum=0, maximum=50, value=7.5, step=0.1\n", " )\n", " seed = gr.Slider(\n", " label=\"Seed\",\n", " minimum=0,\n", " maximum=2147483647,\n", " step=1,\n", " randomize=True,\n", " )\n", " gr.on([text.submit, btn.click], infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)\n", " advanced_button.click(\n", " None,\n", " [],\n", " text,\n", " )\n", "\n", "block.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +>>>>>>> main diff --git a/demo/state_component/run.ipynb b/demo/state_component/run.ipynb index 83e1049d347b8..5a50fd98b3715 100644 --- a/demo/state_component/run.ipynb +++ b/demo/state_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: state_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.State()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: state_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", " gr.State()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/stock_forecast/run.ipynb b/demo/stock_forecast/run.ipynb index dc0b0dc6bc3e5..5fce6697fcb89 100644 --- a/demo/stock_forecast/run.ipynb +++ b/demo/stock_forecast/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stock_forecast"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "\n", "def plot_forecast(final_year, companies, noise, show_legend, point_style):\n", " start_year = 2020\n", " x = np.arange(start_year, final_year + 1)\n", " year_count = x.shape[0]\n", " plt_format = ({\"cross\": \"X\", \"line\": \"-\", \"circle\": \"o--\"})[point_style]\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", " for i, company in enumerate(companies):\n", " series = np.arange(0, year_count, dtype=float)\n", " series = series**2 * (i + 1)\n", " series += np.random.rand(year_count) * noise\n", " ax.plot(x, series, plt_format)\n", " if show_legend:\n", " plt.legend(companies)\n", " return fig\n", "\n", "\n", "demo = gr.Interface(\n", " plot_forecast,\n", " [\n", " gr.Radio([2025, 2030, 2035, 2040], label=\"Project to:\"),\n", " gr.CheckboxGroup([\"Google\", \"Microsoft\", \"Gradio\"], label=\"Company Selection\"),\n", " gr.Slider(1, 100, label=\"Noise Level\"),\n", " gr.Checkbox(label=\"Show Legend\"),\n", " gr.Dropdown([\"cross\", \"line\", \"circle\"], label=\"Style\"),\n", " ],\n", " gr.Plot(label=\"forecast\"),\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stock_forecast"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy matplotlib"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "\n", "def plot_forecast(final_year, companies, noise, show_legend, point_style):\n", " start_year = 2020\n", " x = np.arange(start_year, final_year + 1)\n", " year_count = x.shape[0]\n", " plt_format = ({\"cross\": \"X\", \"line\": \"-\", \"circle\": \"o--\"})[point_style]\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", " for i, company in enumerate(companies):\n", " series = np.arange(0, year_count, dtype=float)\n", " series = series**2 * (i + 1)\n", " series += np.random.rand(year_count) * noise\n", " ax.plot(x, series, plt_format)\n", " if show_legend:\n", " plt.legend(companies)\n", " return fig\n", "\n", "\n", "demo = gr.Interface(\n", " plot_forecast,\n", " [\n", " gr.Radio([2025, 2030, 2035, 2040], label=\"Project to:\"),\n", " gr.CheckboxGroup([\"Google\", \"Microsoft\", \"Gradio\"], label=\"Company Selection\"),\n", " gr.Slider(1, 100, label=\"Noise Level\"),\n", " gr.Checkbox(label=\"Show Legend\"),\n", " gr.Dropdown([\"cross\", \"line\", \"circle\"], label=\"Style\"),\n", " ],\n", " gr.Plot(label=\"forecast\"),\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/stream_asr/run.ipynb b/demo/stream_asr/run.ipynb index c5ce437da6a49..8eec2af351b05 100644 --- a/demo/stream_asr/run.ipynb +++ b/demo/stream_asr/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stream_asr"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchaudio transformers"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import pipeline\n", "import numpy as np\n", "\n", "transcriber = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-base.en\")\n", "\n", "def transcribe(stream, new_chunk):\n", " sr, y = new_chunk\n", " y = y.astype(np.float32)\n", " y /= np.max(np.abs(y))\n", "\n", " if stream is not None:\n", " stream = np.concatenate([stream, y])\n", " else:\n", " stream = y\n", " return stream, transcriber({\"sampling_rate\": sr, \"raw\": stream})[\"text\"]\n", "\n", "\n", "demo = gr.Interface(\n", " transcribe,\n", " [\"state\", gr.Audio(source=\"microphone\", streaming=True)],\n", " [\"state\", \"text\"],\n", " live=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stream_asr"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchaudio transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import pipeline\n", "import numpy as np\n", "\n", "transcriber = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-base.en\")\n", "\n", "def transcribe(stream, new_chunk):\n", " sr, y = new_chunk\n", " y = y.astype(np.float32)\n", " y /= np.max(np.abs(y))\n", "\n", " if stream is not None:\n", " stream = np.concatenate([stream, y])\n", " else:\n", " stream = y\n", " return stream, transcriber({\"sampling_rate\": sr, \"raw\": stream})[\"text\"]\n", "\n", "\n", "demo = gr.Interface(\n", " transcribe,\n", " [\"state\", gr.Audio(source=\"microphone\", streaming=True)],\n", " [\"state\", \"text\"],\n", " live=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/stream_audio/run.ipynb b/demo/stream_audio/run.ipynb index 3cb0d76c525ab..766c73658cd44 100644 --- a/demo/stream_audio/run.ipynb +++ b/demo/stream_audio/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stream_audio"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "\n", "def add_to_stream(audio, instream):\n", " time.sleep(1)\n", " if audio is None:\n", " return gr.Audio(), instream\n", " if instream is None:\n", " ret = audio\n", " else:\n", " ret = (audio[0], np.concatenate((instream[1], audio[1])))\n", " return ret, ret\n", "\n", "\n", "with gr.Blocks() as demo:\n", " inp = gr.Audio(source=\"microphone\")\n", " out = gr.Audio()\n", " stream = gr.State()\n", " clear = gr.Button(\"Clear\")\n", "\n", " inp.stream(add_to_stream, [inp, stream], [out, stream])\n", " clear.click(lambda: [None, None, None], None, [inp, out, stream])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stream_audio"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "\n", "def add_to_stream(audio, instream):\n", " time.sleep(1)\n", " if audio is None:\n", " return gr.Audio(), instream\n", " if instream is None:\n", " ret = audio\n", " else:\n", " ret = (audio[0], np.concatenate((instream[1], audio[1])))\n", " return ret, ret\n", "\n", "\n", "with gr.Blocks() as demo:\n", " inp = gr.Audio(source=\"microphone\")\n", " out = gr.Audio()\n", " stream = gr.State()\n", " clear = gr.Button(\"Clear\")\n", "\n", " inp.stream(add_to_stream, [inp, stream], [out, stream])\n", " clear.click(lambda: [None, None, None], None, [inp, out, stream])\n", "\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/stream_audio_out/run.ipynb b/demo/stream_audio_out/run.ipynb index 25ef1f123bd96..af33bb68d67a1 100644 --- a/demo/stream_audio_out/run.ipynb +++ b/demo/stream_audio_out/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stream_audio_out"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('audio')\n", "!wget -q -O audio/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/stream_audio_out/audio/cantina.wav"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from pydub import AudioSegment\n", "from time import sleep\n", "\n", "with gr.Blocks() as demo:\n", " input_audio = gr.Audio(label=\"Input Audio\", type=\"filepath\", format=\"mp3\")\n", " with gr.Row():\n", " with gr.Column():\n", " stream_as_file_btn = gr.Button(\"Stream as File\")\n", " format = gr.Radio([\"wav\", \"mp3\"], value=\"wav\", label=\"Format\")\n", " stream_as_file_output = gr.Audio(streaming=True)\n", "\n", " def stream_file(audio_file, format):\n", " audio = AudioSegment.from_file(audio_file)\n", " i = 0\n", " chunk_size = 1000\n", " while chunk_size * i < len(audio):\n", " chunk = audio[chunk_size * i : chunk_size * (i + 1)]\n", " i += 1\n", " if chunk:\n", " file = f\"/tmp/{i}.{format}\"\n", " chunk.export(file, format=format)\n", " yield file\n", " sleep(0.5)\n", "\n", " stream_as_file_btn.click(\n", " stream_file, [input_audio, format], stream_as_file_output\n", " )\n", "\n", " gr.Examples(\n", " [[\"audio/cantina.wav\", \"wav\"], [\"audio/cantina.wav\", \"mp3\"]],\n", " [input_audio, format],\n", " fn=stream_file,\n", " outputs=stream_as_file_output,\n", " cache_examples=True,\n", " )\n", "\n", " with gr.Column():\n", " stream_as_bytes_btn = gr.Button(\"Stream as Bytes\")\n", " stream_as_bytes_output = gr.Audio(format=\"bytes\", streaming=True)\n", "\n", " def stream_bytes(audio_file):\n", " chunk_size = 20_000\n", " with open(audio_file, \"rb\") as f:\n", " while True:\n", " chunk = f.read(chunk_size)\n", " if chunk:\n", " yield chunk\n", " sleep(1)\n", " else:\n", " break\n", " stream_as_bytes_btn.click(stream_bytes, input_audio, stream_as_bytes_output)\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue().launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stream_audio_out"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('audio')\n", "!wget -q -O audio/cantina.wav https://github.com/gradio-app/gradio/raw/main/demo/stream_audio_out/audio/cantina.wav"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from pydub import AudioSegment\n", "from time import sleep\n", "\n", "with gr.Blocks() as demo:\n", " input_audio = gr.Audio(label=\"Input Audio\", type=\"filepath\", format=\"mp3\")\n", " with gr.Row():\n", " with gr.Column():\n", " stream_as_file_btn = gr.Button(\"Stream as File\")\n", " format = gr.Radio([\"wav\", \"mp3\"], value=\"wav\", label=\"Format\")\n", " stream_as_file_output = gr.Audio(streaming=True)\n", "\n", " def stream_file(audio_file, format):\n", " audio = AudioSegment.from_file(audio_file)\n", " i = 0\n", " chunk_size = 1000\n", " while chunk_size * i < len(audio):\n", " chunk = audio[chunk_size * i : chunk_size * (i + 1)]\n", " i += 1\n", " if chunk:\n", " file = f\"/tmp/{i}.{format}\"\n", " chunk.export(file, format=format)\n", " yield file\n", " sleep(0.5)\n", "\n", " stream_as_file_btn.click(\n", " stream_file, [input_audio, format], stream_as_file_output\n", " )\n", "\n", " gr.Examples(\n", " [[\"audio/cantina.wav\", \"wav\"], [\"audio/cantina.wav\", \"mp3\"]],\n", " [input_audio, format],\n", " fn=stream_file,\n", " outputs=stream_as_file_output,\n", " cache_examples=True,\n", " )\n", "\n", " with gr.Column():\n", " stream_as_bytes_btn = gr.Button(\"Stream as Bytes\")\n", " stream_as_bytes_output = gr.Audio(format=\"bytes\", streaming=True)\n", "\n", " def stream_bytes(audio_file):\n", " chunk_size = 20_000\n", " with open(audio_file, \"rb\") as f:\n", " while True:\n", " chunk = f.read(chunk_size)\n", " if chunk:\n", " yield chunk\n", " sleep(1)\n", " else:\n", " break\n", " stream_as_bytes_btn.click(stream_bytes, input_audio, stream_as_bytes_output)\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue().launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/stream_frames/run.ipynb b/demo/stream_frames/run.ipynb index f39f16a90dfbc..f71842e9202fe 100644 --- a/demo/stream_frames/run.ipynb +++ b/demo/stream_frames/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stream_frames"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "def flip(im):\n", " return np.flipud(im)\n", "\n", "demo = gr.Interface(\n", " flip, \n", " gr.Image(source=\"webcam\", streaming=True), \n", " \"image\",\n", " live=True\n", ")\n", "if __name__ == \"__main__\":\n", " demo.launch()\n", " "]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stream_frames"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "\n", "def flip(im):\n", " return np.flipud(im)\n", "\n", "demo = gr.Interface(\n", " flip, \n", " gr.Image(source=\"webcam\", streaming=True), \n", " \"image\",\n", " live=True\n", ")\n", "if __name__ == \"__main__\":\n", " demo.launch()\n", " "]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/streaming_wav2vec/run.ipynb b/demo/streaming_wav2vec/run.ipynb index b92040cb1dd9f..28660acf8bc5c 100644 --- a/demo/streaming_wav2vec/run.ipynb +++ b/demo/streaming_wav2vec/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: streaming_wav2vec"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "import gradio as gr\n", "import time\n", "\n", "p = pipeline(\"automatic-speech-recognition\")\n", "\n", "def transcribe(audio, state=\"\"):\n", " time.sleep(2)\n", " text = p(audio)[\"text\"]\n", " state += text + \" \"\n", " return state, state\n", "\n", "demo = gr.Interface(\n", " fn=transcribe, \n", " inputs=[\n", " gr.Audio(source=\"microphone\", type=\"filepath\", streaming=True), \n", " \"state\"\n", " ],\n", " outputs=[\n", " \"textbox\",\n", " \"state\"\n", " ],\n", " live=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: streaming_wav2vec"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "import gradio as gr\n", "import time\n", "\n", "p = pipeline(\"automatic-speech-recognition\")\n", "\n", "def transcribe(audio, state=\"\"):\n", " time.sleep(2)\n", " text = p(audio)[\"text\"]\n", " state += text + \" \"\n", " return state, state\n", "\n", "demo = gr.Interface(\n", " fn=transcribe, \n", " inputs=[\n", " gr.Audio(source=\"microphone\", type=\"filepath\", streaming=True), \n", " \"state\"\n", " ],\n", " outputs=[\n", " \"textbox\",\n", " \"state\"\n", " ],\n", " live=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/stt_or_tts/run.ipynb b/demo/stt_or_tts/run.ipynb index 351ddf363b324..ad53035e0e6fe 100644 --- a/demo/stt_or_tts/run.ipynb +++ b/demo/stt_or_tts/run.ipynb @@ -1 +1,5 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stt_or_tts"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "tts_examples = [\n", " \"I love learning machine learning\",\n", " \"How do you do?\",\n", "]\n", "\n", "tts_demo = gr.load(\n", " \"huggingface/facebook/fastspeech2-en-ljspeech\",\n", " title=None,\n", " examples=tts_examples,\n", " description=\"Give me something to say!\",\n", " cache_examples=False\n", ")\n", "\n", "stt_demo = gr.load(\n", " \"huggingface/facebook/wav2vec2-base-960h\",\n", " title=None,\n", " inputs=\"mic\",\n", " description=\"Let me try to guess what you're saying!\",\n", ")\n", "\n", "demo = gr.TabbedInterface([tts_demo, stt_demo], [\"Text-to-speech\", \"Speech-to-text\"])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +<<<<<<< HEAD +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: stt_or_tts"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "tts_examples = [\n", " \"I love learning machine learning\",\n", " \"How do you do?\",\n", "]\n", "\n", "tts_demo = gr.load(\n", " \"huggingface/facebook/fastspeech2-en-ljspeech\",\n", " title=None,\n", " examples=tts_examples,\n", " description=\"Give me something to say!\",\n", " cache_examples=False\n", ")\n", "\n", "stt_demo = gr.load(\n", " \"huggingface/facebook/wav2vec2-base-960h\",\n", " title=None,\n", " inputs=\"mic\",\n", " description=\"Let me try to guess what you're saying!\",\n", ")\n", "\n", "demo = gr.TabbedInterface([tts_demo, stt_demo], [\"Text-to-speech\", \"Speech-to-text\"])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +======= +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: stt_or_tts"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "tts_examples = [\n", " \"I love learning machine learning\",\n", " \"How do you do?\",\n", "]\n", "\n", "tts_demo = gr.load(\n", " \"huggingface/facebook/fastspeech2-en-ljspeech\",\n", " title=None,\n", " examples=tts_examples,\n", " description=\"Give me something to say!\",\n", ")\n", "\n", "stt_demo = gr.load(\n", " \"huggingface/facebook/wav2vec2-base-960h\",\n", " title=None,\n", " inputs=\"mic\",\n", " description=\"Let me try to guess what you're saying!\",\n", ")\n", "\n", "demo = gr.TabbedInterface([tts_demo, stt_demo], [\"Text-to-speech\", \"Speech-to-text\"])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +>>>>>>> main diff --git a/demo/tax_calculator/run.ipynb b/demo/tax_calculator/run.ipynb index b85290fd4604f..a531071b1b664 100644 --- a/demo/tax_calculator/run.ipynb +++ b/demo/tax_calculator/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: tax_calculator\n", "### Calculate taxes using Textbox, Radio, and Dataframe components\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def tax_calculator(income, marital_status, assets):\n", " tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\n", " total_deductible = sum(assets[\"Cost\"])\n", " taxable_income = income - total_deductible\n", "\n", " total_tax = 0\n", " for bracket, rate in tax_brackets:\n", " if taxable_income > bracket:\n", " total_tax += (taxable_income - bracket) * rate / 100\n", "\n", " if marital_status == \"Married\":\n", " total_tax *= 0.75\n", " elif marital_status == \"Divorced\":\n", " total_tax *= 0.8\n", "\n", " return round(total_tax)\n", "\n", "demo = gr.Interface(\n", " tax_calculator,\n", " [\n", " \"number\",\n", " gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\n", " gr.Dataframe(\n", " headers=[\"Item\", \"Cost\"],\n", " datatype=[\"str\", \"number\"],\n", " label=\"Assets Purchased this Year\",\n", " ),\n", " ],\n", " \"number\",\n", " examples=[\n", " [10000, \"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]],\n", " [80000, \"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]],\n", " ],\n", ")\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: tax_calculator\n", "### Calculate taxes using Textbox, Radio, and Dataframe components\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def tax_calculator(income, marital_status, assets):\n", " tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\n", " total_deductible = sum(assets[\"Cost\"])\n", " taxable_income = income - total_deductible\n", "\n", " total_tax = 0\n", " for bracket, rate in tax_brackets:\n", " if taxable_income > bracket:\n", " total_tax += (taxable_income - bracket) * rate / 100\n", "\n", " if marital_status == \"Married\":\n", " total_tax *= 0.75\n", " elif marital_status == \"Divorced\":\n", " total_tax *= 0.8\n", "\n", " return round(total_tax)\n", "\n", "demo = gr.Interface(\n", " tax_calculator,\n", " [\n", " \"number\",\n", " gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\n", " gr.Dataframe(\n", " headers=[\"Item\", \"Cost\"],\n", " datatype=[\"str\", \"number\"],\n", " label=\"Assets Purchased this Year\",\n", " ),\n", " ],\n", " \"number\",\n", " examples=[\n", " [10000, \"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]],\n", " [80000, \"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]],\n", " ],\n", ")\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/text_analysis/run.ipynb b/demo/text_analysis/run.ipynb index cc53c4e960ec7..b95ad000185ad 100644 --- a/demo/text_analysis/run.ipynb +++ b/demo/text_analysis/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: text_analysis\n", "### This simple demo takes advantage of Gradio's HighlightedText, JSON and HTML outputs to create a clear NER segmentation.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio spacy"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "os.system('python -m spacy download en_core_web_sm')\n", "import spacy\n", "from spacy import displacy\n", "\n", "nlp = spacy.load(\"en_core_web_sm\")\n", "\n", "def text_analysis(text):\n", " doc = nlp(text)\n", " html = displacy.render(doc, style=\"dep\", page=True)\n", " html = (\n", " \"\"\n", " \"\ud83c\udf99\ufe0f Learn more about UniSpeech-SAT | \"\n", " \"\ud83d\udcda UniSpeech-SAT paper | \"\n", " \"\ud83d\udcda X-Vector paper\"\n", " \"
\"\n", ")\n", "examples = [\n", " [\"samples/cate_blanch.mp3\", \"samples/cate_blanch_2.mp3\"],\n", " [\"samples/cate_blanch.mp3\", \"samples/cate_blanch_3.mp3\"],\n", " [\"samples/cate_blanch_2.mp3\", \"samples/cate_blanch_3.mp3\"],\n", " [\"samples/heath_ledger.mp3\", \"samples/heath_ledger_2.mp3\"],\n", " [\"samples/cate_blanch.mp3\", \"samples/kirsten_dunst.wav\"],\n", "]\n", "\n", "demo = gr.Interface(\n", " fn=similarity_fn,\n", " inputs=inputs,\n", " outputs=output,\n", " title=\"Voice Authentication with UniSpeech-SAT + X-Vectors\",\n", " description=description,\n", " article=article,\n", " layout=\"horizontal\",\n", " allow_flagging=\"never\",\n", " live=False,\n", " examples=examples,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n", "\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: unispeech-speaker-verification"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio git+https://github.com/huggingface/transformers torchaudio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('samples')\n", "!wget -q -O samples/cate_blanch.mp3 https://github.com/gradio-app/gradio/raw/main/demo/unispeech-speaker-verification/samples/cate_blanch.mp3\n", "!wget -q -O samples/cate_blanch_2.mp3 https://github.com/gradio-app/gradio/raw/main/demo/unispeech-speaker-verification/samples/cate_blanch_2.mp3\n", "!wget -q -O samples/cate_blanch_3.mp3 https://github.com/gradio-app/gradio/raw/main/demo/unispeech-speaker-verification/samples/cate_blanch_3.mp3\n", "!wget -q -O samples/heath_ledger.mp3 https://github.com/gradio-app/gradio/raw/main/demo/unispeech-speaker-verification/samples/heath_ledger.mp3\n", "!wget -q -O samples/heath_ledger_2.mp3 https://github.com/gradio-app/gradio/raw/main/demo/unispeech-speaker-verification/samples/heath_ledger_2.mp3\n", "!wget -q -O samples/kirsten_dunst.wav https://github.com/gradio-app/gradio/raw/main/demo/unispeech-speaker-verification/samples/kirsten_dunst.wav"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "from torchaudio.sox_effects import apply_effects_file\n", "from transformers import AutoFeatureExtractor, AutoModelForAudioXVector\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "STYLE = \"\"\"\n", "\n", "\"\"\"\n", "OUTPUT_OK = (\n", " STYLE\n", " + \"\"\"\n", "\"\n", " \"\ud83c\udf99\ufe0f Learn more about UniSpeech-SAT | \"\n", " \"\ud83d\udcda UniSpeech-SAT paper | \"\n", " \"\ud83d\udcda X-Vector paper\"\n", " \"
\"\n", ")\n", "examples = [\n", " [\"samples/cate_blanch.mp3\", \"samples/cate_blanch_2.mp3\"],\n", " [\"samples/cate_blanch.mp3\", \"samples/cate_blanch_3.mp3\"],\n", " [\"samples/cate_blanch_2.mp3\", \"samples/cate_blanch_3.mp3\"],\n", " [\"samples/heath_ledger.mp3\", \"samples/heath_ledger_2.mp3\"],\n", " [\"samples/cate_blanch.mp3\", \"samples/kirsten_dunst.wav\"],\n", "]\n", "\n", "demo = gr.Interface(\n", " fn=similarity_fn,\n", " inputs=inputs,\n", " outputs=output,\n", " title=\"Voice Authentication with UniSpeech-SAT + X-Vectors\",\n", " description=description,\n", " article=article,\n", " layout=\"horizontal\",\n", " allow_flagging=\"never\",\n", " live=False,\n", " examples=examples,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n", "\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/upload_button/run.ipynb b/demo/upload_button/run.ipynb index 7acd21453f3da..d8cdaea6ac840 100644 --- a/demo/upload_button/run.ipynb +++ b/demo/upload_button/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: upload_button\n", "### A simple demo showcasing the upload button used with its `upload` event trigger.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def upload_file(files):\n", " file_paths = [file.name for file in files]\n", " return file_paths\n", "\n", "with gr.Blocks() as demo:\n", " file_output = gr.File()\n", " upload_button = gr.UploadButton(\"Click to Upload a File\", file_types=[\"image\", \"video\"], file_count=\"multiple\")\n", " upload_button.upload(upload_file, upload_button, file_output)\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: upload_button\n", "### A simple demo showcasing the upload button used with its `upload` event trigger.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def upload_file(files):\n", " file_paths = [file.name for file in files]\n", " return file_paths\n", "\n", "with gr.Blocks() as demo:\n", " file_output = gr.File()\n", " upload_button = gr.UploadButton(\"Click to Upload a File\", file_types=[\"image\", \"video\"], file_count=\"multiple\")\n", " upload_button.upload(upload_file, upload_button, file_output)\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/uploadbutton_component/run.ipynb b/demo/uploadbutton_component/run.ipynb index e6fcc8e5d1ff6..55dee4d996783 100644 --- a/demo/uploadbutton_component/run.ipynb +++ b/demo/uploadbutton_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: uploadbutton_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def upload_file(files):\n", " file_paths = [file.name for file in files]\n", " return file_paths\n", "\n", "with gr.Blocks() as demo:\n", " file_output = gr.File()\n", " upload_button = gr.UploadButton(\"Click to Upload an Image or Video File\", file_types=[\"image\", \"video\"], file_count=\"multiple\")\n", " upload_button.upload(upload_file, upload_button, file_output)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: uploadbutton_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def upload_file(files):\n", " file_paths = [file.name for file in files]\n", " return file_paths\n", "\n", "with gr.Blocks() as demo:\n", " file_output = gr.File()\n", " upload_button = gr.UploadButton(\"Click to Upload an Image or Video File\", file_types=[\"image\", \"video\"], file_count=\"multiple\")\n", " upload_button.upload(upload_file, upload_button, file_output)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/variable_outputs/run.ipynb b/demo/variable_outputs/run.ipynb index 578fdf9314ccf..7ebbe92b2ba6a 100644 --- a/demo/variable_outputs/run.ipynb +++ b/demo/variable_outputs/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: variable_outputs"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "max_textboxes = 10\n", "\n", "def variable_outputs(k):\n", " k = int(k)\n", " return [gr.Textbox(visible=True)]*k + [gr.Textbox(visible=False)]*(max_textboxes-k)\n", "\n", "with gr.Blocks() as demo:\n", " s = gr.Slider(1, max_textboxes, value=max_textboxes, step=1, label=\"How many textboxes to show:\")\n", " textboxes = []\n", " for i in range(max_textboxes):\n", " t = gr.Textbox(f\"Textbox {i}\")\n", " textboxes.append(t)\n", "\n", " s.change(variable_outputs, s, textboxes)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: variable_outputs"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "max_textboxes = 10\n", "\n", "def variable_outputs(k):\n", " k = int(k)\n", " return [gr.Textbox(visible=True)]*k + [gr.Textbox(visible=False)]*(max_textboxes-k)\n", "\n", "with gr.Blocks() as demo:\n", " s = gr.Slider(1, max_textboxes, value=max_textboxes, step=1, label=\"How many textboxes to show:\")\n", " textboxes = []\n", " for i in range(max_textboxes):\n", " t = gr.Textbox(f\"Textbox {i}\")\n", " textboxes.append(t)\n", "\n", " s.change(variable_outputs, s, textboxes)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/video_component/run.ipynb b/demo/video_component/run.ipynb index c755938a6028a..4e4bccc3eaacf 100644 --- a/demo/video_component/run.ipynb +++ b/demo/video_component/run.ipynb @@ -1 +1,5 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: video_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/a.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/a.mp4\n", "!wget -q -O files/b.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/b.mp4\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "a = os.path.join(os.path.abspath(''), \"files/world.mp4\") # Video\n", "b = os.path.join(os.path.abspath(''), \"files/a.mp4\") # Video\n", "c = os.path.join(os.path.abspath(''), \"files/b.mp4\") # Video\n", "\n", "\n", "demo = gr.Interface(\n", " fn=lambda x: x,\n", " inputs=gr.Video(type=\"file\"),\n", " outputs=gr.Video(),\n", " examples=[\n", " [a],\n", " [b],\n", " [c],\n", " ],\n", " cache_examples=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +<<<<<<< HEAD +{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: video_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/a.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/a.mp4\n", "!wget -q -O files/b.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/b.mp4\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "a = os.path.join(os.path.abspath(''), \"files/world.mp4\") # Video\n", "b = os.path.join(os.path.abspath(''), \"files/a.mp4\") # Video\n", "c = os.path.join(os.path.abspath(''), \"files/b.mp4\") # Video\n", "\n", "\n", "demo = gr.Interface(\n", " fn=lambda x: x,\n", " inputs=gr.Video(type=\"file\"),\n", " outputs=gr.Video(),\n", " examples=[\n", " [a],\n", " [b],\n", " [c],\n", " ],\n", " cache_examples=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +======= +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: video_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/a.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/a.mp4\n", "!wget -q -O files/b.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/b.mp4\n", "!wget -q -O files/world.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "a = os.path.join(os.path.abspath(''), \"files/world.mp4\") # Video\n", "b = os.path.join(os.path.abspath(''), \"files/a.mp4\") # Video\n", "c = os.path.join(os.path.abspath(''), \"files/b.mp4\") # Video\n", "\n", "\n", "demo = gr.Interface(\n", " fn=lambda x: x,\n", " inputs=gr.Video(type=\"file\"),\n", " outputs=gr.Video(),\n", " examples=[\n", " [a],\n", " [b],\n", " [c],\n", " ],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} +>>>>>>> main diff --git a/demo/video_identity/run.ipynb b/demo/video_identity/run.ipynb index 19b4d287be16f..2cbc57bda2df2 100644 --- a/demo/video_identity/run.ipynb +++ b/demo/video_identity/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: video_identity"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('video')\n", "!wget -q -O video/video_sample.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_identity/video/video_sample.mp4"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "def video_identity(video):\n", " return video\n", "\n", "\n", "demo = gr.Interface(video_identity, \n", " gr.Video(), \n", " \"playable_video\", \n", " examples=[\n", " os.path.join(os.path.abspath(''), \n", " \"video/video_sample.mp4\")], \n", " cache_examples=True)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: video_identity"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('video')\n", "!wget -q -O video/video_sample.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_identity/video/video_sample.mp4"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "def video_identity(video):\n", " return video\n", "\n", "\n", "demo = gr.Interface(video_identity, \n", " gr.Video(), \n", " \"playable_video\", \n", " examples=[\n", " os.path.join(os.path.abspath(''), \n", " \"video/video_sample.mp4\")], \n", " cache_examples=True)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/video_subtitle/run.ipynb b/demo/video_subtitle/run.ipynb index 6aa2d0dde03e2..ea6f1959bfb18 100644 --- a/demo/video_subtitle/run.ipynb +++ b/demo/video_subtitle/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: video_subtitle"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/a.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_subtitle/files/a.mp4\n", "!wget -q -O files/b.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_subtitle/files/b.mp4\n", "!wget -q -O files/s1.srt https://github.com/gradio-app/gradio/raw/main/demo/video_subtitle/files/s1.srt\n", "!wget -q -O files/s2.vtt https://github.com/gradio-app/gradio/raw/main/demo/video_subtitle/files/s2.vtt"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "a = os.path.join(os.path.abspath(''), \"files/a.mp4\") # Video\n", "b = os.path.join(os.path.abspath(''), \"files/b.mp4\") # Video\n", "s1 = os.path.join(os.path.abspath(''), \"files/s1.srt\") # Subtitle\n", "s2 = os.path.join(os.path.abspath(''), \"files/s2.vtt\") # Subtitle\n", "\n", "\n", "def video_demo(video, subtitle=None):\n", " if subtitle is None:\n", " return video\n", "\n", " return [video, subtitle.name]\n", "\n", "\n", "demo = gr.Interface(\n", " fn=video_demo,\n", " inputs=[\n", " gr.Video(type=\"file\", label=\"In\", interactive=True),\n", " gr.File(label=\"Subtitle\", file_types=[\".srt\", \".vtt\"]),\n", " ],\n", " outputs=gr.Video(label=\"Out\"),\n", " examples=[\n", " [a, s1],\n", " [b, s2],\n", " [a, None],\n", " ],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: video_subtitle"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/a.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_subtitle/files/a.mp4\n", "!wget -q -O files/b.mp4 https://github.com/gradio-app/gradio/raw/main/demo/video_subtitle/files/b.mp4\n", "!wget -q -O files/s1.srt https://github.com/gradio-app/gradio/raw/main/demo/video_subtitle/files/s1.srt\n", "!wget -q -O files/s2.vtt https://github.com/gradio-app/gradio/raw/main/demo/video_subtitle/files/s2.vtt"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "a = os.path.join(os.path.abspath(''), \"files/a.mp4\") # Video\n", "b = os.path.join(os.path.abspath(''), \"files/b.mp4\") # Video\n", "s1 = os.path.join(os.path.abspath(''), \"files/s1.srt\") # Subtitle\n", "s2 = os.path.join(os.path.abspath(''), \"files/s2.vtt\") # Subtitle\n", "\n", "\n", "def video_demo(video, subtitle=None):\n", " if subtitle is None:\n", " return video\n", "\n", " return [video, subtitle.name]\n", "\n", "\n", "demo = gr.Interface(\n", " fn=video_demo,\n", " inputs=[\n", " gr.Video(type=\"file\", label=\"In\", interactive=True),\n", " gr.File(label=\"Subtitle\", file_types=[\".srt\", \".vtt\"]),\n", " ],\n", " outputs=gr.Video(label=\"Out\"),\n", " examples=[\n", " [a, s1],\n", " [b, s2],\n", " [a, None],\n", " ],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/waveform/run.ipynb b/demo/waveform/run.ipynb index 4da46e83bc908..123780c6ddaa0 100644 --- a/demo/waveform/run.ipynb +++ b/demo/waveform/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: waveform"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "\n", "\n", "COLORS = [\n", " [\"#ff0000\", \"#00ff00\"],\n", " [\"#00ff00\", \"#0000ff\"],\n", " [\"#0000ff\", \"#ff0000\"],\n", "] \n", "\n", "def audio_waveform(audio, image):\n", " return (\n", " audio,\n", " gr.make_waveform(audio),\n", " gr.make_waveform(audio, animate=True),\n", " gr.make_waveform(audio, bg_image=image, bars_color=random.choice(COLORS)),\n", " )\n", "\n", "\n", "gr.Interface(\n", " audio_waveform,\n", " inputs=[gr.Audio(), gr.Image(type=\"filepath\")],\n", " outputs=[\n", " gr.Audio(),\n", " gr.Video(),\n", " gr.Video(),\n", " gr.Video(),\n", " ],\n", ").launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: waveform"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "\n", "\n", "COLORS = [\n", " [\"#ff0000\", \"#00ff00\"],\n", " [\"#00ff00\", \"#0000ff\"],\n", " [\"#0000ff\", \"#ff0000\"],\n", "] \n", "\n", "def audio_waveform(audio, image):\n", " return (\n", " audio,\n", " gr.make_waveform(audio),\n", " gr.make_waveform(audio, animate=True),\n", " gr.make_waveform(audio, bg_image=image, bars_color=random.choice(COLORS)),\n", " )\n", "\n", "\n", "gr.Interface(\n", " audio_waveform,\n", " inputs=[gr.Audio(), gr.Image(type=\"filepath\")],\n", " outputs=[\n", " gr.Audio(),\n", " gr.Video(),\n", " gr.Video(),\n", " gr.Video(),\n", " ],\n", ").launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/webcam/run.ipynb b/demo/webcam/run.ipynb index cb32f0c5cf10e..cfc657b72bb6f 100644 --- a/demo/webcam/run.ipynb +++ b/demo/webcam/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: webcam"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["\n", "import gradio as gr\n", "\n", "\n", "def snap(image, video):\n", " return [image, video]\n", "\n", "\n", "demo = gr.Interface(\n", " snap,\n", " [gr.Image(source=\"webcam\", tool=None), gr.Video(source=\"webcam\")],\n", " [\"image\", \"video\"],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: webcam"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["\n", "import gradio as gr\n", "\n", "\n", "def snap(image, video):\n", " return [image, video]\n", "\n", "\n", "demo = gr.Interface(\n", " snap,\n", " [gr.Image(source=\"webcam\", tool=None), gr.Video(source=\"webcam\")],\n", " [\"image\", \"video\"],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/white_noise_vid_not_playable/run.ipynb b/demo/white_noise_vid_not_playable/run.ipynb index 69139bcbc9ff2..8a76dde6ced72 100644 --- a/demo/white_noise_vid_not_playable/run.ipynb +++ b/demo/white_noise_vid_not_playable/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: white_noise_vid_not_playable"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio opencv-python"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import cv2\n", "import gradio as gr\n", "import numpy as np\n", "\n", "\n", "def gif_maker():\n", " img_array = []\n", " height, width = 50, 50\n", " for i in range(30):\n", " img_array.append(np.random.randint(0, 255, size=(height, width, 3)).astype(np.uint8))\n", " output_file = \"test.mp4\"\n", " out = cv2.VideoWriter(output_file, cv2.VideoWriter_fourcc(*'mp4v'), 15, (height, width))\n", " for i in range(len(img_array)):\n", " out.write(img_array[i])\n", " out.release()\n", " return output_file, output_file\n", "\n", "\n", "demo = gr.Interface(gif_maker, inputs=None, outputs=[gr.Video(), gr.File()])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: white_noise_vid_not_playable"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio opencv-python"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import cv2\n", "import gradio as gr\n", "import numpy as np\n", "\n", "\n", "def gif_maker():\n", " img_array = []\n", " height, width = 50, 50\n", " for i in range(30):\n", " img_array.append(np.random.randint(0, 255, size=(height, width, 3)).astype(np.uint8))\n", " output_file = \"test.mp4\"\n", " out = cv2.VideoWriter(output_file, cv2.VideoWriter_fourcc(*'mp4v'), 15, (height, width))\n", " for i in range(len(img_array)):\n", " out.write(img_array[i])\n", " out.release()\n", " return output_file, output_file\n", "\n", "\n", "demo = gr.Interface(gif_maker, inputs=None, outputs=[gr.Video(), gr.File()])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/xgboost-income-prediction-with-explainability/run.ipynb b/demo/xgboost-income-prediction-with-explainability/run.ipynb index 2d0a707ad6fa3..e1093d6867ec1 100644 --- a/demo/xgboost-income-prediction-with-explainability/run.ipynb +++ b/demo/xgboost-income-prediction-with-explainability/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: xgboost-income-prediction-with-explainability\n", "### This demo takes in 12 inputs from the user in dropdowns and sliders and predicts income. It also has a separate button for explaining the prediction.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy==1.23.2 matplotlib shap xgboost==1.7.6 pandas datasets"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import shap\n", "import xgboost as xgb\n", "from datasets import load_dataset\n", "\n", "\n", "dataset = load_dataset(\"scikit-learn/adult-census-income\")\n", "X_train = dataset[\"train\"].to_pandas()\n", "_ = X_train.pop(\"fnlwgt\")\n", "_ = X_train.pop(\"race\")\n", "y_train = X_train.pop(\"income\")\n", "y_train = (y_train == \">50K\").astype(int)\n", "categorical_columns = [\n", " \"workclass\",\n", " \"education\",\n", " \"marital.status\",\n", " \"occupation\",\n", " \"relationship\",\n", " \"sex\",\n", " \"native.country\",\n", "]\n", "X_train = X_train.astype({col: \"category\" for col in categorical_columns})\n", "data = xgb.DMatrix(X_train, label=y_train, enable_categorical=True)\n", "model = xgb.train(params={\"objective\": \"binary:logistic\"}, dtrain=data)\n", "explainer = shap.TreeExplainer(model)\n", "\n", "def predict(*args):\n", " df = pd.DataFrame([args], columns=X_train.columns)\n", " df = df.astype({col: \"category\" for col in categorical_columns})\n", " pos_pred = model.predict(xgb.DMatrix(df, enable_categorical=True))\n", " return {\">50K\": float(pos_pred[0]), \"<=50K\": 1 - float(pos_pred[0])}\n", "\n", "\n", "def interpret(*args):\n", " df = pd.DataFrame([args], columns=X_train.columns)\n", " df = df.astype({col: \"category\" for col in categorical_columns})\n", " shap_values = explainer.shap_values(xgb.DMatrix(df, enable_categorical=True))\n", " scores_desc = list(zip(shap_values[0], X_train.columns))\n", " scores_desc = sorted(scores_desc)\n", " fig_m = plt.figure(tight_layout=True)\n", " plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])\n", " plt.title(\"Feature Shap Values\")\n", " plt.ylabel(\"Shap Value\")\n", " plt.xlabel(\"Feature\")\n", " plt.tight_layout()\n", " return fig_m\n", "\n", "\n", "unique_class = sorted(X_train[\"workclass\"].unique())\n", "unique_education = sorted(X_train[\"education\"].unique())\n", "unique_marital_status = sorted(X_train[\"marital.status\"].unique())\n", "unique_relationship = sorted(X_train[\"relationship\"].unique())\n", "unique_occupation = sorted(X_train[\"occupation\"].unique())\n", "unique_sex = sorted(X_train[\"sex\"].unique())\n", "unique_country = sorted(X_train[\"native.country\"].unique())\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"\"\"\n", " **Income Classification with XGBoost \ud83d\udcb0**: This demo uses an XGBoost classifier predicts income based on demographic factors, along with Shapley value-based *explanations*. The [source code for this Gradio demo is here](https://huggingface.co/spaces/gradio/xgboost-income-prediction-with-explainability/blob/main/app.py).\n", " \"\"\")\n", " with gr.Row():\n", " with gr.Column():\n", " age = gr.Slider(label=\"Age\", minimum=17, maximum=90, step=1, randomize=True)\n", " work_class = gr.Dropdown(\n", " label=\"Workclass\",\n", " choices=unique_class,\n", " value=lambda: random.choice(unique_class),\n", " )\n", " education = gr.Dropdown(\n", " label=\"Education Level\",\n", " choices=unique_education,\n", " value=lambda: random.choice(unique_education),\n", " )\n", " years = gr.Slider(\n", " label=\"Years of schooling\",\n", " minimum=1,\n", " maximum=16,\n", " step=1,\n", " randomize=True,\n", " )\n", " marital_status = gr.Dropdown(\n", " label=\"Marital Status\",\n", " choices=unique_marital_status,\n", " value=lambda: random.choice(unique_marital_status),\n", " )\n", " occupation = gr.Dropdown(\n", " label=\"Occupation\",\n", " choices=unique_occupation,\n", " value=lambda: random.choice(unique_occupation),\n", " )\n", " relationship = gr.Dropdown(\n", " label=\"Relationship Status\",\n", " choices=unique_relationship,\n", " value=lambda: random.choice(unique_relationship),\n", " )\n", " sex = gr.Dropdown(\n", " label=\"Sex\", choices=unique_sex, value=lambda: random.choice(unique_sex)\n", " )\n", " capital_gain = gr.Slider(\n", " label=\"Capital Gain\",\n", " minimum=0,\n", " maximum=100000,\n", " step=500,\n", " randomize=True,\n", " )\n", " capital_loss = gr.Slider(\n", " label=\"Capital Loss\", minimum=0, maximum=10000, step=500, randomize=True\n", " )\n", " hours_per_week = gr.Slider(\n", " label=\"Hours Per Week Worked\", minimum=1, maximum=99, step=1\n", " )\n", " country = gr.Dropdown(\n", " label=\"Native Country\",\n", " choices=unique_country,\n", " value=lambda: random.choice(unique_country),\n", " )\n", " with gr.Column():\n", " label = gr.Label()\n", " plot = gr.Plot()\n", " with gr.Row():\n", " predict_btn = gr.Button(value=\"Predict\")\n", " interpret_btn = gr.Button(value=\"Explain\")\n", " predict_btn.click(\n", " predict,\n", " inputs=[\n", " age,\n", " work_class,\n", " education,\n", " years,\n", " marital_status,\n", " occupation,\n", " relationship,\n", " sex,\n", " capital_gain,\n", " capital_loss,\n", " hours_per_week,\n", " country,\n", " ],\n", " outputs=[label],\n", " )\n", " interpret_btn.click(\n", " interpret,\n", " inputs=[\n", " age,\n", " work_class,\n", " education,\n", " years,\n", " marital_status,\n", " occupation,\n", " relationship,\n", " sex,\n", " capital_gain,\n", " capital_loss,\n", " hours_per_week,\n", " country,\n", " ],\n", " outputs=[plot],\n", " )\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: xgboost-income-prediction-with-explainability\n", "### This demo takes in 12 inputs from the user in dropdowns and sliders and predicts income. It also has a separate button for explaining the prediction.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy==1.23.2 matplotlib shap xgboost==1.7.6 pandas datasets"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import shap\n", "import xgboost as xgb\n", "from datasets import load_dataset\n", "\n", "\n", "dataset = load_dataset(\"scikit-learn/adult-census-income\")\n", "X_train = dataset[\"train\"].to_pandas()\n", "_ = X_train.pop(\"fnlwgt\")\n", "_ = X_train.pop(\"race\")\n", "y_train = X_train.pop(\"income\")\n", "y_train = (y_train == \">50K\").astype(int)\n", "categorical_columns = [\n", " \"workclass\",\n", " \"education\",\n", " \"marital.status\",\n", " \"occupation\",\n", " \"relationship\",\n", " \"sex\",\n", " \"native.country\",\n", "]\n", "X_train = X_train.astype({col: \"category\" for col in categorical_columns})\n", "data = xgb.DMatrix(X_train, label=y_train, enable_categorical=True)\n", "model = xgb.train(params={\"objective\": \"binary:logistic\"}, dtrain=data)\n", "explainer = shap.TreeExplainer(model)\n", "\n", "def predict(*args):\n", " df = pd.DataFrame([args], columns=X_train.columns)\n", " df = df.astype({col: \"category\" for col in categorical_columns})\n", " pos_pred = model.predict(xgb.DMatrix(df, enable_categorical=True))\n", " return {\">50K\": float(pos_pred[0]), \"<=50K\": 1 - float(pos_pred[0])}\n", "\n", "\n", "def interpret(*args):\n", " df = pd.DataFrame([args], columns=X_train.columns)\n", " df = df.astype({col: \"category\" for col in categorical_columns})\n", " shap_values = explainer.shap_values(xgb.DMatrix(df, enable_categorical=True))\n", " scores_desc = list(zip(shap_values[0], X_train.columns))\n", " scores_desc = sorted(scores_desc)\n", " fig_m = plt.figure(tight_layout=True)\n", " plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc])\n", " plt.title(\"Feature Shap Values\")\n", " plt.ylabel(\"Shap Value\")\n", " plt.xlabel(\"Feature\")\n", " plt.tight_layout()\n", " return fig_m\n", "\n", "\n", "unique_class = sorted(X_train[\"workclass\"].unique())\n", "unique_education = sorted(X_train[\"education\"].unique())\n", "unique_marital_status = sorted(X_train[\"marital.status\"].unique())\n", "unique_relationship = sorted(X_train[\"relationship\"].unique())\n", "unique_occupation = sorted(X_train[\"occupation\"].unique())\n", "unique_sex = sorted(X_train[\"sex\"].unique())\n", "unique_country = sorted(X_train[\"native.country\"].unique())\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(\"\"\"\n", " **Income Classification with XGBoost \ud83d\udcb0**: This demo uses an XGBoost classifier predicts income based on demographic factors, along with Shapley value-based *explanations*. The [source code for this Gradio demo is here](https://huggingface.co/spaces/gradio/xgboost-income-prediction-with-explainability/blob/main/app.py).\n", " \"\"\")\n", " with gr.Row():\n", " with gr.Column():\n", " age = gr.Slider(label=\"Age\", minimum=17, maximum=90, step=1, randomize=True)\n", " work_class = gr.Dropdown(\n", " label=\"Workclass\",\n", " choices=unique_class,\n", " value=lambda: random.choice(unique_class),\n", " )\n", " education = gr.Dropdown(\n", " label=\"Education Level\",\n", " choices=unique_education,\n", " value=lambda: random.choice(unique_education),\n", " )\n", " years = gr.Slider(\n", " label=\"Years of schooling\",\n", " minimum=1,\n", " maximum=16,\n", " step=1,\n", " randomize=True,\n", " )\n", " marital_status = gr.Dropdown(\n", " label=\"Marital Status\",\n", " choices=unique_marital_status,\n", " value=lambda: random.choice(unique_marital_status),\n", " )\n", " occupation = gr.Dropdown(\n", " label=\"Occupation\",\n", " choices=unique_occupation,\n", " value=lambda: random.choice(unique_occupation),\n", " )\n", " relationship = gr.Dropdown(\n", " label=\"Relationship Status\",\n", " choices=unique_relationship,\n", " value=lambda: random.choice(unique_relationship),\n", " )\n", " sex = gr.Dropdown(\n", " label=\"Sex\", choices=unique_sex, value=lambda: random.choice(unique_sex)\n", " )\n", " capital_gain = gr.Slider(\n", " label=\"Capital Gain\",\n", " minimum=0,\n", " maximum=100000,\n", " step=500,\n", " randomize=True,\n", " )\n", " capital_loss = gr.Slider(\n", " label=\"Capital Loss\", minimum=0, maximum=10000, step=500, randomize=True\n", " )\n", " hours_per_week = gr.Slider(\n", " label=\"Hours Per Week Worked\", minimum=1, maximum=99, step=1\n", " )\n", " country = gr.Dropdown(\n", " label=\"Native Country\",\n", " choices=unique_country,\n", " value=lambda: random.choice(unique_country),\n", " )\n", " with gr.Column():\n", " label = gr.Label()\n", " plot = gr.Plot()\n", " with gr.Row():\n", " predict_btn = gr.Button(value=\"Predict\")\n", " interpret_btn = gr.Button(value=\"Explain\")\n", " predict_btn.click(\n", " predict,\n", " inputs=[\n", " age,\n", " work_class,\n", " education,\n", " years,\n", " marital_status,\n", " occupation,\n", " relationship,\n", " sex,\n", " capital_gain,\n", " capital_loss,\n", " hours_per_week,\n", " country,\n", " ],\n", " outputs=[label],\n", " )\n", " interpret_btn.click(\n", " interpret,\n", " inputs=[\n", " age,\n", " work_class,\n", " education,\n", " years,\n", " marital_status,\n", " occupation,\n", " relationship,\n", " sex,\n", " capital_gain,\n", " capital_loss,\n", " hours_per_week,\n", " country,\n", " ],\n", " outputs=[plot],\n", " )\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/zip_files/run.ipynb b/demo/zip_files/run.ipynb index 21810513c773e..a2732e7f190d2 100644 --- a/demo/zip_files/run.ipynb +++ b/demo/zip_files/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: zip_files"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/zip_files/files/titanic.csv"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import os\n", "from zipfile import ZipFile\n", "\n", "import gradio as gr\n", "\n", "\n", "def zip_files(files):\n", " with ZipFile(\"tmp.zip\", \"w\") as zipObj:\n", " for idx, file in enumerate(files):\n", " zipObj.write(file.name, file.name.split(\"/\")[-1])\n", " return \"tmp.zip\"\n", "\n", "demo = gr.Interface(\n", " zip_files,\n", " gr.File(file_count=\"multiple\", file_types=[\"text\", \".json\", \".csv\"]),\n", " \"file\",\n", " examples=[[[os.path.join(os.path.abspath(''),\"files/titanic.csv\"), \n", " os.path.join(os.path.abspath(''),\"files/titanic.csv\"), \n", " os.path.join(os.path.abspath(''),\"files/titanic.csv\")]]], \n", " cache_examples=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: zip_files"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/titanic.csv https://github.com/gradio-app/gradio/raw/main/demo/zip_files/files/titanic.csv"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "from zipfile import ZipFile\n", "\n", "import gradio as gr\n", "\n", "\n", "def zip_files(files):\n", " with ZipFile(\"tmp.zip\", \"w\") as zipObj:\n", " for idx, file in enumerate(files):\n", " zipObj.write(file.name, file.name.split(\"/\")[-1])\n", " return \"tmp.zip\"\n", "\n", "demo = gr.Interface(\n", " zip_files,\n", " gr.File(file_count=\"multiple\", file_types=[\"text\", \".json\", \".csv\"]),\n", " \"file\",\n", " examples=[[[os.path.join(os.path.abspath(''),\"files/titanic.csv\"), \n", " os.path.join(os.path.abspath(''),\"files/titanic.csv\"), \n", " os.path.join(os.path.abspath(''),\"files/titanic.csv\")]]], \n", " cache_examples=True\n", ")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/zip_to_json/run.ipynb b/demo/zip_to_json/run.ipynb index 0928b97b52932..02230b11ed8ae 100644 --- a/demo/zip_to_json/run.ipynb +++ b/demo/zip_to_json/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: zip_to_json"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["from zipfile import ZipFile\n", "\n", "import gradio as gr\n", "\n", "\n", "def zip_to_json(file_obj):\n", " files = []\n", " with ZipFile(file_obj.name) as zfile:\n", " for zinfo in zfile.infolist():\n", " files.append(\n", " {\n", " \"name\": zinfo.filename,\n", " \"file_size\": zinfo.file_size,\n", " \"compressed_size\": zinfo.compress_size,\n", " }\n", " )\n", " return files\n", "\n", "\n", "demo = gr.Interface(zip_to_json, \"file\", \"json\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: zip_to_json"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from zipfile import ZipFile\n", "\n", "import gradio as gr\n", "\n", "\n", "def zip_to_json(file_obj):\n", " files = []\n", " with ZipFile(file_obj.name) as zfile:\n", " for zinfo in zfile.infolist():\n", " files.append(\n", " {\n", " \"name\": zinfo.filename,\n", " \"file_size\": zinfo.file_size,\n", " \"compressed_size\": zinfo.compress_size,\n", " }\n", " )\n", " return files\n", "\n", "\n", "demo = gr.Interface(zip_to_json, \"file\", \"json\")\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/gradio/CHANGELOG.md b/gradio/CHANGELOG.md index bc1703db27c5b..78bc3a913fbff 100644 --- a/gradio/CHANGELOG.md +++ b/gradio/CHANGELOG.md @@ -113,8 +113,6 @@ For more information check the [`FileExplorer` documentation](https://gradio.app ### Fixes - [#5625](https://github.com/gradio-app/gradio/pull/5625) [`9ccc4794a`](https://github.com/gradio-app/gradio/commit/9ccc4794a72ce8319417119f6c370e7af3ffca6d) - Use ContextVar instead of threading.local(). Thanks [@cbensimon](https://github.com/cbensimon)! -<<<<<<< HEAD -<<<<<<< HEAD - [#5636](https://github.com/gradio-app/gradio/pull/5636) [`fb5964fb8`](https://github.com/gradio-app/gradio/commit/fb5964fb88082e7b956853b543c468116811cab9) - Fix bug in example cache loading event. Thanks [@freddyaboulton](https://github.com/freddyaboulton)! - [#5633](https://github.com/gradio-app/gradio/pull/5633) [`341402337`](https://github.com/gradio-app/gradio/commit/34140233794c29d4722020e13c2d045da642dfae) - Allow Gradio apps containing `gr.Radio()`, `gr.Checkboxgroup()`, or `gr.Dropdown()` to be loaded with `gr.load()`. Thanks [@abidlabs](https://github.com/abidlabs)! - [#5593](https://github.com/gradio-app/gradio/pull/5593) [`88d43bd12`](https://github.com/gradio-app/gradio/commit/88d43bd124792d216da445adef932a2b02f5f416) - Fixes avatar image in chatbot being squashed. Thanks [@dawoodkhan82](https://github.com/dawoodkhan82)! diff --git a/gradio/blocks.py b/gradio/blocks.py index 3ed8143247e90..2759dd6041796 100644 --- a/gradio/blocks.py +++ b/gradio/blocks.py @@ -201,14 +201,6 @@ def get_config(self): def update(**kwargs) -> dict: return {} - @classmethod - def get_specific_update(cls, generic_update: dict[str, Any]) -> dict: - generic_update = generic_update.copy() - del generic_update["__type__"] - specific_update = cls.update(**generic_update) - specific_update = utils.delete_none(specific_update, skip_value=True) - return specific_update - class BlockContext(Block): def __init__( @@ -340,16 +332,13 @@ def postprocess_update_dict( ): """ Converts a dictionary of updates into a format that can be sent to the frontend. - E.g. {"__type__": "generic_update", "value": "2", "interactive": False} + E.g. {"__type__": "update", "value": "2", "interactive": False} Into -> {"__type__": "update", "value": 2.0, "mode": "static"} - Parameters: block: The Block that is being updated with this update dictionary. update_dict: The original update dictionary postprocess: Whether to postprocess the "value" key of the update dictionary. """ - if update_dict.get("__type__", "") == "generic_update": - update_dict = block.get_specific_update(update_dict) if update_dict.get("value") is components._Keywords.NO_VALUE: update_dict.pop("value") interactive = update_dict.pop("interactive", None) @@ -1322,7 +1311,6 @@ def postprocess_data( args["_skip_init_processing"] = not block_fn.postprocess state[output_id] = self.blocks[output_id].__class__(**args) - assert isinstance(prediction_value, dict) prediction_value = postprocess_update_dict( block=state[output_id], update_dict=prediction_value, diff --git a/gradio/components/code.py b/gradio/components/code.py index e0d7e2e920124..3bd90f38ebd91 100644 --- a/gradio/components/code.py +++ b/gradio/components/code.py @@ -19,7 +19,7 @@ class Code(Component): """ Creates a Code editor for entering, editing or viewing code. Preprocessing: passes a {str} of code into the function. - Postprocessing: expects the function to return a {str} of code or a single-elment {tuple}: (string filepath,) + Postprocessing: expects the function to return a {str} of code or a single-element {tuple}: {(string_filepath,)} """ languages = [ diff --git a/gradio/components/file.py b/gradio/components/file.py index d54de10471f7d..577e8c11e4c39 100644 --- a/gradio/components/file.py +++ b/gradio/components/file.py @@ -26,7 +26,7 @@ class ListFiles(GradioRootModel): class File(Component): """ Creates a file component that allows uploading generic file (when used as an input) and or displaying generic files (output). - Preprocessing: passes the uploaded file as a {tempfile._TemporaryFileWrapper} or {List[tempfile._TemporaryFileWrapper]} depending on `file_count` (or a {bytes}/{List{bytes}} depending on `type`) + Preprocessing: passes the uploaded file as a {tempfile._TemporaryFileWrapper} or {List[tempfile._TemporaryFileWrapper]} depending on `file_count` (or a {bytes}/{List[bytes]} depending on `type`) Postprocessing: expects function to return a {str} path to a file, or {List[str]} consisting of paths to files. Examples-format: a {str} path to a local file that populates the component. Demos: zip_to_json, zip_files diff --git a/gradio/components/upload_button.py b/gradio/components/upload_button.py index e682b360a5811..27eb6a78474ee 100644 --- a/gradio/components/upload_button.py +++ b/gradio/components/upload_button.py @@ -25,7 +25,7 @@ class ListFiles(GradioRootModel): class UploadButton(Component): """ Used to create an upload button, when clicked allows a user to upload files that satisfy the specified file type or generic files (if file_type not set). - Preprocessing: passes the uploaded file as a {file-object} or {List[file-object]} depending on `file_count` (or a {bytes}/{List{bytes}} depending on `type`) + Preprocessing: passes the uploaded file as a {file-object} or {List[file-object]} depending on `file_count` (or a {bytes}/{List[bytes]} depending on `type`) Postprocessing: expects function to return a {str} path to a file, or {List[str]} consisting of paths to files. Examples-format: a {str} path to a local file that populates the component. Demos: upload_button diff --git a/gradio/helpers.py b/gradio/helpers.py index 98af6c9134ae7..893a789260f7f 100644 --- a/gradio/helpers.py +++ b/gradio/helpers.py @@ -793,53 +793,40 @@ def special_args( return inputs or [], progress_index, event_data_index -def update(**kwargs) -> dict: +def update( + elem_id: str | None = None, + elem_classes: list[str] | str | None = None, + visible: bool | None = None, + **kwargs, +) -> dict: """ - DEPRECATED. Updates component properties. When a function passed into a Gradio Interface or a Blocks events returns a typical value, it updates the value of the output component. But it is also possible to update the properties of an output component (such as the number of lines of a `Textbox` or the visibility of an `Image`) by returning the component's `update()` function, which takes as parameters any of the constructor parameters for that component. - This is a shorthand for using the update method on a component. - For example, rather than using gr.Number.update(...) you can just use gr.update(...). - Note that your editor's autocompletion will suggest proper parameters - if you use the update method on the component. - Demos: blocks_essay, blocks_update, blocks_essay_update + Updates a component's properties. When a function passed into a Gradio Interface or a Blocks events returns a value, it typically updates the value of the output component. But it is also possible to update the *properties* of an output component (such as the number of lines of a `Textbox` or the visibility of an `Row`) by returning a component and passing in the parameters to update in the constructor of the component. Alternatively, you can return `gr.update(...)` with any arbitrary parameters to update. (This is useful as a shorthand or if the same function can be called with different components to update.) Parameters: - kwargs: Key-word arguments used to update the component's properties. + elem_id: Use this to update the id of the component in the HTML DOM + elem_classes: Use this to update the classes of the component in the HTML DOM + visible: Use this to update the visibility of the component + kwargs: Any other keyword arguments to update the component's properties. Example: - # Blocks Example import gradio as gr with gr.Blocks() as demo: radio = gr.Radio([1, 2, 4], label="Set the value of the number") number = gr.Number(value=2, interactive=True) radio.change(fn=lambda value: gr.update(value=value), inputs=radio, outputs=number) demo.launch() - - # Interface example - import gradio as gr - def change_textbox(choice): - if choice == "short": - return gr.Textbox.update(lines=2, visible=True) - elif choice == "long": - return gr.Textbox.update(lines=8, visible=True) - else: - return gr.Textbox.update(visible=False) - gr.Interface( - change_textbox, - gr.Radio( - ["short", "long", "none"], label="What kind of essay would you like to write?" - ), - gr.Textbox(lines=2), - live=True, - ).launch() """ - warnings.warn( - "Using the update method is deprecated. Simply return a new object instead, e.g. `return gr.Textbox(...)` instead of `return gr.update(...)" - ) - kwargs["__type__"] = "generic_update" + kwargs["__type__"] = "update" + if elem_id is not None: + kwargs["elem_id"] = elem_id + if elem_classes is not None: + kwargs["elem_classes"] = elem_classes + if visible is not None: + kwargs["visible"] = visible return kwargs def skip() -> dict: - return {"__type__": "generic_update"} + return {"__type__": "update"} @document() diff --git a/gradio/interface.py b/gradio/interface.py index 33fd981a01e6d..1681b75d56204 100644 --- a/gradio/interface.py +++ b/gradio/interface.py @@ -590,7 +590,7 @@ def attach_submit_events(self, submit_btn: Button | None, stop_btn: Button | Non for component in self.input_components: if component.has_event("stream") and component.streaming: # type: ignore events.append(component.stream) # type: ignore - elif Events.change in component.events: + elif component.has_event("change"): events.append(component.change) # type: ignore on( events, diff --git a/gradio/themes/utils/fonts.py b/gradio/themes/utils/fonts.py index d51dbbfdf4990..5525c2c4d5809 100644 --- a/gradio/themes/utils/fonts.py +++ b/gradio/themes/utils/fonts.py @@ -40,6 +40,12 @@ def stylesheet(self) -> str: def __eq__(self, other: Font) -> bool: return self.name == other.name and self.stylesheet() == other.stylesheet() + def __repr__(self) -> str: + klass = type(self) + class_repr = klass.__module__ + "." + klass.__qualname__ + attrs = ", ".join([k + "=" + repr(v) for k, v in self.__dict__.items()]) + return f"<{class_repr} ({attrs})>" + class GoogleFont(Font): def __init__(self, name: str, weights: Iterable[int] = (400, 600)): diff --git a/js/_website/package.json b/js/_website/package.json index befa29c86bcf9..9a54327cc2a6d 100644 --- a/js/_website/package.json +++ b/js/_website/package.json @@ -1,6 +1,6 @@ { "name": "website", - "version": "0.6.1-beta.0", + "version": "0.7.1", "private": true, "scripts": { "dev": "python generate_jsons/generate.py && vite dev", diff --git a/js/accordion/CHANGELOG.md b/js/accordion/CHANGELOG.md index 3c3a99c4be040..745638beec6c4 100644 --- a/js/accordion/CHANGELOG.md +++ b/js/accordion/CHANGELOG.md @@ -13,28 +13,6 @@ ### Patch Changes - Updated dependencies []: - - @gradio/atoms@0.2.0-beta.2 - - @gradio/statustracker@0.3.0-beta.3 - -## 0.1.0-beta.2 - -### Features - -- [#5620](https://github.com/gradio-app/gradio/pull/5620) [`c4c25ecdf`](https://github.com/gradio-app/gradio/commit/c4c25ecdf8c2fab5e3c41b519564e3b6a9ebfce3) - fix build and broken imports. Thanks [@pngwn](https://github.com/pngwn)! - -## 0.1.0-beta.1 - -### Patch Changes - -- Updated dependencies []: - - @gradio/atoms@0.2.0-beta.1 - - @gradio/statustracker@0.3.0-beta.1 - -## 0.1.0-beta.0 - -### Features - -- [#5507](https://github.com/gradio-app/gradio/pull/5507) [`1385dc688`](https://github.com/gradio-app/gradio/commit/1385dc6881f2d8ae7a41106ec21d33e2ef04d6a9) - Custom components. Thanks [@pngwn](https://github.com/pngwn)! - @gradio/atoms@0.1.3 - @gradio/statustracker@0.2.1 diff --git a/js/checkbox/CHANGELOG.md b/js/checkbox/CHANGELOG.md index bfa31f2c916cd..ad83c76ec97e6 100644 --- a/js/checkbox/CHANGELOG.md +++ b/js/checkbox/CHANGELOG.md @@ -23,7 +23,6 @@ - [#5554](https://github.com/gradio-app/gradio/pull/5554) [`75ddeb390`](https://github.com/gradio-app/gradio/commit/75ddeb390d665d4484667390a97442081b49a423) - Accessibility Improvements. Thanks [@hannahblair](https://github.com/hannahblair)! - ## 0.1.3 ### Patch Changes diff --git a/js/gallery/static/Gallery.svelte b/js/gallery/static/Gallery.svelte index aedc48073bb4a..c1731d80b7489 100644 --- a/js/gallery/static/Gallery.svelte +++ b/js/gallery/static/Gallery.svelte @@ -466,4 +466,4 @@ .icon-buttons a { margin: var(--size-1) 0; } - + \ No newline at end of file diff --git a/js/markdown/CHANGELOG.md b/js/markdown/CHANGELOG.md index d5161b38cae12..91950049707f6 100644 --- a/js/markdown/CHANGELOG.md +++ b/js/markdown/CHANGELOG.md @@ -76,3 +76,4 @@ Thanks [@pngwn](https://github.com/pngwn)! ### Features - [#5268](https://github.com/gradio-app/gradio/pull/5268) [`f49028cf`](https://github.com/gradio-app/gradio/commit/f49028cfe3e21097001ddbda71c560b3d8b42e1c) - Move markdown & latex processing to the frontend for the gr.Markdown and gr.DataFrame components. Thanks [@abidlabs](https://github.com/abidlabs)! +- [#5215](https://github.com/gradio-app/gradio/pull/5215) [`fbdad78a`](https://github.com/gradio-app/gradio/commit/fbdad78af4c47454cbb570f88cc14bf4479bbceb) - Lazy load interactive or static variants of a component individually, rather than loading both variants regardless. This change will improve performance for many applications. Thanks [@pngwn](https://github.com/pngwn)! diff --git a/js/markdown/static/MarkdownCode.svelte b/js/markdown/static/MarkdownCode.svelte index 2b29962bf3b2b..8a563e23365b5 100644 --- a/js/markdown/static/MarkdownCode.svelte +++ b/js/markdown/static/MarkdownCode.svelte @@ -119,4 +119,7 @@ span:not(.chatbot) :global(ol) { list-style-position: inside; } + span :global(p:not(:first-child)) { + margin-top: var(--spacing-xxl); + } diff --git a/js/wasm/src/webworker/index.ts b/js/wasm/src/webworker/index.ts index 43d9af3b29d97..11b5af346c2be 100644 --- a/js/wasm/src/webworker/index.ts +++ b/js/wasm/src/webworker/index.ts @@ -311,6 +311,7 @@ self.onmessage = async (event: MessageEvent