Removes deprecated arguments and parameters from v4 (#5968)

* Removes deprecated arguments and methods for v4

* remove update

* style

* add changeset

* added params

* typign

* fixed all of the typing

* tests

* fix layouts

* layout

* fix config

* fix

* fix form

* fix

* fix blocks tests

* fix some more tests

* lint

* fix test routes

* doc

* notebooks

* remove doc

* format

* format

* remove group

* label docstring

* remov test

---------

Co-authored-by: gradio-pr-bot <gradio-pr-bot@users.noreply.github.com>

.changeset/spicy-streets-stop.md

@@ -0,0 +1,5 @@
+---
+"gradio": minor
+---
+
+feat:Removes deprecated arguments and parameters from v4

client/python/test/conftest.py

@@ -272,13 +272,13 @@ def hello_world_with_group():
             gr.Textbox("Hello!")
 
         def greeting(name):
-            return f"Hello {name}", gr.Group.update(visible=True)
+            return f"Hello {name}", gr.Group(visible=True)
 
         greet.click(
             greeting, inputs=[name], outputs=[output, group], api_name="greeting"
         )
         show_group.click(
-            lambda: gr.Group.update(visible=False), None, group, api_name="show_group"
+            lambda: gr.Group(visible=False), None, group, api_name="show_group"
         )
     return demo
 
@@ -300,7 +300,7 @@ def hello_world_with_state_and_accordion():
 
         def greeting(name, state):
             state += 1
-            return state, f"Hello {name}", state, gr.Accordion.update(open=False)
+            return state, f"Hello {name}", state, gr.Accordion(open=False)
 
         greet.click(
             greeting,
@@ -309,13 +309,13 @@ def greeting(name, state):
             api_name="greeting",
         )
         open_acc.click(
-            lambda state: (state + 1, state + 1, gr.Accordion.update(open=True)),
+            lambda state: (state + 1, state + 1, gr.Accordion(open=True)),
             [n_counts],
             [n_counts, num, accordion],
             api_name="open",
         )
         close_acc.click(
-            lambda state: (state + 1, state + 1, gr.Accordion.update(open=False)),
+            lambda state: (state + 1, state + 1, gr.Accordion(open=False)),
             [n_counts],
             [n_counts, num, accordion],
             api_name="close",
@@ -350,9 +350,7 @@ def _stream_audio(audio_file):
 
 @pytest.fixture
 def video_component():
-    return gr.Interface(
-        fn=lambda x: x, inputs=gr.Video(type="file"), outputs=gr.Video()
-    )
+    return gr.Interface(fn=lambda x: x, inputs=gr.Video(), outputs=gr.Video())
 
 
 @pytest.fixture
@@ -368,7 +366,7 @@ def all_components():
             classes_to_check.extend(children)
         if (
             "value" in inspect.signature(subclass).parameters
-            and subclass != gr.components.IOComponent
+            and subclass != gr.components.Component
             and not getattr(subclass, "is_template", False)
         ):
             subclasses.append(subclass)

demo/depth_estimation/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: depth_estimation\n", "### A demo for predicting the depth of an image and generating a 3D model of it.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch git+https://github.com/nielsrogge/transformers.git@add_dpt_redesign#egg=transformers numpy Pillow jinja2 open3d"]}, {"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/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg https://github.com/gradio-app/gradio/raw/main/demo/depth_estimation/examples/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/depth_estimation/packages.txt"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import DPTFeatureExtractor, DPTForDepthEstimation\n", "import torch\n", "import numpy as np\n", "from PIL import Image\n", "import open3d as o3d\n", "from pathlib import Path\n", "\n", "feature_extractor = DPTFeatureExtractor.from_pretrained(\"Intel/dpt-large\")\n", "model = DPTForDepthEstimation.from_pretrained(\"Intel/dpt-large\")\n", "\n", "def process_image(image_path):\n", "    image_path = Path(image_path)\n", "    image_raw = Image.open(image_path)\n", "    image = image_raw.resize(\n", "        (800, int(800 * image_raw.size[1] / image_raw.size[0])),\n", "        Image.Resampling.LANCZOS)\n", "\n", "    # prepare image for the model\n", "    encoding = feature_extractor(image, return_tensors=\"pt\")\n", "\n", "    # forward pass\n", "    with torch.no_grad():\n", "        outputs = model(**encoding)\n", "        predicted_depth = outputs.predicted_depth\n", "\n", "    # interpolate to original size\n", "    prediction = torch.nn.functional.interpolate(\n", "        predicted_depth.unsqueeze(1),\n", "        size=image.size[::-1],\n", "        mode=\"bicubic\",\n", "        align_corners=False,\n", "    ).squeeze()\n", "    output = prediction.cpu().numpy()\n", "    depth_image = (output * 255 / np.max(output)).astype('uint8')\n", "    try:\n", "        gltf_path = create_3d_obj(np.array(image), depth_image, image_path)\n", "        img = Image.fromarray(depth_image)\n", "        return [img, gltf_path, gltf_path]\n", "    except Exception:\n", "        gltf_path = create_3d_obj(\n", "            np.array(image), depth_image, image_path, depth=8)\n", "        img = Image.fromarray(depth_image)\n", "        return [img, gltf_path, gltf_path]\n", "    except:\n", "        print(\"Error reconstructing 3D model\")\n", "        raise Exception(\"Error reconstructing 3D model\")\n", "\n", "\n", "def create_3d_obj(rgb_image, depth_image, image_path, depth=10):\n", "    depth_o3d = o3d.geometry.Image(depth_image)\n", "    image_o3d = o3d.geometry.Image(rgb_image)\n", "    rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(\n", "        image_o3d, depth_o3d, convert_rgb_to_intensity=False)\n", "    w = int(depth_image.shape[1])\n", "    h = int(depth_image.shape[0])\n", "\n", "    camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()\n", "    camera_intrinsic.set_intrinsics(w, h, 500, 500, w/2, h/2)\n", "\n", "    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(\n", "        rgbd_image, camera_intrinsic)\n", "\n", "    print('normals')\n", "    pcd.normals = o3d.utility.Vector3dVector(\n", "        np.zeros((1, 3)))  # invalidate existing normals\n", "    pcd.estimate_normals(\n", "        search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))\n", "    pcd.orient_normals_towards_camera_location(\n", "        camera_location=np.array([0., 0., 1000.]))\n", "    pcd.transform([[1, 0, 0, 0],\n", "                   [0, -1, 0, 0],\n", "                   [0, 0, -1, 0],\n", "                   [0, 0, 0, 1]])\n", "    pcd.transform([[-1, 0, 0, 0],\n", "                   [0, 1, 0, 0],\n", "                   [0, 0, 1, 0],\n", "                   [0, 0, 0, 1]])\n", "\n", "    print('run Poisson surface reconstruction')\n", "    with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug):\n", "        mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(\n", "            pcd, depth=depth, width=0, scale=1.1, linear_fit=True)\n", "\n", "    voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / 256\n", "    print(f'voxel_size = {voxel_size:e}')\n", "    mesh = mesh_raw.simplify_vertex_clustering(\n", "        voxel_size=voxel_size,\n", "        contraction=o3d.geometry.SimplificationContraction.Average)\n", "\n", "    # vertices_to_remove = densities < np.quantile(densities, 0.001)\n", "    # mesh.remove_vertices_by_mask(vertices_to_remove)\n", "    bbox = pcd.get_axis_aligned_bounding_box()\n", "    mesh_crop = mesh.crop(bbox)\n", "    gltf_path = f'./{image_path.stem}.gltf'\n", "    o3d.io.write_triangle_mesh(\n", "        gltf_path, mesh_crop, write_triangle_uvs=True)\n", "    return gltf_path\n", "\n", "title = \"Demo: zero-shot depth estimation with DPT + 3D Point Cloud\"\n", "description = \"This demo is a variation from the original <a href='https://huggingface.co/spaces/nielsr/dpt-depth-estimation' target='_blank'>DPT Demo</a>. It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object.\"\n", "examples = [[\"examples/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg\"]]\n", "\n", "iface = gr.Interface(fn=process_image,\n", "                     inputs=[gr.Image(\n", "                         type=\"filepath\", label=\"Input Image\")],\n", "                     outputs=[gr.Image(label=\"predicted depth\", type=\"pil\"),\n", "                              gr.Model3D(label=\"3d mesh reconstruction\", clear_color=[\n", "                                                 1.0, 1.0, 1.0, 1.0]),\n", "                              gr.File(label=\"3d gLTF\")],\n", "                     title=title,\n", "                     description=description,\n", "                     examples=examples,\n", "                     allow_flagging=\"never\",\n", "                     cache_examples=False)\n", "\n", "iface.launch(debug=True, enable_queue=False)"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: depth_estimation\n", "### A demo for predicting the depth of an image and generating a 3D model of it.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch git+https://github.com/nielsrogge/transformers.git@add_dpt_redesign#egg=transformers numpy Pillow jinja2 open3d"]}, {"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/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg https://github.com/gradio-app/gradio/raw/main/demo/depth_estimation/examples/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/depth_estimation/packages.txt"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import DPTFeatureExtractor, DPTForDepthEstimation\n", "import torch\n", "import numpy as np\n", "from PIL import Image\n", "import open3d as o3d\n", "from pathlib import Path\n", "\n", "feature_extractor = DPTFeatureExtractor.from_pretrained(\"Intel/dpt-large\")\n", "model = DPTForDepthEstimation.from_pretrained(\"Intel/dpt-large\")\n", "\n", "def process_image(image_path):\n", "    image_path = Path(image_path)\n", "    image_raw = Image.open(image_path)\n", "    image = image_raw.resize(\n", "        (800, int(800 * image_raw.size[1] / image_raw.size[0])),\n", "        Image.Resampling.LANCZOS)\n", "\n", "    # prepare image for the model\n", "    encoding = feature_extractor(image, return_tensors=\"pt\")\n", "\n", "    # forward pass\n", "    with torch.no_grad():\n", "        outputs = model(**encoding)\n", "        predicted_depth = outputs.predicted_depth\n", "\n", "    # interpolate to original size\n", "    prediction = torch.nn.functional.interpolate(\n", "        predicted_depth.unsqueeze(1),\n", "        size=image.size[::-1],\n", "        mode=\"bicubic\",\n", "        align_corners=False,\n", "    ).squeeze()\n", "    output = prediction.cpu().numpy()\n", "    depth_image = (output * 255 / np.max(output)).astype('uint8')\n", "    try:\n", "        gltf_path = create_3d_obj(np.array(image), depth_image, image_path)\n", "        img = Image.fromarray(depth_image)\n", "        return [img, gltf_path, gltf_path]\n", "    except Exception:\n", "        gltf_path = create_3d_obj(\n", "            np.array(image), depth_image, image_path, depth=8)\n", "        img = Image.fromarray(depth_image)\n", "        return [img, gltf_path, gltf_path]\n", "    except:\n", "        print(\"Error reconstructing 3D model\")\n", "        raise Exception(\"Error reconstructing 3D model\")\n", "\n", "\n", "def create_3d_obj(rgb_image, depth_image, image_path, depth=10):\n", "    depth_o3d = o3d.geometry.Image(depth_image)\n", "    image_o3d = o3d.geometry.Image(rgb_image)\n", "    rgbd_image = o3d.geometry.RGBDImage.create_from_color_and_depth(\n", "        image_o3d, depth_o3d, convert_rgb_to_intensity=False)\n", "    w = int(depth_image.shape[1])\n", "    h = int(depth_image.shape[0])\n", "\n", "    camera_intrinsic = o3d.camera.PinholeCameraIntrinsic()\n", "    camera_intrinsic.set_intrinsics(w, h, 500, 500, w/2, h/2)\n", "\n", "    pcd = o3d.geometry.PointCloud.create_from_rgbd_image(\n", "        rgbd_image, camera_intrinsic)\n", "\n", "    print('normals')\n", "    pcd.normals = o3d.utility.Vector3dVector(\n", "        np.zeros((1, 3)))  # invalidate existing normals\n", "    pcd.estimate_normals(\n", "        search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=0.01, max_nn=30))\n", "    pcd.orient_normals_towards_camera_location(\n", "        camera_location=np.array([0., 0., 1000.]))\n", "    pcd.transform([[1, 0, 0, 0],\n", "                   [0, -1, 0, 0],\n", "                   [0, 0, -1, 0],\n", "                   [0, 0, 0, 1]])\n", "    pcd.transform([[-1, 0, 0, 0],\n", "                   [0, 1, 0, 0],\n", "                   [0, 0, 1, 0],\n", "                   [0, 0, 0, 1]])\n", "\n", "    print('run Poisson surface reconstruction')\n", "    with o3d.utility.VerbosityContextManager(o3d.utility.VerbosityLevel.Debug):\n", "        mesh_raw, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(\n", "            pcd, depth=depth, width=0, scale=1.1, linear_fit=True)\n", "\n", "    voxel_size = max(mesh_raw.get_max_bound() - mesh_raw.get_min_bound()) / 256\n", "    print(f'voxel_size = {voxel_size:e}')\n", "    mesh = mesh_raw.simplify_vertex_clustering(\n", "        voxel_size=voxel_size,\n", "        contraction=o3d.geometry.SimplificationContraction.Average)\n", "\n", "    # vertices_to_remove = densities < np.quantile(densities, 0.001)\n", "    # mesh.remove_vertices_by_mask(vertices_to_remove)\n", "    bbox = pcd.get_axis_aligned_bounding_box()\n", "    mesh_crop = mesh.crop(bbox)\n", "    gltf_path = f'./{image_path.stem}.gltf'\n", "    o3d.io.write_triangle_mesh(\n", "        gltf_path, mesh_crop, write_triangle_uvs=True)\n", "    return gltf_path\n", "\n", "title = \"Demo: zero-shot depth estimation with DPT + 3D Point Cloud\"\n", "description = \"This demo is a variation from the original <a href='https://huggingface.co/spaces/nielsr/dpt-depth-estimation' target='_blank'>DPT Demo</a>. It uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object.\"\n", "examples = [[\"examples/1-jonathan-borba-CgWTqYxHEkg-unsplash.jpg\"]]\n", "\n", "iface = gr.Interface(fn=process_image,\n", "                     inputs=[gr.Image(\n", "                         type=\"filepath\", label=\"Input Image\")],\n", "                     outputs=[gr.Image(label=\"predicted depth\", type=\"pil\"),\n", "                              gr.Model3D(label=\"3d mesh reconstruction\", clear_color=[\n", "                                                 1.0, 1.0, 1.0, 1.0]),\n", "                              gr.File(label=\"3d gLTF\")],\n", "                     title=title,\n", "                     description=description,\n", "                     examples=examples,\n", "                     allow_flagging=\"never\",\n", "                     cache_examples=False)\n", "\n", "iface.launch(debug=True)"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/depth_estimation/run.py

@@ -114,4 +114,4 @@ def create_3d_obj(rgb_image, depth_image, image_path, depth=10):
                      allow_flagging="never",
                      cache_examples=False)
 
-iface.launch(debug=True, enable_queue=False)
+iface.launch(debug=True)

demo/video_component/run.ipynb

@@ -1 +1 @@
-{"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(),\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}

demo/video_component/run.py

@@ -9,7 +9,7 @@
 
 demo = gr.Interface(
     fn=lambda x: x,
-    inputs=gr.Video(type="file"),
+    inputs=gr.Video(),
     outputs=gr.Video(),
     examples=[
         [a],