From de36820ef51097b47937b41fb76e4038aaa369cb Mon Sep 17 00:00:00 2001 From: Ali Abdalla Date: Thu, 2 Nov 2023 17:35:07 -0700 Subject: [PATCH] Fix various issues with demos on website (#6268) * fix demos * demos on landing page * make code interactive on playground * add changeset * try new secret * formatting * fix fake_gan * demo notebooks --------- Co-authored-by: gradio-pr-bot Co-authored-by: Abubakar Abid --- .changeset/quick-cases-stay.md | 6 + .github/workflows/deploy-pr-to-spaces.yml | 2 +- demo/Echocardiogram-Segmentation/run.ipynb | 2 +- demo/Echocardiogram-Segmentation/run.py | 4 +- demo/animeganv2/run.ipynb | 2 +- demo/animeganv2/run.py | 4 +- demo/blocks_mask/image.png | Bin 1878 -> 0 bytes demo/blocks_mask/lion.jpg | Bin 18489 -> 0 bytes demo/blocks_mask/lion.webp | Bin 24708 -> 0 bytes demo/blocks_mask/run.ipynb | 1 - demo/blocks_mask/run.py | 157 --- demo/blocks_webcam/run.ipynb | 2 +- demo/blocks_webcam/run.py | 2 +- demo/cancel_events/run.ipynb | 2 +- demo/cancel_events/run.py | 8 +- demo/fake_gan/run.ipynb | 2 +- demo/fake_gan/run.py | 29 +- demo/gallery_component/run.ipynb | 2 +- demo/gallery_component/run.py | 2 - demo/gallery_selections/run.ipynb | 2 +- demo/gallery_selections/run.py | 2 +- demo/generate_english_german/run.ipynb | 2 +- demo/generate_english_german/run.py | 2 +- demo/image_classification/run.ipynb | 2 +- demo/image_classification/run.py | 4 +- .../files/imagenet_labels.json | 1000 ----------------- .../images/cheetah1.jpg | Bin 20552 -> 0 bytes .../images/lion.jpg | Bin 18489 -> 0 bytes .../requirements.txt | 2 - .../image_classifier_interpretation/run.ipynb | 1 - demo/image_classifier_interpretation/run.py | 28 - .../screenshot.gif | Bin 421819 -> 0 bytes .../screenshot.png | Bin 553635 -> 0 bytes demo/leaderboard/run.ipynb | 2 +- demo/leaderboard/run.py | 2 +- demo/pictionary/class_names.txt | 100 -- demo/pictionary/requirements.txt | 3 - demo/pictionary/run.ipynb | 1 - demo/pictionary/run.py | 56 - demo/question-answering/run.ipynb | 2 +- demo/question-answering/run.py | 6 +- demo/same-person-or-different/run.ipynb | 2 +- demo/same-person-or-different/run.py | 6 +- demo/sepia_filter/run.ipynb | 2 +- demo/sepia_filter/run.py | 2 +- demo/stream_frames/run.ipynb | 2 +- demo/stream_frames/run.py | 2 +- demo/text_generation/run.ipynb | 2 +- demo/text_generation/run.py | 4 +- demo/unispeech-speaker-verification/run.ipynb | 2 +- demo/unispeech-speaker-verification/run.py | 5 +- demo/webcam/run.ipynb | 2 +- demo/webcam/run.py | 2 +- gradio/components/image.py | 2 +- gradio/components/label.py | 2 +- .../building-a-pictionary-app.md | 106 -- .../building-a-pictionary-app.md | 108 -- js/_website/src/lib/assets/demo_code.js | 11 +- .../src/lib/components/DemosLanding.svelte | 22 +- .../src/lib/components/DemosLite.svelte | 1 + scripts/copy_demos.py | 1 - 61 files changed, 81 insertions(+), 1649 deletions(-) create mode 100644 .changeset/quick-cases-stay.md delete mode 100644 demo/blocks_mask/image.png delete mode 100644 demo/blocks_mask/lion.jpg delete mode 100644 demo/blocks_mask/lion.webp delete mode 100644 demo/blocks_mask/run.ipynb delete mode 100644 demo/blocks_mask/run.py delete mode 100644 demo/image_classifier_interpretation/files/imagenet_labels.json delete mode 100644 demo/image_classifier_interpretation/images/cheetah1.jpg delete mode 100644 demo/image_classifier_interpretation/images/lion.jpg delete mode 100644 demo/image_classifier_interpretation/requirements.txt delete mode 100644 demo/image_classifier_interpretation/run.ipynb delete mode 100644 demo/image_classifier_interpretation/run.py delete mode 100644 demo/image_classifier_interpretation/screenshot.gif delete mode 100644 demo/image_classifier_interpretation/screenshot.png delete mode 100644 demo/pictionary/class_names.txt delete mode 100644 demo/pictionary/requirements.txt delete mode 100644 demo/pictionary/run.ipynb delete mode 100644 demo/pictionary/run.py delete mode 100644 guides/09_other-tutorials/building-a-pictionary-app.md delete mode 100644 guides/cn/07_other-tutorials/building-a-pictionary-app.md diff --git a/.changeset/quick-cases-stay.md b/.changeset/quick-cases-stay.md new file mode 100644 index 000000000000..59ba282d2172 --- /dev/null +++ b/.changeset/quick-cases-stay.md @@ -0,0 +1,6 @@ +--- +"gradio": minor +"website": minor +--- + +feat:Fix various issues with demos on website diff --git a/.github/workflows/deploy-pr-to-spaces.yml b/.github/workflows/deploy-pr-to-spaces.yml index c00c6ac7e68e..1842b7c4e78b 100644 --- a/.github/workflows/deploy-pr-to-spaces.yml +++ b/.github/workflows/deploy-pr-to-spaces.yml @@ -62,7 +62,7 @@ jobs: github.event.workflow_run.conclusion == 'success' id: upload-website-demos run: | - python scripts/upload_website_demos.py --AUTH_TOKEN ${{ secrets.SPACES_DEPLOY_TOKEN }} \ + python scripts/upload_website_demos.py --AUTH_TOKEN ${{ secrets.WEBSITE_SPACES_DEPLOY_TOKEN }} \ --WHEEL_URL https://gradio-builds.s3.amazonaws.com/${{ steps.set-outputs.outputs.gh_sha }}/ \ --GRADIO_VERSION ${{ steps.set-outputs.outputs.gradio_version }} diff --git a/demo/Echocardiogram-Segmentation/run.ipynb b/demo/Echocardiogram-Segmentation/run.ipynb index 21f9c60b2efe..28e63da38c0a 100644 --- a/demo/Echocardiogram-Segmentation/run.ipynb +++ b/demo/Echocardiogram-Segmentation/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: Echocardiogram-Segmentation"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio -f https://download.pytorch.org/whl/torch_stable.html numpy matplotlib wget 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/Echocardiogram-Segmentation/img1.jpg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/Echocardiogram-Segmentation/img2.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "import numpy as np\n", "import torch\n", "import torchvision\n", "import wget \n", "\n", "\n", "destination_folder = \"output\"\n", "destination_for_weights = \"weights\"\n", "\n", "if os.path.exists(destination_for_weights):\n", " print(\"The weights are at\", destination_for_weights)\n", "else:\n", " print(\"Creating folder at \", destination_for_weights, \" to store weights\")\n", " os.mkdir(destination_for_weights)\n", " \n", "segmentationWeightsURL = 'https://github.com/douyang/EchoNetDynamic/releases/download/v1.0.0/deeplabv3_resnet50_random.pt'\n", "\n", "if not os.path.exists(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL))):\n", " print(\"Downloading Segmentation Weights, \", segmentationWeightsURL,\" to \",os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)))\n", " filename = wget.download(segmentationWeightsURL, out = destination_for_weights)\n", "else:\n", " print(\"Segmentation Weights already present\")\n", "\n", "torch.cuda.empty_cache()\n", "\n", "def collate_fn(x):\n", " x, f = zip(*x)\n", " i = list(map(lambda t: t.shape[1], x))\n", " x = torch.as_tensor(np.swapaxes(np.concatenate(x, 1), 0, 1))\n", " return x, f, i\n", "\n", "model = torchvision.models.segmentation.deeplabv3_resnet50(pretrained=False, aux_loss=False)\n", "model.classifier[-1] = torch.nn.Conv2d(model.classifier[-1].in_channels, 1, kernel_size=model.classifier[-1].kernel_size)\n", "\n", "print(\"loading weights from \", os.path.join(destination_for_weights, \"deeplabv3_resnet50_random\"))\n", "\n", "if torch.cuda.is_available():\n", " print(\"cuda is available, original weights\")\n", " device = torch.device(\"cuda\")\n", " model = torch.nn.DataParallel(model)\n", " model.to(device)\n", " checkpoint = torch.load(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)))\n", " model.load_state_dict(checkpoint['state_dict'])\n", "else:\n", " print(\"cuda is not available, cpu weights\")\n", " device = torch.device(\"cpu\")\n", " checkpoint = torch.load(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)), map_location = \"cpu\")\n", " state_dict_cpu = {k[7:]: v for (k, v) in checkpoint['state_dict'].items()}\n", " model.load_state_dict(state_dict_cpu)\n", "\n", "model.eval()\n", "\n", "def segment(input):\n", " inp = input\n", " x = inp.transpose([2, 0, 1]) # channels-first\n", " x = np.expand_dims(x, axis=0) # adding a batch dimension \n", " \n", " mean = x.mean(axis=(0, 2, 3))\n", " std = x.std(axis=(0, 2, 3))\n", " x = x - mean.reshape(1, 3, 1, 1)\n", " x = x / std.reshape(1, 3, 1, 1)\n", " \n", " with torch.no_grad():\n", " x = torch.from_numpy(x).type('torch.FloatTensor').to(device)\n", " output = model(x) \n", " \n", " y = output['out'].numpy()\n", " y = y.squeeze()\n", " \n", " out = y>0 \n", " \n", " mask = inp.copy()\n", " mask[out] = np.array([0, 0, 255])\n", " \n", " return mask\n", "\n", "import gradio as gr\n", "\n", "i = gr.Image(shape=(112, 112), label=\"Echocardiogram\")\n", "o = gr.Image(label=\"Segmentation Mask\")\n", "\n", "examples = [[\"img1.jpg\"], [\"img2.jpg\"]]\n", "title = None #\"Left Ventricle Segmentation\"\n", "description = \"This semantic segmentation model identifies the left ventricle in echocardiogram images.\"\n", "# videos. Accurate evaluation of the motion and size of the left ventricle is crucial for the assessment of cardiac function and ejection fraction. In this interface, the user inputs apical-4-chamber images from echocardiography videos and the model will output a prediction of the localization of the left ventricle in blue. This model was trained on the publicly released EchoNet-Dynamic dataset of 10k echocardiogram videos with 20k expert annotations of the left ventricle and published as part of \u2018Video-based AI for beat-to-beat assessment of cardiac function\u2019 by Ouyang et al. in Nature, 2020.\"\n", "thumbnail = \"https://raw.githubusercontent.com/gradio-app/hub-echonet/master/thumbnail.png\"\n", "gr.Interface(segment, i, o, examples=examples, allow_flagging=False, analytics_enabled=False, thumbnail=thumbnail, cache_examples=False).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file +{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: Echocardiogram-Segmentation"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio -f https://download.pytorch.org/whl/torch_stable.html numpy matplotlib wget 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/Echocardiogram-Segmentation/img1.jpg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/Echocardiogram-Segmentation/img2.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import os\n", "import numpy as np\n", "import torch\n", "import torchvision\n", "import wget \n", "\n", "\n", "destination_folder = \"output\"\n", "destination_for_weights = \"weights\"\n", "\n", "if os.path.exists(destination_for_weights):\n", " print(\"The weights are at\", destination_for_weights)\n", "else:\n", " print(\"Creating folder at \", destination_for_weights, \" to store weights\")\n", " os.mkdir(destination_for_weights)\n", " \n", "segmentationWeightsURL = 'https://github.com/douyang/EchoNetDynamic/releases/download/v1.0.0/deeplabv3_resnet50_random.pt'\n", "\n", "if not os.path.exists(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL))):\n", " print(\"Downloading Segmentation Weights, \", segmentationWeightsURL,\" to \",os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)))\n", " filename = wget.download(segmentationWeightsURL, out = destination_for_weights)\n", "else:\n", " print(\"Segmentation Weights already present\")\n", "\n", "torch.cuda.empty_cache()\n", "\n", "def collate_fn(x):\n", " x, f = zip(*x)\n", " i = list(map(lambda t: t.shape[1], x))\n", " x = torch.as_tensor(np.swapaxes(np.concatenate(x, 1), 0, 1))\n", " return x, f, i\n", "\n", "model = torchvision.models.segmentation.deeplabv3_resnet50(pretrained=False, aux_loss=False)\n", "model.classifier[-1] = torch.nn.Conv2d(model.classifier[-1].in_channels, 1, kernel_size=model.classifier[-1].kernel_size)\n", "\n", "print(\"loading weights from \", os.path.join(destination_for_weights, \"deeplabv3_resnet50_random\"))\n", "\n", "if torch.cuda.is_available():\n", " print(\"cuda is available, original weights\")\n", " device = torch.device(\"cuda\")\n", " model = torch.nn.DataParallel(model)\n", " model.to(device)\n", " checkpoint = torch.load(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)))\n", " model.load_state_dict(checkpoint['state_dict'])\n", "else:\n", " print(\"cuda is not available, cpu weights\")\n", " device = torch.device(\"cpu\")\n", " checkpoint = torch.load(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)), map_location = \"cpu\")\n", " state_dict_cpu = {k[7:]: v for (k, v) in checkpoint['state_dict'].items()}\n", " model.load_state_dict(state_dict_cpu)\n", "\n", "model.eval()\n", "\n", "def segment(input):\n", " inp = input\n", " x = inp.transpose([2, 0, 1]) # channels-first\n", " x = np.expand_dims(x, axis=0) # adding a batch dimension \n", " \n", " mean = x.mean(axis=(0, 2, 3))\n", " std = x.std(axis=(0, 2, 3))\n", " x = x - mean.reshape(1, 3, 1, 1)\n", " x = x / std.reshape(1, 3, 1, 1)\n", " \n", " with torch.no_grad():\n", " x = torch.from_numpy(x).type('torch.FloatTensor').to(device)\n", " output = model(x) \n", " \n", " y = output['out'].numpy()\n", " y = y.squeeze()\n", " \n", " out = y>0 \n", " \n", " mask = inp.copy()\n", " mask[out] = np.array([0, 0, 255])\n", " \n", " return mask\n", "\n", "import gradio as gr\n", "\n", "i = gr.Image(label=\"Echocardiogram\")\n", "o = gr.Image(label=\"Segmentation Mask\")\n", "\n", "examples = [[\"img1.jpg\"], [\"img2.jpg\"]]\n", "title = None #\"Left Ventricle Segmentation\"\n", "description = \"This semantic segmentation model identifies the left ventricle in echocardiogram images.\"\n", "# videos. Accurate evaluation of the motion and size of the left ventricle is crucial for the assessment of cardiac function and ejection fraction. In this interface, the user inputs apical-4-chamber images from echocardiography videos and the model will output a prediction of the localization of the left ventricle in blue. This model was trained on the publicly released EchoNet-Dynamic dataset of 10k echocardiogram videos with 20k expert annotations of the left ventricle and published as part of \u2018Video-based AI for beat-to-beat assessment of cardiac function\u2019 by Ouyang et al. in Nature, 2020.\"\n", "thumbnail = \"https://raw.githubusercontent.com/gradio-app/hub-echonet/master/thumbnail.png\"\n", "gr.Interface(segment, i, o, examples=examples, analytics_enabled=False, thumbnail=thumbnail, cache_examples=False).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/Echocardiogram-Segmentation/run.py b/demo/Echocardiogram-Segmentation/run.py index 27b2c0ad2959..95c09932a739 100644 --- a/demo/Echocardiogram-Segmentation/run.py +++ b/demo/Echocardiogram-Segmentation/run.py @@ -77,7 +77,7 @@ def segment(input): import gradio as gr -i = gr.Image(shape=(112, 112), label="Echocardiogram") +i = gr.Image(label="Echocardiogram") o = gr.Image(label="Segmentation Mask") examples = [["img1.jpg"], ["img2.jpg"]] @@ -85,4 +85,4 @@ def segment(input): description = "This semantic segmentation model identifies the left ventricle in echocardiogram images." # videos. Accurate evaluation of the motion and size of the left ventricle is crucial for the assessment of cardiac function and ejection fraction. In this interface, the user inputs apical-4-chamber images from echocardiography videos and the model will output a prediction of the localization of the left ventricle in blue. This model was trained on the publicly released EchoNet-Dynamic dataset of 10k echocardiogram videos with 20k expert annotations of the left ventricle and published as part of ‘Video-based AI for beat-to-beat assessment of cardiac function’ by Ouyang et al. in Nature, 2020." thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-echonet/master/thumbnail.png" -gr.Interface(segment, i, o, examples=examples, allow_flagging=False, analytics_enabled=False, thumbnail=thumbnail, cache_examples=False).launch() +gr.Interface(segment, i, o, examples=examples, analytics_enabled=False, thumbnail=thumbnail, cache_examples=False).launch() diff --git a/demo/animeganv2/run.ipynb b/demo/animeganv2/run.ipynb index 970dbdc59bfa..d16af5addf9b 100644 --- a/demo/animeganv2/run.ipynb +++ b/demo/animeganv2/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: animeganv2\n", "### Recreate the viral AnimeGAN image transformation demo.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchvision Pillow gdown numpy scipy cmake onnxruntime-gpu opencv-python-headless"]}, {"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/animeganv2/gongyoo.jpeg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/animeganv2/groot.jpeg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "\n", "model2 = torch.hub.load(\n", " \"AK391/animegan2-pytorch:main\",\n", " \"generator\",\n", " pretrained=True,\n", " progress=False\n", ")\n", "model1 = torch.hub.load(\"AK391/animegan2-pytorch:main\", \"generator\", pretrained=\"face_paint_512_v1\")\n", "face2paint = torch.hub.load(\n", " 'AK391/animegan2-pytorch:main', 'face2paint', \n", " size=512,side_by_side=False\n", ")\n", "\n", "def inference(img, ver):\n", " if ver == 'version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)':\n", " out = face2paint(model2, img)\n", " else:\n", " out = face2paint(model1, img)\n", " return out\n", "\n", "title = \"AnimeGANv2\"\n", "description = \"Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below.\"\n", "article = \"

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\"\n", "examples=[['groot.jpeg','version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)'],['gongyoo.jpeg','version 1 (\ud83d\udd3a stylization, \ud83d\udd3b robustness)']]\n", "\n", "demo = gr.Interface(\n", " fn=inference, \n", " inputs=[gr.inputs.Image(type=\"pil\"),gr.inputs.Radio(['version 1 (\ud83d\udd3a stylization, \ud83d\udd3b robustness)','version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)'], type=\"value\", default='version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)', label='version')], \n", " outputs=gr.outputs.Image(type=\"pil\"),\n", " title=title,\n", " description=description,\n", " article=article,\n", " examples=examples)\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: animeganv2\n", "### Recreate the viral AnimeGAN image transformation demo.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchvision Pillow gdown numpy scipy cmake onnxruntime-gpu opencv-python-headless"]}, {"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/animeganv2/gongyoo.jpeg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/animeganv2/groot.jpeg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import torch\n", "\n", "model2 = torch.hub.load(\n", " \"AK391/animegan2-pytorch:main\",\n", " \"generator\",\n", " pretrained=True,\n", " progress=False\n", ")\n", "model1 = torch.hub.load(\"AK391/animegan2-pytorch:main\", \"generator\", pretrained=\"face_paint_512_v1\")\n", "face2paint = torch.hub.load(\n", " 'AK391/animegan2-pytorch:main', 'face2paint', \n", " size=512,side_by_side=False\n", ")\n", "\n", "def inference(img, ver):\n", " if ver == 'version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)':\n", " out = face2paint(model2, img)\n", " else:\n", " out = face2paint(model1, img)\n", " return out\n", "\n", "title = \"AnimeGANv2\"\n", "description = \"Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below.\"\n", "article = \"

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\"\n", "examples=[['groot.jpeg','version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)'],['gongyoo.jpeg','version 1 (\ud83d\udd3a stylization, \ud83d\udd3b robustness)']]\n", "\n", "demo = gr.Interface(\n", " fn=inference, \n", " inputs=[gr.Image(type=\"pil\"),gr.Radio(['version 1 (\ud83d\udd3a stylization, \ud83d\udd3b robustness)','version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)'], type=\"value\", value='version 2 (\ud83d\udd3a robustness,\ud83d\udd3b stylization)', label='version')], \n", " outputs=gr.Image(type=\"pil\"),\n", " title=title,\n", " description=description,\n", " article=article,\n", " examples=examples)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/animeganv2/run.py b/demo/animeganv2/run.py index dbbe53f764a3..5d8de0171ebe 100644 --- a/demo/animeganv2/run.py +++ b/demo/animeganv2/run.py @@ -27,8 +27,8 @@ def inference(img, ver): demo = gr.Interface( fn=inference, - inputs=[gr.inputs.Image(type="pil"),gr.inputs.Radio(['version 1 (🔺 stylization, 🔻 robustness)','version 2 (🔺 robustness,🔻 stylization)'], type="value", default='version 2 (🔺 robustness,🔻 stylization)', label='version')], - outputs=gr.outputs.Image(type="pil"), + inputs=[gr.Image(type="pil"),gr.Radio(['version 1 (🔺 stylization, 🔻 robustness)','version 2 (🔺 robustness,🔻 stylization)'], type="value", value='version 2 (🔺 robustness,🔻 stylization)', label='version')], + outputs=gr.Image(type="pil"), title=title, description=description, article=article, diff --git a/demo/blocks_mask/image.png b/demo/blocks_mask/image.png deleted file mode 100644 index dc392ea95476974e07a68ad98a9659c240e1089b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1878 zcmeAS@N?(olHy`uVBq!ia0y~yU;;9k7&t&wwUqN(1_pLnPZ!6KiaBqu8Zt5{@EkBW z@ZWati5nMP9QWUwY02TkD+t>Q>2xkl00M^p! zm1nILt@8=M)VFB1fJz0c8Df`q3;#812+#p{M-7C#;+8PZ}|t(B6U@B@9BlWU>t0IoIUi>z(Ld+r|zCOB5FZ<%osw 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zPsV*njqtnKIGsh%Kb0q}?uPMm1!UJkuflz4QQ_WObsITtu0~2Xt6@Qf^SWMecW`aavYzR;#D59=r zJxvK7mnli@+V~o9r!J?1n)zYTf@`w@s~N7F5e_KC!-GIA0PT8!og~n_P3kh1u&LWX zDubpLhrR~p`MmK?X6{|N(^>@;w#%FT`QXwp#b^#0#e13`&~?EHkZPP~Z# diff --git a/demo/blocks_mask/run.ipynb b/demo/blocks_mask/run.ipynb deleted file mode 100644 index 254bb7e332ef..000000000000 --- a/demo/blocks_mask/run.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_mask"]}, {"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/blocks_mask/image.png\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/blocks_mask/lion.jpg\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/blocks_mask/lion.webp"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from gradio.components import Markdown as md\n", "from PIL import Image\n", "\n", "demo = gr.Blocks()\n", "\n", "io1a = gr.Interface(lambda x: x, gr.Image(), gr.Image())\n", "io1b = gr.Interface(lambda x: x, gr.Image(source=\"webcam\"), gr.Image())\n", "\n", "io2a = gr.Interface(lambda x: x, gr.Image(source=\"canvas\"), gr.Image())\n", "io2b = gr.Interface(lambda x: x, gr.Sketchpad(), gr.Image())\n", "io2c = gr.Interface(\n", " lambda x: x, gr.Image(source=\"canvas\", shape=(512, 512)), gr.Image()\n", ")\n", "\n", "io3a = gr.Interface(\n", " lambda x: [x[\"mask\"], x[\"image\"]],\n", " gr.Image(source=\"upload\", tool=\"sketch\"),\n", " [gr.Image(), gr.Image()],\n", ")\n", "\n", "io3b = gr.Interface(\n", " lambda x: [x[\"mask\"], x[\"image\"]],\n", " gr.ImageMask(),\n", " [gr.Image(), gr.Image()],\n", ")\n", "\n", "io3b2 = gr.Interface(\n", " lambda x: [x[\"mask\"], x[\"image\"]],\n", " gr.ImageMask(),\n", " [gr.Image(), gr.Image()],\n", ")\n", "\n", "io3b3 = gr.Interface(\n", " lambda x: [x[\"mask\"], x[\"image\"]],\n", " gr.ImageMask(),\n", " [gr.Image(), gr.Image()],\n", ")\n", "\n", "io3c = gr.Interface(\n", " lambda x: [x[\"mask\"], x[\"image\"]],\n", " gr.Image(source=\"webcam\", tool=\"sketch\"),\n", " [gr.Image(), gr.Image()],\n", ")\n", "\n", "io4a = gr.Interface(\n", " lambda x: x, gr.Image(source=\"canvas\", tool=\"color-sketch\"), gr.Image()\n", ")\n", "io4b = gr.Interface(lambda x: x, gr.Paint(), gr.Image())\n", "\n", "io5a = gr.Interface(\n", " lambda x: x, gr.Image(source=\"upload\", tool=\"color-sketch\"), gr.Image()\n", ")\n", "io5b = gr.Interface(lambda x: x, gr.ImagePaint(), gr.Image())\n", "io5c = gr.Interface(\n", " lambda x: x, gr.Image(source=\"webcam\", tool=\"color-sketch\"), gr.Image()\n", ")\n", "\n", "\n", "def save_image(image):\n", " image.save(\"colorede.png\")\n", " return image\n", "\n", "\n", "img = Image.new(\"RGB\", (512, 512), (150, 150, 150))\n", "img.save(\"image.png\", \"PNG\")\n", "\n", "io5d = gr.Interface(\n", " save_image,\n", " gr.Image(\"image.png\", source=\"upload\", tool=\"color-sketch\", type=\"pil\"),\n", " gr.Image(),\n", ")\n", "\n", "with demo:\n", " md(\"# Different Ways to Use the Image Input Component\")\n", " md(\n", " \"**1a. Standalone Image Upload: `gr.Interface(lambda x: x, gr.Image(), gr.Image())`**\"\n", " )\n", " io1a.render()\n", " md(\n", " \"**1b. Standalone Image from Webcam: `gr.Interface(lambda x: x, gr.Image(source='webcam'), gr.Image())`**\"\n", " )\n", " io1b.render()\n", " md(\n", " \"**2a. Black and White Sketchpad: `gr.Interface(lambda x: x, gr.Image(source='canvas'), gr.Image())`**\"\n", " )\n", " io2a.render()\n", " md(\n", " \"**2b. Black and White Sketchpad: `gr.Interface(lambda x: x, gr.Sketchpad(), gr.Image())`**\"\n", " )\n", " io2b.render()\n", " md(\"**2c. Black and White Sketchpad with `shape=(512,512)`**\")\n", " io2c.render()\n", " md(\"**3a. Binary Mask with image upload:**\")\n", " md(\n", " \"\"\"```python\n", "gr.Interface(\n", " lambda x: [x['mask'], x['image']],\n", " gr.Image(source='upload', tool='sketch'),\n", " [gr.Image(), gr.Image()],\n", ")\n", "```\n", "\"\"\"\n", " )\n", " io3a.render()\n", " md(\"**3b. Binary Mask with image upload:**\")\n", " md(\n", " \"\"\"```python\n", "gr.Interface(\n", " lambda x: [x['mask'], x['image']],\n", " gr.ImageMask(),\n", " [gr.Image(), gr.Image()],\n", ")\n", "```\n", "\"\"\"\n", " )\n", " io3b.render()\n", " md(\"**3c. Binary Mask with webcam upload:**\")\n", " md(\n", " \"\"\"```python\n", "gr.Interface(\n", " lambda x: [x['mask'], x['image']],\n", " gr.Image(source='webcam', tool='sketch'),\n", " [gr.Image(), gr.Image()],\n", ")\n", "```\n", "\"\"\"\n", " )\n", " io3c.render()\n", " md(\n", " \"**4a. Color Sketchpad: `gr.Interface(lambda x: x, gr.Image(source='canvas', tool='color-sketch'), gr.Image())`**\"\n", " )\n", " io4a.render()\n", " md(\"**4b. Color Sketchpad: `gr.Interface(lambda x: x, gr.Paint(), gr.Image())`**\")\n", " io4b.render()\n", " md(\n", " \"**5a. Color Sketchpad with image upload: `gr.Interface(lambda x: x, gr.Image(source='upload', tool='color-sketch'), gr.Image())`**\"\n", " )\n", " io5a.render()\n", " md(\n", " \"**5b. Color Sketchpad with image upload: `gr.Interface(lambda x: x, gr.ImagePaint(), gr.Image())`**\"\n", " )\n", " io5b.render()\n", " md(\n", " \"**5c. Color Sketchpad with webcam upload: `gr.Interface(lambda x: x, gr.Image(source='webcam', tool='color-sketch'), gr.Image())`**\"\n", " )\n", " io5c.render()\n", " md(\"**Tabs**\")\n", " with gr.Tab(\"One\"):\n", " io3b2.render()\n", " with gr.Tab(\"Two\"):\n", " io3b3.render()\n", " md(\"**5d. Color Sketchpad with image upload and a default images**\")\n", " io5d.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/blocks_mask/run.py b/demo/blocks_mask/run.py deleted file mode 100644 index 93e24a12ae3c..000000000000 --- a/demo/blocks_mask/run.py +++ /dev/null @@ -1,157 +0,0 @@ -import gradio as gr -from gradio.components import Markdown as md -from PIL import Image - -demo = gr.Blocks() - -io1a = gr.Interface(lambda x: x, gr.Image(), gr.Image()) -io1b = gr.Interface(lambda x: x, gr.Image(source="webcam"), gr.Image()) - -io2a = gr.Interface(lambda x: x, gr.Image(source="canvas"), gr.Image()) -io2b = gr.Interface(lambda x: x, gr.Sketchpad(), gr.Image()) -io2c = gr.Interface( - lambda x: x, gr.Image(source="canvas", shape=(512, 512)), gr.Image() -) - -io3a = gr.Interface( - lambda x: [x["mask"], x["image"]], - gr.Image(source="upload", tool="sketch"), - [gr.Image(), gr.Image()], -) - -io3b = gr.Interface( - lambda x: [x["mask"], x["image"]], - gr.ImageMask(), - [gr.Image(), gr.Image()], -) - -io3b2 = gr.Interface( - lambda x: [x["mask"], x["image"]], - gr.ImageMask(), - [gr.Image(), gr.Image()], -) - -io3b3 = gr.Interface( - lambda x: [x["mask"], x["image"]], - gr.ImageMask(), - [gr.Image(), gr.Image()], -) - -io3c = gr.Interface( - lambda x: [x["mask"], x["image"]], - gr.Image(source="webcam", tool="sketch"), - [gr.Image(), gr.Image()], -) - -io4a = gr.Interface( - lambda x: x, gr.Image(source="canvas", tool="color-sketch"), gr.Image() -) -io4b = gr.Interface(lambda x: x, gr.Paint(), gr.Image()) - -io5a = gr.Interface( - lambda x: x, gr.Image(source="upload", tool="color-sketch"), gr.Image() -) -io5b = gr.Interface(lambda x: x, gr.ImagePaint(), gr.Image()) -io5c = gr.Interface( - lambda x: x, gr.Image(source="webcam", tool="color-sketch"), gr.Image() -) - - -def save_image(image): - image.save("colorede.png") - return image - - -img = Image.new("RGB", (512, 512), (150, 150, 150)) -img.save("image.png", "PNG") - -io5d = gr.Interface( - save_image, - gr.Image("image.png", source="upload", tool="color-sketch", type="pil"), - gr.Image(), -) - -with demo: - md("# Different Ways to Use the Image Input Component") - md( - "**1a. Standalone Image Upload: `gr.Interface(lambda x: x, gr.Image(), gr.Image())`**" - ) - io1a.render() - md( - "**1b. Standalone Image from Webcam: `gr.Interface(lambda x: x, gr.Image(source='webcam'), gr.Image())`**" - ) - io1b.render() - md( - "**2a. Black and White Sketchpad: `gr.Interface(lambda x: x, gr.Image(source='canvas'), gr.Image())`**" - ) - io2a.render() - md( - "**2b. Black and White Sketchpad: `gr.Interface(lambda x: x, gr.Sketchpad(), gr.Image())`**" - ) - io2b.render() - md("**2c. Black and White Sketchpad with `shape=(512,512)`**") - io2c.render() - md("**3a. Binary Mask with image upload:**") - md( - """```python -gr.Interface( - lambda x: [x['mask'], x['image']], - gr.Image(source='upload', tool='sketch'), - [gr.Image(), gr.Image()], -) -``` -""" - ) - io3a.render() - md("**3b. Binary Mask with image upload:**") - md( - """```python -gr.Interface( - lambda x: [x['mask'], x['image']], - gr.ImageMask(), - [gr.Image(), gr.Image()], -) -``` -""" - ) - io3b.render() - md("**3c. Binary Mask with webcam upload:**") - md( - """```python -gr.Interface( - lambda x: [x['mask'], x['image']], - gr.Image(source='webcam', tool='sketch'), - [gr.Image(), gr.Image()], -) -``` -""" - ) - io3c.render() - md( - "**4a. Color Sketchpad: `gr.Interface(lambda x: x, gr.Image(source='canvas', tool='color-sketch'), gr.Image())`**" - ) - io4a.render() - md("**4b. Color Sketchpad: `gr.Interface(lambda x: x, gr.Paint(), gr.Image())`**") - io4b.render() - md( - "**5a. Color Sketchpad with image upload: `gr.Interface(lambda x: x, gr.Image(source='upload', tool='color-sketch'), gr.Image())`**" - ) - io5a.render() - md( - "**5b. Color Sketchpad with image upload: `gr.Interface(lambda x: x, gr.ImagePaint(), gr.Image())`**" - ) - io5b.render() - md( - "**5c. Color Sketchpad with webcam upload: `gr.Interface(lambda x: x, gr.Image(source='webcam', tool='color-sketch'), gr.Image())`**" - ) - io5c.render() - md("**Tabs**") - with gr.Tab("One"): - io3b2.render() - with gr.Tab("Two"): - io3b3.render() - md("**5d. Color Sketchpad with image upload and a default images**") - io5d.render() - -if __name__ == "__main__": - demo.launch() diff --git a/demo/blocks_webcam/run.ipynb b/demo/blocks_webcam/run.ipynb index 730332f69314..bf4b993fdb53 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, gr.Image(sources=[\"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_webcam/run.py b/demo/blocks_webcam/run.py index 4b9259868408..1076d0b1afd1 100644 --- a/demo/blocks_webcam/run.py +++ b/demo/blocks_webcam/run.py @@ -7,7 +7,7 @@ def snap(image): return np.flipud(image) -demo = gr.Interface(snap, "webcam", "image") +demo = gr.Interface(snap, gr.Image(sources=["webcam"]), "image") if __name__ == "__main__": demo.launch() diff --git a/demo/cancel_events/run.ipynb b/demo/cancel_events/run.ipynb index c258553b8558..4dd11c03ac70 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(1)\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(sources=[\"webcam\"], tool=\"editor\", label=\"Cancel on edit\", interactive=True)\n", " with gr.Column():\n", " video = gr.Video(sources=[\"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(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(1)\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(sources=[\"webcam\"], label=\"Cancel on clear\", interactive=True)\n", " with gr.Column():\n", " video = gr.Video(sources=[\"webcam\"], label=\"Cancel on start recording\", 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.clear(None, None, None, cancels=[click_event, pred_event])\n", " video.start_recording(None, None, None, cancels=[click_event, pred_event])\n", "\n", " demo.queue(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/cancel_events/run.py b/demo/cancel_events/run.py index 83d7b7d73d93..56f8d9ba21ef 100644 --- a/demo/cancel_events/run.py +++ b/demo/cancel_events/run.py @@ -31,9 +31,9 @@ def long_prediction(*args, **kwargs): echo = gr.Textbox(label="Echo") with gr.Row(): with gr.Column(): - image = gr.Image(sources=["webcam"], tool="editor", label="Cancel on edit", interactive=True) + image = gr.Image(sources=["webcam"], label="Cancel on clear", interactive=True) with gr.Column(): - video = gr.Video(sources=["webcam"], label="Cancel on play", interactive=True) + video = gr.Video(sources=["webcam"], label="Cancel on start recording", interactive=True) click_event = run.click(fake_diffusion, n, output) stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event]) @@ -41,8 +41,8 @@ def long_prediction(*args, **kwargs): cancel_on_change.change(None, None, None, cancels=[click_event, pred_event]) cancel_on_submit.submit(lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event]) - image.edit(None, None, None, cancels=[click_event, pred_event]) - video.play(None, None, None, cancels=[click_event, pred_event]) + image.clear(None, None, None, cancels=[click_event, pred_event]) + video.start_recording(None, None, None, cancels=[click_event, pred_event]) demo.queue(max_size=20) diff --git a/demo/fake_gan/run.ipynb b/demo/fake_gan/run.ipynb index e0c21f7cb4d3..77783d1e3ea1 100644 --- a/demo/fake_gan/run.ipynb +++ b/demo/fake_gan/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_gan\n", "### This is a fake GAN that shows how to create a text-to-image interface for image generation. Check out the Stable Diffusion demo for more: https://hf.co/spaces/stabilityai/stable-diffusion/\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": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan/files/cheetah1.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["# This demo needs to be run from the repo folder.\n", "# python demo/fake_gan/run.py\n", "import random\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan():\n", " images = [\n", " (random.choice(\n", " [\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", " \"https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", " \"https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80\",\n", " ]\n", " ), f\"label {i}\" if i != 0 else \"label\" * 50)\n", " for i in range(3)\n", " ]\n", " return images\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column(variant=\"panel\"):\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\", scale=0)\n", "\n", " gallery = gr.Gallery(\n", " label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n", " , columns=[2], rows=[2], object_fit=\"contain\", height=\"auto\")\n", "\n", " btn.click(fake_gan, None, gallery)\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: fake_gan\n", "### This is a fake GAN that shows how to create a text-to-image interface for image generation. Check out the Stable Diffusion demo for more: https://hf.co/spaces/stabilityai/stable-diffusion/\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": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('files')\n", "!wget -q -O files/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan/files/cheetah1.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["# This demo needs to be run from the repo folder.\n", "# python demo/fake_gan/run.py\n", "import random\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan():\n", " images = [\n", " (random.choice(\n", " [\n", " \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg\",\n", " \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg\",\n", " \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg\",\n", " \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg\",\n", " \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg\",\n", " ]\n", " ), f\"label {i}\")\n", " for i in range(3)\n", " ]\n", " return images\n", "\n", "\n", "with gr.Blocks() as demo:\n", " gallery = gr.Gallery(\n", " label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n", " , columns=[3], rows=[1], object_fit=\"contain\", height=\"auto\")\n", " btn = gr.Button(\"Generate images\", scale=0)\n", "\n", " btn.click(fake_gan, None, gallery)\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/fake_gan/run.py b/demo/fake_gan/run.py index b8d8ab732d72..b3ea489cee0f 100644 --- a/demo/fake_gan/run.py +++ b/demo/fake_gan/run.py @@ -9,32 +9,23 @@ def fake_gan(): images = [ (random.choice( [ - "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80", - "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80", - "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80", - "https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80", - "https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80", + "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg", + "http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg", + "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg", + "http://www.marketingtool.online/en/face-generator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg", + "http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg", ] - ), f"label {i}" if i != 0 else "label" * 50) + ), f"label {i}") for i in range(3) ] return images with gr.Blocks() as demo: - with gr.Column(variant="panel"): - with gr.Row(): - text = gr.Textbox( - label="Enter your prompt", - max_lines=1, - placeholder="Enter your prompt", - container=False, - ) - btn = gr.Button("Generate image", scale=0) - - gallery = gr.Gallery( - label="Generated images", show_label=False, elem_id="gallery" - , columns=[2], rows=[2], object_fit="contain", height="auto") + gallery = gr.Gallery( + label="Generated images", show_label=False, elem_id="gallery" + , columns=[3], rows=[1], object_fit="contain", height="auto") + btn = gr.Button("Generate images", scale=0) btn.click(fake_gan, None, gallery) diff --git a/demo/gallery_component/run.ipynb b/demo/gallery_component/run.ipynb index c76f30131a09..ce858d3c1ff2 100644 --- a/demo/gallery_component/run.ipynb +++ b/demo/gallery_component/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: gallery_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", " cheetahs = [\n", " \"https://upload.wikimedia.org/wikipedia/commons/0/09/TheCheethcat.jpg\",\n", " \"https://nationalzoo.si.edu/sites/default/files/animals/cheetah-003.jpg\",\n", " \"https://img.etimg.com/thumb/msid-50159822,width-650,imgsize-129520,,resizemode-4,quality-100/.jpg\",\n", " \"https://nationalzoo.si.edu/sites/default/files/animals/cheetah-002.jpg\",\n", " \"https://images.theconversation.com/files/375893/original/file-20201218-13-a8h8uq.jpg?ixlib=rb-1.1.0&rect=16%2C407%2C5515%2C2924&q=45&auto=format&w=496&fit=clip\",\n", " \"https://www.lifegate.com/app/uploads/ghepardo-primo-piano.jpg\",\n", " \"https://qph.cf2.quoracdn.net/main-qimg-0bbf31c18a22178cb7a8dd53640a3d05-lq\"\n", " ]\n", " gr.Gallery(value=cheetahs, columns=4)\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: gallery_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", " cheetahs = [\n", " \"https://upload.wikimedia.org/wikipedia/commons/0/09/TheCheethcat.jpg\",\n", " \"https://nationalzoo.si.edu/sites/default/files/animals/cheetah-003.jpg\",\n", " \"https://img.etimg.com/thumb/msid-50159822,width-650,imgsize-129520,,resizemode-4,quality-100/.jpg\",\n", " \"https://nationalzoo.si.edu/sites/default/files/animals/cheetah-002.jpg\",\n", " \"https://images.theconversation.com/files/375893/original/file-20201218-13-a8h8uq.jpg?ixlib=rb-1.1.0&rect=16%2C407%2C5515%2C2924&q=45&auto=format&w=496&fit=clip\",\n", " ]\n", " gr.Gallery(value=cheetahs, columns=4)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/gallery_component/run.py b/demo/gallery_component/run.py index 3acc659c7a90..1bc775fb6910 100644 --- a/demo/gallery_component/run.py +++ b/demo/gallery_component/run.py @@ -7,8 +7,6 @@ "https://img.etimg.com/thumb/msid-50159822,width-650,imgsize-129520,,resizemode-4,quality-100/.jpg", "https://nationalzoo.si.edu/sites/default/files/animals/cheetah-002.jpg", "https://images.theconversation.com/files/375893/original/file-20201218-13-a8h8uq.jpg?ixlib=rb-1.1.0&rect=16%2C407%2C5515%2C2924&q=45&auto=format&w=496&fit=clip", - "https://www.lifegate.com/app/uploads/ghepardo-primo-piano.jpg", - "https://qph.cf2.quoracdn.net/main-qimg-0bbf31c18a22178cb7a8dd53640a3d05-lq" ] gr.Gallery(value=cheetahs, columns=4) diff --git a/demo/gallery_selections/run.ipynb b/demo/gallery_selections/run.ipynb index 23d890efbf3a..51a66fce2b29 100644 --- a/demo/gallery_selections/run.ipynb +++ b/demo/gallery_selections/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: gallery_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", " imgs = gr.State()\n", " gallery = gr.Gallery()\n", "\n", " def generate_images():\n", " images = []\n", " for _ in range(9):\n", " image = np.ones((100, 100, 3), dtype=np.uint8) * np.random.randint(\n", " 0, 255, 3\n", " ) # image is a solid single color\n", " images.append(image)\n", " return images, images\n", "\n", " demo.load(generate_images, None, [gallery, imgs])\n", "\n", " with gr.Row():\n", " selected = gr.Number(show_label=False, placeholder=\"Selected\")\n", " darken_btn = gr.Button(\"Darken selected\")\n", "\n", " def get_select_index(evt: gr.SelectData):\n", " return evt.index\n", "\n", " gallery.select(get_select_index, None, selected)\n", "\n", " def darken_img(imgs, index):\n", " index = int(index)\n", " imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)\n", " return imgs, imgs\n", "\n", " darken_btn.click(darken_img, [imgs, selected], [imgs, gallery])\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: gallery_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", " imgs = gr.State()\n", " gallery = gr.Gallery()\n", "\n", " def generate_images():\n", " images = []\n", " for _ in range(9):\n", " image = np.ones((100, 100, 3), dtype=np.uint8) * np.random.randint(\n", " 0, 255, 3\n", " ) # image is a solid single color\n", " images.append(image)\n", " return images, images\n", "\n", " demo.load(generate_images, None, [gallery, imgs])\n", "\n", " with gr.Row():\n", " selected = gr.Number(show_label=False)\n", " darken_btn = gr.Button(\"Darken selected\")\n", "\n", " def get_select_index(evt: gr.SelectData):\n", " return evt.index\n", "\n", " gallery.select(get_select_index, None, selected)\n", "\n", " def darken_img(imgs, index):\n", " index = int(index)\n", " imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)\n", " return imgs, imgs\n", "\n", " darken_btn.click(darken_img, [imgs, selected], [imgs, gallery])\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/gallery_selections/run.py b/demo/gallery_selections/run.py index 77885a5e26e9..446431b55358 100644 --- a/demo/gallery_selections/run.py +++ b/demo/gallery_selections/run.py @@ -17,7 +17,7 @@ def generate_images(): demo.load(generate_images, None, [gallery, imgs]) with gr.Row(): - selected = gr.Number(show_label=False, placeholder="Selected") + selected = gr.Number(show_label=False) darken_btn = gr.Button("Darken selected") def get_select_index(evt: gr.SelectData): diff --git a/demo/generate_english_german/run.ipynb b/demo/generate_english_german/run.ipynb index 15de5a6d8d2b..52608e681203 100644 --- a/demo/generate_english_german/run.ipynb +++ b/demo/generate_english_german/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: generate_english_german"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio transformers torch"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "from transformers import pipeline\n", "\n", "english_translator = gr.Blocks.load(name=\"spaces/gradio/english_translator\")\n", "english_generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n", "\n", "\n", "def generate_text(text):\n", " english_text = english_generator(text)[0][\"generated_text\"]\n", " german_text = english_translator(english_text)\n", " return english_text, german_text\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " seed = gr.Text(label=\"Input Phrase\")\n", " with gr.Column():\n", " english = gr.Text(label=\"Generated English Text\")\n", " german = gr.Text(label=\"Generated German Text\")\n", " btn = gr.Button(\"Generate\")\n", " btn.click(generate_text, inputs=[seed], outputs=[english, german])\n", " gr.Examples([\"My name is Clara and I am\"], inputs=[seed])\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: generate_english_german"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio transformers torch"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "from transformers import pipeline\n", "\n", "english_translator = gr.load(name=\"spaces/gradio/english_translator\")\n", "english_generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n", "\n", "\n", "def generate_text(text):\n", " english_text = english_generator(text)[0][\"generated_text\"]\n", " german_text = english_translator(english_text)\n", " return english_text, german_text\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Row():\n", " with gr.Column():\n", " seed = gr.Text(label=\"Input Phrase\")\n", " with gr.Column():\n", " english = gr.Text(label=\"Generated English Text\")\n", " german = gr.Text(label=\"Generated German Text\")\n", " btn = gr.Button(\"Generate\")\n", " btn.click(generate_text, inputs=[seed], outputs=[english, german])\n", " gr.Examples([\"My name is Clara and I am\"], inputs=[seed])\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/generate_english_german/run.py b/demo/generate_english_german/run.py index ab9d7144a76d..621007e77085 100644 --- a/demo/generate_english_german/run.py +++ b/demo/generate_english_german/run.py @@ -2,7 +2,7 @@ from transformers import pipeline -english_translator = gr.Blocks.load(name="spaces/gradio/english_translator") +english_translator = gr.load(name="spaces/gradio/english_translator") english_generator = pipeline("text-generation", model="distilgpt2") diff --git a/demo/image_classification/run.ipynb b/demo/image_classification/run.ipynb index 434fbbf4ae7e..97e4ed4974cc 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.Image(type=\"pil\"),\n", " outputs=gr.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_classification/run.py b/demo/image_classification/run.py index 9860a9dea5fc..454f94c12369 100644 --- a/demo/image_classification/run.py +++ b/demo/image_classification/run.py @@ -15,8 +15,8 @@ def predict(inp): return confidences demo = gr.Interface(fn=predict, - inputs=gr.inputs.Image(type="pil"), - outputs=gr.outputs.Label(num_top_classes=3), + inputs=gr.Image(type="pil"), + outputs=gr.Label(num_top_classes=3), examples=[["cheetah.jpg"]], ) diff --git a/demo/image_classifier_interpretation/files/imagenet_labels.json b/demo/image_classifier_interpretation/files/imagenet_labels.json deleted file mode 100644 index fa059ceeab5d..000000000000 --- a/demo/image_classifier_interpretation/files/imagenet_labels.json +++ /dev/null @@ -1,1000 +0,0 @@ -["tench", - "goldfish", - "great white shark", - "tiger shark", - "hammerhead shark", - "electric ray", - "stingray", - "cock", - "hen", - "ostrich", - "brambling", - "goldfinch", - "house finch", - "junco", - "indigo bunting", - "American robin", - "bulbul", - "jay", - "magpie", - "chickadee", - "American dipper", - "kite", - "bald eagle", - "vulture", - "great grey owl", - "fire salamander", - "smooth newt", - "newt", - "spotted salamander", - "axolotl", - "American bullfrog", - "tree frog", - "tailed frog", - "loggerhead sea turtle", - "leatherback sea turtle", - "mud turtle", - "terrapin", - "box turtle", - "banded gecko", - "green iguana", - "Carolina anole", - "desert grassland whiptail lizard", - "agama", - "frilled-necked lizard", - "alligator lizard", - "Gila monster", - "European green lizard", - "chameleon", - "Komodo dragon", - "Nile crocodile", - "American alligator", - "triceratops", - "worm snake", - "ring-necked snake", - "eastern hog-nosed snake", - "smooth green snake", - "kingsnake", - "garter snake", - "water snake", - "vine snake", - "night snake", - "boa constrictor", - "African rock python", - "Indian cobra", - "green mamba", - "sea snake", - "Saharan horned viper", - "eastern diamondback rattlesnake", - "sidewinder", - "trilobite", - "harvestman", - "scorpion", - "yellow garden spider", - "barn spider", - "European garden spider", - "southern black widow", - "tarantula", - "wolf spider", - "tick", - "centipede", - "black grouse", - "ptarmigan", - "ruffed grouse", - "prairie grouse", - "peacock", - "quail", - "partridge", - "grey parrot", - "macaw", - "sulphur-crested cockatoo", - "lorikeet", - "coucal", - "bee eater", - "hornbill", - "hummingbird", - "jacamar", - "toucan", - "duck", - "red-breasted merganser", - "goose", - "black swan", - "tusker", - "echidna", - "platypus", - "wallaby", - "koala", - "wombat", - "jellyfish", - "sea anemone", - "brain coral", - "flatworm", - "nematode", - "conch", - "snail", - "slug", - "sea slug", - "chiton", - "chambered nautilus", - "Dungeness crab", - "rock crab", - "fiddler crab", - "red king crab", - "American lobster", - "spiny lobster", - "crayfish", - "hermit crab", - "isopod", - "white stork", - "black stork", - "spoonbill", - "flamingo", - "little blue heron", - "great egret", - "bittern", - "crane (bird)", - "limpkin", - "common gallinule", - "American coot", - "bustard", - "ruddy turnstone", - "dunlin", - "common redshank", - "dowitcher", - "oystercatcher", - "pelican", - "king penguin", - "albatross", - "grey whale", - "killer whale", - "dugong", - "sea lion", - "Chihuahua", - "Japanese Chin", - "Maltese", - "Pekingese", - "Shih Tzu", - "King Charles Spaniel", - "Papillon", - "toy terrier", - "Rhodesian Ridgeback", - "Afghan Hound", - "Basset Hound", - "Beagle", - "Bloodhound", - "Bluetick Coonhound", - "Black and Tan Coonhound", - "Treeing Walker Coonhound", - "English foxhound", - "Redbone Coonhound", - "borzoi", - "Irish Wolfhound", - "Italian Greyhound", - "Whippet", - "Ibizan Hound", - "Norwegian Elkhound", - "Otterhound", - "Saluki", - "Scottish Deerhound", - "Weimaraner", - "Staffordshire Bull Terrier", - "American Staffordshire Terrier", - "Bedlington Terrier", - "Border Terrier", - "Kerry Blue Terrier", - "Irish Terrier", - "Norfolk Terrier", - "Norwich Terrier", - "Yorkshire Terrier", - "Wire Fox Terrier", - "Lakeland Terrier", - "Sealyham Terrier", - "Airedale Terrier", - "Cairn Terrier", - "Australian Terrier", - "Dandie Dinmont Terrier", - "Boston Terrier", - "Miniature Schnauzer", - "Giant Schnauzer", - "Standard Schnauzer", - "Scottish Terrier", - "Tibetan Terrier", - "Australian Silky Terrier", - "Soft-coated Wheaten Terrier", - "West Highland White Terrier", - "Lhasa Apso", - "Flat-Coated Retriever", - "Curly-coated Retriever", - "Golden Retriever", - "Labrador Retriever", - "Chesapeake Bay Retriever", - "German Shorthaired Pointer", - "Vizsla", - "English Setter", - "Irish Setter", - "Gordon Setter", - "Brittany", - "Clumber Spaniel", - "English Springer Spaniel", - "Welsh Springer Spaniel", - "Cocker Spaniels", - "Sussex Spaniel", - "Irish Water Spaniel", - "Kuvasz", - "Schipperke", - "Groenendael", - "Malinois", - "Briard", - "Australian Kelpie", - "Komondor", - "Old English Sheepdog", - "Shetland Sheepdog", - "collie", - "Border Collie", - "Bouvier des Flandres", - "Rottweiler", - "German Shepherd Dog", - "Dobermann", - "Miniature Pinscher", - "Greater Swiss Mountain Dog", - "Bernese Mountain Dog", - "Appenzeller Sennenhund", - "Entlebucher Sennenhund", - "Boxer", - "Bullmastiff", - "Tibetan Mastiff", - "French Bulldog", - "Great Dane", - "St. Bernard", - "husky", - "Alaskan Malamute", - "Siberian Husky", - "Dalmatian", - "Affenpinscher", - "Basenji", - "pug", - "Leonberger", - "Newfoundland", - "Pyrenean Mountain Dog", - "Samoyed", - "Pomeranian", - "Chow Chow", - "Keeshond", - "Griffon Bruxellois", - "Pembroke Welsh Corgi", - "Cardigan Welsh Corgi", - "Toy Poodle", - "Miniature Poodle", - "Standard Poodle", - "Mexican hairless dog", - "grey wolf", - "Alaskan tundra wolf", - "red wolf", - "coyote", - "dingo", - "dhole", - "African wild dog", - "hyena", - "red fox", - "kit fox", - "Arctic fox", - "grey fox", - "tabby cat", - "tiger cat", - "Persian cat", - "Siamese cat", - "Egyptian Mau", - "cougar", - "lynx", - "leopard", - "snow leopard", - "jaguar", - "lion", - "tiger", - "cheetah", - "brown bear", - "American black bear", - "polar bear", - "sloth bear", - "mongoose", - "meerkat", - "tiger beetle", - "ladybug", - "ground beetle", - "longhorn beetle", - "leaf beetle", - "dung beetle", - "rhinoceros beetle", - "weevil", - "fly", - "bee", - "ant", - "grasshopper", - "cricket", - "stick insect", - "cockroach", - "mantis", - "cicada", - "leafhopper", - "lacewing", - "dragonfly", - "damselfly", - "red admiral", - "ringlet", - "monarch butterfly", - "small white", - "sulphur butterfly", - "gossamer-winged butterfly", - "starfish", - "sea urchin", - "sea cucumber", - "cottontail rabbit", - "hare", - "Angora rabbit", - "hamster", - "porcupine", - "fox squirrel", - "marmot", - "beaver", - "guinea pig", - "common sorrel", - "zebra", - "pig", - "wild boar", - "warthog", - "hippopotamus", - "ox", - "water buffalo", - "bison", - "ram", - "bighorn sheep", - "Alpine ibex", - "hartebeest", - "impala", - "gazelle", - "dromedary", - "llama", - "weasel", - "mink", - "European polecat", - "black-footed ferret", - "otter", - "skunk", - "badger", - "armadillo", - "three-toed sloth", - "orangutan", - "gorilla", - "chimpanzee", - "gibbon", - "siamang", - "guenon", - "patas monkey", - "baboon", - "macaque", - "langur", - "black-and-white colobus", - "proboscis monkey", - "marmoset", - "white-headed capuchin", - "howler monkey", - "titi", - "Geoffroy's spider monkey", - "common squirrel monkey", - "ring-tailed lemur", - "indri", - "Asian elephant", - "African bush elephant", - "red panda", - "giant panda", - "snoek", - "eel", - "coho salmon", - "rock beauty", - "clownfish", - "sturgeon", - "garfish", - "lionfish", - "pufferfish", - "abacus", - "abaya", - "academic gown", - "accordion", - "acoustic guitar", - "aircraft carrier", - "airliner", - "airship", - "altar", - "ambulance", - "amphibious vehicle", - "analog clock", - "apiary", - "apron", - "waste container", - "assault rifle", - "backpack", - "bakery", - "balance beam", - "balloon", - "ballpoint pen", - "Band-Aid", - "banjo", - "baluster", - "barbell", - "barber chair", - "barbershop", - "barn", - "barometer", - "barrel", - "wheelbarrow", - "baseball", - "basketball", - "bassinet", - "bassoon", - "swimming cap", - "bath towel", - "bathtub", - "station wagon", - "lighthouse", - "beaker", - "military cap", - "beer bottle", - "beer glass", - "bell-cot", - "bib", - "tandem bicycle", - "bikini", - "ring binder", - "binoculars", - "birdhouse", - "boathouse", - "bobsleigh", - "bolo tie", - "poke bonnet", - "bookcase", - "bookstore", - "bottle cap", - "bow", - "bow tie", - "brass", - "bra", - "breakwater", - "breastplate", - "broom", - "bucket", - "buckle", - "bulletproof vest", - "high-speed train", - "butcher shop", - "taxicab", - "cauldron", - "candle", - "cannon", - "canoe", - "can opener", - "cardigan", - "car mirror", - "carousel", - "tool kit", - "carton", - "car wheel", - "automated teller machine", - "cassette", - "cassette player", - "castle", - "catamaran", - "CD player", - "cello", - "mobile phone", - "chain", - "chain-link fence", - "chain mail", - "chainsaw", - "chest", - "chiffonier", - "chime", - "china cabinet", - "Christmas stocking", - "church", - "movie theater", - "cleaver", - "cliff dwelling", - "cloak", - "clogs", - "cocktail shaker", - "coffee mug", - "coffeemaker", - "coil", - "combination lock", - "computer keyboard", - "confectionery store", - "container ship", - "convertible", - "corkscrew", - "cornet", - "cowboy boot", - "cowboy hat", - "cradle", - "crane (machine)", - "crash helmet", - "crate", - "infant bed", - "Crock Pot", - "croquet ball", - "crutch", - "cuirass", - "dam", - "desk", - "desktop computer", - "rotary dial telephone", - "diaper", - "digital clock", - "digital watch", - "dining table", - "dishcloth", - "dishwasher", - "disc brake", - "dock", - "dog sled", - "dome", - "doormat", - "drilling rig", - "drum", - "drumstick", - "dumbbell", - "Dutch oven", - "electric fan", - "electric guitar", - "electric locomotive", - "entertainment center", - "envelope", - "espresso machine", - "face powder", - "feather boa", - "filing cabinet", - "fireboat", - "fire engine", - "fire screen sheet", - "flagpole", - "flute", - "folding chair", - "football helmet", - "forklift", - "fountain", - "fountain pen", - "four-poster bed", - "freight car", - "French horn", - "frying pan", - "fur coat", - "garbage truck", - "gas mask", - "gas pump", - "goblet", - "go-kart", - "golf ball", - "golf cart", - "gondola", - "gong", - "gown", - "grand piano", - "greenhouse", - "grille", - "grocery store", - "guillotine", - "barrette", - "hair spray", - "half-track", - "hammer", - "hamper", - "hair dryer", - "hand-held computer", - "handkerchief", - "hard disk drive", - "harmonica", - "harp", - "harvester", - "hatchet", - "holster", - "home theater", - "honeycomb", - "hook", - "hoop skirt", - "horizontal bar", - "horse-drawn vehicle", - "hourglass", - "iPod", - "clothes iron", - "jack-o'-lantern", - "jeans", - "jeep", - "T-shirt", - "jigsaw puzzle", - "pulled rickshaw", - "joystick", - "kimono", - "knee pad", - "knot", - "lab coat", - "ladle", - "lampshade", - "laptop computer", - "lawn mower", - "lens cap", - "paper knife", - "library", - "lifeboat", - "lighter", - "limousine", - "ocean liner", - "lipstick", - "slip-on shoe", - "lotion", - "speaker", - "loupe", - "sawmill", - "magnetic compass", - "mail bag", - "mailbox", - "tights", - "tank suit", - "manhole cover", - "maraca", - "marimba", - "mask", - "match", - "maypole", - "maze", - "measuring cup", - "medicine chest", - "megalith", - "microphone", - "microwave oven", - "military uniform", - "milk can", - "minibus", - "miniskirt", - "minivan", - "missile", - "mitten", - "mixing bowl", - "mobile home", - "Model T", - "modem", - "monastery", - "monitor", - "moped", - "mortar", - "square academic cap", - "mosque", - "mosquito net", - "scooter", - "mountain bike", - "tent", - "computer mouse", - "mousetrap", - "moving van", - "muzzle", - "nail", - "neck brace", - "necklace", - "nipple", - "notebook computer", - "obelisk", - "oboe", - "ocarina", - "odometer", - "oil filter", - "organ", - "oscilloscope", - "overskirt", - "bullock cart", - "oxygen mask", - "packet", - "paddle", - "paddle wheel", - "padlock", - "paintbrush", - "pajamas", - "palace", - "pan flute", - "paper towel", - "parachute", - "parallel bars", - "park bench", - "parking meter", - "passenger car", - "patio", - "payphone", - "pedestal", - "pencil case", - "pencil sharpener", - "perfume", - "Petri dish", - "photocopier", - "plectrum", - "Pickelhaube", - "picket fence", - "pickup truck", - "pier", - "piggy bank", - "pill bottle", - "pillow", - "ping-pong ball", - "pinwheel", - "pirate ship", - "pitcher", - "hand plane", - "planetarium", - "plastic bag", - "plate rack", - "plow", - "plunger", - "Polaroid camera", - "pole", - "police van", - "poncho", - "billiard table", - "soda bottle", - "pot", - "potter's wheel", - "power drill", - "prayer rug", - "printer", - "prison", - "projectile", - "projector", - "hockey puck", - "punching bag", - "purse", - "quill", - "quilt", - "race car", - "racket", - "radiator", - "radio", - "radio telescope", - "rain barrel", - "recreational vehicle", - "reel", - "reflex camera", - "refrigerator", - "remote control", - "restaurant", - "revolver", - "rifle", - "rocking chair", - "rotisserie", - "eraser", - "rugby ball", - "ruler", - "running shoe", - "safe", - "safety pin", - "salt shaker", - "sandal", - "sarong", - "saxophone", - "scabbard", - "weighing scale", - "school bus", - "schooner", - "scoreboard", - "CRT screen", - "screw", - "screwdriver", - "seat belt", - "sewing machine", - "shield", - "shoe store", - "shoji", - "shopping basket", - "shopping cart", - "shovel", - "shower cap", - "shower curtain", - "ski", - "ski mask", - "sleeping bag", - "slide rule", - "sliding door", - "slot machine", - "snorkel", - "snowmobile", - "snowplow", - "soap dispenser", - "soccer ball", - "sock", - "solar thermal collector", - "sombrero", - "soup bowl", - "space bar", - "space heater", - "space shuttle", - "spatula", - "motorboat", - "spider web", - "spindle", - "sports car", - "spotlight", - "stage", - "steam locomotive", - "through arch bridge", - "steel drum", - "stethoscope", - "scarf", - "stone wall", - "stopwatch", - "stove", - "strainer", - "tram", - "stretcher", - "couch", - "stupa", - "submarine", - "suit", - "sundial", - "sunglass", - "sunglasses", - "sunscreen", - "suspension bridge", - "mop", - "sweatshirt", - "swimsuit", - "swing", - "switch", - "syringe", - "table lamp", - "tank", - "tape player", - "teapot", - "teddy bear", - "television", - "tennis ball", - "thatched roof", - "front curtain", - "thimble", - "threshing machine", - "throne", - "tile roof", - "toaster", - "tobacco shop", - "toilet seat", - "torch", - "totem pole", - "tow truck", - "toy store", - "tractor", - "semi-trailer truck", - "tray", - "trench coat", - "tricycle", - "trimaran", - "tripod", - "triumphal arch", - "trolleybus", - "trombone", - "tub", - "turnstile", - "typewriter keyboard", - "umbrella", - "unicycle", - "upright piano", - "vacuum cleaner", - "vase", - "vault", - "velvet", - "vending machine", - "vestment", - "viaduct", - "violin", - "volleyball", - "waffle iron", - "wall clock", - "wallet", - "wardrobe", - "military aircraft", - "sink", - "washing machine", - "water bottle", - "water jug", - "water tower", - "whiskey jug", - "whistle", - "wig", - "window screen", - "window shade", - "Windsor tie", - "wine bottle", - "wing", - "wok", - "wooden spoon", - "wool", - "split-rail fence", - "shipwreck", - "yawl", - "yurt", - "website", - "comic book", - "crossword", - "traffic sign", - "traffic light", - "dust jacket", - "menu", - "plate", - "guacamole", - "consomme", - "hot pot", - "trifle", - "ice cream", - "ice pop", - "baguette", - "bagel", - "pretzel", - "cheeseburger", - "hot dog", - "mashed potato", - "cabbage", - "broccoli", - "cauliflower", - "zucchini", - "spaghetti squash", - "acorn squash", - "butternut squash", - "cucumber", - "artichoke", - "bell pepper", - "cardoon", - "mushroom", - "Granny Smith", - "strawberry", - "orange", - "lemon", - "fig", - "pineapple", - "banana", - "jackfruit", - "custard apple", - "pomegranate", - "hay", - "carbonara", - "chocolate syrup", - "dough", - "meatloaf", - "pizza", - "pot pie", - "burrito", - "red wine", - "espresso", - "cup", - "eggnog", - "alp", - "bubble", - "cliff", - "coral reef", - "geyser", - "lakeshore", - "promontory", - "shoal", - "seashore", - "valley", - "volcano", - "baseball player", - "bridegroom", - "scuba diver", - "rapeseed", - "daisy", - "yellow lady's slipper", - "corn", - "acorn", - "rose hip", - "horse chestnut seed", - "coral fungus", - "agaric", - "gyromitra", - "stinkhorn mushroom", - "earth star", - "hen-of-the-woods", - "bolete", - "ear", - "toilet paper"] \ No newline at end of file diff --git a/demo/image_classifier_interpretation/images/cheetah1.jpg b/demo/image_classifier_interpretation/images/cheetah1.jpg deleted file mode 100644 index c510ff30e09c1ce410afa499f0bfc3a63c751134..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 20552 zcma%ib8ux(^yiChy%_Jsw(VqM+qUfqC(b09cw%#6O>Eot#P;lbe_Ok?f9~nJRk!Ng zKIe0*x~o42{deW>4gg(FN>&O00RaF&{2PG3>i}^8EHpGU3^XhZ3@ifdzXu5(78V`} 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z!^+j2x#d`ej9f=OvI25Z!Yt@aDtOXT#@!eHfKE*yp3^jFMo)jSmg(XwDy4Ir#o<<< z6cU|SsgGs{vW(2DW9#T@Z(+hn1(qqbq^zT`(oQODxaAa!JBW$~5dn0NsBYO7z1`eota$H`XdsXfJ)As?YwIg4-xcprhLO|2e=@BH9iMXVs z?IVyeCCzF~qN@|)q>C$yGsYgX-&$e5xPCrR@~nDQ-XH&-rMoe9Fu?b?eP|^l)Sj`@ zmUKClB(b_&>wmWcKcg0>=-lFe-%6JhU14hKh7VpR#x1cnEXj{ftwM)b^`yO6Af6nH zRJ(g!p7UQ!?__@Th`$;1@uaeP<@lpV$+cS1O3QID2i8!8v6fVzD&|Z diff --git a/demo/leaderboard/run.ipynb b/demo/leaderboard/run.ipynb index b4c74a920d4e..0780fa5ae234 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.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/leaderboard/run.py b/demo/leaderboard/run.py index 48e26218c99f..01948369ec2d 100644 --- a/demo/leaderboard/run.py +++ b/demo/leaderboard/run.py @@ -25,7 +25,7 @@ def get_blocks_party_spaces(): with gr.Tabs(): with gr.TabItem("Blocks Party Leaderboard"): with gr.Row(): - data = gr.outputs.Dataframe(type="pandas") + data = gr.Dataframe(type="pandas") with gr.Row(): data_run = gr.Button("Refresh") data_run.click(get_blocks_party_spaces, inputs=None, outputs=data) diff --git a/demo/pictionary/class_names.txt b/demo/pictionary/class_names.txt deleted file mode 100644 index 52a6caf89fcd..000000000000 --- a/demo/pictionary/class_names.txt +++ /dev/null @@ -1,100 +0,0 @@ -airplane -alarm_clock -anvil -apple -axe -baseball -baseball_bat -basketball -beard -bed -bench -bicycle -bird -book -bread -bridge -broom -butterfly -camera -candle -car -cat -ceiling_fan -cell_phone -chair -circle -clock -cloud -coffee_cup -cookie -cup -diving_board -donut -door -drums -dumbbell -envelope -eye -eyeglasses -face -fan -flower -frying_pan -grapes -hammer -hat -headphones -helmet -hot_dog -ice_cream -key -knife -ladder -laptop -light_bulb -lightning -line -lollipop -microphone -moon -mountain -moustache -mushroom -pants -paper_clip -pencil -pillow -pizza -power_outlet -radio -rainbow -rifle -saw -scissors -screwdriver -shorts -shovel -smiley_face -snake -sock -spider -spoon -square -star -stop_sign -suitcase -sun -sword -syringe -t-shirt -table -tennis_racquet -tent -tooth -traffic_light -tree -triangle -umbrella -wheel -wristwatch diff --git a/demo/pictionary/requirements.txt b/demo/pictionary/requirements.txt deleted file mode 100644 index ed9a63afad7f..000000000000 --- a/demo/pictionary/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -torch -gdown -numpy \ No newline at end of file diff --git a/demo/pictionary/run.ipynb b/demo/pictionary/run.ipynb deleted file mode 100644 index 40f5d34b1345..000000000000 --- a/demo/pictionary/run.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: pictionary"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch gdown numpy"]}, {"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/pictionary/class_names.txt"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["from pathlib import Path\n", "\n", "import numpy as np\n", "import torch\n", "import gradio as gr\n", "from torch import nn\n", "import gdown \n", "\n", "url = 'https://drive.google.com/uc?id=1dsk2JNZLRDjC-0J4wIQX_FcVurPaXaAZ'\n", "output = 'pytorch_model.bin'\n", "gdown.download(url, output, quiet=False)\n", "\n", "LABELS = Path('class_names.txt').read_text().splitlines()\n", "\n", "model = nn.Sequential(\n", " nn.Conv2d(1, 32, 3, padding='same'),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(32, 64, 3, padding='same'),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Conv2d(64, 128, 3, padding='same'),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2),\n", " nn.Flatten(),\n", " nn.Linear(1152, 256),\n", " nn.ReLU(),\n", " nn.Linear(256, len(LABELS)),\n", ")\n", "state_dict = torch.load('pytorch_model.bin', map_location='cpu')\n", "model.load_state_dict(state_dict, strict=False)\n", "model.eval()\n", "\n", "def predict(im):\n", " if im is None:\n", " return None\n", " im = np.asarray(im.resize((28, 28)))\n", " \n", " x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.\n", "\n", " with torch.no_grad():\n", " out = model(x)\n", "\n", " probabilities = torch.nn.functional.softmax(out[0], dim=0)\n", "\n", " values, indices = torch.topk(probabilities, 5)\n", "\n", " return {LABELS[i]: v.item() for i, v in zip(indices, values)}\n", "\n", "\n", "interface = gr.Interface(predict, \n", " inputs=gr.Sketchpad(label=\"Draw Here\", brush_radius=5, type=\"pil\", shape=(120, 120)), \n", " outputs=gr.Label(label=\"Guess\"), \n", " live=True)\n", "\n", "interface.queue().launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/pictionary/run.py b/demo/pictionary/run.py deleted file mode 100644 index ce9be4d1f3fa..000000000000 --- a/demo/pictionary/run.py +++ /dev/null @@ -1,56 +0,0 @@ -from pathlib import Path - -import numpy as np -import torch -import gradio as gr -from torch import nn -import gdown - -url = 'https://drive.google.com/uc?id=1dsk2JNZLRDjC-0J4wIQX_FcVurPaXaAZ' -output = 'pytorch_model.bin' -gdown.download(url, output, quiet=False) - -LABELS = Path('class_names.txt').read_text().splitlines() - -model = nn.Sequential( - nn.Conv2d(1, 32, 3, padding='same'), - nn.ReLU(), - nn.MaxPool2d(2), - nn.Conv2d(32, 64, 3, padding='same'), - nn.ReLU(), - nn.MaxPool2d(2), - nn.Conv2d(64, 128, 3, padding='same'), - nn.ReLU(), - nn.MaxPool2d(2), - nn.Flatten(), - nn.Linear(1152, 256), - nn.ReLU(), - nn.Linear(256, len(LABELS)), -) -state_dict = torch.load('pytorch_model.bin', map_location='cpu') -model.load_state_dict(state_dict, strict=False) -model.eval() - -def predict(im): - if im is None: - return None - im = np.asarray(im.resize((28, 28))) - - x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255. - - with torch.no_grad(): - out = model(x) - - probabilities = torch.nn.functional.softmax(out[0], dim=0) - - values, indices = torch.topk(probabilities, 5) - - return {LABELS[i]: v.item() for i, v in zip(indices, values)} - - -interface = gr.Interface(predict, - inputs=gr.Sketchpad(label="Draw Here", brush_radius=5, type="pil", shape=(120, 120)), - outputs=gr.Label(label="Guess"), - live=True) - -interface.queue().launch() diff --git a/demo/question-answering/run.ipynb b/demo/question-answering/run.ipynb index 67be24671afb..b62032797a6e 100644 --- a/demo/question-answering/run.ipynb +++ b/demo/question-answering/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: question-answering"]}, {"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", "\n", "from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\n", "\n", "model_name = \"deepset/roberta-base-squad2\"\n", "\n", "nlp = pipeline(\"question-answering\", model=model_name, tokenizer=model_name)\n", "\n", "context = \"The Amazon rainforest, also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species.\"\n", "question = \"Which continent is the Amazon rainforest in?\"\n", "\n", "\n", "def predict(context, question):\n", " res = nlp({\"question\": question, \"context\": context})\n", " return res[\"answer\"], res[\"score\"]\n", "\n", "\n", "gr.Interface(\n", " predict,\n", " inputs=[\n", " gr.inputs.Textbox(lines=7, default=context, label=\"Context Paragraph\"),\n", " gr.inputs.Textbox(lines=2, default=question, label=\"Question\"),\n", " ],\n", " outputs=[gr.outputs.Textbox(label=\"Answer\"), gr.outputs.Textbox(label=\"Score\")],\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: question-answering"]}, {"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", "\n", "from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\n", "\n", "model_name = \"deepset/roberta-base-squad2\"\n", "\n", "nlp = pipeline(\"question-answering\", model=model_name, tokenizer=model_name)\n", "\n", "context = \"The Amazon rainforest, also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species.\"\n", "question = \"Which continent is the Amazon rainforest in?\"\n", "\n", "\n", "def predict(context, question):\n", " res = nlp({\"question\": question, \"context\": context})\n", " return res[\"answer\"], res[\"score\"]\n", "\n", "\n", "gr.Interface(\n", " predict,\n", " inputs=[\n", " gr.Textbox(lines=7, value=context, label=\"Context Paragraph\"),\n", " gr.Textbox(lines=2, value=question, label=\"Question\"),\n", " ],\n", " outputs=[gr.Textbox(label=\"Answer\"), gr.Textbox(label=\"Score\")],\n", ").launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/question-answering/run.py b/demo/question-answering/run.py index 1f6e48fcd9af..7ccc419cdd06 100644 --- a/demo/question-answering/run.py +++ b/demo/question-answering/run.py @@ -18,8 +18,8 @@ def predict(context, question): gr.Interface( predict, inputs=[ - gr.inputs.Textbox(lines=7, default=context, label="Context Paragraph"), - gr.inputs.Textbox(lines=2, default=question, label="Question"), + gr.Textbox(lines=7, value=context, label="Context Paragraph"), + gr.Textbox(lines=2, value=question, label="Question"), ], - outputs=[gr.outputs.Textbox(label="Answer"), gr.outputs.Textbox(label="Score")], + outputs=[gr.Textbox(label="Answer"), gr.Textbox(label="Score")], ).launch() diff --git a/demo/same-person-or-different/run.ipynb b/demo/same-person-or-different/run.ipynb index 6222162cd6e8..a37c4e344640 100644 --- a/demo/same-person-or-different/run.ipynb +++ b/demo/same-person-or-different/run.ipynb @@ -1 +1 @@ -{"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", "

    The speakers are

    \n", "

    {:.1f}%

    \n", "

    similar

    \n", "

    Welcome, human!

    \n", "
    (You must get at least 85% to be considered the same person)
    \n", "
    \n", "\"\"\"\n", ")\n", "OUTPUT_FAIL = (\n", " \"\"\"\n", "
    \n", "

    The speakers are

    \n", "

    {:.1f}%

    \n", "

    similar

    \n", "

    You shall not pass!

    \n", "
    (You must get at least 85% to be considered the same person)
    \n", "
    \n", "\"\"\"\n", ")\n", "\n", "EFFECTS = [\n", " [\"remix\", \"-\"],\n", " [\"channels\", \"1\"],\n", " [\"rate\", \"16000\"],\n", " [\"gain\", \"-1.0\"],\n", " [\"silence\", \"1\", \"0.1\", \"0.1%\", \"-1\", \"0.1\", \"0.1%\"],\n", " [\"trim\", \"0\", \"10\"],\n", "]\n", "\n", "THRESHOLD = 0.85\n", "\n", "model_name = \"microsoft/unispeech-sat-base-plus-sv\"\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)\n", "model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)\n", "cosine_sim = torch.nn.CosineSimilarity(dim=-1)\n", "\n", "\n", "def similarity_fn(path1, path2):\n", " if not (path1 and path2):\n", " return 'ERROR: Please record audio for *both* speakers!'\n", "\n", " wav1, _ = apply_effects_file(path1, EFFECTS)\n", " wav2, _ = apply_effects_file(path2, EFFECTS)\n", " print(wav1.shape, wav2.shape)\n", "\n", " input1 = feature_extractor(wav1.squeeze(0), return_tensors=\"pt\", sampling_rate=16000).input_values.to(device)\n", " input2 = feature_extractor(wav2.squeeze(0), return_tensors=\"pt\", sampling_rate=16000).input_values.to(device)\n", "\n", " with torch.no_grad():\n", " emb1 = model(input1).embeddings\n", " emb2 = model(input2).embeddings\n", " emb1 = torch.nn.functional.normalize(emb1, dim=-1).cpu()\n", " emb2 = torch.nn.functional.normalize(emb2, dim=-1).cpu()\n", " similarity = cosine_sim(emb1, emb2).numpy()[0]\n", "\n", " if similarity >= THRESHOLD:\n", " output = OUTPUT_OK.format(similarity * 100)\n", " else:\n", " output = OUTPUT_FAIL.format(similarity * 100)\n", "\n", " return output\n", "\n", "inputs = [\n", " gr.inputs.Audio(sources=[\"microphone\"], type=\"filepath\", optional=True, label=\"Speaker #1\"),\n", " gr.inputs.Audio(sources=[\"microphone\"], type=\"filepath\", optional=True, label=\"Speaker #2\"),\n", "]\n", "output = gr.outputs.HTML(label=\"\")\n", "\n", "\n", "description = (\n", " \"This demo from Microsoft will compare two speech samples and determine if they are from the same speaker. \"\n", " \"Try it with your own voice!\"\n", ")\n", "article = (\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 +{"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", "

    The speakers are

    \n", "

    {:.1f}%

    \n", "

    similar

    \n", "

    Welcome, human!

    \n", "
    (You must get at least 85% to be considered the same person)
    \n", "
    \n", "\"\"\"\n", ")\n", "OUTPUT_FAIL = (\n", " \"\"\"\n", "
    \n", "

    The speakers are

    \n", "

    {:.1f}%

    \n", "

    similar

    \n", "

    You shall not pass!

    \n", "
    (You must get at least 85% to be considered the same person)
    \n", "
    \n", "\"\"\"\n", ")\n", "\n", "EFFECTS = [\n", " [\"remix\", \"-\"],\n", " [\"channels\", \"1\"],\n", " [\"rate\", \"16000\"],\n", " [\"gain\", \"-1.0\"],\n", " [\"silence\", \"1\", \"0.1\", \"0.1%\", \"-1\", \"0.1\", \"0.1%\"],\n", " [\"trim\", \"0\", \"10\"],\n", "]\n", "\n", "THRESHOLD = 0.85\n", "\n", "model_name = \"microsoft/unispeech-sat-base-plus-sv\"\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)\n", "model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)\n", "cosine_sim = torch.nn.CosineSimilarity(dim=-1)\n", "\n", "\n", "def similarity_fn(path1, path2):\n", " if not (path1 and path2):\n", " return 'ERROR: Please record audio for *both* speakers!'\n", "\n", " wav1, _ = apply_effects_file(path1, EFFECTS)\n", " wav2, _ = apply_effects_file(path2, EFFECTS)\n", " print(wav1.shape, wav2.shape)\n", "\n", " input1 = feature_extractor(wav1.squeeze(0), return_tensors=\"pt\", sampling_rate=16000).input_values.to(device)\n", " input2 = feature_extractor(wav2.squeeze(0), return_tensors=\"pt\", sampling_rate=16000).input_values.to(device)\n", "\n", " with torch.no_grad():\n", " emb1 = model(input1).embeddings\n", " emb2 = model(input2).embeddings\n", " emb1 = torch.nn.functional.normalize(emb1, dim=-1).cpu()\n", " emb2 = torch.nn.functional.normalize(emb2, dim=-1).cpu()\n", " similarity = cosine_sim(emb1, emb2).numpy()[0]\n", "\n", " if similarity >= THRESHOLD:\n", " output = OUTPUT_OK.format(similarity * 100)\n", " else:\n", " output = OUTPUT_FAIL.format(similarity * 100)\n", "\n", " return output\n", "\n", "inputs = [\n", " gr.Audio(sources=[\"microphone\"], type=\"filepath\", label=\"Speaker #1\"),\n", " gr.Audio(sources=[\"microphone\"], type=\"filepath\", label=\"Speaker #2\"),\n", "]\n", "output = gr.HTML(label=\"\")\n", "\n", "\n", "description = (\n", " \"This demo from Microsoft will compare two speech samples and determine if they are from the same speaker. \"\n", " \"Try it with your own voice!\"\n", ")\n", "article = (\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/same-person-or-different/run.py b/demo/same-person-or-different/run.py index b0d6d95a43fb..5313b2304a39 100644 --- a/demo/same-person-or-different/run.py +++ b/demo/same-person-or-different/run.py @@ -70,10 +70,10 @@ def similarity_fn(path1, path2): return output inputs = [ - gr.inputs.Audio(sources=["microphone"], type="filepath", optional=True, label="Speaker #1"), - gr.inputs.Audio(sources=["microphone"], type="filepath", optional=True, label="Speaker #2"), + gr.Audio(sources=["microphone"], type="filepath", label="Speaker #1"), + gr.Audio(sources=["microphone"], type="filepath", label="Speaker #2"), ] -output = gr.outputs.HTML(label="") +output = gr.HTML(label="") description = ( diff --git a/demo/sepia_filter/run.ipynb b/demo/sepia_filter/run.ipynb index 3f8c9fac800e..eef299cbb92a 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(), \"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/sepia_filter/run.py b/demo/sepia_filter/run.py index b48eb6a5e10e..9977659549b1 100644 --- a/demo/sepia_filter/run.py +++ b/demo/sepia_filter/run.py @@ -11,6 +11,6 @@ def sepia(input_img): sepia_img /= sepia_img.max() return sepia_img -demo = gr.Interface(sepia, gr.Image(shape=(200, 200)), "image") +demo = gr.Interface(sepia, gr.Image(), "image") if __name__ == "__main__": demo.launch() diff --git a/demo/stream_frames/run.ipynb b/demo/stream_frames/run.ipynb index f71842e9202f..954388742113 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(sources=[\"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/stream_frames/run.py b/demo/stream_frames/run.py index 87c2dcafa3ef..547fc11eec2f 100644 --- a/demo/stream_frames/run.py +++ b/demo/stream_frames/run.py @@ -6,7 +6,7 @@ def flip(im): demo = gr.Interface( flip, - gr.Image(source="webcam", streaming=True), + gr.Image(sources=["webcam"], streaming=True), "image", live=True ) diff --git a/demo/text_generation/run.ipynb b/demo/text_generation/run.ipynb index 073b694f9485..f73a87e97640 100644 --- a/demo/text_generation/run.ipynb +++ b/demo/text_generation/run.ipynb @@ -1 +1 @@ -{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: text_generation\n", "### This text generation demo takes in input text and returns generated text. It uses the Transformers library to set up the model and has two examples.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio git+https://github.com/huggingface/transformers gradio torch"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import pipeline\n", "\n", "generator = pipeline('text-generation', model='gpt2')\n", "\n", "def generate(text):\n", " result = generator(text, max_length=30, num_return_sequences=1)\n", " return result[0][\"generated_text\"]\n", "\n", "examples = [\n", " [\"The Moon's orbit around Earth has\"],\n", " [\"The smooth Borealis basin in the Northern Hemisphere covers 40%\"],\n", "]\n", "\n", "demo = gr.Interface(\n", " fn=generate,\n", " inputs=gr.inputs.Textbox(lines=5, label=\"Input Text\"),\n", " outputs=gr.outputs.Textbox(label=\"Generated Text\"),\n", " examples=examples\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: text_generation\n", "### This text generation demo takes in input text and returns generated text. It uses the Transformers library to set up the model and has two examples.\n", " "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio git+https://github.com/huggingface/transformers gradio torch"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import pipeline\n", "\n", "generator = pipeline('text-generation', model='gpt2')\n", "\n", "def generate(text):\n", " result = generator(text, max_length=30, num_return_sequences=1)\n", " return result[0][\"generated_text\"]\n", "\n", "examples = [\n", " [\"The Moon's orbit around Earth has\"],\n", " [\"The smooth Borealis basin in the Northern Hemisphere covers 40%\"],\n", "]\n", "\n", "demo = gr.Interface(\n", " fn=generate,\n", " inputs=gr.Textbox(lines=5, label=\"Input Text\"),\n", " outputs=gr.Textbox(label=\"Generated Text\"),\n", " examples=examples\n", ")\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} \ No newline at end of file diff --git a/demo/text_generation/run.py b/demo/text_generation/run.py index a84d7042d5e9..c78d089f90e2 100644 --- a/demo/text_generation/run.py +++ b/demo/text_generation/run.py @@ -14,8 +14,8 @@ def generate(text): demo = gr.Interface( fn=generate, - inputs=gr.inputs.Textbox(lines=5, label="Input Text"), - outputs=gr.outputs.Textbox(label="Generated Text"), + inputs=gr.Textbox(lines=5, label="Input Text"), + outputs=gr.Textbox(label="Generated Text"), examples=examples ) diff --git a/demo/unispeech-speaker-verification/run.ipynb b/demo/unispeech-speaker-verification/run.ipynb index 38052fe4de30..67bffef6529b 100644 --- a/demo/unispeech-speaker-verification/run.ipynb +++ b/demo/unispeech-speaker-verification/run.ipynb @@ -1 +1 @@ -{"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", "

    The speakers are

    \n", "

    {:.1f}%

    \n", "

    similar

    \n", "

    Welcome, human!

    \n", "
    (You must get at least 85% to be considered the same person)
    \n", "
    \n", "\"\"\"\n", ")\n", "OUTPUT_FAIL = (\n", " STYLE\n", " + \"\"\"\n", "
    \n", "

    The speakers are

    \n", "

    {:.1f}%

    \n", "

    similar

    \n", "

    You shall not pass!

    \n", "
    (You must get at least 85% to be considered the same person)
    \n", "
    \n", "\"\"\"\n", ")\n", "\n", "EFFECTS = [\n", " [\"remix\", \"-\"],\n", " [\"channels\", \"1\"],\n", " [\"rate\", \"16000\"],\n", " [\"gain\", \"-1.0\"],\n", " [\"silence\", \"1\", \"0.1\", \"0.1%\", \"-1\", \"0.1\", \"0.1%\"],\n", " [\"trim\", \"0\", \"10\"],\n", "]\n", "\n", "THRESHOLD = 0.85\n", "\n", "model_name = \"microsoft/unispeech-sat-base-plus-sv\"\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)\n", "model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)\n", "cosine_sim = torch.nn.CosineSimilarity(dim=-1)\n", "\n", "\n", "def similarity_fn(path1, path2):\n", " if not (path1 and path2):\n", " return 'ERROR: Please record audio for *both* speakers!'\n", " wav1, _ = apply_effects_file(path1, EFFECTS)\n", " wav2, _ = apply_effects_file(path2, EFFECTS)\n", " print(wav1.shape, wav2.shape)\n", "\n", " input1 = feature_extractor(wav1.squeeze(0), return_tensors=\"pt\", sampling_rate=16000).input_values.to(device)\n", " input2 = feature_extractor(wav2.squeeze(0), return_tensors=\"pt\", sampling_rate=16000).input_values.to(device)\n", "\n", " with torch.no_grad():\n", " emb1 = model(input1).embeddings\n", " emb2 = model(input2).embeddings\n", " emb1 = torch.nn.functional.normalize(emb1, dim=-1).cpu()\n", " emb2 = torch.nn.functional.normalize(emb2, dim=-1).cpu()\n", " similarity = cosine_sim(emb1, emb2).numpy()[0]\n", "\n", " if similarity >= THRESHOLD:\n", " output = OUTPUT_OK.format(similarity * 100)\n", " else:\n", " output = OUTPUT_FAIL.format(similarity * 100)\n", "\n", " return output\n", "\n", "\n", "inputs = [\n", " gr.Audio(sources=[\"microphone\"], type=\"filepath\", optional=True, label=\"Speaker #1\"),\n", " gr.Audio(sources=[\"microphone\"], type=\"filepath\", optional=True, label=\"Speaker #2\"),\n", "]\n", "output = gr.HTML(label=\"\")\n", "\n", "\n", "description = (\n", " \"This demo will compare two speech samples and determine if they are from the same speaker. \"\n", " \"Try it with your own voice!\"\n", ")\n", "article = (\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", "

    The speakers are

    \n", "

    {:.1f}%

    \n", "

    similar

    \n", "

    Welcome, human!

    \n", "
    (You must get at least 85% to be considered the same person)
    \n", "
    \n", "\"\"\"\n", ")\n", "OUTPUT_FAIL = (\n", " STYLE\n", " + \"\"\"\n", "
    \n", "

    The speakers are

    \n", "

    {:.1f}%

    \n", "

    similar

    \n", "

    You shall not pass!

    \n", "
    (You must get at least 85% to be considered the same person)
    \n", "
    \n", "\"\"\"\n", ")\n", "\n", "EFFECTS = [\n", " [\"remix\", \"-\"],\n", " [\"channels\", \"1\"],\n", " [\"rate\", \"16000\"],\n", " [\"gain\", \"-1.0\"],\n", " [\"silence\", \"1\", \"0.1\", \"0.1%\", \"-1\", \"0.1\", \"0.1%\"],\n", " [\"trim\", \"0\", \"10\"],\n", "]\n", "\n", "THRESHOLD = 0.85\n", "\n", "model_name = \"microsoft/unispeech-sat-base-plus-sv\"\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)\n", "model = AutoModelForAudioXVector.from_pretrained(model_name).to(device)\n", "cosine_sim = torch.nn.CosineSimilarity(dim=-1)\n", "\n", "\n", "def similarity_fn(path1, path2):\n", " if not (path1 and path2):\n", " return 'ERROR: Please record audio for *both* speakers!'\n", " wav1, _ = apply_effects_file(path1, EFFECTS)\n", " wav2, _ = apply_effects_file(path2, EFFECTS)\n", " print(wav1.shape, wav2.shape)\n", "\n", " input1 = feature_extractor(wav1.squeeze(0), return_tensors=\"pt\", sampling_rate=16000).input_values.to(device)\n", " input2 = feature_extractor(wav2.squeeze(0), return_tensors=\"pt\", sampling_rate=16000).input_values.to(device)\n", "\n", " with torch.no_grad():\n", " emb1 = model(input1).embeddings\n", " emb2 = model(input2).embeddings\n", " emb1 = torch.nn.functional.normalize(emb1, dim=-1).cpu()\n", " emb2 = torch.nn.functional.normalize(emb2, dim=-1).cpu()\n", " similarity = cosine_sim(emb1, emb2).numpy()[0]\n", "\n", " if similarity >= THRESHOLD:\n", " output = OUTPUT_OK.format(similarity * 100)\n", " else:\n", " output = OUTPUT_FAIL.format(similarity * 100)\n", "\n", " return output\n", "\n", "\n", "inputs = [\n", " gr.Audio(sources=[\"microphone\"], type=\"filepath\", label=\"Speaker #1\"),\n", " gr.Audio(sources=[\"microphone\"], type=\"filepath\", label=\"Speaker #2\"),\n", "]\n", "output = gr.HTML(label=\"\")\n", "\n", "\n", "description = (\n", " \"This demo will compare two speech samples and determine if they are from the same speaker. \"\n", " \"Try it with your own voice!\"\n", ")\n", "article = (\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", " 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/unispeech-speaker-verification/run.py b/demo/unispeech-speaker-verification/run.py index bf985f1c10f4..206ac25a3dba 100644 --- a/demo/unispeech-speaker-verification/run.py +++ b/demo/unispeech-speaker-verification/run.py @@ -76,8 +76,8 @@ def similarity_fn(path1, path2): inputs = [ - gr.Audio(sources=["microphone"], type="filepath", optional=True, label="Speaker #1"), - gr.Audio(sources=["microphone"], type="filepath", optional=True, label="Speaker #2"), + gr.Audio(sources=["microphone"], type="filepath", label="Speaker #1"), + gr.Audio(sources=["microphone"], type="filepath", label="Speaker #2"), ] output = gr.HTML(label="") @@ -108,7 +108,6 @@ def similarity_fn(path1, path2): title="Voice Authentication with UniSpeech-SAT + X-Vectors", description=description, article=article, - layout="horizontal", allow_flagging="never", live=False, examples=examples, diff --git a/demo/webcam/run.ipynb b/demo/webcam/run.ipynb index a7c9454862fb..28fca1a54b4a 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(sources=[\"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(sources=[\"webcam\"]), gr.Video(sources=[\"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/webcam/run.py b/demo/webcam/run.py index b8ee3d5c054e..c12026ee6cdc 100644 --- a/demo/webcam/run.py +++ b/demo/webcam/run.py @@ -8,7 +8,7 @@ def snap(image, video): demo = gr.Interface( snap, - [gr.Image(source="webcam", tool=None), gr.Video(sources=["webcam"])], + [gr.Image(sources=["webcam"]), gr.Video(sources=["webcam"])], ["image", "video"], ) diff --git a/gradio/components/image.py b/gradio/components/image.py index dc525775a3b7..4e0da7bdf06a 100644 --- a/gradio/components/image.py +++ b/gradio/components/image.py @@ -28,7 +28,7 @@ class Image(StreamingInput, Component): Postprocessing: expects a {numpy.array}, {PIL.Image} or {str} or {pathlib.Path} filepath to an image and displays the image. Examples-format: a {str} local filepath or URL to an image. Demos: image_mod, image_mod_default_image - Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, building-a-pictionary_app, create-your-own-friends-with-a-gan + Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, create-your-own-friends-with-a-gan """ EVENTS = [ diff --git a/gradio/components/label.py b/gradio/components/label.py index 3d437a504e8b..bb910dfd27a7 100644 --- a/gradio/components/label.py +++ b/gradio/components/label.py @@ -34,7 +34,7 @@ class Label(Component): Postprocessing: expects a {Dict[str, float]} of classes and confidences, or {str} with just the class or an {int}/{float} for regression outputs, or a {str} path to a .json file containing a json dictionary in the structure produced by Label.postprocess(). Demos: main_note, titanic_survival - Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, building-a-pictionary-app + Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers """ CONFIDENCES_KEY = "confidences" diff --git a/guides/09_other-tutorials/building-a-pictionary-app.md b/guides/09_other-tutorials/building-a-pictionary-app.md deleted file mode 100644 index 7b71ae216a5c..000000000000 --- a/guides/09_other-tutorials/building-a-pictionary-app.md +++ /dev/null @@ -1,106 +0,0 @@ -# Building a Pictionary App - -Related spaces: https://huggingface.co/spaces/nateraw/quickdraw -Tags: SKETCHPAD, LABELS, LIVE - -## Introduction - -How well can an algorithm guess what you're drawing? A few years ago, Google released the **Quick Draw** dataset, which contains drawings made by humans of a variety of every objects. Researchers have used this dataset to train models to guess Pictionary-style drawings. - -Such models are perfect to use with Gradio's _sketchpad_ input, so in this tutorial we will build a Pictionary web application using Gradio. We will be able to build the whole web application in Python, and it will look like the demo on the bottom of the page. - -Let's get started! This guide covers how to build a pictionary app (step-by-step): - -1. [Set up the Sketch Recognition Model](#1-set-up-the-sketch-recognition-model) -2. [Define a `predict` function](#2-define-a-predict-function) -3. [Create a Gradio Interface](#3-create-a-gradio-interface) - -### Prerequisites - -Make sure you have the `gradio` Python package already [installed](/getting_started). To use the pretrained sketchpad model, also install `torch`. - -## 1. Set up the Sketch Recognition Model - -First, you will need a sketch recognition model. Since many researchers have already trained their own models on the Quick Draw dataset, we will use a pretrained model in this tutorial. Our model is a light 1.5 MB model trained by Nate Raw, that [you can download here](https://huggingface.co/spaces/nateraw/quickdraw/blob/main/pytorch_model.bin). - -If you are interested, here [is the code](https://github.com/nateraw/quickdraw-pytorch) that was used to train the model. We will simply load the pretrained model in PyTorch, as follows: - -```python -import torch -from torch import nn - -model = nn.Sequential( - nn.Conv2d(1, 32, 3, padding='same'), - nn.ReLU(), - nn.MaxPool2d(2), - nn.Conv2d(32, 64, 3, padding='same'), - nn.ReLU(), - nn.MaxPool2d(2), - nn.Conv2d(64, 128, 3, padding='same'), - nn.ReLU(), - nn.MaxPool2d(2), - nn.Flatten(), - nn.Linear(1152, 256), - nn.ReLU(), - nn.Linear(256, len(LABELS)), -) -state_dict = torch.load('pytorch_model.bin', map_location='cpu') -model.load_state_dict(state_dict, strict=False) -model.eval() -``` - -## 2. Define a `predict` function - -Next, you will need to define a function that takes in the _user input_, which in this case is a sketched image, and returns the prediction. The prediction should be returned as a dictionary whose keys are class name and values are confidence probabilities. We will load the class names from this [text file](https://huggingface.co/spaces/nateraw/quickdraw/blob/main/class_names.txt). - -In the case of our pretrained model, it will look like this: - -```python -from pathlib import Path - -LABELS = Path('class_names.txt').read_text().splitlines() - -def predict(img): - x = torch.tensor(img, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255. - with torch.no_grad(): - out = model(x) - probabilities = torch.nn.functional.softmax(out[0], dim=0) - values, indices = torch.topk(probabilities, 5) - confidences = {LABELS[i]: v.item() for i, v in zip(indices, values)} - return confidences -``` - -Let's break this down. The function takes one parameters: - -- `img`: the input image as a `numpy` array - -Then, the function converts the image to a PyTorch `tensor`, passes it through the model, and returns: - -- `confidences`: the top five predictions, as a dictionary whose keys are class labels and whose values are confidence probabilities - -## 3. Create a Gradio Interface - -Now that we have our predictive function set up, we can create a Gradio Interface around it. - -In this case, the input component is a sketchpad. To create a sketchpad input, we can use the convenient string shortcut, `"sketchpad"` which creates a canvas for a user to draw on and handles the preprocessing to convert that to a numpy array. - -The output component will be a `"label"`, which displays the top labels in a nice form. - -Finally, we'll add one more parameter, setting `live=True`, which allows our interface to run in real time, adjusting its predictions every time a user draws on the sketchpad. The code for Gradio looks like this: - -```python -import gradio as gr - -gr.Interface(fn=predict, - inputs="sketchpad", - outputs="label", - live=True).launch() -``` - -This produces the following interface, which you can try right here in your browser (try drawing something, like a "snake" or a "laptop"): - - - ---- - -And you're done! That's all the code you need to build a Pictionary-style guessing app. Have fun and try to find some edge cases 🧐 diff --git a/guides/cn/07_other-tutorials/building-a-pictionary-app.md b/guides/cn/07_other-tutorials/building-a-pictionary-app.md deleted file mode 100644 index 6b5052d39d57..000000000000 --- a/guides/cn/07_other-tutorials/building-a-pictionary-app.md +++ /dev/null @@ -1,108 +0,0 @@ -# 构建一个 Pictionary 应用程序 - -相关空间：https://huggingface.co/spaces/nateraw/quickdraw -标签：SKETCHPAD，LABELS，LIVE - -## 简介 - -一个算法能够有多好地猜出你在画什么？几年前，Google 发布了 **Quick Draw** 数据集，其中包含人类绘制的各种物体的图画。研究人员使用这个数据集训练模型来猜测 Pictionary 风格的图画。 - -这样的模型非常适合与 Gradio 的 _sketchpad_ 输入一起使用，因此在本教程中，我们将使用 Gradio 构建一个 Pictionary 网络应用程序。我们将能够完全使用 Python 构建整个网络应用程序，并且将如下所示（尝试画点什么！）： - - - -让我们开始吧！本指南介绍了如何构建一个 pictionary 应用程序（逐步）： - -1. [设置 Sketch Recognition 模型](#1-set-up-the-sketch-recognition-model) -2. [定义 `predict` 函数](#2-define-a-predict-function) -3. [创建 Gradio 界面](#3-create-a-gradio-interface) - -### 先决条件 - -确保您已经[安装](/getting_started)了 `gradio` Python 包。要使用预训练的草图模型，还需要安装 `torch`。 - -## 1. 设置 Sketch Recognition 模型 - -首先，您将需要一个草图识别模型。由于许多研究人员已经在 Quick Draw 数据集上训练了自己的模型，在本教程中，我们将使用一个预训练模型。我们的模型是一个由 Nate Raw 训练的轻量级 1.5MB 模型，您可以在此处[下载](https://huggingface.co/spaces/nateraw/quickdraw/blob/main/pytorch_model.bin)。 - -如果您感兴趣，这是用于训练模型的[代码](https://github.com/nateraw/quickdraw-pytorch)。我们将简单地使用 PyTorch 加载预训练的模型，如下所示： - -```python -import torch -from torch import nn - -model = nn.Sequential( - nn.Conv2d(1, 32, 3, padding='same'), - nn.ReLU(), - nn.MaxPool2d(2), - nn.Conv2d(32, 64, 3, padding='same'), - nn.ReLU(), - nn.MaxPool2d(2), - nn.Conv2d(64, 128, 3, padding='same'), - nn.ReLU(), - nn.MaxPool2d(2), - nn.Flatten(), - nn.Linear(1152, 256), - nn.ReLU(), - nn.Linear(256, len(LABELS)), -) -state_dict = torch.load('pytorch_model.bin', map_location='cpu') -model.load_state_dict(state_dict, strict=False) -model.eval() -``` - -## 2. 定义 `predict` 函数 - -接下来，您需要定义一个函数，该函数接受*用户输入*（在本例中是一个涂鸦图像）并返回预测结果。预测结果应该作为一个字典返回，其中键是类名，值是置信度概率。我们将从这个[文本文件](https://huggingface.co/spaces/nateraw/quickdraw/blob/main/class_names.txt)加载类名。 - -对于我们的预训练模型，代码如下所示： - -```python -from pathlib import Path - -LABELS = Path('class_names.txt').read_text().splitlines() - -def predict(img): - x = torch.tensor(img, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255. - with torch.no_grad(): - out = model(x) - probabilities = torch.nn.functional.softmax(out[0], dim=0) - values, indices = torch.topk(probabilities, 5) - confidences = {LABELS[i]: v.item() for i, v in zip(indices, values)} - return confidences -``` - -让我们分解一下。该函数接受一个参数： - -- `img`：输入图像，作为一个 `numpy` 数组 - -然后，函数将图像转换为 PyTorch 的 `tensor`，将其通过模型，并返回： - -- `confidences`：前五个预测的字典，其中键是类别标签，值是置信度概率 - -## 3. 创建一个 Gradio 界面 - -现在我们已经设置好预测函数，我们可以在其周围创建一个 Gradio 界面。 - -在本例中，输入组件是一个 `sketchpad`，使用方便的字符串快捷方式 `"sketchpad"` 创建一个用户可以在其上绘制的画布，并处理将其转换为 numpy 数组的预处理。 - -输出组件将是一个 `"label"`，以良好的形式显示前几个标签。 - -最后，我们将添加一个额外的参数，设置 `live=True`，允许我们的界面实时运行，每当用户在涂鸦板上绘制时，就会调整其预测结果。Gradio 的代码如下所示： - -```python -import gradio as gr - -gr.Interface(fn=predict, - inputs="sketchpad", - outputs="label", - live=True).launch() -``` - -这将产生以下界面，您可以在浏览器中尝试（尝试画一些东西，比如 "snake" 或 "laptop"）： - - - ---- - -完成！这就是构建一个 Pictionary 风格的猜词游戏所需的所有代码。玩得开心，并尝试找到一些边缘情况🧐 diff --git a/js/_website/src/lib/assets/demo_code.js b/js/_website/src/lib/assets/demo_code.js index 39d431877402..bc8a81c4aecd 100644 --- a/js/_website/src/lib/assets/demo_code.js +++ b/js/_website/src/lib/assets/demo_code.js @@ -1,9 +1,10 @@ -export const sketch = `
    import gradio as gr
-def sketch_recognition(img):
-    pass # Implement your sketch recognition model here...
+export const hello = `
    import gradio as gr
 
-gr.Interface(fn=sketch_recognition, inputs="sketchpad", outputs="label").launch()
-
    `;
+def greet(name):
+    return "Hello " + name + "!"
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+    demo.launch()
    `; export const chat = `
    import gradio as gr
 def chat(message, history):
diff --git a/js/_website/src/lib/components/DemosLanding.svelte b/js/_website/src/lib/components/DemosLanding.svelte
index 8e3dbb5050a2..a0d16c3f4d2a 100644
--- a/js/_website/src/lib/components/DemosLanding.svelte
+++ b/js/_website/src/lib/components/DemosLanding.svelte
@@ -1,26 +1,26 @@

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