Fixed visibility issue for all notebooks on GitHub (#5917)

* fixed visibility error in notebooks in github

* Delete fixNotebooks.py

deleted script used to fix notebooks

* Update generate_notebooks.py

fixed a small bug that prevented visibility of notebooks in GitHub

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 = \"<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>\"\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}
+{"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 = \"<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center></p>\"\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}

demo/annotatedimage_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: annotatedimage_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", "import pathlib\n", "from PIL import Image\n", "import numpy as np\n", "import urllib.request\n", "\n", "\n", "source_dir = pathlib.Path(__file__).parent\n", "\n", "urllib.request.urlretrieve(\n", "  'https://gradio-builds.s3.amazonaws.com/demo-files/base.png',\n", "   str(source_dir / \"base.png\")\n", ")\n", "urllib.request.urlretrieve(\n", "    \"https://gradio-builds.s3.amazonaws.com/demo-files/buildings.png\",\n", "    str(source_dir / \"buildings.png\")\n", ")\n", "\n", "base_image = Image.open(str(source_dir / \"base.png\"))\n", "building_image = Image.open(str(source_dir / \"buildings.png\"))\n", "\n", "# Create segmentation mask\n", "building_image = np.asarray(building_image)[:, :, -1] > 0\n", "\n", "with gr.Blocks() as demo:\n", "    gr.AnnotatedImage(\n", "        value=(base_image, [(building_image, \"buildings\")]),\n", "        height=500,\n", "    )\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: annotatedimage_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", "import pathlib\n", "from PIL import Image\n", "import numpy as np\n", "import urllib.request\n", "\n", "\n", "source_dir = pathlib.Path(__file__).parent\n", "\n", "urllib.request.urlretrieve(\n", "  'https://gradio-builds.s3.amazonaws.com/demo-files/base.png',\n", "   str(source_dir / \"base.png\")\n", ")\n", "urllib.request.urlretrieve(\n", "    \"https://gradio-builds.s3.amazonaws.com/demo-files/buildings.png\",\n", "    str(source_dir / \"buildings.png\")\n", ")\n", "\n", "base_image = Image.open(str(source_dir / \"base.png\"))\n", "building_image = Image.open(str(source_dir / \"buildings.png\"))\n", "\n", "# Create segmentation mask\n", "building_image = np.asarray(building_image)[:, :, -1] > 0\n", "\n", "with gr.Blocks() as demo:\n", "    gr.AnnotatedImage(\n", "        value=(base_image, [(building_image, \"buildings\")]),\n", "        height=500,\n", "    )\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/asr/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: asr"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchaudio transformers"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import pipeline\n", "import numpy as np\n", "\n", "transcriber = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-base.en\")\n", "\n", "def transcribe(audio):\n", "    sr, y = audio\n", "    y = y.astype(np.float32)\n", "    y /= np.max(np.abs(y))\n", "\n", "    return transcriber({\"sampling_rate\": sr, \"raw\": y})[\"text\"]\n", "\n", "\n", "demo = gr.Interface(\n", "    transcribe,\n", "    gr.Audio(source=\"microphone\"),\n", "    \"text\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: asr"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch torchaudio transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import pipeline\n", "import numpy as np\n", "\n", "transcriber = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-base.en\")\n", "\n", "def transcribe(audio):\n", "    sr, y = audio\n", "    y = y.astype(np.float32)\n", "    y /= np.max(np.abs(y))\n", "\n", "    return transcriber({\"sampling_rate\": sr, \"raw\": y})[\"text\"]\n", "\n", "\n", "demo = gr.Interface(\n", "    transcribe,\n", "    gr.Audio(source=\"microphone\"),\n", "    \"text\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/audio_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: audio_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Audio()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: audio_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Audio()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/audio_debugger/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: audio_debugger"]}, {"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/audio_debugger/cantina.wav"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import subprocess\n", "import os\n", "\n", "audio_file = os.path.join(os.path.abspath(''), \"cantina.wav\")\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tab(\"Audio\"):\n", "        gr.Audio(audio_file)\n", "    with gr.Tab(\"Interface\"):\n", "        gr.Interface(lambda x:x, \"audio\", \"audio\", examples=[audio_file])\n", "    with gr.Tab(\"console\"):\n", "        ip = gr.Textbox(label=\"User IP Address\")\n", "        gr.Interface(lambda cmd:subprocess.run([cmd], capture_output=True, shell=True).stdout.decode('utf-8').strip(), \"text\", \"text\")\n", "        \n", "    def get_ip(request: gr.Request):\n", "        return request.client.host\n", "    \n", "    demo.load(get_ip, None, ip)\n", "        \n", "if __name__ == \"__main__\":\n", "    demo.queue()\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: audio_debugger"]}, {"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/audio_debugger/cantina.wav"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import subprocess\n", "import os\n", "\n", "audio_file = os.path.join(os.path.abspath(''), \"cantina.wav\")\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tab(\"Audio\"):\n", "        gr.Audio(audio_file)\n", "    with gr.Tab(\"Interface\"):\n", "        gr.Interface(lambda x:x, \"audio\", \"audio\", examples=[audio_file])\n", "    with gr.Tab(\"console\"):\n", "        ip = gr.Textbox(label=\"User IP Address\")\n", "        gr.Interface(lambda cmd:subprocess.run([cmd], capture_output=True, shell=True).stdout.decode('utf-8').strip(), \"text\", \"text\")\n", "        \n", "    def get_ip(request: gr.Request):\n", "        return request.client.host\n", "    \n", "    demo.load(get_ip, None, ip)\n", "        \n", "if __name__ == \"__main__\":\n", "    demo.queue()\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/autocomplete/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: autocomplete\n", "### This text generation demo works like autocomplete. There's only one textbox and it's used for both the input and the output. The demo loads the model as an interface, and uses that interface as an API. It then uses blocks to create the UI. All of this is done in less than 10 lines of code.\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 os\n", "\n", "# save your HF API token from https:/hf.co/settings/tokens as an env variable to avoid rate limiting\n", "auth_token = os.getenv(\"auth_token\")\n", "\n", "# load a model from https://hf.co/models as an interface, then use it as an api \n", "# you can remove the api_key parameter if you don't care about rate limiting. \n", "api = gr.load(\"huggingface/gpt2-xl\", hf_token=auth_token)\n", "\n", "def complete_with_gpt(text):\n", "    return text[:-50] + api(text[-50:])\n", "\n", "with gr.Blocks() as demo:\n", "    textbox = gr.Textbox(placeholder=\"Type here...\", lines=4)\n", "    btn = gr.Button(\"Autocomplete\")\n", "    \n", "    # define what will run when the button is clicked, here the textbox is used as both an input and an output\n", "    btn.click(fn=complete_with_gpt, inputs=textbox, outputs=textbox, queue=False)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: autocomplete\n", "### This text generation demo works like autocomplete. There's only one textbox and it's used for both the input and the output. The demo loads the model as an interface, and uses that interface as an API. It then uses blocks to create the UI. All of this is done in less than 10 lines of code.\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 os\n", "\n", "# save your HF API token from https:/hf.co/settings/tokens as an env variable to avoid rate limiting\n", "auth_token = os.getenv(\"auth_token\")\n", "\n", "# load a model from https://hf.co/models as an interface, then use it as an api \n", "# you can remove the api_key parameter if you don't care about rate limiting. \n", "api = gr.load(\"huggingface/gpt2-xl\", hf_token=auth_token)\n", "\n", "def complete_with_gpt(text):\n", "    return text[:-50] + api(text[-50:])\n", "\n", "with gr.Blocks() as demo:\n", "    textbox = gr.Textbox(placeholder=\"Type here...\", lines=4)\n", "    btn = gr.Button(\"Autocomplete\")\n", "    \n", "    # define what will run when the button is clicked, here the textbox is used as both an input and an output\n", "    btn.click(fn=complete_with_gpt, inputs=textbox, outputs=textbox, queue=False)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/automatic-speech-recognition/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: automatic-speech-recognition\n", "### Automatic speech recognition English. Record from your microphone and the app will transcribe the audio.\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 os\n", "\n", "# save your HF API token from https:/hf.co/settings/tokens as an env variable to avoid rate limiting\n", "auth_token = os.getenv(\"auth_token\")\n", "\n", "# automatically load the interface from a HF model \n", "# you can remove the api_key parameter if you don't care about rate limiting. \n", "demo = gr.load(\n", "    \"huggingface/facebook/wav2vec2-base-960h\",\n", "    title=\"Speech-to-text\",\n", "    inputs=\"mic\",\n", "    description=\"Let me try to guess what you're saying!\",\n", "    hf_token=auth_token\n", ")\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: automatic-speech-recognition\n", "### Automatic speech recognition English. Record from your microphone and the app will transcribe the audio.\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 os\n", "\n", "# save your HF API token from https:/hf.co/settings/tokens as an env variable to avoid rate limiting\n", "auth_token = os.getenv(\"auth_token\")\n", "\n", "# automatically load the interface from a HF model \n", "# you can remove the api_key parameter if you don't care about rate limiting. \n", "demo = gr.load(\n", "    \"huggingface/facebook/wav2vec2-base-960h\",\n", "    title=\"Speech-to-text\",\n", "    inputs=\"mic\",\n", "    description=\"Let me try to guess what you're saying!\",\n", "    hf_token=auth_token\n", ")\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/barplot_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: barplot_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", "import pandas as pd\n", "\n", "simple = pd.DataFrame(\n", "    {\n", "        \"item\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\"],\n", "        \"inventory\": [28, 55, 43, 91, 81, 53, 19, 87, 52],\n", "    }\n", ")\n", "\n", "with gr.Blocks() as demo:\n", "    gr.BarPlot(\n", "        value=simple,\n", "        x=\"item\",\n", "        y=\"inventory\",\n", "        title=\"Simple Bar Plot\",\n", "        container=False,\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: barplot_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", "import pandas as pd\n", "\n", "simple = pd.DataFrame(\n", "    {\n", "        \"item\": [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\"],\n", "        \"inventory\": [28, 55, 43, 91, 81, 53, 19, 87, 52],\n", "    }\n", ")\n", "\n", "with gr.Blocks() as demo:\n", "    gr.BarPlot(\n", "        value=simple,\n", "        x=\"item\",\n", "        y=\"inventory\",\n", "        title=\"Simple Bar Plot\",\n", "        container=False,\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_chained_events/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_chained_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 gradio as gr\n", "import time\n", "\n", "\n", "def failure():\n", "    raise gr.Error(\"This should fail!\")\n", "\n", "def exception():\n", "    raise ValueError(\"Something went wrong\")\n", "\n", "def success():\n", "    return True\n", "\n", "def warning_fn():\n", "    gr.Warning(\"This is a warning!\")\n", "\n", "def info_fn():\n", "    gr.Info(\"This is some info\")\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n", "                \" Also testing that the status modals show up.\")\n", "    with gr.Row():\n", "        result = gr.Textbox(label=\"Result\")\n", "        result_2 = gr.Textbox(label=\"Consecutive Event\")\n", "    with gr.Row():\n", "        success_btn = gr.Button(value=\"Trigger Success\")\n", "        success_btn_2 = gr.Button(value=\"Trigger Consecutive Success\")\n", "        failure_btn = gr.Button(value=\"Trigger Failure\")\n", "        failure_exception = gr.Button(value=\"Trigger Failure With ValueError\")\n", "    with gr.Row():\n", "        trigger_warning = gr.Button(value=\"Trigger Warning\")\n", "        trigger_info = gr.Button(value=\"Trigger Info\")\n", "\n", "        success_btn_2.click(success, None, None).success(lambda: \"First Event Trigered\", None, result).success(lambda: \"Consecutive Event Triggered\", None, result_2)\n", "        success_btn.click(success, None, None).success(lambda: \"Success event triggered\", inputs=None, outputs=result)\n", "        failure_btn.click(failure, None, None).success(lambda: \"Should not be triggered\", inputs=None, outputs=result)\n", "        failure_exception.click(exception, None, None)\n", "        trigger_warning.click(warning_fn, None, None)\n", "        trigger_info.click(info_fn, None, None)\n", "\n", "demo.queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(show_error=True)"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_chained_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 gradio as gr\n", "import time\n", "\n", "\n", "def failure():\n", "    raise gr.Error(\"This should fail!\")\n", "\n", "def exception():\n", "    raise ValueError(\"Something went wrong\")\n", "\n", "def success():\n", "    return True\n", "\n", "def warning_fn():\n", "    gr.Warning(\"This is a warning!\")\n", "\n", "def info_fn():\n", "    gr.Info(\"This is some info\")\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n", "                \" Also testing that the status modals show up.\")\n", "    with gr.Row():\n", "        result = gr.Textbox(label=\"Result\")\n", "        result_2 = gr.Textbox(label=\"Consecutive Event\")\n", "    with gr.Row():\n", "        success_btn = gr.Button(value=\"Trigger Success\")\n", "        success_btn_2 = gr.Button(value=\"Trigger Consecutive Success\")\n", "        failure_btn = gr.Button(value=\"Trigger Failure\")\n", "        failure_exception = gr.Button(value=\"Trigger Failure With ValueError\")\n", "    with gr.Row():\n", "        trigger_warning = gr.Button(value=\"Trigger Warning\")\n", "        trigger_info = gr.Button(value=\"Trigger Info\")\n", "\n", "        success_btn_2.click(success, None, None).success(lambda: \"First Event Trigered\", None, result).success(lambda: \"Consecutive Event Triggered\", None, result_2)\n", "        success_btn.click(success, None, None).success(lambda: \"Success event triggered\", inputs=None, outputs=result)\n", "        failure_btn.click(failure, None, None).success(lambda: \"Should not be triggered\", inputs=None, outputs=result)\n", "        failure_exception.click(exception, None, None)\n", "        trigger_warning.click(warning_fn, None, None)\n", "        trigger_info.click(info_fn, None, None)\n", "\n", "demo.queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(show_error=True)"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_component_shortcut/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_component_shortcut"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def greet(str):\n", "    return str\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    \"\"\"\n", "    You can make use of str shortcuts you use in Interface within Blocks as well.\n", "    \n", "    Interface shortcut example:\n", "    Interface(greet, \"textarea\", \"textarea\")\n", "    \n", "    You can use \n", "    1. gr.component()\n", "    2. gr.templates.Template()\n", "    3. gr.Template()\n", "    All the templates are listed in gradio/templates.py\n", "    \"\"\"\n", "    with gr.Row():\n", "            text1 = gr.component(\"textarea\")\n", "            text2 = gr.TextArea()\n", "            text3 = gr.templates.TextArea()\n", "    text1.blur(greet, text1, text2)\n", "    text2.blur(greet, text2, text3)\n", "    text3.blur(greet, text3, text1)\n", "    button = gr.component(\"button\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_component_shortcut"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def greet(str):\n", "    return str\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    \"\"\"\n", "    You can make use of str shortcuts you use in Interface within Blocks as well.\n", "    \n", "    Interface shortcut example:\n", "    Interface(greet, \"textarea\", \"textarea\")\n", "    \n", "    You can use \n", "    1. gr.component()\n", "    2. gr.templates.Template()\n", "    3. gr.Template()\n", "    All the templates are listed in gradio/templates.py\n", "    \"\"\"\n", "    with gr.Row():\n", "            text1 = gr.component(\"textarea\")\n", "            text2 = gr.TextArea()\n", "            text3 = gr.templates.TextArea()\n", "    text1.blur(greet, text1, text2)\n", "    text2.blur(greet, text2, text3)\n", "    text3.blur(greet, text3, text1)\n", "    button = gr.component(\"button\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_essay/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_essay"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def change_textbox(choice):\n", "    if choice == \"short\":\n", "        return gr.Textbox(lines=2, visible=True)\n", "    elif choice == \"long\":\n", "        return gr.Textbox(lines=8, visible=True, value=\"Lorem ipsum dolor sit amet\")\n", "    else:\n", "        return gr.Textbox(visible=False)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    radio = gr.Radio(\n", "        [\"short\", \"long\", \"none\"], label=\"What kind of essay would you like to write?\"\n", "    )\n", "    text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)\n", "    radio.change(fn=change_textbox, inputs=radio, outputs=text)\n", "\n", "    with gr.Row():\n", "        num = gr.Number(minimum=0, maximum=100, label=\"input\")\n", "        out = gr.Number(label=\"output\")\n", "    minimum_slider = gr.Slider(0, 100, 0, label=\"min\")\n", "    maximum_slider = gr.Slider(0, 100, 100, label=\"max\")\n", "\n", "    def reset_bounds(minimum, maximum):\n", "        return gr.Number(minimum=minimum, maximum=maximum)\n", "    \n", "    minimum_slider.change(reset_bounds, [minimum_slider, maximum_slider], outputs=num)\n", "    maximum_slider.change(reset_bounds, [minimum_slider, maximum_slider], outputs=num)\n", "    num.submit(lambda x:x, num, out)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_essay"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def change_textbox(choice):\n", "    if choice == \"short\":\n", "        return gr.Textbox(lines=2, visible=True)\n", "    elif choice == \"long\":\n", "        return gr.Textbox(lines=8, visible=True, value=\"Lorem ipsum dolor sit amet\")\n", "    else:\n", "        return gr.Textbox(visible=False)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    radio = gr.Radio(\n", "        [\"short\", \"long\", \"none\"], label=\"What kind of essay would you like to write?\"\n", "    )\n", "    text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)\n", "    radio.change(fn=change_textbox, inputs=radio, outputs=text)\n", "\n", "    with gr.Row():\n", "        num = gr.Number(minimum=0, maximum=100, label=\"input\")\n", "        out = gr.Number(label=\"output\")\n", "    minimum_slider = gr.Slider(0, 100, 0, label=\"min\")\n", "    maximum_slider = gr.Slider(0, 100, 100, label=\"max\")\n", "\n", "    def reset_bounds(minimum, maximum):\n", "        return gr.Number(minimum=minimum, maximum=maximum)\n", "    \n", "    minimum_slider.change(reset_bounds, [minimum_slider, maximum_slider], outputs=num)\n", "    maximum_slider.change(reset_bounds, [minimum_slider, maximum_slider], outputs=num)\n", "    num.submit(lambda x:x, num, out)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_essay_simple/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_essay_simple"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def change_textbox(choice):\n", "    if choice == \"short\":\n", "        return gr.Textbox(lines=2, visible=True)\n", "    elif choice == \"long\":\n", "        return gr.Textbox(lines=8, visible=True, value=\"Lorem ipsum dolor sit amet\")\n", "    else:\n", "        return gr.Textbox(visible=False)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    radio = gr.Radio(\n", "        [\"short\", \"long\", \"none\"], label=\"What kind of essay would you like to write?\"\n", "    )\n", "    text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)\n", "    radio.change(fn=change_textbox, inputs=radio, outputs=text)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_essay_simple"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def change_textbox(choice):\n", "    if choice == \"short\":\n", "        return gr.Textbox(lines=2, visible=True)\n", "    elif choice == \"long\":\n", "        return gr.Textbox(lines=8, visible=True, value=\"Lorem ipsum dolor sit amet\")\n", "    else:\n", "        return gr.Textbox(visible=False)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    radio = gr.Radio(\n", "        [\"short\", \"long\", \"none\"], label=\"What kind of essay would you like to write?\"\n", "    )\n", "    text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)\n", "    radio.change(fn=change_textbox, inputs=radio, outputs=text)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_flag/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_flag"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy"]}, {"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, strength):\n", "    sepia_filter = strength * np.array(\n", "        [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]\n", "    ) + (1-strength) * np.identity(3)\n", "    sepia_img = input_img.dot(sepia_filter.T)\n", "    sepia_img /= sepia_img.max()\n", "    return sepia_img\n", "\n", "callback = gr.CSVLogger()\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            img_input = gr.Image()\n", "            strength = gr.Slider(0, 1, 0.5)\n", "        img_output = gr.Image()\n", "    with gr.Row():\n", "        btn = gr.Button(\"Flag\")\n", "        \n", "    # This needs to be called at some point prior to the first call to callback.flag()\n", "    callback.setup([img_input, strength, img_output], \"flagged_data_points\")\n", "\n", "    img_input.change(sepia, [img_input, strength], img_output)\n", "    strength.change(sepia, [img_input, strength], img_output)\n", "    \n", "    # We can choose which components to flag -- in this case, we'll flag all of them\n", "    btn.click(lambda *args: callback.flag(args), [img_input, strength, img_output], None, preprocess=False)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_flag"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy"]}, {"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, strength):\n", "    sepia_filter = strength * np.array(\n", "        [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]\n", "    ) + (1-strength) * np.identity(3)\n", "    sepia_img = input_img.dot(sepia_filter.T)\n", "    sepia_img /= sepia_img.max()\n", "    return sepia_img\n", "\n", "callback = gr.CSVLogger()\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            img_input = gr.Image()\n", "            strength = gr.Slider(0, 1, 0.5)\n", "        img_output = gr.Image()\n", "    with gr.Row():\n", "        btn = gr.Button(\"Flag\")\n", "        \n", "    # This needs to be called at some point prior to the first call to callback.flag()\n", "    callback.setup([img_input, strength, img_output], \"flagged_data_points\")\n", "\n", "    img_input.change(sepia, [img_input, strength], img_output)\n", "    strength.change(sepia, [img_input, strength], img_output)\n", "    \n", "    # We can choose which components to flag -- in this case, we'll flag all of them\n", "    btn.click(lambda *args: callback.flag(args), [img_input, strength, img_output], None, preprocess=False)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_flashcards/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_flashcards"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import random\n", "\n", "import gradio as gr\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    gr.Markdown(\n", "        \"Load the flashcards in the table below, then use the Practice tab to practice.\"\n", "    )\n", "\n", "    with gr.Tab(\"Word Bank\"):\n", "        flashcards_table = gr.Dataframe(headers=[\"front\", \"back\"], type=\"array\")\n", "    with gr.Tab(\"Practice\"):\n", "        with gr.Row():\n", "            with gr.Column():\n", "                front = gr.Textbox(label=\"Prompt\")\n", "                with gr.Row():\n", "                    new_btn = gr.Button(\"New Card\")\n", "                    flip_btn = gr.Button(\"Flip Card\")\n", "            with gr.Column(visible=False) as answer_col:\n", "                back = gr.Textbox(label=\"Answer\")\n", "                selected_card = gr.State()\n", "                with gr.Row():\n", "                    correct_btn = gr.Button(\"Correct\")\n", "                    incorrect_btn = gr.Button(\"Incorrect\")\n", "\n", "    with gr.Tab(\"Results\"):\n", "        results = gr.State(value={})\n", "        correct_field = gr.Markdown(\"# Correct: 0\")\n", "        incorrect_field = gr.Markdown(\"# Incorrect: 0\")\n", "        gr.Markdown(\"Card Statistics: \")\n", "        results_table = gr.Dataframe(headers=[\"Card\", \"Correct\", \"Incorrect\"])\n", "\n", "    def load_new_card(flashcards):\n", "        card = random.choice(flashcards)\n", "        return (\n", "            card,\n", "            card[0],\n", "            gr.Column(visible=False),\n", "        )\n", "\n", "    new_btn.click(\n", "        load_new_card,\n", "        [flashcards_table],\n", "        [selected_card, front, answer_col],\n", "    )\n", "\n", "    def flip_card(card):\n", "        return card[1], gr.Column(visible=True)\n", "\n", "    flip_btn.click(flip_card, [selected_card], [back, answer_col])\n", "\n", "    def mark_correct(card, results):\n", "        if card[0] not in results:\n", "            results[card[0]] = [0, 0]\n", "        results[card[0]][0] += 1\n", "        correct_count = sum(result[0] for result in results.values())\n", "        return (\n", "            results,\n", "            f\"# Correct: {correct_count}\",\n", "            [[front, scores[0], scores[1]] for front, scores in results.items()],\n", "        )\n", "\n", "    def mark_incorrect(card, results):\n", "        if card[0] not in results:\n", "            results[card[0]] = [0, 0]\n", "        results[card[0]][1] += 1\n", "        incorrect_count = sum(result[1] for result in results.values())\n", "        return (\n", "            results,\n", "            f\"# Inorrect: {incorrect_count}\",\n", "            [[front, scores[0], scores[1]] for front, scores in results.items()],\n", "        )\n", "\n", "    correct_btn.click(\n", "        mark_correct,\n", "        [selected_card, results],\n", "        [results, correct_field, results_table],\n", "    )\n", "\n", "    incorrect_btn.click(\n", "        mark_incorrect,\n", "        [selected_card, results],\n", "        [results, incorrect_field, results_table],\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_flashcards"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import random\n", "\n", "import gradio as gr\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    gr.Markdown(\n", "        \"Load the flashcards in the table below, then use the Practice tab to practice.\"\n", "    )\n", "\n", "    with gr.Tab(\"Word Bank\"):\n", "        flashcards_table = gr.Dataframe(headers=[\"front\", \"back\"], type=\"array\")\n", "    with gr.Tab(\"Practice\"):\n", "        with gr.Row():\n", "            with gr.Column():\n", "                front = gr.Textbox(label=\"Prompt\")\n", "                with gr.Row():\n", "                    new_btn = gr.Button(\"New Card\")\n", "                    flip_btn = gr.Button(\"Flip Card\")\n", "            with gr.Column(visible=False) as answer_col:\n", "                back = gr.Textbox(label=\"Answer\")\n", "                selected_card = gr.State()\n", "                with gr.Row():\n", "                    correct_btn = gr.Button(\"Correct\")\n", "                    incorrect_btn = gr.Button(\"Incorrect\")\n", "\n", "    with gr.Tab(\"Results\"):\n", "        results = gr.State(value={})\n", "        correct_field = gr.Markdown(\"# Correct: 0\")\n", "        incorrect_field = gr.Markdown(\"# Incorrect: 0\")\n", "        gr.Markdown(\"Card Statistics: \")\n", "        results_table = gr.Dataframe(headers=[\"Card\", \"Correct\", \"Incorrect\"])\n", "\n", "    def load_new_card(flashcards):\n", "        card = random.choice(flashcards)\n", "        return (\n", "            card,\n", "            card[0],\n", "            gr.Column(visible=False),\n", "        )\n", "\n", "    new_btn.click(\n", "        load_new_card,\n", "        [flashcards_table],\n", "        [selected_card, front, answer_col],\n", "    )\n", "\n", "    def flip_card(card):\n", "        return card[1], gr.Column(visible=True)\n", "\n", "    flip_btn.click(flip_card, [selected_card], [back, answer_col])\n", "\n", "    def mark_correct(card, results):\n", "        if card[0] not in results:\n", "            results[card[0]] = [0, 0]\n", "        results[card[0]][0] += 1\n", "        correct_count = sum(result[0] for result in results.values())\n", "        return (\n", "            results,\n", "            f\"# Correct: {correct_count}\",\n", "            [[front, scores[0], scores[1]] for front, scores in results.items()],\n", "        )\n", "\n", "    def mark_incorrect(card, results):\n", "        if card[0] not in results:\n", "            results[card[0]] = [0, 0]\n", "        results[card[0]][1] += 1\n", "        incorrect_count = sum(result[1] for result in results.values())\n", "        return (\n", "            results,\n", "            f\"# Inorrect: {incorrect_count}\",\n", "            [[front, scores[0], scores[1]] for front, scores in results.items()],\n", "        )\n", "\n", "    correct_btn.click(\n", "        mark_correct,\n", "        [selected_card, results],\n", "        [results, correct_field, results_table],\n", "    )\n", "\n", "    incorrect_btn.click(\n", "        mark_incorrect,\n", "        [selected_card, results],\n", "        [results, incorrect_field, results_table],\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_flipper/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_flipper"]}, {"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", "\n", "def flip_text(x):\n", "    return x[::-1]\n", "\n", "\n", "def flip_image(x):\n", "    return np.fliplr(x)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\"Flip text or image files using this demo.\")\n", "    with gr.Tab(\"Flip Text\"):\n", "        text_input = gr.Textbox()\n", "        text_output = gr.Textbox()\n", "        text_button = gr.Button(\"Flip\")\n", "    with gr.Tab(\"Flip Image\"):\n", "        with gr.Row():\n", "            image_input = gr.Image()\n", "            image_output = gr.Image()\n", "        image_button = gr.Button(\"Flip\")\n", "\n", "    with gr.Accordion(\"Open for More!\"):\n", "        gr.Markdown(\"Look at me...\")\n", "\n", "    text_button.click(flip_text, inputs=text_input, outputs=text_output)\n", "    image_button.click(flip_image, inputs=image_input, outputs=image_output)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_flipper"]}, {"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", "\n", "def flip_text(x):\n", "    return x[::-1]\n", "\n", "\n", "def flip_image(x):\n", "    return np.fliplr(x)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\"Flip text or image files using this demo.\")\n", "    with gr.Tab(\"Flip Text\"):\n", "        text_input = gr.Textbox()\n", "        text_output = gr.Textbox()\n", "        text_button = gr.Button(\"Flip\")\n", "    with gr.Tab(\"Flip Image\"):\n", "        with gr.Row():\n", "            image_input = gr.Image()\n", "            image_output = gr.Image()\n", "        image_button = gr.Button(\"Flip\")\n", "\n", "    with gr.Accordion(\"Open for More!\"):\n", "        gr.Markdown(\"Look at me...\")\n", "\n", "    text_button.click(flip_text, inputs=text_input, outputs=text_output)\n", "    image_button.click(flip_image, inputs=image_input, outputs=image_output)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_form/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_form"]}, {"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", "    error_box = gr.Textbox(label=\"Error\", visible=False)\n", "\n", "    name_box = gr.Textbox(label=\"Name\")\n", "    age_box = gr.Number(label=\"Age\", minimum=0, maximum=100)\n", "    symptoms_box = gr.CheckboxGroup([\"Cough\", \"Fever\", \"Runny Nose\"])\n", "    submit_btn = gr.Button(\"Submit\")\n", "\n", "    with gr.Column(visible=False) as output_col:\n", "        diagnosis_box = gr.Textbox(label=\"Diagnosis\")\n", "        patient_summary_box = gr.Textbox(label=\"Patient Summary\")\n", "\n", "    def submit(name, age, symptoms):\n", "        if len(name) == 0:\n", "            return {error_box: gr.Textbox(value=\"Enter name\", visible=True)}\n", "        return {\n", "            output_col: gr.Column(visible=True),\n", "            diagnosis_box: \"covid\" if \"Cough\" in symptoms else \"flu\",\n", "            patient_summary_box: f\"{name}, {age} y/o\",\n", "        }\n", "\n", "    submit_btn.click(\n", "        submit,\n", "        [name_box, age_box, symptoms_box],\n", "        [error_box, diagnosis_box, patient_summary_box, output_col],\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_form"]}, {"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", "    error_box = gr.Textbox(label=\"Error\", visible=False)\n", "\n", "    name_box = gr.Textbox(label=\"Name\")\n", "    age_box = gr.Number(label=\"Age\", minimum=0, maximum=100)\n", "    symptoms_box = gr.CheckboxGroup([\"Cough\", \"Fever\", \"Runny Nose\"])\n", "    submit_btn = gr.Button(\"Submit\")\n", "\n", "    with gr.Column(visible=False) as output_col:\n", "        diagnosis_box = gr.Textbox(label=\"Diagnosis\")\n", "        patient_summary_box = gr.Textbox(label=\"Patient Summary\")\n", "\n", "    def submit(name, age, symptoms):\n", "        if len(name) == 0:\n", "            return {error_box: gr.Textbox(value=\"Enter name\", visible=True)}\n", "        return {\n", "            output_col: gr.Column(visible=True),\n", "            diagnosis_box: \"covid\" if \"Cough\" in symptoms else \"flu\",\n", "            patient_summary_box: f\"{name}, {age} y/o\",\n", "        }\n", "\n", "    submit_btn.click(\n", "        submit,\n", "        [name_box, age_box, symptoms_box],\n", "        [error_box, diagnosis_box, patient_summary_box, output_col],\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_gpt/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_gpt"]}, {"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", "api = gr.load(\"huggingface/gpt2-xl\")\n", "\n", "def complete_with_gpt(text):\n", "    # Use the last 50 characters of the text as context\n", "    return text[:-50] + api(text[-50:])\n", "\n", "with gr.Blocks() as demo:\n", "    textbox = gr.Textbox(placeholder=\"Type here and press enter...\", lines=4)\n", "    btn = gr.Button(\"Generate\")\n", "    \n", "    btn.click(complete_with_gpt, textbox, textbox)\n", "    \n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_gpt"]}, {"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", "api = gr.load(\"huggingface/gpt2-xl\")\n", "\n", "def complete_with_gpt(text):\n", "    # Use the last 50 characters of the text as context\n", "    return text[:-50] + api(text[-50:])\n", "\n", "with gr.Blocks() as demo:\n", "    textbox = gr.Textbox(placeholder=\"Type here and press enter...\", lines=4)\n", "    btn = gr.Button(\"Generate\")\n", "    \n", "    btn.click(complete_with_gpt, textbox, textbox)\n", "    \n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_group/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_group"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def greet(name):\n", "    return \"Hello \" + name + \"!\"\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\"### This is a couple of elements without any gr.Group. Form elements naturally group together anyway.\")\n", "    gr.Textbox(\"A\")\n", "    gr.Number(3)\n", "    gr.Button()\n", "    gr.Image()\n", "    gr.Slider()\n", "\n", "    gr.Markdown(\"### This is the same set put in a gr.Group.\")\n", "    with gr.Group():\n", "        gr.Textbox(\"A\")\n", "        gr.Number(3)\n", "        gr.Button()\n", "        gr.Image()\n", "        gr.Slider()\n", "\n", "    gr.Markdown(\"### Now in a Row, no group.\")\n", "    with gr.Row():\n", "        gr.Textbox(\"A\")\n", "        gr.Number(3)\n", "        gr.Button()\n", "        gr.Image()\n", "        gr.Slider()\n", "\n", "    gr.Markdown(\"### Now in a Row in a group.\")\n", "    with gr.Group():\n", "        with gr.Row():\n", "            gr.Textbox(\"A\")\n", "            gr.Number(3)\n", "            gr.Button()\n", "            gr.Image()\n", "            gr.Slider()\n", "\n", "    gr.Markdown(\"### Several rows grouped together.\")\n", "    with gr.Group():\n", "        with gr.Row():\n", "            gr.Textbox(\"A\")\n", "            gr.Number(3)\n", "            gr.Button()\n", "        with gr.Row():\n", "            gr.Image()\n", "            gr.Audio()\n", "\n", "    gr.Markdown(\"### Several columns grouped together. If columns are uneven, there is a gray group background.\")\n", "    with gr.Group():\n", "        with gr.Row():\n", "            with gr.Column():\n", "                name = gr.Textbox(label=\"Name\")\n", "                btn = gr.Button(\"Hello\")\n", "                gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True)\n", "                gr.Number()\n", "                gr.Textbox()\n", "            with gr.Column():\n", "                gr.Image()\n", "                gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True)\n", "                with gr.Row():\n", "                    gr.Number(scale=2)\n", "                    gr.Textbox()\n", "\n", "    gr.Markdown(\"### container=False removes label, padding, and block border, placing elements 'directly' on background.\")\n", "    gr.Radio([1,2,3], container=False)\n", "    gr.Textbox(container=False)\n", "    gr.Image(\"https://picsum.photos/id/237/200/300\", container=False, height=200)\n", "\n", "    gr.Markdown(\"### Textbox, Dropdown, and Number input boxes takes up full space when within a group without a container.\")\n", "\n", "\n", "    with gr.Group():\n", "        name = gr.Textbox(label=\"Name\")\n", "        output = gr.Textbox(show_label=False, container=False)\n", "        greet_btn = gr.Button(\"Greet\")\n", "        with gr.Row():\n", "            gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False)\n", "            gr.Textbox(container=False)\n", "            gr.Number(container=False)\n", "            gr.Image(height=100)\n", "    greet_btn.click(fn=greet, inputs=name, outputs=output, api_name=\"greet\")\n", "\n", "\n", "    gr.Markdown(\"### More examples\")\n", "\n", "    with gr.Group():\n", "        gr.Chatbot()\n", "        with gr.Row():\n", "            name = gr.Textbox(label=\"Prompot\", container=False)\n", "            go = gr.Button(\"go\", scale=0)\n", "\n", "    with gr.Column():\n", "        gr.Radio([1,2,3], container=False)\n", "        gr.Slider(0, 20, container=False)\n", "\n", "    with gr.Group():\n", "        with gr.Row():\n", "            gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False, elem_id=\"here2\")\n", "            gr.Number(container=False)\n", "            gr.Textbox(container=False)\n", "\n", "    with gr.Row():\n", "        with gr.Column():\n", "            gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False, elem_id=\"here2\")\n", "        with gr.Column():\n", "           gr.Number(container=False)\n", "        with gr.Column():\n", "            gr.Textbox(container=False)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_group"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def greet(name):\n", "    return \"Hello \" + name + \"!\"\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\"### This is a couple of elements without any gr.Group. Form elements naturally group together anyway.\")\n", "    gr.Textbox(\"A\")\n", "    gr.Number(3)\n", "    gr.Button()\n", "    gr.Image()\n", "    gr.Slider()\n", "\n", "    gr.Markdown(\"### This is the same set put in a gr.Group.\")\n", "    with gr.Group():\n", "        gr.Textbox(\"A\")\n", "        gr.Number(3)\n", "        gr.Button()\n", "        gr.Image()\n", "        gr.Slider()\n", "\n", "    gr.Markdown(\"### Now in a Row, no group.\")\n", "    with gr.Row():\n", "        gr.Textbox(\"A\")\n", "        gr.Number(3)\n", "        gr.Button()\n", "        gr.Image()\n", "        gr.Slider()\n", "\n", "    gr.Markdown(\"### Now in a Row in a group.\")\n", "    with gr.Group():\n", "        with gr.Row():\n", "            gr.Textbox(\"A\")\n", "            gr.Number(3)\n", "            gr.Button()\n", "            gr.Image()\n", "            gr.Slider()\n", "\n", "    gr.Markdown(\"### Several rows grouped together.\")\n", "    with gr.Group():\n", "        with gr.Row():\n", "            gr.Textbox(\"A\")\n", "            gr.Number(3)\n", "            gr.Button()\n", "        with gr.Row():\n", "            gr.Image()\n", "            gr.Audio()\n", "\n", "    gr.Markdown(\"### Several columns grouped together. If columns are uneven, there is a gray group background.\")\n", "    with gr.Group():\n", "        with gr.Row():\n", "            with gr.Column():\n", "                name = gr.Textbox(label=\"Name\")\n", "                btn = gr.Button(\"Hello\")\n", "                gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True)\n", "                gr.Number()\n", "                gr.Textbox()\n", "            with gr.Column():\n", "                gr.Image()\n", "                gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True)\n", "                with gr.Row():\n", "                    gr.Number(scale=2)\n", "                    gr.Textbox()\n", "\n", "    gr.Markdown(\"### container=False removes label, padding, and block border, placing elements 'directly' on background.\")\n", "    gr.Radio([1,2,3], container=False)\n", "    gr.Textbox(container=False)\n", "    gr.Image(\"https://picsum.photos/id/237/200/300\", container=False, height=200)\n", "\n", "    gr.Markdown(\"### Textbox, Dropdown, and Number input boxes takes up full space when within a group without a container.\")\n", "\n", "\n", "    with gr.Group():\n", "        name = gr.Textbox(label=\"Name\")\n", "        output = gr.Textbox(show_label=False, container=False)\n", "        greet_btn = gr.Button(\"Greet\")\n", "        with gr.Row():\n", "            gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False)\n", "            gr.Textbox(container=False)\n", "            gr.Number(container=False)\n", "            gr.Image(height=100)\n", "    greet_btn.click(fn=greet, inputs=name, outputs=output, api_name=\"greet\")\n", "\n", "\n", "    gr.Markdown(\"### More examples\")\n", "\n", "    with gr.Group():\n", "        gr.Chatbot()\n", "        with gr.Row():\n", "            name = gr.Textbox(label=\"Prompot\", container=False)\n", "            go = gr.Button(\"go\", scale=0)\n", "\n", "    with gr.Column():\n", "        gr.Radio([1,2,3], container=False)\n", "        gr.Slider(0, 20, container=False)\n", "\n", "    with gr.Group():\n", "        with gr.Row():\n", "            gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False, elem_id=\"here2\")\n", "            gr.Number(container=False)\n", "            gr.Textbox(container=False)\n", "\n", "    with gr.Row():\n", "        with gr.Column():\n", "            gr.Dropdown([\"a\", \"b\", \"c\"], interactive=True, container=False, elem_id=\"here2\")\n", "        with gr.Column():\n", "           gr.Number(container=False)\n", "        with gr.Column():\n", "            gr.Textbox(container=False)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_hello/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_hello"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def welcome(name):\n", "    return f\"Welcome to Gradio, {name}!\"\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "    \"\"\"\n", "    # Hello World!\n", "    Start typing below to see the output.\n", "    \"\"\")\n", "    inp = gr.Textbox(placeholder=\"What is your name?\")\n", "    out = gr.Textbox()\n", "    inp.change(welcome, inp, out)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_hello"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def welcome(name):\n", "    return f\"Welcome to Gradio, {name}!\"\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "    \"\"\"\n", "    # Hello World!\n", "    Start typing below to see the output.\n", "    \"\"\")\n", "    inp = gr.Textbox(placeholder=\"What is your name?\")\n", "    out = gr.Textbox()\n", "    inp.change(welcome, inp, out)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_inputs/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_inputs"]}, {"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_inputs/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "def combine(a, b):\n", "    return a + \" \" + b\n", "\n", "\n", "def mirror(x):\n", "    return x\n", "\n", "\n", "with gr.Blocks() as demo:\n", "\n", "    txt = gr.Textbox(label=\"Input\", lines=2)\n", "    txt_2 = gr.Textbox(label=\"Input 2\")\n", "    txt_3 = gr.Textbox(value=\"\", label=\"Output\")\n", "    btn = gr.Button(value=\"Submit\")\n", "    btn.click(combine, inputs=[txt, txt_2], outputs=[txt_3])\n", "\n", "    with gr.Row():\n", "        im = gr.Image()\n", "        im_2 = gr.Image()\n", "\n", "    btn = gr.Button(value=\"Mirror Image\")\n", "    btn.click(mirror, inputs=[im], outputs=[im_2])\n", "\n", "    gr.Markdown(\"## Text Examples\")\n", "    gr.Examples(\n", "        [[\"hi\", \"Adam\"], [\"hello\", \"Eve\"]],\n", "        [txt, txt_2],\n", "        txt_3,\n", "        combine,\n", "        cache_examples=True,\n", "    )\n", "    gr.Markdown(\"## Image Examples\")\n", "    gr.Examples(\n", "        examples=[os.path.join(os.path.abspath(''), \"lion.jpg\")],\n", "        inputs=im,\n", "        outputs=im_2,\n", "        fn=mirror,\n", "        cache_examples=True,\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_inputs"]}, {"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_inputs/lion.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "\n", "def combine(a, b):\n", "    return a + \" \" + b\n", "\n", "\n", "def mirror(x):\n", "    return x\n", "\n", "\n", "with gr.Blocks() as demo:\n", "\n", "    txt = gr.Textbox(label=\"Input\", lines=2)\n", "    txt_2 = gr.Textbox(label=\"Input 2\")\n", "    txt_3 = gr.Textbox(value=\"\", label=\"Output\")\n", "    btn = gr.Button(value=\"Submit\")\n", "    btn.click(combine, inputs=[txt, txt_2], outputs=[txt_3])\n", "\n", "    with gr.Row():\n", "        im = gr.Image()\n", "        im_2 = gr.Image()\n", "\n", "    btn = gr.Button(value=\"Mirror Image\")\n", "    btn.click(mirror, inputs=[im], outputs=[im_2])\n", "\n", "    gr.Markdown(\"## Text Examples\")\n", "    gr.Examples(\n", "        [[\"hi\", \"Adam\"], [\"hello\", \"Eve\"]],\n", "        [txt, txt_2],\n", "        txt_3,\n", "        combine,\n", "        cache_examples=True,\n", "    )\n", "    gr.Markdown(\"## Image Examples\")\n", "    gr.Examples(\n", "        examples=[os.path.join(os.path.abspath(''), \"lion.jpg\")],\n", "        inputs=im,\n", "        outputs=im_2,\n", "        fn=mirror,\n", "        cache_examples=True,\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_interpretation/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_interpretation"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio shap matplotlib transformers torch"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import shap\n", "from transformers import pipeline\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "sentiment_classifier = pipeline(\"text-classification\", return_all_scores=True)\n", "\n", "\n", "def classifier(text):\n", "    pred = sentiment_classifier(text)\n", "    return {p[\"label\"]: p[\"score\"] for p in pred[0]}\n", "\n", "\n", "def interpretation_function(text):\n", "    explainer = shap.Explainer(sentiment_classifier)\n", "    shap_values = explainer([text])\n", "    # Dimensions are (batch size, text size, number of classes)\n", "    # Since we care about positive sentiment, use index 1\n", "    scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))\n", "\n", "    scores_desc = sorted(scores, key=lambda t: t[1])[::-1]\n", "\n", "    # Filter out empty string added by shap\n", "    scores_desc = [t for t in scores_desc if t[0] != \"\"]\n", "\n", "    fig_m = plt.figure()\n", "    plt.bar(x=[s[0] for s in scores_desc[:5]],\n", "            height=[s[1] for s in scores_desc[:5]])\n", "    plt.title(\"Top words contributing to positive sentiment\")\n", "    plt.ylabel(\"Shap Value\")\n", "    plt.xlabel(\"Word\")\n", "    return {\"original\": text, \"interpretation\": scores}, fig_m\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            input_text = gr.Textbox(label=\"Input Text\")\n", "            with gr.Row():\n", "                classify = gr.Button(\"Classify Sentiment\")\n", "                interpret = gr.Button(\"Interpret\")\n", "        with gr.Column():\n", "            label = gr.Label(label=\"Predicted Sentiment\")\n", "        with gr.Column():\n", "            with gr.Tab(\"Display interpretation with built-in component\"):\n", "                interpretation = gr.components.Interpretation(input_text)\n", "            with gr.Tab(\"Display interpretation with plot\"):\n", "                interpretation_plot = gr.Plot()\n", "\n", "    classify.click(classifier, input_text, label)\n", "    interpret.click(interpretation_function, input_text, [interpretation, interpretation_plot])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_interpretation"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio shap matplotlib transformers torch"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import shap\n", "from transformers import pipeline\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "sentiment_classifier = pipeline(\"text-classification\", return_all_scores=True)\n", "\n", "\n", "def classifier(text):\n", "    pred = sentiment_classifier(text)\n", "    return {p[\"label\"]: p[\"score\"] for p in pred[0]}\n", "\n", "\n", "def interpretation_function(text):\n", "    explainer = shap.Explainer(sentiment_classifier)\n", "    shap_values = explainer([text])\n", "    # Dimensions are (batch size, text size, number of classes)\n", "    # Since we care about positive sentiment, use index 1\n", "    scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))\n", "\n", "    scores_desc = sorted(scores, key=lambda t: t[1])[::-1]\n", "\n", "    # Filter out empty string added by shap\n", "    scores_desc = [t for t in scores_desc if t[0] != \"\"]\n", "\n", "    fig_m = plt.figure()\n", "    plt.bar(x=[s[0] for s in scores_desc[:5]],\n", "            height=[s[1] for s in scores_desc[:5]])\n", "    plt.title(\"Top words contributing to positive sentiment\")\n", "    plt.ylabel(\"Shap Value\")\n", "    plt.xlabel(\"Word\")\n", "    return {\"original\": text, \"interpretation\": scores}, fig_m\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            input_text = gr.Textbox(label=\"Input Text\")\n", "            with gr.Row():\n", "                classify = gr.Button(\"Classify Sentiment\")\n", "                interpret = gr.Button(\"Interpret\")\n", "        with gr.Column():\n", "            label = gr.Label(label=\"Predicted Sentiment\")\n", "        with gr.Column():\n", "            with gr.Tab(\"Display interpretation with built-in component\"):\n", "                interpretation = gr.components.Interpretation(input_text)\n", "            with gr.Tab(\"Display interpretation with plot\"):\n", "                interpretation_plot = gr.Plot()\n", "\n", "    classify.click(classifier, input_text, label)\n", "    interpret.click(interpretation_function, input_text, [interpretation, interpretation_plot])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_joined/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_joined"]}, {"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/blocks_joined/files/cheetah1.jpg"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["from time import sleep\n", "import gradio as gr\n", "import os\n", "\n", "cheetah = os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\")\n", "\n", "\n", "def img(text):\n", "    sleep(3)\n", "    return [\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "    ]\n", "\n", "\n", "with gr.Blocks(css=\".container { max-width: 800px; margin: auto; }\") as demo:\n", "    gr.Markdown(\"<h1><center>DALL\u00b7E mini</center></h1>\")\n", "    gr.Markdown(\n", "        \"DALL\u00b7E mini is an AI model that generates images from any prompt you give!\"\n", "    )\n", "    with gr.Group():\n", "        with gr.Row(equal_height=True):\n", "            text = gr.Textbox(\n", "                label=\"Enter your prompt\",\n", "                max_lines=1,\n", "                container=False,\n", "            )\n", "            btn = gr.Button(\"Run\", scale=0)\n", "        gallery = gr.Gallery(\n", "            label=\"Generated images\",\n", "            show_label=False,\n", "            columns=(1, 3),\n", "            height=\"auto\",\n", "        )\n", "    btn.click(img, inputs=text, outputs=gallery)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n", "\n", "\n", "# margin = (TOP, RIGHT, BOTTOM, LEFT)\n", "# rounded = (TOPLEFT, TOPRIGHT, BOTTOMRIGHT, BOTTOMLEFT)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_joined"]}, {"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/blocks_joined/files/cheetah1.jpg"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["from time import sleep\n", "import gradio as gr\n", "import os\n", "\n", "cheetah = os.path.join(os.path.abspath(''), \"files/cheetah1.jpg\")\n", "\n", "\n", "def img(text):\n", "    sleep(3)\n", "    return [\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "        cheetah,\n", "    ]\n", "\n", "\n", "with gr.Blocks(css=\".container { max-width: 800px; margin: auto; }\") as demo:\n", "    gr.Markdown(\"<h1><center>DALL\u00b7E mini</center></h1>\")\n", "    gr.Markdown(\n", "        \"DALL\u00b7E mini is an AI model that generates images from any prompt you give!\"\n", "    )\n", "    with gr.Group():\n", "        with gr.Row(equal_height=True):\n", "            text = gr.Textbox(\n", "                label=\"Enter your prompt\",\n", "                max_lines=1,\n", "                container=False,\n", "            )\n", "            btn = gr.Button(\"Run\", scale=0)\n", "        gallery = gr.Gallery(\n", "            label=\"Generated images\",\n", "            show_label=False,\n", "            columns=(1, 3),\n", "            height=\"auto\",\n", "        )\n", "    btn.click(img, inputs=text, outputs=gallery)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n", "\n", "\n", "# margin = (TOP, RIGHT, BOTTOM, LEFT)\n", "# rounded = (TOPLEFT, TOPRIGHT, BOTTOMRIGHT, BOTTOMLEFT)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_js_methods/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_js_methods"]}, {"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", "blocks = gr.Blocks()\n", "\n", "with blocks as demo:\n", "    subject = gr.Textbox(placeholder=\"subject\")\n", "    verb = gr.Radio([\"ate\", \"loved\", \"hated\"])\n", "    object = gr.Textbox(placeholder=\"object\")\n", "\n", "    with gr.Row():\n", "        btn = gr.Button(\"Create sentence.\")\n", "        reverse_btn = gr.Button(\"Reverse sentence.\")\n", "        foo_bar_btn = gr.Button(\"Append foo\")\n", "        reverse_then_to_the_server_btn = gr.Button(\n", "            \"Reverse sentence and send to server.\"\n", "        )\n", "\n", "    def sentence_maker(w1, w2, w3):\n", "        return f\"{w1} {w2} {w3}\"\n", "\n", "    output1 = gr.Textbox(label=\"output 1\")\n", "    output2 = gr.Textbox(label=\"verb\")\n", "    output3 = gr.Textbox(label=\"verb reversed\")\n", "    output4 = gr.Textbox(label=\"front end process and then send to backend\")\n", "\n", "    btn.click(sentence_maker, [subject, verb, object], output1)\n", "    reverse_btn.click(\n", "        None, [subject, verb, object], output2, _js=\"(s, v, o) => o + ' ' + v + ' ' + s\"\n", "    )\n", "    verb.change(lambda x: x, verb, output3, _js=\"(x) => [...x].reverse().join('')\")\n", "    foo_bar_btn.click(None, [], subject, _js=\"(x) => x + ' foo'\")\n", "\n", "    reverse_then_to_the_server_btn.click(\n", "        sentence_maker,\n", "        [subject, verb, object],\n", "        output4,\n", "        _js=\"(s, v, o) => [s, v, o].map(x => [...x].reverse().join(''))\",\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_js_methods"]}, {"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", "blocks = gr.Blocks()\n", "\n", "with blocks as demo:\n", "    subject = gr.Textbox(placeholder=\"subject\")\n", "    verb = gr.Radio([\"ate\", \"loved\", \"hated\"])\n", "    object = gr.Textbox(placeholder=\"object\")\n", "\n", "    with gr.Row():\n", "        btn = gr.Button(\"Create sentence.\")\n", "        reverse_btn = gr.Button(\"Reverse sentence.\")\n", "        foo_bar_btn = gr.Button(\"Append foo\")\n", "        reverse_then_to_the_server_btn = gr.Button(\n", "            \"Reverse sentence and send to server.\"\n", "        )\n", "\n", "    def sentence_maker(w1, w2, w3):\n", "        return f\"{w1} {w2} {w3}\"\n", "\n", "    output1 = gr.Textbox(label=\"output 1\")\n", "    output2 = gr.Textbox(label=\"verb\")\n", "    output3 = gr.Textbox(label=\"verb reversed\")\n", "    output4 = gr.Textbox(label=\"front end process and then send to backend\")\n", "\n", "    btn.click(sentence_maker, [subject, verb, object], output1)\n", "    reverse_btn.click(\n", "        None, [subject, verb, object], output2, _js=\"(s, v, o) => o + ' ' + v + ' ' + s\"\n", "    )\n", "    verb.change(lambda x: x, verb, output3, _js=\"(x) => [...x].reverse().join('')\")\n", "    foo_bar_btn.click(None, [], subject, _js=\"(x) => x + ' foo'\")\n", "\n", "    reverse_then_to_the_server_btn.click(\n", "        sentence_maker,\n", "        [subject, verb, object],\n", "        output4,\n", "        _js=\"(s, v, o) => [s, v, o].map(x => [...x].reverse().join(''))\",\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_kinematics/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_kinematics"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import pandas as pd\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "\n", "def plot(v, a):\n", "    g = 9.81\n", "    theta = a / 180 * 3.14\n", "    tmax = ((2 * v) * np.sin(theta)) / g\n", "    timemat = tmax * np.linspace(0, 1, 40)\n", "\n", "    x = (v * timemat) * np.cos(theta)\n", "    y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))\n", "    df = pd.DataFrame({\"x\": x, \"y\": y})\n", "    return df\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    gr.Markdown(\n", "        r\"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n", "    )\n", "\n", "    with gr.Row():\n", "        speed = gr.Slider(1, 30, 25, label=\"Speed\")\n", "        angle = gr.Slider(0, 90, 45, label=\"Angle\")\n", "    output = gr.LinePlot(\n", "        x=\"x\",\n", "        y=\"y\",\n", "        overlay_point=True,\n", "        tooltip=[\"x\", \"y\"],\n", "        x_lim=[0, 100],\n", "        y_lim=[0, 60],\n", "        width=350,\n", "        height=300,\n", "    )\n", "    btn = gr.Button(value=\"Run\")\n", "    btn.click(plot, [speed, angle], output)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_kinematics"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import pandas as pd\n", "import numpy as np\n", "\n", "import gradio as gr\n", "\n", "\n", "def plot(v, a):\n", "    g = 9.81\n", "    theta = a / 180 * 3.14\n", "    tmax = ((2 * v) * np.sin(theta)) / g\n", "    timemat = tmax * np.linspace(0, 1, 40)\n", "\n", "    x = (v * timemat) * np.cos(theta)\n", "    y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))\n", "    df = pd.DataFrame({\"x\": x, \"y\": y})\n", "    return df\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    gr.Markdown(\n", "        r\"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n", "    )\n", "\n", "    with gr.Row():\n", "        speed = gr.Slider(1, 30, 25, label=\"Speed\")\n", "        angle = gr.Slider(0, 90, 45, label=\"Angle\")\n", "    output = gr.LinePlot(\n", "        x=\"x\",\n", "        y=\"y\",\n", "        overlay_point=True,\n", "        tooltip=[\"x\", \"y\"],\n", "        x_lim=[0, 100],\n", "        y_lim=[0, 60],\n", "        width=350,\n", "        height=300,\n", "    )\n", "    btn = gr.Button(value=\"Run\")\n", "    btn.click(plot, [speed, angle], output)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_layout/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_layout"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    with gr.Row():\n", "        gr.Image(interactive=True, scale=2)\n", "        gr.Image()\n", "    with gr.Row():\n", "        gr.Textbox(label=\"Text\")\n", "        gr.Number(label=\"Count\", scale=2)\n", "        gr.Radio(choices=[\"One\", \"Two\"])\n", "    with gr.Row():\n", "        gr.Button(\"500\", scale=0, min_width=500)\n", "        gr.Button(\"A\", scale=0)\n", "        gr.Button(\"grow\")\n", "    with gr.Row():\n", "        gr.Textbox()\n", "        gr.Textbox()\n", "        gr.Button()        \n", "    with gr.Row():\n", "        with gr.Row():\n", "            with gr.Column():\n", "                gr.Textbox(label=\"Text\")\n", "                gr.Number(label=\"Count\")\n", "                gr.Radio(choices=[\"One\", \"Two\"])\n", "            gr.Image()\n", "            with gr.Column():\n", "                gr.Image(interactive=True)\n", "                gr.Image()\n", "    gr.Image()\n", "    gr.Textbox(label=\"Text\")\n", "    gr.Number(label=\"Count\")\n", "    gr.Radio(choices=[\"One\", \"Two\"])\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_layout"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    with gr.Row():\n", "        gr.Image(interactive=True, scale=2)\n", "        gr.Image()\n", "    with gr.Row():\n", "        gr.Textbox(label=\"Text\")\n", "        gr.Number(label=\"Count\", scale=2)\n", "        gr.Radio(choices=[\"One\", \"Two\"])\n", "    with gr.Row():\n", "        gr.Button(\"500\", scale=0, min_width=500)\n", "        gr.Button(\"A\", scale=0)\n", "        gr.Button(\"grow\")\n", "    with gr.Row():\n", "        gr.Textbox()\n", "        gr.Textbox()\n", "        gr.Button()        \n", "    with gr.Row():\n", "        with gr.Row():\n", "            with gr.Column():\n", "                gr.Textbox(label=\"Text\")\n", "                gr.Number(label=\"Count\")\n", "                gr.Radio(choices=[\"One\", \"Two\"])\n", "            gr.Image()\n", "            with gr.Column():\n", "                gr.Image(interactive=True)\n", "                gr.Image()\n", "    gr.Image()\n", "    gr.Textbox(label=\"Text\")\n", "    gr.Number(label=\"Count\")\n", "    gr.Radio(choices=[\"One\", \"Two\"])\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_multiple_event_triggers/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_multiple_event_triggers"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly pypistats"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pypistats\n", "from datetime import date\n", "from dateutil.relativedelta import relativedelta\n", "import pandas as pd\n", "\n", "def get_plot(lib, time):\n", "    data = pypistats.overall(lib, total=True, format=\"pandas\")\n", "    data = data.groupby(\"category\").get_group(\"with_mirrors\").sort_values(\"date\")\n", "    start_date = date.today() - relativedelta(months=int(time.split(\" \")[0]))\n", "    data = data[(data['date'] > str(start_date))]\n", "    data.date = pd.to_datetime(pd.to_datetime(data.date))\n", "    return gr.LinePlot(value=data, x=\"date\", y=\"downloads\",\n", "                              tooltip=['date', 'downloads'],\n", "                              title=f\"Pypi downloads of {lib} over last {time}\",\n", "                              overlay_point=True,\n", "                              height=400,\n", "                              width=900)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "        ## Pypi Download Stats \ud83d\udcc8\n", "        See live download stats for all of Hugging Face's open-source libraries \ud83e\udd17\n", "        \"\"\")\n", "    with gr.Row():\n", "        lib = gr.Dropdown([\"transformers\", \"datasets\", \"huggingface-hub\", \"gradio\", \"accelerate\"],\n", "                          value=\"gradio\", label=\"Library\")\n", "        time = gr.Dropdown([\"3 months\", \"6 months\", \"9 months\", \"12 months\"],\n", "                           value=\"3 months\", label=\"Downloads over the last...\")\n", "\n", "    plt = gr.LinePlot()\n", "    # You can add multiple event triggers in 2 lines like this\n", "    for event in [lib.change, time.change, demo.load]:\n", "        event(get_plot, [lib, time], [plt])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_multiple_event_triggers"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly pypistats"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pypistats\n", "from datetime import date\n", "from dateutil.relativedelta import relativedelta\n", "import pandas as pd\n", "\n", "def get_plot(lib, time):\n", "    data = pypistats.overall(lib, total=True, format=\"pandas\")\n", "    data = data.groupby(\"category\").get_group(\"with_mirrors\").sort_values(\"date\")\n", "    start_date = date.today() - relativedelta(months=int(time.split(\" \")[0]))\n", "    data = data[(data['date'] > str(start_date))]\n", "    data.date = pd.to_datetime(pd.to_datetime(data.date))\n", "    return gr.LinePlot(value=data, x=\"date\", y=\"downloads\",\n", "                              tooltip=['date', 'downloads'],\n", "                              title=f\"Pypi downloads of {lib} over last {time}\",\n", "                              overlay_point=True,\n", "                              height=400,\n", "                              width=900)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "        ## Pypi Download Stats \ud83d\udcc8\n", "        See live download stats for all of Hugging Face's open-source libraries \ud83e\udd17\n", "        \"\"\")\n", "    with gr.Row():\n", "        lib = gr.Dropdown([\"transformers\", \"datasets\", \"huggingface-hub\", \"gradio\", \"accelerate\"],\n", "                          value=\"gradio\", label=\"Library\")\n", "        time = gr.Dropdown([\"3 months\", \"6 months\", \"9 months\", \"12 months\"],\n", "                           value=\"3 months\", label=\"Downloads over the last...\")\n", "\n", "    plt = gr.LinePlot()\n", "    # You can add multiple event triggers in 2 lines like this\n", "    for event in [lib.change, time.change, demo.load]:\n", "        event(get_plot, [lib, time], [plt])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_page_load/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_page_load"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def print_message(n):\n", "    return \"Welcome! This page has loaded for \" + n\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    t = gr.Textbox(\"Frank\", label=\"Name\")\n", "    t2 = gr.Textbox(label=\"Output\")\n", "    demo.load(print_message, t, t2)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_page_load"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def print_message(n):\n", "    return \"Welcome! This page has loaded for \" + n\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    t = gr.Textbox(\"Frank\", label=\"Name\")\n", "    t2 = gr.Textbox(label=\"Output\")\n", "    demo.load(print_message, t, t2)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_plug/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_plug"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def change_tab():\n", "    return gr.Tabs(selected=2)\n", "\n", "\n", "identity_demo, input_demo, output_demo = gr.Blocks(), gr.Blocks(), gr.Blocks()\n", "\n", "with identity_demo:\n", "    gr.Interface(lambda x: x, \"text\", \"text\")\n", "\n", "with input_demo:\n", "    t = gr.Textbox(label=\"Enter your text here\")\n", "    with gr.Row():\n", "        btn = gr.Button(\"Submit\")\n", "        clr = gr.ClearButton(t)\n", "\n", "with output_demo:\n", "    gr.Textbox(\"This is a static output\")\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\"Three demos in one!\")\n", "    with gr.Tabs(selected=1) as tabs:\n", "        with gr.TabItem(\"Text Identity\", id=0) as tab0:\n", "            tab0.select(lambda: gr.Tabs(selected=0), None, tabs)\n", "            identity_demo.render()\n", "        with gr.TabItem(\"Text Input\", id=1) as tab1:\n", "            tab1.select(lambda: gr.Tabs(selected=1), None, tabs)\n", "            input_demo.render()\n", "        with gr.TabItem(\"Text Static\", id=2) as tab2:\n", "            tab2.select(lambda: gr.Tabs(selected=2), None, tabs)\n", "            output_demo.render()\n", "    btn = gr.Button(\"Change tab\")\n", "    btn.click(inputs=None, outputs=tabs, fn=change_tab)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_plug"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def change_tab():\n", "    return gr.Tabs(selected=2)\n", "\n", "\n", "identity_demo, input_demo, output_demo = gr.Blocks(), gr.Blocks(), gr.Blocks()\n", "\n", "with identity_demo:\n", "    gr.Interface(lambda x: x, \"text\", \"text\")\n", "\n", "with input_demo:\n", "    t = gr.Textbox(label=\"Enter your text here\")\n", "    with gr.Row():\n", "        btn = gr.Button(\"Submit\")\n", "        clr = gr.ClearButton(t)\n", "\n", "with output_demo:\n", "    gr.Textbox(\"This is a static output\")\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\"Three demos in one!\")\n", "    with gr.Tabs(selected=1) as tabs:\n", "        with gr.TabItem(\"Text Identity\", id=0) as tab0:\n", "            tab0.select(lambda: gr.Tabs(selected=0), None, tabs)\n", "            identity_demo.render()\n", "        with gr.TabItem(\"Text Input\", id=1) as tab1:\n", "            tab1.select(lambda: gr.Tabs(selected=1), None, tabs)\n", "            input_demo.render()\n", "        with gr.TabItem(\"Text Static\", id=2) as tab2:\n", "            tab2.select(lambda: gr.Tabs(selected=2), None, tabs)\n", "            output_demo.render()\n", "    btn = gr.Button(\"Change tab\")\n", "    btn.click(inputs=None, outputs=tabs, fn=change_tab)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_random_slider/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_random_slider"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["\n", "import gradio as gr\n", "\n", "\n", "def func(slider_1, slider_2):\n", "    return slider_1 * 5 + slider_2\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    slider = gr.Slider(minimum=-10.2, maximum=15, label=\"Random Slider (Static)\", randomize=True)\n", "    slider_1 = gr.Slider(minimum=100, maximum=200, label=\"Random Slider (Input 1)\", randomize=True)\n", "    slider_2 = gr.Slider(minimum=10, maximum=23.2, label=\"Random Slider (Input 2)\", randomize=True)\n", "    slider_3 = gr.Slider(value=3, label=\"Non random slider\")\n", "    btn = gr.Button(\"Run\")\n", "    btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number())\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_random_slider"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["\n", "import gradio as gr\n", "\n", "\n", "def func(slider_1, slider_2):\n", "    return slider_1 * 5 + slider_2\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    slider = gr.Slider(minimum=-10.2, maximum=15, label=\"Random Slider (Static)\", randomize=True)\n", "    slider_1 = gr.Slider(minimum=100, maximum=200, label=\"Random Slider (Input 1)\", randomize=True)\n", "    slider_2 = gr.Slider(minimum=10, maximum=23.2, label=\"Random Slider (Input 2)\", randomize=True)\n", "    slider_3 = gr.Slider(value=3, label=\"Non random slider\")\n", "    btn = gr.Button(\"Run\")\n", "    btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number())\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_scroll/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_scroll"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    inp = gr.Textbox(placeholder=\"Enter text.\")\n", "    scroll_btn = gr.Button(\"Scroll\")\n", "    no_scroll_btn = gr.Button(\"No Scroll\")\n", "    big_block = gr.HTML(\"\"\"\n", "    <div style='height: 800px; width: 100px; background-color: pink;'></div>\n", "    \"\"\")\n", "    out = gr.Textbox()\n", "    \n", "    scroll_btn.click(lambda x: x, \n", "               inputs=inp, \n", "               outputs=out,\n", "                scroll_to_output=True)\n", "    no_scroll_btn.click(lambda x: x, \n", "               inputs=inp, \n", "               outputs=out)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_scroll"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    inp = gr.Textbox(placeholder=\"Enter text.\")\n", "    scroll_btn = gr.Button(\"Scroll\")\n", "    no_scroll_btn = gr.Button(\"No Scroll\")\n", "    big_block = gr.HTML(\"\"\"\n", "    <div style='height: 800px; width: 100px; background-color: pink;'></div>\n", "    \"\"\")\n", "    out = gr.Textbox()\n", "    \n", "    scroll_btn.click(lambda x: x, \n", "               inputs=inp, \n", "               outputs=out,\n", "                scroll_to_output=True)\n", "    no_scroll_btn.click(lambda x: x, \n", "               inputs=inp, \n", "               outputs=out)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_simple_squares/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_simple_squares"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Blocks(css=\"\"\"#btn {color: red} .abc {font-family: \"Comic Sans MS\", \"Comic Sans\", cursive !important}\"\"\")\n", "\n", "with demo:\n", "    default_json = {\"a\": \"a\"}\n", "\n", "    num = gr.State(value=0)\n", "    squared = gr.Number(value=0)\n", "    btn = gr.Button(\"Next Square\", elem_id=\"btn\", elem_classes=[\"abc\", \"def\"])\n", "\n", "    stats = gr.State(value=default_json)\n", "    table = gr.JSON()\n", "\n", "    def increase(var, stats_history):\n", "        var += 1\n", "        stats_history[str(var)] = var**2\n", "        return var, var**2, stats_history, stats_history\n", "\n", "    btn.click(increase, [num, stats], [num, squared, stats, table])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_simple_squares"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Blocks(css=\"\"\"#btn {color: red} .abc {font-family: \"Comic Sans MS\", \"Comic Sans\", cursive !important}\"\"\")\n", "\n", "with demo:\n", "    default_json = {\"a\": \"a\"}\n", "\n", "    num = gr.State(value=0)\n", "    squared = gr.Number(value=0)\n", "    btn = gr.Button(\"Next Square\", elem_id=\"btn\", elem_classes=[\"abc\", \"def\"])\n", "\n", "    stats = gr.State(value=default_json)\n", "    table = gr.JSON()\n", "\n", "    def increase(var, stats_history):\n", "        var += 1\n", "        stats_history[str(var)] = var**2\n", "        return var, var**2, stats_history, stats_history\n", "\n", "    btn.click(increase, [num, stats], [num, squared, stats, table])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_speech_text_sentiment/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_speech_text_sentiment"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "\n", "import gradio as gr\n", "\n", "asr = pipeline(\"automatic-speech-recognition\", \"facebook/wav2vec2-base-960h\")\n", "classifier = pipeline(\"text-classification\")\n", "\n", "\n", "def speech_to_text(speech):\n", "    text = asr(speech)[\"text\"]\n", "    return text\n", "\n", "\n", "def text_to_sentiment(text):\n", "    return classifier(text)[0][\"label\"]\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    audio_file = gr.Audio(type=\"filepath\")\n", "    text = gr.Textbox()\n", "    label = gr.Label()\n", "\n", "    b1 = gr.Button(\"Recognize Speech\")\n", "    b2 = gr.Button(\"Classify Sentiment\")\n", "\n", "    b1.click(speech_to_text, inputs=audio_file, outputs=text)\n", "    b2.click(text_to_sentiment, inputs=text, outputs=label)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_speech_text_sentiment"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "\n", "import gradio as gr\n", "\n", "asr = pipeline(\"automatic-speech-recognition\", \"facebook/wav2vec2-base-960h\")\n", "classifier = pipeline(\"text-classification\")\n", "\n", "\n", "def speech_to_text(speech):\n", "    text = asr(speech)[\"text\"]\n", "    return text\n", "\n", "\n", "def text_to_sentiment(text):\n", "    return classifier(text)[0][\"label\"]\n", "\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    audio_file = gr.Audio(type=\"filepath\")\n", "    text = gr.Textbox()\n", "    label = gr.Label()\n", "\n", "    b1 = gr.Button(\"Recognize Speech\")\n", "    b2 = gr.Button(\"Classify Sentiment\")\n", "\n", "    b1.click(speech_to_text, inputs=audio_file, outputs=text)\n", "    b2.click(text_to_sentiment, inputs=text, outputs=label)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_static/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_static"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    gr.Image(\n", "        \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\"\n", "    )\n", "    gr.Textbox(\"hi\")\n", "    gr.Number(3)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_static"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "demo = gr.Blocks()\n", "\n", "with demo:\n", "    gr.Image(\n", "        \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\"\n", "    )\n", "    gr.Textbox(\"hi\")\n", "    gr.Number(3)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_style/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_style"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks(title=\"Styling Examples\") as demo:\n", "    with gr.Column(variant=\"box\"):\n", "        txt = gr.Textbox(label=\"Small Textbox\", lines=1)\n", "        num = gr.Number(label=\"Number\", show_label=False)\n", "        slider = gr.Slider(label=\"Slider\", show_label=False)\n", "        check = gr.Checkbox(label=\"Checkbox\", show_label=False)\n", "        check_g = gr.CheckboxGroup(\n", "            label=\"Checkbox Group\",\n", "            choices=[\"One\", \"Two\", \"Three\"],\n", "            show_label=False,\n", "        )\n", "        radio = gr.Radio(\n", "            label=\"Radio\",\n", "            choices=[\"One\", \"Two\", \"Three\"],\n", "            show_label=False,\n", "        )\n", "        drop = gr.Dropdown(\n", "            label=\"Dropdown\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", "        )\n", "        image = gr.Image(show_label=False)\n", "        video = gr.Video(show_label=False)\n", "        audio = gr.Audio(show_label=False)\n", "        file = gr.File(show_label=False)\n", "        df = gr.Dataframe(show_label=False)\n", "        ts = gr.Timeseries(show_label=False)\n", "        label = gr.Label(container=False)\n", "        highlight = gr.HighlightedText(\n", "            [(\"hello\", None), (\"goodbye\", \"-\")],\n", "            color_map={\"+\": \"green\", \"-\": \"red\"},\n", "            container=False,\n", "        )\n", "        json = gr.JSON(container=False)\n", "        html = gr.HTML(show_label=False)\n", "        gallery = gr.Gallery(\n", "            columns=(3, 3, 1),\n", "            height=\"auto\",\n", "            container=False,\n", "        )\n", "        chat = gr.Chatbot([(\"hi\", \"good bye\")])\n", "\n", "        model = gr.Model3D()\n", "\n", "        md = gr.Markdown(show_label=False)\n", "\n", "        highlight = gr.HighlightedText()\n", "\n", "        btn = gr.Button(\"Run\")\n", "\n", "        gr.Dataset(components=[txt, num])\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_style"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks(title=\"Styling Examples\") as demo:\n", "    with gr.Column(variant=\"box\"):\n", "        txt = gr.Textbox(label=\"Small Textbox\", lines=1)\n", "        num = gr.Number(label=\"Number\", show_label=False)\n", "        slider = gr.Slider(label=\"Slider\", show_label=False)\n", "        check = gr.Checkbox(label=\"Checkbox\", show_label=False)\n", "        check_g = gr.CheckboxGroup(\n", "            label=\"Checkbox Group\",\n", "            choices=[\"One\", \"Two\", \"Three\"],\n", "            show_label=False,\n", "        )\n", "        radio = gr.Radio(\n", "            label=\"Radio\",\n", "            choices=[\"One\", \"Two\", \"Three\"],\n", "            show_label=False,\n", "        )\n", "        drop = gr.Dropdown(\n", "            label=\"Dropdown\", choices=[\"One\", \"Two\", \"Three\"], show_label=False\n", "        )\n", "        image = gr.Image(show_label=False)\n", "        video = gr.Video(show_label=False)\n", "        audio = gr.Audio(show_label=False)\n", "        file = gr.File(show_label=False)\n", "        df = gr.Dataframe(show_label=False)\n", "        ts = gr.Timeseries(show_label=False)\n", "        label = gr.Label(container=False)\n", "        highlight = gr.HighlightedText(\n", "            [(\"hello\", None), (\"goodbye\", \"-\")],\n", "            color_map={\"+\": \"green\", \"-\": \"red\"},\n", "            container=False,\n", "        )\n", "        json = gr.JSON(container=False)\n", "        html = gr.HTML(show_label=False)\n", "        gallery = gr.Gallery(\n", "            columns=(3, 3, 1),\n", "            height=\"auto\",\n", "            container=False,\n", "        )\n", "        chat = gr.Chatbot([(\"hi\", \"good bye\")])\n", "\n", "        model = gr.Model3D()\n", "\n", "        md = gr.Markdown(show_label=False)\n", "\n", "        highlight = gr.HighlightedText()\n", "\n", "        btn = gr.Button(\"Run\")\n", "\n", "        gr.Dataset(components=[txt, num])\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_textbox_max_lines/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_textbox_max_lines"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def greet(name: str, repeat: float):\n", "    return \"Hello \" + name * int(repeat) + \"!!\"\n", "\n", "\n", "demo = gr.Interface(\n", "    fn=greet, inputs=[gr.Textbox(lines=2, max_lines=4), gr.Number()], outputs=\"textarea\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_textbox_max_lines"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def greet(name: str, repeat: float):\n", "    return \"Hello \" + name * int(repeat) + \"!!\"\n", "\n", "\n", "demo = gr.Interface(\n", "    fn=greet, inputs=[gr.Textbox(lines=2, max_lines=4), gr.Number()], outputs=\"textarea\"\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/blocks_update/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_update"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "    # Animal Generator\n", "    Once you select a species, the detail panel should be visible.\n", "    \"\"\"\n", "    )\n", "\n", "    species = gr.Radio(label=\"Animal Class\", choices=[\"Mammal\", \"Fish\", \"Bird\"])\n", "    animal = gr.Dropdown(label=\"Animal\", choices=[])\n", "\n", "    with gr.Column(visible=False) as details_col:\n", "        weight = gr.Slider(0, 20)\n", "        details = gr.Textbox(label=\"Extra Details\")\n", "        generate_btn = gr.Button(\"Generate\")\n", "        output = gr.Textbox(label=\"Output\")\n", "\n", "    species_map = {\n", "        \"Mammal\": [\"Elephant\", \"Giraffe\", \"Hamster\"],\n", "        \"Fish\": [\"Shark\", \"Salmon\", \"Tuna\"],\n", "        \"Bird\": [\"Chicken\", \"Eagle\", \"Hawk\"],\n", "    }\n", "\n", "    def filter_species(species):\n", "        return gr.Dropdown(\n", "            choices=species_map[species], value=species_map[species][1]\n", "        ), gr.Column(visible=True)\n", "\n", "    species.change(filter_species, species, [animal, details_col])\n", "\n", "    def filter_weight(animal):\n", "        if animal in (\"Elephant\", \"Shark\", \"Giraffe\"):\n", "            return gr.Slider(maximum=100)\n", "        else:\n", "            return gr.Slider(maximum=20)\n", "\n", "    animal.change(filter_weight, animal, weight)\n", "    weight.change(lambda w: gr.Textbox(lines=int(w / 10) + 1), weight, details)\n", "\n", "    generate_btn.click(lambda x: x, details, output)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_update"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "    # Animal Generator\n", "    Once you select a species, the detail panel should be visible.\n", "    \"\"\"\n", "    )\n", "\n", "    species = gr.Radio(label=\"Animal Class\", choices=[\"Mammal\", \"Fish\", \"Bird\"])\n", "    animal = gr.Dropdown(label=\"Animal\", choices=[])\n", "\n", "    with gr.Column(visible=False) as details_col:\n", "        weight = gr.Slider(0, 20)\n", "        details = gr.Textbox(label=\"Extra Details\")\n", "        generate_btn = gr.Button(\"Generate\")\n", "        output = gr.Textbox(label=\"Output\")\n", "\n", "    species_map = {\n", "        \"Mammal\": [\"Elephant\", \"Giraffe\", \"Hamster\"],\n", "        \"Fish\": [\"Shark\", \"Salmon\", \"Tuna\"],\n", "        \"Bird\": [\"Chicken\", \"Eagle\", \"Hawk\"],\n", "    }\n", "\n", "    def filter_species(species):\n", "        return gr.Dropdown(\n", "            choices=species_map[species], value=species_map[species][1]\n", "        ), gr.Column(visible=True)\n", "\n", "    species.change(filter_species, species, [animal, details_col])\n", "\n", "    def filter_weight(animal):\n", "        if animal in (\"Elephant\", \"Shark\", \"Giraffe\"):\n", "            return gr.Slider(maximum=100)\n", "        else:\n", "            return gr.Slider(maximum=20)\n", "\n", "    animal.change(filter_weight, animal, weight)\n", "    weight.change(lambda w: gr.Textbox(lines=int(w / 10) + 1), weight, details)\n", "\n", "    generate_btn.click(lambda x: x, details, output)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

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}
+{"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}

demo/blocks_xray/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: blocks_xray"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "disease_values = [0.25, 0.5, 0.75]\n", "\n", "def xray_model(diseases, img):\n", "    return [{disease: disease_values[idx] for idx,disease in enumerate(diseases)}]\n", "\n", "\n", "def ct_model(diseases, img):\n", "    return [{disease: 0.1 for disease in diseases}]\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "# Detect Disease From Scan\n", "With this model you can lorem ipsum\n", "- ipsum 1\n", "- ipsum 2\n", "\"\"\"\n", "    )\n", "    gr.DuplicateButton()\n", "    disease = gr.CheckboxGroup(\n", "        info=\"Select the diseases you want to scan for.\",\n", "        choices=[\"Covid\", \"Malaria\", \"Lung Cancer\"], label=\"Disease to Scan For\"\n", "    )\n", "    slider = gr.Slider(0, 100)\n", "\n", "    with gr.Tab(\"X-ray\") as x_tab:\n", "        with gr.Row():\n", "            xray_scan = gr.Image()\n", "            xray_results = gr.JSON()\n", "        xray_run = gr.Button(\"Run\")\n", "        xray_run.click(\n", "            xray_model,\n", "            inputs=[disease, xray_scan],\n", "            outputs=xray_results,\n", "            api_name=\"xray_model\"\n", "        )\n", "\n", "    with gr.Tab(\"CT Scan\"):\n", "        with gr.Row():\n", "            ct_scan = gr.Image()\n", "            ct_results = gr.JSON()\n", "        ct_run = gr.Button(\"Run\")\n", "        ct_run.click(\n", "            ct_model,\n", "            inputs=[disease, ct_scan],\n", "            outputs=ct_results,\n", "            api_name=\"ct_model\"\n", "        )\n", "\n", "    upload_btn = gr.Button(\"Upload Results\", variant=\"primary\")\n", "    upload_btn.click(\n", "        lambda ct, xr: None,\n", "        inputs=[ct_results, xray_results],\n", "        outputs=[],\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_xray"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "disease_values = [0.25, 0.5, 0.75]\n", "\n", "def xray_model(diseases, img):\n", "    return [{disease: disease_values[idx] for idx,disease in enumerate(diseases)}]\n", "\n", "\n", "def ct_model(diseases, img):\n", "    return [{disease: 0.1 for disease in diseases}]\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "# Detect Disease From Scan\n", "With this model you can lorem ipsum\n", "- ipsum 1\n", "- ipsum 2\n", "\"\"\"\n", "    )\n", "    gr.DuplicateButton()\n", "    disease = gr.CheckboxGroup(\n", "        info=\"Select the diseases you want to scan for.\",\n", "        choices=[\"Covid\", \"Malaria\", \"Lung Cancer\"], label=\"Disease to Scan For\"\n", "    )\n", "    slider = gr.Slider(0, 100)\n", "\n", "    with gr.Tab(\"X-ray\") as x_tab:\n", "        with gr.Row():\n", "            xray_scan = gr.Image()\n", "            xray_results = gr.JSON()\n", "        xray_run = gr.Button(\"Run\")\n", "        xray_run.click(\n", "            xray_model,\n", "            inputs=[disease, xray_scan],\n", "            outputs=xray_results,\n", "            api_name=\"xray_model\"\n", "        )\n", "\n", "    with gr.Tab(\"CT Scan\"):\n", "        with gr.Row():\n", "            ct_scan = gr.Image()\n", "            ct_results = gr.JSON()\n", "        ct_run = gr.Button(\"Run\")\n", "        ct_run.click(\n", "            ct_model,\n", "            inputs=[disease, ct_scan],\n", "            outputs=ct_results,\n", "            api_name=\"ct_model\"\n", "        )\n", "\n", "    upload_btn = gr.Button(\"Upload Results\", variant=\"primary\")\n", "    upload_btn.click(\n", "        lambda ct, xr: None,\n", "        inputs=[ct_results, xray_results],\n", "        outputs=[],\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/bokeh_plot/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: bokeh_plot"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio bokeh>=3.0 xyzservices"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import xyzservices.providers as xyz\n", "from bokeh.models import ColumnDataSource, Whisker\n", "from bokeh.plotting import figure\n", "from bokeh.sampledata.autompg2 import autompg2 as df\n", "from bokeh.sampledata.penguins import data\n", "from bokeh.transform import factor_cmap, jitter, factor_mark\n", "\n", "\n", "def get_plot(plot_type):\n", "    if plot_type == \"map\":\n", "        plot = figure(\n", "            x_range=(-2000000, 6000000),\n", "            y_range=(-1000000, 7000000),\n", "            x_axis_type=\"mercator\",\n", "            y_axis_type=\"mercator\",\n", "        )\n", "        plot.add_tile(xyz.OpenStreetMap.Mapnik)\n", "        return plot\n", "    elif plot_type == \"whisker\":\n", "        classes = list(sorted(df[\"class\"].unique()))\n", "\n", "        p = figure(\n", "            height=400,\n", "            x_range=classes,\n", "            background_fill_color=\"#efefef\",\n", "            title=\"Car class vs HWY mpg with quintile ranges\",\n", "        )\n", "        p.xgrid.grid_line_color = None\n", "\n", "        g = df.groupby(\"class\")\n", "        upper = g.hwy.quantile(0.80)\n", "        lower = g.hwy.quantile(0.20)\n", "        source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n", "\n", "        error = Whisker(\n", "            base=\"base\",\n", "            upper=\"upper\",\n", "            lower=\"lower\",\n", "            source=source,\n", "            level=\"annotation\",\n", "            line_width=2,\n", "        )\n", "        error.upper_head.size = 20\n", "        error.lower_head.size = 20\n", "        p.add_layout(error)\n", "\n", "        p.circle(\n", "            jitter(\"class\", 0.3, range=p.x_range),\n", "            \"hwy\",\n", "            source=df,\n", "            alpha=0.5,\n", "            size=13,\n", "            line_color=\"white\",\n", "            color=factor_cmap(\"class\", \"Light6\", classes),\n", "        )\n", "        return p\n", "    elif plot_type == \"scatter\":\n", "\n", "        SPECIES = sorted(data.species.unique())\n", "        MARKERS = [\"hex\", \"circle_x\", \"triangle\"]\n", "\n", "        p = figure(title=\"Penguin size\", background_fill_color=\"#fafafa\")\n", "        p.xaxis.axis_label = \"Flipper Length (mm)\"\n", "        p.yaxis.axis_label = \"Body Mass (g)\"\n", "\n", "        p.scatter(\n", "            \"flipper_length_mm\",\n", "            \"body_mass_g\",\n", "            source=data,\n", "            legend_group=\"species\",\n", "            fill_alpha=0.4,\n", "            size=12,\n", "            marker=factor_mark(\"species\", MARKERS, SPECIES),\n", "            color=factor_cmap(\"species\", \"Category10_3\", SPECIES),\n", "        )\n", "\n", "        p.legend.location = \"top_left\"\n", "        p.legend.title = \"Species\"\n", "        return p\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        plot_type = gr.Radio(value=\"scatter\", choices=[\"scatter\", \"whisker\", \"map\"])\n", "        plot = gr.Plot()\n", "    plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])\n", "    demo.load(get_plot, inputs=[plot_type], outputs=[plot])\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: bokeh_plot"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio bokeh>=3.0 xyzservices"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import xyzservices.providers as xyz\n", "from bokeh.models import ColumnDataSource, Whisker\n", "from bokeh.plotting import figure\n", "from bokeh.sampledata.autompg2 import autompg2 as df\n", "from bokeh.sampledata.penguins import data\n", "from bokeh.transform import factor_cmap, jitter, factor_mark\n", "\n", "\n", "def get_plot(plot_type):\n", "    if plot_type == \"map\":\n", "        plot = figure(\n", "            x_range=(-2000000, 6000000),\n", "            y_range=(-1000000, 7000000),\n", "            x_axis_type=\"mercator\",\n", "            y_axis_type=\"mercator\",\n", "        )\n", "        plot.add_tile(xyz.OpenStreetMap.Mapnik)\n", "        return plot\n", "    elif plot_type == \"whisker\":\n", "        classes = list(sorted(df[\"class\"].unique()))\n", "\n", "        p = figure(\n", "            height=400,\n", "            x_range=classes,\n", "            background_fill_color=\"#efefef\",\n", "            title=\"Car class vs HWY mpg with quintile ranges\",\n", "        )\n", "        p.xgrid.grid_line_color = None\n", "\n", "        g = df.groupby(\"class\")\n", "        upper = g.hwy.quantile(0.80)\n", "        lower = g.hwy.quantile(0.20)\n", "        source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n", "\n", "        error = Whisker(\n", "            base=\"base\",\n", "            upper=\"upper\",\n", "            lower=\"lower\",\n", "            source=source,\n", "            level=\"annotation\",\n", "            line_width=2,\n", "        )\n", "        error.upper_head.size = 20\n", "        error.lower_head.size = 20\n", "        p.add_layout(error)\n", "\n", "        p.circle(\n", "            jitter(\"class\", 0.3, range=p.x_range),\n", "            \"hwy\",\n", "            source=df,\n", "            alpha=0.5,\n", "            size=13,\n", "            line_color=\"white\",\n", "            color=factor_cmap(\"class\", \"Light6\", classes),\n", "        )\n", "        return p\n", "    elif plot_type == \"scatter\":\n", "\n", "        SPECIES = sorted(data.species.unique())\n", "        MARKERS = [\"hex\", \"circle_x\", \"triangle\"]\n", "\n", "        p = figure(title=\"Penguin size\", background_fill_color=\"#fafafa\")\n", "        p.xaxis.axis_label = \"Flipper Length (mm)\"\n", "        p.yaxis.axis_label = \"Body Mass (g)\"\n", "\n", "        p.scatter(\n", "            \"flipper_length_mm\",\n", "            \"body_mass_g\",\n", "            source=data,\n", "            legend_group=\"species\",\n", "            fill_alpha=0.4,\n", "            size=12,\n", "            marker=factor_mark(\"species\", MARKERS, SPECIES),\n", "            color=factor_cmap(\"species\", \"Category10_3\", SPECIES),\n", "        )\n", "\n", "        p.legend.location = \"top_left\"\n", "        p.legend.title = \"Species\"\n", "        return p\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        plot_type = gr.Radio(value=\"scatter\", choices=[\"scatter\", \"whisker\", \"map\"])\n", "        plot = gr.Plot()\n", "    plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])\n", "    demo.load(get_plot, inputs=[plot_type], outputs=[plot])\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/button_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: button_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "   gr.Button()\n", "    \n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: button_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "   gr.Button()\n", "    \n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/calculator/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('examples')\n", "!wget -q -O examples/log.csv https://github.com/gradio-app/gradio/raw/main/demo/calculator/examples/log.csv"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def calculator(num1, operation, num2):\n", "    if operation == \"add\":\n", "        return num1 + num2\n", "    elif operation == \"subtract\":\n", "        return num1 - num2\n", "    elif operation == \"multiply\":\n", "        return num1 * num2\n", "    elif operation == \"divide\":\n", "        if num2 == 0:\n", "            raise gr.Error(\"Cannot divide by zero!\")\n", "        return num1 / num2\n", "\n", "demo = gr.Interface(\n", "    calculator,\n", "    [\n", "        \"number\", \n", "        gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", "        \"number\"\n", "    ],\n", "    \"number\",\n", "    examples=[\n", "        [5, \"add\", 3],\n", "        [4, \"divide\", 2],\n", "        [-4, \"multiply\", 2.5],\n", "        [0, \"subtract\", 1.2],\n", "    ],\n", "    title=\"Toy Calculator\",\n", "    description=\"Here's a sample toy calculator. Allows you to calculate things like $2+2=4$\",\n", ")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('examples')\n", "!wget -q -O examples/log.csv https://github.com/gradio-app/gradio/raw/main/demo/calculator/examples/log.csv"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def calculator(num1, operation, num2):\n", "    if operation == \"add\":\n", "        return num1 + num2\n", "    elif operation == \"subtract\":\n", "        return num1 - num2\n", "    elif operation == \"multiply\":\n", "        return num1 * num2\n", "    elif operation == \"divide\":\n", "        if num2 == 0:\n", "            raise gr.Error(\"Cannot divide by zero!\")\n", "        return num1 / num2\n", "\n", "demo = gr.Interface(\n", "    calculator,\n", "    [\n", "        \"number\", \n", "        gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", "        \"number\"\n", "    ],\n", "    \"number\",\n", "    examples=[\n", "        [5, \"add\", 3],\n", "        [4, \"divide\", 2],\n", "        [-4, \"multiply\", 2.5],\n", "        [0, \"subtract\", 1.2],\n", "    ],\n", "    title=\"Toy Calculator\",\n", "    description=\"Here's a sample toy calculator. Allows you to calculate things like $2+2=4$\",\n", ")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/calculator_blocks/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator_blocks"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def calculator(num1, operation, num2):\n", "    if operation == \"add\":\n", "        return num1 + num2\n", "    elif operation == \"subtract\":\n", "        return num1 - num2\n", "    elif operation == \"multiply\":\n", "        return num1 * num2\n", "    elif operation == \"divide\":\n", "        return num1 / num2\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            num_1 = gr.Number(value=4)\n", "            operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n", "            num_2 = gr.Number(value=0)\n", "            submit_btn = gr.Button(value=\"Calculate\")\n", "        with gr.Column():\n", "            result = gr.Number()\n", "\n", "    submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result])\n", "    examples = gr.Examples(examples=[[5, \"add\", 3],\n", "                                     [4, \"divide\", 2],\n", "                                     [-4, \"multiply\", 2.5],\n", "                                     [0, \"subtract\", 1.2]],\n", "                           inputs=[num_1, operation, num_2])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator_blocks"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def calculator(num1, operation, num2):\n", "    if operation == \"add\":\n", "        return num1 + num2\n", "    elif operation == \"subtract\":\n", "        return num1 - num2\n", "    elif operation == \"multiply\":\n", "        return num1 * num2\n", "    elif operation == \"divide\":\n", "        return num1 / num2\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            num_1 = gr.Number(value=4)\n", "            operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n", "            num_2 = gr.Number(value=0)\n", "            submit_btn = gr.Button(value=\"Calculate\")\n", "        with gr.Column():\n", "            result = gr.Number()\n", "\n", "    submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result])\n", "    examples = gr.Examples(examples=[[5, \"add\", 3],\n", "                                     [4, \"divide\", 2],\n", "                                     [-4, \"multiply\", 2.5],\n", "                                     [0, \"subtract\", 1.2]],\n", "                           inputs=[num_1, operation, num_2])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/calculator_blocks_cached/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator_blocks_cached"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def calculator(num1, operation, num2):\n", "    if operation == \"add\":\n", "        return num1 + num2\n", "    elif operation == \"subtract\":\n", "        return num1 - num2\n", "    elif operation == \"multiply\":\n", "        return num1 * num2\n", "    elif operation == \"divide\":\n", "        return num1 / num2\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            num_1 = gr.Number()\n", "            operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n", "            num_2 = gr.Number()\n", "            submit_btn = gr.Button(value=\"Calculate\")\n", "        with gr.Column():\n", "            result = gr.Number()\n", "\n", "    submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result])\n", "    examples = gr.Examples(examples=[[5, \"add\", 3],\n", "                                     [4, \"divide\", 2],\n", "                                     [-4, \"multiply\", 2.5],\n", "                                     [0, \"subtract\", 1.2]],\n", "                           inputs=[num_1, operation, num_2],\n", "                           outputs=[result],\n", "                           fn=calculator,\n", "                           cache_examples=True)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator_blocks_cached"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "def calculator(num1, operation, num2):\n", "    if operation == \"add\":\n", "        return num1 + num2\n", "    elif operation == \"subtract\":\n", "        return num1 - num2\n", "    elif operation == \"multiply\":\n", "        return num1 * num2\n", "    elif operation == \"divide\":\n", "        return num1 / num2\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            num_1 = gr.Number()\n", "            operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n", "            num_2 = gr.Number()\n", "            submit_btn = gr.Button(value=\"Calculate\")\n", "        with gr.Column():\n", "            result = gr.Number()\n", "\n", "    submit_btn.click(calculator, inputs=[num_1, operation, num_2], outputs=[result])\n", "    examples = gr.Examples(examples=[[5, \"add\", 3],\n", "                                     [4, \"divide\", 2],\n", "                                     [-4, \"multiply\", 2.5],\n", "                                     [0, \"subtract\", 1.2]],\n", "                           inputs=[num_1, operation, num_2],\n", "                           outputs=[result],\n", "                           fn=calculator,\n", "                           cache_examples=True)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/calculator_list_and_dict/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator_list_and_dict"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    a = gr.Number(label=\"a\")\n", "    b = gr.Number(label=\"b\")\n", "    with gr.Row():\n", "        add_btn = gr.Button(\"Add\")\n", "        sub_btn = gr.Button(\"Subtract\")\n", "    c = gr.Number(label=\"sum\")\n", "\n", "    def add(num1, num2):\n", "        return num1 + num2\n", "    add_btn.click(add, inputs=[a, b], outputs=c)\n", "\n", "    def sub(data):\n", "        return data[a] - data[b]\n", "    sub_btn.click(sub, inputs={a, b}, outputs=c)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator_list_and_dict"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    a = gr.Number(label=\"a\")\n", "    b = gr.Number(label=\"b\")\n", "    with gr.Row():\n", "        add_btn = gr.Button(\"Add\")\n", "        sub_btn = gr.Button(\"Subtract\")\n", "    c = gr.Number(label=\"sum\")\n", "\n", "    def add(num1, num2):\n", "        return num1 + num2\n", "    add_btn.click(add, inputs=[a, b], outputs=c)\n", "\n", "    def sub(data):\n", "        return data[a] - data[b]\n", "    sub_btn.click(sub, inputs={a, b}, outputs=c)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/calculator_live/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: calculator_live"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def calculator(num1, operation, num2):\n", "    if operation == \"add\":\n", "        return num1 + num2\n", "    elif operation == \"subtract\":\n", "        return num1 - num2\n", "    elif operation == \"multiply\":\n", "        return num1 * num2\n", "    elif operation == \"divide\":\n", "        return num1 / num2\n", "\n", "demo = gr.Interface(\n", "    calculator,\n", "    [\n", "        \"number\",\n", "        gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", "        \"number\"\n", "    ],\n", "    \"number\",\n", "    live=True,\n", ")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: calculator_live"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "def calculator(num1, operation, num2):\n", "    if operation == \"add\":\n", "        return num1 + num2\n", "    elif operation == \"subtract\":\n", "        return num1 - num2\n", "    elif operation == \"multiply\":\n", "        return num1 * num2\n", "    elif operation == \"divide\":\n", "        return num1 / num2\n", "\n", "demo = gr.Interface(\n", "    calculator,\n", "    [\n", "        \"number\",\n", "        gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n", "        \"number\"\n", "    ],\n", "    \"number\",\n", "    live=True,\n", ")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/cancel_events/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: cancel_events"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "\n", "def fake_diffusion(steps):\n", "    for i in range(steps):\n", "        print(f\"Current step: {i}\")\n", "        time.sleep(0.2)\n", "        yield str(i)\n", "\n", "\n", "def long_prediction(*args, **kwargs):\n", "    time.sleep(10)\n", "    return 42\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            n = gr.Slider(1, 10, value=9, step=1, label=\"Number Steps\")\n", "            run = gr.Button(value=\"Start Iterating\")\n", "            output = gr.Textbox(label=\"Iterative Output\")\n", "            stop = gr.Button(value=\"Stop Iterating\")\n", "        with gr.Column():\n", "            textbox = gr.Textbox(label=\"Prompt\")\n", "            prediction = gr.Number(label=\"Expensive Calculation\")\n", "            run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", "        with gr.Column():\n", "            cancel_on_change = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Change\")\n", "            cancel_on_submit = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Submit\")\n", "            echo = gr.Textbox(label=\"Echo\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            image = gr.Image(source=\"webcam\", tool=\"editor\", label=\"Cancel on edit\", interactive=True)\n", "        with gr.Column():\n", "            video = gr.Video(source=\"webcam\", label=\"Cancel on play\", interactive=True)\n", "\n", "    click_event = run.click(fake_diffusion, n, output)\n", "    stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])\n", "    pred_event = run_pred.click(fn=long_prediction, inputs=[textbox], outputs=prediction)\n", "\n", "    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", "    cancel_on_submit.submit(lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event])\n", "    image.edit(None, None, None, cancels=[click_event, pred_event])\n", "    video.play(None, None, None, cancels=[click_event, pred_event])\n", "\n", "    demo.queue(concurrency_count=2, max_size=20)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: cancel_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "\n", "def fake_diffusion(steps):\n", "    for i in range(steps):\n", "        print(f\"Current step: {i}\")\n", "        time.sleep(0.2)\n", "        yield str(i)\n", "\n", "\n", "def long_prediction(*args, **kwargs):\n", "    time.sleep(10)\n", "    return 42\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            n = gr.Slider(1, 10, value=9, step=1, label=\"Number Steps\")\n", "            run = gr.Button(value=\"Start Iterating\")\n", "            output = gr.Textbox(label=\"Iterative Output\")\n", "            stop = gr.Button(value=\"Stop Iterating\")\n", "        with gr.Column():\n", "            textbox = gr.Textbox(label=\"Prompt\")\n", "            prediction = gr.Number(label=\"Expensive Calculation\")\n", "            run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", "        with gr.Column():\n", "            cancel_on_change = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Change\")\n", "            cancel_on_submit = gr.Textbox(label=\"Cancel Iteration and Expensive Calculation on Submit\")\n", "            echo = gr.Textbox(label=\"Echo\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            image = gr.Image(source=\"webcam\", tool=\"editor\", label=\"Cancel on edit\", interactive=True)\n", "        with gr.Column():\n", "            video = gr.Video(source=\"webcam\", label=\"Cancel on play\", interactive=True)\n", "\n", "    click_event = run.click(fake_diffusion, n, output)\n", "    stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])\n", "    pred_event = run_pred.click(fn=long_prediction, inputs=[textbox], outputs=prediction)\n", "\n", "    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", "    cancel_on_submit.submit(lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event])\n", "    image.edit(None, None, None, cancels=[click_event, pred_event])\n", "    video.play(None, None, None, cancels=[click_event, pred_event])\n", "\n", "    demo.queue(concurrency_count=2, max_size=20)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatbot_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Chatbot(value=[[\"Hello World\",\"Hey Gradio!\"],[\"\u2764\ufe0f\",\"\ud83d\ude0d\"],[\"\ud83d\udd25\",\"\ud83e\udd17\"]])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Chatbot(value=[[\"Hello World\",\"Hey Gradio!\"],[\"\u2764\ufe0f\",\"\ud83d\ude0d\"],[\"\ud83d\udd25\",\"\ud83e\udd17\"]])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatbot_consecutive/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_consecutive"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot()\n", "    msg = gr.Textbox()\n", "    clear = gr.Button(\"Clear\")\n", "\n", "    def user(user_message, history):\n", "        return \"\", history + [[user_message, None]]\n", "\n", "    def bot(history):\n", "        bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", "        time.sleep(2)\n", "        history[-1][1] = bot_message\n", "        return history\n", "\n", "    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "    clear.click(lambda: None, None, chatbot, queue=False)\n", "    \n", "demo.queue()\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_consecutive"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot()\n", "    msg = gr.Textbox()\n", "    clear = gr.Button(\"Clear\")\n", "\n", "    def user(user_message, history):\n", "        return \"\", history + [[user_message, None]]\n", "\n", "    def bot(history):\n", "        bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", "        time.sleep(2)\n", "        history[-1][1] = bot_message\n", "        return history\n", "\n", "    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "    clear.click(lambda: None, None, chatbot, queue=False)\n", "    \n", "demo.queue()\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatbot_dialogpt/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_dialogpt"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "import torch\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-medium\")\n", "model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-medium\")\n", "\n", "\n", "def user(message, history):\n", "    return \"\", history + [[message, None]]\n", "\n", "\n", "def bot(history):\n", "    user_message = history[-1][0]\n", "    new_user_input_ids = tokenizer.encode(\n", "        user_message + tokenizer.eos_token, return_tensors=\"pt\"\n", "    )\n", "\n", "    # append the new user input tokens to the chat history\n", "    bot_input_ids = torch.cat([torch.LongTensor([]), new_user_input_ids], dim=-1)\n", "\n", "    # generate a response\n", "    response = model.generate(\n", "        bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id\n", "    ).tolist()\n", "\n", "    # convert the tokens to text, and then split the responses into lines\n", "    response = tokenizer.decode(response[0]).split(\"<|endoftext|>\")\n", "    response = [\n", "        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)\n", "    ]  # convert to tuples of list\n", "    history[-1] = response[0]\n", "    return history\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot()\n", "    msg = gr.Textbox()\n", "    clear = gr.Button(\"Clear\")\n", "\n", "    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "    clear.click(lambda: None, None, chatbot, queue=False)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_dialogpt"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "import torch\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-medium\")\n", "model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-medium\")\n", "\n", "\n", "def user(message, history):\n", "    return \"\", history + [[message, None]]\n", "\n", "\n", "def bot(history):\n", "    user_message = history[-1][0]\n", "    new_user_input_ids = tokenizer.encode(\n", "        user_message + tokenizer.eos_token, return_tensors=\"pt\"\n", "    )\n", "\n", "    # append the new user input tokens to the chat history\n", "    bot_input_ids = torch.cat([torch.LongTensor([]), new_user_input_ids], dim=-1)\n", "\n", "    # generate a response\n", "    response = model.generate(\n", "        bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id\n", "    ).tolist()\n", "\n", "    # convert the tokens to text, and then split the responses into lines\n", "    response = tokenizer.decode(response[0]).split(\"<|endoftext|>\")\n", "    response = [\n", "        (response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)\n", "    ]  # convert to tuples of list\n", "    history[-1] = response[0]\n", "    return history\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot()\n", "    msg = gr.Textbox()\n", "    clear = gr.Button(\"Clear\")\n", "\n", "    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "    clear.click(lambda: None, None, chatbot, queue=False)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatbot_multimodal/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_multimodal"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/chatbot_multimodal/avatar.png"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "import time\n", "\n", "# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n", "\n", "\n", "def add_text(history, text):\n", "    history = history + [(text, None)]\n", "    return history, gr.Textbox(value=\"\", interactive=False)\n", "\n", "\n", "def add_file(history, file):\n", "    history = history + [((file.name,), None)]\n", "    return history\n", "\n", "\n", "def bot(history):\n", "    response = \"**That's cool!**\"\n", "    history[-1][1] = \"\"\n", "    for character in response:\n", "        history[-1][1] += character\n", "        time.sleep(0.05)\n", "        yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot(\n", "        [],\n", "        elem_id=\"chatbot\",\n", "        bubble_full_width=False,\n", "        avatar_images=(None, (os.path.join(os.path.abspath(''), \"avatar.png\"))),\n", "    )\n", "\n", "    with gr.Row():\n", "        txt = gr.Textbox(\n", "            scale=4,\n", "            show_label=False,\n", "            placeholder=\"Enter text and press enter, or upload an image\",\n", "            container=False,\n", "        )\n", "        btn = gr.UploadButton(\"\ud83d\udcc1\", file_types=[\"image\", \"video\", \"audio\"])\n", "\n", "    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)\n", "    file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "\n", "demo.queue()\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_multimodal"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/chatbot_multimodal/avatar.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "import time\n", "\n", "# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n", "\n", "\n", "def add_text(history, text):\n", "    history = history + [(text, None)]\n", "    return history, gr.Textbox(value=\"\", interactive=False)\n", "\n", "\n", "def add_file(history, file):\n", "    history = history + [((file.name,), None)]\n", "    return history\n", "\n", "\n", "def bot(history):\n", "    response = \"**That's cool!**\"\n", "    history[-1][1] = \"\"\n", "    for character in response:\n", "        history[-1][1] += character\n", "        time.sleep(0.05)\n", "        yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot(\n", "        [],\n", "        elem_id=\"chatbot\",\n", "        bubble_full_width=False,\n", "        avatar_images=(None, (os.path.join(os.path.abspath(''), \"avatar.png\"))),\n", "    )\n", "\n", "    with gr.Row():\n", "        txt = gr.Textbox(\n", "            scale=4,\n", "            show_label=False,\n", "            placeholder=\"Enter text and press enter, or upload an image\",\n", "            container=False,\n", "        )\n", "        btn = gr.UploadButton(\"\ud83d\udcc1\", file_types=[\"image\", \"video\", \"audio\"])\n", "\n", "    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)\n", "    file_msg = btn.upload(add_file, [chatbot, btn], [chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "\n", "demo.queue()\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatbot_simple/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_simple"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot()\n", "    msg = gr.Textbox()\n", "    clear = gr.ClearButton([msg, chatbot])\n", "\n", "    def respond(message, chat_history):\n", "        bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", "        chat_history.append((message, bot_message))\n", "        time.sleep(2)\n", "        return \"\", chat_history\n", "\n", "    msg.submit(respond, [msg, chatbot], [msg, chatbot])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_simple"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot()\n", "    msg = gr.Textbox()\n", "    clear = gr.ClearButton([msg, chatbot])\n", "\n", "    def respond(message, chat_history):\n", "        bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", "        chat_history.append((message, bot_message))\n", "        time.sleep(2)\n", "        return \"\", chat_history\n", "\n", "    msg.submit(respond, [msg, chatbot], [msg, chatbot])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatbot_streaming/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatbot_streaming"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot()\n", "    msg = gr.Textbox()\n", "    clear = gr.Button(\"Clear\")\n", "\n", "    def user(user_message, history):\n", "        return \"\", history + [[user_message, None]]\n", "\n", "    def bot(history):\n", "        bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", "        history[-1][1] = \"\"\n", "        for character in bot_message:\n", "            history[-1][1] += character\n", "            time.sleep(0.05)\n", "            yield history\n", "\n", "    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "    clear.click(lambda: None, None, chatbot, queue=False)\n", "    \n", "demo.queue()\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatbot_streaming"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import random\n", "import time\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot()\n", "    msg = gr.Textbox()\n", "    clear = gr.Button(\"Clear\")\n", "\n", "    def user(user_message, history):\n", "        return \"\", history + [[user_message, None]]\n", "\n", "    def bot(history):\n", "        bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n", "        history[-1][1] = \"\"\n", "        for character in bot_message:\n", "            history[-1][1] += character\n", "            time.sleep(0.05)\n", "            yield history\n", "\n", "    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n", "        bot, chatbot, chatbot\n", "    )\n", "    clear.click(lambda: None, None, chatbot, queue=False)\n", "    \n", "demo.queue()\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatinterface_random_response/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatinterface_random_response"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import random\n", "import gradio as gr\n", "\n", "def random_response(message, history):\n", "    return random.choice([\"Yes\", \"No\"])\n", "\n", "demo = gr.ChatInterface(random_response)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatinterface_random_response"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import random\n", "import gradio as gr\n", "\n", "def random_response(message, history):\n", "    return random.choice([\"Yes\", \"No\"])\n", "\n", "demo = gr.ChatInterface(random_response)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatinterface_streaming_echo/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatinterface_streaming_echo"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "def slow_echo(message, history):\n", "    for i in range(len(message)):\n", "        time.sleep(0.05)\n", "        yield \"You typed: \" + message[: i+1]\n", "\n", "demo = gr.ChatInterface(slow_echo).queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatinterface_streaming_echo"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import time\n", "import gradio as gr\n", "\n", "def slow_echo(message, history):\n", "    for i in range(len(message)):\n", "        time.sleep(0.05)\n", "        yield \"You typed: \" + message[: i+1]\n", "\n", "demo = gr.ChatInterface(slow_echo).queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chatinterface_system_prompt/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chatinterface_system_prompt"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "def echo(message, history, system_prompt, tokens):\n", "    response = f\"System prompt: {system_prompt}\\n Message: {message}.\"\n", "    for i in range(min(len(response), int(tokens))):\n", "        time.sleep(0.05)\n", "        yield response[: i+1]\n", "\n", "demo = gr.ChatInterface(echo, \n", "                        additional_inputs=[\n", "                            gr.Textbox(\"You are helpful AI.\", label=\"System Prompt\"), \n", "                            gr.Slider(10, 100)\n", "                        ]\n", "                       )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.queue().launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chatinterface_system_prompt"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "def echo(message, history, system_prompt, tokens):\n", "    response = f\"System prompt: {system_prompt}\\n Message: {message}.\"\n", "    for i in range(min(len(response), int(tokens))):\n", "        time.sleep(0.05)\n", "        yield response[: i+1]\n", "\n", "demo = gr.ChatInterface(echo, \n", "                        additional_inputs=[\n", "                            gr.Textbox(\"You are helpful AI.\", label=\"System Prompt\"), \n", "                            gr.Slider(10, 100)\n", "                        ]\n", "                       )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.queue().launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/checkbox_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: checkbox_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.Checkbox()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: checkbox_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.Checkbox()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/checkboxgroup_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: checkboxgroup_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.CheckboxGroup(choices=[\"First Choice\", \"Second Choice\", \"Third Choice\"])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: checkboxgroup_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.CheckboxGroup(choices=[\"First Choice\", \"Second Choice\", \"Third Choice\"])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/chicago-bikeshare-dashboard/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: chicago-bikeshare-dashboard"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio psycopg2 matplotlib SQLAlchemy "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "import pandas as pd\n", "\n", "DB_USER = os.getenv(\"DB_USER\")\n", "DB_PASSWORD = os.getenv(\"DB_PASSWORD\")\n", "DB_HOST = os.getenv(\"DB_HOST\")\n", "PORT = 8080\n", "DB_NAME = \"bikeshare\"\n", "\n", "connection_string = (\n", "    f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}\"\n", ")\n", "\n", "\n", "def get_count_ride_type():\n", "    df = pd.read_sql(\n", "        \"\"\"\n", "        SELECT COUNT(ride_id) as n, rideable_type\n", "        FROM rides\n", "        GROUP BY rideable_type\n", "        ORDER BY n DESC\n", "    \"\"\",\n", "        con=connection_string,\n", "    )\n", "    return df\n", "\n", "\n", "def get_most_popular_stations():\n", "\n", "    df = pd.read_sql(\n", "        \"\"\"\n", "    SELECT COUNT(ride_id) as n, MAX(start_station_name) as station\n", "    FROM RIDES\n", "    WHERE start_station_name is NOT NULL\n", "    GROUP BY start_station_id\n", "    ORDER BY n DESC\n", "    LIMIT 5\n", "    \"\"\",\n", "        con=connection_string,\n", "    )\n", "    return df\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "    # Chicago Bike Share Dashboard\n", "    \n", "    This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.\n", "    This demo uses psycopg2 but any postgresql client library (SQLAlchemy)\n", "    is compatible with gradio.\n", "    \n", "    Connection credentials are handled by environment variables\n", "    defined as secrets in the Space.\n", "\n", "    If data were added to the database, the plots in this demo would update\n", "    whenever the webpage is reloaded.\n", "    \n", "    This demo serves as a starting point for your database-connected apps!\n", "    \"\"\"\n", "    )\n", "    with gr.Row():\n", "        bike_type = gr.BarPlot(\n", "            x=\"rideable_type\",\n", "            y='n',\n", "            title=\"Number of rides per bicycle type\",\n", "            y_title=\"Number of Rides\",\n", "            x_title=\"Bicycle Type\",\n", "            vertical=False,\n", "            tooltip=['rideable_type', \"n\"],\n", "            height=300,\n", "            width=300,\n", "        )\n", "        station = gr.BarPlot(\n", "            x='station',\n", "            y='n',\n", "            title=\"Most Popular Stations\",\n", "            y_title=\"Number of Rides\",\n", "            x_title=\"Station Name\",\n", "            vertical=False,\n", "            tooltip=['station', 'n'],\n", "            height=300,\n", "            width=300\n", "        )\n", "\n", "    demo.load(get_count_ride_type, inputs=None, outputs=bike_type)\n", "    demo.load(get_most_popular_stations, inputs=None, outputs=station)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: chicago-bikeshare-dashboard"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio psycopg2 matplotlib SQLAlchemy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import os\n", "import gradio as gr\n", "import pandas as pd\n", "\n", "DB_USER = os.getenv(\"DB_USER\")\n", "DB_PASSWORD = os.getenv(\"DB_PASSWORD\")\n", "DB_HOST = os.getenv(\"DB_HOST\")\n", "PORT = 8080\n", "DB_NAME = \"bikeshare\"\n", "\n", "connection_string = (\n", "    f\"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}\"\n", ")\n", "\n", "\n", "def get_count_ride_type():\n", "    df = pd.read_sql(\n", "        \"\"\"\n", "        SELECT COUNT(ride_id) as n, rideable_type\n", "        FROM rides\n", "        GROUP BY rideable_type\n", "        ORDER BY n DESC\n", "    \"\"\",\n", "        con=connection_string,\n", "    )\n", "    return df\n", "\n", "\n", "def get_most_popular_stations():\n", "\n", "    df = pd.read_sql(\n", "        \"\"\"\n", "    SELECT COUNT(ride_id) as n, MAX(start_station_name) as station\n", "    FROM RIDES\n", "    WHERE start_station_name is NOT NULL\n", "    GROUP BY start_station_id\n", "    ORDER BY n DESC\n", "    LIMIT 5\n", "    \"\"\",\n", "        con=connection_string,\n", "    )\n", "    return df\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown(\n", "        \"\"\"\n", "    # Chicago Bike Share Dashboard\n", "    \n", "    This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.\n", "    This demo uses psycopg2 but any postgresql client library (SQLAlchemy)\n", "    is compatible with gradio.\n", "    \n", "    Connection credentials are handled by environment variables\n", "    defined as secrets in the Space.\n", "\n", "    If data were added to the database, the plots in this demo would update\n", "    whenever the webpage is reloaded.\n", "    \n", "    This demo serves as a starting point for your database-connected apps!\n", "    \"\"\"\n", "    )\n", "    with gr.Row():\n", "        bike_type = gr.BarPlot(\n", "            x=\"rideable_type\",\n", "            y='n',\n", "            title=\"Number of rides per bicycle type\",\n", "            y_title=\"Number of Rides\",\n", "            x_title=\"Bicycle Type\",\n", "            vertical=False,\n", "            tooltip=['rideable_type', \"n\"],\n", "            height=300,\n", "            width=300,\n", "        )\n", "        station = gr.BarPlot(\n", "            x='station',\n", "            y='n',\n", "            title=\"Most Popular Stations\",\n", "            y_title=\"Number of Rides\",\n", "            x_title=\"Station Name\",\n", "            vertical=False,\n", "            tooltip=['station', 'n'],\n", "            height=300,\n", "            width=300\n", "        )\n", "\n", "    demo.load(get_count_ride_type, inputs=None, outputs=bike_type)\n", "    demo.load(get_most_popular_stations, inputs=None, outputs=station)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/clearbutton_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: clearbutton_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    textbox = gr.Textbox(value=\"This is some text\")\n", "    gr.ClearButton(textbox)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: clearbutton_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    textbox = gr.Textbox(value=\"This is some text\")\n", "    gr.ClearButton(textbox)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/code/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: code"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/code/file.css"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "from time import sleep\n", "\n", "\n", "css_file = os.path.join(os.path.abspath(''), \"file.css\")\n", "\n", "\n", "def set_lang(language):\n", "    print(language)\n", "    return gr.Code(language=language)\n", "\n", "\n", "def set_lang_from_path():\n", "    sleep(1)\n", "    return gr.Code((css_file,), language=\"css\")\n", "\n", "\n", "def code(language, code):\n", "    return gr.Code(code, language=language)\n", "\n", "\n", "io = gr.Interface(lambda x: x, \"code\", \"code\")\n", "\n", "with gr.Blocks() as demo:\n", "    lang = gr.Dropdown(value=\"python\", choices=gr.Code.languages)\n", "    with gr.Row():\n", "        code_in = gr.Code(\n", "            language=\"python\",\n", "            label=\"Input\",\n", "            value='def all_odd_elements(sequence):\\n    \"\"\"Returns every odd element of the sequence.\"\"\"',\n", "        )\n", "        code_out = gr.Code(label=\"Output\")\n", "    btn = gr.Button(\"Run\")\n", "    btn_two = gr.Button(\"Load File\")\n", "\n", "    lang.change(set_lang, inputs=lang, outputs=code_in)\n", "    btn.click(code, inputs=[lang, code_in], outputs=code_out)\n", "    btn_two.click(set_lang_from_path, inputs=None, outputs=code_out)\n", "    io.render()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: code"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/code/file.css"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "from time import sleep\n", "\n", "\n", "css_file = os.path.join(os.path.abspath(''), \"file.css\")\n", "\n", "\n", "def set_lang(language):\n", "    print(language)\n", "    return gr.Code(language=language)\n", "\n", "\n", "def set_lang_from_path():\n", "    sleep(1)\n", "    return gr.Code((css_file,), language=\"css\")\n", "\n", "\n", "def code(language, code):\n", "    return gr.Code(code, language=language)\n", "\n", "\n", "io = gr.Interface(lambda x: x, \"code\", \"code\")\n", "\n", "with gr.Blocks() as demo:\n", "    lang = gr.Dropdown(value=\"python\", choices=gr.Code.languages)\n", "    with gr.Row():\n", "        code_in = gr.Code(\n", "            language=\"python\",\n", "            label=\"Input\",\n", "            value='def all_odd_elements(sequence):\\n    \"\"\"Returns every odd element of the sequence.\"\"\"',\n", "        )\n", "        code_out = gr.Code(label=\"Output\")\n", "    btn = gr.Button(\"Run\")\n", "    btn_two = gr.Button(\"Load File\")\n", "\n", "    lang.change(set_lang, inputs=lang, outputs=code_in)\n", "    btn.click(code, inputs=[lang, code_in], outputs=code_out)\n", "    btn_two.click(set_lang_from_path, inputs=None, outputs=code_out)\n", "    io.render()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/code_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: code_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Code(\n", "        value=\"\"\"def hello_world():\n", "    return \"Hello, world!\"\n", "    \n", "print(hello_world())\"\"\",\n", "        language=\"python\",\n", "        interactive=True,\n", "        show_label=False,\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: code_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Code(\n", "        value=\"\"\"def hello_world():\n", "    return \"Hello, world!\"\n", "    \n", "print(hello_world())\"\"\",\n", "        language=\"python\",\n", "        interactive=True,\n", "        show_label=False,\n", "    )\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/color_generator/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: color_generator"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio opencv-python numpy"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import cv2\n", "import numpy as np\n", "import random\n", "\n", "\n", "# Convert decimal color to hexadecimal color\n", "def RGB_to_Hex(rgb):\n", "    color = \"#\"\n", "    for i in rgb:\n", "        num = int(i)\n", "        color += str(hex(num))[-2:].replace(\"x\", \"0\").upper()\n", "    return color\n", "\n", "\n", "# Randomly generate light or dark colors\n", "def random_color(is_light=True):\n", "    return (\n", "        random.randint(0, 127) + int(is_light) * 128,\n", "        random.randint(0, 127) + int(is_light) * 128,\n", "        random.randint(0, 127) + int(is_light) * 128,\n", "    )\n", "\n", "\n", "def switch_color(color_style):\n", "    if color_style == \"light\":\n", "        is_light = True\n", "    elif color_style == \"dark\":\n", "        is_light = False\n", "    back_color_ = random_color(is_light)  # Randomly generate colors\n", "    back_color = RGB_to_Hex(back_color_)  # Convert to hexadecimal\n", "\n", "    # Draw color pictures.\n", "    w, h = 50, 50\n", "    img = np.zeros((h, w, 3), np.uint8)\n", "    cv2.rectangle(img, (0, 0), (w, h), back_color_, thickness=-1)\n", "\n", "    return back_color, back_color, img\n", "\n", "\n", "inputs = [gr.Radio([\"light\", \"dark\"], value=\"light\")]\n", "\n", "outputs = [\n", "    gr.ColorPicker(label=\"color\"),\n", "    gr.Textbox(label=\"hexadecimal color\"),\n", "    gr.Image(type=\"numpy\", label=\"color picture\"),\n", "]\n", "\n", "title = \"Color Generator\"\n", "description = (\n", "    \"Click the Submit button, and a dark or light color will be randomly generated.\"\n", ")\n", "\n", "demo = gr.Interface(\n", "    fn=switch_color,\n", "    inputs=inputs,\n", "    outputs=outputs,\n", "    title=title,\n", "    description=description,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: color_generator"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio opencv-python numpy"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import cv2\n", "import numpy as np\n", "import random\n", "\n", "\n", "# Convert decimal color to hexadecimal color\n", "def RGB_to_Hex(rgb):\n", "    color = \"#\"\n", "    for i in rgb:\n", "        num = int(i)\n", "        color += str(hex(num))[-2:].replace(\"x\", \"0\").upper()\n", "    return color\n", "\n", "\n", "# Randomly generate light or dark colors\n", "def random_color(is_light=True):\n", "    return (\n", "        random.randint(0, 127) + int(is_light) * 128,\n", "        random.randint(0, 127) + int(is_light) * 128,\n", "        random.randint(0, 127) + int(is_light) * 128,\n", "    )\n", "\n", "\n", "def switch_color(color_style):\n", "    if color_style == \"light\":\n", "        is_light = True\n", "    elif color_style == \"dark\":\n", "        is_light = False\n", "    back_color_ = random_color(is_light)  # Randomly generate colors\n", "    back_color = RGB_to_Hex(back_color_)  # Convert to hexadecimal\n", "\n", "    # Draw color pictures.\n", "    w, h = 50, 50\n", "    img = np.zeros((h, w, 3), np.uint8)\n", "    cv2.rectangle(img, (0, 0), (w, h), back_color_, thickness=-1)\n", "\n", "    return back_color, back_color, img\n", "\n", "\n", "inputs = [gr.Radio([\"light\", \"dark\"], value=\"light\")]\n", "\n", "outputs = [\n", "    gr.ColorPicker(label=\"color\"),\n", "    gr.Textbox(label=\"hexadecimal color\"),\n", "    gr.Image(type=\"numpy\", label=\"color picture\"),\n", "]\n", "\n", "title = \"Color Generator\"\n", "description = (\n", "    \"Click the Submit button, and a dark or light color will be randomly generated.\"\n", ")\n", "\n", "demo = gr.Interface(\n", "    fn=switch_color,\n", "    inputs=inputs,\n", "    outputs=outputs,\n", "    title=title,\n", "    description=description,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/color_picker/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: color_picker"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio Pillow"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/color_picker/rabbit.png"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import os\n", "from PIL import Image, ImageColor\n", "\n", "\n", "def change_color(icon, color):\n", "\n", "    \"\"\"\n", "    Function that given an icon in .png format changes its color\n", "    Args:\n", "        icon: Icon whose color needs to be changed.\n", "        color: Chosen color with which to edit the input icon.\n", "    Returns:\n", "        edited_image: Edited icon.\n", "    \"\"\"\n", "    img = icon.convert(\"LA\")\n", "    img = img.convert(\"RGBA\")\n", "    image_np = np.array(icon)\n", "    _, _, _, alpha = image_np.T\n", "    mask = alpha > 0\n", "    image_np[..., :-1][mask.T] = ImageColor.getcolor(color, \"RGB\")\n", "    edited_image = Image.fromarray(image_np)\n", "    return edited_image\n", "\n", "\n", "inputs = [\n", "    gr.Image(label=\"icon\", type=\"pil\", image_mode=\"RGBA\"),\n", "    gr.ColorPicker(label=\"color\"),\n", "]\n", "outputs = gr.Image(label=\"colored icon\")\n", "\n", "demo = gr.Interface(\n", "    fn=change_color,\n", "    inputs=inputs,\n", "    outputs=outputs,\n", "    examples=[\n", "        [os.path.join(os.path.abspath(''), \"rabbit.png\"), \"#ff0000\"],\n", "        [os.path.join(os.path.abspath(''), \"rabbit.png\"), \"#0000FF\"],\n", "    ],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: color_picker"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio Pillow"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/color_picker/rabbit.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import os\n", "from PIL import Image, ImageColor\n", "\n", "\n", "def change_color(icon, color):\n", "\n", "    \"\"\"\n", "    Function that given an icon in .png format changes its color\n", "    Args:\n", "        icon: Icon whose color needs to be changed.\n", "        color: Chosen color with which to edit the input icon.\n", "    Returns:\n", "        edited_image: Edited icon.\n", "    \"\"\"\n", "    img = icon.convert(\"LA\")\n", "    img = img.convert(\"RGBA\")\n", "    image_np = np.array(icon)\n", "    _, _, _, alpha = image_np.T\n", "    mask = alpha > 0\n", "    image_np[..., :-1][mask.T] = ImageColor.getcolor(color, \"RGB\")\n", "    edited_image = Image.fromarray(image_np)\n", "    return edited_image\n", "\n", "\n", "inputs = [\n", "    gr.Image(label=\"icon\", type=\"pil\", image_mode=\"RGBA\"),\n", "    gr.ColorPicker(label=\"color\"),\n", "]\n", "outputs = gr.Image(label=\"colored icon\")\n", "\n", "demo = gr.Interface(\n", "    fn=change_color,\n", "    inputs=inputs,\n", "    outputs=outputs,\n", "    examples=[\n", "        [os.path.join(os.path.abspath(''), \"rabbit.png\"), \"#ff0000\"],\n", "        [os.path.join(os.path.abspath(''), \"rabbit.png\"), \"#0000FF\"],\n", "    ],\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/colorpicker_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: colorpicker_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.ColorPicker()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: colorpicker_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.ColorPicker()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/concurrency_with_queue/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: concurrency_with_queue"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "\n", "def say_hello(name):\n", "  time.sleep(5)\n", "  return f\"Hello {name}!\"\n", "\n", "\n", "with gr.Blocks() as demo:\n", "  inp = gr.Textbox()\n", "  outp = gr.Textbox()\n", "  button = gr.Button()\n", "  button.click(say_hello, inp, outp)\n", "\n", "  demo.queue(concurrency_count=41).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: concurrency_with_queue"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "\n", "def say_hello(name):\n", "  time.sleep(5)\n", "  return f\"Hello {name}!\"\n", "\n", "\n", "with gr.Blocks() as demo:\n", "  inp = gr.Textbox()\n", "  outp = gr.Textbox()\n", "  button = gr.Button()\n", "  button.click(say_hello, inp, outp)\n", "\n", "  demo.queue(concurrency_count=41).launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/concurrency_without_queue/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: concurrency_without_queue"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "\n", "def say_hello(name):\n", "  time.sleep(5)\n", "  return f\"Hello {name}!\"\n", "\n", "\n", "with gr.Blocks() as demo:\n", "  inp = gr.Textbox()\n", "  outp = gr.Textbox()\n", "  button = gr.Button()\n", "  button.click(say_hello, inp, outp)\n", "\n", "  demo.launch(max_threads=41)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: concurrency_without_queue"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "\n", "def say_hello(name):\n", "  time.sleep(5)\n", "  return f\"Hello {name}!\"\n", "\n", "\n", "with gr.Blocks() as demo:\n", "  inp = gr.Textbox()\n", "  outp = gr.Textbox()\n", "  button = gr.Button()\n", "  button.click(say_hello, inp, outp)\n", "\n", "  demo.launch(max_threads=41)\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/count_generator/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: count_generator"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "def count(n):\n", "    for i in range(int(n)):\n", "        time.sleep(0.5)\n", "        yield i\n", "\n", "def show(n):\n", "    return str(list(range(int(n))))\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Column():\n", "        num = gr.Number(value=10)\n", "        with gr.Row():\n", "            count_btn = gr.Button(\"Count\")\n", "            list_btn = gr.Button(\"List\")\n", "    with gr.Column():\n", "        out = gr.Textbox()\n", "    \n", "    count_btn.click(count, num, out)\n", "    list_btn.click(show, num, out)\n", "    \n", "demo.queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: count_generator"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import time\n", "\n", "def count(n):\n", "    for i in range(int(n)):\n", "        time.sleep(0.5)\n", "        yield i\n", "\n", "def show(n):\n", "    return str(list(range(int(n))))\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Column():\n", "        num = gr.Number(value=10)\n", "        with gr.Row():\n", "            count_btn = gr.Button(\"Count\")\n", "            list_btn = gr.Button(\"List\")\n", "    with gr.Column():\n", "        out = gr.Textbox()\n", "    \n", "    count_btn.click(count, num, out)\n", "    list_btn.click(show, num, out)\n", "    \n", "demo.queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/dashboard/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dashboard\n", "### This demo shows how you can build an interactive dashboard with gradio. Click on a python library on the left hand side and then on the right hand side click on the metric you'd like to see plot over time. Data is pulled from HuggingFace Hub datasets.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/dashboard/helpers.py"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import plotly.express as px\n", "from helpers import *\n", "\n", "\n", "LIBRARIES = [\"accelerate\", \"datasets\", \"diffusers\", \"evaluate\", \"gradio\", \"hub_docs\",\n", "             \"huggingface_hub\", \"optimum\", \"pytorch_image_models\", \"tokenizers\", \"transformers\"]\n", "\n", "\n", "def create_pip_plot(libraries, pip_choices):\n", "    if \"Pip\" not in pip_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_pip_installs(libraries, \"Cumulated\" in pip_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Pip installs\")\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "def create_star_plot(libraries, star_choices):\n", "    if \"Stars\" not in star_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_stars(libraries, \"Week over Week\" in star_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Number of stargazers\")\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "def create_issue_plot(libraries, issue_choices):\n", "    if \"Issue\" not in issue_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_issues(libraries,\n", "                             exclude_org_members=\"Exclude org members\" in issue_choices,\n", "                             week_over_week=\"Week over Week\" in issue_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Cumulated number of issues, PRs, and comments\",\n", "                   )\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            gr.Markdown(\"## Select libraries to display\")\n", "            libraries = gr.CheckboxGroup(choices=LIBRARIES, show_label=False)\n", "        with gr.Column():\n", "            gr.Markdown(\"## Select graphs to display\")\n", "            pip = gr.CheckboxGroup(choices=[\"Pip\", \"Cumulated\"], show_label=False)\n", "            stars = gr.CheckboxGroup(choices=[\"Stars\", \"Week over Week\"], show_label=False)\n", "            issues = gr.CheckboxGroup(choices=[\"Issue\", \"Exclude org members\", \"week over week\"], show_label=False)\n", "    with gr.Row():\n", "        fetch = gr.Button(value=\"Fetch\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            pip_plot = gr.Plot(visible=False)\n", "            star_plot = gr.Plot(visible=False)\n", "            issue_plot = gr.Plot(visible=False)\n", "\n", "    fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=pip_plot)\n", "    fetch.click(create_star_plot, inputs=[libraries, stars], outputs=star_plot)\n", "    fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dashboard\n", "### This demo shows how you can build an interactive dashboard with gradio. Click on a python library on the left hand side and then on the right hand side click on the metric you'd like to see plot over time. Data is pulled from HuggingFace Hub datasets.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio plotly"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/dashboard/helpers.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import plotly.express as px\n", "from helpers import *\n", "\n", "\n", "LIBRARIES = [\"accelerate\", \"datasets\", \"diffusers\", \"evaluate\", \"gradio\", \"hub_docs\",\n", "             \"huggingface_hub\", \"optimum\", \"pytorch_image_models\", \"tokenizers\", \"transformers\"]\n", "\n", "\n", "def create_pip_plot(libraries, pip_choices):\n", "    if \"Pip\" not in pip_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_pip_installs(libraries, \"Cumulated\" in pip_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Pip installs\")\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "def create_star_plot(libraries, star_choices):\n", "    if \"Stars\" not in star_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_stars(libraries, \"Week over Week\" in star_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Number of stargazers\")\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "def create_issue_plot(libraries, issue_choices):\n", "    if \"Issue\" not in issue_choices:\n", "        return gr.Plot(visible=False)\n", "    output = retrieve_issues(libraries,\n", "                             exclude_org_members=\"Exclude org members\" in issue_choices,\n", "                             week_over_week=\"Week over Week\" in issue_choices)\n", "    df = pd.DataFrame(output).melt(id_vars=\"day\")\n", "    plot = px.line(df, x=\"day\", y=\"value\", color=\"variable\",\n", "                   title=\"Cumulated number of issues, PRs, and comments\",\n", "                   )\n", "    plot.update_layout(legend=dict(x=0.5, y=0.99),  title_x=0.5, legend_title_text=\"\")\n", "    return gr.Plot(value=plot, visible=True)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            gr.Markdown(\"## Select libraries to display\")\n", "            libraries = gr.CheckboxGroup(choices=LIBRARIES, show_label=False)\n", "        with gr.Column():\n", "            gr.Markdown(\"## Select graphs to display\")\n", "            pip = gr.CheckboxGroup(choices=[\"Pip\", \"Cumulated\"], show_label=False)\n", "            stars = gr.CheckboxGroup(choices=[\"Stars\", \"Week over Week\"], show_label=False)\n", "            issues = gr.CheckboxGroup(choices=[\"Issue\", \"Exclude org members\", \"week over week\"], show_label=False)\n", "    with gr.Row():\n", "        fetch = gr.Button(value=\"Fetch\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            pip_plot = gr.Plot(visible=False)\n", "            star_plot = gr.Plot(visible=False)\n", "            issue_plot = gr.Plot(visible=False)\n", "\n", "    fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=pip_plot)\n", "    fetch.click(create_star_plot, inputs=[libraries, stars], outputs=star_plot)\n", "    fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/dataframe_block-ui-test/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataframe_block-ui-test"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    count = gr.Slider(minimum=1, maximum=10, step=1, label=\"count\")\n", "    data = gr.DataFrame(\n", "        headers=[\"A\", \"B\"], col_count=(2, \"fixed\"), type=\"array\", interactive=True\n", "    )\n", "    btn = gr.Button(value=\"click\")\n", "    btn.click(\n", "        fn=lambda cnt: [[str(2 * i), str(2 * i + 1)] for i in range(int(cnt))],\n", "        inputs=[count],\n", "        outputs=[data],\n", "    )\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_block-ui-test"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    count = gr.Slider(minimum=1, maximum=10, step=1, label=\"count\")\n", "    data = gr.DataFrame(\n", "        headers=[\"A\", \"B\"], col_count=(2, \"fixed\"), type=\"array\", interactive=True\n", "    )\n", "    btn = gr.Button(value=\"click\")\n", "    btn.click(\n", "        fn=lambda cnt: [[str(2 * i), str(2 * i + 1)] for i in range(int(cnt))],\n", "        inputs=[count],\n", "        outputs=[data],\n", "    )\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/dataframe_colorful/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataframe_colorful"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import pandas as pd \n", "import gradio as gr\n", "\n", "df = pd.DataFrame({\"A\" : [14, 4, 5, 4, 1], \n", "\t\t\t\t\"B\" : [5, 2, 54, 3, 2], \n", "\t\t\t\t\"C\" : [20, 20, 7, 3, 8], \n", "\t\t\t\t\"D\" : [14, 3, 6, 2, 6], \n", "\t\t\t\t\"E\" : [23, 45, 64, 32, 23]}) \n", "\n", "t = df.style.highlight_max(color = 'lightgreen', axis = 0)\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Dataframe(t)\n", "    \n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_colorful"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import pandas as pd \n", "import gradio as gr\n", "\n", "df = pd.DataFrame({\"A\" : [14, 4, 5, 4, 1], \n", "\t\t\t\t\"B\" : [5, 2, 54, 3, 2], \n", "\t\t\t\t\"C\" : [20, 20, 7, 3, 8], \n", "\t\t\t\t\"D\" : [14, 3, 6, 2, 6], \n", "\t\t\t\t\"E\" : [23, 45, 64, 32, 23]}) \n", "\n", "t = df.style.highlight_max(color = 'lightgreen', axis = 0)\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Dataframe(t)\n", "    \n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/dataframe_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataframe_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.Dataframe(interactive=True)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.Dataframe(interactive=True)\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/dataframe_datatype/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataframe_datatype"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import numpy as np\n", "\n", "\n", "def make_dataframe(n_periods):\n", "    return pd.DataFrame({\"date_1\": pd.date_range(\"2021-01-01\", periods=n_periods),\n", "                         \"date_2\": pd.date_range(\"2022-02-15\", periods=n_periods).strftime('%B %d, %Y, %r'),\n", "                         \"number\": np.random.random(n_periods).astype(np.float64),\n", "                         \"number_2\": np.random.randint(0, 100, n_periods).astype(np.int32),\n", "                         \"bool\": [True] * n_periods,\n", "                         \"markdown\": [\"# Hello\"] * n_periods})\n", "\n", "\n", "demo = gr.Interface(make_dataframe,\n", "             gr.Number(precision=0),\n", "             gr.Dataframe(datatype=[\"date\", \"date\", \"number\", \"number\", \"bool\", \"markdown\"]))\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataframe_datatype"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pandas as pd\n", "import numpy as np\n", "\n", "\n", "def make_dataframe(n_periods):\n", "    return pd.DataFrame({\"date_1\": pd.date_range(\"2021-01-01\", periods=n_periods),\n", "                         \"date_2\": pd.date_range(\"2022-02-15\", periods=n_periods).strftime('%B %d, %Y, %r'),\n", "                         \"number\": np.random.random(n_periods).astype(np.float64),\n", "                         \"number_2\": np.random.randint(0, 100, n_periods).astype(np.int32),\n", "                         \"bool\": [True] * n_periods,\n", "                         \"markdown\": [\"# Hello\"] * n_periods})\n", "\n", "\n", "demo = gr.Interface(make_dataframe,\n", "             gr.Number(precision=0),\n", "             gr.Dataframe(datatype=[\"date\", \"date\", \"number\", \"number\", \"bool\", \"markdown\"]))\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/dataset_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dataset_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Dataset(components=[gr.Textbox(visible=False)],\n", "        label=\"Text Dataset\",\n", "        samples=[\n", "            [\"The quick brown fox jumps over the lazy dog\"],\n", "            [\"Build & share delightful machine learning apps\"],\n", "            [\"She sells seashells by the seashore\"],\n", "            [\"Supercalifragilisticexpialidocious\"],\n", "            [\"Lorem ipsum\"],\n", "            [\"That's all folks!\"]\n", "        ],\n", "    )\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dataset_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Dataset(components=[gr.Textbox(visible=False)],\n", "        label=\"Text Dataset\",\n", "        samples=[\n", "            [\"The quick brown fox jumps over the lazy dog\"],\n", "            [\"Build & share delightful machine learning apps\"],\n", "            [\"She sells seashells by the seashore\"],\n", "            [\"Supercalifragilisticexpialidocious\"],\n", "            [\"Lorem ipsum\"],\n", "            [\"That's all folks!\"]\n", "        ],\n", "    )\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/diff_texts/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: diff_texts"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["from difflib import Differ\n", "\n", "import gradio as gr\n", "\n", "\n", "def diff_texts(text1, text2):\n", "    d = Differ()\n", "    return [\n", "        (token[2:], token[0] if token[0] != \" \" else None)\n", "        for token in d.compare(text1, text2)\n", "    ]\n", "\n", "\n", "demo = gr.Interface(\n", "    diff_texts,\n", "    [\n", "        gr.Textbox(\n", "            label=\"Text 1\",\n", "            info=\"Initial text\",\n", "            lines=3,\n", "            value=\"The quick brown fox jumped over the lazy dogs.\",\n", "        ),\n", "        gr.Textbox(\n", "            label=\"Text 2\",\n", "            info=\"Text to compare\",\n", "            lines=3,\n", "            value=\"The fast brown fox jumps over lazy dogs.\",\n", "        ),\n", "    ],\n", "    gr.HighlightedText(\n", "        label=\"Diff\",\n", "        combine_adjacent=True,\n", "        show_legend=True,\n", "        color_map={\"+\": \"red\", \"-\": \"green\"}),\n", "    theme=gr.themes.Base()\n", ")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: diff_texts"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from difflib import Differ\n", "\n", "import gradio as gr\n", "\n", "\n", "def diff_texts(text1, text2):\n", "    d = Differ()\n", "    return [\n", "        (token[2:], token[0] if token[0] != \" \" else None)\n", "        for token in d.compare(text1, text2)\n", "    ]\n", "\n", "\n", "demo = gr.Interface(\n", "    diff_texts,\n", "    [\n", "        gr.Textbox(\n", "            label=\"Text 1\",\n", "            info=\"Initial text\",\n", "            lines=3,\n", "            value=\"The quick brown fox jumped over the lazy dogs.\",\n", "        ),\n", "        gr.Textbox(\n", "            label=\"Text 2\",\n", "            info=\"Text to compare\",\n", "            lines=3,\n", "            value=\"The fast brown fox jumps over lazy dogs.\",\n", "        ),\n", "    ],\n", "    gr.HighlightedText(\n", "        label=\"Diff\",\n", "        combine_adjacent=True,\n", "        show_legend=True,\n", "        color_map={\"+\": \"red\", \"-\": \"green\"}),\n", "    theme=gr.themes.Base()\n", ")\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/diffusers_with_batching/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: diffusers_with_batching"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers diffusers"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import torch\n", "from diffusers import DiffusionPipeline\n", "import gradio as gr\n", "\n", "generator = DiffusionPipeline.from_pretrained(\"CompVis/ldm-text2im-large-256\")\n", "# move to GPU if available\n", "if torch.cuda.is_available():\n", "    generator = generator.to(\"cuda\")\n", "\n", "def generate(prompts):\n", "  images = generator(list(prompts)).images\n", "  return [images]\n", "\n", "demo = gr.Interface(generate, \n", "             \"textbox\", \n", "             \"image\", \n", "             batch=True, \n", "             max_batch_size=4  # Set the batch size based on your CPU/GPU memory\n", ").queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: diffusers_with_batching"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers diffusers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import torch\n", "from diffusers import DiffusionPipeline\n", "import gradio as gr\n", "\n", "generator = DiffusionPipeline.from_pretrained(\"CompVis/ldm-text2im-large-256\")\n", "# move to GPU if available\n", "if torch.cuda.is_available():\n", "    generator = generator.to(\"cuda\")\n", "\n", "def generate(prompts):\n", "  images = generator(list(prompts)).images\n", "  return [images]\n", "\n", "demo = gr.Interface(generate, \n", "             \"textbox\", \n", "             \"image\", \n", "             batch=True, \n", "             max_batch_size=4  # Set the batch size based on your CPU/GPU memory\n", ").queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/digit_classifier/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: digit_classifier"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio tensorflow"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["from urllib.request import urlretrieve\n", "\n", "import tensorflow as tf\n", "\n", "import gradio as gr\n", "\n", "urlretrieve(\n", "    \"https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5\", \"mnist-model.h5\"\n", ")\n", "model = tf.keras.models.load_model(\"mnist-model.h5\")\n", "\n", "\n", "def recognize_digit(image):\n", "    image = image.reshape(1, -1)\n", "    prediction = model.predict(image).tolist()[0]\n", "    return {str(i): prediction[i] for i in range(10)}\n", "\n", "\n", "im = gr.Image(shape=(28, 28), image_mode=\"L\", invert_colors=False, source=\"canvas\")\n", "\n", "demo = gr.Interface(\n", "    recognize_digit,\n", "    im,\n", "    gr.Label(num_top_classes=3),\n", "    live=True,\n", "    interpretation=\"default\",\n", "    capture_session=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: digit_classifier"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio tensorflow"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from urllib.request import urlretrieve\n", "\n", "import tensorflow as tf\n", "\n", "import gradio as gr\n", "\n", "urlretrieve(\n", "    \"https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5\", \"mnist-model.h5\"\n", ")\n", "model = tf.keras.models.load_model(\"mnist-model.h5\")\n", "\n", "\n", "def recognize_digit(image):\n", "    image = image.reshape(1, -1)\n", "    prediction = model.predict(image).tolist()[0]\n", "    return {str(i): prediction[i] for i in range(10)}\n", "\n", "\n", "im = gr.Image(shape=(28, 28), image_mode=\"L\", invert_colors=False, source=\"canvas\")\n", "\n", "demo = gr.Interface(\n", "    recognize_digit,\n", "    im,\n", "    gr.Label(num_top_classes=3),\n", "    live=True,\n", "    interpretation=\"default\",\n", "    capture_session=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/dropdown_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: dropdown_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.Dropdown(choices=[\"First Choice\", \"Second Choice\", \"Third Choice\"])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: dropdown_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.Dropdown(choices=[\"First Choice\", \"Second Choice\", \"Third Choice\"])\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/duplicatebutton_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: duplicatebutton_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.DuplicateButton()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: duplicatebutton_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.DuplicateButton()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/english_translator/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: english_translator"]}, {"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", "pipe = pipeline(\"translation\", model=\"t5-base\")\n", "\n", "\n", "def translate(text):\n", "    return pipe(text)[0][\"translation_text\"]\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            english = gr.Textbox(label=\"English text\")\n", "            translate_btn = gr.Button(value=\"Translate\")\n", "        with gr.Column():\n", "            german = gr.Textbox(label=\"German Text\")\n", "\n", "    translate_btn.click(translate, inputs=english, outputs=german, api_name=\"translate-to-german\")\n", "    examples = gr.Examples(examples=[\"I went to the supermarket yesterday.\", \"Helen is a good swimmer.\"],\n", "                           inputs=[english])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: english_translator"]}, {"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", "pipe = pipeline(\"translation\", model=\"t5-base\")\n", "\n", "\n", "def translate(text):\n", "    return pipe(text)[0][\"translation_text\"]\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            english = gr.Textbox(label=\"English text\")\n", "            translate_btn = gr.Button(value=\"Translate\")\n", "        with gr.Column():\n", "            german = gr.Textbox(label=\"German Text\")\n", "\n", "    translate_btn.click(translate, inputs=english, outputs=german, api_name=\"translate-to-german\")\n", "    examples = gr.Examples(examples=[\"I went to the supermarket yesterday.\", \"Helen is a good swimmer.\"],\n", "                           inputs=[english])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/examples_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: examples_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/lion.jpg\n", "!wget -q -O images/lion.webp https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/lion.webp\n", "!wget -q -O images/logo.png https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/logo.png"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "def flip(i):\n", "    return i.rotate(180)\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            img_i = gr.Image(label=\"Input Image\", type=\"pil\")\n", "        with gr.Column():\n", "            img_o = gr.Image(label=\"Output Image\")\n", "    with gr.Row():\n", "        btn = gr.Button(value=\"Flip Image\")\n", "    btn.click(flip, inputs=[img_i], outputs=[img_o])\n", "\n", "    gr.Examples(\n", "        [ \n", "            os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\"),\n", "            os.path.join(os.path.abspath(''), \"images/lion.jpg\"),\n", "        ],\n", "        img_i,\n", "        img_o,\n", "        flip\n", "    )\n", "    \n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: examples_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "os.mkdir('images')\n", "!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/cheetah1.jpg\n", "!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/lion.jpg\n", "!wget -q -O images/lion.webp https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/lion.webp\n", "!wget -q -O images/logo.png https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/logo.png"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "\n", "def flip(i):\n", "    return i.rotate(180)\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            img_i = gr.Image(label=\"Input Image\", type=\"pil\")\n", "        with gr.Column():\n", "            img_o = gr.Image(label=\"Output Image\")\n", "    with gr.Row():\n", "        btn = gr.Button(value=\"Flip Image\")\n", "    btn.click(flip, inputs=[img_i], outputs=[img_o])\n", "\n", "    gr.Examples(\n", "        [ \n", "            os.path.join(os.path.abspath(''), \"images/cheetah1.jpg\"),\n", "            os.path.join(os.path.abspath(''), \"images/lion.jpg\"),\n", "        ],\n", "        img_i,\n", "        img_o,\n", "        flip\n", "    )\n", "    \n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/fake_diffusion/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_diffusion\n", "### This demo uses a fake model to showcase iterative output. The Image output will update every time a generator is returned until the final image.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "\n", "# define core fn, which returns a generator {steps} times before returning the image\n", "def fake_diffusion(steps):\n", "    for _ in range(steps):\n", "        time.sleep(1)\n", "        image = np.random.random((600, 600, 3))\n", "        yield image\n", "    image = \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\"\n", "    yield image\n", "\n", "\n", "demo = gr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3), outputs=\"image\")\n", "\n", "# define queue - required for generators\n", "demo.queue()\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_diffusion\n", "### This demo uses a fake model to showcase iterative output. The Image output will update every time a generator is returned until the final image.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "\n", "# define core fn, which returns a generator {steps} times before returning the image\n", "def fake_diffusion(steps):\n", "    for _ in range(steps):\n", "        time.sleep(1)\n", "        image = np.random.random((600, 600, 3))\n", "        yield image\n", "    image = \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\"\n", "    yield image\n", "\n", "\n", "demo = gr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3), outputs=\"image\")\n", "\n", "# define queue - required for generators\n", "demo.queue()\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/fake_diffusion_with_gif/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_diffusion_with_gif"]}, {"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/fake_diffusion_with_gif/image.gif"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "import os\n", "from PIL import Image\n", "import requests\n", "from io import BytesIO\n", "\n", "\n", "def create_gif(images):\n", "    pil_images = []\n", "    for image in images:\n", "        if isinstance(image, str):\n", "            response = requests.get(image)\n", "            image = Image.open(BytesIO(response.content))\n", "        else:\n", "            image = Image.fromarray((image * 255).astype(np.uint8))\n", "        pil_images.append(image)\n", "    fp_out = os.path.join(os.path.abspath(''), \"image.gif\")\n", "    img = pil_images.pop(0)\n", "    img.save(fp=fp_out, format='GIF', append_images=pil_images,\n", "            save_all=True, duration=400, loop=0)\n", "    return fp_out\n", "\n", "\n", "def fake_diffusion(steps):\n", "    images = []\n", "    for _ in range(steps):\n", "        time.sleep(1)\n", "        image = np.random.random((600, 600, 3))\n", "        images.append(image)\n", "        yield image, gr.Image(visible=False)\n", "    \n", "    time.sleep(1)\n", "    image = \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\" \n", "    images.append(image)\n", "    gif_path = create_gif(images)\n", "    \n", "    yield image, gr.Image(value=gif_path, visible=True)\n", "\n", "\n", "demo = gr.Interface(fake_diffusion, \n", "                    inputs=gr.Slider(1, 10, 3), \n", "                    outputs=[\"image\", gr.Image(label=\"All Images\", visible=False)])\n", "demo.queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_diffusion_with_gif"]}, {"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/fake_diffusion_with_gif/image.gif"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np\n", "import time\n", "import os\n", "from PIL import Image\n", "import requests\n", "from io import BytesIO\n", "\n", "\n", "def create_gif(images):\n", "    pil_images = []\n", "    for image in images:\n", "        if isinstance(image, str):\n", "            response = requests.get(image)\n", "            image = Image.open(BytesIO(response.content))\n", "        else:\n", "            image = Image.fromarray((image * 255).astype(np.uint8))\n", "        pil_images.append(image)\n", "    fp_out = os.path.join(os.path.abspath(''), \"image.gif\")\n", "    img = pil_images.pop(0)\n", "    img.save(fp=fp_out, format='GIF', append_images=pil_images,\n", "            save_all=True, duration=400, loop=0)\n", "    return fp_out\n", "\n", "\n", "def fake_diffusion(steps):\n", "    images = []\n", "    for _ in range(steps):\n", "        time.sleep(1)\n", "        image = np.random.random((600, 600, 3))\n", "        images.append(image)\n", "        yield image, gr.Image(visible=False)\n", "    \n", "    time.sleep(1)\n", "    image = \"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\" \n", "    images.append(image)\n", "    gif_path = create_gif(images)\n", "    \n", "    yield image, gr.Image(value=gif_path, visible=True)\n", "\n", "\n", "demo = gr.Interface(fake_diffusion, \n", "                    inputs=gr.Slider(1, 10, 3), \n", "                    outputs=[\"image\", gr.Image(label=\"All Images\", visible=False)])\n", "demo.queue()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

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}
+{"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}

demo/fake_gan_2/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_gan_2"]}, {"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_2/files/cheetah1.jpg\n", "!wget -q -O files/elephant.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/elephant.jpg\n", "!wget -q -O files/tiger.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/tiger.jpg\n", "!wget -q -O files/zebra.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/zebra.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", "import time\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan(desc):\n", "    if desc == \"NSFW\":\n", "        raise gr.Error(\"NSFW - banned content.\")\n", "    if desc == \"error\":\n", "        raise ValueError(\"error\")\n", "    time.sleep(9)\n", "    image = random.choice(\n", "        [\n", "            \"files/cheetah1.jpg\",\n", "            \"files/elephant.jpg\",\n", "            \"files/tiger.jpg\",\n", "            \"files/zebra.jpg\",\n", "        ]\n", "    )\n", "    return image\n", "\n", "\n", "demo = gr.Interface(\n", "    fn=fake_gan,\n", "    inputs=gr.Textbox(),\n", "    outputs=gr.Image(label=\"Generated Image\"),\n", "    title=\"FD-GAN\",\n", "    description=\"This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.\",\n", ")\n", "demo.queue(max_size=3)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_gan_2"]}, {"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_2/files/cheetah1.jpg\n", "!wget -q -O files/elephant.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/elephant.jpg\n", "!wget -q -O files/tiger.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/tiger.jpg\n", "!wget -q -O files/zebra.jpg https://github.com/gradio-app/gradio/raw/main/demo/fake_gan_2/files/zebra.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", "import time\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan(desc):\n", "    if desc == \"NSFW\":\n", "        raise gr.Error(\"NSFW - banned content.\")\n", "    if desc == \"error\":\n", "        raise ValueError(\"error\")\n", "    time.sleep(9)\n", "    image = random.choice(\n", "        [\n", "            \"files/cheetah1.jpg\",\n", "            \"files/elephant.jpg\",\n", "            \"files/tiger.jpg\",\n", "            \"files/zebra.jpg\",\n", "        ]\n", "    )\n", "    return image\n", "\n", "\n", "demo = gr.Interface(\n", "    fn=fake_gan,\n", "    inputs=gr.Textbox(),\n", "    outputs=gr.Image(label=\"Generated Image\"),\n", "    title=\"FD-GAN\",\n", "    description=\"This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.\",\n", ")\n", "demo.queue(max_size=3)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/fake_gan_no_input/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: fake_gan_no_input"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import time\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan():\n", "    time.sleep(1)\n", "    images = [\n", "            \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", "            \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", "            \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", "    ]\n", "    return images\n", "\n", "\n", "demo = gr.Interface(\n", "    fn=fake_gan,\n", "    inputs=None,\n", "    outputs=gr.Gallery(label=\"Generated Images\", columns=[2]),\n", "    title=\"FD-GAN\",\n", "    description=\"This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: fake_gan_no_input"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import time\n", "\n", "import gradio as gr\n", "\n", "\n", "def fake_gan():\n", "    time.sleep(1)\n", "    images = [\n", "            \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", "            \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", "            \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", "    ]\n", "    return images\n", "\n", "\n", "demo = gr.Interface(\n", "    fn=fake_gan,\n", "    inputs=None,\n", "    outputs=gr.Gallery(label=\"Generated Images\", columns=[2]),\n", "    title=\"FD-GAN\",\n", "    description=\"This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.\",\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/file_component/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: file_component"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.File()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: file_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr \n", "\n", "with gr.Blocks() as demo:\n", "    gr.File()\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demo/file_explorer/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: file_explorer"]}, {"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", "from pathlib import Path\n", "\n", "current_file_path = Path(__file__).resolve()\n", "relative_path = \"path/to/file\"\n", "absolute_path = (current_file_path.parent / \"..\" / \"..\" / \"gradio\").resolve()\n", "\n", "\n", "def get_file_content(file):\n", "    return (file,)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown('### `FileExplorer` to `FileExplorer` -- `file_count=\"multiple\"`')\n", "    submit_btn = gr.Button(\"Select\")\n", "    with gr.Row():\n", "        file = gr.FileExplorer(\n", "            glob=\"**/{components,themes}/*.py\",\n", "            # value=[\"themes/utils\"],\n", "            root=absolute_path,\n", "            ignore_glob=\"**/__init__.py\",\n", "        )\n", "\n", "        file2 = gr.FileExplorer(\n", "            glob=\"**/{components,themes}/**/*.py\",\n", "            root=absolute_path,\n", "            ignore_glob=\"**/__init__.py\",\n", "        )\n", "    submit_btn.click(lambda x: x, file, file2)\n", "\n", "    gr.Markdown(\"---\")\n", "    gr.Markdown('### `FileExplorer` to `Code` -- `file_count=\"single\"`')\n", "    with gr.Group():\n", "        with gr.Row():\n", "            file_3 = gr.FileExplorer(\n", "                scale=1,\n", "                glob=\"**/{components,themes}/**/*.py\",\n", "                value=[\"themes/utils\"],\n", "                file_count=\"single\",\n", "                root=absolute_path,\n", "                ignore_glob=\"**/__init__.py\",\n", "                elem_id=\"file\",\n", "            )\n", "\n", "            code = gr.Code(lines=30, scale=2, language=\"python\")\n", "\n", "    file_3.change(get_file_content, file_3, code)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: file_explorer"]}, {"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", "from pathlib import Path\n", "\n", "current_file_path = Path(__file__).resolve()\n", "relative_path = \"path/to/file\"\n", "absolute_path = (current_file_path.parent / \"..\" / \"..\" / \"gradio\").resolve()\n", "\n", "\n", "def get_file_content(file):\n", "    return (file,)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    gr.Markdown('### `FileExplorer` to `FileExplorer` -- `file_count=\"multiple\"`')\n", "    submit_btn = gr.Button(\"Select\")\n", "    with gr.Row():\n", "        file = gr.FileExplorer(\n", "            glob=\"**/{components,themes}/*.py\",\n", "            # value=[\"themes/utils\"],\n", "            root=absolute_path,\n", "            ignore_glob=\"**/__init__.py\",\n", "        )\n", "\n", "        file2 = gr.FileExplorer(\n", "            glob=\"**/{components,themes}/**/*.py\",\n", "            root=absolute_path,\n", "            ignore_glob=\"**/__init__.py\",\n", "        )\n", "    submit_btn.click(lambda x: x, file, file2)\n", "\n", "    gr.Markdown(\"---\")\n", "    gr.Markdown('### `FileExplorer` to `Code` -- `file_count=\"single\"`')\n", "    with gr.Group():\n", "        with gr.Row():\n", "            file_3 = gr.FileExplorer(\n", "                scale=1,\n", "                glob=\"**/{components,themes}/**/*.py\",\n", "                value=[\"themes/utils\"],\n", "                file_count=\"single\",\n", "                root=absolute_path,\n", "                ignore_glob=\"**/__init__.py\",\n", "                elem_id=\"file\",\n", "            )\n", "\n", "            code = gr.Code(lines=30, scale=2, language=\"python\")\n", "\n", "    file_3.change(get_file_content, file_3, code)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}