Commit 1: Add 50 file(s)

demos/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": ["import gradio as gr\n", "import subprocess\n", "\n", "# get_audio returns the path to the audio file\n", "audio_file = gr.get_audio(\"cantina.wav\")\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tab(\"Audio\"):\n", "        gr.Audio(audio_file, buttons=[\"download\"])\n", "    with gr.Tab(\"Interface\"):\n", "        gr.Interface(\n", "            lambda x: x, \n", "            gr.Audio(),\n", "            gr.Audio(),\n", "            examples=[audio_file], \n", "            cache_examples=True,\n", "            api_name=\"predict\"\n", "        )\n", "    with gr.Tab(\"Streaming\"):\n", "        gr.Interface(\n", "            lambda x: x,\n", "            gr.Audio(streaming=True),\n", "            \"audio\",\n", "            examples=[audio_file],\n", "            cache_examples=True,\n", "            api_name=\"predict\"\n", "        )\n", "    with gr.Tab(\"console\"):\n", "        ip = gr.Textbox(label=\"User IP Address\")\n", "        gr.Interface(\n", "            lambda cmd: subprocess.run([cmd], capture_output=True, shell=True, check=False)\n", "            .stdout.decode(\"utf-8\")\n", "            .strip(),\n", "            \"text\",\n", "            \"text\",\n", "            api_name=\"predict\"\n", "        )\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.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": ["import gradio as gr\n", "import subprocess\n", "\n", "# get_audio returns the path to the audio file\n", "audio_file = gr.get_audio(\"cantina.wav\")\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tab(\"Audio\"):\n", "        gr.Audio(audio_file, buttons=[\"download\"])\n", "    with gr.Tab(\"Interface\"):\n", "        gr.Interface(\n", "            lambda x: x,\n", "            gr.Audio(),\n", "            gr.Audio(),\n", "            examples=[audio_file],\n", "            cache_examples=True,\n", "            api_name=\"predict\"\n", "        )\n", "    with gr.Tab(\"Streaming\"):\n", "        gr.Interface(\n", "            lambda x: x,\n", "            gr.Audio(streaming=True),\n", "            \"audio\",\n", "            examples=[audio_file],\n", "            cache_examples=True,\n", "            api_name=\"predict\"\n", "        )\n", "    with gr.Tab(\"console\"):\n", "        ip = gr.Textbox(label=\"User IP Address\")\n", "        gr.Interface(\n", "            lambda cmd: subprocess.run([cmd], capture_output=True, shell=True, check=False)\n", "            .stdout.decode(\"utf-8\")\n", "            .strip(),\n", "            \"text\",\n", "            \"text\",\n", "            api_name=\"predict\"\n", "        )\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.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/audio_debugger/run.py

@@ -9,10 +9,10 @@ with gr.Blocks() as demo:
         gr.Audio(audio_file, buttons=["download"])
     with gr.Tab("Interface"):
         gr.Interface(
-            lambda x: x, 
+            lambda x: x,
             gr.Audio(),
             gr.Audio(),
-            examples=[audio_file], 
+            examples=[audio_file],
             cache_examples=True,
             api_name="predict"
         )

demos/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", "import atexit\n", "import pathlib\n", "\n", "log_file = pathlib.Path(__file__).parent / \"cancel_events_output_log.txt\"\n", "\n", "def fake_diffusion(steps):\n", "    log_file.write_text(\"\")\n", "    for i in range(steps):\n", "        print(f\"Current step: {i}\")\n", "        with log_file.open(\"a\") as f:\n", "            f.write(f\"Current step: {i}\\n\")\n", "        time.sleep(0.2)\n", "        yield str(i)\n", "\n", "def long_prediction(*args, **kwargs):\n", "    time.sleep(4)\n", "    return 42, 42\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", "            loading_box = gr.Textbox(label=\"Loading indicator for expensive calculation\")\n", "            loading_box2 = gr.Textbox(label=\"Loading indicator for expensive calculation\")\n", "            prediction = gr.Number(label=\"Expensive Calculation\")\n", "            prediction2 = gr.Number(label=\"Expensive Calculation\")\n", "            run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", "        with gr.Column():\n", "            cancel_on_change = gr.Textbox(\n", "                label=\"Cancel Iteration and Expensive Calculation on Change\"\n", "            )\n", "            cancel_on_submit = gr.Textbox(\n", "                label=\"Cancel Iteration and Expensive Calculation on Submit\"\n", "            )\n", "            echo = gr.Textbox(label=\"Echo\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            image = gr.Image(\n", "                sources=[\"webcam\"], label=\"Cancel on clear\", interactive=True\n", "            )\n", "        with gr.Column():\n", "            video = gr.Video(\n", "                sources=[\"webcam\"], label=\"Cancel on start recording\", interactive=True\n", "            )\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(\n", "        fn=long_prediction, inputs=[textbox], outputs=[prediction, prediction2], show_progress_on=[loading_box, loading_box2]\n", "    )\n", "\n", "    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", "    cancel_on_submit.submit(\n", "        lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event]\n", "    )\n", "    image.clear(None, None, None, cancels=[click_event, pred_event])\n", "    video.start_recording(None, None, None, cancels=[click_event, pred_event])\n", "\n", "    demo.queue(max_size=20)\n", "    atexit.register(lambda: log_file.unlink())\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", "import atexit\n", "import pathlib\n", "\n", "log_file = pathlib.Path(__file__).parent / \"cancel_events_output_log.txt\"\n", "\n", "\n", "def fake_diffusion(steps):\n", "    log_file.write_text(\"\")\n", "    for i in range(steps):\n", "        print(f\"Current step: {i}\")\n", "        with log_file.open(\"a\") as f:\n", "            f.write(f\"Current step: {i}\\n\")\n", "        time.sleep(0.2)\n", "        yield str(i)\n", "\n", "\n", "def long_prediction(*args, **kwargs):\n", "    time.sleep(4)\n", "    return 42, 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", "            loading_box = gr.Textbox(\n", "                label=\"Loading indicator for expensive calculation\"\n", "            )\n", "            loading_box2 = gr.Textbox(\n", "                label=\"Loading indicator for expensive calculation\"\n", "            )\n", "            prediction = gr.Number(label=\"Expensive Calculation\")\n", "            prediction2 = gr.Number(label=\"Expensive Calculation\")\n", "            run_pred = gr.Button(value=\"Run Expensive Calculation\")\n", "        with gr.Column():\n", "            cancel_on_change = gr.Textbox(\n", "                label=\"Cancel Iteration and Expensive Calculation on Change\"\n", "            )\n", "            cancel_on_submit = gr.Textbox(\n", "                label=\"Cancel Iteration and Expensive Calculation on Submit\"\n", "            )\n", "            echo = gr.Textbox(label=\"Echo\")\n", "    with gr.Row():\n", "        with gr.Column():\n", "            image = gr.Image(\n", "                sources=[\"webcam\"], label=\"Cancel on clear\", interactive=True\n", "            )\n", "        with gr.Column():\n", "            video = gr.Video(\n", "                sources=[\"webcam\"], label=\"Cancel on start recording\", interactive=True\n", "            )\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(\n", "        fn=long_prediction,\n", "        inputs=[textbox],\n", "        outputs=[prediction, prediction2],\n", "        show_progress_on=[loading_box, loading_box2],\n", "    )\n", "\n", "    cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])\n", "    cancel_on_submit.submit(\n", "        lambda s: s, cancel_on_submit, echo, cancels=[click_event, pred_event]\n", "    )\n", "    image.clear(None, None, None, cancels=[click_event, pred_event])\n", "    video.start_recording(None, None, None, cancels=[click_event, pred_event])\n", "\n", "    demo.queue(max_size=20)\n", "    atexit.register(lambda: log_file.unlink(True))\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/cancel_events/run.py

@@ -5,6 +5,7 @@ import pathlib
 
 log_file = pathlib.Path(__file__).parent / "cancel_events_output_log.txt"
 
+
 def fake_diffusion(steps):
     log_file.write_text("")
     for i in range(steps):
@@ -14,10 +15,12 @@ def fake_diffusion(steps):
         time.sleep(0.2)
         yield str(i)
 
+
 def long_prediction(*args, **kwargs):
     time.sleep(4)
     return 42, 42
 
+
 with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
@@ -27,8 +30,12 @@ with gr.Blocks() as demo:
             stop = gr.Button(value="Stop Iterating")
         with gr.Column():
             textbox = gr.Textbox(label="Prompt")
-            loading_box = gr.Textbox(label="Loading indicator for expensive calculation")
-            loading_box2 = gr.Textbox(label="Loading indicator for expensive calculation")
+            loading_box = gr.Textbox(
+                label="Loading indicator for expensive calculation"
+            )
+            loading_box2 = gr.Textbox(
+                label="Loading indicator for expensive calculation"
+            )
             prediction = gr.Number(label="Expensive Calculation")
             prediction2 = gr.Number(label="Expensive Calculation")
             run_pred = gr.Button(value="Run Expensive Calculation")
@@ -53,7 +60,10 @@ with gr.Blocks() as demo:
     click_event = run.click(fake_diffusion, n, output)
     stop.click(fn=None, inputs=None, outputs=None, cancels=[click_event])
     pred_event = run_pred.click(
-        fn=long_prediction, inputs=[textbox], outputs=[prediction, prediction2], show_progress_on=[loading_box, loading_box2]
+        fn=long_prediction,
+        inputs=[textbox],
+        outputs=[prediction, prediction2],
+        show_progress_on=[loading_box, loading_box2],
     )
 
     cancel_on_change.change(None, None, None, cancels=[click_event, pred_event])
@@ -64,7 +74,7 @@ with gr.Blocks() as demo:
     video.start_recording(None, None, None, cancels=[click_event, pred_event])
 
     demo.queue(max_size=20)
-    atexit.register(lambda: log_file.unlink())
+    atexit.register(lambda: log_file.unlink(True))
 
 if __name__ == "__main__":
     demo.launch()

demos/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": ["import gradio as gr\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 print_like_dislike(x: gr.LikeData):\n", "    print(x.index, x.value, x.liked)\n", "\n", "\n", "def add_message(history, message):\n", "    user_msg = {\"role\": \"user\", \"content\": []}\n", "    for x in message[\"files\"]:\n", "        user_msg[\"content\"].append({\"path\": x})\n", "    if message[\"text\"] is not None:\n", "       user_msg[\"content\"].append(message[\"text\"])\n", "    history.append(user_msg)\n", "    return history, gr.MultimodalTextbox(value=None, interactive=False)\n", "\n", "\n", "def bot(history: list):\n", "    response = \"**That's cool!**\"\n", "    history.append({\"role\": \"assistant\", \"content\": \"\"})\n", "    for character in response:\n", "        history[-1][\"content\"] += character\n", "        time.sleep(0.05)\n", "        yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot(elem_id=\"chatbot\")\n", "\n", "    chat_input = gr.MultimodalTextbox(\n", "        interactive=True,\n", "        file_count=\"multiple\",\n", "        placeholder=\"Enter message or upload file...\",\n", "        show_label=False,\n", "        sources=[\"microphone\", \"upload\"],\n", "    )\n", "\n", "    chat_msg = chat_input.submit(\n", "        add_message, [chatbot, chat_input], [chatbot, chat_input]\n", "    )\n", "    bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name=\"bot_response\")\n", "    bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n", "\n", "    chatbot.like(print_like_dislike, None, None, like_user_message=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: 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": ["import gradio as gr\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 print_like_dislike(x: gr.LikeData):\n", "    print(x.index, x.value, x.liked)\n", "\n", "\n", "def add_message(history, message):\n", "    user_msg = {\"role\": \"user\", \"content\": []}\n", "    for x in message[\"files\"]:\n", "        user_msg[\"content\"].append({\"path\": x})\n", "    if message[\"text\"] is not None:\n", "       user_msg[\"content\"].append(message[\"text\"])\n", "    history.append(user_msg)\n", "    return history, gr.MultimodalTextbox(value=None, interactive=False)\n", "\n", "\n", "def bot(history: list):\n", "    response = \"**That's cool!**\"\n", "    history.append({\"role\": \"assistant\", \"content\": \"\"})\n", "    for character in response:\n", "        history[-1][\"content\"] += character\n", "        time.sleep(0.05)\n", "        yield history\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    chatbot = gr.Chatbot(elem_id=\"chatbot\", like_user_message=True)\n", "\n", "    chat_input = gr.MultimodalTextbox(\n", "        interactive=True,\n", "        file_count=\"multiple\",\n", "        placeholder=\"Enter message or upload file...\",\n", "        show_label=False,\n", "        sources=[\"microphone\", \"upload\"],\n", "    )\n", "\n", "    chat_msg = chat_input.submit(\n", "        add_message, [chatbot, chat_input], [chatbot, chat_input]\n", "    )\n", "    bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name=\"bot_response\")\n", "    bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n", "\n", "    chatbot.like(print_like_dislike, None, None)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/chatbot_multimodal/run.py

@@ -28,7 +28,7 @@ def bot(history: list):
 
 
 with gr.Blocks() as demo:
-    chatbot = gr.Chatbot(elem_id="chatbot")
+    chatbot = gr.Chatbot(elem_id="chatbot", like_user_message=True)
 
     chat_input = gr.MultimodalTextbox(
         interactive=True,
@@ -44,7 +44,7 @@ with gr.Blocks() as demo:
     bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
     bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
 
-    chatbot.like(print_like_dislike, None, None, like_user_message=True)
+    chatbot.like(print_like_dislike, None, None)
 
 if __name__ == "__main__":
     demo.launch()

demos/clear_components/run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: clear_components"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy pandas matplotlib "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/clear_components/__init__.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from datetime import datetime\n", "import os\n", "import random\n", "import string\n", "import pandas as pd\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "# get_audio(), get_video() return file paths to sample media included with Gradio\n", "from gradio.media import get_audio, get_video, MEDIA_ROOT\n", "\n", "def random_plot():\n", "    start_year = 2020\n", "    x = np.arange(start_year, start_year + 5)\n", "    year_count = x.shape[0]\n", "    plt_format = \"-\"\n", "    fig = plt.figure()\n", "    ax = fig.add_subplot(111)\n", "    series = np.arange(0, year_count, dtype=float)\n", "    series = series**2\n", "    series += np.random.rand(year_count)\n", "    ax.plot(x, series, plt_format)\n", "    return fig\n", "\n", "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", "highlighted_text_output_1 = [\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9988978,\n", "        \"index\": 2,\n", "        \"word\": \"Chicago\",\n", "        \"start\": 5,\n", "        \"end\": 12,\n", "    },\n", "    {\n", "        \"entity\": \"I-MISC\",\n", "        \"score\": 0.9958592,\n", "        \"index\": 5,\n", "        \"word\": \"Pakistani\",\n", "        \"start\": 22,\n", "        \"end\": 31,\n", "    },\n", "]\n", "highlighted_text_output_2 = [\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9988978,\n", "        \"index\": 2,\n", "        \"word\": \"Chicago\",\n", "        \"start\": 5,\n", "        \"end\": 12,\n", "    },\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9958592,\n", "        \"index\": 5,\n", "        \"word\": \"Pakistan\",\n", "        \"start\": 22,\n", "        \"end\": 30,\n", "    },\n", "]\n", "\n", "highlighted_text = \"Does Chicago have any Pakistani restaurants\"\n", "\n", "components = [\n", "    gr.Textbox(value=lambda: datetime.now(), label=\"Current Time\"),\n", "    gr.Number(value=lambda: random.random(), label=\"Random Percentage\"),\n", "    gr.Slider(minimum=0, maximum=100, randomize=True, label=\"Slider with randomize\"),\n", "    gr.Slider(\n", "        minimum=0,\n", "        maximum=1,\n", "        value=lambda: random.random(),\n", "        label=\"Slider with value func\",\n", "    ),\n", "    gr.Checkbox(value=lambda: random.random() > 0.5, label=\"Random Checkbox\"),\n", "    gr.CheckboxGroup(\n", "        choices=[\"a\", \"b\", \"c\", \"d\"],\n", "        value=lambda: random.choice([\"a\", \"b\", \"c\", \"d\"]),\n", "        label=\"Random CheckboxGroup\",\n", "    ),\n", "    gr.Radio(\n", "        choices=list(string.ascii_lowercase),\n", "        value=lambda: random.choice(string.ascii_lowercase),\n", "    ),\n", "    gr.Dropdown(\n", "        choices=[\"a\", \"b\", \"c\", \"d\", \"e\"],\n", "        value=lambda: random.choice([\"a\", \"b\", \"c\"]),\n", "    ),\n", "    gr.Image(\n", "        value=lambda: random.choice(images)\n", "    ),\n", "    gr.Video(value=lambda: get_video(\"world.mp4\")),\n", "    gr.Audio(value=lambda: get_audio(\"cantina.wav\")),\n", "    gr.File(\n", "        value=gr.get_file\n", "    ),\n", "    gr.Dataframe(\n", "        value=lambda: pd.DataFrame({\"random_number_rows\": range(5)}, columns=[\"one\", \"two\", \"three\"])  # type: ignore\n", "    ),\n", "    gr.ColorPicker(value=lambda: random.choice([\"#000000\", \"#ff0000\", \"#0000FF\"])),\n", "    gr.Label(value=lambda: random.choice([\"Pedestrian\", \"Car\", \"Cyclist\"])),\n", "    gr.HighlightedText(\n", "        value=lambda: random.choice(\n", "            [\n", "                {\"text\": highlighted_text, \"entities\": highlighted_text_output_1},\n", "                {\"text\": highlighted_text, \"entities\": highlighted_text_output_2},\n", "            ]\n", "        ),\n", "    ),\n", "    gr.JSON(value=lambda: random.choice([{\"a\": 1}, {\"b\": 2}])),\n", "    gr.HTML(\n", "        value=lambda: random.choice(\n", "            [\n", "                '<p style=\"color:red;\">I am red</p>',\n", "                '<p style=\"color:blue;\">I am blue</p>',\n", "            ]\n", "        )\n", "    ),\n", "    gr.Gallery(\n", "        value=lambda: images\n", "    ),\n", "    gr.Model3D(value=gr.get_model3d),\n", "    gr.Plot(value=random_plot),\n", "    gr.Markdown(value=lambda: f\"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}\"),\n", "]\n", "\n", "def evaluate_values(*args):\n", "    are_false = []\n", "    for a in args:\n", "        if isinstance(a, (pd.DataFrame, np.ndarray)):\n", "            are_false.append(not a.any().any())  # type: ignore\n", "        elif isinstance(a, str) and a.startswith(\"#\"):\n", "            are_false.append(a == \"#000000\")\n", "        else:\n", "            are_false.append(not a)\n", "    return all(are_false)\n", "\n", "with gr.Blocks() as demo:\n", "    for i, component in enumerate(components):\n", "        component.label = f\"component_{str(i).zfill(2)}\"\n", "        component.render()\n", "    clear = gr.ClearButton(value=\"Clear\", components=components)\n", "    result = gr.Textbox(label=\"Are all cleared?\")\n", "    hide = gr.Button(value=\"Hide\")\n", "    reveal = gr.Button(value=\"Reveal\")\n", "    clear_button_and_components = components + [clear]\n", "    hide.click(\n", "        lambda: [c.__class__(visible=False) for c in clear_button_and_components],\n", "        inputs=[],\n", "        outputs=clear_button_and_components\n", "    )\n", "    reveal.click(\n", "        lambda: [c.__class__(visible=True) for c in clear_button_and_components],\n", "        inputs=[],\n", "        outputs=clear_button_and_components\n", "    )\n", "    get_value = gr.Button(value=\"Get Values\")\n", "    get_value.click(evaluate_values, components, result)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(allowed_paths=[str(MEDIA_ROOT)])"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: clear_components"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio numpy pandas matplotlib "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/clear_components/__init__.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from datetime import datetime\n", "import os\n", "import random\n", "import string\n", "import pandas as pd\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "# get_audio(), get_video() return file paths to sample media included with Gradio\n", "from gradio.media import get_audio, get_video, MEDIA_ROOT\n", "\n", "def random_plot():\n", "    start_year = 2020\n", "    x = np.arange(start_year, start_year + 5)\n", "    year_count = x.shape[0]\n", "    plt_format = \"-\"\n", "    fig = plt.figure()\n", "    ax = fig.add_subplot(111)\n", "    series = np.arange(0, year_count, dtype=float)\n", "    series = series**2\n", "    series += np.random.rand(year_count)\n", "    ax.plot(x, series, plt_format)\n", "    return fig\n", "\n", "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", "highlighted_text_output_1 = [\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9988978,\n", "        \"index\": 2,\n", "        \"word\": \"Chicago\",\n", "        \"start\": 5,\n", "        \"end\": 12,\n", "    },\n", "    {\n", "        \"entity\": \"I-MISC\",\n", "        \"score\": 0.9958592,\n", "        \"index\": 5,\n", "        \"word\": \"Pakistani\",\n", "        \"start\": 22,\n", "        \"end\": 31,\n", "    },\n", "]\n", "highlighted_text_output_2 = [\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9988978,\n", "        \"index\": 2,\n", "        \"word\": \"Chicago\",\n", "        \"start\": 5,\n", "        \"end\": 12,\n", "    },\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9958592,\n", "        \"index\": 5,\n", "        \"word\": \"Pakistan\",\n", "        \"start\": 22,\n", "        \"end\": 30,\n", "    },\n", "]\n", "\n", "highlighted_text = \"Does Chicago have any Pakistani restaurants\"\n", "\n", "components = [\n", "    gr.Textbox(value=lambda: datetime.now(), label=\"Current Time\"),\n", "    gr.Number(value=lambda: random.random(), label=\"Random Percentage\"),\n", "    gr.Slider(minimum=0, maximum=100, randomize=True, label=\"Slider with randomize\"),\n", "    gr.Slider(\n", "        minimum=0,\n", "        maximum=1,\n", "        value=lambda: random.random(),\n", "        label=\"Slider with value func\",\n", "    ),\n", "    gr.Checkbox(value=lambda: random.random() > 0.5, label=\"Random Checkbox\"),\n", "    gr.CheckboxGroup(\n", "        choices=[\"a\", \"b\", \"c\", \"d\"],\n", "        value=lambda: random.choice([\"a\", \"b\", \"c\", \"d\"]),\n", "        label=\"Random CheckboxGroup\",\n", "    ),\n", "    gr.Radio(\n", "        choices=list(string.ascii_lowercase),\n", "        value=lambda: random.choice(string.ascii_lowercase),\n", "    ),\n", "    gr.Dropdown(\n", "        choices=[\"a\", \"b\", \"c\", \"d\", \"e\"],\n", "        value=lambda: random.choice([\"a\", \"b\", \"c\"]),\n", "    ),\n", "    gr.Image(\n", "        value=lambda: random.choice(images)\n", "    ),\n", "    gr.Video(value=lambda: get_video(\"world.mp4\")),\n", "    gr.Audio(value=lambda: get_audio(\"cantina.wav\")),\n", "    gr.File(\n", "        value=gr.get_file\n", "    ),\n", "    # gr.Dataframe(\n", "    #     value=lambda: pd.DataFrame({\"random_number_rows\": range(5)}, columns=[\"one\", \"two\", \"three\"])  # type: ignore\n", "    # ),\n", "    gr.ColorPicker(value=lambda: random.choice([\"#000000\", \"#ff0000\", \"#0000FF\"])),\n", "    gr.Label(value=lambda: random.choice([\"Pedestrian\", \"Car\", \"Cyclist\"])),\n", "    gr.HighlightedText(\n", "        value=lambda: random.choice(\n", "            [\n", "                {\"text\": highlighted_text, \"entities\": highlighted_text_output_1},\n", "                {\"text\": highlighted_text, \"entities\": highlighted_text_output_2},\n", "            ]\n", "        ),\n", "    ),\n", "    gr.JSON(value=lambda: random.choice([{\"a\": 1}, {\"b\": 2}])),\n", "    # gr.HTML(\n", "    #     value=lambda: random.choice(\n", "    #         [\n", "    #             '<p style=\"color:red;\">I am red</p>',\n", "    #             '<p style=\"color:blue;\">I am blue</p>',\n", "    #         ]\n", "    #     )\n", "    # ),\n", "    gr.Gallery(\n", "        value=lambda: images\n", "    ),\n", "    gr.Model3D(value=gr.get_model3d),\n", "    # gr.Plot(value=random_plot),\n", "    gr.Markdown(value=lambda: f\"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}\"),\n", "]\n", "\n", "def evaluate_values(*args):\n", "    are_false = []\n", "    for a in args:\n", "        if isinstance(a, (pd.DataFrame, np.ndarray)):\n", "            are_false.append(not a.any().any())  # type: ignore\n", "        elif isinstance(a, str) and a.startswith(\"#\"):\n", "            are_false.append(a == \"#000000\")\n", "        else:\n", "            are_false.append(not a)\n", "    return all(are_false)\n", "\n", "with gr.Blocks() as demo:\n", "    for i, component in enumerate(components):\n", "        component.label = f\"component_{str(i).zfill(2)}\"\n", "        component.render()\n", "    clear = gr.ClearButton(value=\"Clear\", components=components)\n", "    result = gr.Textbox(label=\"Are all cleared?\")\n", "    hide = gr.Button(value=\"Hide\")\n", "    reveal = gr.Button(value=\"Reveal\")\n", "    clear_button_and_components = components + [clear]\n", "    hide.click(\n", "        lambda: [c.__class__(visible=False) for c in clear_button_and_components],\n", "        inputs=[],\n", "        outputs=clear_button_and_components\n", "    )\n", "    reveal.click(\n", "        lambda: [c.__class__(visible=True) for c in clear_button_and_components],\n", "        inputs=[],\n", "        outputs=clear_button_and_components\n", "    )\n", "    get_value = gr.Button(value=\"Get Values\")\n", "    get_value.click(evaluate_values, components, result)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch(allowed_paths=[str(MEDIA_ROOT)])"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

demos/clear_components/run.py

@@ -99,9 +99,9 @@ components = [
     gr.File(
         value=gr.get_file
     ),
-    gr.Dataframe(
-        value=lambda: pd.DataFrame({"random_number_rows": range(5)}, columns=["one", "two", "three"])  # type: ignore
-    ),
+    # gr.Dataframe(
+    #     value=lambda: pd.DataFrame({"random_number_rows": range(5)}, columns=["one", "two", "three"])  # type: ignore
+    # ),
     gr.ColorPicker(value=lambda: random.choice(["#000000", "#ff0000", "#0000FF"])),
     gr.Label(value=lambda: random.choice(["Pedestrian", "Car", "Cyclist"])),
     gr.HighlightedText(
@@ -113,19 +113,19 @@ components = [
         ),
     ),
     gr.JSON(value=lambda: random.choice([{"a": 1}, {"b": 2}])),
-    gr.HTML(
-        value=lambda: random.choice(
-            [
-                '<p style="color:red;">I am red</p>',
-                '<p style="color:blue;">I am blue</p>',
-            ]
-        )
-    ),
+    # gr.HTML(
+    #     value=lambda: random.choice(
+    #         [
+    #             '<p style="color:red;">I am red</p>',
+    #             '<p style="color:blue;">I am blue</p>',
+    #         ]
+    #     )
+    # ),
     gr.Gallery(
         value=lambda: images
     ),
     gr.Model3D(value=gr.get_model3d),
-    gr.Plot(value=random_plot),
+    # gr.Plot(value=random_plot),
     gr.Markdown(value=lambda: f"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}"),
 ]