File size: 7,374 Bytes
cc5d952
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 "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/clear_components/__init__.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from datetime import datetime\n", "import os\n", "import random\n", "import string\n", "import pandas as pd\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "\n", "def random_plot():\n", "    start_year = 2020\n", "    x = np.arange(start_year, start_year + 5)\n", "    year_count = x.shape[0]\n", "    plt_format = \"-\"\n", "    fig = plt.figure()\n", "    ax = fig.add_subplot(111)\n", "    series = np.arange(0, year_count, dtype=float)\n", "    series = series**2\n", "    series += np.random.rand(year_count)\n", "    ax.plot(x, series, plt_format)\n", "    return fig\n", "\n", "\n", "images = [\n", "    \"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80\",\n", "    \"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80\",\n", "    \"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80\",\n", "]\n", "file_dir = os.path.join(os.path.abspath(''), \"..\", \"kitchen_sink\", \"files\")\n", "model3d_dir = os.path.join(os.path.abspath(''), \"..\", \"model3D\", \"files\")\n", "highlighted_text_output_1 = [\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9988978,\n", "        \"index\": 2,\n", "        \"word\": \"Chicago\",\n", "        \"start\": 5,\n", "        \"end\": 12,\n", "    },\n", "    {\n", "        \"entity\": \"I-MISC\",\n", "        \"score\": 0.9958592,\n", "        \"index\": 5,\n", "        \"word\": \"Pakistani\",\n", "        \"start\": 22,\n", "        \"end\": 31,\n", "    },\n", "]\n", "highlighted_text_output_2 = [\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9988978,\n", "        \"index\": 2,\n", "        \"word\": \"Chicago\",\n", "        \"start\": 5,\n", "        \"end\": 12,\n", "    },\n", "    {\n", "        \"entity\": \"I-LOC\",\n", "        \"score\": 0.9958592,\n", "        \"index\": 5,\n", "        \"word\": \"Pakistan\",\n", "        \"start\": 22,\n", "        \"end\": 30,\n", "    },\n", "]\n", "\n", "highlighted_text = \"Does Chicago have any Pakistani restaurants\"\n", "\n", "\n", "def random_model3d():\n", "    model_3d = random.choice(\n", "        [os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != \"source.txt\"]\n", "    )\n", "    return model_3d\n", "\n", "\n", "\n", "components = [\n", "    gr.Textbox(value=lambda: datetime.now(), label=\"Current Time\"),\n", "    gr.Number(value=lambda: random.random(), label=\"Random Percentage\"),\n", "    gr.Slider(minimum=0, maximum=100, randomize=True, label=\"Slider with randomize\"),\n", "    gr.Slider(\n", "        minimum=0,\n", "        maximum=1,\n", "        value=lambda: random.random(),\n", "        label=\"Slider with value func\",\n", "    ),\n", "    gr.Checkbox(value=lambda: random.random() > 0.5, label=\"Random Checkbox\"),\n", "    gr.CheckboxGroup(\n", "        choices=[\"a\", \"b\", \"c\", \"d\"],\n", "        value=lambda: random.choice([\"a\", \"b\", \"c\", \"d\"]),\n", "        label=\"Random CheckboxGroup\",\n", "    ),\n", "    gr.Radio(\n", "        choices=list(string.ascii_lowercase),\n", "        value=lambda: random.choice(string.ascii_lowercase),\n", "    ),\n", "    gr.Dropdown(\n", "        choices=[\"a\", \"b\", \"c\", \"d\", \"e\"],\n", "        value=lambda: random.choice([\"a\", \"b\", \"c\"]),\n", "    ),\n", "    gr.Image(\n", "        value=lambda: random.choice(images)\n", "    ),\n", "    gr.Video(value=lambda: os.path.join(file_dir, \"world.mp4\")),\n", "    gr.Audio(value=lambda: os.path.join(file_dir, \"cantina.wav\")),\n", "    gr.File(\n", "        value=lambda: random.choice(\n", "            [os.path.join(file_dir, img) for img in os.listdir(file_dir)]\n", "        )\n", "    ),\n", "    gr.Dataframe(\n", "        value=lambda: pd.DataFrame({\"random_number_rows\": range(5)}, columns=[\"one\", \"two\", \"three\"])\n", "    ),\n", "    gr.ColorPicker(value=lambda: random.choice([\"#000000\", \"#ff0000\", \"#0000FF\"])),\n", "    gr.Label(value=lambda: random.choice([\"Pedestrian\", \"Car\", \"Cyclist\"])),\n", "    gr.HighlightedText(\n", "        value=lambda: random.choice(\n", "            [\n", "                {\"text\": highlighted_text, \"entities\": highlighted_text_output_1},\n", "                {\"text\": highlighted_text, \"entities\": highlighted_text_output_2},\n", "            ]\n", "        ),\n", "    ),\n", "    gr.JSON(value=lambda: random.choice([{\"a\": 1}, {\"b\": 2}])),\n", "    gr.HTML(\n", "        value=lambda: random.choice(\n", "            [\n", "                '<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=random_model3d),\n", "    gr.Plot(value=random_plot),\n", "    gr.Markdown(value=lambda: f\"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}\"),\n", "]\n", "\n", "\n", "def evaluate_values(*args):\n", "    are_false = []\n", "    for a in args:\n", "        if isinstance(a, (pd.DataFrame, np.ndarray)):\n", "            are_false.append(not a.any().any())\n", "        elif isinstance(a, str) and a.startswith(\"#\"):\n", "            are_false.append(a == \"#000000\")\n", "        else:\n", "            are_false.append(not a)\n", "    return all(are_false)\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    for i, component in enumerate(components):\n", "        component.label = f\"component_{str(i).zfill(2)}\"\n", "        component.render()\n", "    clear = gr.ClearButton(value=\"Clear\", components=components)\n", "    result = gr.Textbox(label=\"Are all cleared?\")\n", "    hide = gr.Button(value=\"Hide\")\n", "    reveal = gr.Button(value=\"Reveal\")\n", "    hide.click(\n", "        lambda: [c.__class__(visible=False) for c in components],\n", "        inputs=[],\n", "        outputs=components\n", "    )\n", "    reveal.click(\n", "        lambda: [c.__class__(visible=True) for c in components],\n", "        inputs=[],\n", "        outputs=components\n", "    )\n", "    get_value = gr.Button(value=\"Get Values\")\n", "    get_value.click(evaluate_values, components, result)\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}