[{"category": "Text", "demos": [{"name": "Hello World", "dir": "hello_world", "code": "import gradio as gr\n\n\ndef greet(name):\n    return \"Hello \" + name + \"!\"\n\n\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "The simplest possible Gradio demo. It wraps a 'Hello {name}!' function in an Interface that accepts and returns text.", "requirements": []}, {"name": "Hello Blocks", "dir": "hello_blocks", "code": "import gradio as gr\n\n\ndef greet(name):\n    return \"Hello \" + name + \"!\"\n\n\nwith gr.Blocks() as demo:\n    name = gr.Textbox(label=\"Name\")\n    output = gr.Textbox(label=\"Output Box\")\n    greet_btn = gr.Button(\"Greet\")\n    greet_btn.click(fn=greet, inputs=name, outputs=output, api_name=\"greet\")\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}, {"name": "Sentence Builder", "dir": "sentence_builder", "code": "import gradio as gr\n\ndef sentence_builder(quantity, animal, countries, place, activity_list, morning):\n    return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n\ndemo = gr.Interface(\n    sentence_builder,\n    [\n        gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n        gr.Dropdown(\n            [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n        ),\n        gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n        gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n        gr.Dropdown(\n            [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n        ),\n        gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n    ],\n    \"text\",\n    examples=[\n        [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n        [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n        [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n        [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n    ]\n)\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}, {"name": "Diff Texts", "dir": "diff_texts", "code": "from difflib import Differ\n\nimport gradio as gr\n\ndef 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\ndemo = 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)\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}]}, {"category": "Media", "demos": [{"name": "Sepia Filter", "dir": "sepia_filter", "code": "import numpy as np\nimport gradio as gr\n\ndef sepia(input_img):\n    sepia_filter = np.array([\n        [0.393, 0.769, 0.189],\n        [0.349, 0.686, 0.168],\n        [0.272, 0.534, 0.131]\n    ])\n    sepia_img = input_img.dot(sepia_filter.T)\n    sepia_img /= sepia_img.max()\n    return sepia_img\n\ndemo = gr.Interface(sepia, gr.Image(), \"image\")\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": ["numpy"]}, {"name": "Video Identity", "dir": "video_identity_2", "code": "import gradio as gr\n\ndef video_identity(video):\n    return video\n\ndemo = gr.Interface(video_identity,\n                    gr.Video(),\n                    \"playable_video\",\n                    )\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}, {"name": "Iterative Output", "dir": "fake_diffusion", "code": "import gradio as gr\nimport numpy as np\nimport time\n\ndef fake_diffusion(steps):\n    rng = np.random.default_rng()\n    for i in range(steps):\n        time.sleep(1)\n        image = rng.random(size=(600, 600, 3))\n        yield image\n    image = np.ones((1000,1000,3), np.uint8)\n    image[:] = [255, 124, 0]\n    yield image\n\ndemo = gr.Interface(fake_diffusion,\n                    inputs=gr.Slider(1, 10, 3, step=1),\n                    outputs=\"image\")\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "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.", "requirements": ["numpy"]}, {"name": "Generate Tone", "dir": "generate_tone", "code": "import numpy as np\nimport gradio as gr\n\nnotes = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]\n\ndef generate_tone(note, octave, duration):\n    sr = 48000\n    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)\n    frequency = a4_freq * 2 ** (tones_from_a4 / 12)\n    duration = int(duration)\n    audio = np.linspace(0, duration, duration * sr)\n    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)\n    return sr, audio\n\ndemo = gr.Interface(\n    generate_tone,\n    [\n        gr.Dropdown(notes, type=\"index\"),\n        gr.Slider(4, 6, step=1),\n        gr.Textbox(value=\"1\", label=\"Duration in seconds\"),\n    ],\n    \"audio\",\n)\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": ["numpy"]}]}, {"category": "Tabular", "demos": [{"name": "Filter Records", "dir": "filter_records", "code": "import gradio as gr\n\ndef filter_records(records, gender):\n    return records[records[\"gender\"] == gender]\n\ndemo = gr.Interface(\n    filter_records,\n    [\n        gr.Dataframe(\n            headers=[\"name\", \"age\", \"gender\"],\n            datatype=[\"str\", \"number\", \"str\"],\n            row_count=5,\n            col_count=(3, \"fixed\"),\n        ),\n        gr.Dropdown([\"M\", \"F\", \"O\"]),\n    ],\n    \"dataframe\",\n    description=\"Enter gender as 'M', 'F', or 'O' for other.\",\n)\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}, {"name": "Transpose Matrix", "dir": "matrix_transpose", "code": "import numpy as np\n\nimport gradio as gr\n\ndef transpose(matrix):\n    return matrix.T\n\ndemo = gr.Interface(\n    transpose,\n    gr.Dataframe(type=\"numpy\", datatype=\"number\", row_count=5, col_count=3),\n    \"numpy\",\n    examples=[\n        [np.zeros((3, 3)).tolist()],\n        [np.ones((2, 2)).tolist()],\n        [np.random.randint(0, 10, (3, 10)).tolist()],\n        [np.random.randint(0, 10, (10, 3)).tolist()],\n        [np.random.randint(0, 10, (10, 10)).tolist()],\n    ],\n    cache_examples=False\n)\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}, {"name": "Tax Calculator", "dir": "tax_calculator", "code": "import gradio as gr\n\ndef tax_calculator(income, marital_status, assets):\n    tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\n    total_deductible = sum(assets[\"Cost\"])\n    taxable_income = income - total_deductible\n\n    total_tax = 0\n    for bracket, rate in tax_brackets:\n        if taxable_income > bracket:\n            total_tax += (taxable_income - bracket) * rate / 100\n\n    if marital_status == \"Married\":\n        total_tax *= 0.75\n    elif marital_status == \"Divorced\":\n        total_tax *= 0.8\n\n    return round(total_tax)\n\ndemo = gr.Interface(\n    tax_calculator,\n    [\n        \"number\",\n        gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\n        gr.Dataframe(\n            headers=[\"Item\", \"Cost\"],\n            datatype=[\"str\", \"number\"],\n            label=\"Assets Purchased this Year\",\n        ),\n    ],\n    \"number\",\n    examples=[\n        [10000, \"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]],\n        [80000, \"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]],\n    ],\n)\n\ndemo.launch()\n", "text": "Calculate taxes using Textbox, Radio, and Dataframe components", "requirements": []}, {"name": "Kinematics", "dir": "blocks_kinematics", "code": "import pandas as pd\nimport numpy as np\n\nimport gradio as gr\n\ndef 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\ndemo = gr.Blocks()\n\nwith 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\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": ["numpy", "pandas"]}, {"name": "Stock Forecast", "dir": "stock_forecast", "code": "import matplotlib.pyplot as plt\nimport numpy as np\n\nimport gradio as gr\n\ndef plot_forecast(final_year, companies, noise, show_legend, point_style):\n    start_year = 2020\n    x = np.arange(start_year, final_year + 1)\n    year_count = x.shape[0]\n    plt_format = ({\"cross\": \"X\", \"line\": \"-\", \"circle\": \"o--\"})[point_style]\n    fig = plt.figure()\n    ax = fig.add_subplot(111)\n    for i, company in enumerate(companies):\n        series = np.arange(0, year_count, dtype=float)\n        series = series**2 * (i + 1)\n        series += np.random.rand(year_count) * noise\n        ax.plot(x, series, plt_format)\n    if show_legend:\n        plt.legend(companies)\n    return fig\n\ndemo = gr.Interface(\n    plot_forecast,\n    [\n        gr.Radio([2025, 2030, 2035, 2040], label=\"Project to:\"),\n        gr.CheckboxGroup([\"Google\", \"Microsoft\", \"Gradio\"], label=\"Company Selection\"),\n        gr.Slider(1, 100, label=\"Noise Level\"),\n        gr.Checkbox(label=\"Show Legend\"),\n        gr.Dropdown([\"cross\", \"line\", \"circle\"], label=\"Style\"),\n    ],\n    gr.Plot(label=\"forecast\", format=\"png\"),\n)\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": ["numpy", "matplotlib"]}]}, {"category": "Other", "demos": [{"name": "Tabbed Interface", "dir": "tabbed_interface_lite", "code": "import gradio as gr\n\nhello_world = gr.Interface(lambda name: \"Hello \" + name, \"text\", \"text\")\nbye_world = gr.Interface(lambda name: \"Bye \" + name, \"text\", \"text\")\n\ndemo = gr.TabbedInterface([hello_world, bye_world], [\"Hello World\", \"Bye World\"])\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}, {"name": "Chatbot", "dir": "chatinterface_random_response", "code": "import random\nimport gradio as gr\n\ndef random_response(message, history):\n    return random.choice([\"Yes\", \"No\"])\n\ndemo = gr.ChatInterface(random_response, type=\"messages\")\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}, {"name": "Streaming Chatbot", "dir": "chatinterface_streaming_echo", "code": "import time\nimport gradio as gr\n\ndef slow_echo(message, history):\n    for i in range(len(message)):\n        time.sleep(0.05)\n        yield \"You typed: \" + message[: i + 1]\n\ndemo = gr.ChatInterface(slow_echo, type=\"messages\")\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}, {"name": "Layouts", "dir": "blocks_flipper", "code": "import numpy as np\nimport gradio as gr\n\ndef flip_text(x):\n    return x[::-1]\n\ndef flip_image(x):\n    return np.fliplr(x)\n\nwith 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!\", open=False):\n        gr.Markdown(\"Look at me...\")\n        temp_slider = gr.Slider(\n            0, 1,\n            value=0.1,\n            step=0.1,\n            interactive=True,\n            label=\"Slide me\",\n        )\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\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": ["numpy"]}, {"name": "Error", "dir": "calculator", "code": "import gradio as gr\n\ndef 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\ndemo = gr.Interface(\n    calculator,\n    [\n        \"number\",\n        gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n        \"number\"\n    ],\n    \"number\",\n    examples=[\n        [45, \"add\", 3],\n        [3.14, \"divide\", 2],\n        [144, \"multiply\", 2.5],\n        [0, \"subtract\", 1.2],\n    ],\n    title=\"Toy Calculator\",\n    description=\"Here's a sample toy calculator.\",\n)\n\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}, {"name": "Chained Events", "dir": "blocks_chained_events", "code": "import gradio as gr\n\ndef failure():\n    raise gr.Error(\"This should fail!\")\n\ndef exception():\n    raise ValueError(\"Something went wrong\")\n\ndef success():\n    return True\n\ndef warning_fn():\n    gr.Warning(\"This is a warning!\")\n\ndef info_fn():\n    gr.Info(\"This is some info\")\n\nwith 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\nif __name__ == \"__main__\":\n    demo.launch(show_error=True)\n", "text": "", "requirements": []}, {"name": "Change Listener", "dir": "blocks_hello", "code": "import gradio as gr\n\ndef welcome(name):\n    return f\"Welcome to Gradio, {name}!\"\n\nwith 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\nif __name__ == \"__main__\":\n    demo.launch()\n", "text": "", "requirements": []}]}, {"category": "Transformers", "demos": [{"name": "Basic", "dir": "transformers_basic", "lite": true, "code": "import gradio as gr\nfrom transformers_js_py import pipeline\n\npipe = await pipeline('sentiment-analysis')\n\nasync def process(text):\n    return await pipe(text)\n\ndemo = gr.Interface(fn=process, inputs=\"text\", outputs=\"json\")\n\ndemo.launch()\n", "text": "", "requirements": ["transformers_js_py"]}]}]