Playground requirements tab (#9460)

* Add a tab navigation to the playground so the user can specify the requirement packages * Add Transformers.js.py demo and fix the playground to install the requirements immediately after switching the demo * add changeset * Format * add changeset * Fix preview flex * Add requirements to the share link and deploy to Spaces buttons * Add requirements.txt to each demo * Format * Update notebooks * Fix * Update --------- Co-authored-by: gradio-pr-bot <[email protected]> Co-authored-by: aliabd <[email protected]>
gradio-app · Oct 1, 2024 · 7352a89 · 7352a89
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diff --git a/.changeset/brown-queens-begin.md b/.changeset/brown-queens-begin.md
@@ -0,0 +1,9 @@
+---
+"@gradio/lite": minor
+"@gradio/tabitem": minor
+"@gradio/tabs": minor
+"gradio": minor
+"website": minor
+---
+
+feat:Playground requirements tab
diff --git a/demo/annotatedimage_component/requirements.txt b/demo/annotatedimage_component/requirements.txt
@@ -0,0 +1,3 @@
+numpy
+requests
+Pillow
diff --git a/demo/annotatedimage_component/run.ipynb b/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 numpy as np \n", "import requests \n", "from io import BytesIO\n", "from PIL import Image\n", "\n", "base_image = \"https://gradio-docs-json.s3.us-west-2.amazonaws.com/base.png\"\n", "building_image = requests.get(\"https://gradio-docs-json.s3.us-west-2.amazonaws.com/buildings.png\")\n", "building_image = np.asarray(Image.open(BytesIO(building_image.content)))[:, :, -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 numpy requests Pillow "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import numpy as np \n", "import requests \n", "from io import BytesIO\n", "from PIL import Image\n", "\n", "base_image = \"https://gradio-docs-json.s3.us-west-2.amazonaws.com/base.png\"\n", "building_image = requests.get(\"https://gradio-docs-json.s3.us-west-2.amazonaws.com/buildings.png\")\n", "building_image = np.asarray(Image.open(BytesIO(building_image.content)))[:, :, -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}
diff --git a/demo/bar_plot_demo/requirements.txt b/demo/bar_plot_demo/requirements.txt
@@ -0,0 +1 @@
+pandas
diff --git a/demo/bar_plot_demo/run.ipynb b/demo/bar_plot_demo/run.ipynb
@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: bar_plot_demo"]}, {"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", "from random import randint, random\n", "import gradio as gr\n", "\n", "\n", "temp_sensor_data = pd.DataFrame(\n", "    {\n", "        \"time\": pd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200),\n", "        \"temperature\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n", "        \"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n", "        \"location\": [\"indoor\", \"outdoor\"] * 100,\n", "    }\n", ")\n", "\n", "food_rating_data = pd.DataFrame(\n", "    {\n", "        \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in range(100)],\n", "        \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n", "        \"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)],\n", "        \"wait\": [random() for i in range(100)],\n", "    }\n", ")\n", "\n", "with gr.Blocks() as bar_plots:\n", "    with gr.Row():\n", "        start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\")\n", "        end = gr.DateTime(\"2021-01-05 00:00:00\", label=\"End\")\n", "        apply_btn = gr.Button(\"Apply\", scale=0)\n", "    with gr.Row():\n", "        group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Group by\")\n", "        aggregate = gr.Radio([\"sum\", \"mean\", \"median\", \"min\", \"max\"], value=\"sum\", label=\"Aggregation\")\n", "\n", "    temp_by_time = gr.BarPlot(\n", "        temp_sensor_data,\n", "        x=\"time\",\n", "        y=\"temperature\",\n", "    )\n", "    temp_by_time_location = gr.BarPlot(\n", "        temp_sensor_data,\n", "        x=\"time\",\n", "        y=\"temperature\",\n", "        color=\"location\",\n", "    )\n", "\n", "    time_graphs = [temp_by_time, temp_by_time_location]\n", "    group_by.change(\n", "        lambda group: [gr.BarPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n", "        group_by,\n", "        time_graphs\n", "    )\n", "    aggregate.change(\n", "        lambda aggregate: [gr.BarPlot(y_aggregate=aggregate)] * len(time_graphs),\n", "        aggregate,\n", "        time_graphs\n", "    )\n", "\n", "    def rescale(select: gr.SelectData):\n", "        return select.index\n", "    rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])\n", "\n", "    for trigger in [apply_btn.click, rescale_evt.then]:\n", "        trigger(\n", "            lambda start, end: [gr.BarPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs\n", "        )\n", "\n", "    with gr.Row():\n", "        price_by_cuisine = gr.BarPlot(\n", "            food_rating_data,\n", "            x=\"cuisine\",\n", "            y=\"price\",\n", "        )\n", "        with gr.Column(scale=0):\n", "            gr.Button(\"Sort $ > $$$\").click(lambda: gr.BarPlot(sort=\"y\"), None, price_by_cuisine)\n", "            gr.Button(\"Sort $$$ > $\").click(lambda: gr.BarPlot(sort=\"-y\"), None, price_by_cuisine)\n", "            gr.Button(\"Sort A > Z\").click(lambda: gr.BarPlot(sort=[\"Chinese\", \"Italian\", \"Mexican\"]), None, price_by_cuisine)\n", "\n", "    with gr.Row():\n", "        price_by_rating = gr.BarPlot(\n", "            food_rating_data,\n", "            x=\"rating\",\n", "            y=\"price\",\n", "            x_bin=1,\n", "        )\n", "        price_by_rating_color = gr.BarPlot(\n", "            food_rating_data,\n", "            x=\"rating\",\n", "            y=\"price\",\n", "            color=\"cuisine\",\n", "            x_bin=1,\n", "            color_map={\"Italian\": \"red\", \"Mexican\": \"green\", \"Chinese\": \"blue\"},\n", "        )\n", "\n", "if __name__ == \"__main__\":\n", "    bar_plots.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: bar_plot_demo"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio pandas "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import pandas as pd\n", "from random import randint, random\n", "import gradio as gr\n", "\n", "\n", "temp_sensor_data = pd.DataFrame(\n", "    {\n", "        \"time\": pd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200),\n", "        \"temperature\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n", "        \"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n", "        \"location\": [\"indoor\", \"outdoor\"] * 100,\n", "    }\n", ")\n", "\n", "food_rating_data = pd.DataFrame(\n", "    {\n", "        \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in range(100)],\n", "        \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n", "        \"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)],\n", "        \"wait\": [random() for i in range(100)],\n", "    }\n", ")\n", "\n", "with gr.Blocks() as bar_plots:\n", "    with gr.Row():\n", "        start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\")\n", "        end = gr.DateTime(\"2021-01-05 00:00:00\", label=\"End\")\n", "        apply_btn = gr.Button(\"Apply\", scale=0)\n", "    with gr.Row():\n", "        group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Group by\")\n", "        aggregate = gr.Radio([\"sum\", \"mean\", \"median\", \"min\", \"max\"], value=\"sum\", label=\"Aggregation\")\n", "\n", "    temp_by_time = gr.BarPlot(\n", "        temp_sensor_data,\n", "        x=\"time\",\n", "        y=\"temperature\",\n", "    )\n", "    temp_by_time_location = gr.BarPlot(\n", "        temp_sensor_data,\n", "        x=\"time\",\n", "        y=\"temperature\",\n", "        color=\"location\",\n", "    )\n", "\n", "    time_graphs = [temp_by_time, temp_by_time_location]\n", "    group_by.change(\n", "        lambda group: [gr.BarPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n", "        group_by,\n", "        time_graphs\n", "    )\n", "    aggregate.change(\n", "        lambda aggregate: [gr.BarPlot(y_aggregate=aggregate)] * len(time_graphs),\n", "        aggregate,\n", "        time_graphs\n", "    )\n", "\n", "    def rescale(select: gr.SelectData):\n", "        return select.index\n", "    rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])\n", "\n", "    for trigger in [apply_btn.click, rescale_evt.then]:\n", "        trigger(\n", "            lambda start, end: [gr.BarPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs\n", "        )\n", "\n", "    with gr.Row():\n", "        price_by_cuisine = gr.BarPlot(\n", "            food_rating_data,\n", "            x=\"cuisine\",\n", "            y=\"price\",\n", "        )\n", "        with gr.Column(scale=0):\n", "            gr.Button(\"Sort $ > $$$\").click(lambda: gr.BarPlot(sort=\"y\"), None, price_by_cuisine)\n", "            gr.Button(\"Sort $$$ > $\").click(lambda: gr.BarPlot(sort=\"-y\"), None, price_by_cuisine)\n", "            gr.Button(\"Sort A > Z\").click(lambda: gr.BarPlot(sort=[\"Chinese\", \"Italian\", \"Mexican\"]), None, price_by_cuisine)\n", "\n", "    with gr.Row():\n", "        price_by_rating = gr.BarPlot(\n", "            food_rating_data,\n", "            x=\"rating\",\n", "            y=\"price\",\n", "            x_bin=1,\n", "        )\n", "        price_by_rating_color = gr.BarPlot(\n", "            food_rating_data,\n", "            x=\"rating\",\n", "            y=\"price\",\n", "            color=\"cuisine\",\n", "            x_bin=1,\n", "            color_map={\"Italian\": \"red\", \"Mexican\": \"green\", \"Chinese\": \"blue\"},\n", "        )\n", "\n", "if __name__ == \"__main__\":\n", "    bar_plots.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
diff --git a/demo/barplot_component/requirements.txt b/demo/barplot_component/requirements.txt
@@ -0,0 +1 @@
+pandas
diff --git a/demo/barplot_component/run.ipynb b/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 pandas "]}, {"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}
diff --git a/demo/blocks_flipper/requirements.txt b/demo/blocks_flipper/requirements.txt
@@ -0,0 +1 @@
+numpy
diff --git a/demo/blocks_flipper/run.ipynb b/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", "def flip_text(x):\n", "    return x[::-1]\n", "\n", "def flip_image(x):\n", "    return np.fliplr(x)\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!\", 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", "\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 numpy "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import numpy as np\n", "import gradio as gr\n", "\n", "def flip_text(x):\n", "    return x[::-1]\n", "\n", "def flip_image(x):\n", "    return np.fliplr(x)\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!\", 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", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
diff --git a/demo/blocks_kinematics/requirements.txt b/demo/blocks_kinematics/requirements.txt
@@ -0,0 +1,2 @@
+numpy
+pandas
diff --git a/demo/blocks_kinematics/run.ipynb b/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", "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", "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 numpy pandas "]}, {"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", "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", "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}