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README.md

@@ -1,12 +1,11 @@
+
 ---
-title: Native Plots Main
-emoji: 🌍
-colorFrom: gray
-colorTo: green
+title: native_plots_main 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
 sdk_version: 3.13.0
-app_file: app.py
+app_file: run.py
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,2 @@
+vega_datasets
+https://gradio-main-build.s3.amazonaws.com/9c8fa8bf45105032fc71e9ccad68c773464dfccb/gradio-3.13.0-py3-none-any.whl

run.ipynb

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+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: native_plots"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio vega_datasets"]}, {"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/native_plots/scatter_plot_demo.py"]}, {"cell_type": "code", "execution_count": null, "id": 44380577570523278879349135829904343037, "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "from scatter_plot_demo import scatter_plot\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Tabs():\n", "        with gr.TabItem(\"Scatter Plot\"):\n", "            scatter_plot.render()\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+import gradio as gr
+
+from scatter_plot_demo import scatter_plot
+
+
+with gr.Blocks() as demo:
+    with gr.Tabs():
+        with gr.TabItem("Scatter Plot"):
+            scatter_plot.render()
+
+if __name__ == "__main__":
+    demo.launch()

scatter_plot_demo.py

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+import gradio as gr
+
+from vega_datasets import data
+
+cars = data.cars()
+iris = data.iris()
+
+
+def scatter_plot_fn(dataset):
+    if dataset == "iris":
+        return gr.ScatterPlot.update(
+            value=iris,
+            x="petalWidth",
+            y="petalLength",
+            color="species",
+            title="Iris Dataset",
+            color_legend_title="Species",
+            x_title="Petal Width",
+            y_title="Petal Length",
+            tooltip=["petalWidth", "petalLength", "species"],
+            caption="",
+        )
+    else:
+        return gr.ScatterPlot.update(
+            value=cars,
+            x="Horsepower",
+            y="Miles_per_Gallon",
+            color="Origin",
+            tooltip="Name",
+            title="Car Data",
+            y_title="Miles per Gallon",
+            color_legend_title="Origin of Car",
+            caption="MPG vs Horsepower of various cars"
+        )
+
+
+with gr.Blocks() as scatter_plot:
+    with gr.Row():
+        with gr.Column():
+            dataset = gr.Dropdown(choices=["cars", "iris"], value="cars")
+        with gr.Column():
+            plot = gr.ScatterPlot(show_label=False).style(container=True)
+    dataset.change(scatter_plot_fn, inputs=dataset, outputs=plot)
+    scatter_plot.load(fn=scatter_plot_fn, inputs=dataset, outputs=plot)
+
+if __name__ == "__main__":
+    scatter_plot.launch()