Create app.py

app.py

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+import gradio as gr
+from vega_datasets import data
+
+cars = data.cars()
+iris = data.iris()
+
+# # Or generate your own fake data
+
+# import pandas as pd
+# import random
+
+# cars_data = {
+#     "Name": ["car name " + f" {int(i/10)}" for i in range(400)],
+#     "Miles_per_Gallon": [random.randint(10, 30) for _ in range(400)],
+#     "Origin": [random.choice(["USA", "Europe", "Japan"]) for _ in range(400)],
+#     "Horsepower": [random.randint(50, 250) for _ in range(400)],
+# }
+
+# iris_data = {
+#     "petalWidth": [round(random.uniform(0, 2.5), 2) for _ in range(150)],
+#     "petalLength": [round(random.uniform(0, 7), 2) for _ in range(150)],
+#     "species": [
+#         random.choice(["setosa", "versicolor", "virginica"]) for _ in range(150)
+#     ],
+# }
+
+# cars = pd.DataFrame(cars_data)
+# iris = pd.DataFrame(iris_data)
+
+
+def scatter_plot_fn(dataset):
+    if dataset == "iris":
+        return gr.ScatterPlot(
+            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(
+            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()
+    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()