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66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
import gradio as gr
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from vega_datasets import data
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cars = data.cars()
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iris = data.iris()
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# # Or generate your own fake data
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# import pandas as pd
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# import random
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# cars_data = {
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# "Name": ["car name " + f" {int(i/10)}" for i in range(400)],
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# "Miles_per_Gallon": [random.randint(10, 30) for _ in range(400)],
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# "Origin": [random.choice(["USA", "Europe", "Japan"]) for _ in range(400)],
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# "Horsepower": [random.randint(50, 250) for _ in range(400)],
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# }
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# iris_data = {
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# "petalWidth": [round(random.uniform(0, 2.5), 2) for _ in range(150)],
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# "petalLength": [round(random.uniform(0, 7), 2) for _ in range(150)],
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# "species": [
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# random.choice(["setosa", "versicolor", "virginica"]) for _ in range(150)
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# ],
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# }
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# cars = pd.DataFrame(cars_data)
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# iris = pd.DataFrame(iris_data)
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def scatter_plot_fn(dataset):
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if dataset == "iris":
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return gr.ScatterPlot(
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value=iris,
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x="petalWidth",
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y="petalLength",
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color="species",
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title="Iris Dataset",
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x_title="Petal Width",
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y_title="Petal Length",
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tooltip=["petalWidth", "petalLength", "species"],
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caption="",
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)
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else:
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return gr.ScatterPlot(
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value=cars,
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x="Horsepower",
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y="Miles_per_Gallon",
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color="Origin",
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tooltip=["Name"],
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title="Car Data",
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y_title="Miles per Gallon",
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caption="MPG vs Horsepower of various cars",
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)
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with gr.Blocks() as scatter_plot:
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with gr.Row():
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with gr.Column():
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dataset = gr.Dropdown(choices=["cars", "iris"], value="cars")
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with gr.Column():
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plot = gr.ScatterPlot()
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dataset.change(scatter_plot_fn, inputs=dataset, outputs=plot)
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scatter_plot.load(fn=scatter_plot_fn, inputs=dataset, outputs=plot)
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if __name__ == "__main__":
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scatter_plot.launch()
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