gradio-app--gradio
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72 行
2.2 KiB
Python
72 行
2.2 KiB
Python
import gradio as gr
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from data import temp_sensor_data, food_rating_data # type: ignore
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with gr.Blocks() as scatter_plots:
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with gr.Row():
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start = gr.DateTime("2021-01-01 00:00:00", label="Start")
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end = gr.DateTime("2021-01-05 00:00:00", label="End")
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apply_btn = gr.Button("Apply", scale=0)
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with gr.Row():
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group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by")
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aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation")
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temp_by_time = gr.ScatterPlot(
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temp_sensor_data,
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x="time",
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y="temperature",
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buttons=["export"],
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)
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temp_by_time_location = gr.ScatterPlot(
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temp_sensor_data,
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x="time",
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y="temperature",
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color="location",
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buttons=["export"],
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)
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time_graphs = [temp_by_time, temp_by_time_location]
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group_by.change(
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lambda group: [gr.ScatterPlot(x_bin=None if group == "None" else group)] * len(time_graphs),
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group_by,
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time_graphs
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)
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aggregate.change(
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lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs),
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aggregate,
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time_graphs
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)
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# def rescale(select: gr.SelectData):
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# return select.index
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# rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])
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# for trigger in [apply_btn.click, rescale_evt.then]:
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# trigger(
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# lambda start, end: [gr.ScatterPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs
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# )
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price_by_cuisine = gr.ScatterPlot(
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food_rating_data,
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x="cuisine",
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y="price",
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buttons=["export"],
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)
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with gr.Row():
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price_by_rating = gr.ScatterPlot(
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food_rating_data,
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x="rating",
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y="price",
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color="wait",
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buttons=["actions", "export"], # type: ignore
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)
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price_by_rating_color = gr.ScatterPlot(
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food_rating_data,
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x="rating",
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y="price",
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color="cuisine",
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buttons=["export"],
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)
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if __name__ == "__main__":
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scatter_plots.launch()
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