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https://github.com/gradio-app/gradio.git
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b4d9825409
Ported gradio website into gradio repository, now launched as a docker service from gradio/website
33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
import gradio as gr
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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def sales_projections(employee_data):
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sales_data = employee_data.iloc[:, 1:4].astype("int").to_numpy()
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regression_values = np.apply_along_axis(lambda row:
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np.array(np.poly1d(np.polyfit([0,1,2], row, 2))), 0, sales_data)
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projected_months = np.repeat(np.expand_dims(
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np.arange(3,12), 0), len(sales_data), axis=0)
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projected_values = np.array([
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month * month * regression[0] + month * regression[1] + regression[2]
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for month, regression in zip(projected_months, regression_values)])
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plt.plot(projected_values.T)
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plt.legend(employee_data["Name"])
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return employee_data, plt.gcf(), regression_values
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iface = gr.Interface(sales_projections,
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gr.inputs.Dataframe(
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headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"],
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default=[["Jon", 12, 14, 18], ["Alice", 14, 17, 2], ["Sana", 8, 9.5, 12]]
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),
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[
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"dataframe",
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"plot",
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"numpy"
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],
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description="Enter sales figures for employees to predict sales trajectory over year."
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)
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if __name__ == "__main__":
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iface.launch()
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