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* changes * changes * revert changes * changes * add changeset * notebooks script * changes * changes --------- Co-authored-by: Ali Abid <aliabid94@gmail.com> Co-authored-by: gradio-pr-bot <gradio-pr-bot@users.noreply.github.com> Co-authored-by: Ali Abdalla <ali.si3luwa@gmail.com>
35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import gradio as gr
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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(
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lambda row: np.array(np.poly1d(np.polyfit([0, 1, 2], row, 2))), 0, sales_data
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)
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projected_months = np.repeat(
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np.expand_dims(np.arange(3, 12), 0), len(sales_data), axis=0
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)
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projected_values = np.array(
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[
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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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]
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)
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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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demo = gr.Interface(
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sales_projections,
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gr.Dataframe(
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headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"],
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value=[["Jon", 12, 14, 18], ["Alice", 14, 17, 2], ["Sana", 8, 9.5, 12]],
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),
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["dataframe", "plot", "numpy"],
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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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demo.launch()
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