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import matplotlib.pyplot as plt
import numpy as np
import gradio as gr
def sales_projections(employee_data):
sales_data = employee_data.iloc[:, 1:4].astype("int").to_numpy()
regression_values = np.apply_along_axis(
lambda row: np.array(np.poly1d(np.polyfit([0, 1, 2], row, 2))), 0, sales_data
)
projected_months = np.repeat(
np.expand_dims(np.arange(3, 12), 0), len(sales_data), axis=0
)
projected_values = np.array(
[
month * month * regression[0] + month * regression[1] + regression[2]
for month, regression in zip(projected_months, regression_values)
]
)
plt.plot(projected_values.T)
plt.legend(employee_data["Name"])
return employee_data, plt.gcf(), regression_values
demo = gr.Interface(
sales_projections,
gr.Dataframe(
headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"],
value=[["Jon", 12, 14, 18], ["Alice", 14, 17, 2], ["Sana", 8, 9.5, 12]],
),
["dataframe", "plot", "numpy"],
description="Enter sales figures for employees to predict sales trajectory over year.",
)
if __name__ == "__main__":
demo.launch()
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