gradio/demo/sales_projections/run.py

import gradio as gr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

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

iface = gr.Interface(sales_projections, 
    gr.inputs.Dataframe(
        headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"],
        default=[["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__":
    iface.launch()