| import matplotlib | |
| matplotlib.use('Agg') | |
| 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.Blocks() | |
| with demo: | |
| with gr.Tabs(): | |
| with gr.TabItem("Greedy Search"): | |
| gr.Button("New Lion") | |
| with gr.TabItem("Sample"): | |
| gr.Button("New Tiger") | |
| with gr.TabItem("Beam Search"): | |
| gr.Button("New Tiger") | |
| with gr.TabItem("Benchmark Information"): | |
| 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.", | |
| ) | |
| demo.launch() | |