Spaces:
Running
Running
| import pickle | |
| import pandas as pd | |
| import shap | |
| from shap.plots._force_matplotlib import draw_additive_plot | |
| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| # load the model from disk | |
| loaded_model = pickle.load(open("filtered_xgb_model.pkl", 'rb')) | |
| # Setup SHAP | |
| explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS. | |
| # Create the main function for server | |
| def main_func(GM3, WorkEnv3, WellBeing2, GM2, JobSecurity, WellBeing1): | |
| new_row = pd.DataFrame.from_dict({ | |
| 'GM3': GM3, | |
| 'WorkEnv3': WorkEnv3, | |
| 'WellBeing2': WellBeing2, | |
| 'GM2': GM2, | |
| 'JobSecurity': JobSecurity, | |
| 'WellBeing1': WellBeing1 | |
| }, orient='index').transpose() | |
| prob = loaded_model.predict_proba(new_row) | |
| shap_values = explainer(new_row) | |
| # plot = shap.force_plot(shap_values[0], matplotlib=True, figsize=(30,30), show=False) | |
| # plot = shap.plots.waterfall(shap_values[0], max_display=6, show=False) | |
| plot = shap.plots.bar(shap_values[0], max_display=6, order=shap.Explanation.abs, show_data='auto', show=False) | |
| plt.tight_layout() | |
| local_plot = plt.gcf() | |
| plt.rcParams['figure.figsize'] = 6,4 | |
| plt.close() | |
| return {"Leave": float(prob[0][0]), "Stay": 1-float(prob[0][0])}, local_plot | |
| # Create the UI | |
| title = "**Employee Turnover Predictor & Interpreter** 🪐" | |
| description1 = """ | |
| This app predicts whether an employee is likely to stay or leave based on selected workplace factors. | |
| It also provides a SHAP visualization to show how each factor influences the prediction. | |
| """ | |
| description2 = """ | |
| To use the app, click on one of the examples, or adjust the values of the six employee satisfaction factors, and click on Analyze. ✨ | |
| """ | |
| with gr.Blocks(title=title) as demo: | |
| gr.Markdown(f"## {title}") | |
| # gr.Markdown("""""") | |
| gr.Markdown(description1) | |
| gr.Markdown("""---""") | |
| gr.Markdown(description2) | |
| gr.Markdown("""---""") | |
| with gr.Row(): | |
| with gr.Column(): | |
| GM3 = gr.Slider(label="GM3", minimum=1, maximum=5, value=4, step=0.1) | |
| WorkEnv3 = gr.Slider(label="Work Environment 3", minimum=1, maximum=5, value=4, step=0.1) | |
| WellBeing2 = gr.Slider(label="Well-Being 2", minimum=1, maximum=5, value=4, step=0.1) | |
| GM2 = gr.Slider(label="GM2", minimum=1, maximum=5, value=4, step=0.1) | |
| JobSecurity = gr.Slider(label="Job Security", minimum=1, maximum=5, value=4, step=0.1) | |
| WellBeing1 = gr.Slider(label="Well-Being 1", minimum=1, maximum=5, value=4, step=0.1) | |
| submit_btn = gr.Button("Analyze") | |
| with gr.Column(visible=True, scale=1, min_width=600) as output_col: | |
| label = gr.Label(label="Predicted Intent") | |
| local_plot = gr.Plot(label='SHAP Analysis') | |
| submit_btn.click( | |
| main_func, | |
| [GM3, WorkEnv3, WellBeing2, GM2, JobSecurity, WellBeing1], | |
| [label, local_plot], api_name="IntentToStay_Predictor" | |
| ) | |
| gr.Markdown("### Click on any of the examples below to see how it works:") | |
| gr.Examples([[4,4,4,4,5,5], [5,4,5,4,4,4]], | |
| [GM3, WorkEnv3, WellBeing2, GM2, JobSecurity, WellBeing1], | |
| [label, local_plot], main_func, cache_examples=True) | |
| demo.launch() |