app.py

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("clf.pkl", 'rb'))

# Setup SHAP
explainer = shap.Explainer(loaded_model) # PLEASE DO NOT CHANGE THIS.

# Create the main function for server
def main_func(RewardsBenefits2,JobSatisfaction,RecommendToWork,Tenure,Generation,LearningDevelopment1):
    new_row = pd.DataFrame.from_dict({'RewardsBenefits2':RewardsBenefits2,'JobSatisfaction':JobSatisfaction,
              'RecommendToWork':RecommendToWork,'Tenure':Tenure,'Generation':Generation,
              'LearningDevelopment1':LearningDevelopment1}, 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 = "Worlds Greatest Employee Predictor of Turnover"
description1 = """
This app takes six inputs about employees' satisfaction with different aspects of their work (such as work-life balance, ...) and predicts whether the employee intends to stay with the employer or leave. There are two outputs from the app: 1- the predicted probability of stay or leave, 2- Shapley's force-plot which visualizes the extent to which each factor impacts the stay/ leave 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("""![marketing](types-of-employee-turnover.jpg)""")
    gr.Markdown(description1)
    gr.Markdown("""---""")
    gr.Markdown(description2)
    gr.Markdown("""---""")
    with gr.Row():        
        with gr.Column():
            RewardsBenefits2 = gr.Slider(label="Rewards Benefits 2 Score", minimum=1, maximum=5, value=4, step=.1)
            JobSatisfaction = gr.Slider(label="Job Satisfaction Score", minimum=1, maximum=5, value=4, step=.1)
            RecommendToWork = gr.Slider(label="Recommend to Work Score", minimum=1, maximum=5, value=4, step=.1)
            Tenure = gr.Slider(label="Tenure Score", minimum=1, maximum=5, value=4, step=.1)
            Generation = gr.Slider(label="Generation Score", minimum=1, maximum=5, value=4, step=.1)
            LearningDevelopment1 = gr.Slider(label="Learning Development Score", minimum=1, maximum=5, value=4, step=.1)
            submit_btn = gr.Button("Analyze")
        with gr.Column(visible=True,scale=1, min_width=600) as output_col:
            label = gr.Label(label = "Predicted Label")
            local_plot = gr.Plot(label = 'Shap:')
        
            submit_btn.click(
                main_func,
                [RewardsBenefits2,JobSatisfaction,RecommendToWork,Tenure,Generation,LearningDevelopment1],
                [label,local_plot], api_name="Employee_Turnover"
            )
    
    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]], 
                [RewardsBenefits2,JobSatisfaction,RecommendToWork,Tenure,Generation,LearningDevelopment1], 
                [label,local_plot], main_func, cache_examples=True)

demo.launch(share=True)