app.py

import gradio as gr
import pickle
import pandas as pd
import shap
import matplotlib.pyplot as plt

# Load model
filename = 'xgb_h_generation.pkl'
with open(filename, 'rb') as f:
    loaded_model = pickle.load(f)

# Setup SHAP
explainer = shap.Explainer(loaded_model)

# Generation Mapping (Radio Button Labels → Numeric Values)
generation_mapping = {
    "Before 1927": 1,
    "Silent Generation": 2,
    "Baby Boomers": 3,
    "Generation X": 4,
    "Millennials": 5,
    "Generation Z": 6
}

# Employee Profiles (Updated Default Dream Employee Values)
employee_profiles = {
    "🥇 Default Dream Employee": [5.0, 5.0, 5.0, 4.8, 4.8, 4.9],  
    "🏆 Leslie Knope": [4.716, 4.792, 4.864, 4.588, 4.849, 4.601],
    "⚠️ Kevin Malone": [3.045, 3.122, 3.129, 2.886, 3.113, 2.197],
    "🌱 Jim Halpert": [3.885, 3.992, 4.119, 3.704, 4.090, 3.377]
}

# Define the prediction function
def main_func(Generation_Label, WellBeing, SupportiveGM, Engagement, Workload, WorkEnvironment, Merit):
    Generation = generation_mapping.get(Generation_Label, 5)  # Convert label to numeric value

    new_row = pd.DataFrame({
        'Generation': [Generation],
        'WellBeing': [WellBeing],
        'SupportiveGM': [SupportiveGM],
        'Engagement': [Engagement],
        'Workload': [Workload],
        'WorkEnvironment': [WorkEnvironment],
        'Merit': [Merit]
    })

    # Predict probability
    prob = loaded_model.predict_proba(new_row)
    shap_values = explainer(new_row)

    # Calculate probability values
    stay_prob = round((1 - float(prob[0][0])) * 100, 2)
    leave_prob = round(float(prob[0][0]) * 100, 2)

    # Dynamic risk label
    risk_label = "🔴 High Risk of Turnover" if leave_prob > 50 else "🟢 Low Risk of Turnover"
    risk_color = "red" if leave_prob > 50 else "green"

    risk_html = f"""
    <div style='border: 1px solid black; padding: 15px; border-radius: 8px; display: flex;'>
        <div style='width: 50%; padding-right: 15px;'>
            <span style='color: {risk_color}; font-size: 26px; font-weight: bold;'>{risk_label}</span>
            <ul style='list-style-type: none; padding-left: 0; font-size: 20px; font-weight: bold; color: #0057B8;'>
                <li>🧲 Likelihood of Staying: {stay_prob}%</li>
                <li>🚪 Likelihood of Leaving: {leave_prob}%</li>
            </ul>
        </div>
        <div style='width: 50%; border-left: 1px solid black; padding-left: 15px;'>
            <b style='color: #0057B8; font-size: 22px;'>Key Insights:</b>
    """

    # Key Insights (excluding Generation)
    shap_values_df = pd.DataFrame(shap_values.values, columns=new_row.columns)
    shap_values_df = shap_values_df.drop(columns=["Generation"])  # Drop Generation
    for feature in shap_values_df.columns:
        shap_val = shap_values_df[feature].values[0]
        impact = f"{abs(shap_val * 1):.2f}"  # FIXED: Correct decimal place
        icon = "📈" if shap_val > 0 else "📉"
        effect = "raises turnover risk" if shap_val > 0 else "improves retention"
        risk_html += f"<p style='margin: 5px 0;'> {icon} <b>Each 1-point increase in {feature} {effect} by {impact}%.</b></p>"

    risk_html += "</div></div>"

    # Retention vs. Turnover Chart
    fig, ax = plt.subplots()
    categories = ["Stay", "Leave"]
    values = [stay_prob, leave_prob]
    colors = ["#0057B8", "#D43F00"]
    ax.barh(categories, values, color=colors)
    for i, v in enumerate(values):
        ax.text(v + 2, i, f"{v:.2f}%", va='center', fontweight='bold', fontsize=12)
    ax.set_xlabel("Probability (%)")
    ax.set_title("Retention vs. Turnover Probability")
    plt.tight_layout()
    prob_chart_path = "prob_chart.png"
    plt.savefig(prob_chart_path, transparent=True)
    plt.close()

    # SHAP Chart (excluding Generation)
    fig, ax = plt.subplots()
    shap_values_filtered = shap_values[:, 1:]  # Remove Generation from SHAP values
    shap.plots.bar(shap_values_filtered[0], max_display=6, show=False)  # Adjust max_display if needed
    ax.set_title("Key Drivers of Turnover Risk")
    plt.tight_layout()
    shap_plot_path = "shap_plot.png"
    plt.savefig(shap_plot_path, transparent=True)
    plt.close()

    return risk_html, prob_chart_path, shap_plot_path

# Function to update sliders based on selected profile
def update_sliders(profile):
    if profile in employee_profiles:
        return employee_profiles[profile]
    return [5.0, 5.0, 5.0, 4.8, 4.8, 4.9]

# UI Setup
with gr.Blocks() as demo:
 
    gr.Image("HiltonLogoSmall.jpg")
    gr.Markdown("""
    <div style="display: flex; justify-content: center; align-items: center;">
        <img src="file=assets/HiltonLogoSmall.jpg" alt="Hilton Logo" width="250px">
    </div>
    """)
    gr.Markdown("<h1 style='color: #0057B8;'>Hilton Team Member Retention Predictor</h1>")
    gr.Markdown("""
    <div style='font-size: 20px; color: #0057B8;'>
    ✨ <b>Welcome to Hilton’s Employee Retention Predictor</b><br>
    This tool helps <b>HR and People Analytics professionals</b> assess  
    <b>Sales, Marketing, and Front Office Operations teams</b>—<span style='color: #0057B8;'>The Face of Hilton</span>—  
    by analyzing <b>team member engagement</b> and predicting <b>turnover risk</b> using  
    <span style='color: #0057B8;'>AI-powered insights</span>.<br>  
    🔍 <b>Understand what drives retention and make data-driven decisions to keep top talent.</b>  
    </div>
    """)

    # Generation Filter (Radio Button - Independent)
    generation_filter = gr.Radio(choices=list(generation_mapping.keys()), label="Select Generation", value="Millennials")

    # Dropdown for Employee Profiles (Updates Sliders)
    profile_dropdown = gr.Dropdown(choices=list(employee_profiles.keys()), label="Select Employee Profile", value="🥇 Default Dream Employee")

    # Sliders for input features (Updated Order)
    with gr.Row():
        WellBeing = gr.Slider(label="WellBeing Score", minimum=1, maximum=5, value=5.0, step=0.1)
        SupportiveGM = gr.Slider(label="Supportive GM Score", minimum=1, maximum=5, value=5.0, step=0.1)
        Engagement = gr.Slider(label="Engagement Score", minimum=1, maximum=5, value=5.0, step=0.1)
    with gr.Row():
        Workload = gr.Slider(label="Workload Score", minimum=1, maximum=5, value=4.8, step=0.1)
        WorkEnvironment = gr.Slider(label="Work Environment Score", minimum=1, maximum=5, value=4.8, step=0.1)
        Merit = gr.Slider(label="Merit Score", minimum=1, maximum=5, value=4.9, step=0.1)

    submit_btn = gr.Button("🔎 Click Here to Analyze Retention")

    prediction = gr.HTML()

    # Charts Side by Side
    with gr.Row():
        prob_chart = gr.Image(label="Retention vs. Turnover Probability", type="filepath")
        shap_plot = gr.Image(label="Key Drivers of Turnover Risk", type="filepath")

    profile_dropdown.change(update_sliders, inputs=[profile_dropdown], outputs=[WellBeing, SupportiveGM, Engagement, Workload, WorkEnvironment, Merit])

    submit_btn.click(main_func, [generation_filter, WellBeing, SupportiveGM, Engagement, Workload, WorkEnvironment, Merit], 
                     [prediction, prob_chart, shap_plot])

demo.launch()