app.py

import gradio as gr
import xgboost as xgb
import numpy as np
import pandas as pd
import joblib
import os
import warnings
import shap
import matplotlib.pyplot as plt

# Suppress XGBoost warnings
warnings.filterwarnings("ignore", category=UserWarning, message=".*WARNING.*")

# Load your model (automatically detect XGBoost or joblib model)
def load_model():
    model_path = "xgboost_model.json"  # Ensure this matches your file name
    if os.path.exists(model_path):  
        model = xgb.Booster()
        model.load_model(model_path)
        print("✅ Model loaded successfully.")
        return model
    else:
        print("❌ Model file not found.")
        return None

model = load_model()

# Prediction function with consistent feature names
def predict_employee_status(satisfaction_level, last_evaluation, number_project,
                            average_monthly_hours, time_spend_company, 
                            work_accident, promotion_last_5years, salary, department, threshold=0.5):
    
    # One-hot encode the department
    departments = [
        'RandD', 'accounting', 'hr', 'management', 'marketing',
        'product_mng', 'sales', 'support', 'technical'
    ]
    department_features = {f"department_{dept}": 0 for dept in departments}
    if department in departments:
        department_features[f"department_{department}"] = 1

    # Automatically Generate Interaction Features
    satisfaction_evaluation = satisfaction_level * last_evaluation
    work_balance = average_monthly_hours / number_project

    # Prepare the input with all expected features as a DataFrame with column names
    input_data = {
        "satisfaction_level": [satisfaction_level],
        "last_evaluation": [last_evaluation],
        "number_project": [number_project],
        "average_monthly_hours": [average_monthly_hours],
        "time_spend_company": [time_spend_company],  # Corrected to match training data
        "Work_accident": [work_accident],
        "promotion_last_5years": [promotion_last_5years],
        "salary": [salary],
        "satisfaction_evaluation": [satisfaction_evaluation],  # Added for model compatibility
        "work_balance": [work_balance],  # Added for model compatibility
        **department_features
    }

    input_df = pd.DataFrame(input_data)

    # Predict using the model
    if model is None:
        return "❌ No model found. Please upload the model file."

    try:
        dmatrix = xgb.DMatrix(input_df)
        prediction = model.predict(dmatrix)
        prediction_prob = prediction[0]

        # Apply the dynamic threshold
        result = "✅ Employee is likely to quit." if prediction_prob >= threshold else "✅ Employee is likely to stay."
        return f"{result} (Probability: {prediction_prob:.2%})"
    except Exception as e:
        return f"❌ Error: {str(e)}"

# Gradio interface with consistent feature names
def gradio_interface():
    interface = gr.Interface(
        fn=predict_employee_status,
        inputs=[
            gr.Number(label="Satisfaction Level (0.0 - 1.0)"),
            gr.Number(label="Last Evaluation (0.0 - 1.0)"),
            gr.Number(label="Number of Projects (1 - 10)"),
            gr.Number(label="Average Monthly Hours (80 - 320)"),
            gr.Number(label="Time Spend at Company (Years)"),  # Corrected to match the notebook
            gr.Radio([0, 1], label="Work Accident (0 = No, 1 = Yes)"),
            gr.Radio([0, 1], label="Promotion in Last 5 Years (0 = No, 1 = Yes)"),
            gr.Radio([0, 1, 2], label="Salary (0 = Low, 1 = Medium, 2 = High)"),
            gr.Dropdown(
                ['RandD', 'accounting', 'hr', 'management', 'marketing',
                 'product_mng', 'sales', 'support', 'technical'], 
                label="Department"
            ),
            gr.Slider(0.1, 0.9, value=0.5, step=0.05, label="Prediction Threshold")
        ],
        outputs="text",
        title="Employee Retention Prediction System (With SHAP & ROC Threshold)",
        description="Predict whether an employee is likely to stay or quit based on their profile. Adjust the threshold for accurate predictions.",
        theme="dark"
    )
    interface.launch()

gradio_interface()