app.py

import gradio as gr
import joblib
import numpy as np

# Load your model
model = joblib.load('best_model.json')

def predict_retention(satisfaction_level, last_evaluation, number_project,
                      average_monthly_hours, time_spent_company, 
                      work_accident, promotion_last_5years, salary, department):
    # One-hot encode the department
    departments = [
        'RandD', 'accounting', 'hr', 'management', 'marketing',
        'product_mng', 'sales', 'support', 'technical'
    ]
    department_encoded = [1 if dept == department else 0 for dept in departments]

    # Prepare the input with all 18 features
    input_data = np.array([
        satisfaction_level, last_evaluation, number_project, 
        average_monthly_hours, time_spent_company, work_accident, 
        promotion_last_5years, salary
    ] + department_encoded).reshape(1, -1)

    # Predict using the model
    try:
        prediction = model.predict(input_data)
        return "Employee is likely to quit." if prediction[0] == 1 else "Employee is likely to stay."
    except Exception as e:
        return f"Error: {str(e)}"

interface = gr.Interface(
    fn=predict_retention,
    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 Spent at Company (Years)"),
        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"
        )
    ],
    outputs="text",
    title="Employee Retention Prediction System",
    description="Predict whether an employee is likely to stay or quit based on their profile."
)

interface.launch()