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| import gradio as gr | |
| import xgboost as xgb | |
| import numpy as np | |
| import joblib | |
| import os | |
| import warnings | |
| # Suppress XGBoost warnings | |
| warnings.filterwarnings("ignore", category=UserWarning, message=".*WARNING.*") | |
| # Load your model (automatically detect XGBoost or joblib model) | |
| def load_model(): | |
| if os.path.exists("best_model.json"): # Model in root directory | |
| model = xgb.Booster() | |
| model.load_model("best_model.json") | |
| print("✅ Model loaded using XGBoost's native method.") | |
| return model | |
| elif os.path.exists("best_model.pkl"): # Joblib model in root directory | |
| model = joblib.load("best_model.pkl") | |
| print("✅ Model loaded using Joblib.") | |
| return model | |
| else: | |
| print("❌ No model file found.") | |
| return None | |
| model = load_model() | |
| # Prediction function | |
| def predict_employee_status(satisfaction_level, last_evaluation, number_project, | |
| average_monthly_hours, time_spend_company, | |
| work_accident, promotion_last_5years, salary, department): | |
| # Encode the department as numeric (One-Hot Encoding or Label Encoding) | |
| department_mapping = { | |
| "Sales": 0, | |
| "Technical": 1, | |
| "Support": 2, | |
| "IT": 3, | |
| "Management": 4, | |
| "Product Management": 5, | |
| "Marketing": 6, | |
| "HR": 7, | |
| "Accounting": 8, | |
| "R&D": 9 | |
| } | |
| # Convert the department to a numeric value | |
| department_encoded = department_mapping.get(department, 0) | |
| # Prepare input data including the department | |
| input_data = np.array([[satisfaction_level, last_evaluation, number_project, | |
| average_monthly_hours, time_spend_company, | |
| work_accident, promotion_last_5years, salary, department_encoded]]) | |
| if model is None: | |
| return "❌ No model found. Please upload the model file." | |
| if isinstance(model, xgb.Booster): | |
| dmatrix = xgb.DMatrix(input_data) | |
| prediction = model.predict(dmatrix)[0] | |
| result = "✅ The employee is likely to Quit." if prediction > 0.5 else "✅ The employee is likely to Stay." | |
| else: | |
| prediction = model.predict(input_data)[0] | |
| result = "✅ The employee is likely to Quit." if prediction == 1 else "✅ The employee is likely to Stay." | |
| return result | |
| # Gradio interface with enhanced UI including Department | |
| interface = gr.Interface( | |
| fn=predict_employee_status, | |
| inputs=[ | |
| gr.Number(label="Satisfaction Level (0.0 - 1.0)", value=0.5), | |
| gr.Number(label="Last Evaluation (0.0 - 1.0)", value=0.6), | |
| gr.Number(label="Number of Projects (1 - 10)", value=3), | |
| gr.Number(label="Average Monthly Hours (80 - 320)", value=150), | |
| gr.Number(label="Time Spent at Company (Years)", value=3), | |
| 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.Dropdown(choices=[0, 1, 2], label="Salary Level (0 = Low, 1 = Medium, 2 = High)"), | |
| gr.Dropdown( | |
| choices=["Sales", "Technical", "Support", "IT", "Management", | |
| "Product Management", "Marketing", "HR", "Accounting", "R&D"], | |
| label="Department" | |
| ) | |
| ], | |
| outputs="text", | |
| title="🚀 Employee Retention Prediction System", | |
| description="Predict whether an employee is likely to stay or quit based on their profile.", | |
| live=False | |
| ) | |
| # Launch Gradio app | |
| interface.launch() | |