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| import gradio as gr | |
| import xgboost as xgb | |
| import numpy as np | |
| import pandas as pd | |
| 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(): | |
| model_path = "best_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 | |
| def predict_employee_status(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_features = {f"department_{dept}": 0 for dept in departments} | |
| if department in departments: | |
| department_features[f"department_{department}"] = 1 | |
| # Prepare the input with all 18 features as a DataFrame with column names | |
| input_data = { | |
| "satisfaction_level": [satisfaction_level], | |
| "last_evaluation": [last_evaluation], | |
| "number_project": [number_project], | |
| "average_montly_hours": [average_monthly_hours], | |
| "time_spend_company": [time_spent_company], | |
| "Work_accident": [work_accident], | |
| "promotion_last_5years": [promotion_last_5years], | |
| "salary": [salary], | |
| **department_features, | |
| "satisfaction_evaluation": [satisfaction_level * last_evaluation] | |
| } | |
| 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) | |
| return "✅ Employee is likely to quit." if prediction[0] > 0.5 else "✅ Employee is likely to stay." | |
| except Exception as e: | |
| return f"❌ Error: {str(e)}" | |
| # 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 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() | |