"""
Employee Growth Score Prediction - Inference Script
=====================================================
Load the trained model and predict growth scores for new employees.
"""
import joblib
import numpy as np

# Load model
model = joblib.load("employee_growth_model.joblib")

# Feature columns (in order):
# Age, BusinessTravel_Num, DailyRate, Department_Num, DistanceFromHome,
# EnvironmentSatisfaction, JobInvolvement, JobLevel, JobRole_Num,
# JobSatisfaction, MaritalStatus_Num, MonthlyIncome, TotalWorkingYears,
# YearsAtCompany, YearsInCurrentRole, YearsSinceLastPromotion, YearsWithCurrManager

# Example: Predict for a new employee
new_employee = np.array([[
    30,    # Age
    2,     # BusinessTravel_Num (1=No, 2=Travel_Rarely, 3=Travel_Frequently)
    800,   # DailyRate
    2,     # Department_Num
    5,     # DistanceFromHome
    3,     # EnvironmentSatisfaction (1-4)
    3,     # JobInvolvement (1-4)
    2,     # JobLevel (1-5)
    5,     # JobRole_Num
    3,     # JobSatisfaction (1-4)
    1,     # MaritalStatus_Num
    5000,  # MonthlyIncome
    8,     # TotalWorkingYears
    5,     # YearsAtCompany
    3,     # YearsInCurrentRole
    1,     # YearsSinceLastPromotion
    3,     # YearsWithCurrManager
]])

growth_score = model.predict(new_employee)
print(f"Predicted Growth Score: {growth_score[0]:.2f} / 100")

if growth_score[0] >= 75:
    print("Category: HIGH GROWTH potential")
elif growth_score[0] >= 50:
    print("Category: MODERATE GROWTH potential")
elif growth_score[0] >= 25:
    print("Category: LOW GROWTH potential")
else:
    print("Category: AT RISK - needs development support")