# predict_model.py

import pandas as pd
import joblib
# predict_model.py

import pandas as pd
import joblib

class InsuranceClaimPredictor:
    def __init__(self, model_path):
        self.model_path = model_path
        self.model = self.load_model()

    def load_model(self):
        # Load the model
        loaded_model = joblib.load(self.model_path)
        return loaded_model

    def predict(self, data):
        # Make predictions
        predictions = self.model.predict(data)
        return predictions


if __name__ == "__main__":
    predictor = InsuranceClaimPredictor('model/insurance_claim_prediction_model.joblib')

    # # Example of a person who is less likely to make an insurance claim
    # unseen_data_non_claim = pd.DataFrame({
    #     'age': [25],          # Younger age
    #     'sex': ['female'],    # Female (just an example, gender may not significantly affect the outcome)
    #     'bmi': [22.0],        # Lower BMI   
    #     'children': [0],      # No children
    #     'smoker': ['no'],     # Non-smoker
    #     'region': ['southwest'], # Region (doesn't typically affect claims, chosen arbitrarily)
    #     'charges': [1000]     # Lower medical expenses
    # })
    
    # predictions = predictor.predict(unseen_data_non_claim)
    # print("Predictions for the unseen data:", predictions)

    #Example of how to use the function
    unseen_data = pd.DataFrame({
        'age': [40],
        'sex': ['male'],
        'bmi': [25.3],
        'children': [2],
        'smoker': ['no'],
        'region': ['southeast'],
        'charges': [2900]
    })

    predictions = predictor.predict(unseen_data)
    print("Predictions for the unseen data:", predictions)