import os
import sys
import numpy as np
import pandas as pd
from projects.ML_StudentPerformance.src.exception import CustomException
from sklearn.metrics import r2_score
import dill

def save_object(file_path, obj):
    try:
        dir_path = os.path.dirname(file_path)

        os.makedirs(dir_path, exist_ok = True)

        with open (file_path, 'wb') as file_obj:
            dill.dump(obj, file_obj)

    except Exception as e:
        raise CustomException(e,sys)

def evaluate_models(x_train, y_train, x_test, y_test, models):
    try:
        report = {}
        for i in range(len(list(models))):
            model = list(models.values())[i]

            model.fit(x_train, y_train)

            y_train_pred = model.predict(x_train)

            y_test_pred = model.predict(x_test)

            train_model_score = r2_score(y_train, y_train_pred)
            test_model_score = r2_score(y_test,y_test_pred)

            report[list(models.keys())[i]] = test_model_score
        
        return report
    except Exception as e:
        raise CustomException(e,sys)
    
def load_object(file_path):
    try:
        with open(file_path, 'rb') as file_obj:
            return dill.load(file_obj)
    except Exception as e:
        raise CustomException(e,sys)