import os
import sys

import numpy as np 
import pandas as pd
import dill
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV

from src.exception import CustomException

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,param):
    try:
        report={}
        
        for i in range(len(list(models))):
            model=list(models.values())[i]
            para=param[list(models.keys())[i]]
            
            gs = GridSearchCV(model,para,cv=3)
            gs.fit(X_train,y_train)
            
            model.set_params(**gs.best_params_)
            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)