import yaml 
from src.exception.exception import DeliveryTimeException
from src.logging.logger import logging
import os, sys 
import numpy as np 
import pickle 

from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV

def read_yaml_file(file_path:str) -> dict:
    try:
        with open(file_path, 'rb') as yaml_file:
            return yaml.safe_load(yaml_file)

    except Exception as e:
        DeliveryTimeException(e, sys)

def write_yaml_file(file_path:str, content:object, replace:bool=False) -> None:
    try:
        if replace:
            if os.path.exists(file_path):
                os.remove(file_path)
        os.makedirs(os.path.dirname(file_path), exist_ok=True)
        with open(file_path, 'w') as file:
            yaml.dump(content, file)

    except Exception as e:
        raise DeliveryTimeException(e, sys)
    
def save_numpy_array_data(file_path:str, array:np.array):
    """
    Save numpy array data to file 
    file_path: str location of file to save 
    array:np.array data to save
    """
    try:
        dir_path=os.path.dirname(file_path)
        os.makedirs(dir_path, exist_ok=True)
        with open(file_path, 'wb') as file_obj:
            np.save(file_obj, array)
    except Exception as e:
        raise DeliveryTimeException(e, sys)
    
def save_object(file_path:str, obj:object) -> None:
    try:
        logging.info("Entered the save_object method of MainUtils class")
        os.makedirs(os.path.dirname(file_path), exist_ok=True)
        with open(file_path, "wb") as file_obj:
            pickle.dump(obj, file_obj)
        logging.info("Exited the save_object method of MainUtils class")
    except Exception as e:
        raise DeliveryTimeException(e, sys)
    
def load_object(file_path:str) ->object:
    try:
        if not os.path.exists(file_path):
            raise Exception(f"The file: {file_path} does not exist") 
        with open(file_path, 'rb') as file_obj:
            print(file_obj)
            return pickle.load(file_obj)
    except Exception as e:
        raise DeliveryTimeException(e, sys)
    
def load_numpy_array_data(file_path:str) -> np.array:
    """
    Load numpy array data from file
    file_path: str location of file to load
    return: np.array data loaded
    """
    try:
        with open(file_path, 'rb') as file_obj:
            return np.load(file_obj)
    except Exception as e:
        raise DeliveryTimeException(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)
            
            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 DeliveryTimeException(e, sys)