import yaml 
import pandas as pd
from src.exception.exception import NetworkSecurityException
from src.logging.logger import logging
import os, sys
import numpy as np 
import pickle
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import r2_score

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:
        raise NetworkSecurityException(e,sys) from e
    
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 NetworkSecurityException(e, sys)
    
def save_np_array(file_path:str, array:np.array):
    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 NetworkSecurityException(e, sys) from e
    
def save_object(file_path:str, obj:object) -> None:
    try:
        os.makedirs(os.path.dirname(file_path), exist_ok=True)
        with open(file_path, "wb") as file_obj:
            pickle.dump(obj, file_obj)
    except Exception as e:
        raise NetworkSecurityException(e, sys) from e
    
def load_object(file_path:str)-> object:
    try:
        if not os.path.exists(file_path):
            raise Exception(f"The file: {file_path} is not exists")
        with open(file_path, "rb") as file_obj: 
            print(file_obj)
            return pickle.load(file_obj)
    except Exception as e:
            raise NetworkSecurityException(e, sys) from e
        
def load_numpy_array_data(file_path:str) -> np.array:
    try:
        with open(file_path, "rb") as file_obj:
            return np.load(file_obj)
    except Exception as e:
        raise NetworkSecurityException(e, sys) from e
    
    
def evaluate_models(x_train, y_train, x_test, y_test, models, params):
    try:
        report = {}
        for i in range(len(list(models))):
            model = list(models.values())[i]
            param = params[list(models.keys())[i]]
            grid = GridSearchCV(model, param, cv=3)
            grid.fit(x_train, y_train)
            model.set_params(**grid.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 NetworkSecurityException(e, sys) from e