from src.entity.config_entity import DataValidationConfig
from src.entity.artifact_entity import DataValidationArtifact,DataIngestionArtifact
from src.utils.main_utils import read_yaml_file
from src.constants import SCHEMA_FILE_PATH
from src.exception import MyException
import sys
import pandas as pd
import logging
import os
import json
from src.utils.main_utils import write_yaml_file
class DataValidation:
    def __init__(self):
        pass

    async def init_config(self,data_validation_config:DataValidationConfig,data_ingestion_artifact:DataIngestionArtifact=None):  
        try:
            logging.info("Initialising init_config in DataValidatin")
            self.data_validation_config=data_validation_config
            self.data_ingestion_artifact=data_ingestion_artifact
            self._schema_config=await read_yaml_file(SCHEMA_FILE_PATH)
        except Exception as e:
            raise MyException(e,sys)    

    async def validate_number_of_columns(self,dataframe:pd.DataFrame)->bool:
        try:
            status=len(dataframe.columns)==len(self._schema_config['columns'])
            logging.info(f"Is required colummn present: [{status}]")
            return status
        except Exception as e:
            raise MyException(e,sys)

    async def is_column_exists(self,dataframe:pd.DataFrame)->bool:
        try:
            missing_numerical_columns=[]
            missing_categorical_columns=[]
            to_check_col=dataframe.columns
            for col in self._schema_config['numerical_columns']:
                if col not in to_check_col:
                    missing_numerical_columns.append(col)

            for col in self._schema_config['categorical_columns']:
                if col not in to_check_col:
                    missing_categorical_columns.append(col)


            if len(missing_categorical_columns)>0:
                logging.info(f"Missing categorical columns: {missing_categorical_columns}")

            if len(missing_numerical_columns)>0:
                logging.info(f"Missing numerical columns: {missing_numerical_columns}")

            return False if len(missing_numerical_columns) or len(missing_categorical_columns) else True
        except Exception as e:
            raise MyException(e,sys)

    @staticmethod
    async def read_data(file_path:str)->pd.DataFrame:
        try:
            return pd.read_csv(file_path)
        except Exception as e:
            raise MyException(e,sys)  

    async def initiate_data_validation(self,)->DataValidationArtifact:
        try:
            validation_error_msg=None
            logging.info("Starting data validation")
            train_df,test_df=(await DataValidation.read_data(file_path=self.data_ingestion_artifact.test_file_path),
                              await DataValidation.read_data(self.data_ingestion_artifact.trained_file_path))


            # Train_df
            logging.info("Checking validate_number_of_columns training columns")
            status = await self.validate_number_of_columns(dataframe=train_df)  
            if not status:
                validation_error_msg+="Columns are missing in training dataframe.",sys
            logging.info(f"All required columns present in train dataframe: {status}")


            logging.info("Checking is_column_exists")
            status = await self.is_column_exists(dataframe=train_df)  
            if not status:
                validation_error_msg+="Columns are missing in training dataframe.",sys
            logging.info(f"All required columns present in train dataframe: {status}")
            

            # Test_df
            logging.info("Checking validate_number_of_columns testing columns")
            status = await self.validate_number_of_columns(dataframe=test_df)  
            if not status:
                validation_error_msg+="Columns are missing in testing dataframe.",sys
            logging.info(f"All required columns present in test dataframe: {status}")


            logging.info("Checking is_column_exists testing columns")
            status = await self.is_column_exists(dataframe=train_df)  
            if not status:
                validation_error_msg+="Columns are missing in testing dataframe.",sys
            logging.info(f"All required columns present in test dataframe: {status}")
            

            
            data_validation_artifact = DataValidationArtifact(
                validation_status=validation_error_msg==None,
                message=validation_error_msg,
                validation_report_file_path=self.data_validation_config.validation_report_file_path
            )

            # Ensure the directory for validation_report_file_path exists
            report_dir = os.path.dirname(self.data_validation_config.validation_report_file_path)
            os.makedirs(report_dir, exist_ok=True)

            await write_yaml_file(file_path=self.data_validation_config.validation_report_file_path,content=data_validation_artifact)
            

            logging.info("Data validation artifact created and saved to JSON file.")
            logging.info(f"Data validation artifact: {data_validation_artifact}")
            return data_validation_artifact
        except Exception as e:
            raise MyException(e,sys)