src/cnnClassfier/config/configuration.py

import os
from cnnClassfier.constants import *
from cnnClassfier.utils.common import read_yaml, create_directories
from cnnClassfier.entity.config_entity import (DataIngestionConfig,
                                               PrepareBaseModelConfig,
                                               PrepareCallbacksConfig,
                                               TrainingConfig,
                                               EvaluationConfig)


class ConfigurationManager:
    def __init__(
        self,
        config_filepath = CONFIG_FILE_PATH,
        params_filepath = PARAMS_FILE_PATH):

        self.config = read_yaml(config_filepath)
        self.params = read_yaml(params_filepath)

        create_directories([self.config.artifacts_root])


    
    def get_data_ingestion_config(self) -> DataIngestionConfig:
        config = self.config.data_ingestion

        create_directories([config.root_dir])

        data_ingestion_config = DataIngestionConfig(
            root_dir=config.root_dir,
            source_URL=config.source_URL,
            local_data_file=config.local_data_file,
            unzip_dir=config.unzip_dir 
        )

        return data_ingestion_config
      
      
      
      
    def get_prepare_base_model(self) -> PrepareBaseModelConfig:
        config = self.config.prepare_base_model
        
        create_directories([config.root_dir])
        
        prepare_base_model_config = PrepareBaseModelConfig(
            root_dir=Path(config.root_dir),
            base_model_path= Path(config.base_model_path),
            updated_base_model_path= Path(config.updated_base_model_path),
            params_image_size=self.params.IMAZE_SIZE,
            params_learning_rate=self.params.LEARNING_RATE,
            params_include_top=self.params.INCLUDE_TOP,
            params_weights=self.params.WEIGHTS,
            params_classes=self.params.CLASSES
        )
        
        return prepare_base_model_config
    
    
    def get_prepare_callback_config(self) -> PrepareCallbacksConfig:
        config = self.config.prepare_callbacks
        model_ckpt_dir = os.path.dirname(config.checkpoint_model_filepath)
        create_directories([
            Path(model_ckpt_dir),
            Path(config.tensorboard_root_log_dir)
        ])

        prepare_callback_config = PrepareCallbacksConfig(
            root_dir=Path(config.root_dir),
            tensorboard_root_log_dir=Path(config.tensorboard_root_log_dir),
            checkpoint_model_filepath=Path(config.checkpoint_model_filepath)
        )

        return prepare_callback_config
    
    def get_training_config(self) -> TrainingConfig:
        training = self.config.training
        prepare_base_model = self.config.prepare_base_model
        params = self.params
        training_data = os.path.join(self.config.data_ingestion.unzip_dir, "Chicken-fecal-images")
        create_directories([
            Path(training.root_dir)
        ])

        training_config = TrainingConfig(
            root_dir=Path(training.root_dir),
            trained_model_path=Path(training.trained_model_path),
            updated_base_model_path=Path(prepare_base_model.updated_base_model_path),
            training_data=Path(training_data),
            params_epochs=params.EPOCHS,
            params_batch_size=params.BATCH_SIZE,
            params_is_augmentation=params.AUGMENTATION,
            params_image_size=params.IMAZE_SIZE
        )

        return training_config
    
    
    def get_validation_config(self) -> EvaluationConfig:
        eval_config = EvaluationConfig(
            path_of_model="artifacts/training/model.h5",
            training_data="artifacts/data_ingestion/Chicken-fecal-images",
            all_params=self.params,
            params_image_size=self.params.IMAZE_SIZE,
            params_batch_size=self.params.BATCH_SIZE
        )
        return eval_config