import os
import sys
from src.exception import CropException
from src.logger import logging
from datetime import datetime

FILE_NAME = "crop.csv"
TRAIN_FILE_NAME = "train.csv"
TEST_FILE_NAME = "test.csv"
TRANSFORMER_OBJECT_FILE_NAME = "transformer.pkl"
TARGET_ENCODER_OBJECT_FILE_NAME = "target_encoder.pkl"
MODEL_FILE_NAME = "model.pkl"


class TrainingPipelineConfig:
    def __init__(self):
        try:
            self.artifact_dir = os.path.join(
                os.getcwd(), "artifact", f"{datetime.now().strftime('%m%d%Y__%H%M%S')}"
            )
        except Exception as e:
            raise CropException(e, sys)


class DataIngestionConfig:
    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
        try:
            self.database_name = "smartcropguard"
            self.collection_name = "crop"
            self.data_ingestion_dir = os.path.join(
                training_pipeline_config.artifact_dir, "data_ingestion"
            )
            self.feature_store_file_path = os.path.join(
                self.data_ingestion_dir, "feature_store", FILE_NAME
            )
            self.train_file_path = os.path.join(
                self.data_ingestion_dir, "dataset", TRAIN_FILE_NAME
            )
            self.test_file_path = os.path.join(
                self.data_ingestion_dir, "dataset", TEST_FILE_NAME
            )
            self.test_size = 0.2
        except Exception as e:
            raise CropException(e, sys)

    def to_dict(self) -> dict:
        try:
            return self.__dict__
        except Exception as e:
            raise CropException(e, sys)


class DataValidationConfig:
    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
        self.data_validation_dir = os.path.join(
            training_pipeline_config.artifact_dir, "data_validation"
        )
        self.report_file_path = os.path.join(self.data_validation_dir, "report.yaml")
        self.missing_threshold = 0.2
        self.base_file_path = os.path.join(
            "crop-recommendation-dataset/Crop_recommendation.csv"
        )


class DataTransformationConfig:
    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
        self.data_transformation_dir = os.path.join(
            training_pipeline_config.artifact_dir, "data_transformation"
        )
        self.transform_object_path = os.path.join(
            self.data_transformation_dir, 
            "transformer", 
            TRANSFORMER_OBJECT_FILE_NAME
        )
        self.transformed_train_path = os.path.join(
            self.data_transformation_dir,
            "transformed",
            TRAIN_FILE_NAME.replace("csv", "npz"),
        )
        self.transformed_test_path = os.path.join(
            self.data_transformation_dir,
            "transformed",
            TEST_FILE_NAME.replace("csv", "npz"),
        )
        self.target_encoder_path = os.path.join(
            self.data_transformation_dir,
            "target_encoder",
            TARGET_ENCODER_OBJECT_FILE_NAME,
        )


class ModelTrainerConfig:
    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
        self.model_trainer_dir = os.path.join(
            training_pipeline_config.artifact_dir, "model_trainer"
        )
        self.model_path = os.path.join(self.model_trainer_dir, "model", MODEL_FILE_NAME)
        self.expected_score = 0.9
        self.overfitting_threshold = 0.1


class ModelEvaluationConfig:
    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
        self.change_threshold = 0.01


class ModelPusherConfig:
    def __init__(self, training_pipeline_config: TrainingPipelineConfig):
        self.model_pusher_dir = os.path.join(
            training_pipeline_config.artifact_dir, "model_pusher"
        )
        self.saved_model_dir = os.path.join("saved_models")
        self.pusher_model_dir = os.path.join(self.model_pusher_dir, "saved_models")
        self.pusher_model_path = os.path.join(self.pusher_model_dir, MODEL_FILE_NAME)
        self.pusher_transformer_path = os.path.join(
            self.pusher_model_dir, TRANSFORMER_OBJECT_FILE_NAME
        )
        self.pusher_target_encoder_path = os.path.join(
            self.pusher_model_dir, TARGET_ENCODER_OBJECT_FILE_NAME
        )