from abc import abstractmethod
from src.config.logging import logger
from abc import ABC

class Model(ABC):
    """
    The Model abstract class defines a method for accepting visitors.
    Concrete implementations will handle specific visitor interactions.
    """
    @abstractmethod
    def accept(self, visitor: 'Visitor') -> None:
        raise NotImplementedError("Subclasses must implement the `accept` method.")


class ClassificationModel(Model):
    """
    ClassificationModel represents a specific type of AI model.
    It accepts visitors that perform operations like model explanation.
    """
    def accept(self, visitor: 'Visitor') -> None:
        logger.info(f'{self.__class__.__name__}: Accepting visitor {visitor.__class__.__name__}')
        visitor.visit_classification_model(self)


class Visitor(ABC):
    """
    The Visitor abstract class defines a method for visiting a ClassificationModel.
    Concrete visitors will implement specific logic for interacting with the model.
    """
    @abstractmethod
    def visit_classification_model(self, model: ClassificationModel) -> None:
        raise NotImplementedError("Subclasses must implement the `visit_classification_model` method.")


class SHAPVisitor(Visitor):
    """
    SHAPVisitor applies SHAP (SHapley Additive exPlanations) to explain the predictions
    of a classification model.
    """
    def visit_classification_model(self, model: ClassificationModel) -> None:
        logger.info(f'{self.__class__.__name__}: Visiting {model.__class__.__name__}')
        logger.info('Applying SHAP to explain model predictions')
        # Add SHAP-specific implementation here
        self.apply_shap(model)

    def apply_shap(self, model: ClassificationModel) -> None:
        logger.info('SHAP explanation applied successfully.')
        # Placeholder for SHAP logic


class LIMEVisitor(Visitor):
    """
    LIMEVisitor applies LIME (Local Interpretable Model-agnostic Explanations) to explain
    the predictions of a classification model.
    """
    def visit_classification_model(self, model: ClassificationModel) -> None:
        logger.info(f'{self.__class__.__name__}: Visiting {model.__class__.__name__}')
        logger.info('Applying LIME to explain model predictions')
        # Add LIME-specific implementation here
        self.apply_lime(model)

    def apply_lime(self, model: ClassificationModel) -> None:
        logger.info('LIME explanation applied successfully.')
        # Placeholder for LIME logic


if __name__ == "__main__":
    model = ClassificationModel()

    shap_visitor = SHAPVisitor()
    lime_visitor = LIMEVisitor()

    logger.info('Starting SHAP visitor process')
    model.accept(shap_visitor)  # Applying SHAP to explain model predictions

    logger.info('Starting LIME visitor process')
    model.accept(lime_visitor)  # Applying LIME to explain model predictions
