# explainable_ai.py

from NeuroFlex.utils import utils

class ExplainableAI:
    def __init__(self):
        self.model = None
        self.explanations = {}

    def set_model(self, model):
        self.model = model

    def explain_prediction(self, input_data):
        if self.model is None:
            raise ValueError("Model not set. Please set a model using set_model() method.")

        prediction = self.model.predict(input_data)
        explanation = self._generate_explanation(input_data, prediction)
        self.explanations[str(input_data)] = explanation  # Use str(input_data) as key
        return explanation

    def _generate_explanation(self, input_data, prediction):
        # Placeholder for explanation generation logic
        # This should be implemented based on the specific explainable AI technique being used
        return f"Explanation for prediction {prediction} based on input {input_data}"

    def get_feature_importance(self):
        # Placeholder for feature importance calculation
        # This should be implemented based on the specific model and explainable AI technique
        return {"feature1": 0.5, "feature2": 0.3, "feature3": 0.2}

    def visualize_explanation(self, explanation):
        # Placeholder for visualization logic
        # This should be implemented to create visual representations of explanations
        print(f"Visualization of explanation: {explanation}")

# Example usage:
# explainable_model = ExplainableAI()
# explainable_model.set_model(some_ml_model)
# explanation = explainable_model.explain_prediction(some_input_data)
# explainable_model.visualize_explanation(explanation)

# TODO: Implement specific explainable AI techniques (e.g., LIME, SHAP, etc.)
# TODO: Add more sophisticated explanation generation and visualization methods
