"""
ImproveResponseAccuracy
Generated by Eden via recursive self-improvement
2025-11-01 12:49:11.409126
"""

class ImproveResponseAccuracy:
    def __init__(self):
        self._knowledge_base = {}
        self._uncertainty_threshold = 0.15

    def update_knowledge(self, context, response, feedback):
        """Update internal knowledge base based on user feedback."""
        if feedback == 'positive':
            self._knowledge_base[context] = {'response': response, 'confidence': 1}
        elif feedback == 'negative':
            del self._knowledge_base[context]

    def respond(self, user_input):
        """Generate a response and request clarification if uncertain."""
        context = self._extract_context(user_input)
        confidence = self._calculate_confidence(context)

        # If the system is unsure about the answer
        if confidence < self._uncertainty_threshold:
            clarification_question = f"Can you please provide more details on {context}?"
            return clarification_question

        # Retrieve and return a confident response
        if context in self._knowledge_base:
            return self._knowledge_base[context]['response']
        else:
            default_response = "I'm not sure about that. Can you give me some more information?"
            return default_response

    def _extract_context(self, user_input):
        # Placeholder function to extract relevant context from the input
        pass

    def _calculate_confidence(self, context):
        # Placeholder function to calculate confidence level based on available knowledge
        if context in self._knowledge_base:
            return 1.0 - len(self._knowledge_base) / (len(self._knowledge_base) + 1)
        else:
            return 0

# Example usage
if __name__ == "__main__":
    accuracy_improver = ImproveResponseAccuracy()
    
    # Simulate a user interaction
    user_input = "What is the capital of France?"
    response = accuracy_improver.respond(user_input)
    print(response)  # This might prompt for clarification if there's uncertainty
    
    # Update knowledge based on feedback
    user_feedback = 'positive'  # Example positive feedback
    context_info = "capital of France"
    accuracy_improver.update_knowledge(context_info, "Paris", user_feedback)