"""
This module manages feedback collection and application for improving AI responses.
"""

from typing import Dict, Any
from ..utils.database import Database

class ImprovedFeedbackManager:
    def __init__(self, database: Database):
        self.database = database
        self.feedback_history = []
        self.adjustment_strategies = {
            "positive": self._apply_positive_feedback,
            "negative": self._apply_negative_feedback,
            "neutral": self._apply_neutral_feedback
        }

    def collect_feedback(self, user_id: int, response_id: int, feedback_type: str, feedback_text: str = "") -> Dict[str, Any]:
        """
        Collect user feedback for a specific response.
        
        Args:
            user_id: The ID of the user providing feedback
            response_id: The ID of the response being rated
            feedback_type: The type of feedback (positive/negative/neutral)
            feedback_text: Optional detailed feedback text
        
        Returns:
            A dictionary containing the processed feedback data
        """
        feedback_data = {
            "user_id": user_id,
            "response_id": response_id,
            "feedback_type": feedback_type,
            "feedback_text": feedback_text,
            "processed": False
        }
        
        self.feedback_history.append(feedback_data)
        return feedback_data

    def adjust_response_based_on_feedback(self, response: str, feedback: Dict[str, Any]) -> str:
        """
        Adjust a response based on previous feedback.
        
        Args:
            response: The current response to adjust
            feedback: The feedback data to use for adjustment
        
        Returns:
            The adjusted response
        """
        feedback_type = feedback.get("feedback_type", "neutral")
        
        if feedback_type in self.adjustment_strategies:
            return self.adjustment_strategies[feedback_type](response, feedback)
        
        return response

    def _apply_positive_feedback(self, response: str, feedback: Dict[str, Any]) -> str:
        """
        Apply adjustments for positive feedback.
        """
        # For positive feedback, maintain similar response patterns
        return response

    def _apply_negative_feedback(self, response: str, feedback: Dict[str, Any]) -> str:
        """
        Apply adjustments for negative feedback.
        """
        # For negative feedback, try to modify the response
        # This is a simple example - in practice, you'd want more sophisticated adjustments
        return f"I understand the previous response wasn't optimal. Let me try again: {response}"

    def _apply_neutral_feedback(self, response: str, feedback: Dict[str, Any]) -> str:
        """
        Apply adjustments for neutral feedback.
        """
        # For neutral feedback, make minor refinements
        return response.strip()

    def get_feedback_stats(self) -> Dict[str, Any]:
        """
        Get statistics about collected feedback.
        
        Returns:
            A dictionary containing feedback statistics
        """
        total = len(self.feedback_history)
        positive = sum(1 for f in self.feedback_history if f["feedback_type"] == "positive")
        negative = sum(1 for f in self.feedback_history if f["feedback_type"] == "negative")
        neutral = total - positive - negative
        
        return {
            "total": total,
            "positive": positive,
            "negative": negative,
            "neutral": neutral,
            "positive_ratio": positive/total if total > 0 else 0,
            "negative_ratio": negative/total if total > 0 else 0
        }

    def clear_feedback_history(self):
        """
        Clear the feedback history.
        """
        self.feedback_history = []