Update src/components/feedback_manager.py

src/components/feedback_manager.py

@@ -1,107 +1,107 @@
-"""
-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.
-        """
+"""
+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 = []