"""
FEEDBACK LEARNING SYSTEM
Learns from user feedback to improve future responses
"""

import json
import os
from typing import Dict, List, Optional, Any
from datetime import datetime
from collections import defaultdict
import logging

logger = logging.getLogger(__name__)

class FeedbackLearner:
    """Learns from user feedback to improve responses"""
    
    def __init__(self):
        self.feedback_data = defaultdict(list)
        self.response_quality = {}
        self.handler_feedback = defaultdict(lambda: {'positive': 0, 'negative': 0, 'total': 0})
        self.learned_improvements = {}
        self.load_feedback_history()
    
    def record_feedback(self, session_id: str, message_id: str, handler: str, 
                       response: str, feedback_type: str, rating: Optional[int] = None,
                       user_comment: str = '', corrections: Optional[str] = None) -> Dict:
        """
        Record user feedback on a response
        
        Args:
            session_id: Session ID
            message_id: ID of the message/response
            handler: Handler that generated response (code, knowledge, conversation, etc.)
            response: The response text
            feedback_type: 'positive', 'negative', 'neutral'
            rating: Rating from 1-5 (optional)
            user_comment: User's comment (optional)
            corrections: Corrected version if user provided one (optional)
        """
        
        feedback_record = {
            'timestamp': datetime.now().isoformat(),
            'session_id': session_id,
            'message_id': message_id,
            'handler': handler,
            'response': response[:500],  # Store first 500 chars
            'feedback_type': feedback_type,
            'rating': rating,
            'comment': user_comment,
            'corrections': corrections
        }
        
        # Store feedback
        self.feedback_data[handler].append(feedback_record)
        
        # Update statistics
        self.handler_feedback[handler]['total'] += 1
        if feedback_type == 'positive':
            self.handler_feedback[handler]['positive'] += 1
        elif feedback_type == 'negative':
            self.handler_feedback[handler]['negative'] += 1
        
        # Learn from feedback
        self._learn_from_feedback(handler, feedback_record)
        
        logger.info(f"Feedback recorded: {handler} - {feedback_type} - Rating: {rating}")
        
        return feedback_record
    
    def _learn_from_feedback(self, handler: str, feedback: Dict):
        """Extract learning from feedback"""
        
        if handler not in self.learned_improvements:
            self.learned_improvements[handler] = {
                'improvements': [],
                'patterns': [],
                'avoid_patterns': []
            }
        
        # Positive feedback
        if feedback['feedback_type'] == 'positive':
            # Store what made a good response
            self.learned_improvements[handler]['patterns'].append({
                'response_snippet': feedback['response'][:100],
                'rating': feedback['rating'],
                'timestamp': feedback['timestamp']
            })
        
        # Negative feedback
        elif feedback['feedback_type'] == 'negative':
            # Store what to avoid
            self.learned_improvements[handler]['avoid_patterns'].append({
                'bad_response': feedback['response'][:100],
                'correction': feedback['corrections'],
                'comment': feedback['comment'],
                'rating': feedback['rating'],
                'timestamp': feedback['timestamp']
            })
        
        # Learn from corrections
        if feedback['corrections']:
            self.learned_improvements[handler]['improvements'].append({
                'wrong': feedback['response'][:100],
                'correct': feedback['corrections'],
                'handler': handler,
                'timestamp': feedback['timestamp']
            })
    
    def get_handler_quality_score(self, handler: str) -> Dict:
        """Get quality score for a handler based on feedback"""
        
        feedback = self.handler_feedback[handler]
        
        if feedback['total'] == 0:
            return {
                'handler': handler,
                'quality_score': 0.5,
                'total_feedback': 0,
                'positive_percentage': 0.0,
                'rating': 'Unknown'
            }
        
        positive_pct = feedback['positive'] / feedback['total'] * 100
        
        quality_score = (feedback['positive'] - feedback['negative']) / feedback['total']
        quality_score = (quality_score + 1) / 2  # Normalize to 0-1
        
        rating_map = {
            (0.8, 1.0): 'Excellent',
            (0.6, 0.8): 'Good',
            (0.4, 0.6): 'Fair',
            (0.2, 0.4): 'Poor',
            (0.0, 0.2): 'Very Poor'
        }
        
        rating = 'Unknown'
        for (min_val, max_val), rating_name in rating_map.items():
            if min_val <= quality_score < max_val:
                rating = rating_name
                break
        
        return {
            'handler': handler,
            'quality_score': round(quality_score, 3),
            'total_feedback': feedback['total'],
            'positive': feedback['positive'],
            'negative': feedback['negative'],
            'positive_percentage': round(positive_pct, 1),
            'rating': rating
        }
    
    def get_improvement_suggestions(self, handler: str) -> Dict:
        """Get specific improvement suggestions for a handler"""
        
        if handler not in self.learned_improvements:
            return {'handler': handler, 'suggestions': []}
        
        improvements = self.learned_improvements[handler]
        suggestions = []
        
        # Suggest based on avoid patterns
        if improvements['avoid_patterns']:
            most_common_issue = max(
                improvements['avoid_patterns'],
                key=lambda x: len(x.get('comment', ''))
            )
            suggestions.append({
                'type': 'avoid',
                'issue': most_common_issue['bad_response'],
                'fix': most_common_issue['correction'],
                'frequency': len(improvements['avoid_patterns'])
            })
        
        # Suggest based on corrections
        if improvements['improvements']:
            suggestions.append({
                'type': 'common_mistake',
                'count': len(improvements['improvements']),
                'examples': improvements['improvements'][:3]
            })
        
        return {
            'handler': handler,
            'suggestions': suggestions,
            'total_improvements_learned': len(improvements['improvements']),
            'confidence': len(improvements['patterns']) / max(1, len(improvements['patterns']) + len(improvements['avoid_patterns']))
        }
    
    def apply_learned_improvements(self, handler: str, response: str) -> Dict:
        """Apply learned improvements to a response"""
        
        if handler not in self.learned_improvements:
            return {'original': response, 'improved': response, 'applied': []}
        
        improved = response
        applied = []
        
        improvements = self.learned_improvements[handler]['improvements']
        
        # Try to apply learned corrections
        for improvement in improvements:
            if improvement['wrong'] in response:
                improved = improved.replace(
                    improvement['wrong'],
                    improvement['correct']
                )
                applied.append({
                    'from': improvement['wrong'],
                    'to': improvement['correct']
                })
        
        return {
            'original': response,
            'improved': improved,
            'applied': applied,
            'modified': len(applied) > 0
        }
    
    def get_feedback_summary(self) -> Dict:
        """Get overall feedback summary"""
        
        summary = {
            'total_feedback_records': sum(len(v) for v in self.feedback_data.values()),
            'handlers_evaluated': len(self.handler_feedback),
            'handler_scores': {},
            'overall_quality': 0.0,
            'most_improved_handler': None,
            'most_problematic_handler': None
        }
        
        # Calculate scores for each handler
        quality_scores = []
        for handler in self.handler_feedback.keys():
            score = self.get_handler_quality_score(handler)
            summary['handler_scores'][handler] = score
            quality_scores.append((handler, score['quality_score']))
        
        if quality_scores:
            summary['overall_quality'] = sum(s[1] for s in quality_scores) / len(quality_scores)
            summary['most_improved_handler'] = max(quality_scores, key=lambda x: x[1])[0]
            summary['most_problematic_handler'] = min(quality_scores, key=lambda x: x[1])[0]
        
        return summary
    
    def save_feedback_history(self):
        """Save feedback history to file"""
        try:
            os.makedirs('noahski_data', exist_ok=True)
            
            feedback_file = 'noahski_data/user_feedback.json'
            
            # Convert defaultdict to regular dict for JSON serialization
            feedback_dict = {
                'feedback': {k: v for k, v in self.feedback_data.items()},
                'handler_stats': {k: dict(v) for k, v in self.handler_feedback.items()},
                'learned_improvements': self.learned_improvements,
                'timestamp': datetime.now().isoformat()
            }
            
            with open(feedback_file, 'w', encoding='utf-8') as f:
                json.dump(feedback_dict, f, indent=2, ensure_ascii=False)
            
            logger.info(f"Feedback history saved: {feedback_file}")
        except Exception as e:
            logger.error(f"Error saving feedback history: {e}")
    
    def load_feedback_history(self):
        """Load feedback history from file"""
        try:
            feedback_file = 'noahski_data/user_feedback.json'
            if os.path.exists(feedback_file):
                with open(feedback_file, 'r', encoding='utf-8') as f:
                    data = json.load(f)
                
                self.feedback_data = defaultdict(list, data.get('feedback', {}))
                
                # Reconstruct handler_feedback
                for handler, stats in data.get('handler_stats', {}).items():
                    self.handler_feedback[handler] = stats
                
                self.learned_improvements = data.get('learned_improvements', {})
                
                logger.info(f"Feedback history loaded: {feedback_file}")
        except Exception as e:
            logger.error(f"Error loading feedback history: {e}")


# Global instance
_feedback_learner = None

def get_feedback_learner() -> FeedbackLearner:
    """Get or create global feedback learner"""
    global _feedback_learner
    if _feedback_learner is None:
        _feedback_learner = FeedbackLearner()
    return _feedback_learner