"""
Feedback Loop System
Author: AI Generated
Created: 2025-11-24
Purpose: Collect feedback metrics to improve AI models over time
"""

from datetime import datetime
from typing import Dict, Optional
from bson import ObjectId

from database import db


class FeedbackCollector:
    """
    Collect feedback on AI outputs for continuous improvement.
    """
    
    def __init__(self):
        self.collection = "AIFeedback"
    
    def record_email_engagement(self, 
                                segment_id: str,
                                user_id: str,
                                opened: bool = False,
                                clicked: bool = False,
                                converted: bool = False,
                                unsubscribed: bool = False):
        """
        Record email engagement metrics.
        Used to evaluate email generation quality.
        """
        doc = {
            "feedback_type": "email_engagement",
            "segment_id": ObjectId(segment_id),
            "user_id": ObjectId(user_id),
            "opened": opened,
            "clicked": clicked,
            "converted": converted,
            "unsubscribed": unsubscribed,
            "timestamp": datetime.utcnow()
        }
        
        db.get_collection(self.collection).insert_one(doc)
    
    def record_sentiment_correction(self,
                                   analysis_id: str,
                                   original_label: str,
                                   corrected_label: str,
                                   corrected_by: str):
        """
        Record manual corrections to sentiment analysis.
        Used to fine-tune PhoBERT.
        """
        doc = {
            "feedback_type": "sentiment_correction",
            "analysis_id": ObjectId(analysis_id),
            "original_label": original_label,
            "corrected_label": corrected_label,
            "corrected_by": corrected_by,
            "timestamp": datetime.utcnow()
        }
        
        db.get_collection(self.collection).insert_one(doc)
    
    def record_segment_feedback(self,
                                segment_id: str,
                                user_id: str,
                                interaction_type: str,
                                value: Optional[float] = None):
        """
        Record user interactions with segment-targeted campaigns.
        
        interaction_type: 'purchase', 'view', 'ignore', etc.
        value: revenue/engagement metric
        """
        doc = {
            "feedback_type": "segment_interaction",
            "segment_id": ObjectId(segment_id),
            "user_id": ObjectId(user_id),
            "interaction_type": interaction_type,
            "value": value,
            "timestamp": datetime.utcnow()
        }
        
        db.get_collection(self.collection).insert_one(doc)
    
    def record_insight_usefulness(self,
                                  insight_report_id: str,
                                  user_id: str,
                                  rating: int,
                                  implemented: bool = False):
        """
        Record how useful an insight report was.
        rating: 1-5 stars
        """
        doc = {
            "feedback_type": "insight_rating",
            "insight_report_id": ObjectId(insight_report_id),
            "user_id": user_id,
            "rating": rating,
            "implemented": implemented,
            "timestamp": datetime.utcnow()
        }
        
        db.get_collection(self.collection).insert_one(doc)
    
    def get_email_performance(self, segment_id: str) -> Dict:
        """
        Get aggregated email performance for a segment.
        """
        pipeline = [
            {
                "$match": {
                    "feedback_type": "email_engagement",
                    "segment_id": ObjectId(segment_id)
                }
            },
            {
                "$group": {
                    "_id": None,
                    "total_sent": {"$sum": 1},
                    "opened": {"$sum": {"$cond": ["$opened", 1, 0]}},
                    "clicked": {"$sum": {"$cond": ["$clicked", 1, 0]}},
                    "converted": {"$sum": {"$cond": ["$converted", 1, 0]}},
                    "unsubscribed": {"$sum": {"$cond": ["$unsubscribed", 1, 0]}}
                }
            }
        ]
        
        results = list(db.get_collection(self.collection).aggregate(pipeline))
        
        if not results:
            return {"error": "No data"}
        
        data = results[0]
        total = data["total_sent"]
        
        return {
            "total_sent": total,
            "open_rate": data["opened"] / total if total > 0 else 0,
            "click_rate": data["clicked"] / total if total > 0 else 0,
            "conversion_rate": data["converted"] / total if total > 0 else 0,
            "unsubscribe_rate": data["unsubscribed"] / total if total > 0 else 0
        }
    
    def get_sentiment_accuracy(self) -> Dict:
        """
        Calculate sentiment analysis accuracy based on corrections.
        """
        corrections = list(db.get_collection(self.collection).find({
            "feedback_type": "sentiment_correction"
        }))
        
        if not corrections:
            return {"error": "No corrections recorded"}
        
        total = len(corrections)
        correct = sum(1 for c in corrections if c["original_label"] == c["corrected_label"])
        
        accuracy = correct / total
        
        # Breakdown by label
        by_label = {}
        for c in corrections:
            label = c["original_label"]
            if label not in by_label:
                by_label[label] = {"total": 0, "correct": 0}
            by_label[label]["total"] += 1
            if c["original_label"] == c["corrected_label"]:
                by_label[label]["correct"] += 1
        
        for label in by_label:
            data = by_label[label]
            by_label[label]["accuracy"] = data["correct"] / data["total"]
        
        return {
            "overall_accuracy": accuracy,
            "total_corrections": total,
            "by_label": by_label
        }
    
    def get_retaining_dataset(self) -> tuple:
        """
        Get dataset for retraining sentiment model from corrections.
        Returns: (texts, labels)
        """
        corrections = list(db.get_collection(self.collection).find({
            "feedback_type": "sentiment_correction"
        }))
        
        # Fetch original texts
        analysis_ids = [c["analysis_id"] for c in corrections]
        analyses = {
            str(a["_id"]): a 
            for a in db.sentiment_results.find({"_id": {"$in": analysis_ids}})
        }
        
        # Get comment texts
        source_ids = [analyses[str(c["analysis_id"])]["source_id"] for c in corrections if str(c["analysis_id"]) in analyses]
        comments = {
            str(c["_id"]): c.get("CommentText", "")
            for c in db.user_comment_post.find({"_id": {"$in": source_ids}})
        }
        
        # Build training data
        texts = []
        labels = []
        
        for c in corrections:
            analysis_id_str = str(c["analysis_id"])
            if analysis_id_str in analyses:
                source_id_str = str(analyses[analysis_id_str]["source_id"])
                if source_id_str in comments:
                    texts.append(comments[source_id_str])
                    labels.append(c["corrected_label"])
        
        print(f"✓ Built retraining dataset: {len(texts)} samples")
        return texts, labels


# Global feedback collector
feedback = FeedbackCollector()