#!/usr/bin/env python3
"""
Norda Knowledge Base Service
============================

Builds and maintains a knowledge base for Norda GPT from:
1. Forum topics and replies (public discussions)
2. AI chat responses (assistant messages with positive feedback)

PRIVACY: User questions (role='user') are NEVER added to the knowledge base.
Only AI responses (role='assistant') containing public company facts are included.

Author: Norda Biznes Development Team
Created: 2026-01-28
"""

import logging
from datetime import datetime, timedelta
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from enum import Enum

from sqlalchemy import Column, Integer, String, Text, DateTime, Boolean, ForeignKey, Enum as SQLEnum
from sqlalchemy.orm import relationship

from database import (
    Base,
    SessionLocal,
    ForumTopic,
    ForumReply,
    AIChatMessage,
    AIChatConversation,
    Company
)

logger = logging.getLogger(__name__)


class KnowledgeSourceType(Enum):
    """Source types for knowledge chunks"""
    FORUM_TOPIC = "forum_topic"
    FORUM_REPLY = "forum_reply"
    CHAT_RESPONSE = "chat_response"
    CHAT_QUESTION = "chat_question"  # Anonymized user questions (for trend analysis)
    MANUAL = "manual"  # Manually added by admin


class InsightCategory(Enum):
    """Categories for development insights"""
    FEATURE_REQUEST = "feature_request"  # User wants new feature
    BUG_REPORT = "bug_report"  # Something doesn't work
    IMPROVEMENT = "improvement"  # Enhancement to existing feature
    QUESTION = "question"  # Common question (indicates missing docs/feature)
    PAIN_POINT = "pain_point"  # User frustration
    POSITIVE_FEEDBACK = "positive_feedback"  # What works well
    COMPANY_SEARCH = "company_search"  # What companies users look for
    OTHER = "other"


class NordaKnowledgeChunk(Base):
    """
    Knowledge chunks extracted from various sources.
    Used for:
    1. Norda GPT knowledge base
    2. Development insights for roadmap
    3. User feedback analysis
    """
    __tablename__ = 'norda_knowledge_chunks'

    id = Column(Integer, primary_key=True)

    # Content
    content = Column(Text, nullable=False)
    summary = Column(String(500))  # Short summary for quick reference

    # Source tracking
    source_type = Column(String(50), nullable=False)  # forum_topic, forum_reply, chat_response, chat_question, manual
    source_id = Column(Integer)  # ID in source table
    source_url = Column(String(500))  # URL to original source

    # Metadata
    category = Column(String(100))  # Topic category or detected theme
    keywords = Column(Text)  # Comma-separated keywords for search

    # Development insights (for roadmap)
    insight_category = Column(String(50))  # feature_request, bug_report, improvement, question, pain_point, etc.
    insight_priority = Column(Integer, default=0)  # Higher = more important (based on frequency)
    insight_status = Column(String(50), default='new')  # new, reviewed, planned, implemented, rejected

    # Quality indicators
    is_verified = Column(Boolean, default=False)  # Admin verified
    confidence_score = Column(Integer, default=50)  # 0-100

    # Companies mentioned
    mentioned_company_ids = Column(Text)  # Comma-separated company IDs

    # Timestamps
    created_at = Column(DateTime, default=datetime.now)
    updated_at = Column(DateTime, default=datetime.now, onupdate=datetime.now)
    source_created_at = Column(DateTime)  # When the source was created

    # Status
    is_active = Column(Boolean, default=True)

    def __repr__(self):
        return f"<NordaKnowledgeChunk {self.id}: {self.source_type}>"


@dataclass
class KnowledgeChunkResult:
    """Result from knowledge search"""
    chunk_id: int
    content: str
    summary: Optional[str]
    source_type: str
    source_url: Optional[str]
    relevance_score: float
    created_at: datetime


class NordaKnowledgeService:
    """
    Service for building and querying the Norda knowledge base.
    """

    def __init__(self):
        self.min_content_length = 50  # Minimum characters for a chunk
        self.max_content_length = 2000  # Maximum characters per chunk

    def sync_forum_knowledge(self, days_back: int = 30) -> Dict[str, int]:
        """
        Sync knowledge from forum topics and replies.

        Args:
            days_back: How many days back to sync

        Returns:
            Dict with counts of added/updated/skipped items
        """
        db = SessionLocal()
        stats = {'topics_added': 0, 'replies_added': 0, 'skipped': 0, 'errors': 0}

        try:
            cutoff_date = datetime.now() - timedelta(days=days_back)

            # Get forum topics (exclude test category)
            topics = db.query(ForumTopic).filter(
                ForumTopic.created_at >= cutoff_date,
                ForumTopic.category != 'test'
            ).all()

            for topic in topics:
                try:
                    # Check if already exists
                    existing = db.query(NordaKnowledgeChunk).filter_by(
                        source_type=KnowledgeSourceType.FORUM_TOPIC.value,
                        source_id=topic.id
                    ).first()

                    if existing:
                        stats['skipped'] += 1
                        continue

                    # Create knowledge chunk from topic
                    if len(topic.content or '') >= self.min_content_length:
                        chunk = NordaKnowledgeChunk(
                            content=self._truncate_content(topic.content),
                            summary=topic.title[:500] if topic.title else None,
                            source_type=KnowledgeSourceType.FORUM_TOPIC.value,
                            source_id=topic.id,
                            source_url=f"/forum/topic/{topic.id}",
                            category=topic.category,
                            keywords=self._extract_keywords(topic.title + ' ' + topic.content),
                            confidence_score=70,  # Forum topics are moderately reliable
                            source_created_at=topic.created_at,
                            is_active=True
                        )
                        db.add(chunk)
                        stats['topics_added'] += 1

                except Exception as e:
                    logger.error(f"Error processing topic {topic.id}: {e}")
                    stats['errors'] += 1

            # Get forum replies
            replies = db.query(ForumReply).filter(
                ForumReply.created_at >= cutoff_date
            ).all()

            for reply in replies:
                try:
                    # Skip if topic is test category
                    if reply.topic and reply.topic.category == 'test':
                        continue

                    # Check if already exists
                    existing = db.query(NordaKnowledgeChunk).filter_by(
                        source_type=KnowledgeSourceType.FORUM_REPLY.value,
                        source_id=reply.id
                    ).first()

                    if existing:
                        stats['skipped'] += 1
                        continue

                    # Create knowledge chunk from reply
                    if len(reply.content or '') >= self.min_content_length:
                        chunk = NordaKnowledgeChunk(
                            content=self._truncate_content(reply.content),
                            summary=f"Odpowiedź w: {reply.topic.title[:200]}" if reply.topic else None,
                            source_type=KnowledgeSourceType.FORUM_REPLY.value,
                            source_id=reply.id,
                            source_url=f"/forum/topic/{reply.topic_id}#reply-{reply.id}" if reply.topic_id else None,
                            category=reply.topic.category if reply.topic else None,
                            keywords=self._extract_keywords(reply.content),
                            confidence_score=60,  # Replies are slightly less reliable
                            source_created_at=reply.created_at,
                            is_active=True
                        )
                        db.add(chunk)
                        stats['replies_added'] += 1

                except Exception as e:
                    logger.error(f"Error processing reply {reply.id}: {e}")
                    stats['errors'] += 1

            db.commit()
            logger.info(f"Forum knowledge sync complete: {stats}")

        except Exception as e:
            logger.error(f"Error syncing forum knowledge: {e}")
            db.rollback()
            raise
        finally:
            db.close()

        return stats

    def sync_chat_knowledge(self, days_back: int = 30) -> Dict[str, int]:
        """
        Sync knowledge from AI chat responses.

        PRIVACY: Only syncs assistant messages (role='assistant').
        User messages (role='user') are NEVER added.

        Prioritizes responses with positive feedback.

        Args:
            days_back: How many days back to sync

        Returns:
            Dict with counts of added/skipped items
        """
        db = SessionLocal()
        stats = {'responses_added': 0, 'skipped': 0, 'errors': 0}

        try:
            cutoff_date = datetime.now() - timedelta(days=days_back)

            # PRIVACY: Only get assistant messages (NEVER user messages)
            # Prioritize messages with positive feedback
            messages = db.query(AIChatMessage).filter(
                AIChatMessage.role == 'assistant',  # CRITICAL: Only assistant messages
                AIChatMessage.created_at >= cutoff_date,
                # Only include messages with positive or no feedback (exclude negative)
                (AIChatMessage.feedback_rating == 2) | (AIChatMessage.feedback_rating.is_(None))
            ).all()

            for msg in messages:
                try:
                    # Check if already exists
                    existing = db.query(NordaKnowledgeChunk).filter_by(
                        source_type=KnowledgeSourceType.CHAT_RESPONSE.value,
                        source_id=msg.id
                    ).first()

                    if existing:
                        stats['skipped'] += 1
                        continue

                    # Skip short responses or greetings
                    if len(msg.content or '') < self.min_content_length:
                        stats['skipped'] += 1
                        continue

                    # Skip generic responses without company information
                    if not self._contains_company_info(msg.content):
                        stats['skipped'] += 1
                        continue

                    # Calculate confidence based on feedback
                    confidence = 50
                    if msg.feedback_rating == 2:  # Positive feedback
                        confidence = 85
                    if msg.companies_mentioned and msg.companies_mentioned > 0:
                        confidence += 10

                    # Create knowledge chunk
                    chunk = NordaKnowledgeChunk(
                        content=self._truncate_content(msg.content),
                        summary=self._generate_summary(msg.content),
                        source_type=KnowledgeSourceType.CHAT_RESPONSE.value,
                        source_id=msg.id,
                        source_url=None,  # Chat responses don't have public URLs
                        category='chat_knowledge',
                        keywords=self._extract_keywords(msg.content),
                        confidence_score=min(confidence, 100),
                        source_created_at=msg.created_at,
                        is_active=True
                    )
                    db.add(chunk)
                    stats['responses_added'] += 1

                except Exception as e:
                    logger.error(f"Error processing chat message {msg.id}: {e}")
                    stats['errors'] += 1

            db.commit()
            logger.info(f"Chat knowledge sync complete: {stats}")

        except Exception as e:
            logger.error(f"Error syncing chat knowledge: {e}")
            db.rollback()
            raise
        finally:
            db.close()

        return stats

    def analyze_user_questions(self, days_back: int = 30) -> Dict[str, int]:
        """
        Analyze user questions for development insights.

        PRIVACY: Content is anonymized - we only extract PATTERNS/TOPICS, not actual questions.
        This helps identify:
        - Missing features (what users ask for that doesn't exist)
        - Common questions (need better docs/UI)
        - Popular company searches (demand analysis)

        Args:
            days_back: How many days back to analyze

        Returns:
            Dict with counts of insights by category
        """
        db = SessionLocal()
        stats = {'insights_added': 0, 'patterns_found': 0}

        try:
            cutoff_date = datetime.now() - timedelta(days=days_back)

            # Get user messages (for pattern analysis only)
            messages = db.query(AIChatMessage).filter(
                AIChatMessage.role == 'user',
                AIChatMessage.created_at >= cutoff_date
            ).all()

            # Analyze patterns (anonymized)
            pattern_counts = {}
            for msg in messages:
                patterns = self._extract_insight_patterns(msg.content)
                for pattern, category in patterns:
                    key = (pattern, category)
                    pattern_counts[key] = pattern_counts.get(key, 0) + 1

            # Create insight chunks for frequently occurring patterns
            for (pattern, category), count in pattern_counts.items():
                if count >= 3:  # Only if pattern appears 3+ times
                    stats['patterns_found'] += 1

                    # Check if insight already exists
                    existing = db.query(NordaKnowledgeChunk).filter(
                        NordaKnowledgeChunk.source_type == KnowledgeSourceType.CHAT_QUESTION.value,
                        NordaKnowledgeChunk.summary == pattern
                    ).first()

                    if existing:
                        # Update priority based on frequency
                        existing.insight_priority = max(existing.insight_priority, count)
                        existing.updated_at = datetime.now()
                    else:
                        # Create new insight
                        chunk = NordaKnowledgeChunk(
                            content=f"Użytkownicy często pytają o: {pattern} ({count} razy w ostatnich {days_back} dniach)",
                            summary=pattern,
                            source_type=KnowledgeSourceType.CHAT_QUESTION.value,
                            source_id=None,  # Anonymized, no specific source
                            insight_category=category,
                            insight_priority=count,
                            insight_status='new',
                            confidence_score=min(50 + count * 5, 95),
                            is_active=True
                        )
                        db.add(chunk)
                        stats['insights_added'] += 1

            db.commit()
            logger.info(f"User question analysis complete: {stats}")

        except Exception as e:
            logger.error(f"Error analyzing user questions: {e}")
            db.rollback()
            raise
        finally:
            db.close()

        return stats

    def _extract_insight_patterns(self, question: str) -> List[tuple]:
        """
        Extract anonymized patterns from user questions.
        Returns list of (pattern, category) tuples.
        """
        patterns = []
        q_lower = question.lower()

        # Feature requests
        feature_keywords = ['czy można', 'czy da się', 'chciałbym', 'przydałoby się', 'brakuje', 'potrzebuję']
        for kw in feature_keywords:
            if kw in q_lower:
                patterns.append(('Prośba o nową funkcję', InsightCategory.FEATURE_REQUEST.value))
                break

        # Bug reports
        bug_keywords = ['nie działa', 'błąd', 'problem', 'nie mogę', 'nie wyświetla', 'crash']
        for kw in bug_keywords:
            if kw in q_lower:
                patterns.append(('Zgłoszenie problemu', InsightCategory.BUG_REPORT.value))
                break

        # Company searches
        company_keywords = ['firma', 'firmy', 'szukam', 'potrzebuję', 'kto robi', 'kto oferuje']
        for kw in company_keywords:
            if kw in q_lower:
                patterns.append(('Wyszukiwanie firm', InsightCategory.COMPANY_SEARCH.value))
                break

        # Specific service searches (extract service type)
        service_patterns = [
            ('budownictwo', 'Szukanie: usługi budowlane'),
            ('transport', 'Szukanie: transport/logistyka'),
            ('it', 'Szukanie: usługi IT'),
            ('marketing', 'Szukanie: marketing/reklama'),
            ('księgowość', 'Szukanie: księgowość'),
            ('prawo', 'Szukanie: usługi prawne'),
        ]
        for keyword, pattern in service_patterns:
            if keyword in q_lower:
                patterns.append((pattern, InsightCategory.COMPANY_SEARCH.value))

        # Questions about portal
        portal_keywords = ['jak', 'gdzie', 'co to', 'po co', 'dlaczego']
        for kw in portal_keywords:
            if kw in q_lower and ('portal' in q_lower or 'strona' in q_lower or 'norda' in q_lower):
                patterns.append(('Pytanie o portal', InsightCategory.QUESTION.value))
                break

        return patterns

    def get_development_insights(self, status: Optional[str] = None) -> List[Dict[str, Any]]:
        """
        Get development insights for roadmap planning.

        Args:
            status: Filter by status (new, reviewed, planned, implemented, rejected)

        Returns:
            List of insight dicts ordered by priority
        """
        db = SessionLocal()

        try:
            query = db.query(NordaKnowledgeChunk).filter(
                NordaKnowledgeChunk.is_active == True,
                NordaKnowledgeChunk.insight_category.isnot(None)
            )

            if status:
                query = query.filter(NordaKnowledgeChunk.insight_status == status)

            insights = query.order_by(
                NordaKnowledgeChunk.insight_priority.desc(),
                NordaKnowledgeChunk.created_at.desc()
            ).limit(100).all()

            return [
                {
                    'id': i.id,
                    'summary': i.summary,
                    'content': i.content,
                    'category': i.insight_category,
                    'priority': i.insight_priority,
                    'status': i.insight_status,
                    'source_type': i.source_type,
                    'created_at': i.created_at.isoformat() if i.created_at else None
                }
                for i in insights
            ]

        finally:
            db.close()

    def update_insight_status(self, insight_id: int, status: str, note: Optional[str] = None) -> bool:
        """Update the status of a development insight"""
        db = SessionLocal()

        try:
            insight = db.query(NordaKnowledgeChunk).filter_by(id=insight_id).first()
            if not insight:
                return False

            insight.insight_status = status
            insight.updated_at = datetime.now()
            if note:
                insight.content = f"{insight.content}\n\n[Admin note: {note}]"

            db.commit()
            return True

        except Exception as e:
            logger.error(f"Error updating insight status: {e}")
            db.rollback()
            return False
        finally:
            db.close()

    def search_knowledge(
        self,
        query: str,
        limit: int = 5,
        source_types: Optional[List[str]] = None
    ) -> List[KnowledgeChunkResult]:
        """
        Search knowledge base for relevant chunks.

        Args:
            query: Search query
            limit: Maximum results to return
            source_types: Filter by source types (None = all)

        Returns:
            List of KnowledgeChunkResult ordered by relevance
        """
        db = SessionLocal()
        results = []

        try:
            # Build base query
            base_query = db.query(NordaKnowledgeChunk).filter(
                NordaKnowledgeChunk.is_active == True
            )

            # Filter by source types
            if source_types:
                base_query = base_query.filter(
                    NordaKnowledgeChunk.source_type.in_(source_types)
                )

            # Simple keyword search (can be enhanced with FTS later)
            query_words = query.lower().split()
            chunks = base_query.all()

            # Score each chunk
            scored_chunks = []
            for chunk in chunks:
                score = self._calculate_relevance(query_words, chunk)
                if score > 0:
                    scored_chunks.append((chunk, score))

            # Sort by score and limit
            scored_chunks.sort(key=lambda x: x[1], reverse=True)
            top_chunks = scored_chunks[:limit]

            # Convert to results
            for chunk, score in top_chunks:
                results.append(KnowledgeChunkResult(
                    chunk_id=chunk.id,
                    content=chunk.content,
                    summary=chunk.summary,
                    source_type=chunk.source_type,
                    source_url=chunk.source_url,
                    relevance_score=score,
                    created_at=chunk.source_created_at or chunk.created_at
                ))

        finally:
            db.close()

        return results

    def get_knowledge_stats(self) -> Dict[str, Any]:
        """Get statistics about the knowledge base"""
        db = SessionLocal()

        try:
            total = db.query(NordaKnowledgeChunk).filter(
                NordaKnowledgeChunk.is_active == True
            ).count()

            by_source = {}
            for source_type in KnowledgeSourceType:
                count = db.query(NordaKnowledgeChunk).filter(
                    NordaKnowledgeChunk.is_active == True,
                    NordaKnowledgeChunk.source_type == source_type.value
                ).count()
                by_source[source_type.value] = count

            verified = db.query(NordaKnowledgeChunk).filter(
                NordaKnowledgeChunk.is_active == True,
                NordaKnowledgeChunk.is_verified == True
            ).count()

            return {
                'total_chunks': total,
                'by_source': by_source,
                'verified_chunks': verified,
                'last_sync': datetime.now().isoformat()
            }

        finally:
            db.close()

    def _truncate_content(self, content: str) -> str:
        """Truncate content to max length"""
        if len(content) <= self.max_content_length:
            return content
        return content[:self.max_content_length - 3] + "..."

    def _extract_keywords(self, text: str) -> str:
        """Extract keywords from text (simple implementation)"""
        # Remove common words and extract significant terms
        stopwords = {
            'i', 'a', 'the', 'to', 'w', 'z', 'na', 'do', 'jest', 'są', 'być',
            'że', 'o', 'nie', 'się', 'jak', 'co', 'dla', 'po', 'od', 'za',
            'ale', 'lub', 'oraz', 'czy', 'tak', 'już', 'tylko', 'też', 'jeszcze'
        }

        words = text.lower().split()
        keywords = [w for w in words if len(w) > 3 and w not in stopwords]

        # Return unique keywords, limited
        unique_keywords = list(dict.fromkeys(keywords))[:20]
        return ','.join(unique_keywords)

    def _generate_summary(self, content: str) -> Optional[str]:
        """Generate a short summary (first sentence or 100 chars)"""
        if not content:
            return None

        # Try to get first sentence
        sentences = content.split('.')
        if sentences:
            first = sentences[0].strip()
            if len(first) > 10:
                return first[:200] + ('...' if len(first) > 200 else '')

        return content[:100] + '...'

    def _contains_company_info(self, content: str) -> bool:
        """Check if content contains company-related information"""
        company_keywords = [
            'firma', 'firmy', 'firmę', 'spółka', 'przedsiębiorstwo',
            'usługi', 'produkty', 'kontakt', 'telefon', 'email',
            'norda', 'członek', 'biznes', 'współpraca'
        ]

        content_lower = content.lower()
        return any(kw in content_lower for kw in company_keywords)

    def _calculate_relevance(self, query_words: List[str], chunk: NordaKnowledgeChunk) -> float:
        """Calculate relevance score for a chunk"""
        score = 0.0
        content_lower = (chunk.content or '').lower()
        keywords = (chunk.keywords or '').lower()
        summary = (chunk.summary or '').lower()

        for word in query_words:
            # Content matches
            if word in content_lower:
                score += 1.0
            # Keyword matches (higher weight)
            if word in keywords:
                score += 2.0
            # Summary matches
            if word in summary:
                score += 1.5

        # Boost verified chunks
        if chunk.is_verified:
            score *= 1.2

        # Boost by confidence
        score *= (chunk.confidence_score or 50) / 100

        return score


# Global instance
_service_instance: Optional[NordaKnowledgeService] = None


def get_knowledge_service() -> NordaKnowledgeService:
    """Get or create global NordaKnowledgeService instance"""
    global _service_instance
    if _service_instance is None:
        _service_instance = NordaKnowledgeService()
    return _service_instance


# Convenience functions
def sync_all_knowledge(days_back: int = 30) -> Dict[str, Any]:
    """Sync knowledge from all sources"""
    service = get_knowledge_service()
    return {
        'forum': service.sync_forum_knowledge(days_back),
        'chat': service.sync_chat_knowledge(days_back)
    }


def search_knowledge(query: str, limit: int = 5) -> List[KnowledgeChunkResult]:
    """Search the knowledge base"""
    return get_knowledge_service().search_knowledge(query, limit)