"""
AI-Generated Knowledge Base Metadata Extraction Service

Extracts rich metadata from documents during ingestion:
- Title
- Summary
- Tags
- Topics (via LLM)
- Date detection
- Document quality score
"""

import os
import re
from typing import Dict, Any, Optional, List
from datetime import datetime
from ..services.llm_client import LLMClient


class MetadataExtractor:
    """
    Extracts structured metadata from document content using LLM and pattern matching.
    """
    
    def __init__(self, llm_client: Optional[LLMClient] = None):
        self.llm = llm_client or LLMClient(
            backend=os.getenv("LLM_BACKEND", "ollama"),
            url=os.getenv("OLLAMA_URL"),
            api_key=os.getenv("GROQ_API_KEY"),
            model=os.getenv("OLLAMA_MODEL", "llama3.1:latest")
        )
    
    async def extract_metadata(
        self,
        content: str,
        filename: Optional[str] = None,
        url: Optional[str] = None,
        source_type: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Extract comprehensive metadata from document content.
        
        Args:
            content: Document text content
            filename: Original filename (if available)
            url: Source URL (if available)
            source_type: Document type (pdf, docx, txt, etc.)
        
        Returns:
            Dictionary with extracted metadata:
            - title: Extracted or inferred title
            - summary: Brief summary (2-3 sentences)
            - tags: List of relevant tags
            - topics: List of main topics/themes
            - detected_date: Extracted date (ISO format or None)
            - quality_score: Document quality score (0.0-1.0)
            - word_count: Word count
            - language: Detected language (if available)
        """
        # Basic metadata (always available)
        word_count = len(content.split())
        char_count = len(content)
        
        # Extract title (try multiple methods)
        title = self._extract_title(content, filename, url)
        
        # Detect date
        detected_date = self._detect_date(content)
        
        # Try LLM extraction for rich metadata
        llm_metadata = {}
        try:
            llm_metadata = await self._extract_with_llm(content, title)
        except Exception as e:
            print(f"LLM metadata extraction failed: {e}, using fallback")
            llm_metadata = self._extract_fallback(content, title)
        
        # Calculate quality score
        quality_score = self._calculate_quality_score(
            content, word_count, llm_metadata.get("summary", "")
        )
        
        return {
            "title": title,
            "summary": llm_metadata.get("summary", self._generate_basic_summary(content)),
            "tags": llm_metadata.get("tags", self._extract_basic_tags(content)),
            "topics": llm_metadata.get("topics", self._extract_basic_topics(content)),
            "detected_date": detected_date,
            "quality_score": quality_score,
            "word_count": word_count,
            "char_count": char_count,
            "source_type": source_type or "unknown",
            "extraction_method": "llm" if llm_metadata.get("summary") else "fallback"
        }
    
    def _extract_title(self, content: str, filename: Optional[str] = None, url: Optional[str] = None) -> str:
        """Extract title from content, filename, or URL."""
        # Try filename first (remove extension)
        if filename:
            title = filename.rsplit('.', 1)[0] if '.' in filename else filename
            if title and len(title) > 3:
                return title.replace('_', ' ').replace('-', ' ').title()
        
        # Try first line (common in markdown/docs)
        lines = content.split('\n')
        for line in lines[:5]:
            line = line.strip()
            if line and len(line) < 200 and not line.startswith('#'):
                # Check if it looks like a title
                if len(line.split()) <= 15:
                    return line
        
        # Try markdown headers
        for line in lines[:10]:
            if line.startswith('# '):
                return line[2:].strip()
            if line.startswith('## '):
                return line[3:].strip()
        
        # Try URL path
        if url:
            from urllib.parse import urlparse
            parsed = urlparse(url)
            path = parsed.path.strip('/').split('/')[-1]
            if path and len(path) > 3:
                return path.replace('_', ' ').replace('-', ' ').title()
        
        # Fallback: first 50 chars
        return content[:50].strip() + "..." if len(content) > 50 else content.strip()
    
    def _detect_date(self, content: str) -> Optional[str]:
        """Detect dates in various formats."""
        # Common date patterns
        patterns = [
            r'\b(\d{4}-\d{2}-\d{2})\b',  # YYYY-MM-DD
            r'\b(\d{2}/\d{2}/\d{4})\b',   # MM/DD/YYYY
            r'\b(\d{4}/\d{2}/\d{2})\b',   # YYYY/MM/DD
            r'\b(January|February|March|April|May|June|July|August|September|October|November|December)\s+\d{1,2},?\s+\d{4}\b',
            r'\b\d{1,2}\s+(January|February|March|April|May|June|July|August|September|October|November|December)\s+\d{4}\b',
        ]
        
        for pattern in patterns:
            matches = re.findall(pattern, content, re.IGNORECASE)
            if matches:
                try:
                    # Try to parse and normalize
                    date_str = matches[0] if isinstance(matches[0], str) else ' '.join(matches[0])
                    # Return first valid date found
                    return date_str
                except:
                    continue
        
        return None
    
    async def _extract_with_llm(self, content: str, title: str) -> Dict[str, Any]:
        """Extract metadata using LLM."""
        # Truncate content for LLM (first 2000 chars for efficiency)
        preview = content[:2000] + "..." if len(content) > 2000 else content
        
        prompt = f"""Analyze the following document and extract structured metadata.

Title: {title}
Content Preview:
{preview}

Extract the following information:
1. A concise summary (2-3 sentences) of what this document is about
2. 5-8 relevant tags (single words or short phrases, comma-separated)
3. 3-5 main topics/themes (comma-separated)
4. The primary subject matter or domain

Respond in JSON format:
{{
    "summary": "Brief 2-3 sentence summary of the document",
    "tags": ["tag1", "tag2", "tag3"],
    "topics": ["topic1", "topic2", "topic3"],
    "domain": "primary domain or subject area"
}}

Only return valid JSON, no additional text:"""
        
        try:
            import asyncio
            response = await asyncio.wait_for(
                self.llm.simple_call(prompt, temperature=0.3),
                timeout=20.0  # 20 second timeout
            )
            
            # Clean up response
            response = response.strip()
            if response.startswith("```json"):
                response = response[7:]
            if response.startswith("```"):
                response = response[3:]
            if response.endswith("```"):
                response = response[:-3]
            response = response.strip()
            
            import json
            data = json.loads(response)
            
            return {
                "summary": data.get("summary", ""),
                "tags": data.get("tags", []),
                "topics": data.get("topics", []),
                "domain": data.get("domain", "")
            }
        except asyncio.TimeoutError:
            raise Exception("LLM timeout")
        except Exception as e:
            raise Exception(f"LLM extraction failed: {e}")
    
    def _extract_fallback(self, content: str, title: str) -> Dict[str, Any]:
        """Fallback metadata extraction without LLM."""
        return {
            "summary": self._generate_basic_summary(content),
            "tags": self._extract_basic_tags(content),
            "topics": self._extract_basic_topics(content),
            "domain": ""
        }
    
    def _generate_basic_summary(self, content: str) -> str:
        """Generate a basic summary from first sentences."""
        sentences = re.split(r'[.!?]+', content)
        sentences = [s.strip() for s in sentences if s.strip()]
        
        if len(sentences) >= 3:
            return ' '.join(sentences[:3]) + '.'
        elif len(sentences) >= 1:
            return sentences[0] + '.'
        else:
            return content[:200] + "..." if len(content) > 200 else content
    
    def _extract_basic_tags(self, content: str) -> List[str]:
        """Extract basic tags using keyword frequency."""
        # Common keywords that might indicate topics
        keywords = [
            "api", "documentation", "guide", "tutorial", "reference", "manual",
            "policy", "procedure", "process", "workflow", "system", "application",
            "security", "authentication", "authorization", "data", "database",
            "server", "client", "network", "protocol", "framework", "library"
        ]
        
        content_lower = content.lower()
        found_tags = []
        
        for keyword in keywords:
            if keyword in content_lower:
                found_tags.append(keyword)
        
        # Also extract capitalized words (might be proper nouns/important terms)
        capitalized = re.findall(r'\b[A-Z][a-z]+\b', content)
        # Count frequency and take top 5
        from collections import Counter
        top_caps = [word.lower() for word, count in Counter(capitalized).most_common(5)]
        found_tags.extend(top_caps[:3])  # Add top 3
        
        return list(set(found_tags))[:8]  # Return up to 8 unique tags
    
    def _extract_basic_topics(self, content: str) -> List[str]:
        """Extract basic topics from content structure."""
        topics = []
        
        # Look for section headers (markdown style)
        headers = re.findall(r'^#+\s+(.+)$', content, re.MULTILINE)
        if headers:
            topics.extend([h.strip() for h in headers[:5]])
        
        # Look for common topic indicators
        if any(word in content.lower() for word in ["introduction", "overview", "getting started"]):
            topics.append("Introduction")
        if any(word in content.lower() for word in ["api", "endpoint", "request", "response"]):
            topics.append("API")
        if any(word in content.lower() for word in ["example", "sample", "demo"]):
            topics.append("Examples")
        if any(word in content.lower() for word in ["error", "troubleshoot", "issue"]):
            topics.append("Troubleshooting")
        
        return topics[:5] if topics else ["General"]
    
    def _calculate_quality_score(self, content: str, word_count: int, summary: str) -> float:
        """
        Calculate document quality score (0.0-1.0).
        
        Factors:
        - Length (not too short, not too long)
        - Structure (has paragraphs, sentences)
        - Completeness (has summary/metadata)
        """
        score = 0.0
        
        # Length score (optimal: 200-5000 words)
        if 200 <= word_count <= 5000:
            score += 0.3
        elif 100 <= word_count < 200 or 5000 < word_count <= 10000:
            score += 0.2
        elif word_count > 10000:
            score += 0.1
        
        # Structure score (has paragraphs and sentences)
        paragraphs = content.split('\n\n')
        if len(paragraphs) >= 2:
            score += 0.2
        
        sentences = re.split(r'[.!?]+', content)
        if len(sentences) >= 5:
            score += 0.2
        
        # Completeness score (has summary)
        if summary and len(summary) > 20:
            score += 0.2
        
        # Readability score (not too many special chars, has spaces)
        if ' ' in content and len(re.findall(r'[a-zA-Z]', content)) > len(content) * 0.5:
            score += 0.1
        
        return min(score, 1.0)