"""
🧠 Perplexity AI Integration for AI Dataset Studio
Automatically discovers relevant sources based on project descriptions
"""

import os
import requests
import json
import logging
import time
import re
from typing import List, Dict, Optional, Tuple
from urllib.parse import urlparse, urljoin
from dataclasses import dataclass
from enum import Enum

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class SearchType(Enum):
    """Types of searches supported by Perplexity AI"""
    GENERAL = "general"
    ACADEMIC = "academic"
    NEWS = "news"
    SOCIAL = "social"
    TECHNICAL = "technical"

@dataclass
class SourceResult:
    """Structure for individual source results"""
    url: str
    title: str
    description: str
    relevance_score: float
    source_type: str
    domain: str
    publication_date: Optional[str] = None
    author: Optional[str] = None

@dataclass
class SearchResults:
    """Container for search results"""
    query: str
    sources: List[SourceResult]
    total_found: int
    search_time: float
    perplexity_response: str
    suggestions: List[str]

class PerplexityClient:
    """
    🧠 Perplexity AI Client for Smart Source Discovery
    
    Features:
    - Intelligent source discovery based on project descriptions
    - Multiple search strategies (academic, news, technical, etc.)
    - Quality filtering and relevance scoring
    - Rate limiting and error handling
    - Domain validation and safety checks
    """
    
    def __init__(self, api_key: Optional[str] = None):
        """
        Initialize Perplexity AI client
        
        Args:
            api_key: Perplexity API key (if not provided, will try env var)
        """
        self.api_key = api_key or os.getenv('PERPLEXITY_API_KEY')
        self.base_url = "https://api.perplexity.ai"
        self.session = requests.Session()
        
        # Set up headers
        if self.api_key:
            self.session.headers.update({
                'Authorization': f'Bearer {self.api_key}',
                'Content-Type': 'application/json',
                'User-Agent': 'AI-Dataset-Studio/1.0'
            })
        
        # Rate limiting
        self.last_request_time = 0
        self.min_request_interval = 1.0  # Seconds between requests
        
        # Configuration
        self.max_retries = 3
        self.timeout = 30
        
        logger.info("🧠 Perplexity AI client initialized")
    
    def _validate_api_key(self) -> bool:
        """Validate that API key is available and working"""
        if not self.api_key:
            logger.error("❌ No Perplexity API key found. Set PERPLEXITY_API_KEY environment variable.")
            return False
        return True
    
    def _rate_limit(self):
        """Implement rate limiting to respect API limits"""
        current_time = time.time()
        time_since_last = current_time - self.last_request_time
        
        if time_since_last < self.min_request_interval:
            sleep_time = self.min_request_interval - time_since_last
            logger.debug(f"⏱️ Rate limiting: sleeping {sleep_time:.2f}s")
            time.sleep(sleep_time)
        
        self.last_request_time = time.time()
    
    def _make_request(self, payload: Dict) -> Optional[Dict]:
        """
        Make API request to Perplexity with error handling
        
        Args:
            payload: Request payload
            
        Returns:
            API response or None if failed
        """
        if not self._validate_api_key():
            return None
        
        self._rate_limit()
        
        for attempt in range(self.max_retries):
            try:
                logger.debug(f"📡 Making Perplexity API request (attempt {attempt + 1})")
                
                response = self.session.post(
                    f"{self.base_url}/chat/completions",
                    json=payload,
                    timeout=self.timeout
                )
                
                if response.status_code == 200:
                    logger.debug("✅ Perplexity API request successful")
                    return response.json()
                elif response.status_code == 429:
                    logger.warning("🚦 Rate limit hit, waiting longer...")
                    time.sleep(2 ** attempt)  # Exponential backoff
                    continue
                else:
                    logger.error(f"❌ API request failed: {response.status_code} - {response.text}")
                    
            except requests.exceptions.Timeout:
                logger.warning(f"⏰ Request timeout (attempt {attempt + 1})")
            except requests.exceptions.RequestException as e:
                logger.error(f"🔌 Request error: {str(e)}")
            
            if attempt < self.max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
        
        logger.error("❌ All retry attempts failed")
        return None
    
    def discover_sources(
        self,
        project_description: str,
        search_type: SearchType = SearchType.GENERAL,
        max_sources: int = 20,
        include_academic: bool = True,
        include_news: bool = True,
        domain_filter: Optional[List[str]] = None
    ) -> SearchResults:
        """
        🔍 Discover relevant sources based on project description
        
        Args:
            project_description: User's project description
            search_type: Type of search to perform
            max_sources: Maximum number of sources to return
            include_academic: Include academic sources
            include_news: Include news sources
            domain_filter: Optional list of domains to focus on
            
        Returns:
            SearchResults object with discovered sources
        """
        start_time = time.time()
        
        logger.info(f"🔍 Discovering sources for: {project_description[:100]}...")
        
        # Build search prompt
        search_prompt = self._build_search_prompt(
            project_description, 
            search_type, 
            max_sources,
            include_academic,
            include_news,
            domain_filter
        )
        
        # Prepare API payload
        payload = {
            "model": "llama-3.1-sonar-large-128k-online",
            "messages": [
                {
                    "role": "system",
                    "content": "You are an expert research assistant specializing in finding high-quality, relevant sources for AI/ML dataset creation. Always provide specific URLs, titles, and descriptions."
                },
                {
                    "role": "user",
                    "content": search_prompt
                }
            ],
            "max_tokens": 4000,
            "temperature": 0.3,
            "top_p": 0.9
        }
        
        # Make API request
        response = self._make_request(payload)
        
        if not response:
            logger.error("❌ Failed to get response from Perplexity API")
            return self._create_empty_results(project_description, time.time() - start_time)
        
        # Parse response and extract sources
        try:
            content = response['choices'][0]['message']['content']
            sources = self._parse_sources_from_response(content)
            suggestions = self._extract_suggestions(content)
            
            search_time = time.time() - start_time
            
            logger.info(f"✅ Found {len(sources)} sources in {search_time:.2f}s")
            
            return SearchResults(
                query=project_description,
                sources=sources[:max_sources],
                total_found=len(sources),
                search_time=search_time,
                perplexity_response=content,
                suggestions=suggestions
            )
            
        except Exception as e:
            logger.error(f"❌ Error parsing Perplexity response: {str(e)}")
            return self._create_empty_results(project_description, time.time() - start_time)
    
    def _build_search_prompt(
        self,
        project_description: str,
        search_type: SearchType,
        max_sources: int,
        include_academic: bool,
        include_news: bool,
        domain_filter: Optional[List[str]]
    ) -> str:
        """Build optimized search prompt for Perplexity AI"""
        
        prompt = f"""
Find {max_sources} high-quality, diverse sources for an AI/ML dataset creation project:

PROJECT DESCRIPTION: {project_description}

SEARCH REQUIREMENTS:
- Find sources with extractable text content suitable for ML training
- Prioritize sources with structured, high-quality content
- Include diverse perspectives and data types
- Focus on sources that are legally scrapable (respect robots.txt)

SEARCH TYPE: {search_type.value}
"""
        
        if include_academic:
            prompt += "\n- Include academic papers, research articles, and scholarly sources"
        
        if include_news:
            prompt += "\n- Include news articles, press releases, and journalistic content"
        
        if domain_filter:
            prompt += f"\n- Focus on these domains: {', '.join(domain_filter)}"
        
        prompt += f"""

OUTPUT FORMAT:
For each source, provide:
1. **URL**: Direct link to the content
2. **Title**: Clear, descriptive title
3. **Description**: 2-3 sentence summary of content and why it's relevant
4. **Type**: [academic/news/blog/government/technical/forum/social]
5. **Quality Score**: 1-10 rating for dataset suitability

ADDITIONAL REQUIREMENTS:
- Verify URLs are accessible and contain substantial text
- Avoid paywalled or login-required content when possible
- Prioritize sources with consistent formatting
- Include publication dates when available
- Suggest related search terms for expanding the dataset

Please provide concrete, actionable sources that can be immediately scraped for dataset creation.
"""
        
        return prompt
    
    def _parse_sources_from_response(self, content: str) -> List[SourceResult]:
        """Parse source information from Perplexity AI response"""
        sources = []
        
        # Try to extract structured information
        # Look for URL patterns
        url_pattern = r'https?://[^\s<>"{}|\\^`\[\]]+[^\s<>"{}|\\^`\[\].,!?;:]'
        
        # Split content into sections
        sections = re.split(r'\n\s*\n', content)
        
        for section in sections:
            # Look for URLs in this section
            urls = re.findall(url_pattern, section, re.IGNORECASE)
            
            if urls:
                for url in urls:
                    try:
                        # Clean URL
                        url = url.strip()
                        
                        # Extract title (look for text before the URL or after)
                        title = self._extract_title_from_section(section, url)
                        
                        # Extract description
                        description = self._extract_description_from_section(section, url)
                        
                        # Determine source type
                        source_type = self._determine_source_type(url, section)
                        
                        # Calculate relevance score (basic heuristic)
                        relevance_score = self._calculate_relevance_score(section, url)
                        
                        # Get domain
                        domain = self._extract_domain(url)
                        
                        # Validate URL
                        if self._is_valid_url(url):
                            source = SourceResult(
                                url=url,
                                title=title,
                                description=description,
                                relevance_score=relevance_score,
                                source_type=source_type,
                                domain=domain
                            )
                            sources.append(source)
                            
                    except Exception as e:
                        logger.debug(f"⚠️ Error parsing source: {str(e)}")
                        continue
        
        # Remove duplicates based on URL
        seen_urls = set()
        unique_sources = []
        
        for source in sources:
            if source.url not in seen_urls:
                seen_urls.add(source.url)
                unique_sources.append(source)
        
        # Sort by relevance score
        unique_sources.sort(key=lambda x: x.relevance_score, reverse=True)
        
        return unique_sources
    
    def _extract_title_from_section(self, section: str, url: str) -> str:
        """Extract title from section text"""
        lines = section.split('\n')
        
        for line in lines:
            if url in line:
                # Look for title patterns
                title_patterns = [
                    r'\*\*([^*]+)\*\*',  # **Title**
                    r'#{1,6}\s*([^\n]+)',  # # Title
                    r'Title:\s*([^\n]+)',  # Title: Something
                    r'([^:\n]+):?\s*' + re.escape(url),  # Title: URL
                ]
                
                for pattern in title_patterns:
                    match = re.search(pattern, line, re.IGNORECASE)
                    if match:
                        return match.group(1).strip()
        
        # Fallback: use domain name
        return self._extract_domain(url)
    
    def _extract_description_from_section(self, section: str, url: str) -> str:
        """Extract description from section text"""
        # Remove the URL line and look for descriptive text
        lines = section.split('\n')
        description_lines = []
        
        for line in lines:
            if url not in line and line.strip():
                # Skip markdown headers and bullets
                clean_line = re.sub(r'^[#*\-\d\.]+\s*', '', line.strip())
                if len(clean_line) > 20:  # Meaningful content
                    description_lines.append(clean_line)
        
        description = ' '.join(description_lines)
        
        # Truncate if too long
        if len(description) > 200:
            description = description[:200] + "..."
        
        return description or "High-quality source for dataset creation"
    
    def _determine_source_type(self, url: str, section: str) -> str:
        """Determine the type of source based on URL and context"""
        url_lower = url.lower()
        section_lower = section.lower()
        
        # Academic sources
        if any(domain in url_lower for domain in [
            'arxiv.org', 'scholar.google', 'pubmed', 'ieee.org', 
            'acm.org', 'springer.com', 'elsevier.com', 'nature.com',
            'sciencedirect.com', 'jstor.org'
        ]):
            return 'academic'
        
        # News sources
        if any(domain in url_lower for domain in [
            'cnn.com', 'bbc.com', 'reuters.com', 'ap.org', 'nytimes.com',
            'washingtonpost.com', 'theguardian.com', 'bloomberg.com',
            'techcrunch.com', 'wired.com'
        ]):
            return 'news'
        
        # Government sources
        if '.gov' in url_lower or 'government' in section_lower:
            return 'government'
        
        # Technical/Documentation
        if any(domain in url_lower for domain in [
            'docs.', 'documentation', 'github.com', 'stackoverflow.com',
            'medium.com', 'dev.to'
        ]):
            return 'technical'
        
        # Social media
        if any(domain in url_lower for domain in [
            'twitter.com', 'reddit.com', 'linkedin.com', 'facebook.com'
        ]):
            return 'social'
        
        # Default to blog
        return 'blog'
    
    def _calculate_relevance_score(self, section: str, url: str) -> float:
        """Calculate relevance score for a source (0-10)"""
        score = 5.0  # Base score
        
        section_lower = section.lower()
        url_lower = url.lower()
        
        # Boost for quality indicators
        quality_indicators = [
            'research', 'study', 'analysis', 'comprehensive', 'detailed',
            'expert', 'professional', 'authoritative', 'peer-reviewed',
            'dataset', 'data', 'machine learning', 'ai', 'artificial intelligence'
        ]
        
        for indicator in quality_indicators:
            if indicator in section_lower:
                score += 0.5
        
        # Boost for academic sources
        if any(domain in url_lower for domain in ['arxiv.org', 'scholar.google', 'pubmed']):
            score += 2.0
        
        # Boost for government sources
        if '.gov' in url_lower:
            score += 1.5
        
        # Penalize for social media
        if any(domain in url_lower for domain in ['twitter.com', 'facebook.com']):
            score -= 1.0
        
        # Cap at 10
        return min(score, 10.0)
    
    def _extract_domain(self, url: str) -> str:
        """Extract domain from URL"""
        try:
            parsed = urlparse(url)
            return parsed.netloc
        except:
            return "unknown"
    
    def _is_valid_url(self, url: str) -> bool:
        """Validate URL format and basic accessibility"""
        try:
            parsed = urlparse(url)
            return all([parsed.scheme, parsed.netloc])
        except:
            return False
    
    def _extract_suggestions(self, content: str) -> List[str]:
        """Extract search suggestions from Perplexity response"""
        suggestions = []
        
        # Look for suggestion patterns
        suggestion_patterns = [
            r'related search terms?:?\s*([^\n]+)',
            r'you might also search for:?\s*([^\n]+)',
            r'additional keywords?:?\s*([^\n]+)',
            r'suggestions?:?\s*([^\n]+)'
        ]
        
        for pattern in suggestion_patterns:
            matches = re.findall(pattern, content, re.IGNORECASE)
            for match in matches:
                # Split by common delimiters
                terms = re.split(r'[,;|]', match)
                suggestions.extend([term.strip().strip('"\'') for term in terms if term.strip()])
        
        return suggestions[:10]  # Limit to 10 suggestions
    
    def _create_empty_results(self, query: str, search_time: float) -> SearchResults:
        """Create empty results object for failed searches"""
        return SearchResults(
            query=query,
            sources=[],
            total_found=0,
            search_time=search_time,
            perplexity_response="",
            suggestions=[]
        )
    
    def search_with_keywords(self, keywords: List[str], search_type: SearchType = SearchType.GENERAL) -> SearchResults:
        """
        🔎 Search using specific keywords
        
        Args:
            keywords: List of search keywords
            search_type: Type of search to perform
            
        Returns:
            SearchResults object
        """
        query = " ".join(keywords)
        return self.discover_sources(
            project_description=f"Find sources related to: {query}",
            search_type=search_type
        )
    
    def get_domain_sources(self, domain: str, topic: str, max_sources: int = 10) -> SearchResults:
        """
        🌐 Find sources from a specific domain
        
        Args:
            domain: Target domain (e.g., "nature.com")
            topic: Topic to search for
            max_sources: Maximum sources to return
            
        Returns:
            SearchResults object
        """
        return self.discover_sources(
            project_description=f"Find articles about {topic} from {domain}",
            domain_filter=[domain],
            max_sources=max_sources
        )
    
    def validate_sources(self, sources: List[SourceResult]) -> List[SourceResult]:
        """
        ✅ Validate and filter sources for quality and accessibility
        
        Args:
            sources: List of source results to validate
            
        Returns:
            Filtered list of valid sources
        """
        valid_sources = []
        
        for source in sources:
            try:
                # Basic URL validation
                if not self._is_valid_url(source.url):
                    logger.debug(f"⚠️ Invalid URL: {source.url}")
                    continue
                
                # Check if domain is accessible (basic check)
                domain = self._extract_domain(source.url)
                if not domain or domain == "unknown":
                    logger.debug(f"⚠️ Unknown domain: {source.url}")
                    continue
                
                # Quality score threshold
                if source.relevance_score < 3.0:
                    logger.debug(f"⚠️ Low quality score: {source.url}")
                    continue
                
                valid_sources.append(source)
                
            except Exception as e:
                logger.debug(f"⚠️ Error validating source {source.url}: {str(e)}")
                continue
        
        logger.info(f"✅ Validated {len(valid_sources)} out of {len(sources)} sources")
        return valid_sources
    
    def export_sources(self, results: SearchResults, format: str = "json") -> str:
        """
        📄 Export search results to various formats
        
        Args:
            results: SearchResults object to export
            format: Export format ("json", "csv", "markdown")
            
        Returns:
            Exported data as string
        """
        if format.lower() == "json":
            return self._export_json(results)
        elif format.lower() == "csv":
            return self._export_csv(results)
        elif format.lower() == "markdown":
            return self._export_markdown(results)
        else:
            raise ValueError(f"Unsupported export format: {format}")
    
    def _export_json(self, results: SearchResults) -> str:
        """Export results as JSON"""
        data = {
            "query": results.query,
            "total_found": results.total_found,
            "search_time": results.search_time,
            "sources": [
                {
                    "url": source.url,
                    "title": source.title,
                    "description": source.description,
                    "relevance_score": source.relevance_score,
                    "source_type": source.source_type,
                    "domain": source.domain,
                    "publication_date": source.publication_date,
                    "author": source.author
                }
                for source in results.sources
            ],
            "suggestions": results.suggestions
        }
        return json.dumps(data, indent=2)
    
    def _export_csv(self, results: SearchResults) -> str:
        """Export results as CSV"""
        import csv
        from io import StringIO
        
        output = StringIO()
        writer = csv.writer(output)
        
        # Write header
        writer.writerow([
            "URL", "Title", "Description", "Relevance Score", 
            "Source Type", "Domain", "Publication Date", "Author"
        ])
        
        # Write data
        for source in results.sources:
            writer.writerow([
                source.url,
                source.title,
                source.description,
                source.relevance_score,
                source.source_type,
                source.domain,
                source.publication_date or "",
                source.author or ""
            ])
        
        return output.getvalue()
    
    def _export_markdown(self, results: SearchResults) -> str:
        """Export results as Markdown"""
        md = f"# Search Results for: {results.query}\n\n"
        md += f"**Total Sources Found:** {results.total_found}\n"
        md += f"**Search Time:** {results.search_time:.2f} seconds\n\n"
        
        md += "## Sources\n\n"
        
        for i, source in enumerate(results.sources, 1):
            md += f"### {i}. {source.title}\n\n"
            md += f"**URL:** {source.url}\n"
            md += f"**Type:** {source.source_type}\n"
            md += f"**Domain:** {source.domain}\n"
            md += f"**Relevance Score:** {source.relevance_score}/10\n"
            md += f"**Description:** {source.description}\n\n"
        
        if results.suggestions:
            md += "## Related Search Suggestions\n\n"
            for suggestion in results.suggestions:
                md += f"- {suggestion}\n"
        
        return md

# Example usage and testing functions
def test_perplexity_client():
    """Test function for Perplexity client"""
    client = PerplexityClient()
    
    if not client._validate_api_key():
        print("❌ No API key found. Set PERPLEXITY_API_KEY environment variable.")
        return
    
    # Test search
    results = client.discover_sources(
        project_description="Create a dataset for sentiment analysis of product reviews",
        search_type=SearchType.GENERAL,
        max_sources=10
    )
    
    print(f"🔍 Found {len(results.sources)} sources")
    for source in results.sources[:3]:
        print(f"  - {source.title}: {source.url}")
    
    # Test export
    json_export = client.export_sources(results, "json")
    print(f"📄 JSON export: {len(json_export)} characters")

if __name__ == "__main__":
    # Test the client
    test_perplexity_client()