"""
Website Analyzer Service
Converted from website_analyzer.py for FastAPI integration.
"""

from typing import Dict, Any, List, Optional
from sqlalchemy.orm import Session
from loguru import logger
from datetime import datetime
import asyncio
import json
from collections import Counter, defaultdict

# Import existing modules (will be updated to use FastAPI services)
from services.database import get_db_session
from .ai_engine_service import AIEngineService

class WebsiteAnalyzer:
    """Analyzes website content structure and performance."""
    
    def __init__(self):
        """Initialize the website analyzer."""
        self.ai_engine = AIEngineService()
        
        logger.info("WebsiteAnalyzer initialized")
    
    async def analyze_website(self, url: str, industry: str = "general") -> Dict[str, Any]:
        """
        Analyze website content and structure.
        
        Args:
            url: Website URL to analyze
            industry: Industry category
            
        Returns:
            Website analysis results
        """
        try:
            logger.info(f"Starting website analysis for {url}")
            
            results = {
                'website_url': url,
                'industry': industry,
                'content_analysis': {},
                'structure_analysis': {},
                'performance_analysis': {},
                'seo_analysis': {},
                'ai_insights': {},
                'analysis_timestamp': datetime.utcnow().isoformat()
            }
            
            # Analyze content structure
            content_analysis = await self._analyze_content_structure(url)
            results['content_analysis'] = content_analysis
            
            # Analyze website structure
            structure_analysis = await self._analyze_website_structure(url)
            results['structure_analysis'] = structure_analysis
            
            # Analyze performance metrics
            performance_analysis = await self._analyze_performance_metrics(url)
            results['performance_analysis'] = performance_analysis
            
            # Analyze SEO aspects
            seo_analysis = await self._analyze_seo_aspects(url)
            results['seo_analysis'] = seo_analysis
            
            # Generate AI insights
            ai_insights = await self._generate_ai_insights(results)
            results['ai_insights'] = ai_insights
            
            logger.info(f"Website analysis completed for {url}")
            return results
            
        except Exception as e:
            logger.error(f"Error in website analysis: {str(e)}")
            return {}
    
    async def _analyze_content_structure(self, url: str) -> Dict[str, Any]:
        """
        Analyze content structure of the website.
        
        Args:
            url: Website URL
            
        Returns:
            Content structure analysis results
        """
        try:
            logger.info(f"Analyzing content structure for {url}")
            
            # TODO: Integrate with actual content analysis service
            # This will crawl and analyze website content
            
            # Simulate content structure analysis
            content_analysis = {
                'total_pages': 150,
                'content_types': {
                    'blog_posts': 80,
                    'product_pages': 30,
                    'landing_pages': 20,
                    'guides': 20
                },
                'content_topics': [
                    'Industry trends',
                    'Best practices',
                    'Case studies',
                    'Tutorials',
                    'Expert insights',
                    'Product information',
                    'Company news',
                    'Customer testimonials'
                ],
                'content_depth': {
                    'shallow': 20,
                    'medium': 60,
                    'deep': 70
                },
                'content_quality_score': 8.5,
                'content_freshness': {
                    'recent': 40,
                    'moderate': 50,
                    'outdated': 10
                },
                'content_engagement': {
                    'avg_time_on_page': 180,
                    'bounce_rate': 0.35,
                    'pages_per_session': 2.5,
                    'social_shares': 45
                }
            }
            
            logger.info("Content structure analysis completed")
            return content_analysis
            
        except Exception as e:
            logger.error(f"Error in content structure analysis: {str(e)}")
            return {}
    
    async def _analyze_website_structure(self, url: str) -> Dict[str, Any]:
        """
        Analyze website structure and navigation.
        
        Args:
            url: Website URL
            
        Returns:
            Website structure analysis results
        """
        try:
            logger.info(f"Analyzing website structure for {url}")
            
            # TODO: Integrate with actual structure analysis service
            # This will analyze website architecture and navigation
            
            # Simulate website structure analysis
            structure_analysis = {
                'navigation_structure': {
                    'main_menu_items': 8,
                    'footer_links': 15,
                    'breadcrumb_usage': True,
                    'sitemap_available': True
                },
                'url_structure': {
                    'avg_url_length': 45,
                    'seo_friendly_urls': True,
                    'url_depth': 3,
                    'canonical_urls': True
                },
                'internal_linking': {
                    'avg_internal_links_per_page': 8,
                    'link_anchor_text_optimization': 75,
                    'broken_links': 2,
                    'orphaned_pages': 5
                },
                'mobile_friendliness': {
                    'responsive_design': True,
                    'mobile_optimized': True,
                    'touch_friendly': True,
                    'mobile_speed': 85
                },
                'page_speed': {
                    'desktop_speed': 85,
                    'mobile_speed': 75,
                    'first_contentful_paint': 1.2,
                    'largest_contentful_paint': 2.5
                }
            }
            
            logger.info("Website structure analysis completed")
            return structure_analysis
            
        except Exception as e:
            logger.error(f"Error in website structure analysis: {str(e)}")
            return {}
    
    async def _analyze_performance_metrics(self, url: str) -> Dict[str, Any]:
        """
        Analyze website performance metrics.
        
        Args:
            url: Website URL
            
        Returns:
            Performance metrics analysis results
        """
        try:
            logger.info(f"Analyzing performance metrics for {url}")
            
            # TODO: Integrate with actual performance analysis service
            # This will analyze website performance metrics
            
            # Simulate performance metrics analysis
            performance_analysis = {
                'traffic_metrics': {
                    'monthly_visitors': '50K+',
                    'page_views': '150K+',
                    'unique_visitors': '35K+',
                    'traffic_growth': '15%'
                },
                'engagement_metrics': {
                    'avg_session_duration': '3:45',
                    'bounce_rate': '35%',
                    'pages_per_session': 2.5,
                    'return_visitor_rate': '25%'
                },
                'conversion_metrics': {
                    'conversion_rate': '3.5%',
                    'lead_generation': '500+ monthly',
                    'sales_conversion': '2.1%',
                    'email_signups': '200+ monthly'
                },
                'social_metrics': {
                    'social_shares': 45,
                    'social_comments': 12,
                    'social_engagement_rate': '8.5%',
                    'social_reach': '10K+'
                },
                'technical_metrics': {
                    'page_load_time': 2.1,
                    'server_response_time': 0.8,
                    'time_to_interactive': 3.2,
                    'cumulative_layout_shift': 0.1
                }
            }
            
            logger.info("Performance metrics analysis completed")
            return performance_analysis
            
        except Exception as e:
            logger.error(f"Error in performance metrics analysis: {str(e)}")
            return {}
    
    async def _analyze_seo_aspects(self, url: str) -> Dict[str, Any]:
        """
        Analyze SEO aspects of the website.
        
        Args:
            url: Website URL
            
        Returns:
            SEO analysis results
        """
        try:
            logger.info(f"Analyzing SEO aspects for {url}")
            
            # TODO: Integrate with actual SEO analysis service
            # This will analyze SEO aspects of the website
            
            # Simulate SEO analysis
            seo_analysis = {
                'technical_seo': {
                    'title_tag_optimization': 85,
                    'meta_description_optimization': 80,
                    'h1_usage': 95,
                    'image_alt_text': 70,
                    'schema_markup': True,
                    'ssl_certificate': True
                },
                'on_page_seo': {
                    'keyword_density': 2.5,
                    'internal_linking': 8,
                    'external_linking': 3,
                    'content_length': 1200,
                    'readability_score': 75
                },
                'off_page_seo': {
                    'domain_authority': 65,
                    'backlinks': 2500,
                    'referring_domains': 150,
                    'social_signals': 45
                },
                'keyword_rankings': {
                    'ranking_keywords': 85,
                    'top_10_rankings': 25,
                    'top_3_rankings': 8,
                    'featured_snippets': 3
                },
                'mobile_seo': {
                    'mobile_friendly': True,
                    'mobile_speed': 75,
                    'mobile_usability': 90,
                    'amp_pages': 0
                },
                'local_seo': {
                    'google_my_business': True,
                    'local_citations': 45,
                    'local_keywords': 12,
                    'local_rankings': 8
                }
            }
            
            logger.info("SEO analysis completed")
            return seo_analysis
            
        except Exception as e:
            logger.error(f"Error in SEO analysis: {str(e)}")
            return {}
    
    async def _generate_ai_insights(self, analysis_results: Dict[str, Any]) -> Dict[str, Any]:
        """
        Generate AI-powered insights for website analysis.
        
        Args:
            analysis_results: Complete website analysis results
            
        Returns:
            AI-generated insights
        """
        try:
            logger.info("🤖 Generating AI-powered website insights")
            
            # Prepare analysis summary for AI
            analysis_summary = {
                'url': analysis_results.get('website_url', ''),
                'industry': analysis_results.get('industry', ''),
                'content_count': analysis_results.get('content_analysis', {}).get('total_pages', 0),
                'content_quality': analysis_results.get('content_analysis', {}).get('content_quality_score', 0),
                'performance_score': analysis_results.get('performance_analysis', {}).get('traffic_metrics', {}).get('monthly_visitors', ''),
                'seo_score': analysis_results.get('seo_analysis', {}).get('technical_seo', {}).get('title_tag_optimization', 0)
            }
            
            # Generate comprehensive AI insights using AI engine
            ai_insights = await self.ai_engine.analyze_website_performance(analysis_summary)
            
            if ai_insights:
                logger.info("✅ Generated comprehensive AI website insights")
                return ai_insights
            else:
                logger.warning("⚠️ Could not generate AI website insights")
                return {}
                
        except Exception as e:
            logger.error(f"Error generating AI website insights: {str(e)}")
            return {}
    
    async def analyze_content_quality(self, url: str) -> Dict[str, Any]:
        """
        Analyze content quality of the website.
        
        Args:
            url: Website URL
            
        Returns:
            Content quality analysis results
        """
        try:
            logger.info(f"Analyzing content quality for {url}")
            
            # TODO: Integrate with actual content quality analysis service
            # This will analyze content quality metrics
            
            # Simulate content quality analysis
            quality_analysis = {
                'overall_quality_score': 8.5,
                'quality_dimensions': {
                    'readability': 8.0,
                    'comprehensiveness': 9.0,
                    'accuracy': 8.5,
                    'engagement': 7.5,
                    'seo_optimization': 8.0
                },
                'content_strengths': [
                    'Comprehensive topic coverage',
                    'Expert-level insights',
                    'Clear structure and organization',
                    'Accurate information',
                    'Good readability'
                ],
                'content_weaknesses': [
                    'Limited visual content',
                    'Missing interactive elements',
                    'Outdated information in some areas',
                    'Inconsistent content depth'
                ],
                'improvement_areas': [
                    {
                        'area': 'Visual Content',
                        'current_score': 6.0,
                        'target_score': 9.0,
                        'improvement_suggestions': [
                            'Add more images and infographics',
                            'Include video content',
                            'Create visual guides',
                            'Add interactive elements'
                        ]
                    },
                    {
                        'area': 'Content Freshness',
                        'current_score': 7.0,
                        'target_score': 9.0,
                        'improvement_suggestions': [
                            'Update outdated content',
                            'Add recent industry insights',
                            'Include current trends',
                            'Regular content audits'
                        ]
                    }
                ]
            }
            
            logger.info("Content quality analysis completed")
            return quality_analysis
            
        except Exception as e:
            logger.error(f"Error in content quality analysis: {str(e)}")
            return {}
    
    async def analyze_user_experience(self, url: str) -> Dict[str, Any]:
        """
        Analyze user experience aspects of the website.
        
        Args:
            url: Website URL
            
        Returns:
            User experience analysis results
        """
        try:
            logger.info(f"Analyzing user experience for {url}")
            
            # TODO: Integrate with actual UX analysis service
            # This will analyze user experience metrics
            
            # Simulate UX analysis
            ux_analysis = {
                'navigation_experience': {
                    'menu_clarity': 8.5,
                    'search_functionality': 7.0,
                    'breadcrumb_navigation': 9.0,
                    'mobile_navigation': 8.0
                },
                'content_accessibility': {
                    'font_readability': 8.5,
                    'color_contrast': 9.0,
                    'alt_text_usage': 7.5,
                    'keyboard_navigation': 8.0
                },
                'page_speed_experience': {
                    'loading_perception': 7.5,
                    'interactive_elements': 8.0,
                    'smooth_scrolling': 8.5,
                    'mobile_performance': 7.0
                },
                'content_engagement': {
                    'content_clarity': 8.5,
                    'call_to_action_visibility': 7.5,
                    'content_scannability': 8.0,
                    'information_architecture': 8.5
                },
                'overall_ux_score': 8.2,
                'improvement_suggestions': [
                    'Improve search functionality',
                    'Add more visual content',
                    'Optimize mobile experience',
                    'Enhance call-to-action visibility'
                ]
            }
            
            logger.info("User experience analysis completed")
            return ux_analysis
            
        except Exception as e:
            logger.error(f"Error in user experience analysis: {str(e)}")
            return {}
    
    async def get_website_summary(self, analysis_id: str) -> Dict[str, Any]:
        """
        Get a summary of website analysis.
        
        Args:
            analysis_id: Analysis identifier
            
        Returns:
            Website analysis summary
        """
        try:
            logger.info(f"Getting website analysis summary for {analysis_id}")
            
            # TODO: Retrieve analysis from database
            # This will be implemented when database integration is complete
            
            summary = {
                'analysis_id': analysis_id,
                'pages_analyzed': 25,
                'content_score': 8.5,
                'seo_score': 7.8,
                'user_experience_score': 8.2,
                'improvement_areas': [
                    'Content depth and comprehensiveness',
                    'SEO optimization',
                    'Mobile responsiveness'
                ],
                'timestamp': datetime.utcnow().isoformat()
            }
            
            return summary
            
        except Exception as e:
            logger.error(f"Error getting website summary: {str(e)}")
            return {}
    
    async def health_check(self) -> Dict[str, Any]:
        """
        Health check for the website analyzer service.
        
        Returns:
            Health status information
        """
        try:
            logger.info("Performing health check for WebsiteAnalyzer")
            
            health_status = {
                'service': 'WebsiteAnalyzer',
                'status': 'healthy',
                'dependencies': {
                    'ai_engine': 'operational'
                },
                'capabilities': {
                    'content_analysis': 'operational',
                    'structure_analysis': 'operational',
                    'performance_analysis': 'operational',
                    'seo_analysis': 'operational'
                },
                'timestamp': datetime.utcnow().isoformat()
            }
            
            logger.info("WebsiteAnalyzer health check passed")
            return health_status
            
        except Exception as e:
            logger.error(f"WebsiteAnalyzer health check failed: {str(e)}")
            return {
                'service': 'WebsiteAnalyzer',
                'status': 'unhealthy',
                'error': str(e),
                'timestamp': datetime.utcnow().isoformat()
            } 