"""
AI Analytics Service
Advanced AI-powered analytics for content planning and performance prediction.
"""

from typing import Dict, Any, List, Optional, Tuple
from datetime import datetime, timedelta
import json
from loguru import logger
import asyncio
from sqlalchemy.orm import Session

from services.database import get_db_session
from models.content_planning import ContentAnalytics, ContentStrategy, CalendarEvent
from services.content_gap_analyzer.ai_engine_service import AIEngineService

class AIAnalyticsService:
    """Advanced AI analytics service for content planning."""
    
    def __init__(self):
        self.ai_engine = AIEngineService()
        self.db_session = None
    
    def _get_db_session(self) -> Session:
        """Get database session."""
        if not self.db_session:
            self.db_session = get_db_session()
        return self.db_session
    
    async def analyze_content_evolution(self, strategy_id: int, time_period: str = "30d") -> Dict[str, Any]:
        """
        Analyze content evolution over time for a specific strategy.
        
        Args:
            strategy_id: Content strategy ID
            time_period: Analysis period (7d, 30d, 90d, 1y)
            
        Returns:
            Content evolution analysis results
        """
        try:
            logger.info(f"Analyzing content evolution for strategy {strategy_id}")
            
            # Get analytics data for the strategy
            analytics_data = await self._get_analytics_data(strategy_id, time_period)
            
            # Analyze content performance trends
            performance_trends = await self._analyze_performance_trends(analytics_data)
            
            # Analyze content type evolution
            content_evolution = await self._analyze_content_type_evolution(analytics_data)
            
            # Analyze audience engagement patterns
            engagement_patterns = await self._analyze_engagement_patterns(analytics_data)
            
            evolution_analysis = {
                'strategy_id': strategy_id,
                'time_period': time_period,
                'performance_trends': performance_trends,
                'content_evolution': content_evolution,
                'engagement_patterns': engagement_patterns,
                'recommendations': await self._generate_evolution_recommendations(
                    performance_trends, content_evolution, engagement_patterns
                ),
                'analysis_date': datetime.utcnow().isoformat()
            }
            
            logger.info(f"Content evolution analysis completed for strategy {strategy_id}")
            return evolution_analysis
            
        except Exception as e:
            logger.error(f"Error analyzing content evolution: {str(e)}")
            raise
    
    async def analyze_performance_trends(self, strategy_id: int, metrics: List[str] = None) -> Dict[str, Any]:
        """
        Analyze performance trends for content strategy.
        
        Args:
            strategy_id: Content strategy ID
            metrics: List of metrics to analyze (engagement, reach, conversion, etc.)
            
        Returns:
            Performance trend analysis results
        """
        try:
            logger.info(f"Analyzing performance trends for strategy {strategy_id}")
            
            if not metrics:
                metrics = ['engagement_rate', 'reach', 'conversion_rate', 'click_through_rate']
            
            # Get performance data
            performance_data = await self._get_performance_data(strategy_id, metrics)
            
            # Analyze trends for each metric
            trend_analysis = {}
            for metric in metrics:
                trend_analysis[metric] = await self._analyze_metric_trend(performance_data, metric)
            
            # Generate predictive insights
            predictive_insights = await self._generate_predictive_insights(trend_analysis)
            
            # Calculate performance scores
            performance_scores = await self._calculate_performance_scores(trend_analysis)
            
            trend_results = {
                'strategy_id': strategy_id,
                'metrics_analyzed': metrics,
                'trend_analysis': trend_analysis,
                'predictive_insights': predictive_insights,
                'performance_scores': performance_scores,
                'recommendations': await self._generate_trend_recommendations(trend_analysis),
                'analysis_date': datetime.utcnow().isoformat()
            }
            
            logger.info(f"Performance trend analysis completed for strategy {strategy_id}")
            return trend_results
            
        except Exception as e:
            logger.error(f"Error analyzing performance trends: {str(e)}")
            raise
    
    async def predict_content_performance(self, content_data: Dict[str, Any], 
                                       strategy_id: int) -> Dict[str, Any]:
        """
        Predict content performance using AI models.
        
        Args:
            content_data: Content details (title, description, type, platform, etc.)
            strategy_id: Content strategy ID
            
        Returns:
            Performance prediction results
        """
        try:
            logger.info(f"Predicting performance for content in strategy {strategy_id}")
            
            # Get historical performance data
            historical_data = await self._get_historical_performance_data(strategy_id)
            
            # Analyze content characteristics
            content_analysis = await self._analyze_content_characteristics(content_data)
            
            # Calculate success probability
            success_probability = await self._calculate_success_probability({}, historical_data)
            
            # Generate optimization recommendations
            optimization_recommendations = await self._generate_optimization_recommendations(
                content_data, {}, success_probability
            )
            
            prediction_results = {
                'strategy_id': strategy_id,
                'content_data': content_data,
                'performance_prediction': {},
                'success_probability': success_probability,
                'optimization_recommendations': optimization_recommendations,
                'confidence_score': 0.7,
                'prediction_date': datetime.utcnow().isoformat()
            }
            
            logger.info(f"Content performance prediction completed")
            return prediction_results
            
        except Exception as e:
            logger.error(f"Error predicting content performance: {str(e)}")
            raise
    
    async def generate_strategic_intelligence(self, strategy_id: int, 
                                           market_data: Dict[str, Any] = None) -> Dict[str, Any]:
        """
        Generate strategic intelligence for content planning.
        
        Args:
            strategy_id: Content strategy ID
            market_data: Additional market data for analysis
            
        Returns:
            Strategic intelligence results
        """
        try:
            logger.info(f"Generating strategic intelligence for strategy {strategy_id}")
            
            # Get strategy data
            strategy_data = await self._get_strategy_data(strategy_id)
            
            # Analyze market positioning
            market_positioning = await self._analyze_market_positioning(strategy_data, market_data)
            
            # Identify competitive advantages
            competitive_advantages = await self._identify_competitive_advantages(strategy_data)
            
            # Calculate strategic scores
            strategic_scores = await self._calculate_strategic_scores(
                strategy_data, market_positioning, competitive_advantages
            )
            
            intelligence_results = {
                'strategy_id': strategy_id,
                'market_positioning': market_positioning,
                'competitive_advantages': competitive_advantages,
                'strategic_scores': strategic_scores,
                'risk_assessment': await self._assess_strategic_risks(strategy_data),
                'opportunity_analysis': await self._analyze_strategic_opportunities(strategy_data),
                'analysis_date': datetime.utcnow().isoformat()
            }
            
            logger.info(f"Strategic intelligence generation completed")
            return intelligence_results
            
        except Exception as e:
            logger.error(f"Error generating strategic intelligence: {str(e)}")
            raise
    
    # Helper methods for data retrieval and analysis
    async def _get_analytics_data(self, strategy_id: int, time_period: str) -> List[Dict[str, Any]]:
        """Get analytics data for the specified strategy and time period."""
        try:
            session = self._get_db_session()
            
            # Calculate date range
            end_date = datetime.utcnow()
            if time_period == "7d":
                start_date = end_date - timedelta(days=7)
            elif time_period == "30d":
                start_date = end_date - timedelta(days=30)
            elif time_period == "90d":
                start_date = end_date - timedelta(days=90)
            elif time_period == "1y":
                start_date = end_date - timedelta(days=365)
            else:
                start_date = end_date - timedelta(days=30)
            
            # Query analytics data
            analytics = session.query(ContentAnalytics).filter(
                ContentAnalytics.strategy_id == strategy_id,
                ContentAnalytics.recorded_at >= start_date,
                ContentAnalytics.recorded_at <= end_date
            ).all()
            
            return [analytics.to_dict() for analytics in analytics]
            
        except Exception as e:
            logger.error(f"Error getting analytics data: {str(e)}")
            return []
    
    async def _analyze_performance_trends(self, analytics_data: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Analyze performance trends from analytics data."""
        try:
            if not analytics_data:
                return {'trend': 'stable', 'growth_rate': 0, 'insights': 'No data available'}
            
            # Calculate trend metrics
            total_analytics = len(analytics_data)
            avg_performance = sum(item.get('performance_score', 0) for item in analytics_data) / total_analytics
            
            # Determine trend direction
            if avg_performance > 0.7:
                trend = 'increasing'
            elif avg_performance < 0.3:
                trend = 'decreasing'
            else:
                trend = 'stable'
            
            return {
                'trend': trend,
                'average_performance': avg_performance,
                'total_analytics': total_analytics,
                'insights': f'Performance is {trend} with average score of {avg_performance:.2f}'
            }
            
        except Exception as e:
            logger.error(f"Error analyzing performance trends: {str(e)}")
            return {'trend': 'unknown', 'error': str(e)}
    
    async def _analyze_content_type_evolution(self, analytics_data: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Analyze how content types have evolved over time."""
        try:
            content_types = {}
            for data in analytics_data:
                content_type = data.get('content_type', 'unknown')
                if content_type not in content_types:
                    content_types[content_type] = {
                        'count': 0,
                        'total_performance': 0,
                        'avg_performance': 0
                    }
                
                content_types[content_type]['count'] += 1
                content_types[content_type]['total_performance'] += data.get('performance_score', 0)
            
            # Calculate averages
            for content_type in content_types:
                if content_types[content_type]['count'] > 0:
                    content_types[content_type]['avg_performance'] = (
                        content_types[content_type]['total_performance'] / 
                        content_types[content_type]['count']
                    )
            
            return {
                'content_types': content_types,
                'most_performing_type': max(content_types.items(), key=lambda x: x[1]['avg_performance'])[0] if content_types else None,
                'evolution_insights': 'Content type performance analysis completed'
            }
            
        except Exception as e:
            logger.error(f"Error analyzing content type evolution: {str(e)}")
            return {'error': str(e)}
    
    async def _analyze_engagement_patterns(self, analytics_data: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Analyze audience engagement patterns."""
        try:
            if not analytics_data:
                return {'patterns': {}, 'insights': 'No engagement data available'}
            
            # Analyze engagement by platform
            platform_engagement = {}
            for data in analytics_data:
                platform = data.get('platform', 'unknown')
                if platform not in platform_engagement:
                    platform_engagement[platform] = {
                        'total_engagement': 0,
                        'count': 0,
                        'avg_engagement': 0
                    }
                
                metrics = data.get('metrics', {})
                engagement = metrics.get('engagement_rate', 0)
                platform_engagement[platform]['total_engagement'] += engagement
                platform_engagement[platform]['count'] += 1
            
            # Calculate averages
            for platform in platform_engagement:
                if platform_engagement[platform]['count'] > 0:
                    platform_engagement[platform]['avg_engagement'] = (
                        platform_engagement[platform]['total_engagement'] / 
                        platform_engagement[platform]['count']
                    )
            
            return {
                'platform_engagement': platform_engagement,
                'best_platform': max(platform_engagement.items(), key=lambda x: x[1]['avg_engagement'])[0] if platform_engagement else None,
                'insights': 'Platform engagement analysis completed'
            }
            
        except Exception as e:
            logger.error(f"Error analyzing engagement patterns: {str(e)}")
            return {'error': str(e)}
    
    async def _generate_evolution_recommendations(self, performance_trends: Dict[str, Any], 
                                               content_evolution: Dict[str, Any], 
                                               engagement_patterns: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Generate recommendations based on evolution analysis."""
        recommendations = []
        
        try:
            # Performance-based recommendations
            if performance_trends.get('trend') == 'decreasing':
                recommendations.append({
                    'type': 'performance_optimization',
                    'priority': 'high',
                    'title': 'Improve Content Performance',
                    'description': 'Content performance is declining. Focus on quality and engagement.',
                    'action_items': [
                        'Review and improve content quality',
                        'Optimize for audience engagement',
                        'Analyze competitor strategies'
                    ]
                })
            
            # Content type recommendations
            if content_evolution.get('most_performing_type'):
                best_type = content_evolution['most_performing_type']
                recommendations.append({
                    'type': 'content_strategy',
                    'priority': 'medium',
                    'title': f'Focus on {best_type} Content',
                    'description': f'{best_type} content is performing best. Increase focus on this type.',
                    'action_items': [
                        f'Increase {best_type} content production',
                        'Analyze what makes this content successful',
                        'Optimize other content types based on learnings'
                    ]
                })
            
            # Platform recommendations
            if engagement_patterns.get('best_platform'):
                best_platform = engagement_patterns['best_platform']
                recommendations.append({
                    'type': 'platform_strategy',
                    'priority': 'medium',
                    'title': f'Optimize for {best_platform}',
                    'description': f'{best_platform} shows highest engagement. Focus optimization efforts here.',
                    'action_items': [
                        f'Increase content for {best_platform}',
                        f'Optimize content format for platform',
                        'Use platform-specific features'
                    ]
                })
            
            return recommendations
            
        except Exception as e:
            logger.error(f"Error generating evolution recommendations: {str(e)}")
            return [{'error': str(e)}]
    
    async def _get_performance_data(self, strategy_id: int, metrics: List[str]) -> List[Dict[str, Any]]:
        """Get performance data for specified metrics."""
        try:
            session = self._get_db_session()
            
            # Get analytics data for the strategy
            analytics = session.query(ContentAnalytics).filter(
                ContentAnalytics.strategy_id == strategy_id
            ).all()
            
            return [analytics.to_dict() for analytics in analytics]
            
        except Exception as e:
            logger.error(f"Error getting performance data: {str(e)}")
            return []
    
    async def _analyze_metric_trend(self, performance_data: List[Dict[str, Any]], metric: str) -> Dict[str, Any]:
        """Analyze trend for a specific metric."""
        try:
            if not performance_data:
                return {'trend': 'no_data', 'value': 0, 'change': 0}
            
            # Extract metric values
            metric_values = []
            for data in performance_data:
                metrics = data.get('metrics', {})
                if metric in metrics:
                    metric_values.append(metrics[metric])
            
            if not metric_values:
                return {'trend': 'no_data', 'value': 0, 'change': 0}
            
            # Calculate trend
            avg_value = sum(metric_values) / len(metric_values)
            
            # Simple trend calculation
            if len(metric_values) >= 2:
                recent_avg = sum(metric_values[-len(metric_values)//2:]) / (len(metric_values)//2)
                older_avg = sum(metric_values[:len(metric_values)//2]) / (len(metric_values)//2)
                change = ((recent_avg - older_avg) / older_avg * 100) if older_avg > 0 else 0
            else:
                change = 0
            
            # Determine trend direction
            if change > 5:
                trend = 'increasing'
            elif change < -5:
                trend = 'decreasing'
            else:
                trend = 'stable'
            
            return {
                'trend': trend,
                'value': avg_value,
                'change_percent': change,
                'data_points': len(metric_values)
            }
            
        except Exception as e:
            logger.error(f"Error analyzing metric trend: {str(e)}")
            return {'trend': 'error', 'error': str(e)}
    
    async def _generate_predictive_insights(self, trend_analysis: Dict[str, Any]) -> Dict[str, Any]:
        """Generate predictive insights based on trend analysis."""
        try:
            insights = {
                'predicted_performance': 'stable',
                'confidence_level': 'medium',
                'key_factors': [],
                'recommendations': []
            }
            
            # Analyze trends to generate insights
            increasing_metrics = []
            decreasing_metrics = []
            
            for metric, analysis in trend_analysis.items():
                if analysis.get('trend') == 'increasing':
                    increasing_metrics.append(metric)
                elif analysis.get('trend') == 'decreasing':
                    decreasing_metrics.append(metric)
            
            if len(increasing_metrics) > len(decreasing_metrics):
                insights['predicted_performance'] = 'improving'
                insights['confidence_level'] = 'high' if len(increasing_metrics) > 2 else 'medium'
            elif len(decreasing_metrics) > len(increasing_metrics):
                insights['predicted_performance'] = 'declining'
                insights['confidence_level'] = 'high' if len(decreasing_metrics) > 2 else 'medium'
            
            insights['key_factors'] = increasing_metrics + decreasing_metrics
            insights['recommendations'] = [
                f'Focus on improving {", ".join(decreasing_metrics)}' if decreasing_metrics else 'Maintain current performance',
                f'Leverage success in {", ".join(increasing_metrics)}' if increasing_metrics else 'Identify new growth opportunities'
            ]
            
            return insights
            
        except Exception as e:
            logger.error(f"Error generating predictive insights: {str(e)}")
            return {'error': str(e)}
    
    async def _calculate_performance_scores(self, trend_analysis: Dict[str, Any]) -> Dict[str, float]:
        """Calculate performance scores based on trend analysis."""
        try:
            scores = {}
            
            for metric, analysis in trend_analysis.items():
                base_score = analysis.get('value', 0)
                change = analysis.get('change_percent', 0)
                
                # Adjust score based on trend
                if analysis.get('trend') == 'increasing':
                    adjusted_score = base_score * (1 + abs(change) / 100)
                elif analysis.get('trend') == 'decreasing':
                    adjusted_score = base_score * (1 - abs(change) / 100)
                else:
                    adjusted_score = base_score
                
                scores[metric] = min(adjusted_score, 1.0)  # Cap at 1.0
            
            return scores
            
        except Exception as e:
            logger.error(f"Error calculating performance scores: {str(e)}")
            return {}
    
    async def _generate_trend_recommendations(self, trend_analysis: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Generate recommendations based on trend analysis."""
        recommendations = []
        
        try:
            for metric, analysis in trend_analysis.items():
                if analysis.get('trend') == 'decreasing':
                    recommendations.append({
                        'type': 'metric_optimization',
                        'priority': 'high',
                        'metric': metric,
                        'title': f'Improve {metric.replace("_", " ").title()}',
                        'description': f'{metric} is declining. Focus on optimization.',
                        'action_items': [
                            f'Analyze factors affecting {metric}',
                            'Review content strategy for this metric',
                            'Implement optimization strategies'
                        ]
                    })
                elif analysis.get('trend') == 'increasing':
                    recommendations.append({
                        'type': 'metric_leverage',
                        'priority': 'medium',
                        'metric': metric,
                        'title': f'Leverage {metric.replace("_", " ").title()} Success',
                        'description': f'{metric} is improving. Build on this success.',
                        'action_items': [
                            f'Identify what\'s driving {metric} improvement',
                            'Apply successful strategies to other metrics',
                            'Scale successful approaches'
                        ]
                    })
            
            return recommendations
            
        except Exception as e:
            logger.error(f"Error generating trend recommendations: {str(e)}")
            return [{'error': str(e)}]
    
    async def _analyze_single_competitor(self, url: str, analysis_period: str) -> Dict[str, Any]:
        """Analyze a single competitor's content strategy."""
        try:
            # This would integrate with the competitor analyzer service
            # For now, return mock data
            return {
                'url': url,
                'content_frequency': 'weekly',
                'content_types': ['blog', 'video', 'social'],
                'engagement_rate': 0.75,
                'top_performing_content': ['How-to guides', 'Industry insights'],
                'publishing_schedule': ['Tuesday', 'Thursday'],
                'content_themes': ['Educational', 'Thought leadership', 'Engagement']
            }
            
        except Exception as e:
            logger.error(f"Error analyzing competitor {url}: {str(e)}")
            return {'url': url, 'error': str(e)}
    
    async def _compare_competitor_strategies(self, competitor_analyses: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Compare strategies across competitors."""
        try:
            if not competitor_analyses:
                return {'comparison': 'no_data'}
            
            # Analyze common patterns
            content_types = set()
            themes = set()
            schedules = set()
            
            for analysis in competitor_analyses:
                if 'content_types' in analysis:
                    content_types.update(analysis['content_types'])
                if 'content_themes' in analysis:
                    themes.update(analysis['content_themes'])
                if 'publishing_schedule' in analysis:
                    schedules.update(analysis['publishing_schedule'])
            
            return {
                'common_content_types': list(content_types),
                'common_themes': list(themes),
                'common_schedules': list(schedules),
                'competitive_landscape': 'analyzed',
                'insights': f'Found {len(content_types)} content types, {len(themes)} themes across competitors'
            }
            
        except Exception as e:
            logger.error(f"Error comparing competitor strategies: {str(e)}")
            return {'error': str(e)}
    
    async def _identify_market_trends(self, competitor_analyses: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Identify market trends from competitor analysis."""
        try:
            trends = {
                'popular_content_types': [],
                'emerging_themes': [],
                'publishing_patterns': [],
                'engagement_trends': []
            }
            
            # Analyze trends from competitor data
            content_type_counts = {}
            theme_counts = {}
            
            for analysis in competitor_analyses:
                for content_type in analysis.get('content_types', []):
                    content_type_counts[content_type] = content_type_counts.get(content_type, 0) + 1
                
                for theme in analysis.get('content_themes', []):
                    theme_counts[theme] = theme_counts.get(theme, 0) + 1
            
            trends['popular_content_types'] = sorted(content_type_counts.items(), key=lambda x: x[1], reverse=True)
            trends['emerging_themes'] = sorted(theme_counts.items(), key=lambda x: x[1], reverse=True)
            
            return trends
            
        except Exception as e:
            logger.error(f"Error identifying market trends: {str(e)}")
            return {'error': str(e)}
    
    async def _generate_competitor_recommendations(self, competitor_analyses: List[Dict[str, Any]], 
                                                strategy_comparison: Dict[str, Any], 
                                                market_trends: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Generate recommendations based on competitor analysis."""
        recommendations = []
        
        try:
            # Identify opportunities
            popular_types = [item[0] for item in market_trends.get('popular_content_types', [])]
            if popular_types:
                recommendations.append({
                    'type': 'content_strategy',
                    'priority': 'high',
                    'title': 'Focus on Popular Content Types',
                    'description': f'Competitors are successfully using: {", ".join(popular_types[:3])}',
                    'action_items': [
                        'Analyze successful content in these categories',
                        'Develop content strategy for popular types',
                        'Differentiate while following proven patterns'
                    ]
                })
            
            # Identify gaps
            all_competitor_themes = set()
            for analysis in competitor_analyses:
                all_competitor_themes.update(analysis.get('content_themes', []))
            
            if all_competitor_themes:
                recommendations.append({
                    'type': 'competitive_advantage',
                    'priority': 'medium',
                    'title': 'Identify Content Gaps',
                    'description': 'Look for opportunities competitors are missing',
                    'action_items': [
                        'Analyze underserved content areas',
                        'Identify unique positioning opportunities',
                        'Develop differentiated content strategy'
                    ]
                })
            
            return recommendations
            
        except Exception as e:
            logger.error(f"Error generating competitor recommendations: {str(e)}")
            return [{'error': str(e)}]
    
    async def _get_historical_performance_data(self, strategy_id: int) -> List[Dict[str, Any]]:
        """Get historical performance data for the strategy."""
        try:
            session = self._get_db_session()
            
            analytics = session.query(ContentAnalytics).filter(
                ContentAnalytics.strategy_id == strategy_id
            ).all()
            
            return [analytics.to_dict() for analytics in analytics]
            
        except Exception as e:
            logger.error(f"Error getting historical performance data: {str(e)}")
            return []
    
    async def _analyze_content_characteristics(self, content_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze content characteristics for performance prediction."""
        try:
            characteristics = {
                'content_type': content_data.get('content_type', 'unknown'),
                'platform': content_data.get('platform', 'unknown'),
                'estimated_length': content_data.get('estimated_length', 'medium'),
                'complexity': 'medium',
                'engagement_potential': 'medium',
                'seo_potential': 'medium'
            }
            
            # Analyze title and description
            title = content_data.get('title', '')
            description = content_data.get('description', '')
            
            if title and description:
                characteristics['content_richness'] = 'high' if len(description) > 200 else 'medium'
                characteristics['title_optimization'] = 'good' if len(title) > 20 and len(title) < 60 else 'needs_improvement'
            
            return characteristics
            
        except Exception as e:
            logger.error(f"Error analyzing content characteristics: {str(e)}")
            return {'error': str(e)}
    
    async def _calculate_success_probability(self, performance_prediction: Dict[str, Any], 
                                          historical_data: List[Dict[str, Any]]) -> float:
        """Calculate success probability based on prediction and historical data."""
        try:
            base_probability = 0.5
            
            # Adjust based on historical performance
            if historical_data:
                avg_historical_performance = sum(
                    data.get('performance_score', 0) for data in historical_data
                ) / len(historical_data)
                
                if avg_historical_performance > 0.7:
                    base_probability += 0.1
                elif avg_historical_performance < 0.3:
                    base_probability -= 0.1
            
            return min(max(base_probability, 0.0), 1.0)
            
        except Exception as e:
            logger.error(f"Error calculating success probability: {str(e)}")
            return 0.5
    
    async def _generate_optimization_recommendations(self, content_data: Dict[str, Any], 
                                                   performance_prediction: Dict[str, Any], 
                                                   success_probability: float) -> List[Dict[str, Any]]:
        """Generate optimization recommendations for content."""
        recommendations = []
        
        try:
            # Performance-based recommendations
            if success_probability < 0.5:
                recommendations.append({
                    'type': 'content_optimization',
                    'priority': 'high',
                    'title': 'Improve Content Quality',
                    'description': 'Content has low success probability. Focus on quality improvements.',
                    'action_items': [
                        'Enhance content depth and value',
                        'Improve title and description',
                        'Optimize for target audience'
                    ]
                })
            
            # Platform-specific recommendations
            platform = content_data.get('platform', '')
            if platform:
                recommendations.append({
                    'type': 'platform_optimization',
                    'priority': 'medium',
                    'title': f'Optimize for {platform}',
                    'description': f'Ensure content is optimized for {platform} platform.',
                    'action_items': [
                        f'Follow {platform} best practices',
                        'Optimize content format for platform',
                        'Use platform-specific features'
                    ]
                })
            
            return recommendations
            
        except Exception as e:
            logger.error(f"Error generating optimization recommendations: {str(e)}")
            return [{'error': str(e)}]
    
    async def _get_strategy_data(self, strategy_id: int) -> Dict[str, Any]:
        """Get strategy data for analysis."""
        try:
            session = self._get_db_session()
            
            strategy = session.query(ContentStrategy).filter(
                ContentStrategy.id == strategy_id
            ).first()
            
            if strategy:
                return strategy.to_dict()
            else:
                return {}
                
        except Exception as e:
            logger.error(f"Error getting strategy data: {str(e)}")
            return {}
    
    async def _analyze_market_positioning(self, strategy_data: Dict[str, Any], 
                                        market_data: Dict[str, Any] = None) -> Dict[str, Any]:
        """Analyze market positioning for the strategy."""
        try:
            positioning = {
                'industry_position': 'established',
                'competitive_advantage': 'content_quality',
                'market_share': 'medium',
                'differentiation_factors': []
            }
            
            # Analyze based on strategy data
            industry = strategy_data.get('industry', '')
            if industry:
                positioning['industry_position'] = 'established' if industry in ['tech', 'finance', 'healthcare'] else 'emerging'
            
            # Analyze content pillars
            content_pillars = strategy_data.get('content_pillars', [])
            if content_pillars:
                positioning['differentiation_factors'] = [pillar.get('name', '') for pillar in content_pillars]
            
            return positioning
            
        except Exception as e:
            logger.error(f"Error analyzing market positioning: {str(e)}")
            return {'error': str(e)}
    
    async def _identify_competitive_advantages(self, strategy_data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Identify competitive advantages for the strategy."""
        try:
            advantages = []
            
            # Analyze content pillars for advantages
            content_pillars = strategy_data.get('content_pillars', [])
            for pillar in content_pillars:
                advantages.append({
                    'type': 'content_pillar',
                    'name': pillar.get('name', ''),
                    'description': pillar.get('description', ''),
                    'strength': 'high' if pillar.get('frequency') == 'weekly' else 'medium'
                })
            
            # Analyze target audience
            target_audience = strategy_data.get('target_audience', {})
            if target_audience:
                advantages.append({
                    'type': 'audience_focus',
                    'name': 'Targeted Audience',
                    'description': 'Well-defined target audience',
                    'strength': 'high'
                })
            
            return advantages
            
        except Exception as e:
            logger.error(f"Error identifying competitive advantages: {str(e)}")
            return []
    
    async def _calculate_strategic_scores(self, strategy_data: Dict[str, Any], 
                                        market_positioning: Dict[str, Any], 
                                        competitive_advantages: List[Dict[str, Any]]) -> Dict[str, float]:
        """Calculate strategic scores for the strategy."""
        try:
            scores = {
                'market_positioning_score': 0.7,
                'competitive_advantage_score': 0.8,
                'content_strategy_score': 0.75,
                'overall_strategic_score': 0.75
            }
            
            # Adjust scores based on analysis
            if market_positioning.get('industry_position') == 'established':
                scores['market_positioning_score'] += 0.1
            
            if len(competitive_advantages) > 2:
                scores['competitive_advantage_score'] += 0.1
            
            # Calculate overall score
            scores['overall_strategic_score'] = sum(scores.values()) / len(scores)
            
            return scores
            
        except Exception as e:
            logger.error(f"Error calculating strategic scores: {str(e)}")
            return {'error': str(e)}
    
    async def _assess_strategic_risks(self, strategy_data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Assess strategic risks for the strategy."""
        try:
            risks = []
            
            # Analyze potential risks
            content_pillars = strategy_data.get('content_pillars', [])
            if len(content_pillars) < 2:
                risks.append({
                    'type': 'content_diversity',
                    'severity': 'medium',
                    'description': 'Limited content pillar diversity',
                    'mitigation': 'Develop additional content pillars'
                })
            
            target_audience = strategy_data.get('target_audience', {})
            if not target_audience:
                risks.append({
                    'type': 'audience_definition',
                    'severity': 'high',
                    'description': 'Unclear target audience definition',
                    'mitigation': 'Define detailed audience personas'
                })
            
            return risks
            
        except Exception as e:
            logger.error(f"Error assessing strategic risks: {str(e)}")
            return []
    
    async def _analyze_strategic_opportunities(self, strategy_data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Analyze strategic opportunities for the strategy."""
        try:
            opportunities = []
            
            # Identify opportunities based on strategy data
            industry = strategy_data.get('industry', '')
            if industry:
                opportunities.append({
                    'type': 'industry_growth',
                    'priority': 'high',
                    'description': f'Growing {industry} industry presents expansion opportunities',
                    'action_items': [
                        'Monitor industry trends',
                        'Develop industry-specific content',
                        'Expand into emerging sub-sectors'
                    ]
                })
            
            content_pillars = strategy_data.get('content_pillars', [])
            if content_pillars:
                opportunities.append({
                    'type': 'content_expansion',
                    'priority': 'medium',
                    'description': 'Opportunity to expand content pillar coverage',
                    'action_items': [
                        'Identify underserved content areas',
                        'Develop new content pillars',
                        'Expand into new content formats'
                    ]
                })
            
            return opportunities
            
        except Exception as e:
            logger.error(f"Error analyzing strategic opportunities: {str(e)}")
            return [] 