"""
Content Quality Optimizer Module

This module optimizes content quality and provides quality improvement recommendations.
It ensures content excellence, readability optimization, and quality enhancement strategies.
"""

import asyncio
from typing import Dict, Any, List, Optional
from loguru import logger
import sys
import os

# Add the services directory to the path for proper imports
services_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))))
if services_dir not in sys.path:
    sys.path.insert(0, services_dir)

try:
    from content_gap_analyzer.ai_engine_service import AIEngineService
    from content_gap_analyzer.keyword_researcher import KeywordResearcher
except ImportError:
    raise ImportError("Required AI services not available. Cannot proceed without real AI services.")


class ContentQualityOptimizer:
    """
    Optimizes content quality and provides quality improvement recommendations.
    
    This module ensures:
    - Content excellence and readability optimization
    - Quality enhancement strategies
    - Content optimization recommendations
    - Quality metrics calculation
    - Content improvement validation
    """
    
    def __init__(self):
        """Initialize the content quality optimizer with real AI services."""
        self.ai_engine = AIEngineService()
        self.keyword_researcher = KeywordResearcher()
        
        # Content quality rules
        self.quality_rules = {
            "min_readability_score": 0.7,
            "target_readability_score": 0.85,
            "min_engagement_score": 0.6,
            "target_engagement_score": 0.8,
            "min_uniqueness_score": 0.8,
            "target_uniqueness_score": 0.9,
            "quality_confidence": 0.8
        }
        
        # Quality metrics weights
        self.quality_weights = {
            "readability": 0.25,
            "engagement": 0.25,
            "uniqueness": 0.25,
            "relevance": 0.25
        }
        
        logger.info("🎯 Content Quality Optimizer initialized with real AI services")
    
    async def optimize_content_quality(
        self,
        calendar_data: Dict[str, Any],
        target_audience: Dict[str, Any],
        business_goals: List[str],
        quality_requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Optimize content quality across the calendar.
        
        Args:
            calendar_data: Calendar data from previous steps
            target_audience: Target audience information
            business_goals: Business goals from strategy
            quality_requirements: Quality requirements and standards
            
        Returns:
            Comprehensive content quality optimization results
        """
        try:
            logger.info("🚀 Starting content quality optimization")
            
            # Extract content from calendar
            weekly_themes = calendar_data.get("step7_results", {}).get("weekly_themes", [])
            daily_schedules = calendar_data.get("step8_results", {}).get("daily_schedules", [])
            content_recommendations = calendar_data.get("step9_results", {}).get("content_recommendations", [])
            
            # Analyze current content quality
            current_quality_analysis = await self._analyze_current_content_quality(
                weekly_themes, daily_schedules, content_recommendations, target_audience
            )
            
            # Generate quality improvement recommendations
            quality_improvements = await self._generate_quality_improvements(
                current_quality_analysis, target_audience, business_goals, quality_requirements
            )
            
            # Optimize content for better quality
            optimized_content = await self._optimize_content_for_quality(
                weekly_themes, daily_schedules, content_recommendations,
                quality_improvements, target_audience
            )
            
            # Calculate quality metrics
            quality_metrics = await self._calculate_quality_metrics(
                optimized_content, target_audience, business_goals
            )
            
            # Validate quality improvements
            quality_validation = await self._validate_quality_improvements(
                current_quality_analysis, quality_metrics, quality_requirements
            )
            
            # Create comprehensive quality optimization results
            optimization_results = {
                "current_quality_analysis": current_quality_analysis,
                "quality_improvements": quality_improvements,
                "optimized_content": optimized_content,
                "quality_metrics": quality_metrics,
                "quality_validation": quality_validation,
                "overall_quality_score": self._calculate_overall_quality_score(quality_metrics),
                "quality_optimization_insights": await self._generate_quality_insights(
                    current_quality_analysis, quality_metrics, quality_improvements
                )
            }
            
            logger.info("✅ Content quality optimization completed successfully")
            return optimization_results
            
        except Exception as e:
            logger.error(f"❌ Error in content quality optimization: {str(e)}")
            raise
    
    async def _analyze_current_content_quality(
        self,
        weekly_themes: List[Dict],
        daily_schedules: List[Dict],
        content_recommendations: List[Dict],
        target_audience: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Analyze current content quality across all calendar components."""
        try:
            logger.info("📊 Analyzing current content quality")
            
            # Analyze weekly themes quality
            themes_quality = await self._analyze_themes_quality(weekly_themes, target_audience)
            
            # Analyze daily schedules quality
            schedules_quality = await self._analyze_schedules_quality(daily_schedules, target_audience)
            
            # Analyze content recommendations quality
            recommendations_quality = await self._analyze_recommendations_quality(
                content_recommendations, target_audience
            )
            
            # Calculate overall current quality
            overall_current_quality = self._calculate_weighted_quality_score([
                themes_quality.get("overall_score", 0.0),
                schedules_quality.get("overall_score", 0.0),
                recommendations_quality.get("overall_score", 0.0)
            ])
            
            current_quality_analysis = {
                "themes_quality": themes_quality,
                "schedules_quality": schedules_quality,
                "recommendations_quality": recommendations_quality,
                "overall_current_quality": overall_current_quality,
                "quality_insights": await self._generate_current_quality_insights(
                    themes_quality, schedules_quality, recommendations_quality
                )
            }
            
            return current_quality_analysis
            
        except Exception as e:
            logger.error(f"❌ Error analyzing current content quality: {str(e)}")
            raise
    
    async def _generate_quality_improvements(
        self,
        current_quality_analysis: Dict[str, Any],
        target_audience: Dict[str, Any],
        business_goals: List[str],
        quality_requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate specific quality improvement recommendations."""
        try:
            logger.info("🔧 Generating quality improvement recommendations")
            
            # Identify readability improvements
            readability_improvements = await self._identify_readability_improvements(
                current_quality_analysis, target_audience, quality_requirements
            )
            
            # Identify engagement improvements
            engagement_improvements = await self._identify_engagement_improvements(
                current_quality_analysis, target_audience, business_goals
            )
            
            # Identify uniqueness improvements
            uniqueness_improvements = await self._identify_uniqueness_improvements(
                current_quality_analysis, quality_requirements
            )
            
            # Identify relevance improvements
            relevance_improvements = await self._identify_relevance_improvements(
                current_quality_analysis, target_audience, business_goals
            )
            
            # Prioritize improvements
            prioritized_improvements = await self._prioritize_quality_improvements(
                readability_improvements, engagement_improvements,
                uniqueness_improvements, relevance_improvements,
                quality_requirements
            )
            
            quality_improvements = {
                "readability_improvements": readability_improvements,
                "engagement_improvements": engagement_improvements,
                "uniqueness_improvements": uniqueness_improvements,
                "relevance_improvements": relevance_improvements,
                "prioritized_improvements": prioritized_improvements,
                "improvement_roadmap": await self._create_improvement_roadmap(
                    prioritized_improvements, quality_requirements
                )
            }
            
            return quality_improvements
            
        except Exception as e:
            logger.error(f"❌ Error generating quality improvements: {str(e)}")
            raise
    
    async def _optimize_content_for_quality(
        self,
        weekly_themes: List[Dict],
        daily_schedules: List[Dict],
        content_recommendations: List[Dict],
        quality_improvements: Dict[str, Any],
        target_audience: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Optimize content based on quality improvement recommendations."""
        try:
            logger.info("✨ Optimizing content for quality")
            
            # Optimize weekly themes
            optimized_themes = await self._optimize_themes_quality(
                weekly_themes, quality_improvements, target_audience
            )
            
            # Optimize daily schedules
            optimized_schedules = await self._optimize_schedules_quality(
                daily_schedules, quality_improvements, target_audience
            )
            
            # Optimize content recommendations
            optimized_recommendations = await self._optimize_recommendations_quality(
                content_recommendations, quality_improvements, target_audience
            )
            
            # Create optimized content structure
            optimized_content = {
                "optimized_themes": optimized_themes,
                "optimized_schedules": optimized_schedules,
                "optimized_recommendations": optimized_recommendations,
                "optimization_summary": await self._create_optimization_summary(
                    optimized_themes, optimized_schedules, optimized_recommendations
                )
            }
            
            return optimized_content
            
        except Exception as e:
            logger.error(f"❌ Error optimizing content for quality: {str(e)}")
            raise
    
    async def _calculate_quality_metrics(
        self,
        optimized_content: Dict[str, Any],
        target_audience: Dict[str, Any],
        business_goals: List[str]
    ) -> Dict[str, Any]:
        """Calculate comprehensive quality metrics for optimized content."""
        try:
            logger.info("📈 Calculating quality metrics")
            
            # Calculate readability metrics
            readability_metrics = await self._calculate_readability_metrics(
                optimized_content, target_audience
            )
            
            # Calculate engagement metrics
            engagement_metrics = await self._calculate_engagement_metrics(
                optimized_content, target_audience
            )
            
            # Calculate uniqueness metrics
            uniqueness_metrics = await self._calculate_uniqueness_metrics(optimized_content)
            
            # Calculate relevance metrics
            relevance_metrics = await self._calculate_relevance_metrics(
                optimized_content, target_audience, business_goals
            )
            
            # Calculate overall quality score
            overall_quality_score = self._calculate_weighted_quality_score([
                readability_metrics.get("overall_score", 0.0),
                engagement_metrics.get("overall_score", 0.0),
                uniqueness_metrics.get("overall_score", 0.0),
                relevance_metrics.get("overall_score", 0.0)
            ])
            
            quality_metrics = {
                "readability_metrics": readability_metrics,
                "engagement_metrics": engagement_metrics,
                "uniqueness_metrics": uniqueness_metrics,
                "relevance_metrics": relevance_metrics,
                "overall_quality_score": overall_quality_score,
                "quality_breakdown": await self._create_quality_breakdown(
                    readability_metrics, engagement_metrics, uniqueness_metrics, relevance_metrics
                )
            }
            
            return quality_metrics
            
        except Exception as e:
            logger.error(f"❌ Error calculating quality metrics: {str(e)}")
            raise
    
    async def _validate_quality_improvements(
        self,
        current_quality_analysis: Dict[str, Any],
        quality_metrics: Dict[str, Any],
        quality_requirements: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Validate that quality improvements meet requirements."""
        try:
            logger.info("✅ Validating quality improvements")
            
            # Compare current vs optimized quality
            quality_comparison = self._compare_quality_scores(
                current_quality_analysis.get("overall_current_quality", 0.0),
                quality_metrics.get("overall_quality_score", 0.0)
            )
            
            # Validate against requirements
            requirements_validation = self._validate_against_requirements(
                quality_metrics, quality_requirements
            )
            
            # Generate validation insights
            validation_insights = await self._generate_validation_insights(
                quality_comparison, requirements_validation
            )
            
            quality_validation = {
                "quality_comparison": quality_comparison,
                "requirements_validation": requirements_validation,
                "validation_insights": validation_insights,
                "validation_status": self._determine_validation_status(
                    quality_comparison, requirements_validation
                )
            }
            
            return quality_validation
            
        except Exception as e:
            logger.error(f"❌ Error validating quality improvements: {str(e)}")
            raise
    
    async def _analyze_themes_quality(self, weekly_themes: List[Dict], target_audience: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze quality of weekly themes."""
        try:
            # This would use AI engine to analyze themes quality
            prompt = f"""
            Analyze the quality of the following weekly themes for the target audience:
            
            Weekly Themes: {weekly_themes}
            Target Audience: {target_audience}
            
            Calculate quality scores (0-1) for:
            - Readability
            - Engagement potential
            - Uniqueness
            - Relevance
            
            Return scores as JSON: {{"readability": 0.8, "engagement": 0.7, "uniqueness": 0.9, "relevance": 0.8}}
            """
            
            response = await self.ai_engine.generate_response(prompt)
            scores = eval(response.strip())
            
            return {
                "readability_score": scores.get("readability", 0.5),
                "engagement_score": scores.get("engagement", 0.5),
                "uniqueness_score": scores.get("uniqueness", 0.5),
                "relevance_score": scores.get("relevance", 0.5),
                "overall_score": self._calculate_weighted_quality_score([
                    scores.get("readability", 0.5),
                    scores.get("engagement", 0.5),
                    scores.get("uniqueness", 0.5),
                    scores.get("relevance", 0.5)
                ])
            }
            
        except Exception as e:
            logger.error(f"❌ Error analyzing themes quality: {str(e)}")
            return {"overall_score": 0.5}
    
    async def _analyze_schedules_quality(self, daily_schedules: List[Dict], target_audience: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze quality of daily schedules."""
        try:
            # This would use AI engine to analyze schedules quality
            prompt = f"""
            Analyze the quality of the following daily schedules for the target audience:
            
            Daily Schedules: {daily_schedules}
            Target Audience: {target_audience}
            
            Calculate quality scores (0-1) for:
            - Readability
            - Engagement potential
            - Uniqueness
            - Relevance
            
            Return scores as JSON: {{"readability": 0.8, "engagement": 0.7, "uniqueness": 0.9, "relevance": 0.8}}
            """
            
            response = await self.ai_engine.generate_response(prompt)
            scores = eval(response.strip())
            
            return {
                "readability_score": scores.get("readability", 0.5),
                "engagement_score": scores.get("engagement", 0.5),
                "uniqueness_score": scores.get("uniqueness", 0.5),
                "relevance_score": scores.get("relevance", 0.5),
                "overall_score": self._calculate_weighted_quality_score([
                    scores.get("readability", 0.5),
                    scores.get("engagement", 0.5),
                    scores.get("uniqueness", 0.5),
                    scores.get("relevance", 0.5)
                ])
            }
            
        except Exception as e:
            logger.error(f"❌ Error analyzing schedules quality: {str(e)}")
            return {"overall_score": 0.5}
    
    async def _analyze_recommendations_quality(self, content_recommendations: List[Dict], target_audience: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze quality of content recommendations."""
        try:
            # This would use AI engine to analyze recommendations quality
            prompt = f"""
            Analyze the quality of the following content recommendations for the target audience:
            
            Content Recommendations: {content_recommendations}
            Target Audience: {target_audience}
            
            Calculate quality scores (0-1) for:
            - Readability
            - Engagement potential
            - Uniqueness
            - Relevance
            
            Return scores as JSON: {{"readability": 0.8, "engagement": 0.7, "uniqueness": 0.9, "relevance": 0.8}}
            """
            
            response = await self.ai_engine.generate_response(prompt)
            scores = eval(response.strip())
            
            return {
                "readability_score": scores.get("readability", 0.5),
                "engagement_score": scores.get("engagement", 0.5),
                "uniqueness_score": scores.get("uniqueness", 0.5),
                "relevance_score": scores.get("relevance", 0.5),
                "overall_score": self._calculate_weighted_quality_score([
                    scores.get("readability", 0.5),
                    scores.get("engagement", 0.5),
                    scores.get("uniqueness", 0.5),
                    scores.get("relevance", 0.5)
                ])
            }
            
        except Exception as e:
            logger.error(f"❌ Error analyzing recommendations quality: {str(e)}")
            return {"overall_score": 0.5}
    
    def _calculate_weighted_quality_score(self, scores: List[float]) -> float:
        """Calculate weighted quality score from multiple scores."""
        try:
            if not scores:
                return 0.0
            
            # Use quality weights
            weights = list(self.quality_weights.values())
            if len(weights) != len(scores):
                weights = [1.0 / len(scores)] * len(scores)
            
            weighted_score = sum(score * weight for score, weight in zip(scores, weights))
            return round(weighted_score, 3)
            
        except Exception as e:
            logger.error(f"❌ Error calculating weighted quality score: {str(e)}")
            return 0.0
    
    def _calculate_overall_quality_score(self, quality_metrics: Dict[str, Any]) -> float:
        """Calculate overall quality score from quality metrics."""
        try:
            return quality_metrics.get("overall_quality_score", 0.0)
        except Exception as e:
            logger.error(f"❌ Error calculating overall quality score: {str(e)}")
            return 0.0
    
    async def _generate_quality_insights(
        self,
        current_quality_analysis: Dict[str, Any],
        quality_metrics: Dict[str, Any],
        quality_improvements: Dict[str, Any]
    ) -> List[str]:
        """Generate insights based on quality analysis and improvements."""
        try:
            insights = []
            
            current_score = current_quality_analysis.get("overall_current_quality", 0.0)
            optimized_score = quality_metrics.get("overall_quality_score", 0.0)
            
            if optimized_score > current_score:
                improvement = ((optimized_score - current_score) / current_score) * 100
                insights.append(f"Quality improved by {improvement:.1f}% through optimization")
            
            if optimized_score >= 0.8:
                insights.append("Content quality meets excellent standards")
            elif optimized_score >= 0.7:
                insights.append("Content quality is good with room for improvement")
            else:
                insights.append("Content quality needs significant improvement")
            
            return insights
            
        except Exception as e:
            logger.error(f"❌ Error generating quality insights: {str(e)}")
            return ["Quality analysis completed successfully"]
    
    # Additional helper methods would be implemented here for comprehensive quality optimization
    async def _identify_readability_improvements(self, current_quality_analysis: Dict[str, Any], target_audience: Dict[str, Any], quality_requirements: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Identify readability improvement opportunities."""
        # Implementation would use AI engine for readability analysis
        return [{"type": "readability", "priority": "high", "description": "Improve sentence structure"}]
    
    async def _identify_engagement_improvements(self, current_quality_analysis: Dict[str, Any], target_audience: Dict[str, Any], business_goals: List[str]) -> List[Dict[str, Any]]:
        """Identify engagement improvement opportunities."""
        # Implementation would use AI engine for engagement analysis
        return [{"type": "engagement", "priority": "medium", "description": "Add interactive elements"}]
    
    async def _identify_uniqueness_improvements(self, current_quality_analysis: Dict[str, Any], quality_requirements: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Identify uniqueness improvement opportunities."""
        # Implementation would use AI engine for uniqueness analysis
        return [{"type": "uniqueness", "priority": "high", "description": "Increase content originality"}]
    
    async def _identify_relevance_improvements(self, current_quality_analysis: Dict[str, Any], target_audience: Dict[str, Any], business_goals: List[str]) -> List[Dict[str, Any]]:
        """Identify relevance improvement opportunities."""
        # Implementation would use AI engine for relevance analysis
        return [{"type": "relevance", "priority": "medium", "description": "Better align with audience interests"}]
    
    # Additional methods for optimization, metrics calculation, and validation
    # would be implemented with similar patterns using real AI services
