"""
Step 7: Weekly Theme Development Implementation

This step generates weekly themes based on content pillars and strategy alignment.
It ensures content mix diversity, strategic relevance, and quality validation.
"""

import asyncio
import time
from typing import Dict, Any, List, Optional
from datetime import datetime, timedelta
from loguru import logger

from ..base_step import PromptStep
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(__file__)))))
if services_dir not in sys.path:
    sys.path.insert(0, services_dir)

try:
    from calendar_generation_datasource_framework.data_processing import (
        ComprehensiveUserDataProcessor,
        StrategyDataProcessor,
        GapAnalysisDataProcessor
    )
    from content_gap_analyzer.ai_engine_service import AIEngineService
    from content_gap_analyzer.keyword_researcher import KeywordResearcher
    from content_gap_analyzer.competitor_analyzer import CompetitorAnalyzer
except ImportError:
    # Fallback for testing environments - create mock classes
    class ComprehensiveUserDataProcessor:
        async def get_comprehensive_user_data(self, user_id, strategy_id):
            return {
                "user_id": user_id,
                "strategy_id": strategy_id,
                "industry": "technology",
                "onboarding_data": {},
                "strategy_data": {},
                "gap_analysis": {},
                "ai_analysis": {},
                "performance_data": {},
                "competitor_data": {}
            }
    
    class AIEngineService:
        async def generate_content_recommendations(self, analysis_data):
            """Mock implementation with correct method signature"""
            logger.info("📋 Using mock content recommendations for theme generation")
            return [
                {
                    'type': 'content_creation',
                    'title': 'Weekly Theme: AI Implementation Guide',
                    'description': 'Comprehensive guide on AI implementation for businesses',
                    'priority': 'high',
                    'estimated_impact': 'High engagement and lead generation',
                    'implementation_time': '1 week',
                    'ai_confidence': 0.92,
                    'content_suggestions': [
                        'Step-by-step AI implementation tutorial',
                        'Best practices for AI adoption',
                        'Common pitfalls to avoid',
                        'Success case studies'
                    ]
                },
                {
                    'type': 'content_creation',
                    'title': 'Weekly Theme: Digital Transformation Journey',
                    'description': 'Navigating the digital transformation process',
                    'priority': 'high',
                    'estimated_impact': 'Thought leadership and brand authority',
                    'implementation_time': '1 week',
                    'ai_confidence': 0.89,
                    'content_suggestions': [
                        'Digital transformation roadmap',
                        'Technology adoption strategies',
                        'Change management insights',
                        'ROI measurement frameworks'
                    ]
                },
                {
                    'type': 'content_creation',
                    'title': 'Weekly Theme: Innovation and Tech Trends',
                    'description': 'Exploring emerging technologies and innovation',
                    'priority': 'medium',
                    'estimated_impact': 'Industry relevance and engagement',
                    'implementation_time': '1 week',
                    'ai_confidence': 0.87,
                    'content_suggestions': [
                        'Emerging technology analysis',
                        'Innovation case studies',
                        'Future trend predictions',
                        'Technology adoption insights'
                    ]
                },
                {
                    'type': 'content_creation',
                    'title': 'Weekly Theme: Business Strategy and Growth',
                    'description': 'Strategic business insights and growth strategies',
                    'priority': 'medium',
                    'estimated_impact': 'Business value and strategic alignment',
                    'implementation_time': '1 week',
                    'ai_confidence': 0.85,
                    'content_suggestions': [
                        'Strategic planning frameworks',
                        'Growth strategy development',
                        'Business model innovation',
                        'Performance optimization'
                    ]
                }
            ]
    
    class KeywordResearcher:
        async def get_keywords(self, topic):
            return ["keyword1", "keyword2", "keyword3"]
    
    class CompetitorAnalyzer:
        async def analyze_competitors(self, industry):
            return {"competitors": ["comp1", "comp2"], "insights": ["insight1", "insight2"]}


class WeeklyThemeDevelopmentStep(PromptStep):
    """
    Step 7: Weekly Theme Development
    
    This step generates weekly themes based on:
    - Content pillars from Step 5
    - Strategy alignment from Step 1
    - Gap analysis from Step 2
    - Platform strategies from Step 6
    
    Expected Output:
    - Weekly theme structure with 4-5 weeks
    - Theme variety and diversity scoring
    - Strategic alignment validation
    - Content mix optimization
    """
    
    def __init__(self):
        super().__init__("Weekly Theme Development", 7)
        
        # Initialize data processors
        self.comprehensive_user_processor = ComprehensiveUserDataProcessor()
        self.strategy_processor = StrategyDataProcessor()
        self.gap_analysis_processor = GapAnalysisDataProcessor()
        
        # Initialize AI services
        self.ai_engine = AIEngineService()
        self.keyword_researcher = KeywordResearcher()
        self.competitor_analyzer = CompetitorAnalyzer()
        
        logger.info("🎯 Step 7: Weekly Theme Development initialized")
    
    async def execute(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Execute weekly theme development with comprehensive data integration.
        
        Args:
            context: Current context containing user data and previous step results
            
        Returns:
            Dict containing weekly themes, quality metrics, and insights
        """
        try:
            logger.info("🚀 Starting Step 7: Weekly Theme Development")
            
            # Extract user and strategy data
            user_id = context.get("user_id")
            strategy_id = context.get("strategy_id")
            
            if not user_id or not strategy_id:
                raise ValueError("Missing user_id or strategy_id in context")
            
            # Get comprehensive user data
            user_data = await self.comprehensive_user_processor.get_comprehensive_user_data(user_id, strategy_id)
            
            # Extract previous step results using correct context structure
            step_results = context.get("step_results", {})
            
            # Get content pillars from Step 5
            step5_result = step_results.get("step_05", {})
            content_pillars = step5_result.get("result", {}).get("pillarMapping", {}).get("content_pillars", [])
            pillar_weights = step5_result.get("result", {}).get("pillarMapping", {}).get("pillar_weights", {})
            
            # Get strategy data from Step 1
            step1_result = step_results.get("step_01", {})
            business_goals = step1_result.get("result", {}).get("business_goals", [])
            target_audience = step1_result.get("result", {}).get("target_audience", {})
            
            # Get gap analysis from Step 2
            step2_result = step_results.get("step_02", {})
            content_gaps = step2_result.get("result", {}).get("content_gaps", [])
            
            # Get platform strategies from Step 6
            step6_result = step_results.get("step_06", {})
            platform_strategies = step6_result.get("result", {}).get("platformOptimization", {})
            
            # Calculate calendar duration and weeks
            calendar_duration = context.get("calendar_duration", 30)  # days
            num_weeks = max(4, calendar_duration // 7)  # Minimum 4 weeks
            
            # Generate weekly themes
            weekly_themes = await self._generate_weekly_themes(
                content_pillars=content_pillars,
                pillar_weights=pillar_weights,
                business_goals=business_goals,
                target_audience=target_audience,
                content_gaps=content_gaps,
                platform_strategies=platform_strategies,
                num_weeks=num_weeks,
                user_data=user_data
            )
            
            # Calculate theme diversity and variety
            diversity_metrics = self._calculate_theme_diversity(weekly_themes)
            
            # Validate strategic alignment
            alignment_metrics = self._validate_strategic_alignment(
                weekly_themes, business_goals, target_audience
            )
            
            # Generate insights and recommendations
            insights = await self._generate_theme_insights(
                weekly_themes, content_gaps, platform_strategies
            )
            
            # Prepare step result
            result = {
                "weekly_themes": weekly_themes,
                "diversity_metrics": diversity_metrics,
                "alignment_metrics": alignment_metrics,
                "insights": insights,
                "num_weeks": num_weeks,
                "theme_count": len(weekly_themes),
                "content_pillars_used": len(content_pillars),
                "strategic_alignment_score": alignment_metrics.get("overall_score", 0.0),
                "diversity_score": diversity_metrics.get("overall_diversity", 0.0)
            }
            
            logger.info(f"✅ Step 7 completed: Generated {len(weekly_themes)} weekly themes")
            return result
            
        except Exception as e:
            logger.error(f"❌ Step 7 failed: {str(e)}")
            raise
    
    async def _generate_weekly_themes(
        self,
        content_pillars: List[Dict],
        pillar_weights: Dict[str, float],
        business_goals: List[str],
        target_audience: Dict,
        content_gaps: List[Dict],
        platform_strategies: Dict,
        num_weeks: int,
        user_data: Dict
    ) -> List[Dict]:
        """
        Generate weekly themes based on content pillars and strategy.
        
        Args:
            content_pillars: Content pillars from Step 5
            pillar_weights: Weight distribution for pillars
            business_goals: Business goals from strategy
            target_audience: Target audience information
            content_gaps: Content gaps from analysis
            platform_strategies: Platform-specific strategies
            num_weeks: Number of weeks to generate themes for
            user_data: Comprehensive user data
            
        Returns:
            List of weekly theme dictionaries
        """
        try:
            weekly_themes = []
            
            # Get industry and business context
            industry = user_data.get("industry", "technology")
            business_size = user_data.get("business_size", "sme")
            
            # Create theme generation prompt
            prompt = self._create_theme_generation_prompt(
                content_pillars=content_pillars,
                pillar_weights=pillar_weights,
                business_goals=business_goals,
                target_audience=target_audience,
                content_gaps=content_gaps,
                platform_strategies=platform_strategies,
                num_weeks=num_weeks,
                industry=industry,
                business_size=business_size
            )
            
            # Generate themes using AI service - use available method
            analysis_data = {
                "step": "weekly_theme_development",
                "industry": industry,
                "business_size": business_size,
                "num_weeks": num_weeks,
                "content_pillars": content_pillars,
                "pillar_weights": pillar_weights,
                "business_goals": business_goals,
                "target_audience": target_audience,
                "content_gaps": content_gaps,
                "platform_strategies": platform_strategies,
                "prompt": prompt
            }
            ai_response = await self.ai_engine.generate_content_recommendations(analysis_data)
            
            # Parse AI response and structure themes
            generated_themes = self._parse_ai_theme_response(ai_response, num_weeks)
            
            # Enhance themes with additional data
            for i, theme in enumerate(generated_themes):
                # Safety check: ensure theme is a dictionary
                if not isinstance(theme, dict):
                    logger.warning(f"Theme {i+1} is not a dictionary: {type(theme)}, converting to fallback")
                    theme = {
                        "title": f"Week {i+1} Theme: Content Focus",
                        "description": f"Week {i+1} strategic content development",
                        "primary_pillar": "Content Strategy",
                        "content_angles": ["Strategic insights", "Best practices", "Industry trends"],
                        "target_platforms": ["LinkedIn", "Blog", "Twitter"],
                        "strategic_alignment": "Strategic focus",
                        "gap_addressal": "Content development",
                        "priority": "medium",
                        "estimated_impact": "Medium",
                        "ai_confidence": 0.8
                    }
                    generated_themes[i] = theme
                
                week_number = i + 1
                
                # Add week-specific information
                theme["week_number"] = week_number
                theme["week_start_date"] = self._calculate_week_start_date(week_number)
                theme["week_end_date"] = self._calculate_week_end_date(week_number)
                
                # Add pillar alignment
                theme["pillar_alignment"] = self._calculate_pillar_alignment(
                    theme, content_pillars, pillar_weights
                )
                
                # Add gap analysis integration
                theme["gap_integration"] = self._integrate_content_gaps(
                    theme, content_gaps
                )
                
                # Add platform optimization
                theme["platform_optimization"] = self._optimize_for_platforms(
                    theme, platform_strategies
                )
                
                # Add strategic relevance
                theme["strategic_relevance"] = self._calculate_strategic_relevance(
                    theme, business_goals, target_audience
                )
                
                weekly_themes.append(theme)
            
            return weekly_themes
            
        except Exception as e:
            logger.error(f"Error generating weekly themes: {str(e)}")
            # Return fallback themes if AI generation fails
            return self._generate_fallback_themes(num_weeks, content_pillars)
    
    def _create_theme_generation_prompt(
        self,
        content_pillars: List[Dict],
        pillar_weights: Dict[str, float],
        business_goals: List[str],
        target_audience: Dict,
        content_gaps: List[Dict],
        platform_strategies: Dict,
        num_weeks: int,
        industry: str,
        business_size: str
    ) -> str:
        """Create comprehensive prompt for theme generation."""
        
        prompt = f"""
        Generate {num_weeks} weekly content themes for a {business_size} business in the {industry} industry.
        
        CONTENT PILLARS:
        {self._format_content_pillars(content_pillars, pillar_weights)}
        
        BUSINESS GOALS:
        {', '.join(business_goals)}
        
        TARGET AUDIENCE:
        {self._format_target_audience(target_audience)}
        
        CONTENT GAPS TO ADDRESS:
        {self._format_content_gaps(content_gaps)}
        
        PLATFORM STRATEGIES:
        {self._format_platform_strategies(platform_strategies)}
        
        REQUIREMENTS:
        1. Each theme should align with at least one content pillar
        2. Themes should address identified content gaps
        3. Ensure variety and diversity across weeks
        4. Optimize for target audience preferences
        5. Align with business goals and platform strategies
        6. Include specific content ideas and angles
        7. Consider seasonal relevance and industry trends
        
        OUTPUT FORMAT:
        For each week, provide:
        - Theme Title
        - Primary Content Pillar
        - Theme Description
        - Key Content Angles (3-5 ideas)
        - Target Platforms
        - Strategic Alignment Notes
        - Content Gap Addressal
        """
        
        return prompt
    
    def _parse_ai_theme_response(self, ai_response: List[Dict], num_weeks: int) -> List[Dict]:
        """Parse AI response and structure into weekly themes."""
        
        try:
            # Handle response from generate_content_recommendations
            themes = []
            
            # If AI provided structured recommendations, use them
            if ai_response and len(ai_response) >= num_weeks:
                for i, recommendation in enumerate(ai_response[:num_weeks]):
                    theme = {
                        "title": recommendation.get("title", f"Week {i+1} Theme"),
                        "description": recommendation.get("description", f"Week {i+1} strategic theme"),
                        "primary_pillar": recommendation.get("type", "content_creation").replace("_", " ").title(),
                        "content_angles": recommendation.get("content_suggestions", [
                            "Industry insights and trends",
                            "Best practices and tips",
                            "Case studies and examples"
                        ]),
                        "target_platforms": ["LinkedIn", "Blog", "Twitter"],
                        "strategic_alignment": f"Priority: {recommendation.get('priority', 'medium')}, Impact: {recommendation.get('estimated_impact', 'Medium')}",
                        "gap_addressal": f"Addresses content gap with {recommendation.get('ai_confidence', 0.8):.1%} confidence",
                        "priority": recommendation.get("priority", "medium"),
                        "estimated_impact": recommendation.get("estimated_impact", "Medium"),
                        "ai_confidence": recommendation.get("ai_confidence", 0.8)
                    }
                    themes.append(theme)
            else:
                # Generate fallback themes
                for i in range(num_weeks):
                    theme = {
                        "title": f"Week {i+1} Theme: Strategic Content Focus",
                        "description": f"Week {i+1} focuses on strategic content development",
                        "primary_pillar": "Strategic Content",
                        "content_angles": [
                            "Industry insights and trends",
                            "Best practices and tips",
                            "Case studies and examples"
                        ],
                        "target_platforms": ["LinkedIn", "Blog", "Twitter"],
                        "strategic_alignment": "Aligns with overall business strategy",
                        "gap_addressal": "Addresses identified content gaps",
                        "priority": "medium",
                        "estimated_impact": "Medium",
                        "ai_confidence": 0.8
                    }
                    themes.append(theme)
            
            return themes
            
        except Exception as e:
            logger.error(f"Error parsing AI theme response: {str(e)}")
            return self._generate_fallback_themes(num_weeks, [])
    
    def _calculate_theme_diversity(self, weekly_themes: List[Dict]) -> Dict[str, float]:
        """Calculate diversity metrics for weekly themes."""
        
        try:
            # Extract theme characteristics
            pillars_used = [theme.get("primary_pillar", "Unknown") for theme in weekly_themes]
            platforms_used = []
            for theme in weekly_themes:
                platforms_used.extend(theme.get("target_platforms", []))
            
            # Calculate diversity metrics
            unique_pillars = len(set(pillars_used))
            unique_platforms = len(set(platforms_used))
            total_themes = len(weekly_themes)
            
            # Pillar diversity (0-1 scale)
            pillar_diversity = unique_pillars / max(1, total_themes)
            
            # Platform diversity (0-1 scale)
            platform_diversity = unique_platforms / max(1, len(set(platforms_used)))
            
            # Content angle diversity
            total_angles = sum(len(theme.get("content_angles", [])) for theme in weekly_themes)
            unique_angles = len(set([
                angle for theme in weekly_themes 
                for angle in theme.get("content_angles", [])
            ]))
            angle_diversity = unique_angles / max(1, total_angles)
            
            # Overall diversity score
            overall_diversity = (pillar_diversity + platform_diversity + angle_diversity) / 3
            
            return {
                "overall_diversity": overall_diversity,
                "pillar_diversity": pillar_diversity,
                "platform_diversity": platform_diversity,
                "angle_diversity": angle_diversity,
                "unique_pillars": unique_pillars,
                "unique_platforms": unique_platforms,
                "total_themes": total_themes
            }
            
        except Exception as e:
            logger.error(f"Error calculating theme diversity: {str(e)}")
            return {
                "overall_diversity": 0.0,
                "pillar_diversity": 0.0,
                "platform_diversity": 0.0,
                "angle_diversity": 0.0,
                "unique_pillars": 0,
                "unique_platforms": 0,
                "total_themes": len(weekly_themes)
            }
    
    def _validate_strategic_alignment(
        self,
        weekly_themes: List[Dict],
        business_goals: List[str],
        target_audience: Dict
    ) -> Dict[str, float]:
        """Validate strategic alignment of weekly themes."""
        
        try:
            alignment_scores = []
            
            for theme in weekly_themes:
                # Check alignment with business goals
                goal_alignment = self._calculate_goal_alignment(theme, business_goals)
                
                # Check alignment with target audience
                audience_alignment = self._calculate_audience_alignment(theme, target_audience)
                
                # Check strategic relevance - convert string to numeric score
                strategic_relevance_str = theme.get("strategic_alignment", "")
                strategic_relevance = 0.8 if "high" in strategic_relevance_str.lower() else 0.6 if "medium" in strategic_relevance_str.lower() else 0.4
                
                # Calculate overall theme alignment
                theme_alignment = (goal_alignment + audience_alignment + strategic_relevance) / 3
                alignment_scores.append(theme_alignment)
            
            # Calculate overall alignment metrics
            overall_score = sum(alignment_scores) / max(1, len(alignment_scores))
            min_score = min(alignment_scores) if alignment_scores else 0.0
            max_score = max(alignment_scores) if alignment_scores else 0.0
            
            return {
                "overall_score": overall_score,
                "min_score": min_score,
                "max_score": max_score,
                "theme_scores": alignment_scores,
                "alignment_level": self._get_alignment_level(overall_score)
            }
            
        except Exception as e:
            logger.error(f"Error validating strategic alignment: {str(e)}")
            return {
                "overall_score": 0.0,
                "min_score": 0.0,
                "max_score": 0.0,
                "theme_scores": [],
                "alignment_level": "Poor"
            }
    
    async def _generate_theme_insights(
        self,
        weekly_themes: List[Dict],
        content_gaps: List[Dict],
        platform_strategies: Dict
    ) -> List[Dict]:
        """Generate insights and recommendations for weekly themes."""
        
        try:
            insights = []
            
            # Analyze theme distribution
            pillar_distribution = {}
            for theme in weekly_themes:
                pillar = theme.get("primary_pillar", "Unknown")
                pillar_distribution[pillar] = pillar_distribution.get(pillar, 0) + 1
            
            insights.append({
                "type": "distribution_analysis",
                "title": "Theme Distribution Analysis",
                "description": f"Themes distributed across {len(pillar_distribution)} content pillars",
                "data": pillar_distribution
            })
            
            # Analyze gap coverage
            gap_coverage = self._analyze_gap_coverage(weekly_themes, content_gaps)
            insights.append({
                "type": "gap_coverage",
                "title": "Content Gap Coverage",
                "description": f"Coverage analysis for {len(content_gaps)} identified gaps",
                "data": gap_coverage
            })
            
            # Platform optimization insights
            platform_insights = self._analyze_platform_optimization(weekly_themes, platform_strategies)
            insights.append({
                "type": "platform_optimization",
                "title": "Platform Optimization Insights",
                "description": "Platform-specific optimization recommendations",
                "data": platform_insights
            })
            
            return insights
            
        except Exception as e:
            logger.error(f"Error generating theme insights: {str(e)}")
            return []
    
    def get_prompt_template(self) -> str:
        """Get the AI prompt template for weekly theme development."""
        return """
        Generate weekly content themes for a business calendar.
        
        Input: Content pillars, business goals, target audience, content gaps, platform strategies
        Output: Weekly themes with strategic alignment and diversity
        
        Focus on:
        1. Strategic alignment with business goals
        2. Content pillar distribution
        3. Gap analysis integration
        4. Platform optimization
        5. Theme variety and diversity
        """
    
    def validate_result(self, result: Dict[str, Any]) -> bool:
        """Validate the weekly theme development result."""
        
        try:
            # Check required fields
            required_fields = ["weekly_themes", "diversity_metrics", "alignment_metrics"]
            for field in required_fields:
                if field not in result:
                    logger.error(f"Missing required field: {field}")
                    return False
            
            # Validate weekly themes
            weekly_themes = result.get("weekly_themes", [])
            if not weekly_themes or len(weekly_themes) < 4:
                logger.error("Insufficient weekly themes generated")
                return False
            
            # Validate diversity metrics
            diversity_metrics = result.get("diversity_metrics", {})
            overall_diversity = diversity_metrics.get("overall_diversity", 0.0)
            if overall_diversity < 0.3:  # Minimum diversity threshold
                logger.error(f"Diversity too low: {overall_diversity}")
                return False
            
            # Validate alignment metrics
            alignment_metrics = result.get("alignment_metrics", {})
            overall_score = alignment_metrics.get("overall_score", 0.0)
            if overall_score < 0.5:  # Minimum alignment threshold
                logger.error(f"Alignment score too low: {overall_score}")
                return False
            
            return True
            
        except Exception as e:
            logger.error(f"Error validating result: {str(e)}")
            return False
    
    def _calculate_quality_score(self, result: Dict[str, Any], validation_passed: bool) -> float:
        """Calculate quality score for weekly theme development."""
        
        try:
            if not validation_passed:
                return 0.0
            
            # Base score from validation
            base_score = 0.7
            
            # Diversity contribution (0-0.15)
            diversity_metrics = result.get("diversity_metrics", {})
            diversity_score = diversity_metrics.get("overall_diversity", 0.0) * 0.15
            
            # Alignment contribution (0-0.15)
            alignment_metrics = result.get("alignment_metrics", {})
            alignment_score = alignment_metrics.get("overall_score", 0.0) * 0.15
            
            # Theme completeness contribution
            weekly_themes = result.get("weekly_themes", [])
            theme_completeness = min(1.0, len(weekly_themes) / 4.0) * 0.1  # Bonus for more themes
            
            # Calculate final score
            final_score = base_score + diversity_score + alignment_score + theme_completeness
            
            return min(1.0, final_score)
            
        except Exception as e:
            logger.error(f"Error calculating quality score: {str(e)}")
            return 0.0
    
    # Helper methods
    def _format_content_pillars(self, content_pillars: List[Dict], pillar_weights: Dict[str, float]) -> str:
        """Format content pillars for prompt."""
        formatted = []
        for pillar in content_pillars:
            weight = pillar_weights.get(pillar.get("name", ""), 0.0)
            formatted.append(f"- {pillar.get('name', 'Unknown')} (Weight: {weight:.2f})")
        return "\n".join(formatted)
    
    def _format_target_audience(self, target_audience: Dict) -> str:
        """Format target audience for prompt."""
        return f"Demographics: {target_audience.get('demographics', 'N/A')}, Interests: {target_audience.get('interests', 'N/A')}"
    
    def _format_content_gaps(self, content_gaps: List[Dict]) -> str:
        """Format content gaps for prompt."""
        formatted = []
        for gap in content_gaps[:5]:  # Limit to top 5 gaps
            formatted.append(f"- {gap.get('description', 'Unknown gap')}")
        return "\n".join(formatted)
    
    def _format_platform_strategies(self, platform_strategies: Dict) -> str:
        """Format platform strategies for prompt."""
        formatted = []
        for platform, strategy in platform_strategies.items():
            formatted.append(f"- {platform}: {strategy.get('approach', 'N/A')}")
        return "\n".join(formatted)
    
    def _calculate_week_start_date(self, week_number: int) -> str:
        """Calculate week start date."""
        start_date = datetime.now() + timedelta(weeks=week_number-1)
        return start_date.strftime("%Y-%m-%d")
    
    def _calculate_week_end_date(self, week_number: int) -> str:
        """Calculate week end date."""
        end_date = datetime.now() + timedelta(weeks=week_number-1, days=6)
        return end_date.strftime("%Y-%m-%d")
    
    def _calculate_pillar_alignment(self, theme: Dict, content_pillars: List[Dict], pillar_weights: Dict[str, float]) -> float:
        """Calculate pillar alignment score."""
        theme_pillar = theme.get("primary_pillar", "")
        weight = pillar_weights.get(theme_pillar, 0.0)
        return min(1.0, weight * 2)  # Normalize to 0-1 scale
    
    def _integrate_content_gaps(self, theme: Dict, content_gaps: List[Dict]) -> List[str]:
        """Integrate content gaps into theme."""
        return [gap.get("description", "") for gap in content_gaps[:2]]
    
    def _optimize_for_platforms(self, theme: Dict, platform_strategies: Dict) -> Dict[str, str]:
        """Optimize theme for different platforms."""
        return {platform: f"Optimized for {platform}" for platform in theme.get("target_platforms", [])}
    
    def _calculate_strategic_relevance(self, theme: Dict, business_goals: List[str], target_audience: Dict) -> float:
        """Calculate strategic relevance score."""
        return 0.8  # Placeholder score
    
    def _generate_fallback_themes(self, num_weeks: int, content_pillars: List[Dict]) -> List[Dict]:
        """Generate fallback themes if AI generation fails."""
        themes = []
        for i in range(num_weeks):
            theme = {
                "title": f"Week {i+1} Theme: Strategic Content Development",
                "description": f"Week {i+1} focuses on strategic content creation",
                "primary_pillar": "Strategic Content",
                "content_angles": ["Industry insights", "Best practices", "Case studies"],
                "target_platforms": ["LinkedIn", "Blog", "Twitter"],
                "strategic_alignment": "Aligns with business strategy",
                "gap_addressal": "Addresses content gaps"
            }
            themes.append(theme)
        return themes
    
    def _calculate_goal_alignment(self, theme: Dict, business_goals: List[str]) -> float:
        """Calculate alignment with business goals."""
        return 0.8  # Placeholder score
    
    def _calculate_audience_alignment(self, theme: Dict, target_audience: Dict) -> float:
        """Calculate alignment with target audience."""
        return 0.8  # Placeholder score
    
    def _get_alignment_level(self, score: float) -> str:
        """Get alignment level based on score."""
        if score >= 0.8:
            return "Excellent"
        elif score >= 0.6:
            return "Good"
        elif score >= 0.4:
            return "Fair"
        else:
            return "Poor"
    
    def _analyze_gap_coverage(self, weekly_themes: List[Dict], content_gaps: List[Dict]) -> Dict[str, Any]:
        """Analyze content gap coverage."""
        return {
            "total_gaps": len(content_gaps),
            "covered_gaps": len(content_gaps) // 2,  # Placeholder
            "coverage_percentage": 50.0
        }
    
    def _analyze_platform_optimization(self, weekly_themes: List[Dict], platform_strategies: Dict) -> Dict[str, Any]:
        """Analyze platform optimization."""
        return {
            "platforms_covered": list(platform_strategies.keys()),
            "optimization_score": 0.8
        }
