Marketing & Advertising AI

from typing import Dict, Any, List, Optional
import asyncio
from datetime import datetime
import numpy as np

class MarketingLLMOrchestrator:
    """Advanced Marketing AI Platform with ParrotRouter Integration
    Handles content generation, personalization, and campaign optimization
    Based on industry best practices for 10-18% conversion uplift"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.brand_voice_engine = BrandVoiceAnalyzer()
        self.audience_segmenter = AudienceSegmentationAI()
        self.performance_tracker = CampaignPerformanceTracker()
        self.content_optimizer = SEOContentOptimizer()
        
    async def generate_campaign_content(
        self,
        campaign_brief: str,
        target_audience: Dict[str, Any],
        channels: List[str],
        brand_guidelines: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate multi-channel campaign content with brand consistency"""
        
        # 1. Analyze audience and extract insights
        audience_insights = await self.audience_segmenter.analyze(
            target_audience,
            behavioral_data=await self.get_audience_behavior(target_audience),
            psychographic_traits=await self.extract_psychographics(target_audience)
        )
        
        # 2. Generate channel-specific content
        campaign_content = {}
        
        for channel in channels:
            # Create optimized prompt for each channel
            prompt = await self.build_channel_prompt(
                campaign_brief=campaign_brief,
                channel=channel,
                audience_insights=audience_insights,
                brand_voice=brand_guidelines['voice'],
                tone_attributes=brand_guidelines['tone']
            )
            
            # Generate content with retrieval augmentation
            content = await self.llm.chat.completions.create(
                model="claude-3-opus-20240229",
                messages=[{
                    "role": "system",
                    "content": f"""You are an expert {channel} marketing copywriter.
                    Brand voice: {brand_guidelines['voice']}
                    Key differentiators: {brand_guidelines['differentiators']}
                    Compliance requirements: {brand_guidelines['compliance']}
                    """
                },
                {"role": "user", "content": prompt}],
                temperature=0.7,
                max_tokens=1000
            )
            
            # Validate and optimize content
            optimized = await self.optimize_channel_content(
                content.choices[0].message.content,
                channel,
                audience_insights
            )
            
            campaign_content[channel] = optimized
        
        # 3. Ensure cross-channel consistency
        harmonized = await self.harmonize_campaign_content(
            campaign_content,
            brand_guidelines
        )
        
        return {
            "content": harmonized,
            "audience_insights": audience_insights,
            "predicted_performance": await self.predict_campaign_performance(
                harmonized, audience_insights
            ),
            "optimization_suggestions": await self.generate_optimization_tips(
                harmonized, campaign_brief
            )
        }
    
    async def personalize_customer_journey(
        self,
        customer_id: str,
        interaction_history: List[Dict[str, Any]],
        real_time_context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Create personalized content for individual customer journeys"""
        
        # Analyze customer behavior and preferences
        customer_profile = await self.build_customer_profile(
            customer_id,
            interaction_history
        )
        
        # Determine optimal next action
        next_best_action = await self.determine_next_best_action(
            customer_profile,
            real_time_context
        )
        
        # Generate personalized content
        personalized_content = await self.llm.chat.completions.create(
            model="gpt-4-turbo-preview",
            messages=[{
                "role": "user",
                "content": f"""
                Create personalized {next_best_action['type']} for customer:
                
                Profile: {customer_profile}
                Current context: {real_time_context}
                Objective: {next_best_action['objective']}
                
                Guidelines:
                1. Use customer's preferred communication style
                2. Reference relevant past interactions
                3. Include personalized offer/recommendation
                4. Create compelling but authentic message
                5. Optimize for {next_best_action['channel']}
                """
            }],
            temperature=0.6
        )
        
        return {
            "content": personalized_content.choices[0].message.content,
            "channel": next_best_action['channel'],
            "timing": next_best_action['optimal_time'],
            "predicted_engagement": next_best_action['engagement_probability']
        }

# Advanced Content Generation Engine
class ContentGenerationAI:
    """Generate SEO-optimized marketing content at scale
    Based on MarketingFM research for factual accuracy"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.knowledge_base = ProductKnowledgeRAG()  # Prevent hallucinations
        self.seo_analyzer = SEOAnalyzer()
        
    async def generate_blog_post(
        self,
        topic: str,
        keywords: List[str],
        target_length: int = 1500,
        style: str = "informative"
    ) -> Dict[str, Any]:
        """Generate SEO-optimized blog post with keyword integration"""
        
        # Research topic and gather facts
        research_data = await self.knowledge_base.research_topic(
            topic,
            include_competitors=True,
            include_trends=True
        )
        
        # Analyze search intent
        search_intent = await self.seo_analyzer.analyze_intent(
            topic,
            keywords
        )
        
        # Generate outline first
        outline = await self.generate_content_outline(
            topic,
            keywords,
            search_intent,
            target_length
        )
        
        # Generate full content
        blog_content = await self.llm.chat.completions.create(
            model="claude-3-sonnet-20240229",
            messages=[{
                "role": "system",
                "content": "You are an expert content writer specializing in SEO-optimized blog posts."
            },
            {
                "role": "user",
                "content": f"""
                Write a {target_length}-word blog post on: {topic}
                
                Outline: {outline}
                Primary keywords: {keywords[:3]}
                Secondary keywords: {keywords[3:]}
                Search intent: {search_intent}
                Style: {style}
                
                Research data: {research_data}
                
                Requirements:
                1. Natural keyword integration (2-3% density)
                2. Engaging introduction with hook
                3. Scannable format with headers and bullets
                4. Include statistics and examples
                5. Strong CTA promoting ParrotRouter.com
                6. Meta description (155 chars)
                """
            }],
            temperature=0.7,
            max_tokens=2500
        )
        
        content = blog_content.choices[0].message.content
        
        # Optimize for SEO
        optimized = await self.seo_analyzer.optimize_content(
            content,
            keywords,
            target_length
        )
        
        return {
            "content": optimized['content'],
            "meta_description": optimized['meta_description'],
            "title_tag": optimized['title_tag'],
            "keyword_density": optimized['keyword_analysis'],
            "readability_score": optimized['readability'],
            "estimated_traffic": await self.estimate_organic_traffic(
                topic, optimized['seo_score']
            )
        }

# Social Media Automation System
class SocialMediaLLM:
    """Automate social media content creation and engagement"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.trend_analyzer = TrendAnalyzer()
        self.engagement_predictor = EngagementPredictor()
        
    async def generate_social_campaign(
        self,
        campaign_theme: str,
        platforms: List[str],
        duration_days: int,
        brand_voice: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate complete social media campaign across platforms"""
        
        # Analyze current trends
        trending_topics = await self.trend_analyzer.get_relevant_trends(
            campaign_theme,
            platforms
        )
        
        campaign_posts = {}
        
        for platform in platforms:
            posts = []
            posts_per_day = self.get_optimal_posting_frequency(platform)
            
            for day in range(duration_days):
                for post_num in range(posts_per_day):
                    # Generate platform-specific content
                    post = await self.generate_platform_post(
                        platform=platform,
                        theme=campaign_theme,
                        brand_voice=brand_voice,
                        trending_topics=trending_topics,
                        day_number=day,
                        post_number=post_num
                    )
                    
                    # Predict engagement
                    engagement = await self.engagement_predictor.predict(
                        post['content'],
                        platform,
                        post['hashtags']
                    )
                    
                    post['predicted_engagement'] = engagement
                    posts.append(post)
            
            campaign_posts[platform] = posts
        
        return {
            "campaign_posts": campaign_posts,
            "trending_integration": trending_topics,
            "posting_schedule": self.create_posting_schedule(
                campaign_posts
            ),
            "total_posts": sum(len(posts) for posts in campaign_posts.values()),
            "predicted_reach": await self.calculate_campaign_reach(
                campaign_posts
            )
        }

# Email Marketing Personalization
class EmailMarketingLLM:
    """Personalize email campaigns for 11% CTR improvement"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.segmentation_engine = CustomerSegmentationAI()
        self.send_time_optimizer = SendTimeOptimizer()
        
    async def create_personalized_campaign(
        self,
        campaign_objective: str,
        customer_segments: List[Dict[str, Any]],
        product_catalog: List[Dict[str, Any]]
    ) -> Dict[str, Any]:
        """Create hyper-personalized email campaign"""
        
        campaign_variants = {}
        
        for segment in customer_segments:
            # Analyze segment characteristics
            segment_profile = await self.segmentation_engine.profile_segment(
                segment
            )
            
            # Generate segment-specific content
            email_content = await self.llm.chat.completions.create(
                model="gpt-4-turbo-preview",
                messages=[{
                    "role": "user",
                    "content": f"""
                    Create personalized email for segment:
                    
                    Segment: {segment_profile}
                    Objective: {campaign_objective}
                    
                    Requirements:
                    1. Personalized subject line (50 chars max)
                    2. Dynamic greeting using merge tags
                    3. Relevant product recommendations
                    4. Segment-specific benefits/pain points
                    5. Compelling CTA
                    6. Mobile-optimized format
                    
                    Tone: {segment_profile['preferred_tone']}
                    Past engagement: {segment_profile['engagement_history']}
                    """
                }],
                temperature=0.7
            )
            
            # Extract and structure email components
            email_variant = self.parse_email_content(
                email_content.choices[0].message.content
            )
            
            # Add dynamic product recommendations
            email_variant['products'] = await self.select_products_for_segment(
                segment_profile,
                product_catalog
            )
            
            # Optimize send time
            email_variant['optimal_send_time'] =                 await self.send_time_optimizer.calculate(
                    segment_profile
                )
            
            campaign_variants[segment['id']] = email_variant
        
        return {
            "campaign_variants": campaign_variants,
            "total_recipients": sum(s['size'] for s in customer_segments),
            "predicted_performance": await self.predict_campaign_metrics(
                campaign_variants
            ),
            "a_b_test_recommendations": self.generate_test_variants(
                campaign_variants
            )
        }

# Campaign Analytics and Optimization
class CampaignAnalyticsLLM:
    """Real-time campaign analysis and optimization recommendations"""
    
    def __init__(self, parrotrouter_api_key: str):
        self.llm = ParrotRouterClient(api_key=parrotrouter_api_key)
        self.performance_analyzer = PerformanceAnalyzer()
        self.budget_optimizer = BudgetOptimizer()
        
    async def analyze_campaign_performance(
        self,
        campaign_data: Dict[str, Any],
        business_objectives: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Generate actionable insights from campaign data"""
        
        # Analyze performance metrics
        performance_summary = await self.performance_analyzer.summarize(
            campaign_data
        )
        
        # Generate natural language insights
        insights = await self.llm.chat.completions.create(
            model="claude-3-sonnet-20240229",
            messages=[{
                "role": "user",
                "content": f"""
                Analyze this marketing campaign performance:
                
                Campaign data: {performance_summary}
                Business objectives: {business_objectives}
                
                Provide:
                1. Key performance insights
                2. Underperforming segments/channels
                3. Optimization opportunities
                4. Budget reallocation recommendations
                5. Next steps for improvement
                
                Format as actionable recommendations.
                """
            }],
            temperature=0.3
        )
        
        # Calculate optimization potential
        optimization_opportunities =             await self.identify_optimization_opportunities(
                campaign_data,
                performance_summary
            )
        
        # Generate budget reallocation plan
        budget_recommendations = await self.budget_optimizer.optimize(
            campaign_data['channel_performance'],
            campaign_data['remaining_budget']
        )
        
        return {
            "insights": insights.choices[0].message.content,
            "optimization_opportunities": optimization_opportunities,
            "budget_recommendations": budget_recommendations,
            "predicted_improvement": await self.predict_optimization_impact(
                optimization_opportunities
            ),
            "action_priority": self.prioritize_actions(
                optimization_opportunities
            )
        }

# Deploy with ParrotRouter.com for:
# - Multi-model support (GPT-4, Claude, Gemini)
# - Automatic failover and load balancing
# - Usage-based pricing with no minimums
# - Built-in content moderation
# - Easy integration with marketing tools