AI Analysis and Content Strategy fixes. Enhanced Strategy Routes refactoring.
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ajaysi
303
backend/services/campaign_creator/prompt_builder.py
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303
backend/services/campaign_creator/prompt_builder.py
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"""
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Campaign Creator Prompt Builder
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Extends AIPromptOptimizer with campaign-specific prompt enhancement.
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"""
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from typing import Dict, Any, Optional
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from loguru import logger
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from services.ai_prompt_optimizer import AIPromptOptimizer
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from services.onboarding import OnboardingDataService
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from services.onboarding.database_service import OnboardingDatabaseService
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from services.persona_data_service import PersonaDataService
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from services.database import SessionLocal
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class CampaignPromptBuilder(AIPromptOptimizer):
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"""Specialized prompt builder for campaign assets with onboarding data integration."""
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def __init__(self):
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"""Initialize Campaign Prompt Builder."""
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super().__init__()
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self.onboarding_data_service = OnboardingDataService()
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self.logger = logger
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logger.info("[Campaign Prompt Builder] Initialized")
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def build_marketing_image_prompt(
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self,
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base_prompt: str,
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user_id: str,
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channel: Optional[str] = None,
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asset_type: str = "hero_image",
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product_context: Optional[Dict[str, Any]] = None
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) -> str:
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"""
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Build enhanced marketing image prompt with brand DNA and persona data.
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Args:
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base_prompt: Base product description or image concept
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user_id: User ID to fetch onboarding data
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channel: Target channel (instagram, linkedin, tiktok, etc.)
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asset_type: Type of asset (hero_image, product_photo, lifestyle, etc.)
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product_context: Additional product information
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Returns:
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Enhanced prompt with brand DNA, persona style, and marketing context
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"""
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try:
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# Get onboarding data
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db = SessionLocal()
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try:
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onboarding_db = OnboardingDatabaseService(db)
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website_analysis = onboarding_db.get_website_analysis(user_id, db)
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persona_data = onboarding_db.get_persona_data(user_id, db)
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competitor_analyses = onboarding_db.get_competitor_analysis(user_id, db)
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finally:
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db.close()
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# Build prompt layers
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enhanced_prompt = base_prompt
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# Layer 1: Brand DNA (from website_analysis)
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if website_analysis:
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writing_style = website_analysis.get('writing_style', {})
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target_audience = website_analysis.get('target_audience', {})
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brand_analysis = website_analysis.get('brand_analysis', {})
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style_guidelines = website_analysis.get('style_guidelines', {})
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# Add brand tone and style
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tone = writing_style.get('tone', 'professional')
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voice = writing_style.get('voice', 'authoritative')
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brand_enhancement = f", {tone} tone, {voice} voice"
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# Add target audience context
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demographics = target_audience.get('demographics', [])
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if demographics:
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audience_context = f", targeting {', '.join(demographics[:2])}"
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enhanced_prompt += audience_context
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# Add brand visual identity if available
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if brand_analysis:
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color_palette = brand_analysis.get('color_palette', [])
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if color_palette:
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colors = ', '.join(color_palette[:3])
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enhanced_prompt += f", brand colors: {colors}"
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# Layer 2: Persona Visual Style (from persona_data)
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if persona_data:
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core_persona = persona_data.get('corePersona', {})
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platform_personas = persona_data.get('platformPersonas', {})
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if core_persona:
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persona_name = core_persona.get('persona_name', '')
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archetype = core_persona.get('archetype', '')
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if persona_name:
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enhanced_prompt += f", {persona_name} style"
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# Channel-specific persona adaptation
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if channel and platform_personas:
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platform_persona = platform_personas.get(channel, {})
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if platform_persona:
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visual_identity = platform_persona.get('visual_identity', {})
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if visual_identity:
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aesthetic = visual_identity.get('aesthetic_preferences', '')
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if aesthetic:
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enhanced_prompt += f", {aesthetic} aesthetic"
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# Layer 3: Channel Optimization
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channel_enhancements = {
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'instagram': ', Instagram-optimized composition, vibrant colors, engaging visual',
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'linkedin': ', professional photography, clean composition, business-focused',
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'tiktok': ', dynamic composition, eye-catching, vertical format optimized',
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'facebook': ', social media optimized, engaging, shareable visual',
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'twitter': ', Twitter card optimized, clear focal point, readable at small size',
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'pinterest': ', Pinterest-optimized, vertical format, detailed and informative',
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}
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if channel and channel.lower() in channel_enhancements:
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enhanced_prompt += channel_enhancements[channel.lower()]
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# Layer 4: Asset Type Specific
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asset_type_enhancements = {
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'hero_image': ', hero image style, prominent product placement, professional photography',
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'product_photo': ', product photography, clean background, detailed product showcase',
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'lifestyle': ', lifestyle photography, natural setting, authentic scene',
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'social_post': ', social media post, engaging composition, optimized for engagement',
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}
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if asset_type in asset_type_enhancements:
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enhanced_prompt += asset_type_enhancements[asset_type]
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# Layer 5: Competitive Differentiation
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if competitor_analyses and len(competitor_analyses) > 0:
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# Extract unique positioning from competitor analysis
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enhanced_prompt += ", unique positioning, differentiated visual style"
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# Layer 6: Quality Descriptors
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enhanced_prompt += ", professional photography, high quality, detailed, sharp focus, natural lighting"
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# Layer 7: Marketing Context
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if product_context:
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marketing_goal = product_context.get('marketing_goal', '')
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if marketing_goal:
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enhanced_prompt += f", {marketing_goal} focused"
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logger.info(f"[Campaign Prompt] Enhanced prompt for user {user_id}: {enhanced_prompt[:200]}...")
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return enhanced_prompt
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except Exception as e:
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logger.error(f"[Campaign Prompt] Error building prompt: {str(e)}")
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# Return base prompt with minimal enhancement if error
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return f"{base_prompt}, professional photography, high quality"
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def build_marketing_copy_prompt(
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self,
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base_request: str,
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user_id: str,
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channel: Optional[str] = None,
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content_type: str = "caption",
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product_context: Optional[Dict[str, Any]] = None
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) -> str:
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"""
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Build enhanced marketing copy prompt with persona linguistic fingerprint.
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Args:
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base_request: Base content request (e.g., "Write Instagram caption for product launch")
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user_id: User ID to fetch onboarding data
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channel: Target channel (instagram, linkedin, etc.)
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content_type: Type of content (caption, cta, email, ad_copy, etc.)
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product_context: Additional product information
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Returns:
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Enhanced prompt with persona style, brand voice, and marketing context
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"""
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try:
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# Get onboarding data
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db = SessionLocal()
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try:
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onboarding_db = OnboardingDatabaseService(db)
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website_analysis = onboarding_db.get_website_analysis(user_id, db)
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persona_data = onboarding_db.get_persona_data(user_id, db)
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competitor_analyses = onboarding_db.get_competitor_analysis(user_id, db)
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finally:
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db.close()
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# Build enhanced prompt
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enhanced_prompt = base_request
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# Add persona linguistic fingerprint
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if persona_data:
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core_persona = persona_data.get('corePersona', {})
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platform_personas = persona_data.get('platformPersonas', {})
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if core_persona:
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persona_name = core_persona.get('persona_name', '')
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linguistic_fingerprint = core_persona.get('linguistic_fingerprint', {})
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if persona_name:
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enhanced_prompt += f"\n\nFollow {persona_name} persona style:"
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if linguistic_fingerprint:
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sentence_metrics = linguistic_fingerprint.get('sentence_metrics', {})
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lexical_features = linguistic_fingerprint.get('lexical_features', {})
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if sentence_metrics:
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avg_length = sentence_metrics.get('average_sentence_length_words', '')
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if avg_length:
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enhanced_prompt += f"\n- Average sentence length: {avg_length} words"
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if lexical_features:
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go_to_words = lexical_features.get('go_to_words', [])
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avoid_words = lexical_features.get('avoid_words', [])
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vocabulary_level = lexical_features.get('vocabulary_level', '')
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if go_to_words:
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enhanced_prompt += f"\n- Use these words: {', '.join(go_to_words[:5])}"
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if avoid_words:
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enhanced_prompt += f"\n- Avoid these words: {', '.join(avoid_words[:5])}"
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if vocabulary_level:
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enhanced_prompt += f"\n- Vocabulary level: {vocabulary_level}"
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# Channel-specific persona adaptation
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if channel and platform_personas:
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platform_persona = platform_personas.get(channel, {})
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if platform_persona:
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content_format_rules = platform_persona.get('content_format_rules', {})
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engagement_patterns = platform_persona.get('engagement_patterns', {})
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if content_format_rules:
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char_limit = content_format_rules.get('character_limit', '')
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hashtag_strategy = content_format_rules.get('hashtag_strategy', '')
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if char_limit:
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enhanced_prompt += f"\n- Character limit: {char_limit}"
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if hashtag_strategy:
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enhanced_prompt += f"\n- Hashtag strategy: {hashtag_strategy}"
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# Add brand voice
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if website_analysis:
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writing_style = website_analysis.get('writing_style', {})
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target_audience = website_analysis.get('target_audience', {})
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tone = writing_style.get('tone', 'professional')
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voice = writing_style.get('voice', 'authoritative')
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enhanced_prompt += f"\n- Brand tone: {tone}, Brand voice: {voice}"
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demographics = target_audience.get('demographics', [])
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expertise_level = target_audience.get('expertise_level', 'intermediate')
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if demographics:
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enhanced_prompt += f"\n- Target audience: {', '.join(demographics[:2])}, {expertise_level} level"
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# Add competitive positioning
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if competitor_analyses and len(competitor_analyses) > 0:
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enhanced_prompt += "\n- Differentiate from competitors, highlight unique value propositions"
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# Add marketing context
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if product_context:
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marketing_goal = product_context.get('marketing_goal', '')
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if marketing_goal:
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enhanced_prompt += f"\n- Marketing goal: {marketing_goal}"
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logger.info(f"[Campaign Copy Prompt] Enhanced for user {user_id}: {enhanced_prompt[:200]}...")
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return enhanced_prompt
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except Exception as e:
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logger.error(f"[Campaign Copy Prompt] Error building prompt: {str(e)}")
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return base_request
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def optimize_marketing_prompt(
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self,
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prompt_type: str,
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base_prompt: str,
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user_id: str,
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context: Optional[Dict[str, Any]] = None
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) -> str:
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"""
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Main entry point for marketing prompt optimization.
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Args:
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prompt_type: Type of prompt (image, copy, video_script, etc.)
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base_prompt: Base prompt to enhance
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user_id: User ID for personalization
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context: Additional context (channel, asset_type, product_context, etc.)
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Returns:
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Optimized marketing prompt
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"""
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context = context or {}
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channel = context.get('channel')
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asset_type = context.get('asset_type', 'hero_image')
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content_type = context.get('content_type', 'caption')
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product_context = context.get('product_context')
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if prompt_type == 'image':
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return self.build_marketing_image_prompt(
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base_prompt, user_id, channel, asset_type, product_context
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)
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elif prompt_type in ['copy', 'caption', 'cta', 'email', 'ad_copy']:
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return self.build_marketing_copy_prompt(
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base_prompt, user_id, channel, content_type, product_context
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)
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else:
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# Default: minimal enhancement
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return f"{base_prompt}, professional quality, marketing optimized"
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