AI Analysis and Content Strategy fixes. Enhanced Strategy Routes refactoring.
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ajaysi
179
backend/services/campaign_creator/channel_pack.py
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179
backend/services/campaign_creator/channel_pack.py
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"""
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Channel Pack Service
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Maps channels to templates, copy frameworks, and platform-specific optimizations.
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"""
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from typing import Dict, Any, List, Optional
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from loguru import logger
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from services.image_studio.templates import Platform, TemplateManager
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from services.image_studio.social_optimizer_service import SocialOptimizerService
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class ChannelPackService:
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"""Service to build channel-specific asset packs."""
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def __init__(self):
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"""Initialize Channel Pack Service."""
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self.template_manager = TemplateManager()
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self.social_optimizer = SocialOptimizerService()
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self.logger = logger
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logger.info("[Channel Pack] Service initialized")
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def get_channel_pack(
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self,
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channel: str,
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asset_type: str = "social_post"
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) -> Dict[str, Any]:
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"""
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Get channel-specific pack configuration.
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Args:
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channel: Target channel (instagram, linkedin, tiktok, facebook, twitter, pinterest, youtube)
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asset_type: Type of asset (social_post, story, reel, cover, etc.)
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Returns:
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Channel pack configuration with templates, dimensions, copy frameworks
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"""
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try:
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# Map channel string to Platform enum
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platform_map = {
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'instagram': Platform.INSTAGRAM,
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'linkedin': Platform.LINKEDIN,
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'tiktok': Platform.TIKTOK,
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'facebook': Platform.FACEBOOK,
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'twitter': Platform.TWITTER,
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'pinterest': Platform.PINTEREST,
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'youtube': Platform.YOUTUBE,
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}
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platform = platform_map.get(channel.lower())
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if not platform:
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raise ValueError(f"Unsupported channel: {channel}")
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# Get templates for this platform
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templates = self.template_manager.get_platform_templates().get(platform, [])
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# Get platform formats
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formats = self.social_optimizer.get_platform_formats(platform)
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# Build channel pack
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pack = {
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"channel": channel,
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"platform": platform.value,
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"asset_type": asset_type,
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"templates": [
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{
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"id": t.id,
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"name": t.name,
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"dimensions": f"{t.aspect_ratio.width}x{t.aspect_ratio.height}",
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"aspect_ratio": t.aspect_ratio.ratio,
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"recommended_provider": t.recommended_provider,
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"quality": t.quality,
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}
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for t in templates
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],
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"formats": formats,
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"copy_framework": self._get_copy_framework(channel, asset_type),
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"optimization_tips": self._get_optimization_tips(channel),
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}
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logger.info(f"[Channel Pack] Built pack for {channel} ({asset_type})")
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return pack
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except Exception as e:
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logger.error(f"[Channel Pack] Error building pack: {str(e)}")
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return {
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"channel": channel,
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"error": str(e),
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}
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def _get_copy_framework(
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self,
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channel: str,
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asset_type: str
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) -> Dict[str, Any]:
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"""Get copy framework for channel and asset type."""
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frameworks = {
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"instagram": {
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"social_post": {
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"caption_length": "125-150 words optimal",
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"hashtags": "5-10 relevant hashtags",
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"cta": "Clear call-to-action in first line",
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"emoji": "Use 1-3 emojis strategically",
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},
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"story": {
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"text_overlay": "Keep text minimal, readable at small size",
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"cta": "Swipe-up or link sticker",
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},
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},
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"linkedin": {
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"social_post": {
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"length": "150-300 words for maximum engagement",
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"hashtags": "3-5 professional hashtags",
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"tone": "Professional, thought-leadership focused",
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"cta": "Engage with question or call-to-action",
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},
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},
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"tiktok": {
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"video": {
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"hook": "Strong hook in first 3 seconds",
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"caption": "Short, engaging, use trending hashtags",
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"hashtags": "3-5 trending hashtags",
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},
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},
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}
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return frameworks.get(channel, {}).get(asset_type, {})
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def _get_optimization_tips(self, channel: str) -> List[str]:
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"""Get optimization tips for channel."""
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tips = {
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"instagram": [
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"Use square (1:1) or portrait (4:5) for feed posts",
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"Include text overlay safe zones (15% top/bottom, 10% left/right)",
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"Optimize for mobile viewing",
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],
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"linkedin": [
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"Use landscape (1.91:1) for feed posts",
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"Professional photography style",
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"Include clear value proposition",
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],
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"tiktok": [
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"Vertical format (9:16) required",
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"Eye-catching first frame",
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"Fast-paced, engaging content",
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],
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}
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return tips.get(channel, [])
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def build_multi_channel_pack(
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self,
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channels: List[str],
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source_image_base64: str
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) -> Dict[str, Any]:
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"""
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Build optimized asset pack for multiple channels from single source.
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Args:
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channels: List of target channels
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source_image_base64: Source image to optimize
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Returns:
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Multi-channel pack with optimized variants
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"""
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pack_results = []
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for channel in channels:
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pack = self.get_channel_pack(channel)
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pack_results.append({
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"channel": channel,
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"pack": pack,
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})
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return {
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"source_image": "provided",
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"channels": pack_results,
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"total_variants": len(channels),
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}
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