AI Analysis and Content Strategy fixes. Enhanced Strategy Routes refactoring.
This commit is contained in:
ajaysi
653
backend/services/campaign_creator/orchestrator.py
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653
backend/services/campaign_creator/orchestrator.py
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"""
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Campaign Creator Orchestrator
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Main service that orchestrates campaign workflows and asset generation.
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"""
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from typing import Dict, Any, List, Optional
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from dataclasses import dataclass
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from loguru import logger
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from services.image_studio import ImageStudioManager, CreateStudioRequest
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from .prompt_builder import CampaignPromptBuilder
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from services.product_marketing.brand_dna_sync import BrandDNASyncService
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from .asset_audit import AssetAuditService
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from .channel_pack import ChannelPackService
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from services.database import SessionLocal
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from services.subscription import PricingService
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from services.subscription.preflight_validator import validate_image_generation_operations
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@dataclass
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class CampaignAssetNode:
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"""Represents an asset node in the campaign graph."""
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asset_id: str
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asset_type: str # image, video, text, audio
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channel: str
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status: str # draft, generating, ready, approved
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prompt: Optional[str] = None
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template_id: Optional[str] = None
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provider: Optional[str] = None
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cost_estimate: Optional[float] = None
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generated_asset_id: Optional[int] = None # Asset Library ID
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@dataclass
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class CampaignBlueprint:
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"""Campaign blueprint with phases and asset nodes."""
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campaign_id: str
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campaign_name: str
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goal: str
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kpi: Optional[str] = None
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phases: List[Dict[str, Any]] = None # teaser, launch, nurture
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asset_nodes: List[CampaignAssetNode] = None
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channels: List[str] = None
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status: str = "draft" # draft, generating, ready, published
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class CampaignOrchestrator:
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"""Main orchestrator for Campaign Creator."""
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def __init__(self):
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"""Initialize Campaign Orchestrator."""
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self.image_studio = ImageStudioManager()
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self.prompt_builder = CampaignPromptBuilder()
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self.brand_dna_sync = BrandDNASyncService()
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self.asset_audit = AssetAuditService()
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self.channel_pack = ChannelPackService()
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self.logger = logger
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logger.info("[Campaign Orchestrator] Initialized")
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def create_campaign_blueprint(
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self,
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user_id: str,
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campaign_data: Dict[str, Any]
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) -> CampaignBlueprint:
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"""
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Create campaign blueprint from user input and onboarding data.
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Args:
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user_id: User ID
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campaign_data: Campaign information (name, goal, channels, etc.)
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Returns:
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Campaign blueprint with asset nodes
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"""
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try:
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import time
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campaign_id = campaign_data.get('campaign_id') or f"campaign_{user_id}_{int(time.time())}"
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campaign_name = campaign_data.get('campaign_name', 'New Campaign')
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goal = campaign_data.get('goal', 'product_launch')
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channels = campaign_data.get('channels', [])
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# Get brand DNA for personalization
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brand_dna = self.brand_dna_sync.get_brand_dna_tokens(user_id)
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# Build campaign phases
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phases = self._build_campaign_phases(goal, channels)
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# Generate asset nodes for each phase and channel
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asset_nodes = []
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for phase in phases:
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phase_name = phase.get('name')
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for channel in channels:
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# Determine required assets for this phase + channel
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required_assets = self._get_required_assets(phase_name, channel)
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for asset_type in required_assets:
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asset_node = CampaignAssetNode(
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asset_id=f"{campaign_id}_{phase_name}_{channel}_{asset_type}",
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asset_type=asset_type,
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channel=channel,
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status="draft",
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)
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asset_nodes.append(asset_node)
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blueprint = CampaignBlueprint(
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campaign_id=campaign_id,
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campaign_name=campaign_name,
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goal=goal,
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kpi=campaign_data.get('kpi'),
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phases=phases,
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asset_nodes=asset_nodes,
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channels=channels,
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status="draft",
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)
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logger.info(f"[Orchestrator] Created blueprint for campaign {campaign_id} with {len(asset_nodes)} assets")
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return blueprint
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except Exception as e:
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logger.error(f"[Orchestrator] Error creating blueprint: {str(e)}")
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raise
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def generate_asset_proposals(
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self,
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user_id: str,
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blueprint: CampaignBlueprint,
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product_context: Optional[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""
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Generate AI proposals for each asset node in the blueprint.
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Args:
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user_id: User ID
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blueprint: Campaign blueprint
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product_context: Product information
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Returns:
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Dictionary with proposals for each asset node
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"""
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try:
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proposals = {}
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for asset_node in blueprint.asset_nodes:
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# Build specialized prompt based on asset type and channel
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if asset_node.asset_type == "image":
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base_prompt = product_context.get('product_description', 'Product image') if product_context else 'Marketing image'
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enhanced_prompt = self.prompt_builder.build_marketing_image_prompt(
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base_prompt=base_prompt,
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user_id=user_id,
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channel=asset_node.channel,
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asset_type="hero_image",
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product_context=product_context,
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)
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# Get channel pack for template recommendations
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channel_pack = self.channel_pack.get_channel_pack(asset_node.channel)
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recommended_template = channel_pack.get('templates', [{}])[0] if channel_pack.get('templates') else None
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# Estimate cost
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cost_estimate = self._estimate_asset_cost("image", asset_node.channel)
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proposals[asset_node.asset_id] = {
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"asset_id": asset_node.asset_id,
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"asset_type": asset_node.asset_type,
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"channel": asset_node.channel,
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"campaign_id": blueprint.campaign_id, # Include campaign_id for tracking
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"proposed_prompt": enhanced_prompt,
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"recommended_template": recommended_template.get('id') if recommended_template else None,
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"recommended_provider": recommended_template.get('recommended_provider', 'wavespeed') if recommended_template else 'wavespeed',
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"cost_estimate": cost_estimate,
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"concept_summary": self._generate_concept_summary(enhanced_prompt),
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}
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elif asset_node.asset_type == "video":
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# Video asset proposals - determine if animation (image-to-video) or demo (text-to-video)
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# Default to animation if we have product image, otherwise demo
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video_subtype = asset_proposal.get('video_subtype', 'animation') if 'asset_proposal' in locals() else 'demo'
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# For demo videos (text-to-video), we need product description
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if video_subtype == "demo" or not product_context or not product_context.get('product_image_base64'):
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# Text-to-video demo video
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video_type = "demo" # Default, can be customized
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if asset_node.channel in ["tiktok", "instagram"]:
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video_type = "storytelling" # Storytelling for social media
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elif asset_node.channel in ["linkedin", "youtube"]:
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video_type = "feature_highlight" # Feature highlights for professional
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# Estimate cost for text-to-video (WAN 2.5: $0.05-$0.15/second)
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duration = 10 # Default 10s for demo videos
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resolution = "720p" # Default
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cost_per_second = 0.10 if resolution == "720p" else (0.15 if resolution == "1080p" else 0.05)
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cost_estimate = duration * cost_per_second
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proposals[asset_node.asset_id] = {
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"asset_id": asset_node.asset_id,
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"asset_type": asset_node.asset_type,
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"video_subtype": "demo", # Text-to-video
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"channel": asset_node.channel,
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"campaign_id": blueprint.campaign_id,
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"video_type": video_type,
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"duration": duration,
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"resolution": resolution,
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"cost_estimate": cost_estimate,
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"concept_summary": f"Product {video_type} video optimized for {asset_node.channel}",
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"note": "Text-to-video demo - requires product description",
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}
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else:
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# Image-to-video animation
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animation_type = "reveal" # Default
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if asset_node.channel in ["tiktok", "instagram", "youtube"]:
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animation_type = "demo" # Demo animations for social media
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elif asset_node.channel in ["linkedin", "facebook"]:
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animation_type = "reveal" # Professional reveal for B2B
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# Estimate cost for image-to-video (WAN 2.5: $0.05-$0.15/second)
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duration = 5 # Default 5s for animations
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resolution = "720p" # Default
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cost_per_second = 0.10 if resolution == "720p" else (0.15 if resolution == "1080p" else 0.05)
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cost_estimate = duration * cost_per_second
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proposals[asset_node.asset_id] = {
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"asset_id": asset_node.asset_id,
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"asset_type": asset_node.asset_type,
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"video_subtype": "animation", # Image-to-video
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"channel": asset_node.channel,
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"campaign_id": blueprint.campaign_id,
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"animation_type": animation_type,
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"duration": duration,
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"resolution": resolution,
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"cost_estimate": cost_estimate,
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"concept_summary": f"Product {animation_type} animation optimized for {asset_node.channel}",
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"note": "Requires product image - will be provided during generation",
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}
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elif asset_node.asset_type == "text":
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base_request = f"Write {asset_node.channel} {asset_node.asset_type} for product launch"
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enhanced_prompt = self.prompt_builder.build_marketing_copy_prompt(
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base_request=base_request,
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user_id=user_id,
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channel=asset_node.channel,
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content_type="caption",
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product_context=product_context,
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)
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proposals[asset_node.asset_id] = {
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"asset_id": asset_node.asset_id,
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"asset_type": asset_node.asset_type,
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"channel": asset_node.channel,
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"campaign_id": blueprint.campaign_id, # Include campaign_id for tracking
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"proposed_prompt": enhanced_prompt,
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"cost_estimate": 0.0, # Text generation cost is minimal
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"concept_summary": "Marketing copy optimized for channel and persona",
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}
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logger.info(f"[Orchestrator] Generated {len(proposals)} asset proposals")
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return {"proposals": proposals, "total_assets": len(proposals)}
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except Exception as e:
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logger.error(f"[Orchestrator] Error generating proposals: {str(e)}")
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raise
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async def generate_asset(
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self,
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user_id: str,
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asset_proposal: Dict[str, Any],
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product_context: Optional[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""
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Generate a single asset using Image Studio APIs.
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Args:
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user_id: User ID
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asset_proposal: Asset proposal from generate_asset_proposals
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product_context: Product information
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Returns:
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Generated asset result
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"""
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try:
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asset_type = asset_proposal.get('asset_type')
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if asset_type == "image":
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# Build CreateStudioRequest
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create_request = CreateStudioRequest(
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prompt=asset_proposal.get('proposed_prompt'),
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template_id=asset_proposal.get('recommended_template'),
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provider=asset_proposal.get('recommended_provider', 'wavespeed'),
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quality="premium",
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enhance_prompt=True,
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use_persona=True,
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num_variations=1,
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)
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# Generate image using Image Studio
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result = await self.image_studio.create_image(create_request, user_id=user_id)
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# Asset is automatically tracked in Asset Library via Image Studio
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return {
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"success": True,
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"asset_type": "image",
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"result": result,
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"asset_library_ids": [
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r.get('asset_id') for r in result.get('results', [])
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if r.get('asset_id')
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],
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}
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elif asset_type == "video":
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# Check video subtype: "animation" (image-to-video) or "demo" (text-to-video)
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video_subtype = asset_proposal.get('video_subtype', 'animation')
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if video_subtype == "demo":
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# Text-to-video: Product demo video from description
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from services.product_marketing.product_video_service import ProductVideoService, ProductVideoRequest
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# Get product info from context
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product_name = product_context.get('product_name', 'Product') if product_context else 'Product'
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product_description = product_context.get('product_description', '') if product_context else ''
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if not product_description:
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raise ValueError("Product description required for text-to-video demo generation")
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# Get brand context
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brand_dna = self.brand_dna_sync.get_brand_dna_tokens(user_id)
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brand_context = {
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"visual_identity": brand_dna.get("visual_identity", {}),
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"persona": brand_dna.get("persona", {}),
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}
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# Get video type from proposal or default
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video_type = asset_proposal.get('video_type', 'demo')
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# Create video service
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video_service = ProductVideoService()
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# Create video request
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video_request = ProductVideoRequest(
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product_name=product_name,
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product_description=product_description,
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video_type=video_type,
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resolution=asset_proposal.get('resolution', '720p'),
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duration=asset_proposal.get('duration', 10),
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audio_base64=asset_proposal.get('audio_base64'),
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brand_context=brand_context,
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additional_context=asset_proposal.get('additional_context'),
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)
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# Generate video using unified ai_video_generate()
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result = await video_service.generate_product_video(video_request, user_id)
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# Extract campaign_id for metadata
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campaign_id = asset_proposal.get('campaign_id')
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asset_id = asset_proposal.get('asset_id', '')
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return {
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"success": True,
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"asset_type": "video",
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"video_subtype": "demo",
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"video_url": result.get('file_url'),
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"video_filename": result.get('filename'),
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"cost": result.get('cost', 0.0),
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"video_type": video_type,
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"campaign_id": campaign_id,
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"asset_id": asset_id,
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}
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else:
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# Image-to-video: Product animation
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from services.product_marketing.product_animation_service import ProductAnimationService, ProductAnimationRequest
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# Get product image from proposal or product context
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product_image_base64 = asset_proposal.get('product_image_base64')
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if not product_image_base64 and product_context:
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product_image_base64 = product_context.get('product_image_base64')
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if not product_image_base64:
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raise ValueError("Product image required for image-to-video animation generation")
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# Get animation type from proposal or default to "reveal"
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animation_type = asset_proposal.get('animation_type', 'reveal')
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product_name = product_context.get('product_name', 'Product') if product_context else 'Product'
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product_description = product_context.get('product_description') if product_context else None
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# Get brand context
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brand_dna = self.brand_dna_sync.get_brand_dna_tokens(user_id)
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brand_context = {
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"visual_identity": brand_dna.get("visual_identity", {}),
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"persona": brand_dna.get("persona", {}),
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}
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# Create animation service
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animation_service = ProductAnimationService()
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# Create animation request
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animation_request = ProductAnimationRequest(
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product_image_base64=product_image_base64,
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animation_type=animation_type,
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product_name=product_name,
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product_description=product_description,
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resolution=asset_proposal.get('resolution', '720p'),
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duration=asset_proposal.get('duration', 5),
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audio_base64=asset_proposal.get('audio_base64'),
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brand_context=brand_context,
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additional_context=asset_proposal.get('additional_context'),
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)
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# Generate video
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result = await animation_service.animate_product(animation_request, user_id)
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# Extract campaign_id for metadata
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campaign_id = asset_proposal.get('campaign_id')
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asset_id = asset_proposal.get('asset_id', '')
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return {
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"success": True,
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"asset_type": "video",
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"video_subtype": "animation",
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"video_url": result.get('video_url'),
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"video_filename": result.get('filename'),
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"cost": result.get('cost', 0.0),
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"animation_type": animation_type,
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"campaign_id": campaign_id,
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"asset_id": asset_id,
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}
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elif asset_type == "text":
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# Import text generation service and tracker
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import asyncio
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from services.llm_providers.main_text_generation import llm_text_gen
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from utils.text_asset_tracker import save_and_track_text_content
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from services.database import SessionLocal
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# Get enhanced prompt from proposal
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text_prompt = asset_proposal.get('proposed_prompt', '')
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channel = asset_proposal.get('channel', 'social')
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asset_id = asset_proposal.get('asset_id', '')
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# Extract campaign_id - try from asset_proposal first, then from asset_id
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# asset_id format: {campaign_id}_{phase}_{channel}_{type}
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campaign_id = asset_proposal.get('campaign_id')
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if not campaign_id and asset_id and '_' in asset_id:
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# Try to extract: asset_id might be "campaign_user123_1234567890_teaser_instagram_text"
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# We need to find where phase_name starts (common phases: teaser, launch, nurture)
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parts = asset_id.split('_')
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# Find phase indicator (usually one of: teaser, launch, nurture)
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phase_indicators = ['teaser', 'launch', 'nurture', 'prelaunch', 'postlaunch']
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phase_idx = None
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for i, part in enumerate(parts):
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if part.lower() in phase_indicators:
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phase_idx = i
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break
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if phase_idx and phase_idx > 0:
|
||||
# Campaign ID is everything before the phase
|
||||
campaign_id = '_'.join(parts[:phase_idx])
|
||||
|
||||
# If still not found, use None (metadata will work without it)
|
||||
if not campaign_id:
|
||||
logger.warning(f"[Orchestrator] Could not extract campaign_id from asset_id: {asset_id}")
|
||||
|
||||
# Build system prompt for marketing copy
|
||||
system_prompt = f"""You are an expert marketing copywriter specializing in {channel} content.
|
||||
Generate compelling, on-brand marketing copy that:
|
||||
- Is optimized for {channel} platform best practices
|
||||
- Includes a clear call-to-action
|
||||
- Uses appropriate tone and style for the platform
|
||||
- Is concise and engaging
|
||||
- Aligns with the product marketing context provided
|
||||
|
||||
Return only the final copy text without explanations or markdown formatting."""
|
||||
|
||||
# Run synchronous llm_text_gen in thread pool
|
||||
logger.info(f"[Orchestrator] Generating text asset for channel: {channel}")
|
||||
generated_text = await asyncio.to_thread(
|
||||
llm_text_gen,
|
||||
prompt=text_prompt,
|
||||
system_prompt=system_prompt,
|
||||
user_id=user_id
|
||||
)
|
||||
|
||||
if not generated_text or not generated_text.strip():
|
||||
raise ValueError("Text generation returned empty content")
|
||||
|
||||
# Save to Asset Library
|
||||
db = SessionLocal()
|
||||
asset_library_id = None
|
||||
try:
|
||||
asset_library_id = save_and_track_text_content(
|
||||
db=db,
|
||||
user_id=user_id,
|
||||
content=generated_text.strip(),
|
||||
source_module="campaign_creator",
|
||||
title=f"{channel.title()} Copy: {asset_id.split('_')[-1] if '_' in asset_id else 'Marketing Copy'}",
|
||||
description=f"Marketing copy for {channel} platform generated from campaign proposal",
|
||||
prompt=text_prompt,
|
||||
tags=["campaign_creator", channel.lower(), "text", "copy"],
|
||||
asset_metadata={
|
||||
"campaign_id": campaign_id,
|
||||
"asset_id": asset_id,
|
||||
"asset_type": "text",
|
||||
"channel": channel,
|
||||
"concept_summary": asset_proposal.get('concept_summary'),
|
||||
},
|
||||
subdirectory="campaigns",
|
||||
file_extension=".txt"
|
||||
)
|
||||
|
||||
if asset_library_id:
|
||||
logger.info(f"[Orchestrator] ✅ Text asset saved to library: ID={asset_library_id}")
|
||||
else:
|
||||
logger.warning(f"[Orchestrator] ⚠️ Text asset tracking returned None")
|
||||
|
||||
except Exception as save_error:
|
||||
logger.error(f"[Orchestrator] ⚠️ Failed to save text asset to library: {str(save_error)}")
|
||||
# Continue even if save fails - text is still generated
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"asset_type": "text",
|
||||
"content": generated_text.strip(),
|
||||
"asset_library_id": asset_library_id,
|
||||
"channel": channel,
|
||||
}
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unsupported asset type: {asset_type}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Orchestrator] Error generating asset: {str(e)}")
|
||||
raise
|
||||
|
||||
def validate_campaign_preflight(
|
||||
self,
|
||||
user_id: str,
|
||||
blueprint: CampaignBlueprint
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Validate campaign blueprint against subscription limits before generation.
|
||||
|
||||
Args:
|
||||
user_id: User ID
|
||||
blueprint: Campaign blueprint
|
||||
|
||||
Returns:
|
||||
Pre-flight validation results
|
||||
"""
|
||||
try:
|
||||
db = SessionLocal()
|
||||
try:
|
||||
pricing_service = PricingService(db)
|
||||
|
||||
# Count operations needed
|
||||
image_count = sum(1 for node in blueprint.asset_nodes if node.asset_type == "image")
|
||||
text_count = sum(1 for node in blueprint.asset_nodes if node.asset_type == "text")
|
||||
|
||||
# Estimate total cost
|
||||
total_cost = 0.0
|
||||
for node in blueprint.asset_nodes:
|
||||
if node.cost_estimate:
|
||||
total_cost += node.cost_estimate
|
||||
|
||||
# Validate image generation limits
|
||||
operations = []
|
||||
if image_count > 0:
|
||||
operations.append({
|
||||
'provider': 'stability', # Default provider
|
||||
'tokens_requested': 0,
|
||||
'actual_provider_name': 'wavespeed',
|
||||
'operation_type': 'image_generation',
|
||||
})
|
||||
|
||||
can_proceed, message, error_details = pricing_service.check_comprehensive_limits(
|
||||
user_id=user_id,
|
||||
operations=operations * image_count if operations else []
|
||||
)
|
||||
|
||||
return {
|
||||
"can_proceed": can_proceed,
|
||||
"message": message,
|
||||
"error_details": error_details,
|
||||
"summary": {
|
||||
"total_assets": len(blueprint.asset_nodes),
|
||||
"image_count": image_count,
|
||||
"text_count": text_count,
|
||||
"estimated_cost": total_cost,
|
||||
},
|
||||
}
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Orchestrator] Error in pre-flight validation: {str(e)}")
|
||||
return {
|
||||
"can_proceed": False,
|
||||
"message": f"Validation error: {str(e)}",
|
||||
"error_details": {},
|
||||
}
|
||||
|
||||
def _build_campaign_phases(
|
||||
self,
|
||||
goal: str,
|
||||
channels: List[str]
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Build campaign phases based on goal."""
|
||||
if goal == "product_launch":
|
||||
return [
|
||||
{"name": "teaser", "duration_days": 7, "purpose": "Build anticipation"},
|
||||
{"name": "launch", "duration_days": 3, "purpose": "Official launch"},
|
||||
{"name": "nurture", "duration_days": 14, "purpose": "Sustain engagement"},
|
||||
]
|
||||
else:
|
||||
return [
|
||||
{"name": "campaign", "duration_days": 30, "purpose": "Campaign execution"},
|
||||
]
|
||||
|
||||
def _get_required_assets(
|
||||
self,
|
||||
phase: str,
|
||||
channel: str
|
||||
) -> List[str]:
|
||||
"""Get required asset types for phase and channel."""
|
||||
# Default: image for all phases and channels
|
||||
assets = ["image"]
|
||||
|
||||
# Add text/copy for social channels
|
||||
if channel in ["instagram", "linkedin", "facebook", "twitter"]:
|
||||
assets.append("text")
|
||||
|
||||
return assets
|
||||
|
||||
def _estimate_asset_cost(
|
||||
self,
|
||||
asset_type: str,
|
||||
channel: str
|
||||
) -> float:
|
||||
"""Estimate cost for asset generation."""
|
||||
if asset_type == "image":
|
||||
# Premium quality image: ~5-6 credits
|
||||
return 5.0
|
||||
elif asset_type == "video":
|
||||
# WAN 2.5 Image-to-Video: $0.05-$0.15/second
|
||||
# Default: 5 seconds at 720p = $0.50
|
||||
return 0.50
|
||||
elif asset_type == "text":
|
||||
return 0.0 # Text generation is typically included
|
||||
else:
|
||||
return 0.0
|
||||
|
||||
def _generate_concept_summary(self, prompt: str) -> str:
|
||||
"""Generate a brief concept summary from prompt."""
|
||||
# Simple extraction: take first 100 chars
|
||||
return prompt[:100] + "..." if len(prompt) > 100 else prompt
|
||||
Reference in New Issue
Block a user