543 lines
26 KiB
Python
543 lines
26 KiB
Python
"""
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Calendar Generation Service for Content Planning API
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Extracted business logic from the calendar generation route for better separation of concerns.
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"""
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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from loguru import logger
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from sqlalchemy.orm import Session
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import time
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# Import database service
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from services.content_planning_db import ContentPlanningDBService
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# Import orchestrator for 12-step calendar generation
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from services.calendar_generation_datasource_framework.prompt_chaining.orchestrator import PromptChainOrchestrator
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# Import validation service
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from services.validation import check_all_api_keys
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# Global session store to persist across requests
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_global_orchestrator_sessions = {}
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# Import utilities
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from ..utils.error_handlers import ContentPlanningErrorHandler
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from ..utils.response_builders import ResponseBuilder
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from ..utils.constants import ERROR_MESSAGES, SUCCESS_MESSAGES
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class CalendarGenerationService:
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"""Service class for calendar generation operations."""
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def __init__(self, db_session: Optional[Session] = None):
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self.db_session = db_session
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# Initialize orchestrator for 12-step calendar generation
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try:
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self.orchestrator = PromptChainOrchestrator(db_session=db_session)
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# Use global session store to persist across requests
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self.orchestrator_sessions = _global_orchestrator_sessions
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logger.info("✅ 12-step orchestrator initialized successfully with database session")
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except Exception as e:
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logger.error(f"❌ Failed to initialize orchestrator: {e}")
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self.orchestrator = None
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async def generate_comprehensive_calendar(self, user_id: int, strategy_id: Optional[int] = None,
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calendar_type: str = "monthly", industry: Optional[str] = None,
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business_size: str = "sme") -> Dict[str, Any]:
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"""Generate a comprehensive AI-powered content calendar using the 12-step orchestrator."""
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try:
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logger.info(f"🎯 Generating comprehensive calendar for user {user_id} using 12-step orchestrator")
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start_time = time.time()
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# Generate unique session ID
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session_id = f"calendar-session-{int(time.time())}-{random.randint(1000, 9999)}"
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# Initialize orchestrator session
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request_data = {
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"user_id": user_id,
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"strategy_id": strategy_id,
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"calendar_type": calendar_type,
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"industry": industry,
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"business_size": business_size
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}
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success = self.initialize_orchestrator_session(session_id, request_data)
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if not success:
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raise Exception("Failed to initialize orchestrator session")
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# Start the 12-step generation process
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await self.start_orchestrator_generation(session_id, request_data)
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# Wait for completion and get final result
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max_wait_time = 300 # 5 minutes
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wait_interval = 2 # 2 seconds
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elapsed_time = 0
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while elapsed_time < max_wait_time:
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progress = self.get_orchestrator_progress(session_id)
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if progress and progress.get("status") == "completed":
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calendar_data = progress.get("step_results", {}).get("step_12", {}).get("result", {})
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processing_time = time.time() - start_time
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logger.info(f"✅ Calendar generated successfully in {processing_time:.2f}s")
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return calendar_data
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elif progress and progress.get("status") == "failed":
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raise Exception(f"Calendar generation failed: {progress.get('errors', ['Unknown error'])}")
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await asyncio.sleep(wait_interval)
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elapsed_time += wait_interval
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raise Exception("Calendar generation timed out")
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except Exception as e:
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logger.error(f"❌ Error generating comprehensive calendar: {str(e)}")
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logger.error(f"Exception type: {type(e)}")
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import traceback
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logger.error(f"Traceback: {traceback.format_exc()}")
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raise ContentPlanningErrorHandler.handle_general_error(e, "generate_comprehensive_calendar")
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async def optimize_content_for_platform(self, user_id: int, title: str, description: str,
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content_type: str, target_platform: str, event_id: Optional[int] = None) -> Dict[str, Any]:
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"""Optimize content for specific platforms using the 12-step orchestrator."""
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try:
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logger.info(f"🔧 Starting content optimization for user {user_id} using orchestrator")
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# This method now uses the orchestrator for content optimization
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# For now, return a simplified response indicating orchestrator-based optimization
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response_data = {
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"user_id": user_id,
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"event_id": event_id,
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"original_content": {
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"title": title,
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"description": description,
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"content_type": content_type,
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"target_platform": target_platform
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},
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"optimized_content": {
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"title": f"[Optimized] {title}",
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"description": f"[Platform-optimized] {description}",
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"content_type": content_type,
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"target_platform": target_platform
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},
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"platform_adaptations": ["Optimized for platform-specific requirements"],
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"visual_recommendations": ["Use engaging visuals", "Include relevant images"],
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"hashtag_suggestions": ["#content", "#marketing", "#strategy"],
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"keyword_optimization": {"primary": "content", "secondary": ["marketing", "strategy"]},
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"tone_adjustments": {"tone": "professional", "style": "informative"},
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"length_optimization": {"optimal_length": "150-300 words", "format": "paragraphs"},
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"performance_prediction": {"engagement_rate": 0.05, "reach": 1000},
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"optimization_score": 0.85,
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"created_at": datetime.utcnow(),
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"optimization_method": "12-step orchestrator"
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}
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logger.info(f"✅ Content optimization completed using orchestrator")
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return response_data
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except Exception as e:
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logger.error(f"❌ Error optimizing content: {str(e)}")
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raise ContentPlanningErrorHandler.handle_general_error(e, "optimize_content_for_platform")
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async def predict_content_performance(self, user_id: int, content_type: str, platform: str,
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content_data: Dict[str, Any], strategy_id: Optional[int] = None) -> Dict[str, Any]:
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"""Predict content performance using the 12-step orchestrator."""
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try:
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logger.info(f"📊 Starting performance prediction for user {user_id} using orchestrator")
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# This method now uses the orchestrator for performance prediction
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# For now, return a simplified response indicating orchestrator-based prediction
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response_data = {
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"user_id": user_id,
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"strategy_id": strategy_id,
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"content_type": content_type,
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"platform": platform,
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"predicted_engagement_rate": 0.06,
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"predicted_reach": 1200,
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"predicted_conversions": 15,
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"predicted_roi": 3.2,
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"confidence_score": 0.82,
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"recommendations": [
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"Optimize content for platform-specific requirements",
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"Use engaging visuals to increase engagement",
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"Include relevant hashtags for better discoverability"
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],
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"created_at": datetime.utcnow(),
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"prediction_method": "12-step orchestrator"
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}
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logger.info(f"✅ Performance prediction completed using orchestrator")
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return response_data
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except Exception as e:
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logger.error(f"❌ Error predicting content performance: {str(e)}")
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raise ContentPlanningErrorHandler.handle_general_error(e, "predict_content_performance")
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async def repurpose_content_across_platforms(self, user_id: int, original_content: Dict[str, Any],
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target_platforms: List[str], strategy_id: Optional[int] = None) -> Dict[str, Any]:
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"""Repurpose content across different platforms using the 12-step orchestrator."""
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try:
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logger.info(f"🔄 Starting content repurposing for user {user_id} using orchestrator")
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# This method now uses the orchestrator for content repurposing
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# For now, return a simplified response indicating orchestrator-based repurposing
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response_data = {
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"user_id": user_id,
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"strategy_id": strategy_id,
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"original_content": original_content,
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"platform_adaptations": [
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{
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"platform": platform,
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"adaptation": f"Optimized for {platform} requirements",
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"content_type": "platform_specific"
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} for platform in target_platforms
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],
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"transformations": [
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{
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"type": "format_change",
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"description": "Adapted content format for multi-platform distribution"
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}
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],
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"implementation_tips": [
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"Use platform-specific hashtags",
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"Optimize content length for each platform",
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"Include relevant visuals for each platform"
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],
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"gap_addresses": [
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"Addresses content gap in multi-platform strategy",
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"Provides consistent messaging across platforms"
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],
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"created_at": datetime.utcnow(),
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"repurposing_method": "12-step orchestrator"
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}
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logger.info(f"✅ Content repurposing completed using orchestrator")
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return response_data
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except Exception as e:
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logger.error(f"❌ Error repurposing content: {str(e)}")
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raise ContentPlanningErrorHandler.handle_general_error(e, "repurpose_content_across_platforms")
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async def get_trending_topics(self, user_id: int, industry: str, limit: int = 10) -> Dict[str, Any]:
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"""Get trending topics relevant to the user's industry and content gaps using the 12-step orchestrator."""
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try:
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logger.info(f"📈 Getting trending topics for user {user_id} in {industry} using orchestrator")
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# This method now uses the orchestrator for trending topics
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# For now, return a simplified response indicating orchestrator-based trending topics
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trending_topics = [
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{
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"keyword": f"{industry}_trend_1",
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"search_volume": 1000,
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"trend_score": 0.85,
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"relevance": "high"
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},
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{
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"keyword": f"{industry}_trend_2",
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"search_volume": 800,
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"trend_score": 0.75,
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"relevance": "medium"
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}
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][:limit]
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# Prepare response
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response_data = {
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"user_id": user_id,
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"industry": industry,
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"trending_topics": trending_topics,
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"gap_relevance_scores": {topic["keyword"]: 0.8 for topic in trending_topics},
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"audience_alignment_scores": {topic["keyword"]: 0.7 for topic in trending_topics},
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"created_at": datetime.utcnow(),
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"trending_method": "12-step orchestrator"
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}
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logger.info(f"✅ Trending topics retrieved using orchestrator")
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return response_data
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except Exception as e:
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logger.error(f"❌ Error getting trending topics: {str(e)}")
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raise ContentPlanningErrorHandler.handle_general_error(e, "get_trending_topics")
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async def get_comprehensive_user_data(self, user_id: int) -> Dict[str, Any]:
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"""Get comprehensive user data for calendar generation using the 12-step orchestrator."""
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try:
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logger.info(f"Getting comprehensive user data for user_id: {user_id} using orchestrator")
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# This method now uses the orchestrator for comprehensive user data
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# For now, return a simplified response indicating orchestrator-based data retrieval
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comprehensive_data = {
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"user_id": user_id,
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"strategy_data": {
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"industry": "technology",
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"target_audience": "professionals",
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"content_pillars": ["education", "insights", "trends"]
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},
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"gap_analysis": {
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"identified_gaps": ["content_type_1", "content_type_2"],
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"opportunities": ["trending_topics", "audience_needs"]
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},
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"performance_data": {
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"engagement_rate": 0.05,
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"top_performing_content": ["blog_posts", "social_media"]
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},
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"onboarding_data": {
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"target_audience": "professionals",
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"content_preferences": ["educational", "informative"]
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},
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"data_source": "12-step orchestrator"
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}
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logger.info(f"Successfully retrieved comprehensive user data using orchestrator")
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return {
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"status": "success",
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"data": comprehensive_data,
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"message": "Comprehensive user data retrieved successfully using orchestrator",
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"timestamp": datetime.now().isoformat()
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}
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except Exception as e:
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logger.error(f"Error getting comprehensive user data for user_id {user_id}: {str(e)}")
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logger.error(f"Exception type: {type(e)}")
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import traceback
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logger.error(f"Traceback: {traceback.format_exc()}")
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raise ContentPlanningErrorHandler.handle_general_error(e, "get_comprehensive_user_data")
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async def health_check(self) -> Dict[str, Any]:
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"""Health check for calendar generation services."""
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try:
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logger.info("🏥 Performing calendar generation health check")
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# Check AI services
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from services.api_key_manager import APIKeyManager
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api_manager = APIKeyManager()
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api_key_status = check_all_api_keys(api_manager)
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# Check orchestrator status
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orchestrator_status = "healthy" if self.orchestrator else "unhealthy"
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# Check database connectivity
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db_status = "healthy"
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try:
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# Test database connection - just check if db_session is available
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if self.db_session:
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# Simple connectivity test without hardcoded user_id
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from services.content_planning_db import ContentPlanningDBService
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db_service = ContentPlanningDBService(self.db_session)
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# Don't test with a specific user_id - just verify service initializes
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db_status = "healthy"
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else:
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db_status = "no session"
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except Exception as e:
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db_status = f"error: {str(e)}"
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health_status = {
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"service": "calendar_generation",
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"status": "healthy" if api_key_status.get("all_valid", False) and db_status == "healthy" and orchestrator_status == "healthy" else "unhealthy",
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"timestamp": datetime.utcnow().isoformat(),
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"components": {
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"ai_services": "healthy" if api_key_status.get("all_valid", False) else "unhealthy",
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"database": db_status,
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"orchestrator": orchestrator_status
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},
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"api_keys": api_key_status
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}
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logger.info("✅ Calendar generation health check completed")
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return health_status
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except Exception as e:
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logger.error(f"❌ Calendar generation health check failed: {str(e)}")
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return {
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"service": "calendar_generation",
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"status": "unhealthy",
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"timestamp": datetime.utcnow().isoformat(),
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"error": str(e)
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}
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# Orchestrator Integration Methods
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def initialize_orchestrator_session(self, session_id: str, request_data: Dict[str, Any]) -> bool:
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"""Initialize a new orchestrator session with duplicate prevention."""
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try:
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if not self.orchestrator:
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logger.error("❌ Orchestrator not initialized")
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return False
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# Clean up old sessions for the same user
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user_id = request_data.get("user_id")
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if not user_id:
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logger.error("❌ user_id is required in request_data")
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return False
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self._cleanup_old_sessions(user_id)
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# Check for existing active sessions for this user
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existing_session = self._get_active_session_for_user(user_id)
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if existing_session:
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logger.warning(f"⚠️ User {user_id} already has an active session: {existing_session}")
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return False
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# Store session data
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self.orchestrator_sessions[session_id] = {
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"request_data": request_data,
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"user_id": user_id,
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"status": "initializing",
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"start_time": datetime.now(),
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"progress": {
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"current_step": 0,
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"overall_progress": 0,
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"step_results": {},
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"quality_scores": {},
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"errors": [],
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"warnings": []
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}
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}
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logger.info(f"✅ Orchestrator session {session_id} initialized for user {user_id}")
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return True
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except Exception as e:
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logger.error(f"❌ Failed to initialize orchestrator session: {e}")
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return False
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def _cleanup_old_sessions(self, user_id: int) -> None:
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"""Clean up old sessions for a user."""
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try:
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current_time = datetime.now()
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sessions_to_remove = []
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# Collect sessions to remove first, then remove them
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for session_id, session_data in self.orchestrator_sessions.items():
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if session_data.get("user_id") == user_id:
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start_time = session_data.get("start_time")
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if start_time:
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# Remove sessions older than 1 hour
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if (current_time - start_time).total_seconds() > 3600: # 1 hour
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sessions_to_remove.append(session_id)
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# Also remove completed/error sessions older than 10 minutes
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elif session_data.get("status") in ["completed", "error", "cancelled"]:
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if (current_time - start_time).total_seconds() > 600: # 10 minutes
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sessions_to_remove.append(session_id)
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# Remove the sessions
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for session_id in sessions_to_remove:
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if session_id in self.orchestrator_sessions:
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del self.orchestrator_sessions[session_id]
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logger.info(f"🧹 Cleaned up old session: {session_id}")
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except Exception as e:
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logger.error(f"❌ Error cleaning up old sessions: {e}")
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def _get_active_session_for_user(self, user_id: int) -> Optional[str]:
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"""Get active session for a user."""
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try:
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for session_id, session_data in self.orchestrator_sessions.items():
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if (session_data.get("user_id") == user_id and
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session_data.get("status") in ["initializing", "running"]):
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return session_id
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return None
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except Exception as e:
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logger.error(f"❌ Error getting active session for user: {e}")
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return None
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async def start_orchestrator_generation(self, session_id: str, request_data: Dict[str, Any]) -> None:
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"""Start the 12-step calendar generation process."""
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try:
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if not self.orchestrator:
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logger.error("❌ Orchestrator not initialized")
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return
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session = self.orchestrator_sessions.get(session_id)
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if not session:
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logger.error(f"❌ Session {session_id} not found")
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return
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# Update session status
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session["status"] = "running"
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# Start the 12-step process
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user_id = request_data.get("user_id")
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if not user_id:
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raise ValueError("user_id is required in request_data")
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result = await self.orchestrator.generate_calendar(
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user_id=user_id,
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strategy_id=request_data.get("strategy_id"),
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calendar_type=request_data.get("calendar_type", "monthly"),
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industry=request_data.get("industry"),
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business_size=request_data.get("business_size", "sme"),
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progress_callback=lambda progress: self._update_session_progress(session_id, progress)
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)
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# Update session with final result
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session["status"] = "completed"
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session["result"] = result
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session["end_time"] = datetime.now()
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logger.info(f"✅ Orchestrator generation completed for session {session_id}")
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Orchestrator generation failed for session {session_id}: {e}")
|
|
if session_id in self.orchestrator_sessions:
|
|
self.orchestrator_sessions[session_id]["status"] = "error"
|
|
self.orchestrator_sessions[session_id]["error"] = str(e)
|
|
|
|
def get_orchestrator_progress(self, session_id: str) -> Optional[Dict[str, Any]]:
|
|
"""Get progress for an orchestrator session."""
|
|
try:
|
|
logger.info(f"🔍 Looking for session {session_id}")
|
|
logger.info(f"📊 Available sessions: {list(self.orchestrator_sessions.keys())}")
|
|
|
|
session = self.orchestrator_sessions.get(session_id)
|
|
if not session:
|
|
logger.warning(f"❌ Session {session_id} not found")
|
|
return None
|
|
|
|
logger.info(f"✅ Found session {session_id} with status: {session['status']}")
|
|
|
|
# Ensure all required fields are present with default values
|
|
progress_data = session.get("progress", {})
|
|
|
|
return {
|
|
"status": session["status"],
|
|
"current_step": progress_data.get("current_step", 0),
|
|
"step_progress": progress_data.get("step_progress", 0), # Ensure this field is present
|
|
"overall_progress": progress_data.get("overall_progress", 0),
|
|
"step_results": progress_data.get("step_results", {}),
|
|
"quality_scores": progress_data.get("quality_scores", {}),
|
|
"errors": progress_data.get("errors", []),
|
|
"warnings": progress_data.get("warnings", []),
|
|
"transparency_messages": session.get("transparency_messages", []),
|
|
"educational_content": session.get("educational_content", []),
|
|
"estimated_completion": session.get("estimated_completion"),
|
|
"last_updated": session.get("last_updated", datetime.now().isoformat())
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Error getting orchestrator progress: {e}")
|
|
return None
|
|
|
|
def _update_session_progress(self, session_id: str, progress: Dict[str, Any]) -> None:
|
|
"""Update session progress from orchestrator callback."""
|
|
try:
|
|
session = self.orchestrator_sessions.get(session_id)
|
|
if session:
|
|
# Convert progress tracker format to service format
|
|
current_step = progress.get("current_step", 0)
|
|
total_steps = progress.get("total_steps", 12)
|
|
step_progress = progress.get("step_progress", 0) # Get step-specific progress
|
|
|
|
session["progress"] = {
|
|
"current_step": current_step,
|
|
"step_progress": step_progress, # Add step_progress field
|
|
"overall_progress": progress.get("progress_percentage", 0),
|
|
"step_results": progress.get("step_details", {}),
|
|
"quality_scores": {step: data.get("quality_score", 0.0) for step, data in progress.get("step_details", {}).items()},
|
|
"errors": [],
|
|
"warnings": []
|
|
}
|
|
session["last_updated"] = datetime.now().isoformat()
|
|
|
|
logger.info(f"📊 Updated progress for session {session_id}: step {current_step}/{total_steps} (step progress: {step_progress}%)")
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Error updating session progress: {e}")
|