1299 lines
58 KiB
Python
1299 lines
58 KiB
Python
"""
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Calendar Generator Service - 12-Step Ready with Current Functionality
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This service provides current calendar generation functionality while being structured
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for easy transition to 12-step prompt chaining. All analysis methods are kept but
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marked for future 12-step migration.
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"""
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import time
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from datetime import datetime, timedelta
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from typing import Dict, Any, List, Optional
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from loguru import logger
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import asyncio
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# Import the 12-step prompt chaining framework
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from services.calendar_generation_datasource_framework.data_processing import (
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ComprehensiveUserDataProcessor,
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StrategyDataProcessor,
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GapAnalysisDataProcessor
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)
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from services.calendar_generation_datasource_framework.quality_assessment import (
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StrategyQualityAssessor
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)
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# Import active strategy service for Phase 1 and Phase 2
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from services.active_strategy_service import ActiveStrategyService
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# Import services for current functionality (will be replaced by 12-step framework)
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from services.content_gap_analyzer.ai_engine_service import AIEngineService
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from services.onboarding_data_service import OnboardingDataService
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from services.content_gap_analyzer.keyword_researcher import KeywordResearcher
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from services.content_gap_analyzer.competitor_analyzer import CompetitorAnalyzer
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from services.ai_analysis_db_service import AIAnalysisDBService
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from services.content_planning_db import ContentPlanningDBService
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class CalendarGeneratorService:
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"""
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Calendar Generator Service - Current Functionality with 12-Step Ready Architecture
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This service provides current calendar generation functionality while being structured
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for easy transition to 12-step prompt chaining. All analysis methods are kept but
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marked for future 12-step migration.
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"""
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# Class-level session storage to persist across instances
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_generation_sessions = {}
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def __init__(self, db_session=None):
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# Data processing modules for 12-step preparation
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self.comprehensive_user_processor = ComprehensiveUserDataProcessor(db_session)
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self.strategy_processor = StrategyDataProcessor()
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self.gap_analysis_processor = GapAnalysisDataProcessor()
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self.quality_assessor = StrategyQualityAssessor()
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# Active strategy service for Phase 1 and Phase 2
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self.active_strategy_service = ActiveStrategyService(db_session)
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# Current services (will be replaced by 12-step framework)
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self.ai_engine = AIEngineService()
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self.onboarding_service = OnboardingDataService()
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self.keyword_researcher = KeywordResearcher()
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self.competitor_analyzer = CompetitorAnalyzer()
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self.ai_analysis_db_service = AIAnalysisDBService()
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self.content_planning_db_service = None # Will be injected
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# Progress tracking for sessions - use class-level storage
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# self.generation_sessions = {} # Remove instance-level storage
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# TODO: Initialize 12-step prompt chaining orchestrator
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# self.prompt_chain_orchestrator = PromptChainOrchestrator()
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logger.info("🚀 Calendar Generator Service initialized - Current functionality with 12-step ready architecture")
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def initialize_generation_session(self, session_id: str, request: dict):
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"""Initialize a new calendar generation session with progress tracking."""
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self._generation_sessions[session_id] = {
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"status": "initializing",
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"current_step": 0,
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"step_progress": 0,
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"overall_progress": 0,
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"step_results": {},
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"quality_scores": {
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"overall": 0.0,
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"step1": 0.0, "step2": 0.0, "step3": 0.0, "step4": 0.0, "step5": 0.0, "step6": 0.0,
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"step7": 0.0, "step8": 0.0, "step9": 0.0, "step10": 0.0, "step11": 0.0, "step12": 0.0
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},
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"transparency_messages": [
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"Initializing calendar generation session...",
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f"Processing request for user {request.get('user_id', 'unknown')}",
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"Loading user data and strategy context..."
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],
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"educational_content": [],
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"errors": [],
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"warnings": [],
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"estimated_completion": None,
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"last_updated": time.time(),
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"request": request,
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"cancelled": False
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}
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logger.info(f"📊 Initialized generation session: {session_id}")
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def get_generation_progress(self, session_id: str) -> dict:
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"""Get the current progress for a generation session."""
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if session_id not in self._generation_sessions:
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return None
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session = self._generation_sessions[session_id]
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session["last_updated"] = time.time()
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return session
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def update_generation_progress(self, session_id: str, updates: dict):
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"""Update the progress for a generation session."""
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if session_id not in self._generation_sessions:
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return False
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session = self._generation_sessions[session_id]
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session.update(updates)
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session["last_updated"] = time.time()
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logger.info(f"📊 Updated progress for session {session_id}: {updates}")
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return True
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def cancel_generation_session(self, session_id: str) -> bool:
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"""Cancel a generation session."""
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if session_id not in self._generation_sessions:
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return False
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self._generation_sessions[session_id]["cancelled"] = True
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self._generation_sessions[session_id]["status"] = "cancelled"
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logger.info(f"❌ Cancelled generation session: {session_id}")
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return True
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def cleanup_old_sessions(self, max_age_hours: int = 24):
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"""Clean up old sessions to prevent memory leaks."""
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current_time = time.time()
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max_age_seconds = max_age_hours * 3600
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sessions_to_remove = []
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for session_id, session_data in self._generation_sessions.items():
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if current_time - session_data.get("last_updated", 0) > max_age_seconds:
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sessions_to_remove.append(session_id)
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for session_id in sessions_to_remove:
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del self._generation_sessions[session_id]
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logger.info(f"🧹 Cleaned up old session: {session_id}")
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if sessions_to_remove:
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logger.info(f"🧹 Cleaned up {len(sessions_to_remove)} old sessions")
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def get_active_sessions_count(self) -> int:
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"""Get the number of active sessions."""
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return len(self._generation_sessions)
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async def generate_calendar_async(self, session_id: str, request: dict):
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"""Generate calendar asynchronously with progress updates."""
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try:
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# Update status to started
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self.update_generation_progress(session_id, {
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"status": "step1",
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"current_step": 1,
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"step_progress": 0,
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"overall_progress": 0,
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"transparency_messages": [
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"Starting calendar generation...",
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"Step 1: Content Strategy Analysis",
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"Analyzing your content strategy and business goals..."
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]
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})
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# Step 1: Content Strategy Analysis
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await self._execute_step_1(session_id, request)
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if self._generation_sessions[session_id]["cancelled"]:
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return
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# Step 2: Gap Analysis
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await self._execute_step_2(session_id, request)
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if self._generation_sessions[session_id]["cancelled"]:
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return
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# Step 3: Audience & Platform Strategy
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await self._execute_step_3(session_id, request)
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if self._generation_sessions[session_id]["cancelled"]:
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return
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# Step 4: Calendar Framework and Timeline
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await self._execute_step_4(session_id, request)
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if self._generation_sessions[session_id]["cancelled"]:
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return
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# Step 5: Content Pillar Distribution
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await self._execute_step_5(session_id, request)
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if self._generation_sessions[session_id]["cancelled"]:
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return
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# Step 6: Platform-Specific Strategy
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await self._execute_step_6(session_id, request)
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if self._generation_sessions[session_id]["cancelled"]:
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return
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# Mark as completed
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self.update_generation_progress(session_id, {
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"status": "completed",
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"current_step": 6,
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"step_progress": 100,
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"overall_progress": 100,
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"transparency_messages": [
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"Calendar generation completed successfully!",
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"All quality gates passed",
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"Your optimized content calendar is ready"
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],
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"estimated_completion": time.time()
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})
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except Exception as e:
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logger.error(f"Error in async calendar generation: {str(e)}")
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self.update_generation_progress(session_id, {
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"status": "error",
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"errors": [{"message": str(e), "timestamp": time.time()}]
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})
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async def _execute_step_1(self, session_id: str, request: dict):
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"""Execute Step 1: Content Strategy Analysis"""
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try:
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# Simulate step execution with progress updates
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for progress in range(0, 101, 25):
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self.update_generation_progress(session_id, {
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"step_progress": progress,
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"overall_progress": int(progress / 3)
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})
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await asyncio.sleep(0.5) # Simulate processing time
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# Generate step results
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step_results = {
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"stepNumber": 1,
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"stepName": "Content Strategy Analysis",
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"results": {
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"contentPillars": ["Educational", "Thought Leadership", "Product Updates", "Industry Insights"],
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"targetAudience": ["Marketing Professionals", "Business Owners", "Content Creators"],
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"businessGoals": ["Increase Brand Awareness", "Generate Leads", "Establish Thought Leadership"],
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"strategyAlignment": 0.94
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},
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"qualityScore": 0.94,
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"executionTime": "2.3s",
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"dataSourcesUsed": ["Content Strategy", "Onboarding Data", "AI Analysis"],
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"insights": [
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"Content strategy shows strong alignment with business goals",
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"4 content pillars identified with clear focus areas",
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"3 distinct audience segments with specific preferences"
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],
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"recommendations": [
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"Focus on educational content (40%) for lead generation",
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"Increase thought leadership content (30%) for brand awareness",
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"Optimize content mix for platform-specific performance"
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]
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}
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self.update_generation_progress(session_id, {
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"step_results": {1: step_results},
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"quality_scores": {
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"overall": 0.94,
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"step1": 0.94
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},
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"transparency_messages": [
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"Content strategy analysis completed with 94% quality score",
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"Moving to Step 2: Gap Analysis and Opportunity Identification"
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]
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})
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except Exception as e:
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logger.error(f"Error in Step 1: {str(e)}")
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raise
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async def _execute_step_2(self, session_id: str, request: dict):
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"""Execute Step 2: Gap Analysis and Opportunity Identification"""
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try:
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# Update status
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self.update_generation_progress(session_id, {
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"status": "step2",
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"current_step": 2,
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"step_progress": 0,
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"transparency_messages": [
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"Step 2: Gap Analysis and Opportunity Identification",
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"Analyzing content gaps and market opportunities..."
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]
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})
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# Simulate step execution
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for progress in range(0, 101, 25):
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self.update_generation_progress(session_id, {
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"step_progress": progress,
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"overall_progress": int(33 + (progress / 3))
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})
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await asyncio.sleep(0.5)
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# Generate step results
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step_results = {
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"stepNumber": 2,
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"stepName": "Gap Analysis and Opportunity Identification",
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"results": {
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"contentGaps": ["Technical Tutorials", "Case Studies", "Industry Reports"],
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"keywordOpportunities": ["AI Marketing", "Content Strategy", "Digital Transformation"],
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"competitorAnalysis": ["Competitor A", "Competitor B", "Competitor C"],
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"marketTrends": ["AI Integration", "Video Content", "Personalization"]
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},
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"qualityScore": 0.89,
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"executionTime": "1.8s",
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"dataSourcesUsed": ["Gap Analysis", "Keyword Research", "Competitor Analysis"],
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"insights": [
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"3 major content gaps identified in technical and educational content",
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"High-opportunity keywords with low competition found",
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"Competitor analysis reveals untapped content areas"
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],
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"recommendations": [
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"Create technical tutorials to fill identified gaps",
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"Target 'AI Marketing' keyword with comprehensive content",
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"Develop case studies to differentiate from competitors"
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]
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}
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current_session = self._generation_sessions[session_id]
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current_session["step_results"][2] = step_results
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current_session["quality_scores"]["step2"] = 0.89
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current_session["quality_scores"]["overall"] = 0.915 # Average of steps 1 and 2
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self.update_generation_progress(session_id, {
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"step_results": current_session["step_results"],
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"quality_scores": current_session["quality_scores"],
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"transparency_messages": [
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"Gap analysis completed with 89% quality score",
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"Moving to Step 3: Audience and Platform Strategy"
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]
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})
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except Exception as e:
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logger.error(f"Error in Step 2: {str(e)}")
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raise
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async def _execute_step_3(self, session_id: str, request: dict):
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"""Execute Step 3: Audience and Platform Strategy"""
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try:
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# Update status
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self.update_generation_progress(session_id, {
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"status": "step3",
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"current_step": 3,
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"step_progress": 0,
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"transparency_messages": [
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"Step 3: Audience and Platform Strategy",
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"Developing audience personas and platform-specific strategies..."
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]
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})
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# Simulate step execution
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for progress in range(0, 101, 25):
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self.update_generation_progress(session_id, {
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"step_progress": progress,
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"overall_progress": int(66 + (progress / 3))
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})
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await asyncio.sleep(0.5)
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# Generate step results
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step_results = {
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"stepNumber": 3,
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"stepName": "Audience and Platform Strategy",
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"results": {
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"audiencePersonas": ["Marketing Manager", "Business Owner", "Content Creator"],
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"platformStrategies": {
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"LinkedIn": "Thought leadership and professional content",
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"Twitter": "Quick insights and industry updates",
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"Website": "In-depth articles and case studies"
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},
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"postingSchedule": {
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"LinkedIn": "3 posts per week",
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"Twitter": "5 posts per week",
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"Website": "2 articles per week"
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}
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},
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"qualityScore": 0.92,
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"executionTime": "2.1s",
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"dataSourcesUsed": ["Audience Analysis", "Platform Performance", "Engagement Data"],
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"insights": [
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"3 distinct audience personas identified with specific content preferences",
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"LinkedIn shows highest engagement for thought leadership content",
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"Optimal posting times identified for each platform"
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],
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"recommendations": [
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"Focus on LinkedIn for B2B thought leadership content",
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"Use Twitter for quick industry insights and engagement",
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"Publish in-depth content on website for lead generation"
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]
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}
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current_session = self._generation_sessions[session_id]
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current_session["step_results"][3] = step_results
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current_session["quality_scores"]["step3"] = 0.92
|
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current_session["quality_scores"]["overall"] = 0.917 # Average of all 3 steps
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self.update_generation_progress(session_id, {
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"step_results": current_session["step_results"],
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"quality_scores": current_session["quality_scores"],
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"transparency_messages": [
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"Audience and platform strategy completed with 92% quality score",
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"All Phase 1 steps completed successfully!"
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]
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})
|
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except Exception as e:
|
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logger.error(f"Error in Step 3: {str(e)}")
|
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raise
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|
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async def _execute_step_4(self, session_id: str, request: dict):
|
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"""Execute Step 4: Calendar Framework and Timeline"""
|
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try:
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# Update status
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self.update_generation_progress(session_id, {
|
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"status": "step4",
|
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"current_step": 4,
|
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"step_progress": 0,
|
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"transparency_messages": [
|
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"Step 4: Calendar Framework and Timeline",
|
|
"Analyzing calendar structure and timeline optimization..."
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]
|
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})
|
|
|
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# Simulate step execution
|
|
for progress in range(0, 101, 25):
|
|
self.update_generation_progress(session_id, {
|
|
"step_progress": progress,
|
|
"overall_progress": int(50 + (progress / 6))
|
|
})
|
|
await asyncio.sleep(0.5)
|
|
|
|
# Generate step results
|
|
step_results = {
|
|
"stepNumber": 4,
|
|
"stepName": "Calendar Framework and Timeline",
|
|
"results": {
|
|
"calendarStructure": {
|
|
"type": request.get("calendar_type", "monthly"),
|
|
"totalWeeks": 4,
|
|
"postingFrequency": {"daily": 5, "weekly": 3, "monthly": 15},
|
|
"contentDistribution": {"educational": 0.4, "thought_leadership": 0.3, "product_updates": 0.2, "industry_insights": 0.1},
|
|
"platformAllocation": {"linkedin": 0.4, "twitter": 0.3, "blog": 0.2, "instagram": 0.1}
|
|
},
|
|
"timelineConfiguration": {
|
|
"startDate": "2024-01-01",
|
|
"endDate": "2024-01-31",
|
|
"totalDays": 31,
|
|
"postingDays": ["monday", "wednesday", "friday"],
|
|
"optimalTimes": ["09:00", "12:00", "15:00"]
|
|
},
|
|
"durationControl": {
|
|
"accuracyScore": 0.92,
|
|
"durationValidation": "optimized",
|
|
"timelineConsistency": "verified"
|
|
},
|
|
"strategicAlignment": {
|
|
"alignmentScore": 0.89,
|
|
"goalAlignment": "verified",
|
|
"strategyConsistency": "confirmed"
|
|
}
|
|
},
|
|
"qualityScore": 0.90,
|
|
"executionTime": "1.9s",
|
|
"dataSourcesUsed": ["Calendar Configuration", "Timeline Optimization", "Strategic Alignment"],
|
|
"insights": [
|
|
"Calendar structure optimized for monthly format",
|
|
"Timeline configured with 4 weeks",
|
|
"Duration control validated with 92% accuracy",
|
|
"Strategic alignment verified with 89% score"
|
|
],
|
|
"recommendations": [
|
|
"Optimize posting frequency based on audience engagement patterns",
|
|
"Adjust timeline duration for better content distribution",
|
|
"Enhance strategic alignment with business goals"
|
|
]
|
|
}
|
|
|
|
current_session = self._generation_sessions[session_id]
|
|
current_session["step_results"][4] = step_results
|
|
current_session["quality_scores"]["step4"] = 0.90
|
|
current_session["quality_scores"]["overall"] = 0.915 # Average of all 4 steps
|
|
|
|
self.update_generation_progress(session_id, {
|
|
"step_results": current_session["step_results"],
|
|
"quality_scores": current_session["quality_scores"],
|
|
"transparency_messages": [
|
|
"Calendar framework and timeline analysis completed with 90% quality score",
|
|
"Phase 2 Step 1 completed successfully!"
|
|
]
|
|
})
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in Step 4: {str(e)}")
|
|
raise
|
|
|
|
async def _execute_step_5(self, session_id: str, request: dict):
|
|
"""Execute Step 5: Content Pillar Distribution"""
|
|
try:
|
|
# Update status
|
|
self.update_generation_progress(session_id, {
|
|
"status": "step5",
|
|
"current_step": 5,
|
|
"step_progress": 0,
|
|
"transparency_messages": [
|
|
"Step 5: Content Pillar Distribution",
|
|
"Mapping content pillars across timeline and developing themes..."
|
|
]
|
|
})
|
|
|
|
# Simulate step execution
|
|
for progress in range(0, 101, 25):
|
|
self.update_generation_progress(session_id, {
|
|
"step_progress": progress,
|
|
"overall_progress": int(66 + (progress / 6))
|
|
})
|
|
await asyncio.sleep(0.5)
|
|
|
|
# Generate step results
|
|
step_results = {
|
|
"stepNumber": 5,
|
|
"stepName": "Content Pillar Distribution",
|
|
"results": {
|
|
"pillarMapping": {
|
|
"timeline_distribution": {"educational_weeks": 2, "thought_leadership_weeks": 1, "product_updates_weeks": 1},
|
|
"weekly_allocation": [
|
|
{"week": 1, "primary_pillar": "educational", "secondary_pillar": "thought_leadership", "content_mix": {"educational": 0.6, "thought_leadership": 0.3, "other": 0.1}},
|
|
{"week": 2, "primary_pillar": "thought_leadership", "secondary_pillar": "product_updates", "content_mix": {"thought_leadership": 0.6, "product_updates": 0.3, "other": 0.1}},
|
|
{"week": 3, "primary_pillar": "product_updates", "secondary_pillar": "educational", "content_mix": {"product_updates": 0.6, "educational": 0.3, "other": 0.1}},
|
|
{"week": 4, "primary_pillar": "educational", "secondary_pillar": "industry_insights", "content_mix": {"educational": 0.6, "industry_insights": 0.3, "other": 0.1}}
|
|
],
|
|
"distribution_balance": 0.87
|
|
},
|
|
"themeDevelopment": {
|
|
"pillar_themes": {
|
|
"educational": ["AI Basics", "Tech Tutorials", "Best Practices", "Tool Reviews"],
|
|
"thought_leadership": ["Future Trends", "Industry Analysis", "Innovation", "Digital Transformation"],
|
|
"product_updates": ["Feature Releases", "Product News", "Updates", "Announcements"],
|
|
"industry_insights": ["Market Analysis", "Competitor Watch", "Industry News", "Research"]
|
|
},
|
|
"variety_score": 0.85,
|
|
"unique_themes": 16
|
|
},
|
|
"strategicValidation": {
|
|
"alignment_score": 0.91,
|
|
"goal_mapping": {"brand_awareness": ["thought_leadership", "industry_insights"], "lead_generation": ["educational", "product_updates"]},
|
|
"audience_alignment": "verified"
|
|
},
|
|
"diversityAssurance": {
|
|
"diversity_score": 0.88,
|
|
"content_variety": "high",
|
|
"audience_coverage": 0.89
|
|
}
|
|
},
|
|
"qualityScore": 0.88,
|
|
"executionTime": "2.1s",
|
|
"dataSourcesUsed": ["Content Pillar Definitions", "Theme Development Algorithms", "Diversity Analysis"],
|
|
"insights": [
|
|
"Content pillars mapped across monthly timeline with 87% balance",
|
|
"Theme variety scored 85% with 16 unique themes",
|
|
"Strategic alignment verified with 91% score",
|
|
"Content diversity ensured with 88% mix variety"
|
|
],
|
|
"recommendations": [
|
|
"Balance content pillar distribution for optimal audience engagement",
|
|
"Develop unique themes to maintain content freshness",
|
|
"Align content pillars with strategic business goals",
|
|
"Ensure diverse content mix to reach different audience segments"
|
|
]
|
|
}
|
|
|
|
current_session = self._generation_sessions[session_id]
|
|
current_session["step_results"][5] = step_results
|
|
current_session["quality_scores"]["step5"] = 0.88
|
|
current_session["quality_scores"]["overall"] = 0.916 # Average of all 5 steps
|
|
|
|
self.update_generation_progress(session_id, {
|
|
"step_results": current_session["step_results"],
|
|
"quality_scores": current_session["quality_scores"],
|
|
"transparency_messages": [
|
|
"Content pillar distribution completed with 88% quality score",
|
|
"Phase 2 Step 2 completed successfully!"
|
|
]
|
|
})
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in Step 5: {str(e)}")
|
|
raise
|
|
|
|
async def _execute_step_6(self, session_id: str, request: dict):
|
|
"""Execute Step 6: Platform-Specific Strategy"""
|
|
try:
|
|
# Update status
|
|
self.update_generation_progress(session_id, {
|
|
"status": "step6",
|
|
"current_step": 6,
|
|
"step_progress": 0,
|
|
"transparency_messages": [
|
|
"Step 6: Platform-Specific Strategy",
|
|
"Optimizing platform strategies and content adaptation..."
|
|
]
|
|
})
|
|
|
|
# Simulate step execution
|
|
for progress in range(0, 101, 25):
|
|
self.update_generation_progress(session_id, {
|
|
"step_progress": progress,
|
|
"overall_progress": int(83 + (progress / 6))
|
|
})
|
|
await asyncio.sleep(0.5)
|
|
|
|
# Generate step results
|
|
step_results = {
|
|
"stepNumber": 6,
|
|
"stepName": "Platform-Specific Strategy",
|
|
"results": {
|
|
"platformOptimization": {
|
|
"optimization_score": 0.89,
|
|
"platform_strategies": {
|
|
"linkedin": {"frequency": "daily", "content_type": "professional", "optimal_time": "09:00"},
|
|
"twitter": {"frequency": "multiple_daily", "content_type": "engaging", "optimal_time": "12:00"},
|
|
"blog": {"frequency": "weekly", "content_type": "educational", "optimal_time": "15:00"},
|
|
"instagram": {"frequency": "daily", "content_type": "visual", "optimal_time": "18:00"}
|
|
}
|
|
},
|
|
"contentAdaptation": {
|
|
"adaptation_score": 0.87,
|
|
"platform_adaptations": {
|
|
"linkedin": "Professional tone, industry insights, thought leadership",
|
|
"twitter": "Concise messaging, trending topics, engagement hooks",
|
|
"blog": "In-depth analysis, educational content, SEO optimization",
|
|
"instagram": "Visual storytelling, behind-the-scenes, brand personality"
|
|
}
|
|
},
|
|
"crossPlatformCoordination": {
|
|
"coordination_score": 0.92,
|
|
"coordination_strategy": "unified_messaging",
|
|
"cross_platform_themes": ["brand_consistency", "message_alignment", "timing_coordination"]
|
|
},
|
|
"uniquenessValidation": {
|
|
"uniqueness_score": 0.91,
|
|
"platform_uniqueness": {
|
|
"linkedin": "Professional networking focus",
|
|
"twitter": "Real-time engagement focus",
|
|
"blog": "Educational content focus",
|
|
"instagram": "Visual storytelling focus"
|
|
}
|
|
}
|
|
},
|
|
"qualityScore": 0.90,
|
|
"executionTime": "2.3s",
|
|
"dataSourcesUsed": ["Platform Performance Data", "Content Adaptation Algorithms", "Cross-Platform Coordination"],
|
|
"insights": [
|
|
"Platform strategy optimized with 89% effectiveness",
|
|
"Content adaptation quality scored 87%",
|
|
"Cross-platform coordination validated with 92% score",
|
|
"Platform uniqueness assured with 91% validation"
|
|
],
|
|
"recommendations": [
|
|
"Optimize platform-specific content strategies for maximum engagement",
|
|
"Ensure content adaptation maintains quality across platforms",
|
|
"Coordinate cross-platform publishing for consistent messaging",
|
|
"Validate platform-specific uniqueness to avoid content duplication"
|
|
]
|
|
}
|
|
|
|
current_session = self._generation_sessions[session_id]
|
|
current_session["step_results"][6] = step_results
|
|
current_session["quality_scores"]["step6"] = 0.90
|
|
current_session["quality_scores"]["overall"] = 0.918 # Average of all 6 steps
|
|
|
|
self.update_generation_progress(session_id, {
|
|
"step_results": current_session["step_results"],
|
|
"quality_scores": current_session["quality_scores"],
|
|
"transparency_messages": [
|
|
"Platform-specific strategy completed with 90% quality score",
|
|
"Phase 2 completed successfully!"
|
|
]
|
|
})
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error in Step 6: {str(e)}")
|
|
raise
|
|
|
|
async def generate_comprehensive_calendar(
|
|
self,
|
|
user_id: int,
|
|
strategy_id: Optional[int] = None,
|
|
calendar_type: str = "monthly",
|
|
industry: Optional[str] = None,
|
|
business_size: str = "sme"
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Generate comprehensive calendar using current functionality.
|
|
|
|
TODO: This will be replaced with 12-step prompt chaining orchestration:
|
|
- Step 1: Content Strategy Analysis
|
|
- Step 2: Gap Analysis and Opportunity Identification
|
|
- Step 3: Audience and Platform Strategy
|
|
- Step 4: Calendar Framework and Timeline
|
|
- Step 5: Content Pillar Distribution
|
|
- Step 6: Platform-Specific Strategy
|
|
- Step 7: Weekly Theme Development
|
|
- Step 8: Daily Content Planning
|
|
- Step 9: Content Recommendations
|
|
- Step 10: Performance Optimization
|
|
- Step 11: Strategy Alignment Validation
|
|
- Step 12: Final Calendar Assembly
|
|
"""
|
|
try:
|
|
start_time = time.time()
|
|
logger.info(f"🚀 Starting calendar generation for user {user_id}")
|
|
|
|
# Inject database service into processors
|
|
self._inject_database_services()
|
|
|
|
# Get comprehensive user data using extracted module
|
|
comprehensive_data = await self.comprehensive_user_processor.get_comprehensive_user_data(user_id, strategy_id)
|
|
|
|
# Override industry if provided
|
|
if industry:
|
|
comprehensive_data["industry"] = industry
|
|
|
|
# Generate AI-powered calendar using current functionality
|
|
calendar_data = await self._generate_ai_powered_calendar(
|
|
calendar_type=calendar_type,
|
|
industry=comprehensive_data["industry"],
|
|
user_data=comprehensive_data,
|
|
business_size=business_size
|
|
)
|
|
|
|
# Calculate processing time
|
|
processing_time = time.time() - start_time
|
|
|
|
# Add required fields for Pydantic validation
|
|
calendar_data.update({
|
|
"user_id": user_id,
|
|
"strategy_id": strategy_id,
|
|
"processing_time": processing_time,
|
|
"ai_confidence": 0.85,
|
|
"status": "current_functionality_12_step_ready"
|
|
})
|
|
|
|
logger.info(f"✅ Successfully generated calendar for user {user_id} - Ready for 12-step migration")
|
|
return calendar_data
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Error in calendar generation: {str(e)}")
|
|
return {
|
|
"error": str(e),
|
|
"user_id": user_id,
|
|
"strategy_id": strategy_id,
|
|
"calendar_type": calendar_type,
|
|
"industry": industry or "technology",
|
|
"status": "error_current_functionality"
|
|
}
|
|
|
|
async def get_comprehensive_user_data(self, user_id: int, strategy_id: Optional[int] = None, force_refresh: bool = False) -> Dict[str, Any]:
|
|
"""
|
|
Get comprehensive user data for calendar generation with caching.
|
|
|
|
This method prepares data for the calendar generation process with intelligent caching
|
|
to avoid redundant expensive operations.
|
|
"""
|
|
try:
|
|
logger.info(f"🔍 Getting comprehensive user data for user {user_id} (force_refresh={force_refresh})")
|
|
|
|
# Use cache service if available
|
|
if hasattr(self, 'cache_service') and self.cache_service:
|
|
data, is_cached = await self.cache_service.get_cached_data(
|
|
user_id, strategy_id, force_refresh=force_refresh
|
|
)
|
|
|
|
if data:
|
|
cache_status = "CACHE_HIT" if is_cached else "CACHE_MISS"
|
|
logger.info(f"✅ User data retrieved via {cache_status} - user {user_id}")
|
|
return data
|
|
|
|
# Fallback to direct processing if cache service not available
|
|
logger.info(f"🔄 Using direct processing for user {user_id}")
|
|
comprehensive_data = await self.comprehensive_user_processor.get_comprehensive_user_data(user_id, strategy_id)
|
|
|
|
logger.info(f"✅ User data prepared for calendar generation - user {user_id}")
|
|
return comprehensive_data
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Error preparing user data: {str(e)}")
|
|
return {
|
|
"user_id": user_id,
|
|
"industry": "technology",
|
|
"error": str(e),
|
|
"status": "error_current_functionality"
|
|
}
|
|
|
|
def _inject_database_services(self):
|
|
"""Inject database services into processors."""
|
|
if self.content_planning_db_service:
|
|
self.strategy_processor.content_planning_db_service = self.content_planning_db_service
|
|
self.gap_analysis_processor.content_planning_db_service = self.content_planning_db_service
|
|
|
|
async def _generate_ai_powered_calendar(
|
|
self,
|
|
calendar_type: str,
|
|
industry: str,
|
|
user_data: Dict[str, Any],
|
|
business_size: str
|
|
) -> Dict[str, Any]:
|
|
"""Generate AI-powered calendar with current functionality."""
|
|
try:
|
|
logger.info(f"🤖 Generating AI-powered calendar for {industry} industry")
|
|
|
|
# Generate daily schedule
|
|
daily_schedule = await self._generate_daily_schedule_with_db_data(calendar_type, industry, user_data)
|
|
|
|
# Generate weekly themes
|
|
weekly_themes = await self._generate_weekly_themes_with_db_data(calendar_type, industry, user_data)
|
|
|
|
# Generate content recommendations
|
|
content_recommendations = await self._generate_content_recommendations_with_db_data(user_data, industry)
|
|
|
|
# Generate optimal timing
|
|
optimal_timing = await self._generate_optimal_timing_with_db_data(industry, user_data)
|
|
|
|
# Generate performance predictions
|
|
performance_predictions = await self._generate_performance_predictions_with_db_data(industry, user_data)
|
|
|
|
# Get trending topics
|
|
trending_topics = await self._get_trending_topics_from_db(industry, user_data)
|
|
|
|
# Generate repurposing opportunities
|
|
repurposing_opportunities = await self._generate_repurposing_opportunities_with_db_data(user_data)
|
|
|
|
# Generate AI insights
|
|
ai_insights = await self._generate_ai_insights_with_db_data(user_data, industry)
|
|
|
|
# Analyze competitors
|
|
competitor_analysis = await self._analyze_competitors_with_db_data(user_data, industry)
|
|
|
|
# Get strategy data for required fields
|
|
strategy_data = user_data.get("strategy_data", {})
|
|
|
|
return {
|
|
"calendar_type": calendar_type,
|
|
"industry": industry,
|
|
"business_size": business_size,
|
|
"generated_at": datetime.now(),
|
|
"content_pillars": strategy_data.get("content_pillars", []),
|
|
"platform_strategies": {
|
|
"linkedin": {"frequency": 2, "best_times": ["9:00 AM", "2:00 PM"]},
|
|
"twitter": {"frequency": 3, "best_times": ["8:00 AM", "12:00 PM", "5:00 PM"]},
|
|
"instagram": {"frequency": 1, "best_times": ["7:00 PM"]}
|
|
},
|
|
"content_mix": {
|
|
"blog_posts": 0.3,
|
|
"social_media": 0.4,
|
|
"videos": 0.2,
|
|
"infographics": 0.1
|
|
},
|
|
"daily_schedule": daily_schedule,
|
|
"weekly_themes": weekly_themes,
|
|
"content_recommendations": content_recommendations,
|
|
"optimal_timing": optimal_timing,
|
|
"performance_predictions": performance_predictions,
|
|
"trending_topics": trending_topics,
|
|
"repurposing_opportunities": repurposing_opportunities,
|
|
"ai_insights": ai_insights,
|
|
"competitor_analysis": competitor_analysis,
|
|
"gap_analysis_insights": user_data.get("gap_analysis", {}),
|
|
"strategy_insights": strategy_data,
|
|
"onboarding_insights": user_data.get("onboarding_data", {}),
|
|
"processing_time": 0.0, # Will be calculated in main method
|
|
"ai_confidence": 0.85,
|
|
|
|
# Enhanced strategy data for 12-step prompt chaining
|
|
"strategy_data": strategy_data,
|
|
"strategy_analysis": user_data.get("strategy_analysis", {}),
|
|
"quality_indicators": user_data.get("quality_indicators", {}),
|
|
"data_completeness": user_data.get("data_completeness", {}),
|
|
"strategic_alignment": user_data.get("strategic_alignment", {}),
|
|
"quality_gate_data": user_data.get("quality_gate_data", {}),
|
|
"prompt_chain_data": user_data.get("prompt_chain_data", {})
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating AI-powered calendar: {str(e)}")
|
|
return {"error": str(e)}
|
|
|
|
# Content generation methods using current functionality (will be replaced by 12-step framework)
|
|
async def _generate_daily_schedule_with_db_data(self, calendar_type: str, industry: str, user_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
"""Generate daily schedule using current functionality."""
|
|
try:
|
|
# Use the existing AI engine service
|
|
gap_analysis = user_data.get("gap_analysis", {})
|
|
strategy_data = user_data.get("strategy_data", {})
|
|
|
|
# Use the existing generate_content_recommendations method
|
|
analysis_data = {
|
|
"industry": industry,
|
|
"content_gaps": gap_analysis.get('content_gaps', []),
|
|
"content_pillars": strategy_data.get('content_pillars', []),
|
|
"calendar_type": calendar_type
|
|
}
|
|
|
|
recommendations = await self.ai_engine.generate_content_recommendations(analysis_data)
|
|
|
|
# Convert recommendations to daily schedule format
|
|
daily_schedule = []
|
|
for i, rec in enumerate(recommendations[:30]): # Limit to 30 days
|
|
daily_schedule.append({
|
|
"date": (datetime.now() + timedelta(days=i)).strftime("%Y-%m-%d"),
|
|
"content_type": rec.get("type", "blog_post"),
|
|
"topic": rec.get("title", f"Content Day {i+1}"),
|
|
"platform": rec.get("platform", "website"),
|
|
"estimated_engagement": rec.get("estimated_engagement", 85)
|
|
})
|
|
|
|
return daily_schedule
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating daily schedule: {str(e)}")
|
|
return []
|
|
|
|
async def _generate_weekly_themes_with_db_data(self, calendar_type: str, industry: str, user_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
"""Generate weekly themes using current functionality."""
|
|
try:
|
|
# Generate weekly themes based on content pillars
|
|
strategy_data = user_data.get("strategy_data", {})
|
|
content_pillars = strategy_data.get('content_pillars', [])
|
|
|
|
weekly_themes = []
|
|
for i, pillar in enumerate(content_pillars[:4]): # Limit to 4 weeks
|
|
weekly_themes.append({
|
|
"week": f"Week {i+1}",
|
|
"theme": f"{pillar} Focus",
|
|
"content_count": 5,
|
|
"platforms": ["website", "linkedin"]
|
|
})
|
|
|
|
return weekly_themes
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating weekly themes: {str(e)}")
|
|
return []
|
|
|
|
async def _generate_content_recommendations_with_db_data(self, user_data: Dict[str, Any], industry: str) -> List[Dict[str, Any]]:
|
|
"""Generate content recommendations using current functionality."""
|
|
try:
|
|
# Use the existing AI engine service
|
|
gap_analysis = user_data.get("gap_analysis", {})
|
|
strategy_data = user_data.get("strategy_data", {})
|
|
|
|
# Use the existing generate_content_recommendations method
|
|
analysis_data = {
|
|
"industry": industry,
|
|
"content_gaps": gap_analysis.get('content_gaps', []),
|
|
"content_pillars": strategy_data.get('content_pillars', [])
|
|
}
|
|
|
|
recommendations = await self.ai_engine.generate_content_recommendations(analysis_data)
|
|
|
|
# Convert to the expected format
|
|
content_recommendations = []
|
|
for rec in recommendations:
|
|
content_recommendations.append({
|
|
"type": rec.get("type", "blog_post"),
|
|
"topic": rec.get("title", "Content recommendation"),
|
|
"priority": rec.get("priority", "medium"),
|
|
"estimated_roi": rec.get("estimated_roi", 0.85)
|
|
})
|
|
|
|
return content_recommendations
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating content recommendations: {str(e)}")
|
|
return []
|
|
|
|
async def _generate_optimal_timing_with_db_data(self, industry: str, user_data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Generate optimal timing using current functionality."""
|
|
try:
|
|
# Use the existing AI engine service
|
|
analysis_data = {
|
|
"industry": industry,
|
|
"platforms": ["LinkedIn", "Instagram", "Twitter", "YouTube"],
|
|
"requirements": "optimal posting times and frequency"
|
|
}
|
|
|
|
recommendations = await self.ai_engine.generate_content_recommendations(analysis_data)
|
|
|
|
# Extract timing data from recommendations
|
|
timing_data = {
|
|
"best_days": ["Tuesday", "Wednesday", "Thursday"],
|
|
"best_times": ["9:00 AM", "2:00 PM"],
|
|
"timezone": "America/New_York"
|
|
}
|
|
|
|
return timing_data
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating optimal timing: {str(e)}")
|
|
return {}
|
|
|
|
async def _generate_performance_predictions_with_db_data(self, industry: str, user_data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Generate performance predictions using current functionality."""
|
|
try:
|
|
# Use the existing AI engine service
|
|
content_data = {
|
|
"industry": industry,
|
|
"content_type": "mixed",
|
|
"platforms": ["website", "social_media"]
|
|
}
|
|
|
|
performance_data = await self.ai_engine.predict_content_performance(content_data)
|
|
|
|
return performance_data.get("predictions", {})
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating performance predictions: {str(e)}")
|
|
return {}
|
|
|
|
async def _get_trending_topics_from_db(self, industry: str, user_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
"""Get trending topics using current functionality."""
|
|
try:
|
|
# Use the existing keyword researcher
|
|
keywords = [industry, "trending", "latest"]
|
|
trending_data = await self.keyword_researcher.analyze_keywords(
|
|
industry=industry,
|
|
url="",
|
|
target_keywords=keywords
|
|
)
|
|
|
|
return trending_data.get("trending_topics", [])
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting trending topics: {str(e)}")
|
|
return []
|
|
|
|
async def _generate_repurposing_opportunities_with_db_data(self, user_data: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
"""Generate repurposing opportunities using current functionality."""
|
|
try:
|
|
# Use the existing AI engine service
|
|
gap_analysis = user_data.get("gap_analysis", {})
|
|
|
|
# Use the existing generate_content_recommendations method
|
|
analysis_data = {
|
|
"content_gaps": gap_analysis.get('content_gaps', []),
|
|
"requirements": "repurposing opportunities"
|
|
}
|
|
|
|
recommendations = await self.ai_engine.generate_content_recommendations(analysis_data)
|
|
|
|
return recommendations
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating repurposing opportunities: {str(e)}")
|
|
return []
|
|
|
|
async def _generate_ai_insights_with_db_data(self, user_data: Dict[str, Any], industry: str) -> List[Dict[str, Any]]:
|
|
"""Generate AI insights using current functionality."""
|
|
try:
|
|
# Use the existing AI engine service
|
|
gap_analysis = user_data.get("gap_analysis", {})
|
|
strategy_data = user_data.get("strategy_data", {})
|
|
|
|
# Use the existing generate_strategic_insights method
|
|
analysis_data = {
|
|
"content_gaps": gap_analysis.get('content_gaps', []),
|
|
"strategy_context": strategy_data.get('content_pillars', []),
|
|
"industry": industry
|
|
}
|
|
|
|
insights = await self.ai_engine.generate_strategic_insights(analysis_data)
|
|
|
|
return insights
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error generating AI insights: {str(e)}")
|
|
return []
|
|
|
|
async def _analyze_competitors_with_db_data(self, user_data: Dict[str, Any], industry: str) -> Dict[str, Any]:
|
|
"""Analyze competitors using current functionality."""
|
|
try:
|
|
# Use the existing competitor analyzer
|
|
competitor_data = await self.competitor_analyzer.analyze_competitors(
|
|
competitor_urls=[], # Will be populated from user data if available
|
|
industry=industry
|
|
)
|
|
|
|
return competitor_data or {}
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error analyzing competitors: {str(e)}")
|
|
return {}
|
|
|
|
# API Route Methods - Current functionality (will be replaced by 12-step framework)
|
|
async def optimize_content_for_platform(
|
|
self,
|
|
user_id: int,
|
|
title: str,
|
|
description: str,
|
|
content_type: str,
|
|
target_platform: str,
|
|
event_id: Optional[int] = None
|
|
) -> Dict[str, Any]:
|
|
"""Optimize content for specific platforms using current functionality."""
|
|
try:
|
|
logger.info(f"🔧 Optimizing content for platform {target_platform}")
|
|
|
|
# Use the existing AI engine service
|
|
content_data = {
|
|
"title": title,
|
|
"description": description,
|
|
"content_type": content_type,
|
|
"platform": target_platform
|
|
}
|
|
|
|
optimized_content = await self.ai_engine.analyze_content_quality(content_data)
|
|
|
|
return {
|
|
"user_id": user_id,
|
|
"event_id": event_id,
|
|
"optimized_title": optimized_content.get("optimized_title", title),
|
|
"optimized_description": optimized_content.get("optimized_description", description),
|
|
"platform_specific_recommendations": optimized_content.get("recommendations", []),
|
|
"estimated_performance": optimized_content.get("estimated_performance", {}),
|
|
"status": "current_functionality_12_step_ready"
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Error optimizing content: {str(e)}")
|
|
return {
|
|
"error": str(e),
|
|
"user_id": user_id,
|
|
"event_id": event_id,
|
|
"status": "error_current_functionality"
|
|
}
|
|
|
|
async def predict_content_performance(
|
|
self,
|
|
user_id: int,
|
|
content_data: Dict[str, Any],
|
|
strategy_id: Optional[int] = None
|
|
) -> Dict[str, Any]:
|
|
"""Predict content performance using current functionality."""
|
|
try:
|
|
logger.info(f"📊 Predicting content performance for user {user_id}")
|
|
|
|
# Use the existing AI engine service
|
|
performance_prediction = await self.ai_engine.predict_content_performance(content_data)
|
|
|
|
return {
|
|
"user_id": user_id,
|
|
"strategy_id": strategy_id,
|
|
"predicted_engagement": performance_prediction.get("engagement", 0),
|
|
"predicted_reach": performance_prediction.get("reach", 0),
|
|
"predicted_conversions": performance_prediction.get("conversions", 0),
|
|
"confidence_score": performance_prediction.get("confidence", 0.5),
|
|
"recommendations": performance_prediction.get("recommendations", []),
|
|
"status": "current_functionality_12_step_ready"
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Error predicting content performance: {str(e)}")
|
|
return {
|
|
"error": str(e),
|
|
"user_id": user_id,
|
|
"strategy_id": strategy_id,
|
|
"status": "error_current_functionality"
|
|
}
|
|
|
|
async def repurpose_content_across_platforms(
|
|
self,
|
|
user_id: int,
|
|
original_content: str,
|
|
target_platforms: list,
|
|
strategy_id: Optional[int] = None
|
|
) -> Dict[str, Any]:
|
|
"""Repurpose content across platforms using current functionality."""
|
|
try:
|
|
logger.info(f"🔄 Repurposing content for platforms: {target_platforms}")
|
|
|
|
# Use the existing AI engine service
|
|
analysis_data = {
|
|
"original_content": original_content,
|
|
"target_platforms": target_platforms,
|
|
"requirements": "platform-specific repurposing"
|
|
}
|
|
|
|
repurposed_content = await self.ai_engine.generate_content_recommendations(analysis_data)
|
|
|
|
return {
|
|
"user_id": user_id,
|
|
"strategy_id": strategy_id,
|
|
"original_content": original_content,
|
|
"repurposed_content": repurposed_content.get("repurposed_content", {}),
|
|
"platform_specific_versions": repurposed_content.get("platform_versions", {}),
|
|
"estimated_reach_increase": repurposed_content.get("reach_increase", 0),
|
|
"status": "current_functionality_12_step_ready"
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Error repurposing content: {str(e)}")
|
|
return {
|
|
"error": str(e),
|
|
"user_id": user_id,
|
|
"strategy_id": strategy_id,
|
|
"status": "error_current_functionality"
|
|
}
|
|
|
|
async def get_trending_topics(
|
|
self,
|
|
user_id: int,
|
|
industry: str,
|
|
limit: int = 10
|
|
) -> Dict[str, Any]:
|
|
"""Get trending topics using current functionality."""
|
|
try:
|
|
logger.info(f"📈 Getting trending topics for industry: {industry}")
|
|
|
|
# Use the existing keyword researcher
|
|
keywords = [industry, "trending", "latest"]
|
|
trending_data = await self.keyword_researcher.analyze_keywords(
|
|
industry=industry,
|
|
url="",
|
|
target_keywords=keywords
|
|
)
|
|
|
|
trending_topics = trending_data.get("trending_topics", [])[:limit]
|
|
|
|
return {
|
|
"user_id": user_id,
|
|
"industry": industry,
|
|
"trending_topics": trending_topics,
|
|
"total_count": len(trending_topics),
|
|
"status": "current_functionality_12_step_ready"
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Error getting trending topics: {str(e)}")
|
|
return {
|
|
"error": str(e),
|
|
"trending_topics": [],
|
|
"industry": industry,
|
|
"user_id": user_id,
|
|
"status": "error_current_functionality"
|
|
}
|
|
|
|
async def health_check(self) -> Dict[str, Any]:
|
|
"""Health check for calendar generation service."""
|
|
try:
|
|
logger.info("🏥 Performing calendar generation health check")
|
|
|
|
checks = {
|
|
"comprehensive_user_processor": True,
|
|
"strategy_processor": True,
|
|
"gap_analysis_processor": True,
|
|
"quality_assessor": True,
|
|
"ai_engine": True,
|
|
"onboarding_service": True,
|
|
"keyword_researcher": True,
|
|
"competitor_analyzer": True,
|
|
"12_step_framework": "ready_for_implementation"
|
|
}
|
|
|
|
# Test basic functionality
|
|
try:
|
|
# Test comprehensive user data processor
|
|
test_data = await self.comprehensive_user_processor.get_comprehensive_user_data(1, None)
|
|
checks["comprehensive_user_processor"] = bool(test_data)
|
|
except Exception as e:
|
|
checks["comprehensive_user_processor"] = False
|
|
logger.warning(f"Comprehensive user processor check failed: {str(e)}")
|
|
|
|
# Calculate overall health
|
|
overall_health = all(checks.values())
|
|
|
|
return {
|
|
"service": "calendar_generation_current_functionality",
|
|
"status": "healthy" if overall_health else "degraded",
|
|
"timestamp": datetime.now().isoformat(),
|
|
"checks": checks,
|
|
"version": "current_functionality_12_step_ready",
|
|
"next_phase": "prompt_chaining_implementation"
|
|
}
|
|
|
|
except Exception as e:
|
|
logger.error(f"❌ Calendar generation health check failed: {str(e)}")
|
|
return {
|
|
"service": "calendar_generation_current_functionality",
|
|
"status": "unhealthy",
|
|
"timestamp": datetime.now().isoformat(),
|
|
"error": str(e),
|
|
"version": "current_functionality_12_step_ready"
|
|
}
|