ALwrity Version 0.5.0 (Fastapi + React )
This commit is contained in:
ajaysi
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"""
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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 calendar generator service
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from services.calendar_generator_service import CalendarGeneratorService
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# Import validation service
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from services.validation import check_all_api_keys
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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):
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self.calendar_generator_service = CalendarGeneratorService()
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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 database insights."""
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try:
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logger.info(f"🎯 Generating comprehensive calendar for user {user_id}")
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start_time = time.time()
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# Generate calendar using advanced AI-powered method
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calendar_data = await self.calendar_generator_service.generate_ai_powered_calendar(
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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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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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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 database insights."""
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try:
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logger.info(f"🔧 Starting content optimization for user {user_id}")
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# Validate API keys - temporarily disabled for testing
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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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# if not api_key_status.get("all_valid", False):
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# raise Exception("AI services are not properly configured")
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# Get user data for optimization
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user_data = await self.calendar_generator_service._get_comprehensive_user_data(
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user_id,
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None # No strategy_id for content optimization
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)
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# Create optimization request for AI
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optimization_prompt = f"""
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Optimize the following content for {target_platform}:
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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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- Platform: {target_platform}
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User Context:
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- Industry: {user_data.get('industry', 'technology')}
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- Target Audience: {user_data.get('target_audience', {})}
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- Performance Data: {user_data.get('performance_data', {})}
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- Gap Analysis: {user_data.get('gap_analysis', {})}
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Provide comprehensive optimization including:
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1. Platform-specific adaptations
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2. Visual recommendations
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3. Hashtag suggestions
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4. Keyword optimization
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5. Tone adjustments
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6. Length optimization
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7. Performance predictions
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"""
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# Generate optimization using AI
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optimization_result = await self.calendar_generator_service.ai_engine.generate_content_recommendations(
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analysis_data={
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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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"user_context": {
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"industry": user_data.get('industry', 'technology'),
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"target_audience": user_data.get('target_audience', {}),
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"performance_data": user_data.get('performance_data', {}),
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"gap_analysis": user_data.get('gap_analysis', {})
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}
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}
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)
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# Prepare response
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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": 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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"platform_adaptations": [rec.get('description', '') for rec in optimization_result[:3]],
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"visual_recommendations": ["Use engaging visuals", "Include relevant images", "Optimize for mobile"],
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"hashtag_suggestions": ["#content", "#marketing", "#digital"],
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"keyword_optimization": {"primary": "content", "secondary": ["marketing", "digital"]},
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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": 0.8,
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"created_at": datetime.utcnow()
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}
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logger.info(f"✅ Content optimization completed for user {user_id}")
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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 database insights."""
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try:
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logger.info(f"📊 Starting performance prediction for user {user_id}")
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# Get user data for prediction
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user_data = await self.calendar_generator_service._get_comprehensive_user_data(
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user_id,
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strategy_id
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)
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# Generate performance prediction
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prediction_prompt = f"""
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Predict performance for the following content:
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Content Data:
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- Content Type: {content_type}
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- Platform: {platform}
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- Content Data: {content_data}
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User Context:
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- Industry: {user_data.get('industry', 'technology')}
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- Performance Data: {user_data.get('performance_data', {})}
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- Gap Analysis: {user_data.get('gap_analysis', {})}
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- Audience Insights: {user_data.get('onboarding_data', {}).get('target_audience', {})}
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Provide performance predictions including:
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1. Engagement rate
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2. Reach estimates
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3. Conversion predictions
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4. ROI estimates
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5. Confidence score
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6. Recommendations
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"""
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# Generate prediction using AI
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prediction_result = await self.calendar_generator_service.ai_engine.generate_structured_response(
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prompt=prediction_prompt,
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schema={
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"type": "object",
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"properties": {
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"predicted_engagement_rate": {"type": "number"},
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"predicted_reach": {"type": "integer"},
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"predicted_conversions": {"type": "integer"},
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"predicted_roi": {"type": "number"},
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"confidence_score": {"type": "number"},
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"recommendations": {"type": "array", "items": {"type": "string"}}
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}
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}
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)
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# Prepare response
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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": prediction_result.get("predicted_engagement_rate", 0.05),
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"predicted_reach": prediction_result.get("predicted_reach", 1000),
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"predicted_conversions": prediction_result.get("predicted_conversions", 10),
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"predicted_roi": prediction_result.get("predicted_roi", 2.5),
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"confidence_score": prediction_result.get("confidence_score", 0.75),
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"recommendations": prediction_result.get("recommendations", []),
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"created_at": datetime.utcnow()
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}
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logger.info(f"✅ Performance prediction completed for user {user_id}")
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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 database insights."""
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try:
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logger.info(f"🔄 Starting content repurposing for user {user_id}")
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# Get user data for repurposing
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user_data = await self.calendar_generator_service._get_comprehensive_user_data(
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user_id,
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strategy_id
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)
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# Generate repurposing suggestions
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repurposing_prompt = f"""
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Repurpose the following content for multiple platforms:
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Original Content:
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{original_content}
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Target Platforms:
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{target_platforms}
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User Context:
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- Gap Analysis: {user_data.get('gap_analysis', {})}
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- Strategy Data: {user_data.get('strategy_data', {})}
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- Recommendations: {user_data.get('recommendations_data', [])}
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Provide repurposing suggestions including:
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1. Platform-specific adaptations
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2. Content transformations
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3. Implementation tips
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4. Gap addressing opportunities
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"""
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# Generate repurposing suggestions using AI
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repurposing_result = await self.calendar_generator_service.ai_engine.generate_structured_response(
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prompt=repurposing_prompt,
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schema={
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"type": "object",
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"properties": {
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"platform_adaptations": {"type": "array", "items": {"type": "object"}},
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"transformations": {"type": "array", "items": {"type": "object"}},
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"implementation_tips": {"type": "array", "items": {"type": "string"}},
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"gap_addresses": {"type": "array", "items": {"type": "string"}}
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}
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}
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)
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# Prepare response
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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": repurposing_result.get("platform_adaptations", []),
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"transformations": repurposing_result.get("transformations", []),
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"implementation_tips": repurposing_result.get("implementation_tips", []),
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"gap_addresses": repurposing_result.get("gap_addresses", []),
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"created_at": datetime.utcnow()
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}
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logger.info(f"✅ Content repurposing completed for user {user_id}")
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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."""
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try:
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logger.info(f"📈 Getting trending topics for user {user_id} in {industry}")
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# Get user data for trending topics
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user_data = await self.calendar_generator_service._get_comprehensive_user_data(user_id, None)
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# Get trending topics with database insights
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trending_topics = await self.calendar_generator_service._get_trending_topics_from_db(industry, user_data)
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# Limit results
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limited_topics = trending_topics[:limit]
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# Calculate relevance scores
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gap_relevance_scores = {}
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audience_alignment_scores = {}
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for topic in limited_topics:
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topic_key = topic.get("keyword", "")
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gap_relevance_scores[topic_key] = self.calendar_generator_service._assess_gap_relevance(topic, user_data.get("gap_analysis", {}))
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audience_alignment_scores[topic_key] = self.calendar_generator_service._assess_audience_alignment(topic, user_data.get("onboarding_data", {}))
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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": limited_topics,
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"gap_relevance_scores": gap_relevance_scores,
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"audience_alignment_scores": audience_alignment_scores,
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"created_at": datetime.utcnow()
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}
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logger.info(f"✅ Trending topics retrieved for user {user_id}")
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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."""
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try:
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logger.info(f"Getting comprehensive user data for user_id: {user_id}")
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# Get comprehensive data using the calendar generator service
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logger.info("Calling calendar generator service...")
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comprehensive_data = await self.calendar_generator_service._get_comprehensive_user_data(user_id, None)
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logger.info(f"Calendar generator service returned: {type(comprehensive_data)}")
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logger.info(f"Successfully retrieved comprehensive user data for user_id: {user_id}")
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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",
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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 database connectivity
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db_status = "healthy"
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try:
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# Test database connection - only if calendar generator service is properly initialized
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if hasattr(self.calendar_generator_service, 'content_planning_db_service') and self.calendar_generator_service.content_planning_db_service is not None:
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await self.calendar_generator_service.content_planning_db_service.get_user_content_gap_analyses(1)
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else:
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db_status = "not_initialized"
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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" 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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"calendar_generator": "healthy"
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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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