ALwrity persona system
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ajaysi
@@ -45,6 +45,24 @@ class PersonaGenerationResponse(BaseModel):
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data_sufficiency: Optional[float] = None
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platforms_generated: List[str] = []
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class LinkedInPersonaValidationRequest(BaseModel):
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"""Request model for LinkedIn persona validation."""
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persona_data: Dict[str, Any]
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class LinkedInPersonaValidationResponse(BaseModel):
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"""Response model for LinkedIn persona validation."""
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is_valid: bool
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quality_score: float
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completeness_score: float
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professional_context_score: float
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linkedin_optimization_score: float
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missing_fields: List[str]
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incomplete_fields: List[str]
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recommendations: List[str]
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quality_issues: List[str]
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strengths: List[str]
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validation_details: Dict[str, Any]
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# Dependency to get persona service
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def get_persona_service() -> PersonaAnalysisService:
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"""Get the persona analysis service instance."""
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@@ -380,6 +398,211 @@ async def get_supported_platforms():
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"description": "Newsletter platform for building subscriber relationships",
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"format": "email newsletter",
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"subscription_focus": True
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}
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]
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}
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class LinkedInOptimizationRequest(BaseModel):
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"""Request model for LinkedIn algorithm optimization."""
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persona_data: Dict[str, Any]
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class LinkedInOptimizationResponse(BaseModel):
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"""Response model for LinkedIn algorithm optimization."""
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optimized_persona: Dict[str, Any]
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optimization_applied: bool
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optimization_details: Dict[str, Any]
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async def validate_linkedin_persona(
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request: LinkedInPersonaValidationRequest,
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persona_service: PersonaAnalysisService = Depends(get_persona_service)
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):
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"""
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Validate LinkedIn persona data for completeness and quality.
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This endpoint provides comprehensive validation of LinkedIn persona data,
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including core fields, LinkedIn-specific optimizations, professional context,
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and content quality assessments.
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"""
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try:
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logger.info("Validating LinkedIn persona data")
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# Get LinkedIn persona service
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from services.persona.linkedin.linkedin_persona_service import LinkedInPersonaService
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linkedin_service = LinkedInPersonaService()
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# Validate the persona data
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validation_results = linkedin_service.validate_linkedin_persona(request.persona_data)
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logger.info(f"LinkedIn persona validation completed: Quality Score: {validation_results['quality_score']:.1f}%")
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return LinkedInPersonaValidationResponse(**validation_results)
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except Exception as e:
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logger.error(f"Error validating LinkedIn persona: {str(e)}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to validate LinkedIn persona: {str(e)}"
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)
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async def optimize_linkedin_persona(
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request: LinkedInOptimizationRequest,
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persona_service: PersonaAnalysisService = Depends(get_persona_service)
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):
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"""
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Optimize LinkedIn persona data for maximum algorithm performance.
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This endpoint applies comprehensive LinkedIn algorithm optimization to persona data,
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including content quality optimization, multimedia strategy, engagement optimization,
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timing optimization, and professional context optimization.
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"""
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try:
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logger.info("Optimizing LinkedIn persona for algorithm performance")
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# Get LinkedIn persona service
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from services.persona.linkedin.linkedin_persona_service import LinkedInPersonaService
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linkedin_service = LinkedInPersonaService()
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# Apply algorithm optimization
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optimized_persona = linkedin_service.optimize_for_linkedin_algorithm(request.persona_data)
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# Extract optimization details
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optimization_details = optimized_persona.get("algorithm_optimization", {})
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logger.info("✅ LinkedIn persona algorithm optimization completed successfully")
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return LinkedInOptimizationResponse(
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optimized_persona=optimized_persona,
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optimization_applied=True,
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optimization_details={
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"optimization_categories": list(optimization_details.keys()),
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"total_optimization_strategies": sum(len(strategies) if isinstance(strategies, list) else 1
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for category in optimization_details.values()
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for strategies in category.values() if isinstance(category, dict)),
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"optimization_timestamp": datetime.utcnow().isoformat()
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}
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]
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}
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)
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except Exception as e:
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logger.error(f"Error optimizing LinkedIn persona: {str(e)}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to optimize LinkedIn persona: {str(e)}"
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)
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class FacebookPersonaValidationRequest(BaseModel):
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"""Request model for Facebook persona validation."""
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persona_data: Dict[str, Any]
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class FacebookPersonaValidationResponse(BaseModel):
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"""Response model for Facebook persona validation."""
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is_valid: bool
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quality_score: float
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completeness_score: float
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facebook_optimization_score: float
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engagement_strategy_score: float
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content_format_score: float
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audience_targeting_score: float
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community_building_score: float
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missing_fields: List[str]
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incomplete_fields: List[str]
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recommendations: List[str]
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quality_issues: List[str]
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strengths: List[str]
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validation_details: Dict[str, Any]
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class FacebookOptimizationRequest(BaseModel):
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"""Request model for Facebook algorithm optimization."""
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persona_data: Dict[str, Any]
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class FacebookOptimizationResponse(BaseModel):
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"""Response model for Facebook algorithm optimization."""
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optimized_persona: Dict[str, Any]
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optimization_applied: bool
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optimization_details: Dict[str, Any]
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async def validate_facebook_persona(
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request: FacebookPersonaValidationRequest,
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persona_service: PersonaAnalysisService = Depends(get_persona_service)
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):
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"""
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Validate Facebook persona data for completeness and quality.
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This endpoint provides comprehensive validation of Facebook persona data,
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including core fields, Facebook-specific optimizations, engagement strategies,
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content formats, audience targeting, and community building assessments.
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"""
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try:
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logger.info("Validating Facebook persona data")
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# Get Facebook persona service
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from services.persona.facebook.facebook_persona_service import FacebookPersonaService
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facebook_service = FacebookPersonaService()
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# Validate the persona data
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validation_results = facebook_service.validate_facebook_persona(request.persona_data)
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logger.info(f"Facebook persona validation completed: Quality Score: {validation_results['quality_score']:.1f}%")
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return FacebookPersonaValidationResponse(**validation_results)
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except Exception as e:
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logger.error(f"Error validating Facebook persona: {str(e)}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to validate Facebook persona: {str(e)}"
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)
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async def optimize_facebook_persona(
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request: FacebookOptimizationRequest,
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persona_service: PersonaAnalysisService = Depends(get_persona_service)
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):
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"""
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Optimize Facebook persona data for maximum algorithm performance.
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This endpoint applies comprehensive Facebook algorithm optimization to persona data,
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including engagement optimization, content quality optimization, timing optimization,
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audience targeting optimization, and community building strategies.
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"""
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try:
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logger.info("Optimizing Facebook persona for algorithm performance")
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# Get Facebook persona service
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from services.persona.facebook.facebook_persona_service import FacebookPersonaService
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facebook_service = FacebookPersonaService()
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# Apply algorithm optimization
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optimized_persona = facebook_service.optimize_for_facebook_algorithm(request.persona_data)
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# Extract optimization details
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optimization_details = optimized_persona.get("algorithm_optimization", {})
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logger.info("✅ Facebook persona algorithm optimization completed successfully")
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# Use the optimization metadata from the service
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optimization_metadata = optimized_persona.get("optimization_metadata", {})
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return FacebookOptimizationResponse(
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optimized_persona=optimized_persona,
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optimization_applied=True,
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optimization_details={
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"optimization_categories": optimization_metadata.get("optimization_categories", []),
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"total_optimization_strategies": optimization_metadata.get("total_optimization_strategies", 0),
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"optimization_timestamp": optimization_metadata.get("optimization_timestamp", datetime.utcnow().isoformat())
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}
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)
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except Exception as e:
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logger.error(f"Error optimizing Facebook persona: {str(e)}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to optimize Facebook persona: {str(e)}"
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)
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@@ -16,7 +16,19 @@ from api.persona import (
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validate_persona_generation_readiness,
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generate_persona_preview,
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get_supported_platforms,
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PersonaGenerationRequest
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validate_linkedin_persona,
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optimize_linkedin_persona,
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validate_facebook_persona,
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optimize_facebook_persona,
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PersonaGenerationRequest,
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LinkedInPersonaValidationRequest,
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LinkedInPersonaValidationResponse,
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LinkedInOptimizationRequest,
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LinkedInOptimizationResponse,
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FacebookPersonaValidationRequest,
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FacebookPersonaValidationResponse,
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FacebookOptimizationRequest,
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FacebookOptimizationResponse
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)
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from services.persona_replication_engine import PersonaReplicationEngine
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@@ -89,6 +101,34 @@ async def get_supported_platforms_endpoint():
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"""Get list of supported platforms for persona generation."""
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return await get_supported_platforms()
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@router.post("/linkedin/validate", response_model=LinkedInPersonaValidationResponse)
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async def validate_linkedin_persona_endpoint(
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request: LinkedInPersonaValidationRequest
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):
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"""Validate LinkedIn persona data for completeness and quality."""
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return await validate_linkedin_persona(request)
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@router.post("/linkedin/optimize", response_model=LinkedInOptimizationResponse)
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async def optimize_linkedin_persona_endpoint(
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request: LinkedInOptimizationRequest
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):
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"""Optimize LinkedIn persona data for maximum algorithm performance."""
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return await optimize_linkedin_persona(request)
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@router.post("/facebook/validate", response_model=FacebookPersonaValidationResponse)
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async def validate_facebook_persona_endpoint(
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request: FacebookPersonaValidationRequest
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):
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"""Validate Facebook persona data for completeness and quality."""
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return await validate_facebook_persona(request)
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@router.post("/facebook/optimize", response_model=FacebookOptimizationResponse)
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async def optimize_facebook_persona_endpoint(
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request: FacebookOptimizationRequest
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):
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"""Optimize Facebook persona data for maximum algorithm performance."""
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return await optimize_facebook_persona(request)
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@router.post("/generate-content")
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async def generate_content_with_persona_endpoint(
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request: Dict[str, Any]
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