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Advanced Content Hyper-Personalization Implementation

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ajaysi
2025-09-04 22:49:15 +05:30
parent d57f7feb4a
commit ccbdc9e8c6
18 changed files with 1452 additions and 974 deletions
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10
backend/services/persona_analysis_service.py
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@@ -317,8 +317,8 @@ class PersonaAnalysisService:
def _build_persona_analysis_prompt(self, onboarding_data: Dict[str, Any]) -> str:
"""Build the main persona analysis prompt."""
website_analysis = onboarding_data.get("website_analysis", {})
research_prefs = onboarding_data.get("research_preferences", {})
website_analysis = onboarding_data.get("website_analysis", {}) or {}
research_prefs = onboarding_data.get("research_preferences", {}) or {}
prompt = f"""
PERSONA GENERATION TASK: Create a comprehensive writing persona based on user onboarding data.
@@ -328,7 +328,7 @@ ONBOARDING DATA ANALYSIS:
Website Analysis:
- URL: {website_analysis.get('website_url', 'Not provided')}
- Writing Style: {json.dumps(website_analysis.get('writing_style', {}), indent=2)}
- Content Characteristics: {json.dumps(website_analysis.get('content_characteristics', {}), indent=2)}
- Content Characteristics: {json.dumps(website_analysis.get('content_characteristics', {}) or {}, indent=2)}
- Target Audience: {json.dumps(website_analysis.get('target_audience', {}), indent=2)}
- Content Type: {json.dumps(website_analysis.get('content_type', {}), indent=2)}
- Style Patterns: {json.dumps(website_analysis.get('style_patterns', {}), indent=2)}
@@ -521,8 +521,8 @@ Generate a platform-optimized persona adaptation that maintains brand consistenc
linguistic_fingerprint=core_persona.get("linguistic_fingerprint", {}),
platform_adaptations={"platforms": list(platform_personas.keys())},
onboarding_session_id=onboarding_data.get("session_info", {}).get("session_id"),
source_website_analysis=onboarding_data.get("website_analysis"),
source_research_preferences=onboarding_data.get("research_preferences"),
source_website_analysis=onboarding_data.get("website_analysis") or {},
source_research_preferences=onboarding_data.get("research_preferences") or {},
ai_analysis_version="gemini_v1.0",
confidence_score=core_persona.get("confidence_score", 0.0)
)

202
backend/test_persona_system.py
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@@ -1,202 +0,0 @@
#!/usr/bin/env python3
"""
Test script for the persona generation system.
Tests the complete flow from onboarding data to persona creation.
"""
import sys
import os
import json
from datetime import datetime
# Add the backend directory to the Python path
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from loguru import logger
def test_persona_system():
"""Test the complete persona generation system."""
logger.info("🧪 Testing Persona Generation System")
try:
# Test 1: Check database models
logger.info("📊 Test 1: Checking database models...")
from models.persona_models import WritingPersona, PlatformPersona, PersonaAnalysisResult
logger.info("✅ Persona models imported successfully")
# Test 2: Check service initialization
logger.info("🔧 Test 2: Testing service initialization...")
from services.persona_analysis_service import PersonaAnalysisService
persona_service = PersonaAnalysisService()
logger.info("✅ PersonaAnalysisService initialized successfully")
# Test 3: Create sample onboarding data
logger.info("📝 Test 3: Creating sample onboarding data...")
sample_onboarding_data = create_sample_onboarding_data()
logger.info("✅ Sample onboarding data created")
# Test 4: Test core persona generation
logger.info("🤖 Test 4: Testing core persona generation...")
core_persona = persona_service._generate_core_persona(sample_onboarding_data)
if "error" in core_persona:
logger.error(f"❌ Core persona generation failed: {core_persona['error']}")
return False
else:
logger.info("✅ Core persona generated successfully")
logger.info(f" Persona Name: {core_persona.get('identity', {}).get('persona_name', 'N/A')}")
logger.info(f" Archetype: {core_persona.get('identity', {}).get('archetype', 'N/A')}")
logger.info(f" Confidence: {core_persona.get('confidence_score', 0)}%")
# Test 5: Test platform adaptations
logger.info("📱 Test 5: Testing platform adaptations...")
platforms = ["twitter", "linkedin", "blog"]
for platform in platforms:
platform_persona = persona_service._generate_single_platform_persona(
core_persona, platform, sample_onboarding_data
)
if "error" in platform_persona:
logger.warning(f"⚠️ {platform} persona generation failed: {platform_persona['error']}")
else:
logger.info(f"✅ {platform} persona generated successfully")
# Test 6: Test data sufficiency calculation
logger.info("📊 Test 6: Testing data sufficiency calculation...")
data_sufficiency = persona_service._calculate_data_sufficiency(sample_onboarding_data)
logger.info(f"✅ Data sufficiency calculated: {data_sufficiency}%")
logger.info("🎉 All persona system tests completed successfully!")
return True
except Exception as e:
logger.error(f"❌ Persona system test failed: {str(e)}")
return False
def create_sample_onboarding_data():
"""Create realistic sample onboarding data for testing."""
return {
"session_info": {
"session_id": 1,
"current_step": 6,
"progress": 100.0,
"started_at": datetime.utcnow().isoformat()
},
"website_analysis": {
"id": 1,
"website_url": "https://techstartup.example.com",
"writing_style": {
"tone": "professional",
"voice": "authoritative",
"complexity": "intermediate",
"engagement_level": "high"
},
"content_characteristics": {
"sentence_structure": "varied",
"vocabulary": "technical",
"paragraph_organization": "logical",
"average_sentence_length": 15.2
},
"target_audience": {
"demographics": ["startup founders", "tech professionals", "investors"],
"expertise_level": "intermediate",
"industry_focus": "technology"
},
"content_type": {
"primary_type": "blog",
"secondary_types": ["case_study", "tutorial"],
"purpose": "educational"
},
"style_patterns": {
"common_phrases": ["let's dive in", "the key insight", "bottom line"],
"sentence_starters": ["Here's the thing:", "The reality is", "Consider this:"],
"rhetorical_devices": ["metaphors", "data_points", "examples"]
},
"style_guidelines": {
"tone_guidelines": "Maintain professional but approachable tone",
"structure_guidelines": "Use clear headings and bullet points",
"voice_guidelines": "Confident and knowledgeable without being condescending"
},
"status": "completed"
},
"research_preferences": {
"id": 1,
"research_depth": "Comprehensive",
"content_types": ["blog", "case_study", "whitepaper"],
"auto_research": True,
"factual_content": True,
"writing_style": {
"tone": "professional",
"voice": "authoritative",
"complexity": "intermediate"
}
}
}
def test_gemini_structured_response():
"""Test Gemini structured response functionality."""
logger.info("🔬 Testing Gemini Structured Response")
try:
from services.llm_providers.gemini_provider import gemini_structured_json_response
# Simple test schema
test_schema = {
"type": "object",
"properties": {
"test_field": {"type": "string"},
"confidence": {"type": "number"}
},
"required": ["test_field", "confidence"]
}
test_prompt = "Generate a test response with test_field='Hello World' and confidence=95.5"
response = gemini_structured_json_response(
prompt=test_prompt,
schema=test_schema,
temperature=0.1,
max_tokens=1024
)
if "error" in response:
logger.error(f"❌ Gemini test failed: {response['error']}")
return False
else:
logger.info(f"✅ Gemini structured response test successful: {response}")
return True
except Exception as e:
logger.error(f"❌ Gemini test error: {str(e)}")
return False
def run_comprehensive_test():
"""Run comprehensive test of the persona system."""
logger.info("🚀 Starting Comprehensive Persona System Test")
# Test 1: Gemini functionality
gemini_works = test_gemini_structured_response()
# Test 2: Persona system
persona_works = test_persona_system()
# Summary
logger.info("📋 Test Summary:")
logger.info(f" Gemini Structured Response: {'✅ PASS' if gemini_works else '❌ FAIL'}")
logger.info(f" Persona Generation System: {'✅ PASS' if persona_works else '❌ FAIL'}")
if gemini_works and persona_works:
logger.info("🎉 All tests passed! Persona system is ready for production.")
return True
else:
logger.error("❌ Some tests failed. Please check the logs and fix issues.")
return False
if __name__ == "__main__":
success = run_comprehensive_test()
sys.exit(0 if success else 1)