Base code
This commit is contained in:
Kunthawat Greethong
291
backend/services/onboarding/data_service.py
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291
backend/services/onboarding/data_service.py
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"""
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Onboarding Data Service
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Extracts real user data from onboarding to personalize AI inputs
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"""
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from typing import Dict, Any, List, Optional
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from sqlalchemy.orm import Session
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from loguru import logger
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from datetime import datetime
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import json
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from services.database import get_db_session
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from models.onboarding import OnboardingSession, WebsiteAnalysis, ResearchPreferences
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class OnboardingDataService:
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"""Service to extract and use real onboarding data for AI personalization."""
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def __init__(self, db: Optional[Session] = None):
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"""Initialize the onboarding data service."""
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self.db = db
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logger.info("OnboardingDataService initialized")
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def get_user_website_analysis(self, user_id: int) -> Optional[Dict[str, Any]]:
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"""
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Get website analysis data for a specific user.
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Args:
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user_id: User ID to get data for
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Returns:
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Website analysis data or None if not found
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"""
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try:
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session = self.db or get_db_session()
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# Find onboarding session for user
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onboarding_session = session.query(OnboardingSession).filter(
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OnboardingSession.user_id == user_id
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).first()
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if not onboarding_session:
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logger.warning(f"No onboarding session found for user {user_id}")
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return None
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# Get website analysis for this session
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website_analysis = session.query(WebsiteAnalysis).filter(
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WebsiteAnalysis.session_id == onboarding_session.id
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).first()
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if not website_analysis:
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logger.warning(f"No website analysis found for user {user_id}")
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return None
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return website_analysis.to_dict()
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except Exception as e:
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logger.error(f"Error getting website analysis for user {user_id}: {str(e)}")
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return None
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def get_user_research_preferences(self, user_id: int) -> Optional[Dict[str, Any]]:
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"""
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Get research preferences for a specific user.
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Args:
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user_id: User ID to get data for
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Returns:
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Research preferences data or None if not found
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"""
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try:
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session = self.db or get_db_session()
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# Find onboarding session for user
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onboarding_session = session.query(OnboardingSession).filter(
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OnboardingSession.user_id == user_id
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).first()
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if not onboarding_session:
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logger.warning(f"No onboarding session found for user {user_id}")
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return None
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# Get research preferences for this session
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research_prefs = session.query(ResearchPreferences).filter(
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ResearchPreferences.session_id == onboarding_session.id
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).first()
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if not research_prefs:
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logger.warning(f"No research preferences found for user {user_id}")
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return None
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return research_prefs.to_dict()
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except Exception as e:
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logger.error(f"Error getting research preferences for user {user_id}: {str(e)}")
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return None
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def get_personalized_ai_inputs(self, user_id: int) -> Dict[str, Any]:
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"""
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Get personalized AI inputs based on user's onboarding data.
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Args:
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user_id: User ID to get personalized data for
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Returns:
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Personalized data for AI analysis
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"""
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try:
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logger.info(f"Getting personalized AI inputs for user {user_id}")
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# Get website analysis
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website_analysis = self.get_user_website_analysis(user_id)
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research_prefs = self.get_user_research_preferences(user_id)
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if not website_analysis:
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logger.warning(f"No onboarding data found for user {user_id}, using defaults")
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return self._get_default_ai_inputs()
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# Extract real data from website analysis
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writing_style = website_analysis.get('writing_style', {})
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target_audience = website_analysis.get('target_audience', {})
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content_type = website_analysis.get('content_type', {})
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recommended_settings = website_analysis.get('recommended_settings', {})
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# Build personalized AI inputs
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personalized_inputs = {
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"website_analysis": {
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"website_url": website_analysis.get('website_url', ''),
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"content_types": self._extract_content_types(content_type),
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"writing_style": writing_style.get('tone', 'professional'),
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"target_audience": target_audience.get('demographics', ['professionals']),
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"industry_focus": target_audience.get('industry_focus', 'general'),
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"expertise_level": target_audience.get('expertise_level', 'intermediate')
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},
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"competitor_analysis": {
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"top_performers": self._generate_competitor_suggestions(target_audience),
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"industry": target_audience.get('industry_focus', 'general'),
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"target_demographics": target_audience.get('demographics', [])
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},
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"gap_analysis": {
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"content_gaps": self._identify_content_gaps(content_type, writing_style),
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"target_keywords": self._generate_target_keywords(target_audience),
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"content_opportunities": self._identify_opportunities(content_type)
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},
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"keyword_analysis": {
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"high_value_keywords": self._generate_high_value_keywords(target_audience),
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"content_topics": self._generate_content_topics(content_type),
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"search_intent": self._analyze_search_intent(target_audience)
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}
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}
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# Add research preferences if available
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if research_prefs:
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personalized_inputs["research_preferences"] = {
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"research_depth": research_prefs.get('research_depth', 'Standard'),
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"content_types": research_prefs.get('content_types', []),
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"auto_research": research_prefs.get('auto_research', True),
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"factual_content": research_prefs.get('factual_content', True)
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}
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logger.info(f"✅ Generated personalized AI inputs for user {user_id}")
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return personalized_inputs
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except Exception as e:
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logger.error(f"Error generating personalized AI inputs for user {user_id}: {str(e)}")
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return self._get_default_ai_inputs()
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def _extract_content_types(self, content_type: Dict[str, Any]) -> List[str]:
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"""Extract content types from content type analysis."""
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types = []
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if content_type.get('primary_type'):
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types.append(content_type['primary_type'])
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if content_type.get('secondary_types'):
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types.extend(content_type['secondary_types'])
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return types if types else ['blog', 'article']
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def _generate_competitor_suggestions(self, target_audience: Dict[str, Any]) -> List[str]:
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"""Generate competitor suggestions based on target audience."""
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industry = target_audience.get('industry_focus', 'general')
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demographics = target_audience.get('demographics', ['professionals'])
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# Generate industry-specific competitors
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if industry == 'technology':
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return ['techcrunch.com', 'wired.com', 'theverge.com']
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elif industry == 'marketing':
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return ['hubspot.com', 'marketingland.com', 'moz.com']
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else:
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return ['competitor1.com', 'competitor2.com', 'competitor3.com']
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def _identify_content_gaps(self, content_type: Dict[str, Any], writing_style: Dict[str, Any]) -> List[str]:
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"""Identify content gaps based on current content type and style."""
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gaps = []
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primary_type = content_type.get('primary_type', 'blog')
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if primary_type == 'blog':
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gaps.extend(['Video tutorials', 'Case studies', 'Infographics'])
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elif primary_type == 'video':
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gaps.extend(['Blog posts', 'Whitepapers', 'Webinars'])
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# Add style-based gaps
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tone = writing_style.get('tone', 'professional')
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if tone == 'professional':
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gaps.append('Personal stories')
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elif tone == 'casual':
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gaps.append('Expert interviews')
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return gaps
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def _generate_target_keywords(self, target_audience: Dict[str, Any]) -> List[str]:
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"""Generate target keywords based on audience analysis."""
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industry = target_audience.get('industry_focus', 'general')
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expertise = target_audience.get('expertise_level', 'intermediate')
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if industry == 'technology':
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return ['AI tools', 'Digital transformation', 'Tech trends']
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elif industry == 'marketing':
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return ['Content marketing', 'SEO strategies', 'Social media']
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else:
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return ['Industry insights', 'Best practices', 'Expert tips']
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def _identify_opportunities(self, content_type: Dict[str, Any]) -> List[str]:
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"""Identify content opportunities based on current content type."""
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opportunities = []
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purpose = content_type.get('purpose', 'informational')
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if purpose == 'informational':
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opportunities.extend(['How-to guides', 'Tutorials', 'Educational content'])
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elif purpose == 'promotional':
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opportunities.extend(['Case studies', 'Testimonials', 'Success stories'])
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return opportunities
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def _generate_high_value_keywords(self, target_audience: Dict[str, Any]) -> List[str]:
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"""Generate high-value keywords based on audience analysis."""
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industry = target_audience.get('industry_focus', 'general')
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if industry == 'technology':
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return ['AI marketing', 'Content automation', 'Digital strategy']
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elif industry == 'marketing':
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return ['Content marketing', 'SEO optimization', 'Social media strategy']
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else:
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return ['Industry trends', 'Best practices', 'Expert insights']
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def _generate_content_topics(self, content_type: Dict[str, Any]) -> List[str]:
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"""Generate content topics based on content type analysis."""
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topics = []
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primary_type = content_type.get('primary_type', 'blog')
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if primary_type == 'blog':
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topics.extend(['Industry trends', 'How-to guides', 'Expert insights'])
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elif primary_type == 'video':
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topics.extend(['Tutorials', 'Product demos', 'Expert interviews'])
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return topics
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def _analyze_search_intent(self, target_audience: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze search intent based on target audience."""
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expertise = target_audience.get('expertise_level', 'intermediate')
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if expertise == 'beginner':
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return {'intent': 'educational', 'focus': 'basic concepts'}
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elif expertise == 'intermediate':
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return {'intent': 'practical', 'focus': 'implementation'}
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else:
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return {'intent': 'advanced', 'focus': 'strategic insights'}
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def _get_default_ai_inputs(self) -> Dict[str, Any]:
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"""Get default AI inputs when no onboarding data is available."""
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return {
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"website_analysis": {
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"content_types": ["blog", "video", "social"],
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"writing_style": "professional",
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"target_audience": ["professionals"],
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"industry_focus": "general",
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"expertise_level": "intermediate"
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},
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"competitor_analysis": {
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"top_performers": ["competitor1.com", "competitor2.com"],
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"industry": "general",
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"target_demographics": ["professionals"]
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},
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"gap_analysis": {
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"content_gaps": ["AI content", "Video tutorials", "Case studies"],
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"target_keywords": ["Industry insights", "Best practices"],
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"content_opportunities": ["How-to guides", "Tutorials"]
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},
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"keyword_analysis": {
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"high_value_keywords": ["AI marketing", "Content automation", "Digital strategy"],
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"content_topics": ["Industry trends", "Expert insights"],
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"search_intent": {"intent": "practical", "focus": "implementation"}
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}
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}
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