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"""
Insights Engine module for LifeUnity AI Cognitive Twin System.
Generates proactive AI-powered insights and recommendations.
"""
from typing import Dict, List, Optional
from datetime import datetime, timedelta
import random
from app.utils.logger import get_logger
from app.user_profile import get_user_profile
from app.memory_graph import get_memory_graph
logger = get_logger("InsightsEngine")
class InsightsEngine:
"""AI-powered insights and recommendations engine."""
def __init__(self):
"""Initialize the insights engine."""
self.user_profile = get_user_profile()
self.memory_graph = get_memory_graph()
logger.info("InsightsEngine initialized")
def generate_daily_report(self) -> Dict:
"""
Generate a comprehensive daily AI report.
Returns:
Dictionary containing the daily report
"""
try:
# Get user data
stress_level = self.user_profile.calculate_stress_level()
productivity = self.user_profile.calculate_productivity_score()
emotion_history = self.user_profile.get_emotion_history(limit=20)
# Analyze patterns
fatigue_risk = self._predict_fatigue(emotion_history, stress_level)
stress_insights = self._analyze_stress(stress_level)
productivity_insights = self._analyze_productivity(productivity)
# Generate recommendations
recommendations = self._generate_recommendations(
stress_level, productivity, fatigue_risk
)
report = {
'date': datetime.now().strftime('%Y-%m-%d'),
'generated_at': datetime.now().isoformat(),
'metrics': {
'stress_level': stress_level,
'productivity_score': productivity,
'fatigue_risk': fatigue_risk
},
'insights': {
'stress': stress_insights,
'productivity': productivity_insights
},
'recommendations': recommendations,
'alerts': self._generate_alerts(stress_level, fatigue_risk)
}
logger.info("Generated daily report")
return report
except Exception as e:
logger.error(f"Error generating daily report: {str(e)}", exc_info=True)
return self._get_default_report()
def _predict_fatigue(self, emotion_history: List[Dict], stress_level: float) -> str:
"""
Predict fatigue level based on emotions and stress.
Args:
emotion_history: Recent emotion records
stress_level: Current stress level
Returns:
Fatigue risk level (low, moderate, high)
"""
if not emotion_history:
return "moderate"
# Count negative emotions
negative_emotions = ['sad', 'angry', 'fear', 'disgust']
negative_count = sum(
1 for record in emotion_history
if record.get('emotion') in negative_emotions
)
negative_ratio = negative_count / len(emotion_history)
# Combine with stress level
fatigue_score = (negative_ratio * 100 + stress_level) / 2
if fatigue_score < 40:
return "low"
elif fatigue_score < 70:
return "moderate"
else:
return "high"
def _analyze_stress(self, stress_level: float) -> Dict:
"""
Analyze stress level and provide insights.
Args:
stress_level: Current stress level
Returns:
Stress analysis
"""
if stress_level < 30:
status = "Low"
description = "Your stress levels are well-managed. Keep maintaining your current lifestyle and coping strategies."
elif stress_level < 60:
status = "Moderate"
description = "Your stress levels are within a manageable range. Consider incorporating more relaxation techniques."
else:
status = "High"
description = "Your stress levels are elevated. It's important to take breaks and practice stress-reduction activities."
return {
'level': stress_level,
'status': status,
'description': description
}
def _analyze_productivity(self, productivity: float) -> Dict:
"""
Analyze productivity score and provide insights.
Args:
productivity: Current productivity score
Returns:
Productivity analysis
"""
if productivity >= 70:
status = "Excellent"
description = "You're in a great productive state! Your emotional balance is supporting high performance."
elif productivity >= 50:
status = "Good"
description = "Your productivity is stable. Small improvements in mood management could enhance performance."
else:
status = "Needs Attention"
description = "Your productivity may be affected by emotional factors. Focus on well-being to improve output."
return {
'score': productivity,
'status': status,
'description': description
}
def _generate_recommendations(
self,
stress_level: float,
productivity: float,
fatigue_risk: str
) -> List[Dict]:
"""
Generate personalized recommendations.
Args:
stress_level: Current stress level
productivity: Current productivity score
fatigue_risk: Fatigue risk level
Returns:
List of recommendations
"""
recommendations = []
# Stress-based recommendations
if stress_level > 70:
recommendations.append({
'category': 'Stress Management',
'priority': 'high',
'suggestion': 'Take a 10-minute meditation break to reduce elevated stress levels.',
'action': 'Practice deep breathing exercises or use a meditation app.'
})
elif stress_level > 50:
recommendations.append({
'category': 'Stress Management',
'priority': 'medium',
'suggestion': 'Consider a short walk or stretching session to manage stress.',
'action': 'Take a 5-minute break every hour.'
})
# Fatigue-based recommendations
if fatigue_risk == "high":
recommendations.append({
'category': 'Energy Management',
'priority': 'high',
'suggestion': 'High fatigue risk detected. Ensure adequate rest and avoid overexertion.',
'action': 'Schedule a longer break or end your work session early if possible.'
})
elif fatigue_risk == "moderate":
recommendations.append({
'category': 'Energy Management',
'priority': 'medium',
'suggestion': 'Monitor your energy levels and take breaks when needed.',
'action': 'Stay hydrated and have a healthy snack.'
})
# Productivity-based recommendations
if productivity < 50:
recommendations.append({
'category': 'Productivity Enhancement',
'priority': 'medium',
'suggestion': 'Your mood may be affecting productivity. Focus on emotional well-being first.',
'action': 'Engage in a mood-boosting activity like listening to music or talking to a friend.'
})
elif productivity >= 70:
recommendations.append({
'category': 'Productivity Enhancement',
'priority': 'low',
'suggestion': 'Great productive state! Use this momentum to tackle challenging tasks.',
'action': 'Focus on high-priority items while you\'re in this optimal state.'
})
# General well-being recommendations
recommendations.append({
'category': 'Well-being',
'priority': 'low',
'suggestion': 'Maintain work-life balance by setting clear boundaries.',
'action': 'Schedule time for hobbies and social connections.'
})
return recommendations
def _generate_alerts(self, stress_level: float, fatigue_risk: str) -> List[Dict]:
"""
Generate alerts for critical conditions.
Args:
stress_level: Current stress level
fatigue_risk: Fatigue risk level
Returns:
List of alerts
"""
alerts = []
if stress_level > 80:
alerts.append({
'type': 'warning',
'message': 'Critical stress level detected. Immediate action recommended.',
'timestamp': datetime.now().isoformat()
})
if fatigue_risk == "high":
alerts.append({
'type': 'warning',
'message': 'High fatigue risk. Consider resting to prevent burnout.',
'timestamp': datetime.now().isoformat()
})
return alerts
def analyze_emotion_patterns(self, days: int = 7) -> Dict:
"""
Analyze emotion patterns over a period.
Args:
days: Number of days to analyze
Returns:
Pattern analysis
"""
try:
emotion_history = self.user_profile.get_emotion_history()
if not emotion_history:
return {
'pattern': 'No data available',
'dominant_emotions': [],
'trend': 'neutral'
}
# Filter recent emotions
cutoff_date = datetime.now() - timedelta(days=days)
recent_emotions = [
e for e in emotion_history
if datetime.fromisoformat(e['timestamp']) > cutoff_date
]
if not recent_emotions:
recent_emotions = emotion_history[-10:] # Use last 10 if no recent
# Count emotions
emotion_counts = {}
for record in recent_emotions:
emotion = record.get('emotion', 'neutral')
emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1
# Get dominant emotions
sorted_emotions = sorted(
emotion_counts.items(),
key=lambda x: x[1],
reverse=True
)
# Determine trend
positive_emotions = ['happy', 'surprise']
negative_emotions = ['sad', 'angry', 'fear', 'disgust']
positive_count = sum(
emotion_counts.get(e, 0) for e in positive_emotions
)
negative_count = sum(
emotion_counts.get(e, 0) for e in negative_emotions
)
if positive_count > negative_count * 1.5:
trend = 'positive'
elif negative_count > positive_count * 1.5:
trend = 'negative'
else:
trend = 'neutral'
return {
'period_days': days,
'total_records': len(recent_emotions),
'dominant_emotions': [e[0] for e in sorted_emotions[:3]],
'emotion_distribution': emotion_counts,
'trend': trend
}
except Exception as e:
logger.error(f"Error analyzing emotion patterns: {str(e)}", exc_info=True)
return {'pattern': 'Error analyzing patterns', 'dominant_emotions': [], 'trend': 'neutral'}
def suggest_memory_insights(self, limit: int = 5) -> List[Dict]:
"""
Generate insights from memory graph.
Args:
limit: Number of insights to generate
Returns:
List of memory-based insights
"""
try:
memories = self.memory_graph.get_all_memories()
if not memories:
return []
insights = []
# Get recent memories
recent_memories = sorted(
memories,
key=lambda x: x['timestamp'],
reverse=True
)[:limit]
for memory in recent_memories:
# Find related memories
related = self.memory_graph.get_related_memories(memory['id'])
insight = {
'memory_id': memory['id'],
'content_preview': memory['content'][:100] + '...' if len(memory['content']) > 100 else memory['content'],
'timestamp': memory['timestamp'],
'related_count': len(related),
'tags': memory.get('tags', [])
}
insights.append(insight)
return insights
except Exception as e:
logger.error(f"Error generating memory insights: {str(e)}", exc_info=True)
return []
def _get_default_report(self) -> Dict:
"""Get a default report when generation fails."""
return {
'date': datetime.now().strftime('%Y-%m-%d'),
'generated_at': datetime.now().isoformat(),
'metrics': {
'stress_level': 50.0,
'productivity_score': 50.0,
'fatigue_risk': 'moderate'
},
'insights': {
'stress': {
'level': 50.0,
'status': 'Moderate',
'description': 'Unable to generate insights at this time.'
},
'productivity': {
'score': 50.0,
'status': 'Good',
'description': 'Unable to generate insights at this time.'
}
},
'recommendations': [],
'alerts': []
}
# Global insights engine instance
_insights_engine = None
def get_insights_engine() -> InsightsEngine:
"""
Get or create a global insights engine instance.
Returns:
InsightsEngine instance
"""
global _insights_engine
if _insights_engine is None:
_insights_engine = InsightsEngine()
return _insights_engine
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