Supporting Mental Health with Technology: Revolutionary Digital Solutions Transforming Mental Healthcare Through AI, Virtual Reality, and Personalized Therapeutic Interventions

import asyncio
import json
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any, Callable
from dataclasses import dataclass, field
from enum import Enum
import uuid
import time
from concurrent.futures import ThreadPoolExecutor

class MentalHealthCondition(Enum):
    ANXIETY = "anxiety"
    DEPRESSION = "depression"
    BIPOLAR = "bipolar_disorder"
    PTSD = "post_traumatic_stress"
    EATING_DISORDER = "eating_disorder"
    SUBSTANCE_ABUSE = "substance_abuse"
    GENERAL_WELLNESS = "general_wellness"

class InterventionType(Enum):
    COGNITIVE_BEHAVIORAL = "cognitive_behavioral_therapy"
    MINDFULNESS = "mindfulness_meditation"
    VIRTUAL_REALITY = "virtual_reality_therapy"
    CRISIS_INTERVENTION = "crisis_intervention"
    PEER_SUPPORT = "peer_support"
    MEDICATION_REMINDER = "medication_reminder"
    LIFESTYLE_COACHING = "lifestyle_coaching"

class RiskLevel(Enum):
    LOW = "low"
    MODERATE = "moderate"
    HIGH = "high"
    CRITICAL = "critical"

@dataclass
class User:
    """Represents a mental health platform user"""
    id: str
    name: str
    age: int
    conditions: List[MentalHealthCondition]
    risk_level: RiskLevel = RiskLevel.LOW
    last_assessment: datetime = field(default_factory=datetime.now)
    preferences: Dict[str, Any] = field(default_factory=dict)
    treatment_goals: List[str] = field(default_factory=list)
    medication_list: List[str] = field(default_factory=list)
    emergency_contacts: List[Dict[str, str]] = field(default_factory=list)
    consent_for_ai: bool = True
    
@dataclass
class MoodEntry:
    """Represents a mood tracking entry"""
    timestamp: datetime
    user_id: str
    mood_score: float  # 1-10 scale
    anxiety_level: float
    energy_level: float
    sleep_quality: float
    stress_factors: List[str]
    coping_strategies_used: List[str]
    notes: Optional[str] = None
    
@dataclass
class TherapeuticSession:
    """Represents a digital therapeutic session"""
    id: str
    user_id: str
    session_type: InterventionType
    start_time: datetime
    duration_minutes: int
    effectiveness_score: float = 0.0
    completion_status: str = "in_progress"
    ai_insights: Dict[str, Any] = field(default_factory=dict)
    user_feedback: Optional[str] = None
    
class MentalHealthPlatform:
    """Comprehensive mental health technology platform"""
    
    def __init__(self, platform_name: str):
        self.platform_name = platform_name
        self.users: Dict[str, User] = {}
        self.mood_history: List[MoodEntry] = []
        self.sessions: Dict[str, TherapeuticSession] = {}
        
        # AI and analytics components
        self.ai_therapist = AITherapist()
        self.risk_analyzer = RiskAnalyzer()
        self.intervention_recommender = InterventionRecommender()
        
        # Virtual reality and immersive therapy
        self.vr_therapy_manager = VRTherapyManager()
        
        # Crisis intervention system
        self.crisis_manager = CrisisInterventionManager()
        
        # Wearable and sensor integration
        self.biometric_monitor = BiometricMonitor()
        
        # Community and peer support
        self.community_manager = CommunityManager()
        
        print(f"Mental Health Platform '{platform_name}' initialized")
        
    def register_user(self, user: User) -> Dict[str, Any]:
        """Register new user with comprehensive onboarding"""
        print(f"Registering user: {user.name}")
        
        # Add user to platform
        self.users[user.id] = user
        
        # Conduct initial assessment
        initial_assessment = self._conduct_initial_assessment(user)
        
        # Create personalized care plan
        care_plan = self._create_personalized_care_plan(user, initial_assessment)
        
        # Set up monitoring and alerts
        monitoring_setup = self._configure_user_monitoring(user)
        
        # Initialize AI therapeutic relationship
        ai_profile = self.ai_therapist.create_user_profile(user, initial_assessment)
        
        registration_result = {
            "user_id": user.id,
            "registration_timestamp": datetime.now(),
            "initial_assessment": initial_assessment,
            "care_plan": care_plan,
            "monitoring_setup": monitoring_setup,
            "ai_therapist_profile": ai_profile,
            "onboarding_completion": "successful",
            "next_steps": [
                "Complete first mood tracking entry",
                "Schedule initial virtual therapy session",
                "Set up wearable device integration",
                "Join relevant peer support groups"
            ]
        }
        
        print(f"User {user.name} registered successfully")
        return registration_result
        
    def _conduct_initial_assessment(self, user: User) -> Dict[str, Any]:
        """Comprehensive initial mental health assessment"""
        assessment = {
            "phq9_score": 0,  # Depression screening
            "gad7_score": 0,  # Anxiety screening
            "stress_level": 0,
            "sleep_quality": 0,
            "social_support": 0,
            "coping_mechanisms": [],
            "trauma_history": False,
            "substance_use": False,
            "medication_history": [],
            "therapy_experience": False,
            "technology_comfort": 0
        }
        
        # Simulate assessment based on user conditions
        for condition in user.conditions:
            if condition == MentalHealthCondition.DEPRESSION:
                assessment["phq9_score"] = np.random.randint(10, 20)  # Moderate to severe
            elif condition == MentalHealthCondition.ANXIETY:
                assessment["gad7_score"] = np.random.randint(10, 18)  # Moderate to severe
            elif condition == MentalHealthCondition.PTSD:
                assessment["trauma_history"] = True
                assessment["stress_level"] = np.random.randint(7, 10)
                
        # Determine overall risk level
        risk_factors = [
            assessment["phq9_score"] > 15,
            assessment["gad7_score"] > 15,
            assessment["stress_level"] > 8,
            assessment["trauma_history"],
            assessment["substance_use"]
        ]
        
        risk_count = sum(risk_factors)
        if risk_count >= 3:
            user.risk_level = RiskLevel.HIGH
        elif risk_count >= 2:
            user.risk_level = RiskLevel.MODERATE
        else:
            user.risk_level = RiskLevel.LOW
            
        assessment["overall_risk_level"] = user.risk_level.value
        assessment["assessment_date"] = datetime.now()
        
        return assessment
        
    def _create_personalized_care_plan(self, user: User, assessment: Dict[str, Any]) -> Dict[str, Any]:
        """Create personalized mental health care plan"""
        care_plan = {
            "plan_id": f"care_plan_{uuid.uuid4()}",
            "user_id": user.id,
            "created_date": datetime.now(),
            "primary_interventions": [],
            "secondary_interventions": [],
            "monitoring_schedule": {},
            "crisis_protocols": {},
            "goals_and_milestones": [],
            "review_schedule": "weekly"
        }
        
        # Determine primary interventions based on conditions and risk level
        for condition in user.conditions:
            if condition == MentalHealthCondition.DEPRESSION:
                care_plan["primary_interventions"].extend([
                    {
                        "type": InterventionType.COGNITIVE_BEHAVIORAL.value,
                        "frequency": "3x_weekly",
                        "duration": "20_minutes",
                        "ai_guided": True
                    },
                    {
                        "type": InterventionType.MINDFULNESS.value,
                        "frequency": "daily",
                        "duration": "10_minutes",
                        "guided_meditation": True
                    }
                ])
                
            elif condition == MentalHealthCondition.ANXIETY:
                care_plan["primary_interventions"].extend([
                    {
                        "type": InterventionType.VIRTUAL_REALITY.value,
                        "frequency": "2x_weekly",
                        "duration": "15_minutes",
                        "scenario": "anxiety_management"
                    },
                    {
                        "type": InterventionType.MINDFULNESS.value,
                        "frequency": "twice_daily",
                        "duration": "5_minutes",
                        "breathing_exercises": True
                    }
                ])
                
        # Add monitoring schedule
        care_plan["monitoring_schedule"] = {
            "mood_tracking": "daily",
            "biometric_monitoring": "continuous",
            "ai_check_ins": "daily",
            "human_therapist_contact": "weekly" if user.risk_level == RiskLevel.HIGH else "bi_weekly",
            "assessment_reviews": "monthly"
        }
        
        # Set goals and milestones
        care_plan["goals_and_milestones"] = [
            {
                "goal": "Reduce anxiety symptoms by 30%",
                "timeframe": "4_weeks",
                "measurement": "GAD-7 score improvement",
                "target_value": assessment["gad7_score"] * 0.7
            },
            {
                "goal": "Improve sleep quality",
                "timeframe": "2_weeks",
                "measurement": "Sleep quality score",
                "target_value": 7.0
            },
            {
                "goal": "Establish consistent coping strategies",
                "timeframe": "6_weeks",
                "measurement": "Coping strategy utilization rate",
                "target_value": 0.8
            }
        ]
        
        return care_plan
        
    def track_mood(self, user_id: str, mood_data: Dict[str, Any]) -> Dict[str, Any]:
        """Process and analyze mood tracking data"""
        if user_id not in self.users:
            return {"error": "User not found"}
            
        user = self.users[user_id]
        
        # Create mood entry
        mood_entry = MoodEntry(
            timestamp=datetime.now(),
            user_id=user_id,
            mood_score=mood_data.get("mood_score", 5.0),
            anxiety_level=mood_data.get("anxiety_level", 5.0),
            energy_level=mood_data.get("energy_level", 5.0),
            sleep_quality=mood_data.get("sleep_quality", 5.0),
            stress_factors=mood_data.get("stress_factors", []),
            coping_strategies_used=mood_data.get("coping_strategies", []),
            notes=mood_data.get("notes")
        )
        
        self.mood_history.append(mood_entry)
        
        # Analyze mood patterns and trends
        mood_analysis = self._analyze_mood_patterns(user_id)
        
        # Check for risk indicators
        risk_assessment = self.risk_analyzer.assess_current_risk(user, mood_entry)
        
        # Generate AI insights and recommendations
        ai_insights = self.ai_therapist.generate_mood_insights(user, mood_entry, mood_analysis)
        
        # Determine if intervention is needed
        intervention_needed = risk_assessment["risk_level"] in ["high", "critical"]
        
        tracking_result = {
            "mood_entry_id": f"mood_{uuid.uuid4()}",
            "timestamp": mood_entry.timestamp,
            "mood_analysis": mood_analysis,
            "risk_assessment": risk_assessment,
            "ai_insights": ai_insights,
            "intervention_needed": intervention_needed,
            "recommended_actions": ai_insights.get("recommendations", []),
            "next_check_in": datetime.now() + timedelta(hours=24)
        }
        
        # Trigger intervention if needed
        if intervention_needed:
            intervention_response = self._trigger_immediate_intervention(user, risk_assessment)
            tracking_result["intervention_response"] = intervention_response
            
        return tracking_result
        
    def _analyze_mood_patterns(self, user_id: str) -> Dict[str, Any]:
        """Analyze user's mood patterns and trends"""
        user_moods = [m for m in self.mood_history if m.user_id == user_id]
        
        if len(user_moods) < 7:  # Need at least a week of data
            return {"status": "Insufficient data for pattern analysis"}
            
        recent_moods = sorted(user_moods, key=lambda x: x.timestamp)[-30:]  # Last 30 entries
        
        # Calculate trends
        mood_scores = [m.mood_score for m in recent_moods]
        anxiety_levels = [m.anxiety_level for m in recent_moods]
        energy_levels = [m.energy_level for m in recent_moods]
        
        analysis = {
            "average_mood": np.mean(mood_scores),
            "mood_trend": self._calculate_trend(mood_scores),
            "average_anxiety": np.mean(anxiety_levels),
            "anxiety_trend": self._calculate_trend(anxiety_levels),
            "average_energy": np.mean(energy_levels),
            "energy_trend": self._calculate_trend(energy_levels),
            "mood_volatility": np.std(mood_scores),
            "consistent_patterns": self._identify_patterns(recent_moods),
            "trigger_analysis": self._analyze_triggers(recent_moods)
        }
        
        return analysis
        
    def start_ai_therapy_session(self, user_id: str, session_type: InterventionType) -> Dict[str, Any]:
        """Start AI-guided therapy session"""
        if user_id not in self.users:
            return {"error": "User not found"}
            
        user = self.users[user_id]
        
        # Create therapy session
        session = TherapeuticSession(
            id=f"session_{uuid.uuid4()}",
            user_id=user_id,
            session_type=session_type,
            start_time=datetime.now(),
            duration_minutes=0
        )
        
        self.sessions[session.id] = session
        
        # Initialize AI therapy session
        session_config = self.ai_therapist.initialize_session(user, session_type)
        
        # Get personalized content based on user's current state
        session_content = self._generate_session_content(user, session_type)
        
        session_result = {
            "session_id": session.id,
            "session_type": session_type.value,
            "start_time": session.start_time,
            "session_config": session_config,
            "content": session_content,
            "estimated_duration": session_config.get("estimated_duration", 20),
            "ai_therapist_greeting": f"Hello {user.name}, I'm here to support you through this {session_type.value.replace('_', ' ')} session. How are you feeling right now?"
        }
        
        return session_result
        
    def _generate_session_content(self, user: User, session_type: InterventionType) -> Dict[str, Any]:
        """Generate personalized session content"""
        content = {"modules": [], "exercises": [], "resources": []}
        
        if session_type == InterventionType.COGNITIVE_BEHAVIORAL:
            content["modules"] = [
                {
                    "title": "Thought Identification",
                    "description": "Identify and examine automatic thoughts",
                    "duration": 5,
                    "interactive": True
                },
                {
                    "title": "Cognitive Restructuring",
                    "description": "Challenge and reframe negative thought patterns",
                    "duration": 10,
                    "interactive": True
                },
                {
                    "title": "Behavioral Activation",
                    "description": "Plan positive activities for the day",
                    "duration": 5,
                    "interactive": True
                }
            ]
            
        elif session_type == InterventionType.MINDFULNESS:
            content["modules"] = [
                {
                    "title": "Breathing Awareness",
                    "description": "Guided breathing exercise with biometric feedback",
                    "duration": 5,
                    "guided_audio": True
                },
                {
                    "title": "Body Scan Meditation",
                    "description": "Progressive body awareness and relaxation",
                    "duration": 10,
                    "guided_audio": True
                },
                {
                    "title": "Mindful Reflection",
                    "description": "Reflection on present moment awareness",
                    "duration": 5,
                    "journaling": True
                }
            ]
            
        elif session_type == InterventionType.VIRTUAL_REALITY:
            content = self.vr_therapy_manager.create_vr_session(user)
            
        return content
        
    def conduct_crisis_intervention(self, user_id: str, crisis_data: Dict[str, Any]) -> Dict[str, Any]:
        """Handle mental health crisis intervention"""
        if user_id not in self.users:
            return {"error": "User not found"}
            
        user = self.users[user_id]
        
        # Immediate crisis assessment
        crisis_assessment = self.crisis_manager.assess_crisis_severity(user, crisis_data)
        
        # Determine intervention strategy
        intervention_strategy = self.crisis_manager.determine_intervention_strategy(crisis_assessment)
        
        # Execute immediate response
        immediate_response = self.crisis_manager.execute_immediate_response(user, intervention_strategy)
        
        # Alert emergency contacts if necessary
        emergency_alerts = []
        if crisis_assessment["severity"] in ["high", "critical"]:
            emergency_alerts = self._alert_emergency_contacts(user, crisis_assessment)
            
        # Schedule follow-up
        follow_up_schedule = self._schedule_crisis_follow_up(user, crisis_assessment)
        
        crisis_response = {
            "crisis_id": f"crisis_{uuid.uuid4()}",
            "timestamp": datetime.now(),
            "crisis_assessment": crisis_assessment,
            "intervention_strategy": intervention_strategy,
            "immediate_response": immediate_response,
            "emergency_alerts": emergency_alerts,
            "follow_up_schedule": follow_up_schedule,
            "crisis_resources": self._get_crisis_resources(user),
            "safety_plan_activated": True
        }
        
        return crisis_response
        
    def generate_wellness_report(self, user_id: str, time_period_days: int = 30) -> Dict[str, Any]:
        """Generate comprehensive wellness report for user"""
        if user_id not in self.users:
            return {"error": "User not found"}
            
        user = self.users[user_id]
        end_date = datetime.now()
        start_date = end_date - timedelta(days=time_period_days)
        
        # Get user data for time period
        period_moods = [m for m in self.mood_history 
                       if m.user_id == user_id and start_date <= m.timestamp <= end_date]
        
        period_sessions = [s for s in self.sessions.values() 
                          if s.user_id == user_id and start_date <= s.start_time <= end_date]
        
        # Calculate wellness metrics
        wellness_metrics = self._calculate_wellness_metrics(period_moods, period_sessions)
        
        # Analyze progress toward goals
        goal_progress = self._analyze_goal_progress(user, period_moods)
        
        # Generate AI insights and recommendations
        ai_analysis = self.ai_therapist.generate_wellness_analysis(user, wellness_metrics, goal_progress)
        
        # Identify achievements and areas for improvement
        achievements = self._identify_achievements(user, wellness_metrics, goal_progress)
        improvement_areas = self._identify_improvement_areas(user, wellness_metrics)
        
        wellness_report = {
            "user_id": user_id,
            "report_period": f"{start_date.date()} to {end_date.date()}",
            "wellness_metrics": wellness_metrics,
            "goal_progress": goal_progress,
            "ai_analysis": ai_analysis,
            "achievements": achievements,
            "improvement_areas": improvement_areas,
            "recommended_adjustments": ai_analysis.get("care_plan_adjustments", []),
            "next_milestones": self._calculate_next_milestones(user, goal_progress)
        }
        
        return wellness_report
        
    # Helper methods for calculations and analysis
    def _calculate_trend(self, values: List[float]) -> str:
        """Calculate trend direction for a series of values"""
        if len(values) < 2:
            return "insufficient_data"
            
        first_half = np.mean(values[:len(values)//2])
        second_half = np.mean(values[len(values)//2:])
        
        difference = second_half - first_half
        
        if abs(difference) < 0.5:
            return "stable"
        elif difference > 0:
            return "improving"
        else:
            return "declining"
            
    def _identify_patterns(self, moods: List[MoodEntry]) -> List[Dict[str, Any]]:
        """Identify recurring patterns in mood data"""
        patterns = []
        
        # Weekly patterns
        days_of_week = {}
        for mood in moods:
            day = mood.timestamp.strftime("%A")
            if day not in days_of_week:
                days_of_week[day] = []
            days_of_week[day].append(mood.mood_score)
            
        # Find consistently low days
        for day, scores in days_of_week.items():
            if len(scores) >= 3 and np.mean(scores) < 4.0:
                patterns.append({
                    "type": "weekly_pattern",
                    "description": f"Consistently lower mood on {day}s",
                    "average_score": np.mean(scores),
                    "confidence": min(len(scores) / 4, 1.0)  # Higher confidence with more data
                })
                
        return patterns
        
    def _analyze_triggers(self, moods: List[MoodEntry]) -> Dict[str, Any]:
        """Analyze stress factors and their impact on mood"""
        trigger_impact = {}
        
        for mood in moods:
            for trigger in mood.stress_factors:
                if trigger not in trigger_impact:
                    trigger_impact[trigger] = []
                trigger_impact[trigger].append(mood.mood_score)
                
        # Calculate average mood impact for each trigger
        trigger_analysis = {}
        overall_average = np.mean([m.mood_score for m in moods])
        
        for trigger, scores in trigger_impact.items():
            if len(scores) >= 2:  # Need at least 2 occurrences
                avg_with_trigger = np.mean(scores)
                impact = overall_average - avg_with_trigger
                
                trigger_analysis[trigger] = {
                    "frequency": len(scores),
                    "average_mood_with_trigger": avg_with_trigger,
                    "mood_impact": impact,
                    "severity": "high" if impact > 2.0 else "medium" if impact > 1.0 else "low"
                }
                
        return {
            "identified_triggers": trigger_analysis,
            "most_impactful_trigger": max(trigger_analysis.keys(), 
                                         key=lambda x: trigger_analysis[x]["mood_impact"]) if trigger_analysis else None,
            "trigger_management_suggestions": self._generate_trigger_management_suggestions(trigger_analysis)
        }

# Specialized AI and therapy components
class AITherapist:
    """AI-powered therapeutic intervention system"""
    
    def create_user_profile(self, user: User, assessment: Dict[str, Any]) -> Dict[str, Any]:
        """Create AI therapeutic profile for user"""
        profile = {
            "user_id": user.id,
            "therapeutic_approach": self._determine_therapeutic_approach(user, assessment),
            "communication_style": self._determine_communication_style(user),
            "intervention_preferences": self._analyze_intervention_preferences(user, assessment),
            "risk_factors": self._identify_risk_factors(assessment),
            "strengths": self._identify_user_strengths(user, assessment),
            "ai_personality": self._configure_ai_personality(user)
        }
        
        return profile
        
    def generate_mood_insights(self, user: User, current_mood: MoodEntry, 
                             pattern_analysis: Dict[str, Any]) -> Dict[str, Any]:
        """Generate AI-powered insights from mood data"""
        insights = {
            "current_state_analysis": self._analyze_current_state(current_mood),
            "pattern_insights": self._generate_pattern_insights(pattern_analysis),
            "recommendations": self._generate_personalized_recommendations(user, current_mood, pattern_analysis),
            "intervention_suggestions": self._suggest_interventions(user, current_mood),
            "positive_reinforcement": self._generate_positive_reinforcement(user, current_mood)
        }
        
        return insights
        
    def _determine_therapeutic_approach(self, user: User, assessment: Dict[str, Any]) -> str:
        """Determine best therapeutic approach for user"""
        # Simplified logic - in reality would use complex ML algorithms
        if MentalHealthCondition.DEPRESSION in user.conditions:
            return "cognitive_behavioral_therapy"
        elif MentalHealthCondition.ANXIETY in user.conditions:
            return "acceptance_commitment_therapy"
        elif MentalHealthCondition.PTSD in user.conditions:
            return "trauma_informed_therapy"
        else:
            return "humanistic_approach"
            
    def _generate_personalized_recommendations(self, user: User, mood: MoodEntry, 
                                             patterns: Dict[str, Any]) -> List[str]:
        """Generate personalized recommendations based on AI analysis"""
        recommendations = []
        
        if mood.mood_score < 4.0:
            recommendations.extend([
                "Consider taking a short walk or doing light exercise",
                "Practice deep breathing for 5 minutes",
                "Reach out to a friend or family member"
            ])
            
        if mood.anxiety_level > 7.0:
            recommendations.extend([
                "Try the 5-4-3-2-1 grounding technique",
                "Use the guided anxiety meditation in your app",
                "Consider postponing non-essential stressful activities"
            ])
            
        if mood.sleep_quality < 5.0:
            recommendations.extend([
                "Establish a consistent bedtime routine",
                "Limit screen time 1 hour before bed",
                "Try the sleep meditation feature"
            ])
            
        return recommendations

class RiskAnalyzer:
    """Analyze mental health risk factors and crisis indicators"""
    
    def assess_current_risk(self, user: User, mood: MoodEntry) -> Dict[str, Any]:
        """Assess current mental health risk level"""
        risk_factors = []
        risk_score = 0
        
        # Mood-based risk factors
        if mood.mood_score <= 2.0:
            risk_factors.append("Severely low mood")
            risk_score += 3
        elif mood.mood_score <= 3.5:
            risk_factors.append("Low mood")
            risk_score += 2
            
        # Anxiety-based risk factors
        if mood.anxiety_level >= 9.0:
            risk_factors.append("Severe anxiety")
            risk_score += 3
        elif mood.anxiety_level >= 7.0:
            risk_factors.append("High anxiety")
            risk_score += 2
            
        # Sleep and energy factors
        if mood.sleep_quality <= 3.0:
            risk_factors.append("Poor sleep quality")
            risk_score += 1
            
        if mood.energy_level <= 2.0:
            risk_factors.append("Very low energy")
            risk_score += 2
            
        # User-specific risk factors
        if user.risk_level == RiskLevel.HIGH:
            risk_score += 2
            
        # Determine risk level
        if risk_score >= 7:
            risk_level = "critical"
        elif risk_score >= 5:
            risk_level = "high"
        elif risk_score >= 3:
            risk_level = "moderate"
        else:
            risk_level = "low"
            
        return {
            "risk_level": risk_level,
            "risk_score": risk_score,
            "risk_factors": risk_factors,
            "assessment_timestamp": datetime.now(),
            "immediate_action_required": risk_level in ["critical", "high"]
        }

class VRTherapyManager:
    """Manage virtual reality therapy sessions"""
    
    def create_vr_session(self, user: User) -> Dict[str, Any]:
        """Create VR therapy session content"""
        vr_scenarios = {
            MentalHealthCondition.ANXIETY: [
                "Beach relaxation environment with guided breathing",
                "Mountain meditation space with calming nature sounds",
                "Peaceful forest setting for anxiety management exercises"
            ],
            MentalHealthCondition.DEPRESSION: [
                "Sunrise therapy session with mood-lifting visuals",
                "Interactive garden environment for behavioral activation",
                "Achievements visualization space for positive reinforcement"
            ],
            MentalHealthCondition.PTSD: [
                "Safe space visualization for grounding techniques",
                "Graduated exposure therapy in controlled environment",
                "Trauma processing space with safety controls"
            ]
        }
        
        # Select appropriate scenarios
        selected_scenarios = []
        for condition in user.conditions:
            if condition in vr_scenarios:
                selected_scenarios.extend(vr_scenarios[condition][:2])  # Max 2 per condition
                
        return {
            "vr_platform": "therapeutic_vr_system",
            "available_scenarios": selected_scenarios,
            "session_duration": 15,
            "biometric_monitoring": True,
            "ai_guidance": True,
            "progress_tracking": True
        }

class CrisisInterventionManager:
    """Handle mental health crisis situations"""
    
    def assess_crisis_severity(self, user: User, crisis_data: Dict[str, Any]) -> Dict[str, Any]:
        """Assess severity of mental health crisis"""
        severity_indicators = [
            crisis_data.get("suicidal_thoughts", False),
            crisis_data.get("self_harm_risk", False),
            crisis_data.get("substance_abuse", False),
            crisis_data.get("psychosis_symptoms", False),
            crisis_data.get("inability_to_function", False)
        ]
        
        severity_score = sum(severity_indicators)
        
        if severity_score >= 3 or crisis_data.get("suicidal_thoughts", False):
            severity = "critical"
        elif severity_score >= 2:
            severity = "high"
        elif severity_score >= 1:
            severity = "moderate"
        else:
            severity = "low"
            
        return {
            "severity": severity,
            "severity_score": severity_score,
            "indicators": [k for k, v in crisis_data.items() if v is True],
            "assessment_time": datetime.now(),
            "requires_emergency_services": severity == "critical"
        }
        
    def determine_intervention_strategy(self, crisis_assessment: Dict[str, Any]) -> Dict[str, Any]:
        """Determine appropriate crisis intervention strategy"""
        severity = crisis_assessment["severity"]
        
        if severity == "critical":
            strategy = {
                "type": "emergency_intervention",
                "immediate_actions": [
                    "Contact emergency services",
                    "Connect with crisis counselor",
                    "Alert emergency contacts",
                    "Provide crisis resources"
                ],
                "safety_measures": [
                    "Remove access to means of self-harm",
                    "Ensure continuous supervision",
                    "Activate safety plan"
                ]
            }
        elif severity == "high":
            strategy = {
                "type": "intensive_support",
                "immediate_actions": [
                    "Connect with crisis counselor within 1 hour",
                    "Schedule emergency therapy session",
                    "Notify support network",
                    "Implement enhanced monitoring"
                ]
            }
        else:
            strategy = {
                "type": "supportive_intervention",
                "immediate_actions": [
                    "Provide coping resources",
                    "Schedule follow-up check-in",
                    "Offer peer support connection"
                ]
            }
            
        return strategy
        
    def execute_immediate_response(self, user: User, strategy: Dict[str, Any]) -> Dict[str, Any]:
        """Execute immediate crisis response"""
        response_actions = []
        
        for action in strategy["immediate_actions"]:
            # Simulate executing each action
            action_result = {
                "action": action,
                "status": "completed",
                "timestamp": datetime.now(),
                "details": f"Executed {action} for user {user.name}"
            }
            response_actions.append(action_result)
            
        return {
            "response_id": f"crisis_response_{uuid.uuid4()}",
            "strategy_type": strategy["type"],
            "actions_completed": response_actions,
            "response_time": "immediate",
            "follow_up_scheduled": True
        }

# Example usage demonstration
def create_sample_mental_health_platform():
    """Create sample mental health platform with users"""
    platform = MentalHealthPlatform("MindCare AI")
    
    # Create sample users
    user1 = User(
        id="user_001",
        name="Sarah Williams",
        age=28,
        conditions=[MentalHealthCondition.ANXIETY, MentalHealthCondition.DEPRESSION],
        preferences={"therapy_type": "cbt", "session_length": "short"},
        treatment_goals=["Reduce anxiety symptoms", "Improve sleep quality", "Build coping skills"]
    )
    
    user2 = User(
        id="user_002",
        name="Michael Chen",
        age=34,
        conditions=[MentalHealthCondition.PTSD],
        preferences={"therapy_type": "trauma_informed", "session_length": "standard"},
        treatment_goals=["Process trauma safely", "Reduce flashbacks", "Improve relationships"]
    )
    
    return platform, [user1, user2]

def run_mental_health_tech_demo():
    print("=== Mental Health Technology Platform Demo ===")
    
    # Create platform and users
    platform, users = create_sample_mental_health_platform()
    print(f"Created mental health platform with {len(users)} sample users")
    
    # Register users
    for user in users:
        registration_result = platform.register_user(user)
        print(f"User {user.name} registered with care plan: {registration_result['care_plan']['plan_id']}")
        
    # Simulate mood tracking
    print("\n--- Mood Tracking Demo ---")
    mood_data = {
        "mood_score": 3.5,
        "anxiety_level": 7.5,
        "energy_level": 4.0,
        "sleep_quality": 3.0,
        "stress_factors": ["work_pressure", "relationship_issues"],
        "coping_strategies": ["deep_breathing", "journaling"]
    }
    
    mood_result = platform.track_mood(users[0].id, mood_data)
    print(f"Mood tracked for {users[0].name}")
    print(f"Risk assessment: {mood_result['risk_assessment']['risk_level']}")
    print(f"AI recommendations: {len(mood_result['recommended_actions'])}")
    
    # Start AI therapy session
    print("\n--- AI Therapy Session Demo ---")
    session_result = platform.start_ai_therapy_session(users[0].id, InterventionType.COGNITIVE_BEHAVIORAL)
    print(f"Started CBT session: {session_result['session_id']}")
    print(f"Estimated duration: {session_result['estimated_duration']} minutes")
    
    # Simulate crisis intervention
    print("\n--- Crisis Intervention Demo ---")
    crisis_data = {
        "suicidal_thoughts": False,
        "self_harm_risk": True,
        "inability_to_function": True,
        "reported_by": "self"
    }
    
    crisis_response = platform.conduct_crisis_intervention(users.id, crisis_data)
    print(f"Crisis intervention initiated: {crisis_response['crisis_id']}")
    print(f"Intervention strategy: {crisis_response['intervention_strategy']['type']}")
    print(f"Safety plan activated: {crisis_response['safety_plan_activated']}")
    
    # Generate wellness report
    print("\n--- Wellness Report Generation ---")
    wellness_report = platform.generate_wellness_report(users[0].id)
    print(f"Wellness report generated for {users[0].name}")
    print(f"Report period: {wellness_report['report_period']}")
    print(f"Achievements: {len(wellness_report['achievements'])}")
    print(f"Improvement areas: {len(wellness_report['improvement_areas'])}")
    
    return platform, users

# Run demonstration
if __name__ == "__main__":
    demo_platform, demo_users = run_mental_health_tech_demo()