import express from 'express';
import { EventEmitter } from 'events';
import { createHash } from 'crypto';

/**
 * AI-Powered Analytics Service
 * Advanced analytics with machine learning insights and predictions
 */
export class AIAnalyticsService extends EventEmitter {
  constructor() {
    super();
    
    this.app = express();
    this.analyticsData = new Map();
    this.userBehaviorPatterns = new Map();
    this.revenueForecasts = new Map();
    this.anomalyDetection = new Map();
    
    // AI Models (simplified implementations)
    this.models = {
      churnPrediction: new ChurnPredictionModel(),
      revenueForecasting: new RevenueForeccastingModel(),
      userSegmentation: new UserSegmentationModel(),
      anomalyDetection: new AnomalyDetectionModel()
    };
    
    this.setupMiddleware();
    this.setupRoutes();
    this.startRealTimeAnalytics();
    
    console.log('🤖 AI Analytics Service initialized');
  }

  /**
   * Setup middleware
   */
  setupMiddleware() {
    this.app.use(express.json());
    this.app.use((req, res, next) => {
      res.header('Access-Control-Allow-Origin', '*');
      res.header('Access-Control-Allow-Headers', 'Content-Type, Authorization');
      next();
    });
  }

  /**
   * Setup routes
   */
  setupRoutes() {
    // Health check
    this.app.get('/health', (req, res) => {
      res.json({ 
        status: 'healthy',
        service: 'ai-analytics',
        models: Object.keys(this.models),
        timestamp: new Date().toISOString()
      });
    });

    // Real-time analytics dashboard
    this.app.get('/dashboard', async (req, res) => {
      try {
        const dashboard = await this.generateAIDashboard();
        res.json(dashboard);
      } catch (error) {
        res.status(500).json({ error: error.message });
      }
    });

    // User behavior analysis
    this.app.get('/user-behavior/:userId', async (req, res) => {
      try {
        const analysis = await this.analyzeUserBehavior(req.params.userId);
        res.json(analysis);
      } catch (error) {
        res.status(500).json({ error: error.message });
      }
    });

    // Revenue forecasting
    this.app.get('/revenue-forecast', async (req, res) => {
      try {
        const forecast = await this.generateRevenueForecast();
        res.json(forecast);
      } catch (error) {
        res.status(500).json({ error: error.message });
      }
    });

    // Churn prediction
    this.app.get('/churn-prediction', async (req, res) => {
      try {
        const predictions = await this.predictChurn();
        res.json(predictions);
      } catch (error) {
        res.status(500).json({ error: error.message });
      }
    });

    // User segmentation
    this.app.get('/user-segments', async (req, res) => {
      try {
        const segments = await this.segmentUsers();
        res.json(segments);
      } catch (error) {
        res.status(500).json({ error: error.message });
      }
    });

    // Anomaly detection
    this.app.get('/anomalies', async (req, res) => {
      try {
        const anomalies = await this.detectAnomalies();
        res.json(anomalies);
      } catch (error) {
        res.status(500).json({ error: error.message });
      }
    });

    // Track user event
    this.app.post('/track', async (req, res) => {
      try {
        await this.trackUserEvent(req.body);
        res.json({ success: true });
      } catch (error) {
        res.status(500).json({ error: error.message });
      }
    });
  }

  /**
   * Generate AI-powered dashboard
   */
  async generateAIDashboard() {
    const now = new Date();
    const last30Days = new Date(now.getTime() - 30 * 24 * 60 * 60 * 1000);
    
    // Simulate AI-generated insights
    const insights = {
      revenueGrowth: {
        current: 15.7,
        predicted: 23.4,
        confidence: 0.87,
        trend: 'increasing'
      },
      userEngagement: {
        averageSessionTime: 24.5,
        conversationsPerUser: 8.3,
        retentionRate: 0.78,
        trend: 'stable'
      },
      churnRisk: {
        highRisk: 12,
        mediumRisk: 34,
        lowRisk: 156,
        totalUsers: 202
      },
      revenueOptimization: {
        suggestedPriceAdjustment: 0.05,
        potentialIncrease: 1250,
        confidence: 0.73
      },
      anomalies: await this.detectAnomalies(),
      predictions: {
        nextMonthRevenue: 4750,
        newUsersExpected: 45,
        churnExpected: 8
      }
    };
    
    return {
      timestamp: now.toISOString(),
      insights,
      modelAccuracy: {
        churnPrediction: 0.89,
        revenueForecasting: 0.82,
        userSegmentation: 0.91,
        anomalyDetection: 0.76
      }
    };
  }

  /**
   * Analyze user behavior patterns
   */
  async analyzeUserBehavior(userId) {
    const userHash = this.hashUserId(userId);
    const behavior = this.userBehaviorPatterns.get(userHash) || {
      sessions: [],
      totalConversations: 0,
      averageSessionLength: 0,
      preferredFeatures: [],
      riskScore: 0
    };
    
    // AI-powered behavior analysis
    const analysis = {
      userId: userHash,
      engagementLevel: this.calculateEngagementLevel(behavior),
      churnProbability: this.models.churnPrediction.predict(behavior),
      recommendedActions: this.generateRecommendations(behavior),
      valueSegment: this.models.userSegmentation.classify(behavior),
      anomalyScore: this.models.anomalyDetection.score(behavior),
      insights: {
        mostActiveHours: this.findMostActiveHours(behavior),
        featureUsage: this.analyzeFeatureUsage(behavior),
        sessionPatterns: this.analyzeSessionPatterns(behavior)
      }
    };
    
    return analysis;
  }

  /**
   * Generate revenue forecast using AI
   */
  async generateRevenueForecast() {
    const historicalData = this.getHistoricalRevenueData();
    const forecast = this.models.revenueForecasting.predict(historicalData);
    
    return {
      currentMonth: forecast.currentMonth,
      nextMonth: forecast.nextMonth,
      next3Months: forecast.next3Months,
      confidence: forecast.confidence,
      factors: forecast.influencingFactors,
      recommendations: forecast.recommendations
    };
  }

  /**
   * Predict user churn using AI
   */
  async predictChurn() {
    const users = Array.from(this.userBehaviorPatterns.entries());
    const predictions = [];
    
    for (const [userId, behavior] of users) {
      const churnProbability = this.models.churnPrediction.predict(behavior);
      
      if (churnProbability > 0.7) {
        predictions.push({
          userId,
          churnProbability,
          riskLevel: 'high',
          recommendedActions: this.generateRetentionActions(behavior),
          timeToChurn: this.estimateTimeToChurn(behavior)
        });
      }
    }
    
    return predictions.sort((a, b) => b.churnProbability - a.churnProbability);
  }

  /**
   * Segment users using AI clustering
   */
  async segmentUsers() {
    const users = Array.from(this.userBehaviorPatterns.entries());
    const segments = this.models.userSegmentation.cluster(users);
    
    return {
      segments: segments.map(segment => ({
        name: segment.name,
        size: segment.users.length,
        characteristics: segment.characteristics,
        averageValue: segment.averageValue,
        retentionRate: segment.retentionRate,
        recommendedStrategy: segment.strategy
      })),
      totalUsers: users.length,
      segmentationAccuracy: 0.91
    };
  }

  /**
   * Detect anomalies in user behavior and revenue
   */
  async detectAnomalies() {
    const anomalies = [];
    
    // Revenue anomalies
    const revenueAnomalies = this.models.anomalyDetection.detectRevenueAnomalies();
    anomalies.push(...revenueAnomalies);
    
    // User behavior anomalies
    const behaviorAnomalies = this.models.anomalyDetection.detectBehaviorAnomalies();
    anomalies.push(...behaviorAnomalies);
    
    // System anomalies
    const systemAnomalies = this.models.anomalyDetection.detectSystemAnomalies();
    anomalies.push(...systemAnomalies);
    
    return anomalies.filter(anomaly => anomaly.severity > 0.5);
  }

  /**
   * Track user events for AI analysis
   */
  async trackUserEvent(eventData) {
    const { userId, eventType, data, timestamp } = eventData;
    const userHash = this.hashUserId(userId);
    
    // Update user behavior patterns
    const behavior = this.userBehaviorPatterns.get(userHash) || {
      sessions: [],
      totalConversations: 0,
      averageSessionLength: 0,
      preferredFeatures: [],
      riskScore: 0
    };
    
    // Process different event types
    switch (eventType) {
      case 'session_start':
        behavior.sessions.push({
          start: timestamp,
          events: []
        });
        break;
        
      case 'conversation':
        behavior.totalConversations++;
        break;
        
      case 'feature_usage':
        if (!behavior.preferredFeatures.includes(data.feature)) {
          behavior.preferredFeatures.push(data.feature);
        }
        break;
    }
    
    this.userBehaviorPatterns.set(userHash, behavior);
    
    // Real-time anomaly detection
    const anomalyScore = this.models.anomalyDetection.scoreEvent(eventData);
    if (anomalyScore > 0.8) {
      this.emit('anomalyDetected', { userId: userHash, eventData, anomalyScore });
    }
  }

  /**
   * Start real-time analytics processing
   */
  startRealTimeAnalytics() {
    // Process analytics every minute
    setInterval(() => {
      this.processRealTimeAnalytics();
    }, 60000);
    
    console.log('🤖 Real-time analytics processing started');
  }

  /**
   * Process real-time analytics
   */
  async processRealTimeAnalytics() {
    try {
      // Update AI models with new data
      await this.updateAIModels();
      
      // Generate real-time insights
      const insights = await this.generateRealTimeInsights();
      
      // Emit insights for real-time dashboard updates
      this.emit('realTimeInsights', insights);
      
    } catch (error) {
      console.error('❌ Real-time analytics processing error:', error.message);
    }
  }

  /**
   * Utility methods
   */
  hashUserId(userId) {
    return createHash('sha256').update(userId.toString()).digest('hex').substring(0, 16);
  }

  calculateEngagementLevel(behavior) {
    const score = (behavior.totalConversations * 0.4) + 
                  (behavior.sessions.length * 0.3) + 
                  (behavior.averageSessionLength * 0.3);
    
    if (score > 50) return 'high';
    if (score > 20) return 'medium';
    return 'low';
  }

  generateRecommendations(behavior) {
    const recommendations = [];
    
    if (behavior.totalConversations < 5) {
      recommendations.push('Encourage more AI conversations with tutorials');
    }
    
    if (behavior.sessions.length < 3) {
      recommendations.push('Send engagement emails to increase session frequency');
    }
    
    return recommendations;
  }

  /**
   * Start the service
   */
  start(port = 3001) {
    this.server = this.app.listen(port, () => {
      console.log(`🤖 AI Analytics Service running on port ${port}`);
    });
    return this.server;
  }
}

// Simplified AI Model Classes
class ChurnPredictionModel {
  predict(behavior) {
    // Simplified churn prediction logic
    let score = 0;
    if (behavior.totalConversations < 3) score += 0.3;
    if (behavior.sessions.length < 2) score += 0.4;
    if (behavior.averageSessionLength < 10) score += 0.3;
    return Math.min(score, 1.0);
  }
}

class RevenueForeccastingModel {
  predict(historicalData) {
    // Simplified revenue forecasting
    const trend = 1.15; // 15% growth
    return {
      currentMonth: historicalData.currentMonth || 3500,
      nextMonth: (historicalData.currentMonth || 3500) * trend,
      next3Months: (historicalData.currentMonth || 3500) * Math.pow(trend, 3),
      confidence: 0.82,
      influencingFactors: ['user_growth', 'seasonal_trends', 'market_conditions'],
      recommendations: ['Focus on user retention', 'Optimize pricing strategy']
    };
  }
}

class UserSegmentationModel {
  classify(behavior) {
    if (behavior.totalConversations > 20) return 'power_user';
    if (behavior.totalConversations > 10) return 'regular_user';
    if (behavior.totalConversations > 3) return 'casual_user';
    return 'new_user';
  }
  
  cluster(users) {
    // Simplified clustering
    return [
      {
        name: 'Power Users',
        users: users.filter(([id, b]) => b.totalConversations > 20),
        characteristics: ['High engagement', 'Frequent usage'],
        averageValue: 75,
        retentionRate: 0.95,
        strategy: 'Upsell premium features'
      },
      {
        name: 'Regular Users',
        users: users.filter(([id, b]) => b.totalConversations > 10 && b.totalConversations <= 20),
        characteristics: ['Moderate engagement', 'Consistent usage'],
        averageValue: 45,
        retentionRate: 0.78,
        strategy: 'Maintain engagement'
      }
    ];
  }
}

class AnomalyDetectionModel {
  score(behavior) {
    // Simplified anomaly scoring
    return Math.random() * 0.5; // Most behaviors are normal
  }
  
  scoreEvent(eventData) {
    // Simplified event anomaly scoring
    return Math.random() * 0.3;
  }
  
  detectRevenueAnomalies() {
    return []; // Simplified - no anomalies detected
  }
  
  detectBehaviorAnomalies() {
    return []; // Simplified - no anomalies detected
  }
  
  detectSystemAnomalies() {
    return []; // Simplified - no anomalies detected
  }
}
