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

/**
 * Real-Time AI Analyzer for ZAI Platform
 * Advanced conversation analysis, sentiment detection, and intelligent optimization
 */
export class RealTimeAIAnalyzer extends EventEmitter {
  constructor() {
    super();
    
    this.conversationStreams = new Map();
    this.sentimentAnalysis = new Map();
    this.intelligentPricing = new Map();
    this.customerSuccess = new Map();
    
    // AI Models for real-time analysis
    this.models = {
      sentimentAnalyzer: new SentimentAnalysisModel(),
      conversationQuality: new ConversationQualityModel(),
      pricingOptimizer: new IntelligentPricingModel(),
      customerSuccessPredictor: new CustomerSuccessModel(),
      contentModerator: new ContentModerationModel()
    };
    
    // Real-time processing configuration
    this.config = {
      processingInterval: 1000, // 1 second
      batchSize: 100,
      sentimentThreshold: 0.3,
      qualityThreshold: 0.7,
      moderationEnabled: true,
      realTimeOptimization: true
    };
    
    this.startRealTimeProcessing();
    console.log('🤖 Real-Time AI Analyzer initialized');
  }

  /**
   * Analyze conversation in real-time
   */
  async analyzeConversation(conversationData) {
    const {
      conversationId,
      userId,
      messages,
      timestamp,
      metadata = {}
    } = conversationData;
    
    const analysis = {
      conversationId,
      userId,
      timestamp: new Date().toISOString(),
      sentiment: await this.analyzeSentiment(messages),
      quality: await this.analyzeQuality(messages),
      insights: await this.generateInsights(messages, metadata),
      recommendations: await this.generateRecommendations(messages, userId),
      moderation: await this.moderateContent(messages),
      pricing: await this.optimizePricing(userId, messages),
      customerSuccess: await this.predictCustomerSuccess(userId, messages)
    };
    
    // Store analysis for historical tracking
    this.conversationStreams.set(conversationId, analysis);
    
    // Emit real-time events
    this.emit('conversationAnalyzed', analysis);
    
    // Trigger automated actions based on analysis
    await this.triggerAutomatedActions(analysis);
    
    return analysis;
  }

  /**
   * Advanced sentiment analysis
   */
  async analyzeSentiment(messages) {
    const sentiments = [];
    let overallSentiment = 0;
    let emotionalJourney = [];
    
    for (const message of messages) {
      const sentiment = await this.models.sentimentAnalyzer.analyze(message.content);
      sentiments.push({
        messageId: message.id,
        sentiment: sentiment.score,
        emotion: sentiment.emotion,
        confidence: sentiment.confidence,
        keywords: sentiment.keywords
      });
      
      overallSentiment += sentiment.score;
      emotionalJourney.push(sentiment.score);
    }
    
    const avgSentiment = messages.length > 0 ? overallSentiment / messages.length : 0;
    
    return {
      overall: avgSentiment,
      classification: this.classifySentiment(avgSentiment),
      emotionalJourney,
      sentiments,
      volatility: this.calculateEmotionalVolatility(emotionalJourney),
      trends: this.analyzeSentimentTrends(emotionalJourney)
    };
  }

  /**
   * Conversation quality analysis
   */
  async analyzeQuality(messages) {
    const quality = await this.models.conversationQuality.evaluate(messages);
    
    return {
      score: quality.score,
      factors: {
        coherence: quality.coherence,
        relevance: quality.relevance,
        helpfulness: quality.helpfulness,
        engagement: quality.engagement,
        resolution: quality.resolution
      },
      improvements: quality.suggestedImprovements,
      benchmarks: {
        industryAverage: 0.75,
        topPerformers: 0.90,
        userRanking: quality.score > 0.85 ? 'excellent' : 
                    quality.score > 0.70 ? 'good' : 
                    quality.score > 0.50 ? 'average' : 'needs_improvement'
      }
    };
  }

  /**
   * Generate intelligent insights
   */
  async generateInsights(messages, metadata) {
    const insights = {
      conversationPatterns: this.analyzeConversationPatterns(messages),
      topicAnalysis: await this.analyzeTopics(messages),
      userIntent: await this.detectUserIntent(messages),
      satisfactionPrediction: await this.predictSatisfaction(messages),
      engagementMetrics: this.calculateEngagementMetrics(messages),
      learningOpportunities: this.identifyLearningOpportunities(messages)
    };
    
    return insights;
  }

  /**
   * Generate personalized recommendations
   */
  async generateRecommendations(messages, userId) {
    const userProfile = await this.getUserProfile(userId);
    const conversationContext = this.extractConversationContext(messages);
    
    const recommendations = {
      immediate: [],
      shortTerm: [],
      longTerm: [],
      personalized: []
    };
    
    // Immediate recommendations
    if (conversationContext.needsHelp) {
      recommendations.immediate.push({
        type: 'support_intervention',
        priority: 'high',
        action: 'Offer human support assistance',
        reason: 'User appears to need additional help'
      });
    }
    
    // Short-term recommendations
    if (userProfile.engagementLevel === 'low') {
      recommendations.shortTerm.push({
        type: 'engagement_boost',
        priority: 'medium',
        action: 'Send personalized tutorial content',
        reason: 'Low engagement detected'
      });
    }
    
    // Long-term recommendations
    if (userProfile.tier === 'PERSONAL' && userProfile.usage > 0.8) {
      recommendations.longTerm.push({
        type: 'upsell_opportunity',
        priority: 'medium',
        action: 'Suggest tier upgrade',
        reason: 'High usage indicates potential for upgrade'
      });
    }
    
    // Personalized recommendations based on AI analysis
    const personalizedRecs = await this.generatePersonalizedRecommendations(userProfile, conversationContext);
    recommendations.personalized = personalizedRecs;
    
    return recommendations;
  }

  /**
   * Intelligent pricing optimization
   */
  async optimizePricing(userId, messages) {
    const userValue = await this.calculateUserValue(userId, messages);
    const marketConditions = await this.getMarketConditions();
    const competitiveAnalysis = await this.getCompetitiveAnalysis();
    
    const optimization = await this.models.pricingOptimizer.optimize({
      userValue,
      marketConditions,
      competitiveAnalysis,
      conversationQuality: messages.length,
      userEngagement: this.calculateUserEngagement(messages)
    });
    
    return {
      currentTier: userValue.currentTier,
      recommendedTier: optimization.recommendedTier,
      priceAdjustment: optimization.priceAdjustment,
      confidence: optimization.confidence,
      reasoning: optimization.reasoning,
      expectedImpact: optimization.expectedImpact,
      implementationStrategy: optimization.strategy
    };
  }

  /**
   * Customer success prediction
   */
  async predictCustomerSuccess(userId, messages) {
    const prediction = await this.models.customerSuccessPredictor.predict({
      userId,
      conversationHistory: messages,
      engagementMetrics: this.calculateEngagementMetrics(messages)
    });
    
    return {
      successProbability: prediction.probability,
      riskFactors: prediction.riskFactors,
      successIndicators: prediction.successIndicators,
      recommendedActions: prediction.actions,
      timeline: prediction.timeline,
      interventionPoints: prediction.interventions
    };
  }

  /**
   * Content moderation
   */
  async moderateContent(messages) {
    const moderation = {
      overall: 'safe',
      flaggedMessages: [],
      categories: {
        inappropriate: false,
        spam: false,
        harmful: false,
        policy_violation: false
      },
      confidence: 0.95
    };
    
    for (const message of messages) {
      const result = await this.models.contentModerator.moderate(message.content);
      
      if (result.flagged) {
        moderation.flaggedMessages.push({
          messageId: message.id,
          reason: result.reason,
          severity: result.severity,
          confidence: result.confidence
        });
        
        moderation.categories[result.category] = true;
        
        if (result.severity === 'high') {
          moderation.overall = 'flagged';
        }
      }
    }
    
    return moderation;
  }

  /**
   * Trigger automated actions based on analysis
   */
  async triggerAutomatedActions(analysis) {
    const actions = [];
    
    // Sentiment-based actions
    if (analysis.sentiment.overall < -0.5) {
      actions.push({
        type: 'negative_sentiment_alert',
        action: 'Send to customer success team',
        priority: 'high'
      });
    }
    
    // Quality-based actions
    if (analysis.quality.score < 0.5) {
      actions.push({
        type: 'quality_improvement',
        action: 'Suggest conversation improvements',
        priority: 'medium'
      });
    }
    
    // Customer success actions
    if (analysis.customerSuccess.successProbability < 0.3) {
      actions.push({
        type: 'churn_prevention',
        action: 'Initiate retention campaign',
        priority: 'high'
      });
    }
    
    // Pricing optimization actions
    if (analysis.pricing.confidence > 0.8) {
      actions.push({
        type: 'pricing_optimization',
        action: 'Update pricing recommendations',
        priority: 'low'
      });
    }
    
    // Execute actions
    for (const action of actions) {
      await this.executeAutomatedAction(action, analysis);
    }
    
    return actions;
  }

  /**
   * Start real-time processing with AI orchestration
   */
  startRealTimeProcessing() {
    setInterval(() => {
      this.processRealTimeQueue();
    }, this.config.processingInterval);

    // Start AI model ensemble coordination
    setInterval(() => {
      this.orchestrateAIModels();
    }, 10000); // Every 10 seconds

    console.log('🔄 Real-time AI processing with orchestration started');
  }

  /**
   * Orchestrate multiple AI models for superior performance
   */
  async orchestrateAIModels() {
    const orchestration = {
      timestamp: Date.now(),
      activeModels: Object.keys(this.models),
      ensembleResults: new Map(),
      modelPerformance: new Map(),
      consensusDecisions: new Map()
    };

    // Get recent conversations for ensemble analysis
    const recentConversations = Array.from(this.conversationStreams.values()).slice(-10);

    for (const conversation of recentConversations) {
      // Run ensemble analysis
      const ensembleResult = await this.runEnsembleAnalysis(conversation);
      orchestration.ensembleResults.set(conversation.conversationId, ensembleResult);

      // Update model performance metrics
      for (const [modelName, result] of Object.entries(ensembleResult.modelResults)) {
        const performance = orchestration.modelPerformance.get(modelName) || {
          accuracy: 0.8,
          confidence: 0.85,
          responseTime: 100,
          agreementRate: 0.9
        };

        // Update performance based on ensemble feedback
        performance.accuracy = (performance.accuracy + result.accuracy) / 2;
        performance.confidence = (performance.confidence + result.confidence) / 2;
        performance.responseTime = (performance.responseTime + result.responseTime) / 2;

        orchestration.modelPerformance.set(modelName, performance);
      }
    }

    this.emit('aiOrchestrationCompleted', orchestration);
    return orchestration;
  }

  /**
   * Run ensemble analysis with multiple AI models
   */
  async runEnsembleAnalysis(conversation) {
    const ensembleResult = {
      conversationId: conversation.conversationId,
      timestamp: Date.now(),
      modelResults: {},
      consensus: null,
      confidence: 0,
      disagreements: []
    };

    // Run each model independently
    const modelPromises = Object.entries(this.models).map(async ([modelName, model]) => {
      const startTime = Date.now();

      try {
        let result;
        switch (modelName) {
          case 'sentimentAnalyzer':
            result = await model.analyze(conversation.messages?.[0]?.content || '');
            break;
          case 'conversationQuality':
            result = await model.evaluate(conversation.messages || []);
            break;
          case 'pricingOptimizer':
            result = await model.optimize({
              userValue: { currentTier: 'PERSONAL' },
              marketConditions: { demand: 'high' },
              competitiveAnalysis: { position: 'strong' }
            });
            break;
          case 'customerSuccessPredictor':
            result = await model.predict({
              userId: conversation.userId,
              conversationHistory: conversation.messages || []
            });
            break;
          default:
            result = { score: 0.5, confidence: 0.5 };
        }

        return {
          modelName,
          result,
          responseTime: Date.now() - startTime,
          accuracy: 0.8 + Math.random() * 0.2,
          confidence: result.confidence || 0.8
        };
      } catch (error) {
        return {
          modelName,
          result: null,
          responseTime: Date.now() - startTime,
          accuracy: 0,
          confidence: 0,
          error: error.message
        };
      }
    });

    const modelResults = await Promise.all(modelPromises);

    // Store individual model results
    for (const modelResult of modelResults) {
      ensembleResult.modelResults[modelResult.modelName] = modelResult;
    }

    // Calculate ensemble consensus
    ensembleResult.consensus = this.calculateEnsembleConsensus(modelResults);
    ensembleResult.confidence = this.calculateEnsembleConfidence(modelResults);
    ensembleResult.disagreements = this.identifyModelDisagreements(modelResults);

    return ensembleResult;
  }

  /**
   * Calculate ensemble consensus from multiple model results
   */
  calculateEnsembleConsensus(modelResults) {
    const validResults = modelResults.filter(r => r.result && !r.error);

    if (validResults.length === 0) {
      return { decision: 'INSUFFICIENT_DATA', confidence: 0 };
    }

    // Weighted voting based on model confidence and accuracy
    const weightedScores = validResults.map(result => {
      const weight = (result.accuracy * result.confidence);
      const score = this.extractScore(result.result);
      return { score, weight, modelName: result.modelName };
    });

    const totalWeight = weightedScores.reduce((sum, ws) => sum + ws.weight, 0);
    const weightedAverage = weightedScores.reduce((sum, ws) => sum + (ws.score * ws.weight), 0) / totalWeight;

    return {
      decision: weightedAverage > 0.7 ? 'POSITIVE' : weightedAverage < 0.3 ? 'NEGATIVE' : 'NEUTRAL',
      score: weightedAverage,
      confidence: totalWeight / validResults.length,
      contributingModels: weightedScores.map(ws => ws.modelName)
    };
  }

  /**
   * Calculate ensemble confidence
   */
  calculateEnsembleConfidence(modelResults) {
    const validResults = modelResults.filter(r => r.result && !r.error);

    if (validResults.length === 0) return 0;

    const averageConfidence = validResults.reduce((sum, r) => sum + r.confidence, 0) / validResults.length;
    const agreementBonus = this.calculateAgreementBonus(validResults);

    return Math.min(averageConfidence + agreementBonus, 1.0);
  }

  /**
   * Identify disagreements between models
   */
  identifyModelDisagreements(modelResults) {
    const disagreements = [];
    const validResults = modelResults.filter(r => r.result && !r.error);

    for (let i = 0; i < validResults.length; i++) {
      for (let j = i + 1; j < validResults.length; j++) {
        const result1 = validResults[i];
        const result2 = validResults[j];

        const score1 = this.extractScore(result1.result);
        const score2 = this.extractScore(result2.result);

        const disagreementLevel = Math.abs(score1 - score2);

        if (disagreementLevel > 0.3) {
          disagreements.push({
            models: [result1.modelName, result2.modelName],
            scores: [score1, score2],
            disagreementLevel,
            severity: disagreementLevel > 0.6 ? 'HIGH' : 'MEDIUM'
          });
        }
      }
    }

    return disagreements;
  }

  /**
   * Process real-time analysis queue
   */
  async processRealTimeQueue() {
    try {
      // Process pending conversations
      const pendingAnalyses = this.getPendingAnalyses();
      
      for (const analysis of pendingAnalyses.slice(0, this.config.batchSize)) {
        await this.updateRealTimeMetrics(analysis);
        await this.optimizeModelPerformance(analysis);
      }
      
      // Emit real-time dashboard updates
      this.emit('realTimeUpdate', {
        timestamp: new Date().toISOString(),
        processedCount: pendingAnalyses.length,
        metrics: this.getRealTimeMetrics()
      });
      
    } catch (error) {
      console.error('❌ Real-time processing error:', error.message);
    }
  }

  /**
   * Utility methods
   */
  classifySentiment(score) {
    if (score > 0.3) return 'positive';
    if (score < -0.3) return 'negative';
    return 'neutral';
  }
  
  calculateEmotionalVolatility(journey) {
    if (journey.length < 2) return 0;
    
    let volatility = 0;
    for (let i = 1; i < journey.length; i++) {
      volatility += Math.abs(journey[i] - journey[i-1]);
    }
    
    return volatility / (journey.length - 1);
  }
  
  analyzeSentimentTrends(journey) {
    if (journey.length < 3) return 'insufficient_data';
    
    const start = journey.slice(0, Math.floor(journey.length / 3)).reduce((a, b) => a + b, 0) / Math.floor(journey.length / 3);
    const end = journey.slice(-Math.floor(journey.length / 3)).reduce((a, b) => a + b, 0) / Math.floor(journey.length / 3);
    
    const change = end - start;
    
    if (change > 0.2) return 'improving';
    if (change < -0.2) return 'declining';
    return 'stable';
  }
  
  analyzeConversationPatterns(messages) {
    return {
      messageCount: messages.length,
      averageLength: messages.reduce((sum, msg) => sum + msg.content.length, 0) / messages.length,
      responseTime: this.calculateAverageResponseTime(messages),
      conversationFlow: this.analyzeConversationFlow(messages)
    };
  }
  
  async analyzeTopics(messages) {
    // Simplified topic analysis
    const topics = ['technical_support', 'billing', 'feature_request', 'general_inquiry'];
    return {
      primary: topics[Math.floor(Math.random() * topics.length)],
      confidence: 0.8,
      keywords: ['ai', 'conversation', 'platform']
    };
  }
  
  async detectUserIntent(messages) {
    return {
      intent: 'seeking_help',
      confidence: 0.85,
      subIntents: ['technical_assistance', 'feature_understanding']
    };
  }
  
  async predictSatisfaction(messages) {
    return {
      probability: 0.75,
      factors: ['response_quality', 'resolution_speed'],
      confidence: 0.80
    };
  }
  
  calculateEngagementMetrics(messages) {
    return {
      messageFrequency: messages.length / 10, // messages per minute
      responseRate: 0.95,
      sessionDuration: 15 // minutes
    };
  }
  
  identifyLearningOpportunities(messages) {
    return [
      {
        area: 'conversation_quality',
        opportunity: 'Improve response relevance',
        impact: 'medium'
      }
    ];
  }
  
  async getUserProfile(userId) {
    return {
      tier: 'PERSONAL',
      engagementLevel: 'medium',
      usage: 0.6,
      satisfactionScore: 0.8
    };
  }
  
  extractConversationContext(messages) {
    return {
      needsHelp: messages.some(msg => msg.content.includes('help')),
      complexity: 'medium',
      urgency: 'low'
    };
  }
  
  async generatePersonalizedRecommendations(userProfile, context) {
    return [
      {
        type: 'feature_suggestion',
        content: 'Try the new AI conversation templates',
        relevance: 0.8
      }
    ];
  }
  
  async calculateUserValue(userId, messages) {
    return {
      currentTier: 'PERSONAL',
      lifetimeValue: 25,
      engagementScore: 0.7
    };
  }
  
  async getMarketConditions() {
    return { demand: 'high', competition: 'medium', growth: 'positive' };
  }
  
  async getCompetitiveAnalysis() {
    return { position: 'strong', advantages: ['ai_quality', 'pricing'], threats: ['new_entrants'] };
  }
  
  calculateUserEngagement(messages) {
    return messages.length * 0.1; // Simplified engagement calculation
  }
  
  async executeAutomatedAction(action, analysis) {
    console.log(`🤖 Executing automated action: ${action.type} (${action.priority} priority)`);
    this.emit('automatedAction', { action, analysis });
  }
  
  getPendingAnalyses() {
    return Array.from(this.conversationStreams.values()).slice(0, 10);
  }
  
  async updateRealTimeMetrics(analysis) {
    // Update real-time metrics
  }
  
  async optimizeModelPerformance(analysis) {
    // Optimize AI model performance based on feedback
  }
  
  getRealTimeMetrics() {
    return {
      totalConversations: this.conversationStreams.size,
      averageSentiment: 0.3,
      averageQuality: 0.75,
      automatedActions: 15
    };
  }
  
  calculateAverageResponseTime(messages) {
    return 2.5; // seconds
  }
  
  analyzeConversationFlow(messages) {
    return 'natural';
  }
}

// Simplified AI Model Classes
class SentimentAnalysisModel {
  async analyze(content) {
    const score = (Math.random() - 0.5) * 2; // -1 to 1
    return {
      score,
      emotion: score > 0.3 ? 'positive' : score < -0.3 ? 'negative' : 'neutral',
      confidence: 0.85,
      keywords: ['ai', 'conversation']
    };
  }
}

class ConversationQualityModel {
  async evaluate(messages) {
    const score = 0.7 + (Math.random() * 0.3); // 0.7 to 1.0
    return {
      score,
      coherence: score * 0.9,
      relevance: score * 0.95,
      helpfulness: score * 0.85,
      engagement: score * 0.8,
      resolution: score * 0.75,
      suggestedImprovements: ['Improve response clarity', 'Add more examples']
    };
  }
}

class IntelligentPricingModel {
  async optimize(data) {
    return {
      recommendedTier: data.userValue.currentTier,
      priceAdjustment: 0.05,
      confidence: 0.75,
      reasoning: 'Market conditions favor slight price increase',
      expectedImpact: { revenue: '+5%', retention: '-1%' },
      strategy: 'gradual_implementation'
    };
  }
}

class CustomerSuccessModel {
  async predict(data) {
    return {
      probability: 0.8,
      riskFactors: ['low_engagement'],
      successIndicators: ['regular_usage', 'positive_feedback'],
      actions: ['Send engagement email', 'Offer tutorial'],
      timeline: '30_days',
      interventions: ['day_7', 'day_14', 'day_21']
    };
  }
}

class ContentModerationModel {
  async moderate(content) {
    return {
      flagged: false,
      reason: null,
      severity: 'low',
      confidence: 0.95,
      category: null
    };
  }
}
