import { textProcessing, nlu } from '@kit.NaturalLanguageKit';

class NLPCoreEngine {
    private textProcessor: textProcessing.TextProcessor;
    private nluEngine: nlu.NaturalLanguageUnderstanding;
    
    async initNLPEngine(): Promise<void> {
        // 初始化文本處理引擎
        this.textProcessor = await textProcessing.createTextProcessor({
            language: 'zh-CN',
            enableGPU: true  // 啓用GPU加速
        });
        
        // 初始化語義理解引擎
        this.nluEngine = await nlu.createNLUEngine({
            modelType: nlu.ModelType.STANDARD,
            features: [
                nlu.Feature.TOKENIZE,
                nlu.Feature.ENTITY,
                nlu.Feature.SENTIMENT,
                nlu.Feature.CLASSIFY
            ]
        });
    }
}

import { textClassification } from '@kit.NaturalLanguageKit';

class TextClassifier {
    private classifier: textClassification.TextClassifier;
    private categories: string[];
    
    async initClassifier(customCategories?: string[]): Promise<void> {
        // 支持自定義分類體系或使用預定義分類
        this.categories = customCategories || [
            '科技', '體育', '財經', '娛樂', '教育', '健康'
        ];
        
        const config: textClassification.ClassificationConfig = {
            modelPath: 'models/text_classification.pt',
            categories: this.categories,
            confidenceThreshold: 0.6,  // 置信度閾值
            maxResults: 3              // 最大返回結果數
        };
        
        this.classifier = await textClassification.createClassifier(config);
    }
    
    // 執行文本分類
    async classifyText(text: string): Promise<ClassificationResult[]> {
        const input: textClassification.ClassificationInput = {
            text: text,
            language: 'zh-CN',
            context: 'news'  // 提供上下文提升準確率
        };
        
        try {
            const results = await this.classifier.classify(input);
            return this.filterValidResults(results);
        } catch (error) {
            console.error(`文本分類失敗: ${error.code}`);
            return this.fallbackClassification(text);  // 降級處理
        }
    }
    
    // 過濾有效結果
    private filterValidResults(results: textClassification.ClassificationResult[]): ClassificationResult[] {
        return results.filter(result => 
            result.confidence >= 0.6 && 
            this.categories.includes(result.category)
        );
    }
}

class AdvancedTextClassifier extends TextClassifier {
    private cache: Map<string, ClassificationResult[]>;
    private performanceMonitor: PerformanceMonitor;
    
    constructor() {
        super();
        this.cache = new Map();
        this.performanceMonitor = new PerformanceMonitor();
    }
    
    // 帶緩存的分類方法
    async classifyWithCache(text: string, useCache: boolean = true): Promise<ClassificationResult[]> {
        const cacheKey = this.generateCacheKey(text);
        
        // 緩存命中
        if (useCache && this.cache.has(cacheKey)) {
            return this.cache.get(cacheKey)!;
        }
        
        // 執行分類
        const startTime = Date.now();
        const results = await this.classifyText(text);
        const endTime = Date.now();
        
        // 性能監控
        this.performanceMonitor.recordClassification(endTime - startTime, text.length);
        
        // 更新緩存
        if (useCache) {
            this.cache.set(cacheKey, results);
        }
        
        return results;
    }
    
    // 批量分類處理
    async batchClassify(texts: string[], batchSize: number = 10): Promise<BatchClassificationResult> {
        const batches: string[][] = [];
        for (let i = 0; i < texts.length; i += batchSize) {
            batches.push(texts.slice(i, i + batchSize));
        }
        
        const results: ClassificationResult[][] = [];
        
        // 並行處理批次
        for (const batch of batches) {
            const batchPromises = batch.map(text => this.classifyWithCache(text));
            const batchResults = await Promise.all(batchPromises);
            results.push(...batchResults);
        }
        
        return {
            results: results,
            statistics: this.performanceMonitor.getStats()
        };
    }
    
    // 動態調整分類閾值
    adjustThresholdBasedOnContext(context: ClassificationContext): void {
        let threshold: number;
        
        switch (context.domain) {
            case 'news':
                threshold = 0.7;  // 新聞分類要求高精度
                break;
            case 'social':
                threshold = 0.5;  // 社交內容可接受較低精度
                break;
            case 'critical':
                threshold = 0.8;  // 關鍵應用需要更高置信度
                break;
            default:
                threshold = 0.6;
        }
        
        this.classifier.setConfidenceThreshold(threshold);
    }
    
    private generateCacheKey(text: string): string {
        // 簡單的文本哈希作為緩存鍵
        return Buffer.from(text).toString('base64').substring(0, 32);
    }
}

import { sentimentAnalysis } from '@kit.NaturalLanguageKit';

class SentimentAnalyzer {
    private analyzer: sentimentAnalysis.SentimentAnalyzer;
    private sentimentLexicon: Map<string, number>;
    
    async initAnalyzer(): Promise<void> {
        const config: sentimentAnalysis.AnalyzerConfig = {
            modelType: sentimentAnalysis.ModelType.MULTI_DIMENSIONAL,
            features: [
                sentimentAnalysis.Feature.BASIC_SENTIMENT,  // 基礎情感
                sentimentAnalysis.Feature.EMOTION_DETAIL,  // 詳細情緒
                sentimentAnalysis.Feature.INTENSITY        // 情感強度
            ],
            language: 'zh-CN'
        };
        
        this.analyzer = await sentimentAnalysis.createAnalyzer(config);
        await this.loadCustomLexicon();  // 加載領域詞典
    }
    
    // 執行情感分析
    async analyzeSentiment(text: string, context?: AnalysisContext): Promise<SentimentResult> {
        const input: sentimentAnalysis.AnalysisInput = {
            text: text,
            context: context || {},
            options: {
                enableSarcasmDetection: true,  // 啓用反諷檢測
                analyzeEmotions: true          // 分析詳細情緒
            }
        };
        
        const result = await this.analyzer.analyze(input);
        return this.enhanceWithLexicon(result, text);  // 使用詞典增強
    }
    
    // 使用自定義詞典增強分析結果
    private enhanceWithLexicon(result: sentimentAnalysis.SentimentResult, text: string): SentimentResult {
        let enhancedScore = result.score;
        const words = this.tokenizeText(text);
        
        // 基於詞典調整情感分數
        words.forEach(word => {
            if (this.sentimentLexicon.has(word)) {
                const wordScore = this.sentimentLexicon.get(word)!;
                enhancedScore = (enhancedScore + wordScore) / 2;  // 加權平均
            }
        });
        
        return {
            ...result,
            score: enhancedScore,
            label: this.getSentimentLabel(enhancedScore)
        };
    }
    
    private getSentimentLabel(score: number): string {
        if (score > 0.6) return 'positive';
        if (score < 0.4) return 'negative';
        return 'neutral';
    }
}

class AdvancedSentimentAnalyzer extends SentimentAnalyzer {
    private emotionDetector: emotion.EmotionDetector;
    
    // 多維度情感分析
    async comprehensiveSentimentAnalysis(text: string, authorInfo?: AuthorInfo): Promise<ComprehensiveSentiment> {
        const basicSentiment = await this.analyzeSentiment(text);
        const emotions = await this.detectEmotions(text);
        const intensity = await this.analyzeIntensity(text);
        const sarcasm = await this.detectSarcasm(text, authorInfo);
        
        return {
            basicSentiment,
            emotions,
            intensity,
            isSarcastic: sarcasm,
            confidence: this.calculateOverallConfidence(basicSentiment, emotions, intensity)
        };
    }
    
    // 情感趨勢分析
    async analyzeSentimentTrend(texts: TimedText[]): Promise<SentimentTrend> {
        const sentiments: number[] = [];
        
        for (const timedText of texts) {
            const result = await this.analyzeSentiment(timedText.text);
            sentiments.push({
                timestamp: timedText.timestamp,
                score: result.score,
                intensity: result.intensity
            });
        }
        
        // 計算情感趨勢
        return this.calculateTrend(sentiments);
    }
    
    // 基於上下文的智能情感修正
    async contextAwareSentimentAnalysis(conversation: ConversationTurn[]): Promise<TurnByTurnSentiment> {
        const turnAnalysis: TurnAnalysis[] = [];
        let context: AnalysisContext = {};
        
        for (const turn of conversation) {
            // 使用對話上下文增強當前分析
            const result = await this.analyzeSentiment(turn.text, context);
            
            turnAnalysis.push({
                speaker: turn.speaker,
                text: turn.text,
                sentiment: result,
                context: { ...context }
            });
            
            // 更新上下文
            context = this.updateContext(context, result, turn);
        }
        
        return { turns: turnAnalysis };
    }
    
    private calculateTrend(sentiments: TimedSentiment[]): SentimentTrend {
        if (sentiments.length < 2) {
            return { trend: 'stable', slope: 0 };
        }
        
        // 簡單線性迴歸計算趨勢
        const n = sentiments.length;
        const sumX = sentiments.reduce((sum, s, i) => sum + i, 0);
        const sumY = sentiments.reduce((sum, s) => sum + s.score, 0);
        const sumXY = sentiments.reduce((sum, s, i) => sum + i * s.score, 0);
        const sumX2 = sentiments.reduce((sum, s, i) => sum + i * i, 0);
        
        const slope = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);
        
        if (Math.abs(slope) < 0.01) return { trend: 'stable', slope };
        return slope > 0 ? { trend: 'improving', slope } : { trend: 'deteriorating', slope };
    }
}

import { dialogueManager, intentRecognizer } from '@kit.ConversationKit';

class IntelligentDialogSystem {
    private dialogueManager: dialogueManager.DialogueManager;
    private intentRecognizer: intentRecognizer.IntentRecognizer;
    private conversationMemory: ConversationMemory;
    
    async initDialogSystem(): Promise<void> {
        // 初始化對話管理器
        this.dialogueManager = await dialogueManager.createManager({
            responseStyle: 'friendly',  // 響應風格
            personality: 'professional', // 個性設置
            contextWindow: 10           // 上下文窗口大小
        });
        
        // 初始化意圖識別器
        this.intentRecognizer = await intentRecognizer.createRecognizer({
            domains: ['general', 'weather', 'news', 'entertainment'],
            enableMultiIntent: true  // 支持多意圖識別
        });
        
        this.conversationMemory = new ConversationMemory(100);  // 保存最近100輪對話
    }
    
    // 處理用户輸入生成響應
    async processUserInput(userInput: UserInput): Promise<DialogResponse> {
        // 1. 意圖識別
        const intent = await this.recognizeIntent(userInput.text);
        
        // 2. 情感分析
        const sentiment = await this.analyzeSentiment(userInput.text);
        
        // 3. 上下文理解
        const context = this.buildContext(userInput, intent, sentiment);
        
        // 4. 生成響應
        const response = await this.generateResponse(context);
        
        // 5. 更新對話記憶
        this.updateConversationMemory(userInput, response, context);
        
        return response;
    }
    
    // 多輪對話管理
    private buildContext(userInput: UserInput, intent: Intent, sentiment: Sentiment): DialogContext {
        const recentHistory = this.conversationMemory.getRecentTurns(5);
        
        return {
            currentInput: userInput,
            recognizedIntent: intent,
            userSentiment: sentiment,
            conversationHistory: recentHistory,
            dialogState: this.getCurrentDialogState(),
            userProfile: userInput.profile
        };
    }
}

class DomainAdaptiveDialogSystem extends IntelligentDialogSystem {
    private domainExperts: Map<string, DomainExpert>;
    private domainClassifier: textClassification.TextClassifier;
    
    constructor() {
        super();
        this.domainExperts = new Map();
        this.initDomainExperts();
    }
    
    // 初始化領域專家
    private initDomainExperts(): void {
        this.domainExperts.set('weather', new WeatherDomainExpert());
        this.domainExperts.set('news', new NewsDomainExpert());
        this.domainExperts.set('entertainment', new EntertainmentDomainExpert());
        this.domainExperts.set('general', new GeneralDomainExpert());
    }
    
    // 領域自適應響應生成
    async generateDomainAdaptiveResponse(context: DialogContext): Promise<DialogResponse> {
        // 識別用户查詢的領域
        const domain = await this.classifyDomain(context.currentInput.text);
        
        // 獲取對應領域的專家
        const domainExpert = this.domainExperts.get(domain) || this.domainExperts.get('general');
        
        // 生成領域特定響應
        const response = await domainExpert.generateResponse(context);
        
        // 根據用户情感調整響應風格
        return this.adaptResponseToSentiment(response, context.userSentiment);
    }
    
    // 動態領域識別
    private async classifyDomain(text: string): Promise<string> {
        const domains = ['weather', 'news', 'entertainment', 'sports', 'technology'];
        const classification = await this.domainClassifier.classifyText(text);
        
        if (classification.length > 0 && classification[0].confidence > 0.7) {
            return classification[0].category;
        }
        
        return 'general';
    }
    
    // 個性化響應適配
    private adaptResponseToSentiment(response: DialogResponse, sentiment: Sentiment): DialogResponse {
        let adaptedResponse = { ...response };
        
        // 根據情感強度調整響應
        switch (sentiment.label) {
            case 'positive':
                adaptedResponse.text = this.addPositiveEmphasis(response.text);
                break;
            case 'negative':
                adaptedResponse.text = this.addEmpatheticLanguage(response.text);
                adaptedResponse.shouldShowEmpathy = true;
                break;
            case 'neutral':
                // 保持中性專業風格
                break;
        }
        
        // 根據情感強度調整詳細程度
        if (sentiment.intensity > 0.7) {
            adaptedResponse.detailLevel = 'high';
        }
        
        return adaptedResponse;
    }
}

class IntelligentCustomerService {
    private textClassifier: AdvancedTextClassifier;
    private sentimentAnalyzer: AdvancedSentimentAnalyzer;
    private dialogSystem: DomainAdaptiveDialogSystem;
    private ticketManager: TicketManager;
    
    async initCustomerService(): Promise<void> {
        await Promise.all([
            this.textClassifier.initClassifier([
                'billing', 'technical', 'account', 'general', 'complaint', 'praise'
            ]),
            this.sentimentAnalyzer.initAnalyzer(),
            this.dialogSystem.initDialogSystem()
        ]);
        
        this.ticketManager = new TicketManager();
    }
    
    // 處理客户諮詢
    async handleCustomerInquiry(inquiry: CustomerInquiry): Promise<ServiceResponse> {
        // 1. 自動分類工單類型
        const category = await this.classifyInquiry(inquiry.text);
        
        // 2. 分析客户情感狀態
        const sentiment = await this.analyzeCustomerSentiment(inquiry);
        
        // 3. 生成個性化響應
        const response = await this.generateServiceResponse(inquiry, category, sentiment);
        
        // 4. 必要時創建或更新工單
        if (this.requiresTicket(category, sentiment)) {
            await this.createOrUpdateTicket(inquiry, category, sentiment, response);
        }
        
        // 5. 關鍵情況觸發人工客服
        if (this.requiresHumanIntervention(sentiment, category)) {
            response.escalateToHuman = true;
            response.humanTransferReason = this.getTransferReason(sentiment, category);
        }
        
        return response;
    }
    
    // 智能路由決策
    private requiresHumanIntervention(sentiment: Sentiment, category: string): boolean {
        // 負面情感強烈的問題轉人工
        if (sentiment.label === 'negative' && sentiment.intensity > 0.8) {
            return true;
        }
        
        // 特定複雜類別轉人工
        const complexCategories = ['billing_dispute', 'legal', 'security'];
        if (complexCategories.includes(category)) {
            return true;
        }
        
        return false;
    }
}

class OptimizedCustomerService extends IntelligentCustomerService {
    private performanceMonitor: PerformanceMonitor;
    private qualityAssurance: QualityAssurance;
    
    // 帶性能監控的查詢處理
    async handleInquiryWithMonitoring(inquiry: CustomerInquiry): Promise<ServiceResponse> {
        const startTime = Date.now();
        
        try {
            const response = await super.handleCustomerInquiry(inquiry);
            const endTime = Date.now();
            
            // 記錄性能指標
            this.performanceMonitor.recordInquiryProcessing(
                endTime - startTime, 
                inquiry.text.length,
                response.escalateToHuman
            );
            
            // 質量檢查
            this.qualityAssurance.checkResponseQuality(inquiry, response);
            
            return response;
        } catch (error) {
            // 錯誤處理和降級方案
            return this.getFallbackResponse(inquiry, error);
        }
    }
    
    // A/B測試不同響應策略
    async experimentalResponseGeneration(inquiry: CustomerInquiry, strategy: ResponseStrategy): Promise<ServiceResponse> {
        const baseResponse = await this.handleCustomerInquiry(inquiry);
        
        switch (strategy) {
            case 'detailed':
                return this.enhanceWithDetailedExplanation(baseResponse);
            case 'empathetic':
                return this.addEmpatheticElements(baseResponse, inquiry);
            case 'concise':
                return this.makeResponseConcise(baseResponse);
            default:
                return baseResponse;
        }
    }
    
    // 持續學習優化
    async learnFromFeedback(feedback: CustomerFeedback): Promise<void> {
        // 基於用户反饋調整分類器
        if (feedback.rating < 3) {
            await this.adjustClassificationBasedOnFeedback(feedback);
        }
        
        // 更新情感分析詞典
        if (feedback.sentimentFeedback) {
            await this.updateSentimentLexicon(feedback);
        }
        
        // 優化對話策略
        this.dialogSystem.learnFromInteraction(feedback);
    }
}

class NLPPerformanceOptimizer {
    private static instance: NLPPerformanceOptimizer;
    private modelCache: Map<string, any> = new Map();
    private memoryMonitor: MemoryMonitor;
    
    // 模型預熱和懶加載
    async preloadCriticalModels(): Promise<void> {
        const criticalModels = [
            'text_classification',
            'sentiment_analysis',
            'intent_recognition'
        ];
        
        await Promise.all(
            criticalModels.map(model => 
                this.loadModelToCache(model)
            )
        );
    }
    
    // 動態內存管理
    manageMemoryBasedOnUsage(): void {
        const memoryInfo = system.memory.getMemoryInfo();
        
        if (memoryInfo.availMemory < 50 * 1024 * 1024) {  // 可用內存小於50MB
            this.clearModelCache();
            this.reducePrecisionModels();
        }
    }
    
    // 自適應模型精度
    private reducePrecisionModels(): void {
        const models = this.modelCache.values();
        for (const model of models) {
            if (model.setPrecision) {
                model.setPrecision('medium');  // 降低精度節省內存
            }
        }
    }
    
    // 批量處理優化
    optimizeBatchProcessing(batchSize: number): number {
        const optimalBatchSize = this.calculateOptimalBatchSize();
        return Math.min(batchSize, optimalBatchSize);
    }
    
    private calculateOptimalBatchSize(): number {
        const memoryInfo = system.memory.getMemoryInfo();
        const availableMemory = memoryInfo.availMemory;
        
        // 根據可用內存計算最佳批次大小
        if (availableMemory > 200 * 1024 * 1024) return 20;
        if (availableMemory > 100 * 1024 * 1024) return 10;
        if (availableMemory > 50 * 1024 * 1024) return 5;
        return 1;  // 內存緊張時逐條處理
    }
}

class NLPErrorHandler {
    private fallbackStrategies: Map<string, FallbackStrategy>;
    
    constructor() {
        this.initFallbackStrategies();
    }
    
    private initFallbackStrategies(): void {
        this.fallbackStrategies.set('classification_failed', {
            priority: 1,
            handler: (error: NLPError) => this.keywordBasedClassification(error.context)
        });
        
        this.fallbackStrategies.set('sentiment_analysis_failed', {
            priority: 2,
            handler: (error: NLPError) => this.lexiconBasedSentiment(error.context)
        });
        
        this.fallbackStrategies.set('dialog_generation_failed', {
            priority: 3,
            handler: (error: NLPError) => this.templateBasedResponse(error.context)
        });
    }
    
    // 關鍵詞降級分類
    private keywordBasedClassification(context: ErrorContext): ClassificationResult[] {
        const text = context.text.toLowerCase();
        const keywordCategories = this.getCategoryKeywords();
        
        for (const [category, keywords] of keywordCategories) {
            if (keywords.some(keyword => text.includes(keyword))) {
                return [{
                    category: category,
                    confidence: 0.6,  // 降級置信度
                    reason: 'keyword_fallback'
                }];
            }
        }
        
        return [{ category: 'general', confidence: 0.5, reason: 'default_fallback' }];
    }
    
    // 基於詞典的情感分析降級
    private lexiconBasedSentiment(context: ErrorContext): SentimentResult {
        const positiveWords = ['好', '優秀', '滿意', '喜歡'];
        const negativeWords = ['差', '糟糕', '不滿意', '討厭'];
        
        const text = context.text;
        const positiveCount = positiveWords.filter(word => text.includes(word)).length;
        const negativeCount = negativeWords.filter(word => text.includes(word)).length;
        
        if (positiveCount > negativeCount) {
            return { label: 'positive', score: 0.7, intensity: 0.6 };
        } else if (negativeCount > positiveCount) {
            return { label: 'negative', score: 0.3, intensity: 0.6 };
        } else {
            return { label: 'neutral', score: 0.5, intensity: 0.5 };
        }
    }
}