// Image Recognition for Products
// AI-powered image analysis for product management

import { EventEmitter } from 'events';

interface ImageAnalysis {
  id: string;
  tenantId: string;
  imageUrl: string;
  status: 'pending' | 'processing' | 'completed' | 'failed';
  results?: {
    objects: Array<{
      label: string;
      confidence: number;
      bbox: { x: number; y: number; width: number; height: number };
    }>;
    labels: Array<{
      name: string;
      confidence: number;
      parents: string[];
    }>;
    text?: string;
    colors: Array<{
      color: string;
      hex: string;
      percentage: number;
    }>;
    faces?: Array<{
      bbox: { x: number; y: number; width: number; height: number };
      emotions: Record<string, number>;
      ageRange?: { low: number; high: number };
      gender?: string;
    }>;
    moderation?: {
      adult: number;
      violence: number;
      racy: number;
    };
  };
  createdAt: Date;
  processingTime?: number;
}

interface ProductMatch {
  id: string;
  imageUrl: string;
  productId?: string;
  confidence: number;
  matches: Array<{
    productId: string;
    title: string;
    imageUrl: string;
    similarity: number;
    features: string[];
  }>;
  createdAt: Date;
}

interface VisualSearchIndex {
  tenantId: string;
  productCount: number;
  lastUpdatedAt: Date;
  status: 'building' | 'ready' | 'updating';
}

// Vision Recognition Manager
export class VisionRecognitionManager extends EventEmitter {
  private analyses: Map<string, ImageAnalysis> = new Map();
  private matches: Map<string, ProductMatch> = new Map();
  private indexes: Map<string, VisualSearchIndex> = new Map();

  // Analyze image
  async analyzeImage(
    tenantId: string,
    imageUrl: string,
    options: {
      detectObjects?: boolean;
      detectText?: boolean;
      detectColors?: boolean;
      detectFaces?: boolean;
      moderation?: boolean;
    } = {}
  ): Promise<ImageAnalysis> {
    const analysis: ImageAnalysis = {
      id: crypto.randomUUID(),
      tenantId,
      imageUrl,
      status: 'pending',
      createdAt: new Date(),
    };

    this.analyses.set(analysis.id, analysis);
    this.emit('analysisStarted', analysis);

    // Process asynchronously
    this.processAnalysis(analysis, options).catch(console.error);

    return analysis;
  }

  // Find similar products
  async findSimilarProducts(
    tenantId: string,
    imageUrl: string,
    options: {
      limit?: number;
      minSimilarity?: number;
      category?: string;
    } = {}
  ): Promise<ProductMatch> {
    const match: ProductMatch = {
      id: crypto.randomUUID(),
      imageUrl,
      confidence: 0,
      matches: [],
      createdAt: new Date(),
    };

    // First, analyze the query image
    const analysis = await this.analyzeImage(tenantId, imageUrl, {
      detectObjects: true,
      detectColors: true,
    });

    // Wait for analysis to complete
    while (analysis.status === 'pending' || analysis.status === 'processing') {
      await new Promise(resolve => setTimeout(resolve, 100));
    }

    if (analysis.status === 'completed' && analysis.results) {
      // Search in visual index
      match.matches = await this.searchVisualIndex(tenantId, analysis.results, options);
      match.confidence = match.matches.length > 0 ? match.matches[0].similarity : 0;
    }

    this.matches.set(match.id, match);
    this.emit('similarProductsFound', match);

    return match;
  }

  // Auto-tag products based on images
  async autoTagProduct(
    tenantId: string,
    productId: string,
    imageUrls: string[]
  ): Promise<{
    productId: string;
    tags: string[];
    category?: string;
    colors: string[];
    attributes: Record<string, string>;
    confidence: number;
  }> {
    const allTags = new Set<string>();
    const allColors = new Set<string>();
    const allLabels: string[] = [];

    for (const imageUrl of imageUrls) {
      const analysis = await this.analyzeImage(tenantId, imageUrl, {
        detectObjects: true,
        detectColors: true,
      });

      // Wait for completion
      while (analysis.status === 'pending' || analysis.status === 'processing') {
        await new Promise(resolve => setTimeout(resolve, 100));
      }

      if (analysis.results) {
        // Extract labels
        for (const label of analysis.results.labels) {
          if (label.confidence > 0.7) {
            allLabels.push(label.name);
            allTags.add(label.name.toLowerCase());
          }
        }

        // Extract colors
        for (const color of analysis.results.colors) {
          if (color.percentage > 10) {
            allColors.add(color.color);
          }
        }

        // Extract objects
        for (const obj of analysis.results.objects) {
          if (obj.confidence > 0.7) {
            allTags.add(obj.label.toLowerCase());
          }
        }
      }
    }

    // Determine category from labels
    const category = this.inferCategory(Array.from(allTags));

    // Extract attributes
    const attributes = this.extractAttributes(allLabels);

    return {
      productId,
      tags: Array.from(allTags).slice(0, 20),
      category,
      colors: Array.from(allColors),
      attributes,
      confidence: 0.85,
    };
  }

  // Extract text from product images (OCR)
  async extractText(
    tenantId: string,
    imageUrl: string
  ): Promise<{
    text: string;
    blocks: Array<{
      text: string;
      bbox: { x: number; y: number; width: number; height: number };
      confidence: number;
    }>;
    extractedInfo?: {
      brand?: string;
      model?: string;
      size?: string;
      price?: string;
      barcode?: string;
    };
  }> {
    const analysis = await this.analyzeImage(tenantId, imageUrl, { detectText: true });

    // Wait for completion
    while (analysis.status === 'pending' || analysis.status === 'processing') {
      await new Promise(resolve => setTimeout(resolve, 100));
    }

    const text = analysis.results?.text || '';
    const blocks: Array<{ text: string; bbox: any; confidence: number }> = [];

    // Extract structured information
    const extractedInfo: { brand?: string; model?: string; size?: string; price?: string; barcode?: string } = {};

    // Brand detection
    const brandMatch = text.match(/(Nike|Adidas|Zara|H&M|Mango|Koton|LC Waikiki|Defacto)/i);
    if (brandMatch) extractedInfo.brand = brandMatch[1];

    // Price detection
    const priceMatch = text.match(/(\d+[,.]?\d*)\s*(TL|₺|USD|\$)/i);
    if (priceMatch) extractedInfo.price = `${priceMatch[1]} ${priceMatch[2]}`;

    // Size detection
    const sizeMatch = text.match(/(XS|S|M|L|XL|XXL|XXXL|\d{2,3}\s*(cm|ml|g|kg)?)/i);
    if (sizeMatch) extractedInfo.size = sizeMatch[1];

    // Barcode detection
    const barcodeMatch = text.match(/(\d{8,13})/);
    if (barcodeMatch) extractedInfo.barcode = barcodeMatch[1];

    return {
      text,
      blocks,
      extractedInfo,
    };
  }

  // Check image quality
  async checkQuality(
    tenantId: string,
    imageUrl: string
  ): Promise<{
    quality: 'excellent' | 'good' | 'fair' | 'poor';
    score: number;
    issues: string[];
    recommendations: string[];
  }> {
    // In production, check:
    // - Resolution
    // - Sharpness/blur
    // - Lighting
    // - Background
    // - Composition
    // - Color balance

    const checks = {
      resolution: Math.random() > 0.2,
      sharpness: Math.random() > 0.3,
      lighting: Math.random() > 0.3,
      background: Math.random() > 0.4,
    };

    const score = Object.values(checks).filter(Boolean).length / Object.keys(checks).length * 100;

    let quality: 'excellent' | 'good' | 'fair' | 'poor' = 'poor';
    if (score >= 90) quality = 'excellent';
    else if (score >= 75) quality = 'good';
    else if (score >= 50) quality = 'fair';

    const issues: string[] = [];
    if (!checks.resolution) issues.push('Düşük çözünürlük');
    if (!checks.sharpness) issues.push('Bulanık görüntü');
    if (!checks.lighting) issues.push('Yetersiz aydınlatma');
    if (!checks.background) issues.push('Uygunsuz arka plan');

    const recommendations: string[] = [];
    if (!checks.resolution) recommendations.push('En az 1000x1000 piksel kullanın');
    if (!checks.sharpness) recommendations.push('Görüntüyü netleştirin veya yeniden çekin');
    if (!checks.lighting) recommendations.push('Daha iyi aydınlatma sağlayın');
    if (!checks.background) recommendations.push('Düz, beyaz arka plan kullanın');

    return {
      quality,
      score,
      issues,
      recommendations,
    };
  }

  // Moderate image content
  async moderateContent(
    tenantId: string,
    imageUrl: string
  ): Promise<{
    safe: boolean;
    confidence: number;
    flags: string[];
    categories: Record<string, number>;
  }> {
    const analysis = await this.analyzeImage(tenantId, imageUrl, { moderation: true });

    // Wait for completion
    while (analysis.status === 'pending' || analysis.status === 'processing') {
      await new Promise(resolve => setTimeout(resolve, 100));
    }

    const moderation = analysis.results?.moderation || {
      adult: 0,
      violence: 0,
      racy: 0,
    };

    const flags: string[] = [];
    const threshold = 0.5;

    if (moderation.adult > threshold) flags.push('adult');
    if (moderation.violence > threshold) flags.push('violence');
    if (moderation.racy > threshold) flags.push('racy');

    const safe = flags.length === 0;
    const confidence = Math.max(moderation.adult, moderation.violence, moderation.racy);

    return {
      safe,
      confidence: 1 - confidence,
      flags,
      categories: moderation,
    };
  }

  // Build visual search index
  async buildVisualIndex(tenantId: string): Promise<VisualSearchIndex> {
    const index: VisualSearchIndex = {
      tenantId,
      productCount: 0,
      lastUpdatedAt: new Date(),
      status: 'building',
    };

    this.indexes.set(tenantId, index);
    this.emit('indexBuilding', index);

    // In production:
    // 1. Fetch all products with images
    // 2. Extract features from each image
    // 3. Build vector index (e.g., using FAISS, Annoy, or Elasticsearch)
    // 4. Store embeddings

    // Mock building process
    setTimeout(() => {
      index.productCount = 10000;
      index.status = 'ready';
      index.lastUpdatedAt = new Date();
      this.emit('indexReady', index);
    }, 5000);

    return index;
  }

  // Get index status
  getIndexStatus(tenantId: string): VisualSearchIndex | null {
    return this.indexes.get(tenantId) || null;
  }

  // Private methods
  private async processAnalysis(
    analysis: ImageAnalysis,
    options: {
      detectObjects?: boolean;
      detectText?: boolean;
      detectColors?: boolean;
      detectFaces?: boolean;
      moderation?: boolean;
    }
  ): Promise<void> {
    const startTime = Date.now();
    analysis.status = 'processing';

    try {
      // In production, call Google Vision, AWS Rekognition, or Azure Computer Vision
      await new Promise(resolve => setTimeout(resolve, 1000)); // Simulate processing

      analysis.results = {
        objects: options.detectObjects ? this.mockObjects() : [],
        labels: this.mockLabels(),
        text: options.detectText ? this.mockText() : undefined,
        colors: options.detectColors ? this.mockColors() : [],
        faces: options.detectFaces ? this.mockFaces() : undefined,
        moderation: options.moderation ? this.mockModeration() : undefined,
      };

      analysis.status = 'completed';
      analysis.processingTime = Date.now() - startTime;

      this.emit('analysisCompleted', analysis);

    } catch (error) {
      analysis.status = 'failed';
      this.emit('analysisFailed', { analysis, error });
    }
  }

  private async searchVisualIndex(
    tenantId: string,
    queryResults: ImageAnalysis['results'],
    options: {
      limit?: number;
      minSimilarity?: number;
      category?: string;
    }
  ): Promise<ProductMatch['matches']> {
    // In production:
    // 1. Extract features from query image
    // 2. Search in vector index
    // 3. Return similar products

    const limit = options.limit || 10;
    const minSimilarity = options.minSimilarity || 0.7;

    // Mock results
    return Array.from({ length: limit }, (_, i) => ({
      productId: `prod-${i}`,
      title: `Similar Product ${i + 1}`,
      imageUrl: `https://cdn.example.com/similar-${i}.jpg`,
      similarity: 0.95 - (i * 0.05),
      features: ['color', 'shape', 'texture'].slice(0, Math.floor(Math.random() * 3) + 1),
    })).filter(m => m.similarity >= minSimilarity);
  }

  private inferCategory(tags: string[]): string | undefined {
    const categories: Record<string, string[]> = {
      'Giyim': ['elbise', 'tişört', 'pantolon', 'gömlek', 'etek', 'mont', 'ceket'],
      'Ayakkabı': ['ayakkabı', 'bot', 'çizme', 'sandalet', 'terlik'],
      'Aksesuar': ['çanta', 'cüzdan', 'kemer', 'şapka', 'atkı', 'eldiven'],
      'Elektronik': ['telefon', 'bilgisayar', 'kulaklık', 'saat'],
    };

    for (const [category, keywords] of Object.entries(categories)) {
      for (const tag of tags) {
        if (keywords.some(k => tag.includes(k))) {
          return category;
        }
      }
    }

    return undefined;
  }

  private extractAttributes(labels: string[]): Record<string, string> {
    const attributes: Record<string, string> = {};

    // Extract material
    const materials = ['cotton', 'polyester', 'leather', 'wool', 'silk', 'denim'];
    for (const label of labels) {
      const material = materials.find(m => label.toLowerCase().includes(m));
      if (material) {
        attributes.material = material;
        break;
      }
    }

    // Extract pattern
    const patterns = ['striped', 'plaid', 'floral', 'solid', 'printed'];
    for (const label of labels) {
      const pattern = patterns.find(p => label.toLowerCase().includes(p));
      if (pattern) {
        attributes.pattern = pattern;
        break;
      }
    }

    return attributes;
  }

  private mockObjects(): ImageAnalysis['results']['objects'] {
    return [
      { label: 'Clothing', confidence: 0.95, bbox: { x: 10, y: 10, width: 200, height: 300 } },
      { label: 'Shoe', confidence: 0.87, bbox: { x: 50, y: 320, width: 100, height: 80 } },
    ];
  }

  private mockLabels(): ImageAnalysis['results']['labels'] {
    return [
      { name: 'Clothing', confidence: 0.95, parents: ['Apparel'] },
      { name: 'Shoe', confidence: 0.87, parents: ['Clothing', 'Footwear'] },
      { name: 'Fashion', confidence: 0.82, parents: [] },
      { name: 'Sneaker', confidence: 0.76, parents: ['Shoe', 'Footwear'] },
    ];
  }

  private mockText(): string {
    return 'NIKE\nAIR MAX\nSize: 42\n$129.99';
  }

  private mockColors(): ImageAnalysis['results']['colors'] {
    return [
      { color: 'Red', hex: '#FF0000', percentage: 45 },
      { color: 'White', hex: '#FFFFFF', percentage: 35 },
      { color: 'Black', hex: '#000000', percentage: 20 },
    ];
  }

  private mockFaces(): ImageAnalysis['results']['faces'] {
    return [
      {
        bbox: { x: 100, y: 50, width: 80, height: 100 },
        emotions: { happy: 0.8, neutral: 0.15, sad: 0.05 },
        ageRange: { low: 25, high: 35 },
        gender: 'female',
      },
    ];
  }

  private mockModeration(): ImageAnalysis['results']['moderation'] {
    return {
      adult: 0.01,
      violence: 0.0,
      racy: 0.02,
    };
  }
}

// Export singleton
export const visionRecognitionManager = new VisionRecognitionManager();

export { ImageAnalysis, ProductMatch, VisualSearchIndex };