import { createWorker } from 'tesseract.js';

export interface OpusMagnumData {
  puzzle: string;
  cost: string;
  cycles: string;
  area: string;
  confidence: {
    puzzle: number;
    cost: number;
    cycles: number;
    area: number;
    overall: number;
  };
}

export interface OCRRegion {
  x: number;
  y: number;
  width: number;
  height: number;
}

export class OpusMagnumOCRService {
  private worker: Tesseract.Worker | null = null;
  private availablePuzzleNames: string[] = [];

  // Regions based on main.py coordinates (adjusted for web usage)
  private readonly regions: Record<string, OCRRegion> = {
    puzzle: { x: 15, y: 600, width: 330, height: 28 },
    cost: { x: 412, y: 603, width: 65, height: 22 },
    cycles: { x: 577, y: 603, width: 65, height: 22 },
    area: { x: 739, y: 603, width: 65, height: 22 }
  };

  async initialize(): Promise<void> {
    if (this.worker) return;
    
    this.worker = await createWorker('eng');
    await this.worker.setParameters({
      tessedit_ocr_engine_mode: '3',
      tessedit_pageseg_mode: 7 as any
    });
  }

  /**
   * Set the list of available puzzle names for better OCR matching
   */
  setAvailablePuzzleNames(puzzleNames: string[]): void {
    this.availablePuzzleNames = puzzleNames;
    console.log('OCR service updated with puzzle names:', puzzleNames);
  }

  /**
   * Configure OCR specifically for puzzle name recognition
   * Uses aggressive character whitelisting and dictionary constraints
   */
  private async configurePuzzleOCR(): Promise<void> {
    if (!this.worker) return;

    // Configure Tesseract for maximum constraint to our puzzle names
    await this.worker.setParameters({
      // Disable all system dictionaries to prevent interference
      load_system_dawg: '0',
      load_freq_dawg: '0', 
      load_punc_dawg: '0',
      load_number_dawg: '0',
      load_unambig_dawg: '0',
      load_bigram_dawg: '0',
      load_fixed_length_dawgs: '0',
      
      // Use only characters from our puzzle names
      tessedit_char_whitelist: this.getPuzzleCharacterSet(),
      
      // Optimize for single words/short phrases
      tessedit_pageseg_mode: 8 as any, // Single word
      
      // Increase penalties for non-dictionary words
      segment_penalty_dict_nonword: '2.0',
      segment_penalty_dict_frequent_word: '0.001',
      segment_penalty_dict_case_ok: '0.001',
      segment_penalty_dict_case_bad: '0.1',
      
      // Make OCR more conservative about character recognition
      classify_enable_learning: '0',
      classify_enable_adaptive_matcher: '1',
      
      // Preserve word boundaries
      preserve_interword_spaces: '1'
    });

    console.log('OCR configured for puzzle names with character set:', this.getPuzzleCharacterSet());
  }

  /**
   * Get character set from available puzzle names for more accurate OCR (fallback)
   */
  private getPuzzleCharacterSet(): string {
    if (this.availablePuzzleNames.length === 0) {
      // Fallback to common characters
      return 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789 -'
    }

    // Extract unique characters from all puzzle names
    const chars = new Set<string>()
    this.availablePuzzleNames.forEach(name => {
      for (const char of name) {
        chars.add(char)
      }
    })

    return Array.from(chars).join('')
  }

  async extractOpusMagnumData(imageFile: File): Promise<OpusMagnumData> {
    if (!this.worker) {
      await this.initialize();
    }

    // Convert file to image element for canvas processing
    const imageUrl = URL.createObjectURL(imageFile);
    const img = new Image();
    
    return new Promise((resolve, reject) => {
      img.onload = async () => {
        try {
          const canvas = document.createElement('canvas');
          const ctx = canvas.getContext('2d')!;
          
          canvas.width = img.width;
          canvas.height = img.height;
          ctx.drawImage(img, 0, 0);

          // Extract text from each region
          const results: Partial<OpusMagnumData> = {};
          const confidenceScores: Record<string, number> = {};

          for (const [key, region] of Object.entries(this.regions)) {
            const regionCanvas = document.createElement('canvas');
            const regionCtx = regionCanvas.getContext('2d')!;
            
            regionCanvas.width = region.width;
            regionCanvas.height = region.height;
            
            // Extract region from main image
            regionCtx.drawImage(
              canvas,
              region.x, region.y, region.width, region.height,
              0, 0, region.width, region.height
            );

            // Convert to grayscale and invert (similar to main.py processing)
            const imageData = regionCtx.getImageData(0, 0, region.width, region.height);
            this.preprocessImage(imageData);
            regionCtx.putImageData(imageData, 0, 0);

            // Configure OCR based on content type
            if (key === 'cost') {
              // Cost field has digits + 'G' for gold (content type: 'digits_with_6')
              await this.worker!.setParameters({
                tessedit_char_whitelist: '0123456789G'
              });
            } else if (key === 'cycles' || key === 'area') {
              // Pure digits (content type: 'digits')
              await this.worker!.setParameters({
                tessedit_char_whitelist: '0123456789'
              });
            } else if (key === 'puzzle') {
              // Puzzle name - use user words file for better matching
              await this.configurePuzzleOCR();
            } else {
              // Default - allow all characters
              await this.worker!.setParameters({
                tessedit_char_whitelist: ''
              });
            }

            // Perform OCR on the region
            const { data: { text, confidence } } = await this.worker!.recognize(regionCanvas);
            let cleanText = text.trim();
            
            // Store the confidence score for this field
            confidenceScores[key] = confidence / 100; // Tesseract returns 0-100, we want 0-1

            // Post-process based on field type
            if (key === 'cost') {
              // Handle common OCR misreadings where G is read as 6
              // If the text ends with 6 and looks like it should be G, remove it
              if (cleanText.endsWith('6') && cleanText.length > 1) {
                // Check if removing the last character gives a reasonable cost value
                const withoutLast = cleanText.slice(0, -1);
                if (/^\d+$/.test(withoutLast)) {
                  cleanText = withoutLast;
                }
              }
              // Remove any trailing G characters
              cleanText = cleanText.replace(/G+$/g, '');
              // Ensure only digits remain
              cleanText = cleanText.replace(/[^0-9]/g, '');
            } else if (key === 'cycles' || key === 'area') {
              // Ensure only digits remain
              cleanText = cleanText.replace(/[^0-9]/g, '');
            } else if (key === 'puzzle') {
              // Post-process puzzle names with aggressive matching to force selection from available puzzles
              cleanText = this.findBestPuzzleMatch(cleanText);
              
              // If we still don't have a match and we have available puzzles, force the best match
              if (this.availablePuzzleNames.length > 0 && !this.availablePuzzleNames.includes(cleanText)) {
                const forcedMatch = this.findBestPuzzleMatchForced(cleanText);
                if (forcedMatch) {
                  cleanText = forcedMatch;
                  console.log(`Forced OCR match: "${text.trim()}" -> "${cleanText}"`);
                }
              }
            }

            (results as any)[key] = cleanText;
          }

          URL.revokeObjectURL(imageUrl);
          
          // Calculate overall confidence as the average of all field confidences
          const confidenceValues = Object.values(confidenceScores);
          const overallConfidence = confidenceValues.length > 0 
            ? confidenceValues.reduce((sum, conf) => sum + conf, 0) / confidenceValues.length 
            : 0;
          
          resolve({
            puzzle: results.puzzle || '',
            cost: results.cost || '',
            cycles: results.cycles || '',
            area: results.area || '',
            confidence: {
              puzzle: confidenceScores.puzzle || 0,
              cost: confidenceScores.cost || 0,
              cycles: confidenceScores.cycles || 0,
              area: confidenceScores.area || 0,
              overall: overallConfidence
            }
          });
        } catch (error) {
          URL.revokeObjectURL(imageUrl);
          reject(error);
        }
      };

      img.onerror = () => {
        URL.revokeObjectURL(imageUrl);
        reject(new Error('Failed to load image'));
      };

      img.src = imageUrl;
    });
  }

  private preprocessImage(imageData: ImageData): void {
    // Convert to grayscale and invert (similar to cv2.bitwise_not in main.py)
    const data = imageData.data;
    
    for (let i = 0; i < data.length; i += 4) {
      // Convert to grayscale
      const gray = Math.round(0.299 * data[i] + 0.587 * data[i + 1] + 0.114 * data[i + 2]);
      
      // Invert the grayscale value
      const inverted = 255 - gray;
      
      data[i] = inverted;     // Red
      data[i + 1] = inverted; // Green
      data[i + 2] = inverted; // Blue
      // Alpha channel (data[i + 3]) remains unchanged
    }
  }

  /**
   * Calculate Levenshtein distance between two strings
   */
  private levenshteinDistance(str1: string, str2: string): number {
    const matrix = Array(str2.length + 1).fill(null).map(() => Array(str1.length + 1).fill(null));
    
    for (let i = 0; i <= str1.length; i++) matrix[0][i] = i;
    for (let j = 0; j <= str2.length; j++) matrix[j][0] = j;
    
    for (let j = 1; j <= str2.length; j++) {
      for (let i = 1; i <= str1.length; i++) {
        const indicator = str1[i - 1] === str2[j - 1] ? 0 : 1;
        matrix[j][i] = Math.min(
          matrix[j][i - 1] + 1,     // deletion
          matrix[j - 1][i] + 1,     // insertion
          matrix[j - 1][i - 1] + indicator // substitution
        );
      }
    }
    
    return matrix[str2.length][str1.length];
  }

  /**
   * Find the best matching puzzle name from available options using multiple strategies
   */
  private findBestPuzzleMatch(ocrText: string): string {
    if (!this.availablePuzzleNames.length) {
      return ocrText.trim();
    }

    const cleanedOcr = ocrText.trim();
    if (!cleanedOcr) return '';
    
    // Strategy 1: Exact match (case insensitive)
    const exactMatch = this.availablePuzzleNames.find(
      name => name.toLowerCase() === cleanedOcr.toLowerCase()
    );
    if (exactMatch) return exactMatch;

    // Strategy 2: Substring match (either direction)
    const substringMatch = this.availablePuzzleNames.find(
      name => name.toLowerCase().includes(cleanedOcr.toLowerCase()) ||
              cleanedOcr.toLowerCase().includes(name.toLowerCase())
    );
    if (substringMatch) return substringMatch;

    // Strategy 3: Multiple fuzzy matching approaches
    let bestMatch = cleanedOcr;
    let bestScore = 0;
    
    for (const puzzleName of this.availablePuzzleNames) {
      const scores = [
        this.calculateLevenshteinSimilarity(cleanedOcr, puzzleName),
        this.calculateJaroWinklerSimilarity(cleanedOcr, puzzleName),
        this.calculateNGramSimilarity(cleanedOcr, puzzleName, 2)
      ];
      
      // Use the maximum score from all algorithms
      const maxScore = Math.max(...scores);
      
      // Lower threshold for better matching - force selection even with moderate confidence
      if (maxScore > bestScore && maxScore > 0.4) {
        bestScore = maxScore;
        bestMatch = puzzleName;
      }
    }
    
    // Strategy 4: If no good match found, try character-based matching
    if (bestScore < 0.6) {
      const charMatch = this.findBestCharacterMatch(cleanedOcr);
      if (charMatch) {
        bestMatch = charMatch;
      }
    }
    
    return bestMatch;
  }

  /**
   * Calculate Levenshtein similarity (normalized)
   */
  private calculateLevenshteinSimilarity(str1: string, str2: string): number {
    const distance = this.levenshteinDistance(str1.toLowerCase(), str2.toLowerCase());
    const maxLength = Math.max(str1.length, str2.length);
    return maxLength === 0 ? 1 : 1 - (distance / maxLength);
  }

  /**
   * Calculate Jaro-Winkler similarity
   */
  private calculateJaroWinklerSimilarity(str1: string, str2: string): number {
    const s1 = str1.toLowerCase();
    const s2 = str2.toLowerCase();
    
    if (s1 === s2) return 1;
    
    const matchWindow = Math.floor(Math.max(s1.length, s2.length) / 2) - 1;
    if (matchWindow < 0) return 0;
    
    const s1Matches = new Array(s1.length).fill(false);
    const s2Matches = new Array(s2.length).fill(false);
    
    let matches = 0;
    let transpositions = 0;
    
    // Find matches
    for (let i = 0; i < s1.length; i++) {
      const start = Math.max(0, i - matchWindow);
      const end = Math.min(i + matchWindow + 1, s2.length);
      
      for (let j = start; j < end; j++) {
        if (s2Matches[j] || s1[i] !== s2[j]) continue;
        s1Matches[i] = true;
        s2Matches[j] = true;
        matches++;
        break;
      }
    }
    
    if (matches === 0) return 0;
    
    // Count transpositions
    let k = 0;
    for (let i = 0; i < s1.length; i++) {
      if (!s1Matches[i]) continue;
      while (!s2Matches[k]) k++;
      if (s1[i] !== s2[k]) transpositions++;
      k++;
    }
    
    const jaro = (matches / s1.length + matches / s2.length + (matches - transpositions / 2) / matches) / 3;
    
    // Jaro-Winkler bonus for common prefix
    let prefix = 0;
    for (let i = 0; i < Math.min(s1.length, s2.length, 4); i++) {
      if (s1[i] === s2[i]) prefix++;
      else break;
    }
    
    return jaro + (0.1 * prefix * (1 - jaro));
  }

  /**
   * Calculate N-gram similarity
   */
  private calculateNGramSimilarity(str1: string, str2: string, n: number): number {
    const s1 = str1.toLowerCase();
    const s2 = str2.toLowerCase();
    
    if (s1 === s2) return 1;
    if (s1.length < n || s2.length < n) return 0;
    
    const ngrams1 = new Set<string>();
    const ngrams2 = new Set<string>();
    
    for (let i = 0; i <= s1.length - n; i++) {
      ngrams1.add(s1.substr(i, n));
    }
    
    for (let i = 0; i <= s2.length - n; i++) {
      ngrams2.add(s2.substr(i, n));
    }
    
    const intersection = new Set([...ngrams1].filter(x => ngrams2.has(x)));
    const union = new Set([...ngrams1, ...ngrams2]);
    
    return intersection.size / union.size;
  }

  /**
   * Find best match based on character frequency
   */
  private findBestCharacterMatch(ocrText: string): string | null {
    let bestMatch = null;
    let bestScore = 0;
    
    for (const puzzleName of this.availablePuzzleNames) {
      const score = this.calculateCharacterFrequencyScore(ocrText.toLowerCase(), puzzleName.toLowerCase());
      if (score > bestScore && score > 0.3) {
        bestScore = score;
        bestMatch = puzzleName;
      }
    }
    
    return bestMatch;
  }

  /**
   * Calculate character frequency similarity
   */
  private calculateCharacterFrequencyScore(str1: string, str2: string): number {
    const freq1 = new Map<string, number>();
    const freq2 = new Map<string, number>();
    
    for (const char of str1) {
      freq1.set(char, (freq1.get(char) || 0) + 1);
    }
    
    for (const char of str2) {
      freq2.set(char, (freq2.get(char) || 0) + 1);
    }
    
    const allChars = new Set([...freq1.keys(), ...freq2.keys()]);
    let similarity = 0;
    let totalChars = 0;
    
    for (const char of allChars) {
      const count1 = freq1.get(char) || 0;
      const count2 = freq2.get(char) || 0;
      similarity += Math.min(count1, count2);
      totalChars += Math.max(count1, count2);
    }
    
    return totalChars === 0 ? 0 : similarity / totalChars;
  }

  /**
   * Force a match to available puzzle names - always returns a puzzle name
   * This is used as a last resort to ensure OCR always selects from available puzzles
   */
  private findBestPuzzleMatchForced(ocrText: string): string | null {
    if (!this.availablePuzzleNames.length || !ocrText.trim()) {
      return null;
    }

    const cleanedOcr = ocrText.trim().toLowerCase();
    let bestMatch = this.availablePuzzleNames[0]; // Default to first puzzle
    let bestScore = 0;

    // Try all matching algorithms and pick the best overall score
    for (const puzzleName of this.availablePuzzleNames) {
      const scores = [
        this.calculateLevenshteinSimilarity(cleanedOcr, puzzleName),
        this.calculateJaroWinklerSimilarity(cleanedOcr, puzzleName),
        this.calculateNGramSimilarity(cleanedOcr, puzzleName, 2),
        this.calculateCharacterFrequencyScore(cleanedOcr, puzzleName.toLowerCase()),
        // Add length similarity bonus
        this.calculateLengthSimilarity(cleanedOcr, puzzleName.toLowerCase())
      ];

      // Use weighted average with emphasis on character frequency and length
      const weightedScore = (
        scores[0] * 0.25 + // Levenshtein
        scores[1] * 0.25 + // Jaro-Winkler  
        scores[2] * 0.2 +  // N-gram
        scores[3] * 0.2 +  // Character frequency
        scores[4] * 0.1    // Length similarity
      );

      if (weightedScore > bestScore) {
        bestScore = weightedScore;
        bestMatch = puzzleName;
      }
    }

    console.log(`Forced match for "${ocrText}": "${bestMatch}" (score: ${bestScore.toFixed(3)})`);
    return bestMatch;
  }

  /**
   * Calculate similarity based on string length
   */
  private calculateLengthSimilarity(str1: string, str2: string): number {
    const len1 = str1.length;
    const len2 = str2.length;
    const maxLen = Math.max(len1, len2);
    const minLen = Math.min(len1, len2);
    
    return maxLen === 0 ? 1 : minLen / maxLen;
  }


  async terminate(): Promise<void> {
    if (this.worker) {
      await this.worker.terminate();
      this.worker = null;
    }
  }

  // Utility method to validate if an image looks like an Opus Magnum screenshot
  static isValidOpusMagnumImage(file: File): boolean {
    // Basic validation - could be enhanced with actual image analysis
    const validTypes = ['image/jpeg', 'image/jpg', 'image/png', 'image/gif'];
    return validTypes.includes(file.type);
  }

  // Debug method to visualize OCR regions (similar to main.py debug rectangles)
  static drawDebugRegions(imageFile: File): Promise<string> {
    return new Promise((resolve, reject) => {
      const imageUrl = URL.createObjectURL(imageFile);
      const img = new Image();
      
      img.onload = () => {
        const canvas = document.createElement('canvas');
        const ctx = canvas.getContext('2d')!;
        
        canvas.width = img.width;
        canvas.height = img.height;
        ctx.drawImage(img, 0, 0);

        // Draw debug rectangles
        ctx.strokeStyle = '#00ff00';
        ctx.lineWidth = 2;
        
        const service = new OpusMagnumOCRService();
        Object.values(service.regions).forEach(region => {
          ctx.strokeRect(region.x, region.y, region.width, region.height);
        });

        URL.revokeObjectURL(imageUrl);
        resolve(canvas.toDataURL());
      };

      img.onerror = () => {
        URL.revokeObjectURL(imageUrl);
        reject(new Error('Failed to load image for debug'));
      };

      img.src = imageUrl;
    });
  }
}

// Singleton instance for the application
export const ocrService = new OpusMagnumOCRService();