tfm_ainventory/frontend/hooks/useScanner.ts

import { useState, useCallback, useEffect } from 'react';
import { db, Item } from '@/lib/db';
import { toast } from 'react-hot-toast';

interface UseScannerOptions {
  inventory: Item[];
  isOnline: boolean;
  onSync: () => Promise<void>;
  onMatchFound?: (item: Item, adjustType: 'ADD' | 'REMOVE') => void;
  onMultipleMatches?: (items: Item[]) => void;
  onFieldCapture?: (field: string, value: string) => void;
}

// Fuzzy string matching with Levenshtein distance
// Returns true if strings are similar enough (allowing 1-2 character differences)
function fuzzyMatch(str1: string, str2: string, maxDistance: number = 2): boolean {
  const s1 = str1.toLowerCase().replace(/\s+/g, '');
  const s2 = str2.toLowerCase().replace(/\s+/g, '');

  if (s1 === s2) return true;
  if (Math.abs(s1.length - s2.length) > maxDistance) return false;

  // Levenshtein distance
  const matrix: number[][] = Array(s2.length + 1).fill(null).map(() => Array(s1.length + 1).fill(0));
  for (let i = 0; i <= s1.length; i++) matrix[0][i] = i;
  for (let j = 0; j <= s2.length; j++) matrix[j][0] = j;

  for (let j = 1; j <= s2.length; j++) {
    for (let i = 1; i <= s1.length; i++) {
      const indicator = s1[i - 1] === s2[j - 1] ? 0 : 1;
      matrix[j][i] = Math.min(
        matrix[j][i - 1] + 1,
        matrix[j - 1][i] + 1,
        matrix[j - 1][i - 1] + indicator
      );
    }
  }

  return matrix[s2.length][s1.length] <= maxDistance;
}

export function useScanner(options: UseScannerOptions) {
  const { inventory, isOnline, onSync, onMatchFound, onMultipleMatches, onFieldCapture } = options;
  const [mode, setMode] = useState<'CHECK_IN' | 'CHECK_OUT' | 'TRASH'>('CHECK_OUT');
  const [showScanner, setShowScanner] = useState(false);
  const [lastScanned, setLastScanned] = useState<string | null>(null);
  const [isScannerReady, setIsScannerReady] = useState(false);
  const [fieldScanning, setFieldScanning] = useState<{ active: boolean, field: string } | null>(null);

  const preloadOCR = useCallback(async () => {
    try {
      const { createWorker } = await import('tesseract.js');
      const worker = await createWorker('eng');
      await worker.terminate();
      setIsScannerReady(true);
    } catch (e) {
      console.warn("OCR Preload failed - will retry on demand", e);
    }
  }, []);

  useEffect(() => {
    preloadOCR();
  }, [preloadOCR]);

  const onScanSuccess = useCallback(async (barcode: string) => {
    setLastScanned(barcode);
    setShowScanner(false);

    const normalizedBarcode = barcode.toLowerCase();
    const item = await db.items.where('barcode').equals(barcode)
      .or('part_number').equals(normalizedBarcode).first();

    if (!item) {
      toast.error(`Item ${barcode} not found in catalog.`);
      return;
    }

    await db.pendingOperations.add({
      type: mode,
      barcode: item.barcode,
      quantity: 1,
      timestamp: Date.now(),
      synced: 0,
      uuid: crypto.randomUUID()
    });

    const newQty = mode === 'CHECK_IN' ? item.quantity + 1 : item.quantity - 1;
    await db.items.update(item.id!, { quantity: newQty });

    toast.success(`${mode === 'CHECK_IN' ? 'Checked in' : 'Checked out'} ${item.name}`);

    if (isOnline) {
      await onSync();
    }
  }, [mode, isOnline, onSync]);

  const onOCRMatch = useCallback(async (text: string) => {
    const cleanText = text.toLowerCase().replace(/[^a-z0-9\s/+-]/g, ' ');

    // Garbage Filter: Ignore noisy strings
    const tokens = cleanText.split(/[\s\n,]+/)
      .filter(t => t.length >= 3)
      .filter(t => !/^\d+\.\d+$/.test(t))
      .filter(t => !/^\d{2,4}-\d{2}-\d{2}$/.test(t));

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

    // Targeted Field Scan Logic
    if (fieldScanning?.active) {
      if (fieldScanning.field === 'box_label') {
        const potentialLabel = tokens[0] || cleanText;
        toast.success(`Captured: ${potentialLabel}`);
        setFieldScanning(null);
        if (onFieldCapture) {
          onFieldCapture('box_label', potentialLabel);
        }
        return;
      }
    }

    toast(`Scanning: ${cleanText.substring(0, 30)}...`, { icon: '🔍', duration: 1500, id: 'ocr-scan' });

    // BOX SCANNING LOGIC
    const possibleBoxItems = inventory.filter(item => {
      if (!item.box_label) return false;
      const boxText = item.box_label.toLowerCase().replace(/[^a-z0-9\s/+-]/g, ' ');
      if (cleanText.includes(boxText)) return true;
      const boxTokens = boxText.split(/[\s/+-]/).filter(t => t.length >= 4);
      const matchedTokens = boxTokens.filter(bt => tokens.includes(bt));
      return matchedTokens.length >= 2 || (boxTokens.length === 1 && matchedTokens.length === 1);
    });

    if (possibleBoxItems.length === 1) {
      toast.success(`Box identified: 1 item found`, { duration: 3000, id: 'ocr-success' });
      setShowScanner(false);
      if (onMatchFound) {
        onMatchFound(possibleBoxItems[0], mode === 'CHECK_IN' ? 'ADD' : 'REMOVE');
      }
      return;
    } else if (possibleBoxItems.length > 1) {
      toast.success(`Box identified: ${possibleBoxItems.length} items found`, { duration: 3000, id: 'ocr-success' });
      setShowScanner(false);
      if (onMultipleMatches) {
        onMultipleMatches(possibleBoxItems);
      }
      return;
    }

    // INDIVIDUAL ITEM MATCHING
    let bestMatch = null;
    let maxMatchScore = 0;

    for (const item of inventory) {
      let score = 0;
      const pn = (item.part_number || '').toLowerCase();
      const sn = (item.serial_number || '').toLowerCase();
      const name = item.name.toLowerCase();
      const category = item.category.toLowerCase();
      const ocrKey = (item.ocr_text || '').toLowerCase().replace(/[^a-z0-9\s/+-]/g, ' ');

      if (ocrKey) {
        if (cleanText.includes(ocrKey)) {
          score += 1000;
        } else {
          const ocrTokens = ocrKey.split(/[\s/+-]+/).filter(t => t.length >= 2);
          const matchedTokens = ocrTokens.filter(token => {
            return tokens.some(t => fuzzyMatch(t, token, 2));
          });
          if (matchedTokens.length >= Math.ceil(ocrTokens.length * 0.7)) {
            score += 800;
          }
        }
      }

      if (sn && cleanText.includes(sn)) score += 500;

      if (pn) {
        if (cleanText.includes(pn)) {
          score += 200;
        } else {
          const pnTokens = pn.split(/[\s/+-]/).filter(t => t.length >= 2);
          const matchedPnTokens = pnTokens.filter(pnToken =>
            tokens.some(t => fuzzyMatch(t, pnToken, 1))
          );
          if (matchedPnTokens.length >= Math.max(2, Math.ceil(pnTokens.length * 0.6))) {
            score += 150;
          }
        }
      }

      if (pn) {
        const pnTokens = pn.split(/[\s/+-]/).filter(t => t.length >= 3);
        pnTokens.forEach(t => {
          if (cleanText.includes(t)) {
            score += 50;
          } else if (tokens.some(scanToken => fuzzyMatch(scanToken, t, 1))) {
            score += 30;
          }
        });
      }

      const nameTokens = name.split(/[\s/+-]/).filter(t => t.length >= 3);
      nameTokens.forEach(t => { if (cleanText.includes(t)) score += 10; });

      if (category && cleanText.includes(category)) score += 20;

      if (score > maxMatchScore) {
        maxMatchScore = score;
        bestMatch = item;
      }
    }

    if (bestMatch && maxMatchScore >= 40) {
      toast.success(`Matched: ${bestMatch.name}`, { duration: 3000, id: 'ocr-success' });
      setShowScanner(false);
      if (onMatchFound) {
        onMatchFound(bestMatch, mode === 'CHECK_IN' ? 'ADD' : 'REMOVE');
      }
    }
  }, [mode, inventory, fieldScanning, isOnline, onMatchFound, onMultipleMatches, onFieldCapture]);

  return {
    mode,
    setMode,
    showScanner,
    setShowScanner,
    lastScanned,
    setLastScanned,
    isScannerReady,
    fieldScanning,
    setFieldScanning,
    onScanSuccess,
    onOCRMatch,
    preloadOCR
  };
}