Cross-reference photos against assets DB
- Scanned all 15 photos in uploads/photos/ via vision + Tesseract OCR - Matched keurig_label.jpg → Asset #5144 (machine_id 636671) - Updated Asset #5144 photo_path in assets.db - Created scripts/crossref_photos.py for reusable batch matching - Reported unmatched IDs for coca_cola_label.jpg and telemetry_device.jpg - Generated crossref_report.md with full findings
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# Photo Cross-Reference Report
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**Generated:** 2026-05-29
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## Database
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- assets.db: 7,488 assets
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## Photos Scanned
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15 files in uploads/photos/
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## Results
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### Matched (1 photo → 1 asset update)
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| Photo | Asset ID | Machine ID | Name | Action |
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|-------|----------|------------|------|--------|
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| keurig_label.jpg | 5144 | 636671 | 636671 / 956 Cypress Way | ✅ photo_path set to /uploads/photos/keurig_label.jpg |
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### Unmatched — For Investigation (2 photos)
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**coca_cola_label.jpg** — Coca-Cola branded label
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- Extracted IDs: RY10006338, RVCC-660-8, 201037BA00039
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- RY10006338 is Royal-pattern serial but not in DB
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- 201037BA00039 is Royal serial format; closest DB match: Asset #6815 (A201037BA00030)
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- Likely a pulled/retired or unregistered machine
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**telemetry_device.jpg** — 4G telemetry device
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- Extracted IDs: 4434331624226353 (ICCID), 48602143 (Monyx ID)
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- These are telemetry/sim identifiers, not vending machine assets
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- Consider tracking in a separate telemetry devices table
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### Test Data (3 photos)
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- 557ba8d1..., 59dd8174..., a8c7e553...: "Machine 1D 12845-678901" — app test pattern, no DB match
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### No Text (9 photos)
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- Icon-sized images (100x100 to 200x100): no readable text
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## Files Changed
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- `/home/oplabs/projects/canteen-asset-tracker/assets.db` — Asset #5144 photo_path updated
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- `/home/oplabs/projects/canteen-asset-tracker/scripts/crossref_photos.py` — reusable batch matching script
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#!/usr/bin/env python3
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"""
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Cross-reference extracted photo IDs against assets DB.
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Processes all photos in uploads/photos/, runs vision/OCR text extraction,
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matches identifiers against assets.machine_id / serial_number / connect_id,
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updates matched assets with photo_path, and reports unmatched IDs.
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Usage:
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python3 scripts/crossref_photos.py # process all photos
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python3 scripts/crossref_photos.py --photo <name> # single photo
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python3 scripts/crossref_photos.py --text "S/N: 1234" # from text only
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"""
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import argparse
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import json
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import os
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import re
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import sqlite3
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import sys
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from datetime import datetime
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from classify_makes import normalize_identifier, find_asset_by_normalized_id
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DB_PATH = str(Path(__file__).resolve().parent.parent / "assets.db")
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PHOTOS_DIR = str(Path(__file__).resolve().parent.parent / "uploads" / "photos")
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def process_photo(photo_path: str, db_path: str = DB_PATH) -> dict:
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"""Process a single photo: extract text, match DB, update photo_path."""
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fname = os.path.basename(photo_path)
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result = {
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"photo": fname,
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"photo_path": f"/uploads/photos/{fname}",
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"size_bytes": os.path.getsize(photo_path),
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"vision_text": "",
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"extracted_ids": [],
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"matches": [],
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"updated": False,
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}
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# Try Tesseract OCR
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try:
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import pytesseract
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from PIL import Image as PILImage
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img = PILImage.open(photo_path)
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img_gray = img.convert("L")
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text = pytesseract.image_to_string(img_gray, config="--psm 6")
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text = text.strip()
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clean_chars = sum(1 for c in text if c.isalnum() or c in " \n/-.")
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if text and clean_chars >= 4:
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result["vision_text"] = text
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except ImportError:
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pass
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if not result["vision_text"]:
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result["vision_source"] = "none"
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return result
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result["vision_source"] = "tesseract"
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# Extract identifiers from text
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identifiers = []
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lines = result["vision_text"].split("\n")
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for line in lines:
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line = line.strip()
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if not line or len(line) < 4:
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continue
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norm = normalize_identifier(line)
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if norm and len(norm) >= 4:
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identifiers.append(norm)
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tokens = re.findall(r"[A-Za-z0-9]{4,}", line)
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for token in tokens:
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norm = normalize_identifier(token)
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if norm and len(norm) >= 4 and norm not in identifiers:
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identifiers.append(norm)
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result["extracted_ids"] = identifiers
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# Match against DB
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conn = sqlite3.connect(db_path)
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conn.row_factory = sqlite3.Row
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seen_ids = set()
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for norm_id in identifiers:
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assets = find_asset_by_normalized_id(db_path, norm_id)
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for a in assets:
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if a["id"] not in seen_ids:
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seen_ids.add(a["id"])
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result["matches"].append(a)
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# Update photo_path if empty
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existing = conn.execute(
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"SELECT photo_path FROM assets WHERE id = ?", (a["id"],)
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).fetchone()
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if existing and not existing["photo_path"]:
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conn.execute(
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"UPDATE assets SET photo_path = ?, updated_at = datetime('now') WHERE id = ?",
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(result["photo_path"], a["id"]),
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)
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result["updated"] = True
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conn.commit()
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conn.close()
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return result
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def main():
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parser = argparse.ArgumentParser(
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description="Cross-reference photo IDs against assets DB"
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)
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parser.add_argument(
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"--photo", "-p", help="Process a single photo by filename"
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)
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parser.add_argument(
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"--text", "-t", help="Process raw text directly (skip OCR)"
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)
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parser.add_argument(
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"--db",
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default=DB_PATH,
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help=f"Database path (default: {DB_PATH})",
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)
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args = parser.parse_args()
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results = []
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if args.text:
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# Process from text directly
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print(f"\n{'=' * 70}")
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print(f"PROCESSING: supplied text ({len(args.text)} chars)")
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print(f"{'=' * 70}")
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print(f" Text: {args.text[:500]}")
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identifiers = []
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norm = normalize_identifier(args.text)
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if norm and len(norm) >= 4:
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identifiers.append(norm)
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tokens = re.findall(r"[A-Za-z0-9]{4,}", args.text)
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for token in tokens:
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n = normalize_identifier(token)
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if n and len(n) >= 4 and n not in identifiers:
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identifiers.append(n)
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print(f"\n Normalized IDs: {identifiers}")
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conn = sqlite3.connect(args.db)
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conn.row_factory = sqlite3.Row
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for norm_id in identifiers:
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assets = find_asset_by_normalized_id(args.db, norm_id)
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if assets:
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for a in assets:
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print(f"\n ✓ MATCH → Asset #{a['id']}")
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print(f" Machine ID: {a['machine_id']}")
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print(f" Name: {a['name'][:60]}")
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else:
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print(f"\n ✗ No match for '{norm_id}'")
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conn.close()
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return
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if args.photo:
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photo_path = os.path.join(PHOTOS_DIR, args.photo)
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if not os.path.exists(photo_path):
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print(f"ERROR: {photo_path} not found")
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sys.exit(1)
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results.append(process_photo(photo_path, args.db))
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else:
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# Process all photos
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for fname in sorted(os.listdir(PHOTOS_DIR)):
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photo_path = os.path.join(PHOTOS_DIR, fname)
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if os.path.isfile(photo_path):
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results.append(process_photo(photo_path, args.db))
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# Print summary
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matched = [r for r in results if r["matches"]]
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unmatched = [r for r in results if not r["matches"] and r["vision_text"]]
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no_text = [r for r in results if not r["vision_text"]]
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print(f"\n{'=' * 70}")
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print(f"CROSS-REFERENCE SUMMARY ({datetime.now().isoformat()})")
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print(f"{'=' * 70}")
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print(f" Photos scanned: {len(results)}")
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print(f" Photos with text: {len(results) - len(no_text)}")
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print(f" Matches found: {len(matched)}")
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print(f" Unmatched: {len(unmatched)}")
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print(f" No text (small/thumbs): {len(no_text)}")
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for r in matched:
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for a in r["matches"]:
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status = "UPDATED" if r["updated"] else "already set"
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print(
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f"\n ✓ {r['photo']} → Asset #{a['id']} "
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f"(machine_id={a['machine_id']}) [{status}]"
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)
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for r in unmatched:
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print(f"\n ✗ {r['photo']} — no DB match")
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print(f" IDs: {r['extracted_ids'][:5]}")
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print(f" Text preview: {r['vision_text'][:100]}")
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print()
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if __name__ == "__main__":
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main()
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