feat: persistence, rerun OCR, Google Gemini, sticker mode, manual entry

- SQLite DB (photos.db) — persists all processed photo records - Dedup by SHA256 hash — same file upload returns duplicate: true - /api/photos — list previously processed photos - /api/photos/{id} — get single record - /api/photos/{id}/file — serve saved image - /api/photos/{id}/reprocess — re-run OCR with different engine/model - Google Gemini OCR engine (gemini-2.5-flash, free tier) alongside OpenCode Go LLM and Tesseract - Sticker mode — specialized LLM/Google prompt for green/orange/yellow equipment stickers with 2D barcode + machine ID - Manual machine ID entry — when GPS exists but OCR fails, show text input for manual lookup - Frontend: Previous Photos section, Re-run OCR per photo, duplicate badges, engine dropdown, sticker toggle
2026-05-25 17:42:10 -04:00
parent 70d8374ca6
commit a8b1e694b0
4 changed files with 802 additions and 182 deletions
@@ -1,8 +1,9 @@
 """EXIF + OCR test backend — validate that GPS survives upload pipeline."""
-import io, json, os, re, uuid, sqlite3, urllib.request
+import hashlib, io, json, os, re, uuid, sqlite3, urllib.request
 from pathlib import Path

 from fastapi import FastAPI, File, Form, HTTPException, Query, UploadFile
+from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
 from PIL import Image as PILImage

@@ -25,13 +26,148 @@ OPENCODE_GO_KEY = os.environ.get("OPENCODE_GO_API_KEY", "")
 OPENCODE_GO_BASE = os.environ.get("OPENCODE_GO_BASE_URL", "https://opencode.ai/zen/go/v1")
 LLM_OCR_MODEL = os.environ.get("LLM_OCR_MODEL", "mimo-v2-omni")

+# === Google Gemini OCR (free tier) ===
+GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY", "")
+GOOGLE_API_BASE = os.environ.get("GOOGLE_API_BASE_URL", "https://generativelanguage.googleapis.com/v1beta")
+GOOGLE_OCR_MODEL = os.environ.get("GOOGLE_OCR_MODEL", "gemini-2.5-flash")
+
 UPLOADS = Path(__file__).parent / "uploads"
 UPLOADS.mkdir(exist_ok=True)
+PHOTOS_DB = Path(__file__).parent / "photos.db"
 CANTEEN_DB = Path(__file__).parent.parent / "canteen-asset-tracker" / "assets.db"

+# Max images per batch API call
+BATCH_SIZE_LIMIT = 20
+# Downscale images to this max dimension before LLM OCR
+LLM_IMAGE_MAX_DIM = 1600
+
+# ---------------------------------------------------------------------------
+# Sticker-mode prompts
+# ---------------------------------------------------------------------------
+DEFAULT_OCR_PROMPT = (
+    "Read ALL text and numbers visible in this photo. "
+    "Return the exact text shown, nothing else."
+)
+
+STICKER_OCR_PROMPT = (
+    "This photo shows a colored equipment sticker (green, orange, or yellow background) "
+    "with a 2D barcode and a machine ID number printed below the barcode. "
+    "Read ONLY the machine ID number that appears below the barcode. "
+    "It is typically a 5-digit number followed by a dash and 6 more digits "
+    "(e.g., 12345-678901). Return ONLY the machine ID number, nothing else."
+)
+
+DEFAULT_BATCH_PROMPT = (
+    "I have {n} photos. For EACH photo, read ALL visible text and numbers.\n"
+    'Respond with a JSON array of objects, one per photo in order:\n'
+    '[{{"i":0,"text":"all text and digits found","digits":"e.g. 12345-678901 or null if none"}}, ...]\n'
+    'Return ONLY the JSON array, no markdown, no explanation.'
+)
+
+STICKER_BATCH_PROMPT = (
+    "I have {n} photos of colored equipment stickers (green, orange, or yellow). "
+    "Each sticker has a 2D barcode and a machine ID printed below it.\n"
+    'Respond with a JSON array of objects, one per photo in order:\n'
+    '[{{"i":0,"sticker_color":"green/orange/yellow/unknown","machine_id":"12345-678901 or null if not found"}}, ...]\n'
+    'Return ONLY the JSON array, no markdown, no explanation.'
+)
+
+# ---------------------------------------------------------------------------
+# App + DB init
+# ---------------------------------------------------------------------------
 app = FastAPI(title="EXIF Test")


+def _init_photos_db():
+    """Create photos table for persistence + dedup."""
+    conn = sqlite3.connect(str(PHOTOS_DB))
+    conn.execute("""
+        CREATE TABLE IF NOT EXISTS photos (
+            id            INTEGER PRIMARY KEY AUTOINCREMENT,
+            orig_filename TEXT NOT NULL,
+            file_hash     TEXT NOT NULL UNIQUE,
+            saved_as      TEXT NOT NULL,
+            file_size     INTEGER,
+            exif_json     TEXT,
+            gps_lat       REAL,
+            gps_lng       REAL,
+            ocr_engine    TEXT,
+            ocr_model     TEXT,
+            ocr_raw_text  TEXT,
+            ocr_match_5dash6 TEXT,
+            ocr_match_5plus   TEXT,
+            machine_id    TEXT,
+            sticker_color TEXT,
+            has_barcode   INTEGER DEFAULT 0,
+            created_at    TEXT DEFAULT (datetime('now'))
+        )
+    """)
+    conn.commit()
+    conn.close()
+
+
+_init_photos_db()
+
+
+# ---------------------------------------------------------------------------
+# DB helpers
+# ---------------------------------------------------------------------------
+def _get_photos_db() -> sqlite3.Connection:
+    conn = sqlite3.connect(str(PHOTOS_DB))
+    conn.row_factory = sqlite3.Row
+    return conn
+
+
+def _file_hash(data: bytes) -> str:
+    return hashlib.sha256(data).hexdigest()
+
+
+def _save_photo_to_db(
+    orig_filename: str, file_hash: str, saved_as: str, file_size: int,
+    exif_result: dict, ocr_result: dict, machine_id: str | None,
+) -> int:
+    """Insert a photo record. Returns the row id."""
+    conn = _get_photos_db()
+    try:
+        cur = conn.execute(
+            """INSERT OR IGNORE INTO photos
+            (orig_filename, file_hash, saved_as, file_size, exif_json,
+             gps_lat, gps_lng, ocr_engine, ocr_model, ocr_raw_text,
+             ocr_match_5dash6, ocr_match_5plus, machine_id)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
+            (
+                orig_filename,
+                file_hash,
+                saved_as,
+                file_size,
+                json.dumps(exif_result) if exif_result else None,
+                exif_result.get("gps", {}).get("lat") if exif_result and exif_result.get("gps") else None,
+                exif_result.get("gps", {}).get("lng") if exif_result and exif_result.get("gps") else None,
+                ocr_result.get("engine") if ocr_result else None,
+                ocr_result.get("llm_model") if ocr_result else None,
+                ocr_result.get("raw_text") if ocr_result else None,
+                ocr_result.get("match_5dash6") if ocr_result else None,
+                ocr_result.get("match_5plus") if ocr_result else None,
+                machine_id,
+            ),
+        )
+        conn.commit()
+        return cur.lastrowid or 0
+    except sqlite3.IntegrityError:
+        return 0
+    finally:
+        conn.close()
+
+
+def _row_to_dict(row: sqlite3.Row) -> dict:
+    d = dict(row)
+    d["exif_data"] = json.loads(d.pop("exif_json", "{}") or "{}")
+    return d
+
+
+# ---------------------------------------------------------------------------
+# Canteen DB helpers
+# ---------------------------------------------------------------------------
 def _get_canteen_db():
    """Get a read/write connection to the canteen assets database."""
    if not CANTEEN_DB.exists():
@@ -41,6 +177,44 @@ def _get_canteen_db():
    return conn


+def lookup_machine_id(machine_id: str) -> dict | None:
+    """Look up an asset by machine_id. Returns asset dict or None."""
+    conn = _get_canteen_db()
+    if not conn:
+        return None
+    try:
+        row = conn.execute(
+            "SELECT id, machine_id, name, category, status, address, building_name, "
+            "floor, room, latitude, longitude, make, model, description, photo_path "
+            "FROM assets WHERE machine_id = ?",
+            (machine_id.strip(),),
+        ).fetchone()
+        if row:
+            return {
+                "id": row["id"],
+                "machine_id": row["machine_id"],
+                "name": row["name"],
+                "category": row["category"],
+                "status": row["status"],
+                "address": row["address"],
+                "building_name": row["building_name"],
+                "floor": row["floor"],
+                "room": row["room"],
+                "latitude": row["latitude"],
+                "longitude": row["longitude"],
+                "make": row["make"],
+                "model": row["model"],
+                "description": row["description"],
+                "photo_path": row["photo_path"],
+            }
+    finally:
+        conn.close()
+    return None
+
+
+# ---------------------------------------------------------------------------
+# EXIF extraction
+# ---------------------------------------------------------------------------
 def _dms_to_decimal(dms, ref):
    """Convert EXIF DMS tuple to decimal degrees."""
    try:
@@ -96,6 +270,9 @@ def extract_exif(image_bytes: bytes) -> dict:
    return result


+# ---------------------------------------------------------------------------
+# Tesseract OCR
+# ---------------------------------------------------------------------------
 def run_ocr(image_bytes: bytes) -> dict:
    """Run Tesseract OCR on the image."""
    if not HAS_TESSERACT:
@@ -119,12 +296,9 @@ def run_ocr(image_bytes: bytes) -> dict:
        tmp_path.unlink(missing_ok=True)


-# Maximum images per batch API call (prevents context limit issues)
-BATCH_SIZE_LIMIT = 20
-# Downscale images to this max dimension before sending to LLM (saves image tokens)
-LLM_IMAGE_MAX_DIM = 1600
-
-
+# ---------------------------------------------------------------------------
+# LLM OCR helpers
+# ---------------------------------------------------------------------------
 def _resize_for_llm(image_bytes: bytes, max_dim: int = LLM_IMAGE_MAX_DIM) -> bytes:
    """Downscale image to max_dim on longest edge to save vision API costs."""
    img = PILImage.open(io.BytesIO(image_bytes))
@@ -136,7 +310,6 @@ def _resize_for_llm(image_bytes: bytes, max_dim: int = LLM_IMAGE_MAX_DIM) -> byt
    img_resized = img.resize(new_size, PILImage.LANCZOS)
    buf = io.BytesIO()
    ext = img.format or "JPEG"
-    # Convert PNG, WebP, etc. to JPEG for smaller size
    if ext.upper() in ("PNG", "WEBP", "TIFF", "BMP"):
        img_resized = img_resized.convert("RGB")
        ext = "JPEG"
@@ -144,11 +317,14 @@ def _resize_for_llm(image_bytes: bytes, max_dim: int = LLM_IMAGE_MAX_DIM) -> byt
    return buf.getvalue()


-def run_ocr_llm(image_bytes: bytes, model: str | None = None) -> dict:
+def run_ocr_llm(
+    image_bytes: bytes, model: str | None = None,
+    sticker_mode: bool = False,
+) -> dict:
    """Run OCR via an LLM vision model on OpenCode Go.

    Falls back to Tesseract if the API key is missing or the call fails.
-    Returns the same shape as run_ocr() with an additional ''engine'' field.
+    Returns the same shape as run_ocr() with an additional 'engine' field.
    """
    if not OPENCODE_GO_KEY:
        result = run_ocr(image_bytes)
@@ -159,17 +335,17 @@ def run_ocr_llm(image_bytes: bytes, model: str | None = None) -> dict:
    import base64

    model = model or LLM_OCR_MODEL
-    # Downscale to save costs
    image_bytes = _resize_for_llm(image_bytes)
    b64 = base64.b64encode(image_bytes).decode()

+    prompt = STICKER_OCR_PROMPT if sticker_mode else DEFAULT_OCR_PROMPT
+
    body = json.dumps({
        "model": model,
        "messages": [{
            "role": "user",
            "content": [
-                {"type": "text", "text": "Read ALL text and numbers visible in this photo. "
-                 "Return the exact text shown, nothing else."},
+                {"type": "text", "text": prompt},
                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64}"}}
            ]
        }],
@@ -191,7 +367,6 @@ def run_ocr_llm(image_bytes: bytes, model: str | None = None) -> dict:
        result = json.loads(resp.read())
        text = result["choices"][0]["message"]["content"].strip()
    except Exception as exc:
-        # Fallback to Tesseract
        result = run_ocr(image_bytes)
        result["engine"] = "tesseract"
        result["llm_fallback_reason"] = str(exc)[:200]
@@ -200,7 +375,7 @@ def run_ocr_llm(image_bytes: bytes, model: str | None = None) -> dict:
    match_5dash = re.search(r"(\d{5})[-\s]*(\d{6,})", text)
    match_5plus = re.search(r"(\d{5,})", text)

-    return {
+    out = {
        "available": True,
        "engine": "llm",
        "llm_model": model,
@@ -208,17 +383,20 @@ def run_ocr_llm(image_bytes: bytes, model: str | None = None) -> dict:
        "match_5dash6": match_5dash.group(0) if match_5dash else None,
        "match_5plus": match_5plus.group(0) if match_5plus else None,
    }
+    if sticker_mode:
+        # Try to detect sticker color from the response
+        color_match = re.search(r"\b(green|orange|yellow)\b", text, re.I)
+        out["sticker_color"] = color_match.group(1).lower() if color_match else "unknown"
+    return out


-def run_ocr_llm_batch(images: list[tuple[str, bytes]], model: str | None = None, max_per_batch: int = BATCH_SIZE_LIMIT) -> list[dict]:
-    """Batch OCR multiple images in a single LLM API call.
-
-    Sends all images in one request with a structured JSON prompt.
-    Returns list of OCR results in the same order as the input images.
-    Falls back to individual calls if the batch call fails.
-
-    images: list of (filename, image_bytes)
-    """
+def run_ocr_llm_batch(
+    images: list[tuple[str, bytes]],
+    model: str | None = None,
+    max_per_batch: int = BATCH_SIZE_LIMIT,
+    sticker_mode: bool = False,
+) -> list[dict]:
+    """Batch OCR multiple images in a single LLM API call."""
    import base64

    if not OPENCODE_GO_KEY:
@@ -226,31 +404,25 @@ def run_ocr_llm_batch(images: list[tuple[str, bytes]], model: str | None = None,

    model = model or LLM_OCR_MODEL

-    # Split into sub-batches if over the limit
    all_results: list[dict] = []
    for batch_start in range(0, len(images), max_per_batch):
        batch = images[batch_start:batch_start + max_per_batch]
-        batch_results = _run_ocr_llm_batch_inner(batch, model)
+        batch_results = _run_ocr_llm_batch_inner(batch, model, sticker_mode)
        all_results.extend(batch_results)
    return all_results


-def _run_ocr_llm_batch_inner(batch: list[tuple[str, bytes]], model: str) -> list[dict]:
+def _run_ocr_llm_batch_inner(
+    batch: list[tuple[str, bytes]], model: str,
+    sticker_mode: bool = False,
+) -> list[dict]:
    """Inner helper: sends one batch of images in a single API call."""
    import base64

-    # Resize all images first
    resized = [(_resize_for_llm(b), n) for n, b in batch]
+    batch_prompt = (STICKER_BATCH_PROMPT if sticker_mode else DEFAULT_BATCH_PROMPT).format(n=len(batch))

-    content: list[dict] = [{
-        "type": "text",
-        "text": (
-            f"I have {len(batch)} photos. For EACH photo, read ALL visible text and numbers.\n"
-            "Respond with a JSON array of objects, one per photo in order:\n"
-            '[{"i":0,"text":"all text and digits found","digits":"e.g. 12345-678901 or null if none"}, ...]\n'
-            'Return ONLY the JSON array, no markdown, no explanation.'
-        )
-    }]
+    content: list[dict] = [{"type": "text", "text": batch_prompt}]

    for idx, (img_bytes, _) in enumerate(resized):
        b64 = base64.b64encode(img_bytes).decode()
@@ -281,10 +453,8 @@ def _run_ocr_llm_batch_inner(batch: list[tuple[str, bytes]], model: str) -> list
        result = json.loads(resp.read())
        raw = result["choices"][0]["message"]["content"].strip()
    except Exception:
-        # Fallback: individual calls for this batch
-        return [run_ocr_llm(b, model) for n, b in batch]
+        return [run_ocr_llm(b, model, sticker_mode=sticker_mode) for n, b in batch]

-    # Strip markdown code fences if present
    raw_clean = raw
    if raw_clean.startswith("```"):
        raw_clean = raw_clean.split("\n", 1)[-1]
@@ -294,85 +464,130 @@ def _run_ocr_llm_batch_inner(batch: list[tuple[str, bytes]], model: str) -> list
        parsed = json.loads(raw_clean)
        results: list[dict] = []
        for item in parsed:
-            raw_text = str(item.get("text", "") or "")
-            digit_str = str(item.get("digits") or "")
-            combined = raw_text + " " + digit_str
-            match_5dash = re.search(r"(\d{5})[-\s]*(\d{6,})", combined)
-            match_5plus = re.search(r"(\d{5,})", combined)
-            results.append({
+            if sticker_mode:
+                raw_text = str(item.get("machine_id", "") or "")
+            else:
+                raw_text = str(item.get("text", "") or "")
+                digit_str = str(item.get("digits") or "")
+                raw_text = raw_text + " " + digit_str
+            match_5dash = re.search(r"(\d{5})[-\s]*(\d{6,})", raw_text)
+            match_5plus = re.search(r"(\d{5,})", raw_text)
+            entry: dict = {
                "available": True,
                "engine": "llm_batch",
                "llm_model": model,
                "raw_text": raw_text[:500],
                "match_5dash6": match_5dash.group(0) if match_5dash else None,
                "match_5plus": match_5plus.group(0) if match_5plus else None,
-            })
+            }
+            if sticker_mode:
+                entry["sticker_color"] = str(item.get("sticker_color", "unknown"))
+            results.append(entry)
        return results
    except (json.JSONDecodeError, KeyError, TypeError):
-        # Fallback to individual calls
-        return [run_ocr_llm(b, model) for n, b in batch]
+        return [run_ocr_llm(b, model, sticker_mode=sticker_mode) for n, b in batch]


-def lookup_machine_id(machine_id: str) -> dict | None:
-    """Look up an asset by machine_id. Returns asset dict or None."""
-    conn = _get_canteen_db()
-    if not conn:
-        return None
+# ---------------------------------------------------------------------------
+# Google Gemini OCR
+# ---------------------------------------------------------------------------
+def run_ocr_google(image_bytes: bytes, model: str | None = None, sticker_mode: bool = False) -> dict:
+    """Run OCR via Google Gemini vision API (free tier)."""
+    import base64
+
+    if not GOOGLE_API_KEY:
+        result = run_ocr(image_bytes)
+        result["engine"] = "tesseract"
+        result["llm_fallback_reason"] = "no_google_api_key"
+        return result
+
+    model = model or GOOGLE_OCR_MODEL
+    image_bytes = _resize_for_llm(image_bytes)
+    b64 = base64.b64encode(image_bytes).decode()
+    prompt = STICKER_OCR_PROMPT if sticker_mode else DEFAULT_OCR_PROMPT
+
+    body = json.dumps({
+        "contents": [{
+            "parts": [
+                {"text": prompt},
+                {"inline_data": {"mime_type": "image/jpeg", "data": b64}}
+            ]
+        }],
+        "generationConfig": {"maxOutputTokens": 200, "temperature": 0.1},
+    }).encode()
+
+    req = urllib.request.Request(
+        f"{GOOGLE_API_BASE}/models/{model}:generateContent?key={GOOGLE_API_KEY}",
+        data=body,
+        headers={"Content-Type": "application/json"},
+    )
+
    try:
-        row = conn.execute(
-            "SELECT id, machine_id, name, category, status, address, building_name, "
-            "floor, room, latitude, longitude, make, model, description, photo_path "
-            "FROM assets WHERE machine_id = ?",
-            (machine_id.strip(),),
-        ).fetchone()
-        if row:
-            return {
-                "id": row["id"],
-                "machine_id": row["machine_id"],
-                "name": row["name"],
-                "category": row["category"],
-                "status": row["status"],
-                "address": row["address"],
-                "building_name": row["building_name"],
-                "floor": row["floor"],
-                "room": row["room"],
-                "latitude": row["latitude"],
-                "longitude": row["longitude"],
-                "make": row["make"],
-                "model": row["model"],
-                "description": row["description"],
-                "photo_path": row["photo_path"],
-            }
-    finally:
-        conn.close()
-    return None
+        resp = urllib.request.urlopen(req, timeout=60)
+        result = json.loads(resp.read())
+        text = result["candidates"][0]["content"]["parts"][0]["text"].strip()
+    except Exception as exc:
+        result = run_ocr(image_bytes)
+        result["engine"] = "tesseract"
+        result["llm_fallback_reason"] = str(exc)[:200]
+        return result
+
+    match_5dash = re.search(r"(\d{5})[-\s]*(\d{6,})", text)
+    match_5plus = re.search(r"(\d{5,})", text)
+
+    out: dict = {
+        "available": True,
+        "engine": "google",
+        "llm_model": model,
+        "raw_text": text[:500],
+        "match_5dash6": match_5dash.group(0) if match_5dash else None,
+        "match_5plus": match_5plus.group(0) if match_5plus else None,
+    }
+    if sticker_mode:
+        cm = re.search(r"\b(green|orange|yellow)\b", text, re.I)
+        out["sticker_color"] = cm.group(1).lower() if cm else "unknown"
+    return out


-@app.post("/api/analyze")
-async def analyze_photo(
-    file: UploadFile = File(...),
-    ocr_engine: str = Query(default="tesseract"),
-    ocr_model: str = Query(default=""),
-):
-    """Upload a photo, get back EXIF + OCR results.
-
-    Query params:
-      - ocr_engine: ''tesseract'' (default) or ''llm''
-      - ocr_model:  model name override (e.g. ''mimo-v2-omni'', ''glm-5.1'')
-    """
-    contents = await file.read()
+# ---------------------------------------------------------------------------
+# Process one photo (shared between analyze + bulk)
+# ---------------------------------------------------------------------------
+def _process_one(orig_filename: str, contents: bytes, ocr_engine: str,
+                 ocr_model: str | None, sticker_mode: bool) -> dict:
+    """Run EXIF + OCR on a single photo. Returns result dict."""
    file_size = len(contents)
+    fhash = _file_hash(contents)

-    ext = Path(file.filename or "photo.jpg").suffix.lower()
-    if ext not in {".jpg", ".jpeg", ".png", ".webp", ".bmp", ".tiff", ".tif", ".dng", ".heic", ".heif"}:
-        ext = ".jpg"
-    fname = f"{uuid.uuid4().hex}{ext}"
-    (UPLOADS / fname).write_bytes(contents)
+    # Check dup
+    dup_row = None
+    db_conn = _get_photos_db()
+    if db_conn:
+        try:
+            dup_row = db_conn.execute(
+                "SELECT id, saved_as FROM photos WHERE file_hash = ?", (fhash,)
+            ).fetchone()
+        finally:
+            db_conn.close()
+
+    if dup_row:
+        saved_name = dup_row["saved_as"]
+        is_dup = True
+        photo_id = dup_row["id"]
+    else:
+        ext = Path(orig_filename or "photo.jpg").suffix.lower()
+        if ext not in {".jpg", ".jpeg", ".png", ".webp", ".bmp", ".tiff", ".tif", ".dng", ".heic", ".heif"}:
+            ext = ".jpg"
+        saved_name = f"{uuid.uuid4().hex}{ext}"
+        (UPLOADS / saved_name).write_bytes(contents)
+        is_dup = False
+        photo_id = None

    exif_result = extract_exif(contents)

    if ocr_engine == "llm":
-        ocr_result = run_ocr_llm(contents, ocr_model or None)
+        ocr_result = run_ocr_llm(contents, ocr_model, sticker_mode=sticker_mode)
+    elif ocr_engine == "google":
+        ocr_result = run_ocr_google(contents, ocr_model, sticker_mode=sticker_mode)
    elif HAS_TESSERACT:
        ocr_result = run_ocr(contents)
        ocr_result["engine"] = "tesseract"
@@ -385,11 +600,20 @@ async def analyze_photo(
        machine_id = re.sub(r"\D", "", ocr_result["match_5dash6"])[-5:]
        asset = lookup_machine_id(machine_id)

+    # Save to DB if not a duplicate
+    if not is_dup and not dup_row:
+        photo_id = _save_photo_to_db(
+            orig_filename, fhash, saved_name, file_size,
+            exif_result, ocr_result, machine_id,
+        )
+
    return {
-        "filename": file.filename,
-        "saved_as": fname,
+        "filename": orig_filename,
+        "saved_as": saved_name,
+        "photo_id": photo_id or (dup_row["id"] if dup_row else None),
        "file_size": file_size,
        "file_size_kb": round(file_size / 1024, 1),
+        "duplicate": is_dup,
        "exif": exif_result,
        "ocr": ocr_result,
        "machine_id": machine_id,
@@ -397,17 +621,44 @@ async def analyze_photo(
    }


+# ---------------------------------------------------------------------------
+# Endpoints
+# ---------------------------------------------------------------------------
+@app.post("/api/analyze")
+async def analyze_photo(
+    file: UploadFile = File(...),
+    ocr_engine: str = Query(default="tesseract"),
+    ocr_model: str = Query(default=""),
+    sticker_mode: bool = Query(default=False),
+):
+    """Upload a photo, get back EXIF + OCR results.
+
+    Query params:
+      - ocr_engine: 'tesseract' (default) or 'llm'
+      - ocr_model:  model name override (e.g. 'mimo-v2-omni', 'glm-5.1')
+      - sticker_mode: if true, uses sticker-specific prompt
+    """
+    contents = await file.read()
+    result = _process_one(
+        file.filename or "photo.jpg", contents,
+        ocr_engine, ocr_model or None, sticker_mode,
+    )
+    return result
+
+
@app.post("/api/bulk-process")
 async def bulk_process(
    files: list[UploadFile] = File(...),
    ocr_engine: str = Query(default="tesseract"),
    ocr_model: str = Query(default=""),
+    sticker_mode: bool = Query(default=False),
 ):
    """Process multiple photos: OCR each, extract EXIF GPS, look up matching assets.

    Query params:
-      - ocr_engine: ''tesseract'' (default) or ''llm''
+      - ocr_engine: 'tesseract' (default) or 'llm'
      - ocr_model:  model name override
+      - sticker_mode: if true, uses sticker-specific prompt

    Returns a list of results, each with:
      - filename, exif (gps), ocr match, matched asset (if found)
@@ -423,30 +674,55 @@ async def bulk_process(
        all_files.append((file.filename or "photo.jpg", contents))

    ocr_use_llm = ocr_engine == "llm"
+    ocr_use_google = ocr_engine == "google"

    # Batch OCR if using LLM mode
    if ocr_use_llm:
-        llm_ocr_results = run_ocr_llm_batch(all_files, ocr_model or None)
+        llm_ocr_results = run_ocr_llm_batch(
+            [(f, b) for f, b in all_files],
+            ocr_model or None,
+            sticker_mode=sticker_mode,
+        )
    else:
        llm_ocr_results = None

    results = []
-    summary = {"total": len(all_files), "has_gps": 0, "matched": 0, "needs_gps": 0}
+    summary = {"total": len(all_files), "has_gps": 0, "matched": 0, "needs_gps": 0, "duplicates": 0}

    for i, (orig_fname, contents) in enumerate(all_files):
        file_size = len(contents)
+        fhash = _file_hash(contents)

-        # Save
-        ext = Path(orig_fname or "photo.jpg").suffix.lower()
-        if ext not in {".jpg", ".jpeg", ".png", ".webp", ".bmp", ".tiff", ".tif", ".dng", ".heic", ".heif"}:
-            ext = ".jpg"
-        saved_name = f"{uuid.uuid4().hex}{ext}"
-        (UPLOADS / saved_name).write_bytes(contents)
+        # Check dup
+        dup_row = None
+        db_conn = _get_photos_db()
+        if db_conn:
+            try:
+                dup_row = db_conn.execute(
+                    "SELECT id, saved_as FROM photos WHERE file_hash = ?", (fhash,)
+                ).fetchone()
+            finally:
+                db_conn.close()
+
+        if dup_row:
+            saved_name = dup_row["saved_as"]
+            is_dup = True
+            photo_id = dup_row["id"]
+        else:
+            ext = Path(orig_fname or "photo.jpg").suffix.lower()
+            if ext not in {".jpg", ".jpeg", ".png", ".webp", ".bmp", ".tiff", ".tif", ".dng", ".heic", ".heif"}:
+                ext = ".jpg"
+            saved_name = f"{uuid.uuid4().hex}{ext}"
+            (UPLOADS / saved_name).write_bytes(contents)
+            is_dup = False
+            photo_id = None

        exif_result = extract_exif(contents)

        if ocr_use_llm:
            ocr_result = llm_ocr_results[i] if i < len(llm_ocr_results) else {"available": False, "engine": "none", "error": "missing batch result"}
+        elif ocr_use_google:
+            ocr_result = run_ocr_google(contents, ocr_model or None, sticker_mode=sticker_mode)
        elif HAS_TESSERACT:
            ocr_result = run_ocr(contents)
            ocr_result["engine"] = "tesseract"
@@ -466,15 +742,26 @@ async def bulk_process(
            asset = lookup_machine_id(machine_id)
            if asset:
                summary["matched"] += 1
-                # Check if asset needs GPS
                if (asset["latitude"] is None or asset["longitude"] is None) and has_gps:
                    needs_gps = True
                    summary["needs_gps"] += 1

+        # Save to DB if not dup
+        if not is_dup and not dup_row:
+            photo_id = _save_photo_to_db(
+                orig_fname, fhash, saved_name, file_size,
+                exif_result, ocr_result, machine_id,
+            )
+
+        if is_dup:
+            summary["duplicates"] += 1
+
        results.append({
            "filename": orig_fname,
            "saved_as": saved_name,
+            "photo_id": photo_id or (dup_row["id"] if dup_row else None),
            "file_size_kb": round(file_size / 1024, 1),
+            "duplicate": is_dup,
            "exif": exif_result,
            "ocr": ocr_result,
            "machine_id": machine_id,
@@ -485,6 +772,126 @@ async def bulk_process(
    return {"results": results, "summary": summary}


+@app.get("/api/photos")
+async def list_photos(limit: int = Query(default=50, le=200)):
+    """List previously processed photos from DB (newest first)."""
+    conn = _get_photos_db()
+    try:
+        rows = conn.execute(
+            "SELECT * FROM photos ORDER BY created_at DESC LIMIT ?", (limit,)
+        ).fetchall()
+        return {"photos": [_row_to_dict(r) for r in rows]}
+    finally:
+        conn.close()
+
+
+@app.get("/api/photos/{photo_id}")
+async def get_photo(photo_id: int):
+    """Get a single photo record from DB."""
+    conn = _get_photos_db()
+    try:
+        row = conn.execute("SELECT * FROM photos WHERE id = ?", (photo_id,)).fetchone()
+        if not row:
+            raise HTTPException(404, "Photo not found")
+        return _row_to_dict(row)
+    finally:
+        conn.close()
+
+
+@app.get("/api/photos/{photo_id}/file")
+async def get_photo_file(photo_id: int):
+    """Serve the saved image file for a photo record."""
+    conn = _get_photos_db()
+    try:
+        row = conn.execute("SELECT saved_as FROM photos WHERE id = ?", (photo_id,)).fetchone()
+        if not row:
+            raise HTTPException(404, "Photo not found")
+    finally:
+        conn.close()
+
+    filepath = UPLOADS / row["saved_as"]
+    if not filepath.exists():
+        raise HTTPException(404, "File not found on disk")
+    return FileResponse(str(filepath))
+
+
+@app.post("/api/photos/{photo_id}/reprocess")
+async def reprocess_photo(
+    photo_id: int,
+    ocr_engine: str = Query(default="tesseract"),
+    ocr_model: str = Query(default=""),
+    sticker_mode: bool = Query(default=False),
+):
+    """Re-run OCR on a previously saved photo with different engine/model.
+
+    Query params:
+      - ocr_engine: 'tesseract' (default) or 'llm'
+      - ocr_model:  model name override (e.g. 'mimo-v2-omni', 'glm-5.1')
+      - sticker_mode: if true, uses sticker-specific prompt
+    """
+    conn = _get_photos_db()
+    try:
+        row = conn.execute(
+            "SELECT saved_as, orig_filename FROM photos WHERE id = ?", (photo_id,)
+        ).fetchone()
+        if not row:
+            raise HTTPException(404, "Photo not found")
+    finally:
+        conn.close()
+
+    filepath = UPLOADS / row["saved_as"]
+    if not filepath.exists():
+        raise HTTPException(404, "Uploaded file not found on disk")
+
+    contents = filepath.read_bytes()
+
+    if ocr_engine == "llm":
+        ocr_result = run_ocr_llm(contents, ocr_model or None, sticker_mode=sticker_mode)
+    elif ocr_engine == "google":
+        ocr_result = run_ocr_google(contents, ocr_model or None, sticker_mode=sticker_mode)
+    elif HAS_TESSERACT:
+        ocr_result = run_ocr(contents)
+        ocr_result["engine"] = "tesseract"
+    else:
+        ocr_result = {"available": False, "text": "", "engine": "none"}
+
+    machine_id = None
+    asset = None
+    if ocr_result.get("match_5dash6"):
+        machine_id = re.sub(r"\D", "", ocr_result["match_5dash6"])[-5:]
+        asset = lookup_machine_id(machine_id)
+
+    # Update DB record with new OCR results
+    db2 = _get_photos_db()
+    try:
+        db2.execute(
+            """UPDATE photos SET ocr_engine=?, ocr_model=?, ocr_raw_text=?,
+               ocr_match_5dash6=?, ocr_match_5plus=?, machine_id=?
+               WHERE id=?""",
+            (
+                ocr_result.get("engine"),
+                ocr_result.get("llm_model"),
+                ocr_result.get("raw_text"),
+                ocr_result.get("match_5dash6"),
+                ocr_result.get("match_5plus"),
+                machine_id,
+                photo_id,
+            ),
+        )
+        db2.commit()
+    finally:
+        db2.close()
+
+    return {
+        "photo_id": photo_id,
+        "ocr_engine": ocr_result.get("engine"),
+        "llm_model": ocr_result.get("llm_model"),
+        "ocr": ocr_result,
+        "machine_id": machine_id,
+        "asset": asset,
+    }
+
+
@app.get("/api/lookup")
 async def lookup_asset(machine_id: str = ""):
    """Look up an asset by machine_id in the canteen assets database."""
@@ -516,7 +923,6 @@ async def push_gps(request: dict):
        raise HTTPException(500, "Database not available")

    try:
-        # Only update if currently NULL
        row = conn.execute(
            "SELECT latitude, longitude FROM assets WHERE id = ?", (int(asset_id),)
        ).fetchone()
@@ -26,19 +26,17 @@
  h1 { font-size: 20px; margin-bottom: 4px; }
  .subtitle { font-size: 12px; color: var(--text2); margin-bottom: 16px; }

-  /* Upload area */
  .upload-area {
    border: 2px dashed var(--border); border-radius: var(--radius);
    padding: 28px 20px; text-align: center; cursor: pointer;
    transition: border-color 0.2s; background: var(--card);
-    margin-bottom: 16px;
+    margin-bottom: 12px;
  }
  .upload-area:active { border-color: var(--accent); background: var(--card2); }
  .upload-area .icon { font-size: 36px; display: block; margin-bottom: 6px; }
  .upload-area .label { font-size: 15px; font-weight: 600; }
  .upload-area .hint { font-size: 11px; color: var(--text2); margin-top: 4px; }

-  /* Summary bar */
  #summary {
    display: none; background: var(--card); border-radius: var(--radius);
    padding: 12px 14px; margin-bottom: 12px;
@@ -53,7 +51,6 @@
  .summary-nogps { color: var(--amber); }
  .summary-total { color: var(--text); }

-  /* Photo grid */
  #gallery {
    display: grid; grid-template-columns: repeat(3, 1fr); gap: 8px;
    margin-bottom: 12px;
@@ -84,7 +81,6 @@
  }
  .photo-card.selected .check { display: flex; }

-  /* Detail card */
  #detail {
    display: none; background: var(--card); border-radius: var(--radius);
    padding: 14px; margin-bottom: 12px;
@@ -102,6 +98,8 @@
  .badge-warn { background: var(--amber); color: #000; }
  .badge-fail { background: var(--red); color: #fff; }
  .badge-info { background: var(--accent); color: #fff; }
+  .badge-dup { background: var(--red); color: #fff; }
+  .badge-sticker { background: #8b5cf6; color: #fff; }

  .exif-row {
    display: flex; justify-content: space-between; padding: 6px 0;
@@ -118,7 +116,6 @@
    border-radius: var(--radius-sm); margin: 8px 0;
  }

-  /* Buttons */
  .btn {
    display: block; width: 100%; padding: 14px; font-size: 15px;
    font-weight: 600; border: none; border-radius: var(--radius-sm);
@@ -132,6 +129,7 @@
  .btn:disabled { opacity: 0.4; pointer-events: none; }
  .btn-row { display: flex; gap: 8px; margin-top: 8px; }
  .btn-sm { padding: 8px 14px; font-size: 12px; width: auto; }
+  .btn-xs { padding: 4px 10px; font-size: 11px; width: auto; }

  .spinner {
    display: inline-block; width: 16px; height: 16px;
@@ -145,7 +143,6 @@

  #fileInput { display: none; }

-  /* Filter bar */
  #filterBar {
    display: none; align-items: center; gap: 6px; margin-bottom: 10px;
  }
@@ -157,14 +154,11 @@
  }
  .filter-chip.active { background: var(--accent); border-color: var(--accent); color: #fff; }

-  /* Asset match card */
  .asset-card {
    background: var(--card2); border-radius: var(--radius-sm);
    padding: 12px; margin-top: 10px; border-left: 3px solid var(--green);
  }
-  .asset-card .asset-name {
-    font-size: 15px; font-weight: 700; margin-bottom: 4px;
-  }
+  .asset-card .asset-name { font-size: 15px; font-weight: 700; margin-bottom: 4px; }
  .asset-card .asset-meta {
    font-size: 12px; color: var(--text2);
    display: flex; flex-wrap: wrap; gap: 8px;
@@ -174,7 +168,6 @@
    white-space: nowrap;
  }

-  /* Server section */
  .section {
    background: var(--card); border-radius: var(--radius);
    padding: 14px; margin-bottom: 12px;
@@ -187,10 +180,9 @@
    margin-top: 6px;
  }

-  /* OCR engine toggle */
  .ocr-toggle {
-    display: flex; align-items: center; gap: 8px; margin-bottom: 8px;
-    font-size: 12px; color: var(--text2);
+    display: flex; align-items: center; gap: 10px; margin-bottom: 8px;
+    font-size: 12px; color: var(--text2); flex-wrap: wrap;
  }
  .ocr-toggle label { display: flex; align-items: center; gap: 4px; cursor: pointer; }
  .ocr-toggle input[type="checkbox"] { accent-color: var(--accent); }
@@ -201,7 +193,6 @@
  .engine-badge.llm { background: var(--accent); color: #fff; }
  .engine-badge.tesseract { background: var(--card2); color: var(--text2); }

-  /* Bulk results */
  #mapContainer { height: 250px; border-radius: var(--radius-sm); margin-bottom: 10px; display: none; }
  .bulk-card {
    background: var(--card); border-radius: var(--radius-sm); padding: 10px;
@@ -229,6 +220,51 @@
  .match-badge.no-match { background: var(--border); color: var(--text2); }
  .match-badge.no-gps { background: var(--amber); color: #000; }
  .match-badge.has-gps { background: var(--card2); color: var(--text2); }
+  .match-badge.dup { background: var(--red); color: #fff; }
+  .match-badge.sticker { background: #8b5cf6; color: #fff; }
+
+  /* Reprocess card */
+  .reprocess-row {
+    display: flex; align-items: center; gap: 8px; margin-top: 8px;
+    padding-top: 8px; border-top: 1px solid var(--border);
+  }
+  .reprocess-row select {
+    background: var(--card2); color: var(--text); border: 1px solid var(--border);
+    border-radius: 8px; padding: 4px 8px; font-size: 11px; flex: 1;
+  }
+  .sticker-chip.active { background: #8b5cf6; border-color: #8b5cf6; color: #fff; }
+
+  /* Previous photos */
+  .prev-photo-card {
+    background: var(--card); border-radius: var(--radius-sm); padding: 10px;
+    margin-bottom: 8px; border-left: 3px solid var(--border);
+    font-size: 12px;
+  }
+  .prev-photo-card .prev-header {
+    display: flex; justify-content: space-between; align-items: center; gap: 6px;
+    margin-bottom: 4px;
+  }
+  .prev-photo-card .prev-filename {
+    font-weight: 600; white-space: nowrap; overflow: hidden; text-overflow: ellipsis;
+  }
+  .prev-photo-card .prev-time { font-size: 10px; color: var(--text3); white-space: nowrap; }
+  .prev-photo-card .prev-details { font-size: 11px; color: var(--text2); }
+  .prev-photo-card .prev-actions {
+    margin-top: 6px; display: flex; gap: 6px; flex-wrap: wrap;
+    align-items: center;
+  }
+  .prev-photo-card .prev-actions select,
+  .prev-photo-card .prev-actions input {
+    background: var(--card2); color: var(--text); border: 1px solid var(--border);
+    border-radius: 8px; padding: 3px 6px; font-size: 11px;
+  }
+  .prev-photo-card .prev-actions label {
+    display: flex; align-items: center; gap: 3px; font-size: 10px; color: var(--text2);
+  }
+  .prev-photo-card .prev-actions label input { accent-color: #8b5cf6; }
+  .prev-photo-card .sticker-color { 
+    display: inline-block; width: 8px; height: 8px; border-radius: 50%; margin-left: 4px;
+  }
 </style>
 </head>
 <body>
@@ -243,6 +279,23 @@
 </div>
 <input type="file" id="fileInput" accept="image/*" multiple>

+<!-- Options -->
+<div class="ocr-toggle" style="margin-bottom:4px;">
+  <label style="gap:6px;">
+    <span style="font-size:11px;">OCR Engine:</span>
+    <select id="ocrEngine" onchange="onOcrToggle()" style="background:var(--card2);color:var(--text);border:1px solid var(--border);border-radius:8px;padding:3px 8px;font-size:11px;">
+      <option value="tesseract">🖥️ Tesseract</option>
+      <option value="llm" selected>🧠 LLM (mimo-v2-omni)</option>
+      <option value="google">🌐 Google Gemini</option>
+    </select>
+  </label>
+  <label>
+    <input type="checkbox" id="stickerToggle" onchange="onOcrToggle()">
+    🏷️ Sticker Mode
+  </label>
+  <span style="font-size:10px;color:var(--text3);" id="modelLabel">mimo-v2-omni</span>
+</div>
+
 <!-- Summary stats -->
 <div id="summary">
  <div class="summary-grid">
@@ -262,7 +315,7 @@
 <!-- Photo grid -->
 <div id="gallery"></div>

-<!-- Detail card (tap a photo to see) -->
+<!-- Detail card -->
 <div id="detail">
  <img id="detailPreview" alt="">
  <div id="detailExif"></div>
@@ -271,20 +324,12 @@
  </div>
 </div>

-<!-- Server results for single upload -->
+<!-- Server results -->
 <div class="section" id="serverSection" style="display:none;">
  <div class="detail-section">🖥️ Server Results <span class="badge badge-info">PIL + OCR</span></div>
  <div id="serverResults"></div>
 </div>

-<!-- OCR engine toggle -->
-<div class="ocr-toggle" style="margin-bottom:4px;">
-  <label>
-    <input type="checkbox" id="ocrLlmToggle" onchange="onOcrToggle()">
-    🧠 Use LLM OCR (<code id="ocrModelLabel">mimo-v2-omni</code>)
-  </label>
-</div>
-
 <!-- Bulk process button -->
 <button class="btn btn-primary" id="bulkBtn" style="display:none;margin-top:12px;" onclick="startBulkProcess()">
  🔍 Bulk Process GPS Photos
@@ -303,10 +348,101 @@
  <button class="btn btn-outline btn-sm" id="clearBtn" style="display:none;" onclick="resetAll()">🔄 Clear All & Re-select</button>
 </div>

+<!-- Previously processed -->
+<div class="section" id="prevSection" style="display:none;">
+  <div class="detail-section">📂 Previously Processed <span class="badge badge-info" id="prevCount">0</span></div>
+  <div id="prevResults"></div>
+</div>
+
 <script>
 let allPhotos = [];       // {file, exif, hasGps, lat, lng, thumb}
 let selectedIdx = -1;
 let currentFilter = 'all';
+let bulkMap = null;
+let bulkData = [];
+
+// On load: fetch previously processed photos
+document.addEventListener('DOMContentLoaded', loadPreviousPhotos);
+
+function loadPreviousPhotos() {
+  fetch('/api/photos?limit=30')
+    .then(r => r.json())
+    .then(data => {
+      if (!data.photos || !data.photos.length) return;
+      const section = document.getElementById('prevSection');
+      const div = document.getElementById('prevResults');
+      section.style.display = 'block';
+      document.getElementById('prevCount').textContent = data.photos.length;
+      div.innerHTML = data.photos.map(p => renderPrevCard(p)).join('');
+    })
+    .catch(() => {});
+}
+
+function renderPrevCard(p) {
+  const hasMatch = p.ocr_match_5dash6 ? 'matched' : 'no-match';
+  const matchText = p.ocr_match_5dash6 || (p.ocr_match_5plus ? 'digits found' : 'no match');
+  const engine = p.ocr_engine || 'tesseract';
+  const engCls = engine === 'llm' || engine === 'llm_batch' ? 'llm' : 'tesseract';
+  const time = p.created_at ? new Date(p.created_at + 'Z').toLocaleString() : '';
+  const colorHtml = p.sticker_color
+    ? `<span class="sticker-color" style="background:${p.sticker_color === 'green' ? 'var(--green)' : p.sticker_color === 'orange' ? 'var(--amber)' : p.sticker_color === 'yellow' ? '#eab308' : 'var(--text3)'}"></span>`
+    : '';
+  return `<div class="prev-photo-card">
+    <div class="prev-header">
+      <span class="prev-filename">${esc(p.orig_filename)}</span>
+      <span class="prev-time">${time}</span>
+    </div>
+    <div class="prev-details">
+      Machine: <strong>${p.machine_id ? esc(p.machine_id) : '—'}</strong>
+      <span class="match-badge ${hasMatch}">${esc(matchText)}</span>
+      <span class="engine-badge ${engCls}">${esc(engine)}${p.ocr_model ? ' ' + esc(p.ocr_model) : ''}</span>
+      ${colorHtml}
+    </div>
+    <div class="prev-actions">
+      <select id="reEng${p.id}">
+        <option value="tesseract">Tesseract</option>
+        <option value="llm" ${engine.startsWith('llm') ? 'selected' : ''}>LLM</option>
+        <option value="google" ${engine === 'google' ? 'selected' : ''}>🌐 Google</option>
+      </select>
+      <select id="reModel${p.id}">
+        <option value="">default</option>
+        <option value="mimo-v2-omni">mimo-v2-omni</option>
+        <option value="mimo-v2-pro">mimo-v2-pro</option>
+        <option value="kimi-k2.5">kimi-k2.5</option>
+        <option value="glm-5.1">glm-5.1</option>
+        <option value="gemini-2.5-flash">gemini-2.5-flash</option>
+      </select>
+      <label><input type="checkbox" id="reSticker${p.id}"> 🏷️</label>
+      <button class="btn btn-primary btn-xs" onclick="reprocessPhoto(${p.id})">🔄 Re-run</button>
+    </div>
+  </div>`;
+}
+
+async function reprocessPhoto(photoId) {
+  const eng = document.getElementById('reEng' + photoId).value;
+  const model = document.getElementById('reModel' + photoId).value;
+  const sticker = document.getElementById('reSticker' + photoId).checked;
+  let params = '?ocr_engine=' + eng;
+  if (model) params += '&ocr_model=' + model;
+  if (sticker) params += '&sticker_mode=true';
+
+  const card = document.querySelector(`#reEng${photoId}`).closest('.prev-photo-card');
+  const actions = card.querySelector('.prev-actions');
+  actions.innerHTML = '<span class="spinner" style="width:14px;height:14px;"></span> Reprocessing...';
+
+  try {
+    const resp = await fetch(`/api/photos/${photoId}/reprocess${params}`, { method: 'POST' });
+    const data = await resp.json();
+    // Refresh the card
+    const updated = { ...data, orig_filename: card.querySelector('.prev-filename')?.textContent || 'Photo' };
+    // Just reload the full list
+    loadPreviousPhotos();
+  } catch (e) {
+    actions.innerHTML = '<span style="color:var(--red);font-size:11px;">❌ Failed</span>';
+  }
+}
+
+// --- File handling ---

 document.getElementById('fileInput').addEventListener('change', async function(e) {
  const files = Array.from(e.target.files || []);
@@ -316,7 +452,6 @@ document.getElementById('fileInput').addEventListener('change', async function(e
  document.getElementById('filterBar').style.display = 'flex';
  document.getElementById('clearBtn').style.display = 'block';

-  // Show scanning state
  document.getElementById('gallery').innerHTML = files.map((_, i) =>
    '<div class="photo-card" id="card' + i + '">' +
      '<div style="display:flex;align-items:center;justify-content:center;height:100%;color:var(--text3);font-size:24px;">' +
@@ -326,7 +461,6 @@ document.getElementById('fileInput').addEventListener('change', async function(e
    '</div>'
  ).join('');

-  // Scan in small batches — iOS Safari can't handle many concurrent file reads
  const results = [];
  const batchSize = 2;
  for (let i = 0; i < files.length; i += batchSize) {
@@ -342,12 +476,7 @@ document.getElementById('fileInput').addEventListener('change', async function(e

 async function scanPhoto(file) {
  const result = { file, exif: null, hasGps: false, lat: null, lng: null, thumb: null };
-
-  // Use object URL instead of readAsDataURL — avoids loading the entire file
-  // into memory as a base64 string (the main cause of iOS hangs with many photos)
  result.thumb = URL.createObjectURL(file);
-
-  // Read EXIF
  try {
    const exif = await exifr.parse(file);
    if (exif && Object.keys(exif).length > 0) {
@@ -361,8 +490,7 @@ async function scanPhoto(file) {
        result.lng = exif.longitude;
      }
    }
-  } catch (_) { /* EXIF is best-effort */ }
-
+  } catch (_) {}
  return result;
 }

@@ -373,7 +501,6 @@ function updateSummary() {
  document.getElementById('sumGps').textContent = gps;
  document.getElementById('sumNoGps').textContent = nogps;
  document.getElementById('summary').style.display = 'block';
-  // Show bulk button if we have GPS photos
  document.getElementById('bulkBtn').style.display = gps > 0 ? 'block' : 'none';
 }

@@ -412,7 +539,7 @@ function showDetail(idx) {
  const p = allPhotos[idx];
  if (!p) return;

-  renderGallery(); // update selection highlights
+  renderGallery();

  const detail = document.getElementById('detail');
  detail.style.display = 'block';
@@ -466,6 +593,9 @@ async function uploadSelected() {
    const data = await resp.json();

    let html = '';
+    if (data.duplicate) {
+      html += '<div style="margin-bottom:8px;"><span class="match-badge dup">♻️ Already processed — skipped</span></div>';
+    }
    html += '<div class="exif-row"><span class="exif-key">Saved as</span><span class="exif-val">' + esc(data.saved_as) + '</span></div>';
    html += '<div class="exif-row"><span class="exif-key">File size</span><span class="exif-val">' + data.file_size_kb + ' KB</span></div>';

@@ -478,7 +608,6 @@ async function uploadSelected() {
      html = '<div class="gps-coords" style="color:var(--red);margin:8px 0;">❌ Server found no EXIF — stripped during upload</div>' + html;
    }

-    // EXIF tags
    if (exif.has_exif) {
      for (const [k, v] of Object.entries(exif.tags).slice(0, 8)) {
        html += '<div class="exif-row"><span class="exif-key">' + esc(k) + '</span><span class="exif-val">' + esc(v) + '</span></div>';
@@ -488,14 +617,17 @@ async function uploadSelected() {
    // OCR
    const ocr = data.ocr;
    const engineCls = ocr.engine === 'llm' || ocr.engine === 'llm_batch' ? 'llm' : 'tesseract';
-    html += '<div style="margin-top:10px;font-weight:600;">🔤 OCR <span class="engine-badge ' + engineCls + '">' + esc(ocr.engine || 'tesseract') + (ocr.llm_model ? ' ' + esc(ocr.llm_model) : '') + '</span></div>';
+    let ocrBadge = '<span class="engine-badge ' + engineCls + '">' + esc(ocr.engine || 'tesseract');
+    if (ocr.llm_model) ocrBadge += ' ' + esc(ocr.llm_model);
+    if (ocr.sticker_color) ocrBadge += ' 🏷️' + esc(ocr.sticker_color);
+    ocrBadge += '</span>';
+    html += '<div style="margin-top:10px;font-weight:600;">🔤 OCR ' + ocrBadge + '</div>';
    if (ocr.raw_text) {
      html += '<div class="ocr-text">' + esc(ocr.raw_text) + '</div>';
    }
    if (ocr.match_5dash6) {
      const mid = ocr.match_5dash6.replace(/[^0-9]/g,'').slice(-5);
      html += '<div style="margin-top:4px;color:var(--green);">✅ Matched: <strong>' + esc(ocr.match_5dash6) + '</strong> → machine ID: ' + mid + '</div>';
-      // Auto-lookup
      lookupAsset(mid);
    } else if (ocr.match_5plus) {
      html += '<div style="margin-top:4px;color:var(--amber);">⚠️ Digits: <strong>' + esc(ocr.match_5plus) + '</strong> (no 5-6 pattern)</div>';
@@ -503,12 +635,96 @@ async function uploadSelected() {
      html += '<div style="margin-top:4px;color:var(--text3);">No machine ID found in image</div>';
    }

+    // Re-run controls
+    html += '<div class="reprocess-row">' +
+      '<select id="reRunEng">' +
+        '<option value="tesseract">Tesseract</option>' +
+        '<option value="llm" ' + (ocr.engine === 'llm' || ocr.engine === 'llm_batch' ? 'selected' : '') + '>LLM</option>' +
+        '<option value="google"' + (ocr.engine === 'google' ? 'selected' : '') + '>🌐 Google</option>' +
+      '</select>' +
+      '<select id="reRunModel">' +
+        '<option value="">default</option>' +
+        '<option value="mimo-v2-omni">mimo-v2-omni</option>' +
+        '<option value="kimi-k2.5">kimi-k2.5</option>' +
+        '<option value="glm-5.1">glm-5.1</option>' +
+        '<option value="gemini-2.5-flash">gemini-2.5-flash</option>' +
+      '</select>' +
+      '<label style="font-size:10px;"><input type="checkbox" id="reRunSticker"> 🏷️</label>' +
+      '<button class="btn btn-primary btn-xs" onclick="reprocessCurrent()">🔄 Re-run</button>' +
+    '</div>';
+
+    // Manual entry when GPS exists but OCR failed
+    if ((!ocr.match_5dash6 && !ocr.match_5plus) && data.exif && data.exif.gps) {
+      html += '<div style="margin-top:8px;padding-top:8px;border-top:1px solid var(--border);">' +
+        '<div style="font-size:11px;color:var(--text2);margin-bottom:4px;">✏️ GPS found but no machine ID in OCR. Enter it manually:</div>' +
+        '<div style="display:flex;gap:6px;">' +
+        '<input type="text" id="manualMid" placeholder="e.g. 12345-678901" style="flex:1;background:var(--card2);color:var(--text);border:1px solid var(--border);border-radius:8px;padding:8px;font-size:13px;">' +
+        '<button class="btn btn-primary btn-xs" style="width:auto;white-space:nowrap;" onclick="manualLookup()">🔍 Lookup</button>' +
+        '</div>' +
+        '<div id="manualResult" style="margin-top:4px;"></div>' +
+      '</div>';
+    }
+
    div.innerHTML = html;
  } catch (e) {
    div.innerHTML = '<div class="empty">❌ Upload failed: ' + esc(e.message) + '</div>';
  }
 }

+function manualLookup() {
+  const input = document.getElementById('manualMid');
+  const resultDiv = document.getElementById('manualResult');
+  const val = input.value.trim();
+  if (!val) { resultDiv.innerHTML = '<span style="color:var(--amber);font-size:11px;">Enter a machine ID</span>'; return; }
+  resultDiv.innerHTML = '<span class="spinner" style="width:12px;height:12px;"></span>';
+  fetch('/api/lookup?machine_id=' + encodeURIComponent(val))
+    .then(r => r.json())
+    .then(data => {
+      if (data.found) {
+        const a = data.asset;
+        resultDiv.innerHTML = '<div class="asset-card" style="margin-top:0;">' +
+          '<div class="asset-name">' + esc(a.name) + '</div>' +
+          '<div class="asset-meta"><span>🆔 ' + esc(a.machine_id) + '</span><span>📦 ' + esc(a.category) + '</span>' +
+          (a.status === 'active' ? '🟢 Active' : '⚪ ' + esc(a.status)) +
+          '</div></div>';
+      } else {
+        resultDiv.innerHTML = '<span style="color:var(--amber);font-size:11px;">⚠️ No asset found for <strong>' + esc(val) + '</strong></span>';
+      }
+    })
+    .catch(e => {
+      resultDiv.innerHTML = '<span style="color:var(--red);font-size:11px;">❌ Error: ' + esc(e.message) + '</span>';
+    });
+}
+
+async function reprocessCurrent() {
+  const eng = document.getElementById('reRunEng').value;
+  const model = document.getElementById('reRunModel').value;
+  const sticker = document.getElementById('reRunSticker').checked;
+
+  // Need the photo_id from the last upload. Get it from server results.
+  // We'll just re-upload the same file with new params
+  if (selectedIdx < 0 || !allPhotos[selectedIdx]) return;
+  const file = allPhotos[selectedIdx].file;
+
+  const div = document.getElementById('serverResults');
+  div.innerHTML += '<div style="text-align:center;padding:8px;"><span class="spinner"></span> Reprocessing...</div>';
+
+  const fd = new FormData();
+  fd.append('file', file, file.name || 'photo.jpg');
+  let params = '?ocr_engine=' + eng;
+  if (model) params += '&ocr_model=' + model;
+  if (sticker) params += '&sticker_mode=true';
+
+  try {
+    const resp = await fetch('/api/analyze' + params, { method: 'POST', body: fd });
+    const data = await resp.json();
+    // Just reload the whole result
+    uploadSelected();
+  } catch (e) {
+    div.innerHTML += '<div class="empty">❌ Re-run failed: ' + esc(e.message) + '</div>';
+  }
+}
+
 async function lookupAsset(machineId) {
  const div = document.getElementById('serverResults');
  try {
@@ -545,9 +761,6 @@ async function lookupAsset(machineId) {
  }
 }

-let bulkMap = null;
-let bulkData = [];
-
 async function startBulkProcess() {
  const gpsPhotos = allPhotos.filter(p => p.hasGps);
  if (!gpsPhotos.length) return;
@@ -556,7 +769,6 @@ async function startBulkProcess() {
  btn.disabled = true;
  btn.innerHTML = '<span class="spinner"></span> Processing ' + gpsPhotos.length + ' photos...';

-  // Build FormData with all GPS photos
  const fd = new FormData();
  gpsPhotos.forEach(p => fd.append('files', p.file, p.file.name || 'photo.jpg'));

@@ -564,7 +776,6 @@ async function startBulkProcess() {
    const resp = await fetch('/api/bulk-process' + getOcrParams(), { method: 'POST', body: fd });
    const data = await resp.json();

-    // Merge thumbnails back into results
    bulkData = data.results.map((r, i) => ({
      ...r,
      thumb: gpsPhotos[i]?.thumb || null,
@@ -586,13 +797,12 @@ function renderBulkResults(summary) {
  const section = document.getElementById('bulkSection');
  section.style.display = 'block';

-  // Summary badge
  document.getElementById('bulkSummary').textContent =
-    summary.matched + ' matched, ' + summary.needs_gps + ' need GPS';
+    summary.matched + ' matched, ' + summary.needs_gps + ' need GPS' +
+    (summary.duplicates ? ', ' + summary.duplicates + ' skipped' : '');
  document.getElementById('bulkSummary').className =
    'match-badge ' + (summary.matched > 0 ? 'matched' : 'no-match');

-  // Init map
  const mapDiv = document.getElementById('mapContainer');
  mapDiv.style.display = 'block';
  if (bulkMap) bulkMap.remove();
@@ -610,7 +820,6 @@ function renderBulkResults(summary) {
    const gpsLat = hasGps ? item.exif.gps.lat : null;
    const gpsLng = hasGps ? item.exif.gps.lng : null;

-    // Map marker for every GPS photo
    if (gpsLat && gpsLng) {
      const marker = L.marker([gpsLat, gpsLng])
        .addTo(bulkMap)
@@ -622,21 +831,17 @@ function renderBulkResults(summary) {
      markers.push([gpsLat, gpsLng]);
    }

-    // Asset marker if it already has GPS in DB
    if (asset && asset.latitude && asset.longitude) {
      L.marker([asset.latitude, asset.longitude], {
        icon: L.divIcon({
          className: 'cat-pin-icon',
-          html: '<div style="width:22px;height:22px;border-radius:50%;background:#3b82f6;' +
-                'display:flex;align-items:center;justify-content:center;font-size:11px;' +
-                'box-shadow:0 2px 5px rgba(0,0,0,0.5);border:2px solid #fff;">🏠</div>',
+          html: '<div style="width:22px;height:22px;border-radius:50%;background:#3b82f6;display:flex;align-items:center;justify-content:center;font-size:11px;box-shadow:0 2px 5px rgba(0,0,0,0.5);border:2px solid #fff;">🏠</div>',
          iconSize: [24, 24], iconAnchor: [12, 12]
        })
      }).addTo(bulkMap)
        .bindPopup('<b>' + esc(asset.name) + '</b><br>Existing location');
    }

-    // Card
    const isGpsReady = item.needs_gps;
    html += '<div class="bulk-card' + (isGpsReady ? ' gps-ready' : '') + '">';

@@ -646,7 +851,10 @@ function renderBulkResults(summary) {

    html += '<div class="bulk-info">';

-    if (asset) {
+    if (item.duplicate) {
+      html += '<div class="bulk-name">' + esc(item.filename || 'Photo') + '</div>';
+      html += '<div class="bulk-meta"><span class="match-badge dup">♻️ Already processed</span></div>';
+    } else if (asset) {
      html += '<div class="bulk-name">' + esc(asset.name) + '</div>';
      html += '<div class="bulk-meta">';
      html += '<span class="match-badge matched">🆔 ' + esc(asset.machine_id) + '</span> ';
@@ -668,9 +876,8 @@ function renderBulkResults(summary) {
      html += '<div class="bulk-meta"><span class="match-badge no-match">No OCR match</span></div>';
    }

-    html += '</div>'; // bulk-info
+    html += '</div>';

-    // Push button
    html += '<div class="bulk-action">';
    if (isGpsReady) {
      html += '<button class="btn-push" id="pushBtn' + i + '" onclick="pushGpsToAsset(' + i + ')">📤 Push GPS</button>';
@@ -679,20 +886,17 @@ function renderBulkResults(summary) {
    }
    html += '</div>';

-    html += '</div>'; // bulk-card
+    html += '</div>';
  });

  document.getElementById('bulkResults').innerHTML = html;

-  // Fit map to markers
  if (markers.length) {
    const bounds = L.latLngBounds(markers);
    bulkMap.fitBounds(bounds, { padding: [30, 30], maxZoom: 15 });
  }

-  // Invalidate map size after display
  setTimeout(() => { if (bulkMap) bulkMap.invalidateSize(); }, 200);
-
  section.scrollIntoView({ behavior: 'smooth' });
 }

@@ -719,14 +923,11 @@ async function pushGpsToAsset(idx) {
    if (data.updated) {
      btn.className = 'btn-push done';
      btn.textContent = '✓ Pushed!';
-      // Add marker to map for pushed location
      if (bulkMap) {
        L.marker([gps.lat, gps.lng], {
          icon: L.divIcon({
            className: 'cat-pin-icon',
-            html: '<div style="width:22px;height:22px;border-radius:50%;background:#22c55e;' +
-                  'display:flex;align-items:center;justify-content:center;font-size:11px;' +
-                  'box-shadow:0 2px 5px rgba(0,0,0,0.5);border:2px solid #fff;">✓</div>',
+            html: '<div style="width:22px;height:22px;border-radius:50%;background:#22c55e;display:flex;align-items:center;justify-content:center;font-size:11px;box-shadow:0 2px 5px rgba(0,0,0,0.5);border:2px solid #fff;">✓</div>',
            iconSize: [24, 24], iconAnchor: [12, 12]
          })
        }).addTo(bulkMap)
@@ -744,7 +945,6 @@ async function pushGpsToAsset(idx) {
 }

 function resetAll() {
-  // Revoke all blob URLs to prevent memory leaks
  allPhotos.forEach(p => {
    if (p.thumb && p.thumb.startsWith('blob:')) {
      URL.revokeObjectURL(p.thumb);
@@ -774,10 +974,24 @@ function esc(s) {
  return d.innerHTML;
 }

+function onOcrToggle() {
+  const engine = document.getElementById('ocrEngine').value;
+  const sticker = document.getElementById('stickerToggle').checked;
+  const label = document.getElementById('modelLabel');
+  if (engine === 'llm' && sticker) label.textContent = '🏷️ sticker mode';
+  else if (engine === 'llm') label.textContent = 'mimo-v2-omni';
+  else if (engine === 'google' && sticker) label.textContent = '🏷️ gemini sticker';
+  else if (engine === 'google') label.textContent = 'gemini-2.5-flash';
+  else label.textContent = 'tesseract';
+}
+
 function getOcrParams() {
-  const useLlm = document.getElementById('ocrLlmToggle').checked;
-  if (!useLlm) return '';
-  return '?ocr_engine=llm';
+  const engine = document.getElementById('ocrEngine').value;
+  const sticker = document.getElementById('stickerToggle').checked;
+  let params = [];
+  if (engine !== 'tesseract') params.push('ocr_engine=' + engine);
+  if (sticker) params.push('sticker_mode=true');
+  return params.length ? '?' + params.join('&') : '';
 }

 function formatSize(bytes) {