feat: Ollama vision OCR via Windows PC for label photo matching

- Add persistent SSH tunnel (systemd service) to Windows PC Ollama
- Add _HAS_OLLAMA check at server startup (qwen2.5vl:3b)
- Replace Tesseract-only OCR with Ollama-first strategy
- Ollama (qwen2.5vl:3b) handles dark/complex labels Tesseract can't read
- Keurig serial 2500.0100.0025534 now correctly matched as Asset #5144
- Add ocr_source field to /api/ocr response ('ollama' or 'tesseract')
- Fix match_label_photo.py argparse bug (image_paths -> images)
- Update match_label_photo.py CLI to read vision key from Hermes config

scripts/match_label_photo.py

@@ -163,7 +163,7 @@ def main():
         _process_text(text, db_path)
         return
 
-    for img_path in args.image_paths:
+    for img_path in args.images:
         if not Path(img_path).exists():
             print(f"\n=== SKIP: {img_path} (not found) ===")
             continue

server.py

@@ -20,13 +20,30 @@ import uuid
 from contextlib import asynccontextmanager
 from datetime import date
 from pathlib import Path
-
+_HAS_TESSERACT: bool
 try:
     import pytesseract
     _HAS_TESSERACT = True
 except ImportError:
-    pytesseract = None
+    pytesseract = None  # type: ignore
     _HAS_TESSERACT = False
+
+# Ollama vision model (Windows PC via SSH tunnel)
+_OLLAMA_BASE = "http://127.0.0.1:11434"
+_OLLAMA_VISION_MODEL = "qwen2.5vl:3b"
+_HAS_OLLAMA: bool = False
+try:
+    import urllib.request as _ollama_urllib
+    import json as _ollama_json
+    _req = _ollama_urllib.Request(
+        f"{_OLLAMA_BASE}/api/generate",
+        data=_ollama_json.dumps({"model": _OLLAMA_VISION_MODEL, "prompt": "ping", "stream": False}).encode(),
+        headers={"Content-Type": "application/json"},
+    )
+    _resp = _ollama_urllib.urlopen(_req, timeout=5)
+    _HAS_OLLAMA = True
+except Exception:
+    _HAS_OLLAMA = False
 import piexif
 from PIL import Image as PILImage
 
@@ -2186,38 +2203,81 @@ async def ocr_sticker(file: UploadFile = File(...), exif_data: str = Form(None))
         ocr_path = temp_dir / f"{uuid.uuid4().hex}.{ext or 'jpg'}"
         ocr_path.write_bytes(contents)
 
-    try:
-        img = PILImage.open(ocr_path)
-        # Preprocess: convert to grayscale and increase contrast for better OCR
-        img_gray = img.convert("L")
-        if not _HAS_TESSERACT:
-            raise RuntimeError(
-                "Tesseract OCR library (pytesseract) is not installed. "
-                "Run: pip install pytesseract && apt-get install -y tesseract-ocr"
-            )
-        text = pytesseract.image_to_string(img_gray, config="--psm 6")
-    except (RuntimeError, ImportError) as e:
-        if not saved_path:
-            ocr_path.unlink(missing_ok=True)
-        raise HTTPException(
-            status_code=503,
-            detail=f"OCR service unavailable: {str(e)}",
-        )
-    except Exception as e:
-        if not saved_path:
-            ocr_path.unlink(missing_ok=True)
-        raise HTTPException(
-            status_code=500,
-            detail=f"OCR processing error: {str(e)}. Tesseract binary may not be installed system-wide. Run: apt-get install -y tesseract-ocr",
-        )
+    text = ""
+    ocr_source = "none"
+    ollama_text = ""
 
-    # Clean up temp file (but keep permanent photos)
+    # Try Ollama vision model first (Windows PC) — most accurate
+    if _HAS_OLLAMA:
+        try:
+            import urllib.request as _ourl
+            import base64 as _b64
+            img_vision = PILImage.open(ocr_path)
+            img_vision.thumbnail((640, 480))
+            temp_vision = ocr_path.parent / f"vis_{ocr_path.name}"
+            img_vision.save(temp_vision)
+            with open(temp_vision, "rb") as _f:
+                _b64_data = _b64.b64encode(_f.read()).decode()
+            temp_vision.unlink(missing_ok=True)
+
+            _vdata = _json.dumps({
+                "model": _OLLAMA_VISION_MODEL,
+                "prompt": "Extract all text, numbers, serial numbers, and IDs visible on this sticker or label. Return ONLY the raw text content, one item per line. Do not describe the image.",
+                "images": [_b64_data],
+                "stream": False,
+            }).encode()
+            _vreq = _ourl.Request(
+                f"{_OLLAMA_BASE}/api/generate",
+                data=_vdata,
+                headers={"Content-Type": "application/json"},
+            )
+            _vresp = _ourl.urlopen(_vreq, timeout=120)
+            _vresult = _json.loads(_vresp.read().decode())
+            ollama_text = _vresult.get("response", "").strip()
+            if ollama_text:
+                ocr_source = "ollama"
+        except Exception:
+            pass  # Fall through to Tesseract
+
+    # Try Tesseract if Ollama didn't yield usable text
+    if ocr_source == "none":
+        try:
+            img = PILImage.open(ocr_path)
+            img_gray = img.convert("L")
+            if _HAS_TESSERACT:
+                tess_text = pytesseract.image_to_string(img_gray, config="--psm 6")
+                tess_text = tess_text.strip()
+                clean_chars = sum(1 for c in tess_text if c.isalnum() or c in ' \n/-.')
+                if len(tess_text) > 0 and clean_chars >= 10:
+                    ocr_source = "tesseract"
+        except Exception:
+            pass
+
+    # Prefer Ollama text when available (much more accurate)
+    if ollama_text:
+        text = ollama_text
+    elif ocr_source == "tesseract":
+        text = tess_text
+    else:
+        text = ""
+
+    # Clean up temp file
     if not saved_path:
         ocr_path.unlink(missing_ok=True)
 
+    if not text:
+        if _HAS_OLLAMA:
+            detail = "Could not read any text from the image via Ollama vision model."
+        elif _HAS_TESSERACT:
+            detail = "Could not read any text from the image via Tesseract. Try a clearer photo of the label."
+        else:
+            detail = "No OCR service available (Tesseract not installed, Ollama not connected)."
+        raise HTTPException(status_code=422, detail=detail)
+
     # Build response — search for identifiers in the OCR text
     result: dict = {
         "raw_text": text.strip()[:1000],
+        "ocr_source": ocr_source,
     }
 
     # 1. Legacy pattern: XXXXX-XXXXXX (5 digits - 6+ digits = Connect ID)