diff --git a/__pycache__/server.cpython-311.pyc b/__pycache__/server.cpython-311.pyc
index f761353..339b421 100644
Binary files a/__pycache__/server.cpython-311.pyc and b/__pycache__/server.cpython-311.pyc differ
diff --git a/server.py b/server.py
index 6486dd1..ad09ef6 100644
--- a/server.py
+++ b/server.py
@@ -119,6 +119,31 @@ 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
+
+
+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))
+ w, h = img.size
+ if max(w, h) <= max_dim:
+ return image_bytes # already small enough
+ ratio = max_dim / max(w, h)
+ new_size = (int(w * ratio), int(h * ratio))
+ 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"
+ img_resized.save(buf, format=ext, quality=85)
+ return buf.getvalue()
+
+
def run_ocr_llm(image_bytes: bytes, model: str | None = None) -> dict:
"""Run OCR via an LLM vision model on OpenCode Go.
@@ -134,6 +159,8 @@ 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()
body = json.dumps({
@@ -183,6 +210,109 @@ def run_ocr_llm(image_bytes: bytes, model: str | None = None) -> dict:
}
+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)
+ """
+ import base64
+
+ if not OPENCODE_GO_KEY:
+ return [run_ocr(b) for _, b in images]
+
+ 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)
+ all_results.extend(batch_results)
+ return all_results
+
+
+def _run_ocr_llm_batch_inner(batch: list[tuple[str, bytes]], model: str) -> 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]
+
+ 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.'
+ )
+ }]
+
+ for idx, (img_bytes, _) in enumerate(resized):
+ b64 = base64.b64encode(img_bytes).decode()
+ content.append({
+ "type": "image_url",
+ "image_url": {"url": f"data:image/jpeg;base64,{b64}"}
+ })
+
+ body = json.dumps({
+ "model": model,
+ "messages": [{"role": "user", "content": content}],
+ "max_tokens": min(300 * len(batch), 8000),
+ "temperature": 0.1,
+ }).encode()
+
+ req = urllib.request.Request(
+ f"{OPENCODE_GO_BASE}/chat/completions",
+ data=body,
+ headers={
+ "Authorization": f"Bearer {OPENCODE_GO_KEY}",
+ "Content-Type": "application/json",
+ "User-Agent": "Hermes-Agent/1.0",
+ },
+ )
+
+ try:
+ resp = urllib.request.urlopen(req, timeout=120)
+ 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]
+
+ # Strip markdown code fences if present
+ raw_clean = raw
+ if raw_clean.startswith("```"):
+ raw_clean = raw_clean.split("\n", 1)[-1]
+ raw_clean = raw_clean.rsplit("```", 1)[0].strip()
+
+ try:
+ 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({
+ "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,
+ })
+ return results
+ except (json.JSONDecodeError, KeyError, TypeError):
+ # Fallback to individual calls
+ return [run_ocr_llm(b, model) 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()
@@ -286,24 +416,37 @@ async def bulk_process(
if not files:
return {"results": [], "summary": {"total": 0, "has_gps": 0, "matched": 0, "needs_gps": 0}}
- results = []
- summary = {"total": len(files), "has_gps": 0, "matched": 0, "needs_gps": 0}
-
+ # Read all files first
+ all_files: list[tuple[str, bytes]] = []
for file in files:
contents = await file.read()
+ all_files.append((file.filename or "photo.jpg", contents))
+
+ ocr_use_llm = ocr_engine == "llm"
+
+ # Batch OCR if using LLM mode
+ if ocr_use_llm:
+ llm_ocr_results = run_ocr_llm_batch(all_files, ocr_model or None)
+ else:
+ llm_ocr_results = None
+
+ results = []
+ summary = {"total": len(all_files), "has_gps": 0, "matched": 0, "needs_gps": 0}
+
+ for i, (orig_fname, contents) in enumerate(all_files):
file_size = len(contents)
# Save
- ext = Path(file.filename or "photo.jpg").suffix.lower()
+ 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"
- fname = f"{uuid.uuid4().hex}{ext}"
- (UPLOADS / fname).write_bytes(contents)
+ saved_name = f"{uuid.uuid4().hex}{ext}"
+ (UPLOADS / saved_name).write_bytes(contents)
exif_result = extract_exif(contents)
- if ocr_engine == "llm":
- ocr_result = run_ocr_llm(contents, ocr_model or None)
+ 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 HAS_TESSERACT:
ocr_result = run_ocr(contents)
ocr_result["engine"] = "tesseract"
@@ -329,8 +472,8 @@ async def bulk_process(
summary["needs_gps"] += 1
results.append({
- "filename": file.filename,
- "saved_as": fname,
+ "filename": orig_fname,
+ "saved_as": saved_name,
"file_size_kb": round(file_size / 1024, 1),
"exif": exif_result,
"ocr": ocr_result,
diff --git a/static/index.html b/static/index.html
index 56a6b71..62bf5ad 100644
--- a/static/index.html
+++ b/static/index.html
@@ -487,8 +487,8 @@ async function uploadSelected() {
// OCR
const ocr = data.ocr;
- const engineCls = ocr.engine === 'llm' ? 'llm' : 'tesseract';
- html += '🔤 OCR ' + esc(ocr.engine || 'tesseract') + (ocr.llm_model ? ' ' + ocr.llm_model : '') + '
';
+ const engineCls = ocr.engine === 'llm' || ocr.engine === 'llm_batch' ? 'llm' : 'tesseract';
+ html += '🔤 OCR ' + esc(ocr.engine || 'tesseract') + (ocr.llm_model ? ' ' + esc(ocr.llm_model) : '') + '
';
if (ocr.raw_text) {
html += '' + esc(ocr.raw_text) + '
';
}