update app

deepseek_ocr_2_unlimited_ocr.py

@@ -88,10 +88,6 @@ print("Determining device...")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 print(f"✅ Using device: {device}")
 
-# ---------------------------------------------------------------------------
-# Model registry — each entry is lazy-loaded on first use and then cached,
-# so switching the dropdown doesn't reload a model already resident on GPU.
-# ---------------------------------------------------------------------------
 DEFAULT_MODEL = "DeepSeek-OCR-2"
 
 MODEL_REGISTRY = {
@@ -166,26 +162,6 @@ TASK_PROMPTS = {
     "Custom": {"prompt": "", "has_grounding": False}
 }
 
-# ---------------------------------------------------------------------------
-# Grounding parsing — DeepSeek-OCR-2 and Unlimited-OCR emit boxes in two
-# different conventions even though both inherit DeepSeek-OCR's DeepEncoder
-# and 0-999 normalized coordinate grid:
-#
-#   DeepSeek-OCR-2:  <|ref|>label<|/ref|><|det|>[[x1,y1,x2,y2], ...]<|/det|>
-#                    (tag pair sits on its own metadata line; recognized
-#                    text for that region follows on subsequent lines)
-#
-#   Unlimited-OCR:   <|det|>label [x1, y1, x2, y2]<|/det|>RECOGNIZED TEXT
-#                    (single tag, label + one box inline; the OCR'd text for
-#                    that region follows immediately on the SAME line, so it
-#                    must not be deleted when the tag is stripped)
-#
-# Both patterns are parsed and normalized to (full_match, label, boxes)
-# tuples, where boxes is always a list of [x1, y1, x2, y2] ints. Downstream
-# code (draw_bounding_boxes, clean_output) consumes this normalized shape
-# regardless of which model produced it.
-# ---------------------------------------------------------------------------
-
 REF_DET_PATTERN = re.compile(r'<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>', re.DOTALL)
 DET_ONLY_PATTERN = re.compile(
     r'<\|det\|>([^\[<]+?)\s*\[\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\]<\|/det\|>',