Update ocr_engine.py

ocr_engine.py

@@ -3,33 +3,34 @@ import numpy as np
 import cv2
 import re
 
+# Load EasyOCR reader
 reader = easyocr.Reader(['en'], gpu=False)
 
 def extract_weight_from_image(pil_img):
     try:
         img = np.array(pil_img)
 
-        # Resize if image is too big
+        # Resize very large images
         max_dim = 1000
         height, width = img.shape[:2]
         if max(height, width) > max_dim:
             scale = max_dim / max(height, width)
             img = cv2.resize(img, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
 
-        # OCR
+        # OCR recognition
         results = reader.readtext(img)
         print("DEBUG OCR RESULTS:", results)
 
         raw_texts = []
         weight_candidates = []
-
         fallback_weight = None
         fallback_conf = 0.0
 
-        for _, text, conf in results:
+        for _, (text, conf) in results:
             original = text
             cleaned = text.lower().strip()
 
+            # Fix common OCR misreads
             cleaned = cleaned.replace(",", ".")
             cleaned = cleaned.replace("o", "0").replace("O", "0")
             cleaned = cleaned.replace("s", "5").replace("S", "5")
@@ -39,14 +40,16 @@ def extract_weight_from_image(pil_img):
 
             raw_texts.append(f"{original} → {cleaned} (conf: {round(conf, 2)})")
 
-            # Save fallback if any number
+            # Save fallback if no match later
             if cleaned and cleaned.replace(".", "").isdigit() and not fallback_weight:
                 fallback_weight = cleaned
                 fallback_conf = conf
 
+            # Match proper weight format: 75.02, 97.2, 105
             if cleaned.count(".") <= 1 and re.fullmatch(r"\d{2,4}(\.\d{1,3})?", cleaned):
                 weight_candidates.append((cleaned, conf))
 
+        # Choose best candidate
         if weight_candidates:
             best_weight, best_conf = sorted(weight_candidates, key=lambda x: -x[1])[0]
         elif fallback_weight:
@@ -54,7 +57,7 @@ def extract_weight_from_image(pil_img):
         else:
             return "Not detected", 0.0, "\n".join(raw_texts)
 
-        # Clean up leading zeros
+        # Strip unnecessary leading zeros
         if "." in best_weight:
             int_part, dec_part = best_weight.split(".")
             int_part = int_part.lstrip("0") or "0"