Update ocr_engine.py

ocr_engine.py

@@ -3,26 +3,23 @@ import numpy as np
 import cv2
 import re
 
+# Load OCR model once
 reader = easyocr.Reader(['en'], gpu=False)
 
 def enhance_image(img):
-    # Resize to make text clearer
+    # Enlarge and convert to grayscale
     img = cv2.resize(img, None, fx=4, fy=4, interpolation=cv2.INTER_CUBIC)
-
-    # Convert to gray
     gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
 
     # Denoise
     gray = cv2.fastNlMeansDenoising(gray, h=15)
 
     # Sharpen
-    kernel = np.array([[0, -1, 0],
-                       [-1, 5,-1],
-                       [0, -1, 0]])
+    kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
     sharp = cv2.filter2D(gray, -1, kernel)
 
-    # Contrast enhancement (CLAHE)
-    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
+    # Contrast enhance
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
     contrast = clahe.apply(sharp)
 
     return contrast
@@ -33,12 +30,13 @@ def extract_weight_from_image(pil_img):
         enhanced = enhance_image(img)
 
         results = reader.readtext(enhanced)
+        print("DEBUG OCR RESULTS:", results)
+
         ocr_texts = [text for _, text, _ in results]
         weight_candidates = []
 
         for _, text, conf in results:
-            cleaned = text.lower()
-            cleaned = cleaned.replace("kg", "").replace("kgs", "")
+            cleaned = text.lower().replace("kg", "").replace("kgs", "")
             cleaned = cleaned.replace("o", "0").replace("s", "5").replace("g", "9")
             cleaned = re.sub(r"[^\d\.]", "", cleaned)