Update ocr_engine.py

ocr_engine.py

@@ -3,22 +3,27 @@ import numpy as np
 import cv2
 import re
 
-# Load OCR model once
 reader = easyocr.Reader(['en'], gpu=False)
 
 def enhance_image(img):
-    # Enlarge and convert to grayscale
-    img = cv2.resize(img, None, fx=4, fy=4, interpolation=cv2.INTER_CUBIC)
+    # Resize big 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)
+
+    # Grayscale
     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 enhance
+    # Contrast
     clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
     contrast = clahe.apply(sharp)
 
@@ -36,7 +41,8 @@ def extract_weight_from_image(pil_img):
         weight_candidates = []
 
         for _, text, conf in results:
-            cleaned = text.lower().replace("kg", "").replace("kgs", "")
+            cleaned = text.lower()
+            cleaned = cleaned.replace("kg", "").replace("kgs", "")
             cleaned = cleaned.replace("o", "0").replace("s", "5").replace("g", "9")
             cleaned = re.sub(r"[^\d\.]", "", cleaned)