Update ocr_engine.py

ocr_engine.py

@@ -1,46 +1,30 @@
-import easyocr
-import numpy as np
-import cv2
-import re
-
-reader = easyocr.Reader(['en'], gpu=False)
-
 def extract_weight_from_image(pil_img):
     try:
         img = np.array(pil_img)
-
-        # Resize and grayscale
         img = cv2.resize(img, None, fx=3.5, fy=3.5, interpolation=cv2.INTER_LINEAR)
         gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-
-        # Denoise & Adaptive Thresholding
         gray = cv2.bilateralFilter(gray, 11, 17, 17)
         thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                        cv2.THRESH_BINARY_INV, 11, 2)
 
-        # Run OCR
         results = reader.readtext(thresh)
-
-        print("OCR Results:", results)
+        all_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("O", "0")
             cleaned = cleaned.replace("s", "5").replace("S", "5")
             cleaned = cleaned.replace("g", "9").replace("G", "6")
-
             cleaned = re.sub(r"[^\d\.]", "", cleaned)
-
             if re.fullmatch(r"\d{2,4}(\.\d{1,2})?", cleaned):
                 weight_candidates.append((cleaned, conf))
 
         if not weight_candidates:
-            return "Not detected", 0.0
+            return "Not detected", 0.0, "\n".join(all_texts)
 
         best_weight, best_conf = sorted(weight_candidates, key=lambda x: -x[1])[0]
-        return best_weight, round(best_conf * 100, 2)
+        return best_weight, round(best_conf * 100, 2), "\n".join(all_texts)
 
     except Exception as e:
-        return f"Error: {str(e)}", 0.0
+        return f"Error: {str(e)}", 0.0, "OCR failed"