Update ocr_engine.py

ocr_engine.py

@@ -1,28 +1,37 @@
 from transformers import TrOCRProcessor, VisionEncoderDecoderModel
-from PIL import Image
+from PIL import Image, ImageEnhance
 import re
 
-# Load lightweight, fast, public model
+# Load processor + model
 processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
 model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
 
 def extract_weight(image: Image.Image) -> str:
-    image = image.convert("RGB")
-    pixel_values = processor(images=image, return_tensors="pt").pixel_values
+    # Crop only display region (adjust based on your image format)
+    width, height = image.size
+    display_area = image.crop((width * 0.35, height * 0.1, width * 0.65, height * 0.25))  # crop display center
+
+    # Enhance contrast & sharpness
+    display_area = display_area.convert("L")  # grayscale
+    display_area = ImageEnhance.Contrast(display_area).enhance(2.0)
+    display_area = ImageEnhance.Sharpness(display_area).enhance(2.5)
+    display_area = display_area.convert("RGB")
+
+    # OCR
+    pixel_values = processor(images=display_area, return_tensors="pt").pixel_values
     generated_ids = model.generate(pixel_values, max_length=32)
     full_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 
-    # Extract number
+    # Clean & parse
     cleaned = full_text.lower().replace(" ", "")
     match = re.search(r"(\d+(\.\d+)?)", cleaned)
     weight = match.group(1) if match else None
 
-    # Detect unit
     if any(u in cleaned for u in ["kg", "kgs", "kilogram", "kilo"]):
         unit = "kg"
     elif any(u in cleaned for u in ["g", "gram", "grams"]):
         unit = "grams"
     else:
-        unit = "kg" if weight and float(weight) >= 5 else "grams"
+        unit = "kg" if weight and float(weight) >= 20 else "grams"
 
     return f"{weight} {unit}" if weight else ""