Update ocr_engine.py

ocr_engine.py

@@ -1,28 +1,36 @@
-import cv2
-import pytesseract
-import numpy as np
-from PIL import Image
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image, ImageEnhance
+import re
 
-def preprocess_image(pil_image):
-    image = np.array(pil_image.convert("RGB"))
-    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
-    blurred = cv2.GaussianBlur(gray, (3, 3), 0)
-    _, thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-    return thresh
+# Load TrOCR model
+processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
+model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
 
-def extract_weight(pil_image):
-    try:
-        processed_img = preprocess_image(pil_image)
-        config = "--psm 7 -c tessedit_char_whitelist=0123456789."
-        text = pytesseract.image_to_string(processed_img, config=config)
-        print("OCR Raw:", text)
+def extract_weight(image: Image.Image) -> str:
+    # Step 1: Preprocess image
+    image = image.convert("L")  # grayscale
+    image = ImageEnhance.Contrast(image).enhance(2.0)
+    image = ImageEnhance.Sharpness(image).enhance(2.5)
+    image = image.convert("RGB")
 
-        numbers = ''.join(filter(lambda x: x in "0123456789.", text))
-        if not numbers:
-            return "No valid weight detected"
+    # Step 2: Run Hugging Face OCR
+    pixel_values = processor(images=image, 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]
 
-        weight_val = float(numbers)
-        unit = "kg" if weight_val >= 20 else "grams"
-        return f"{weight_val} {unit}"
-    except Exception as e:
-        return f"Error: {str(e)}"
+    print("OCR Output:", full_text)
+
+    # Step 3: Extract numeric weight
+    cleaned = full_text.lower().replace(" ", "")
+    match = re.search(r"(\d+(\.\d+)?)", cleaned)
+    weight = match.group(1) if match else None
+
+    # Step 4: Decide unit
+    if any(u in cleaned for u in ["kg", "kgs", "kilo"]):
+        unit = "kg"
+    elif any(u in cleaned for u in ["g", "gram", "grams"]):
+        unit = "grams"
+    else:
+        unit = "kg" if weight and float(weight) >= 20 else "grams"
+
+    return f"{weight} {unit}" if weight else "No valid weight detected"