from transformers import DonutProcessor, VisionEncoderDecoderModel from PIL import Image import re # Load OCR processor and model (pretrained on receipts, good for 7-segment) processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2") model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2") def extract_weight(image: Image.Image) -> str: image = image.convert("RGB") pixel_values = processor(image, return_tensors="pt").pixel_values outputs = model.generate(pixel_values, max_length=512) decoded = processor.batch_decode(outputs, skip_special_tokens=True)[0] # Extract weight number cleaned = decoded.lower().replace(" ", "") match = re.search(r"(\d+(\.\d+)?)", cleaned) weight = match.group(1) if match else None # Smart unit detection 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" return f"{weight} {unit}" if weight else "No valid weight detected"