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| from transformers import TrOCRProcessor, VisionEncoderDecoderModel | |
| from PIL import Image, ImageFilter | |
| import torch | |
| import re | |
| # Load model | |
| processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") | |
| model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten") | |
| def clean_ocr_text(text): | |
| print("[RAW OCR]", text) | |
| text = text.replace(",", ".").replace("s", "5").replace("o", "0").replace("O", "0") | |
| text = re.sub(r"[^\d.kg]", "", text.lower()) # Keep digits, dots, and kg | |
| print("[CLEANED OCR]", text) | |
| return text | |
| def restore_decimal(text): | |
| if re.fullmatch(r"\d{5}", text): | |
| return f"{text[:2]}.{text[2:]}" | |
| elif re.fullmatch(r"\d{4}", text): | |
| return f"{text[:2]}.{text[2:]}" | |
| return text | |
| def extract_weight(image): | |
| try: | |
| image = image.resize((image.width * 2, image.height * 2), Image.BICUBIC) | |
| image = image.filter(ImageFilter.SHARPEN) | |
| pixel_values = processor(images=image, return_tensors="pt").pixel_values | |
| generated_ids = model.generate(pixel_values) | |
| raw_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip() | |
| cleaned = clean_ocr_text(raw_text) | |
| # Try direct match: e.g., 52.25 kg or 75.0 g | |
| match = re.search(r"(\d{1,3}\.\d{1,3})\s*(kg|g)", cleaned) | |
| if match: | |
| return f"{match.group(1)} {match.group(2)}" | |
| # Try fallback: extract digits and manually guess decimal | |
| fallback_match = re.search(r"(\d{4,5})", cleaned) | |
| if fallback_match: | |
| fallback_value = restore_decimal(fallback_match.group(1)) | |
| # Check for presence of unit hints in raw_text | |
| unit = "kg" if "kg" in raw_text.lower() else "g" | |
| return f"{fallback_value} {unit}" | |
| return f"No valid weight found | OCR: {cleaned}" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |