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from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import torch
import re
# Load model and processor
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").lower()
text = re.sub(r"[^\d\.kg]", "", text)
print("[CLEANED OCR]", text)
return text
def extract_weight(image):
try:
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)
# First try with unit
match = re.search(r'(\d{1,5}(?:\.\d{1,3})?)\s*(kg|g)', cleaned)
if match:
return f"{match.group(1)} {match.group(2)}"
# Fallback: only number, assume grams
fallback = re.search(r'(\d{1,5}(?:\.\d{1,3})?)', cleaned)
if fallback:
return f"{fallback.group(1)} g"
return f"No valid weight found | OCR: {cleaned}"
except Exception as e:
return f"Error: {str(e)}"
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