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from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import torch
import re
# Load TrOCR processor and model once
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
def clean_ocr_text(text):
# Fix common OCR mistakes
text = text.replace(",", ".") # comma to dot
text = re.sub(r"[^\d\.kg]", "", text.lower()) # keep only digits, dot, k, g
return text
def extract_weight(image):
try:
# TrOCR inference
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()
print("OCR Raw Output:", raw_text)
# Clean and normalize text
cleaned_text = clean_ocr_text(raw_text)
print("Cleaned OCR:", cleaned_text)
# Flexible regex to catch even minor issues (e.g., 52.2g, 98.7kg)
pattern = r'(\d{1,5}(?:\.\d{1,3})?)\s*(kg|g)'
match = re.search(pattern, cleaned_text)
if match:
value = match.group(1)
unit = match.group(2)
return f"{value} {unit}"
else:
return "No valid weight found"
except Exception as e:
return f"Error: {str(e)}"
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