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from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import re
# Load model + processor
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-stage1")
model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-stage1")
def extract_weight(image: Image.Image) -> str:
image = image.convert("RGB")
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values, max_length=20)
full_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
# For debugging (optional):
print("OCR Output:", full_text)
# Extract number (weight)
match = re.search(r"(\d+(\.\d+)?)", full_text)
if match:
weight = match.group(1)
else:
return "No valid weight detected"
# Detect unit — smarter match
text_lower = full_text.lower().replace(" ", "")
if any(unit in text_lower for unit in ["kg", "kgs", "kilogram", "kilo", "k.g"]):
unit = "kg"
elif any(unit in text_lower for unit in ["g", "gram", "grams"]):
unit = "grams"
else:
# Smart fallback: use value
if float(weight) >= 5:
unit = "kg"
else:
unit = "grams"
return f"{weight} {unit}"
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