from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image, ImageEnhance
import re

# Load TrOCR model
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")

def extract_weight(image: Image.Image) -> str:
    # Step 1: Preprocess image
    image = image.convert("L")  # grayscale
    image = ImageEnhance.Contrast(image).enhance(2.0)
    image = ImageEnhance.Sharpness(image).enhance(2.5)
    image = image.convert("RGB")

    # Step 2: Run Hugging Face OCR
    pixel_values = processor(images=image, return_tensors="pt").pixel_values
    generated_ids = model.generate(pixel_values, max_length=32)
    full_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

    print("OCR Output:", full_text)

    # Step 3: Extract numeric weight
    cleaned = full_text.lower().replace(" ", "")
    match = re.search(r"(\d+(\.\d+)?)", cleaned)
    weight = match.group(1) if match else None

    # Step 4: Decide unit
    if any(u in cleaned for u in ["kg", "kgs", "kilo"]):
        unit = "kg"
    elif any(u in cleaned for u in ["g", "gram", "grams"]):
        unit = "grams"
    else:
        unit = "kg" if weight and float(weight) >= 20 else "grams"

    return f"{weight} {unit}" if weight else "No valid weight detected"