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)}"