from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image, ImageFilter
import torch
import re

# Load model
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").replace("O", "0")
    text = re.sub(r"[^\d.kg]", "", text.lower())  # Keep digits, dots, and kg
    print("[CLEANED OCR]", text)
    return text

def restore_decimal(text):
    if re.fullmatch(r"\d{5}", text):
        return f"{text[:2]}.{text[2:]}"
    elif re.fullmatch(r"\d{4}", text):
        return f"{text[:2]}.{text[2:]}"
    return text

def extract_weight(image):
    try:
        image = image.resize((image.width * 2, image.height * 2), Image.BICUBIC)
        image = image.filter(ImageFilter.SHARPEN)

        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)

        # Try direct match: e.g., 52.25 kg or 75.0 g
        match = re.search(r"(\d{1,3}\.\d{1,3})\s*(kg|g)", cleaned)
        if match:
            return f"{match.group(1)} {match.group(2)}"

        # Try fallback: extract digits and manually guess decimal
        fallback_match = re.search(r"(\d{4,5})", cleaned)
        if fallback_match:
            fallback_value = restore_decimal(fallback_match.group(1))

            # Check for presence of unit hints in raw_text
            unit = "kg" if "kg" in raw_text.lower() else "g"
            return f"{fallback_value} {unit}"

        return f"No valid weight found | OCR: {cleaned}"
    except Exception as e:
        return f"Error: {str(e)}"