from mmocr.apis import MMOCRInferencer
import numpy as np
import cv2
import re
from PIL import Image

# Initialize MMOCR
ocr = MMOCRInferencer(det='DBNet', recog='SAR', device='cpu')  # or 'cuda' if GPU available

def extract_weight_from_image(pil_img):
    try:
        # Convert PIL to OpenCV image (BGR)
        img = np.array(pil_img.convert("RGB"))[:, :, ::-1]

        # Run MMOCR inference
        result = ocr(img)

        raw_texts = []
        weight_candidates = []
        fallback_weight = None
        fallback_conf = 0.0

        for item in result['predictions'][0]:
            text = item['text']
            conf = item.get('score', 0.8)  # Fallback confidence
            original = text
            cleaned = text.lower().strip()

            # Fix common OCR misreads
            cleaned = cleaned.replace(",", ".")
            cleaned = cleaned.replace("o", "0").replace("O", "0")
            cleaned = cleaned.replace("s", "5").replace("S", "5")
            cleaned = cleaned.replace("g", "9").replace("G", "6")
            cleaned = cleaned.replace("kg", "").replace("kgs", "")
            cleaned = re.sub(r"[^0-9\.]", "", cleaned)

            raw_texts.append(f"{original} → {cleaned} (conf: {round(conf, 2)})")

            if cleaned and cleaned.replace(".", "").isdigit() and not fallback_weight:
                fallback_weight = cleaned
                fallback_conf = conf

            if cleaned.count(".") <= 1 and re.fullmatch(r"\d{2,4}(\.\d{1,3})?", cleaned):
                weight_candidates.append((cleaned, conf))

        if weight_candidates:
            best_weight, best_conf = sorted(weight_candidates, key=lambda x: -x[1])[0]
        elif fallback_weight:
            best_weight, best_conf = fallback_weight, fallback_conf
        else:
            return "Not detected", 0.0, "\n".join(raw_texts)

        if "." in best_weight:
            int_part, dec_part = best_weight.split(".")
            int_part = int_part.lstrip("0") or "0"
            best_weight = f"{int_part}.{dec_part}"
        else:
            best_weight = best_weight.lstrip("0") or "0"

        return best_weight, round(best_conf * 100, 2), "\n".join(raw_texts)

    except Exception as e:
        return f"Error: {str(e)}", 0.0, "OCR failed"