from mmocr.utils.ocr import MMOCR import numpy as np import cv2 import re from PIL import Image # Load MMOCR (det + recog) ocr = MMOCR(det='DBPANet', recog='SAR', device='cpu') # CPU mode for Hugging Face def extract_weight_from_image(pil_img): try: img = np.array(pil_img.convert("RGB"))[:, :, ::-1] result = ocr.readtext(img, print_result=False, output=None)[0]['result'] raw_texts = [] weight_candidates = [] fallback_weight = None fallback_conf = 0.0 for text, conf in result: original = text cleaned = text.lower().strip() 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"