import easyocr
import numpy as np
import cv2
import re

reader = easyocr.Reader(['en'], gpu=False)

def extract_weight_from_image(pil_img):
    try:
        img = np.array(pil_img)

        # Resize and grayscale
        img = cv2.resize(img, None, fx=4, fy=4, interpolation=cv2.INTER_LINEAR)
        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
        gray = cv2.bilateralFilter(gray, 11, 17, 17)
        _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

        results = reader.readtext(thresh)

        ocr_raw_texts = []
        weight_candidates = []

        for _, text, conf in results:
            ocr_raw_texts.append(text)
            t = text.lower()
            t = t.replace("kg", "").replace("kgs", "")
            t = t.replace("o", "0").replace("O", "0")
            t = t.replace("s", "5").replace("S", "5")
            t = t.replace("g", "9").replace("G", "6")
            t = re.sub(r"[^\d\.]", "", t)

            if re.fullmatch(r"\d{2,4}(\.\d{1,2})?", t):
                weight_candidates.append((t, conf))

        if not weight_candidates:
            return "Not detected", 0.0, "\n".join(ocr_raw_texts)

        best_weight, best_conf = sorted(weight_candidates, key=lambda x: -x[1])[0]
        return best_weight, round(best_conf * 100, 2), "\n".join(ocr_raw_texts)

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