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)

        # Convert to grayscale and resize
        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
        gray = cv2.resize(gray, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)

        # Histogram equalization and adaptive threshold
        gray = cv2.equalizeHist(gray)
        thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                       cv2.THRESH_BINARY, 11, 2)
        thresh = cv2.bitwise_not(thresh)

        # OCR with bounding boxes
        results = reader.readtext(thresh)

        # Filter potential weight values
        candidates = []
        for (bbox, text, confidence) in results:
            # Clean text
            clean_text = text.replace('kg', '').strip()
            if re.fullmatch(r"\d{2,4}(\.\d{1,2})?", clean_text):
                candidates.append((clean_text, confidence))

        if not candidates:
            return "Not detected", 0.0

        # Choose the highest confidence match
        best_weight, conf = sorted(candidates, key=lambda x: -x[1])[0]
        return best_weight, round(conf, 2)

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