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 for better clarity
        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
        gray = cv2.resize(gray, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)

        # Histogram equalization
        gray = cv2.equalizeHist(gray)

        # Adaptive threshold to enhance text
        thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                       cv2.THRESH_BINARY, 11, 2)

        # Invert for LCD-like contrast
        thresh = cv2.bitwise_not(thresh)

        # OCR read
        result = reader.readtext(thresh, detail=0)
        combined_text = " ".join(result)
        print("OCR Text:", combined_text)

        # Regex to match weight like 25, 46.5, 75.45 etc.
        match = re.search(r"\b\d{2,4}\.?\d{0,2}\b", combined_text)
        if match:
            return match.group(), 95.0
        else:
            return "No weight detected", 0.0

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