import easyocr
import numpy as np
import re
import cv2

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

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

        # Step 1: Convert to grayscale
        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

        # Step 2: Apply adaptive threshold to handle lighting
        thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
                                       cv2.THRESH_BINARY_INV, 11, 2)

        # Step 3: Dilate to make digits thicker
        kernel = np.ones((2, 2), np.uint8)
        dilated = cv2.dilate(thresh, kernel, iterations=1)

        # Step 4: OCR on the preprocessed image
        result = reader.readtext(dilated, detail=0)
        text = " ".join(result).strip()
        print("OCR Text:", text)

        # Step 5: Match numeric values like 52.30 or 003.25
        match = re.search(r"\b\d{2,4}\.?\d{0,2}\b", 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