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
        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

        # Enhance contrast
        gray = cv2.equalizeHist(gray)

        # Resize to enhance small text
        gray = cv2.resize(gray, None, fx=2, fy=2, interpolation=cv2.INTER_LINEAR)

        # Add light blur to reduce noise
        gray = cv2.GaussianBlur(gray, (3, 3), 0)

        # Invert for LCD screens with dark backgrounds
        inverted = cv2.bitwise_not(gray)

        # OCR
        result = reader.readtext(inverted, detail=0)
        combined_text = " ".join(result)
        print("OCR Result:", combined_text)

        # Try to detect weight pattern like "25kg" or "25.3kg"
        match = re.search(r"(\d{1,4}(?:\.\d{1,2})?)\s?(kg)", combined_text, re.IGNORECASE)
        if match:
            return f"{match.group(1)} kg", 95.0

        # Fallback: detect numbers only
        fallback = re.search(r"\d{1,4}(?:\.\d{1,2})?", combined_text)
        if fallback:
            return f"{fallback.group(0)} kg", 75.0

        return "No weight detected kg", 0.0

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