import numpy as np
import re
import cv2
from PIL import Image
import easyocr

# ✅ Initialize EasyOCR Reader once
reader = easyocr.Reader(['en'], gpu=False)

def preprocess_image(image):
    """
    Convert to grayscale and apply adaptive thresholding
    to enhance contrast for digital scale OCR.
    """
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    thresh = cv2.adaptiveThreshold(
        gray, 255,
        cv2.ADAPTIVE_THRESH_MEAN_C,
        cv2.THRESH_BINARY_INV,
        11, 10
    )
    return thresh

def extract_weight_from_image(pil_image):
    try:
        # ✅ Convert PIL image to OpenCV format
        image = np.array(pil_image.convert("RGB"))

        # ✅ Preprocess image
        processed = preprocess_image(image)

        # ✅ Optional: Save debug image for troubleshooting
        debug_path = "debug_processed_image.png"
        Image.fromarray(processed).save(debug_path)
        print(f"[DEBUG] Preprocessed image saved to: {debug_path}")

        # ✅ Perform OCR using EasyOCR
        result = reader.readtext(processed)

        print("🔍 OCR Results:")
        for detection in result:
            print(f"  • Text: '{detection[1]}' | Confidence: {detection[2]*100:.2f}%")

        # ✅ Extract first matching numeric value
        for detection in result:
            text = detection[1].replace(",", ".")  # normalize decimal
            conf = detection[2]
            match = re.search(r"\b\d{1,4}(\.\d{1,2})?\b", text)
            if match:
                return match.group(), round(conf * 100, 2)

        # ❌ No weight found
        return "No weight detected", 0.0

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