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

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

def preprocess_image(image):
    """
    Preprocess the image to improve OCR detection.
    Converts to grayscale and applies adaptive threshold.
    """
    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    # Apply adaptive threshold to isolate digits better
    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:
        # ✅ Step 1: Convert to OpenCV format
        image = np.array(pil_image.convert("RGB"))

        # ✅ Step 2: Preprocess image
        processed = preprocess_image(image)

        # ✅ Step 3: Optional - Save debug image
        debug_path = "debug_processed_image.png"
        Image.fromarray(processed).save(debug_path)
        print(f"[DEBUG] Saved preprocessed image to {debug_path}")

        # ✅ Step 4: Run EasyOCR
        result = reader.readtext(processed)

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

        # ✅ Step 5: Look for a decimal number like 53.25
        weight = None
        confidence = 0.0

        for detection in result:
            text = detection[1].replace(",", ".")  # Handle comma decimal (if any)
            conf = detection[2]

            # Look for numbers like 53.25 or 100
            match = re.search(r"\b\d{1,3}(\.\d{1,2})?\b", text)
            if match:
                weight = match.group()
                confidence = conf
                break

        if weight:
            return weight, round(confidence * 100, 2)
        else:
            return "No weight detected", 0.0

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