ocr_engine.py

import cv2
import pytesseract
import numpy as np
from PIL import Image

def extract_weight_from_image(pil_img):
    try:
        # Step 1: Convert PIL to OpenCV
        img = pil_img.convert("L")  # grayscale
        img = np.array(img)

        # Step 2: Resize image for better OCR accuracy
        img = cv2.resize(img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)

        # Step 3: Apply Gaussian Blur to remove noise
        blur = cv2.GaussianBlur(img, (5, 5), 0)

        # Step 4: Apply Adaptive Thresholding
        thresh = cv2.adaptiveThreshold(
            blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
            cv2.THRESH_BINARY_INV, 11, 2
        )

        # Step 5: OCR Config - digits only
        config = r'--oem 3 --psm 6 -c tessedit_char_whitelist=0123456789.'

        # Step 6: Run OCR
        text = pytesseract.image_to_string(thresh, config=config)
        print("🔍 OCR RAW OUTPUT:", repr(text))  # view this in Hugging Face logs

        # Step 7: Extract numbers
        weight = ''.join(filter(lambda c: c in '0123456789.', text))

        confidence = 95 if weight else 0
        return weight.strip(), confidence

    except Exception as e:
        print("❌ OCR Exception:", str(e))
        return "", 0