def input_handler()

in src/inference.py [0:0]

• 12 lines of code
• 3 McCabe index (conditional complexity)

def input_handler(data, context):
    """ Pre-process request input before it is sent to TensorFlow Serving REST API
    Args:
        data (obj): the request data, in format of dict or string
        context (Context): an object containing request and configuration details
    Returns:
        (dict): a JSON-serializable dict that contains request body and headers
    """
    if context.request_content_type == 'application/x-image':
        # pass through json (assumes it's correctly formed)
        #read image as bytes
        image_as_bytes = io.BytesIO(data.read())
        img = Image.open(image_as_bytes)
        img = img.resize((WIDTH, HEIGHT))
        # convert PIL image instance to numpy array
        img_array = image.img_to_array(img, data_format = "channels_first")
        # the image is now in an array of shape (3, 224, 224)
        # need to expand it to (1, 3, 224, 224) as it's expecting a list
        expanded_img_array = tf.expand_dims(img_array, axis=0)
        #preprocessing the image array with channel first
        preprocessed_img = preprocess_input(expanded_img_array, data_format = "channels_first")
        #converting to numpy list
        preprocessed_img_lst = preprocessed_img.numpy().tolist()
        return json.dumps({"instances": preprocessed_img_lst})
    else:
        _return_error(415, 'Unsupported content type "{}"'.format(context.request_content_type or 'Unknown'))