def complete_model_pipeline(image):
    """
    Complete model pipeline for deploying the model using Gradio.

    This function takes an image as input, saves it temporarily to a file,
    loads the image as a test sample, performs prediction using the model,
    and then removes the temporarily saved image. The function returns the
    predicted dog breed label.

    Args:
        image (np.ndarray): The NumPy array representing the input image.

    Returns:
        str: The predicted dog breed label.

    Parameters:
        - image: A NumPy array representing the input image.

    Note: This function is designed to be used with Gradio to deploy the model
    and obtain predictions interactively.

    """

    # Define the file path to temporarily save the image
    image_file_path = '/content/drive/MyDrive/Colab Notebooks/dog-vision-project/custom_images/myImage.jpg'

    # Convert the input NumPy array to a Pillow image object and save it as a file
    image = Image.fromarray(image)
    image.save(image_file_path)

    # Prepare test data for the model using the saved image file
    test_data = get_data_batches(X=[image_file_path], test_data=True, batch_size=1, deployment = True)

    # Perform prediction using the model
    predictions = model.predict(test_data, verbose = 0)

    # Get the index of the highest predicted value and obtain the corresponding breed label
    predicted_label_index = np.argmax(predictions[0])
    predicted_breed_label = unique_breeds[predicted_label_index]

    # Remove the temporarily saved image file
    os.remove(image_file_path)

    return format_underscores(predicted_breed_label)