code: def create_gradio_app(self):
      import gradio as gr
      import tempfile
      import os
      from PIL import Image
  
      def inference(image, use_gpu=False):
          with tempfile.TemporaryDirectory() as temp_dir:
              self.segment(
                  paths=[image],
                  use_gpu=use_gpu,
                  visualization=True,
                  output_dir=temp_dir,
              )
              return Image.open(
                  os.path.join(temp_dir, os.listdir(temp_dir)[0])
              )
  
      interface = gr.Interface(
          inference,
          [
              gr.inputs.Image(type="filepath"),
              gr.Checkbox(label="use_gpu"),
          ],
          gr.outputs.Image(type="ndarray"),
          title="humanseg_lite",
      )
      return interface
  
imports: import argparse
  import ast
  import os
  import os.path
  import cv2
  import numpy
  import paddle.jit
  import paddle.static
  from paddle.inference import Config
  from paddle.inference import create_predictor
  from .data_feed import preprocess_v
  from .data_feed import reader
  from .optimal import postprocess_v
  from .optimal import threshold_mask
  from .processor import base64_to_cv2
  from .processor import check_dir
  from .processor import cv2_to_base64
  from .processor import postprocess
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import runnable
  from paddlehub.module.module import serving
label_desc: Use image node_id 10 for calling the ShufflenetHumanSeg obj's underlying member method code with example usage: obj.create_gradio_app() and returns: interface, Image