code: def gradio_display():
      import gradio as gr
  
      examples_sam = [
          ["./examples/cityscapes_demo.png", "a photo of car"],
          ["examples/dog.jpg", "dog"],
          ["examples/zixingche.jpeg", "kid"],
      ]
  
      demo_mask_sam = gr.Interface(
          fn=get_id_photo_output,
          inputs=[
              gr.Image(
                  value=ID_PHOTO_IMAGE_DEMO, label="Input image"
              ).style(height=400),
              gr.inputs.Textbox(
                  lines=3,
                  placeholder=None,
                  default="a photo of car",
                  label="🔥 Input text prompt 🔥",
                  optional=False,
              ),
          ],
          outputs=[
              gr.Image(
                  label="Output based on text", interactive=False
              ).style(height=300),
              gr.Image(label="Output mask", interactive=False).style(
                  height=300
              ),
          ],
          examples=examples_sam,
          description="<p> \
                          <strong>SAM+CLIP:  Text prompt for segmentation. </strong> <br>\
                          Choose an example below; Or, upload by yourself: <br>\
                          1. Upload images to be tested to 'input image'. 2. Input a text prompt to 'input text prompt' and click 'submit'</strong>.  <br>\
                          </p>",
          cache_examples=False,
          allow_flagging="never",
      )
  
      demo = gr.TabbedInterface(
          [
              demo_mask_sam,
          ],
          ["SAM+CLIP(Text to Segment)"],
          title=" 🔥 Text to Segment Anything with PaddleSeg 🔥",
      )
      demo.launch(
          server_name="0.0.0.0",
          enable_queue=False,
          server_port=8078,
          share=True,
      )
  
imports: import os
  import cv2
  import time
  import sys
  import argparse
  import paddle
  import paddle.nn.functional
  import numpy
  from PIL import Image, ImageDraw
  import matplotlib.pyplot
  from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
  from segment_anything.modeling.clip_paddle import build_clip_model, _transform
  from segment_anything.utils.sample_tokenizer import tokenize
  from paddleseg.utils.visualize import get_pseudo_color_map, get_color_map_list
label_desc: Use image node_id 7 for calling a global function with example usage: gradio_display() without return types