gradio实用小技能汇总

gradio简介

• gradio库是一个可以快速实现了一个机器学习或者深度学习模型的web端框架，该库已经集成到Hugging Face的Spaces中。
• 只需要几行代码便可快速写出一个带有web界面的demo。

参数对应关系

# inference函数前几个参数分别一一对应下面的gr.Interface中inputs的几个参数
def inference(img, box_thresh, unclip_ratio, text_score):
    img_path = img.name
    img = cv2.imread(img_path)
    dt_boxes, rec_res = text_sys(img,
                                 box_thresh=box_thresh,
                                 unclip_ratio=unclip_ratio,
                                 text_score=text_score)
    img_save_path = visualize(img_path, dt_boxes, rec_res)
    return img_save_path, rec_res

gr.Interface(
    inference,
    inputs=[
        gr.inputs.Image(type='file', label='Input'),
        gr.Slider(minimum=0, maximum=1.0, value=0.5,
                  label='box_thresh', step=0.1),
        gr.Slider(minimum=1.5, maximum=2.0, value=1.6,
                  label='unclip_ratio', step=0.1),
        gr.Slider(minimum=0, maximum=1.0, value=0.5,
                  label='text_score', step=0.1),
    ],
    outputs=[
        gr.outputs.Image(type='file', label='Output_image'),
        gr.outputs.Textbox(type='text', label='Output_text')
    ],
    title=title,
    description=description,
    article=article,
    css=css,
    allow_flagging='never',
    ).launch(debug=True, enable_queue=True)

Slider的change用法

import gradio as gr

def update_value(val):
  return f'Value is set to {val}'

demo = gr.Blocks()

with demo:
  inp = gr.Slider(0, 100, label='Value')
  md = gr.Markdown('Select a value')

  inp.change(fn=update_value, inputs=inp, outputs=md)

demo.launch()