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
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()