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import gradio as gr
from bald_face import BaldFace
bald = BaldFace()
def predict(image):
return bald.make(image)
footer = r"""
<center>
<b>
Demo for <a href=''>Lightweight OpenPose</a>
</b>
</center>
"""
with gr.Blocks(title="") as app:
gr.HTML("<center><h1></h1></center>")
gr.HTML("<center><h3></h3></center>")
with gr.Row(equal_height=False):
with gr.Column():
input_img = gr.Image(type="numpy", label="Input image")
run_btn = gr.Button(variant="primary")
with gr.Column():
output_img = gr.Image(type="pil", label="Output image")
gr.ClearButton(components=[input_img, output_img], variant="stop")
run_btn.click(predict, [input_img], [output_img])
with gr.Row():
blobs = [[f"examples/{x:02d}.jpg"] for x in range(1, 5)]
examples = gr.Dataset(components=[input_img], samples=blobs)
examples.click(lambda x: x[0], [examples], [input_img])
with gr.Row():
gr.HTML(footer)
app.launch(share=False, debug=True, show_error=True)
app.queue()
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