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import os
import gradio as gr
from gradio_client import Client, handle_file
HF_TOKEN = os.environ.get('HF_KEY')
client = Client("geyongtao/video-matting",
max_workers=3,
hf_token=HF_TOKEN)
def process_image_check(path_input):
if path_input is None:
raise gr.Error(
"Missing image in the left pane: please upload an image first."
)
def process_pipe_matting(matting_image_input):
return client.predict(
path_input=handle_file(matting_image_input),
api_name="/gradio_handler"
)
def run_demo_server():
gradio_theme = gr.themes.Default()
with gr.Blocks(
theme=gradio_theme,
title="Matting",
) as demo:
with gr.Row():
gr.Markdown("# Matting Demo")
with gr.Row():
gr.Markdown("### Due to the GPU quota limit, if an error occurs, please wait for 5 minutes before retrying.")
with gr.Row():
with gr.Column():
matting_image_input = gr.Image(
label="Input Image",
type="filepath",
)
with gr.Row():
matting_image_submit_btn = gr.Button(
value="Estimate Matting", variant="primary"
)
matting_image_reset_btn = gr.Button(value="Reset")
with gr.Column():
matting_image_output = gr.Image(label='Output')
matting_image_submit_btn.click(
fn=process_image_check,
inputs=matting_image_input,
outputs=None,
preprocess=False,
queue=False,
).success(
fn=process_pipe_matting,
inputs=[
matting_image_input,
],
outputs=[matting_image_output],
concurrency_limit=1,
)
matting_image_reset_btn.click(
fn=lambda: (
None,
None,
),
inputs=[],
outputs=[
matting_image_input,
matting_image_output,
],
queue=False,
)
demo.queue(
api_open=False,
).launch()
if __name__ == '__main__':
run_demo_server() |