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import gradio as gr
from backend import Infer
DEBUG = False
infer = Infer(DEBUG)
example_image_path = [
"assets/example_1.jpg",
"assets/example_2.jpg",
"assets/example_3.jpg",
]
outputs = [
gr.Image(label="Thumb"),
gr.Number(label="DeepNAPSI Thumb", precision=0),
gr.Image(label="Index"),
gr.Number(label="DeepNAPSI Index", precision=0),
gr.Image(label="Middle"),
gr.Number(label="DeepNAPSI Middle", precision=0),
gr.Image(label="Ring"),
gr.Number(label="DeepNAPSI Ring", precision=0),
gr.Image(label="Pinky"),
gr.Number(label="DeepNAPSI Pinky", precision=0),
gr.Number(label="DeepNAPSI Sum", precision=0),
]
with gr.Blocks(analytics_enabled=False, title="DeepNAPSI") as demo:
with gr.Column():
gr.Markdown("## Welcome to the DeepNAPSI application!")
gr.Markdown(
"Upload an image of the one hand and click **Predict NAPSI** to see the output."
)
gr.Markdown(
"*Note*: Make sure there are no identifying information present in the image. The prediction can take up to 4.5 minutes."
)
gr.Markdown(
"*Note*: This is not a medical product and cannot be used for a patient diagnosis in any way."
)
with gr.Column():
with gr.Row():
with gr.Column():
with gr.Row():
image_input = gr.Image()
example_images = gr.Examples(
example_image_path,
image_input,
outputs,
fn=infer.predict,
cache_examples=True,
)
with gr.Row():
image_button = gr.Button("Predict NAPSI")
with gr.Row():
with gr.Column():
outputs[0].render()
outputs[1].render()
with gr.Column():
outputs[2].render()
outputs[3].render()
with gr.Column():
outputs[4].render()
outputs[5].render()
with gr.Column():
outputs[6].render()
outputs[7].render()
with gr.Column():
outputs[8].render()
outputs[9].render()
outputs[10].render()
image_button.click(infer.predict, inputs=image_input, outputs=outputs)
demo.launch(
share=True if DEBUG else False,
favicon_path="assets/favicon-32x32.png",
)
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