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import os
import pip
import gradio as gr
from PIL import Image
from backend import Infer
DEBUG = False
infer = Infer(DEBUG)
example_image_path = ["assets/example_1.jpg", "assets/example_2.jpg", "assets/example_3.jpg"]
with gr.Blocks(analytics_enabled=False, title="DeepNAPSI") as demo:
outputs = []
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.\n" \
"> Note: Make sure there are no identifying information present in the image. The prediction can take up to 1 minute.")
with gr.Column():
with gr.Row():
with gr.Column():
with gr.Row():
image_input = gr.Image()
with gr.Row():
image_button = gr.Button("Predict NAPSI")
with gr.Row():
with gr.Column():
outputs.append(gr.Image(label="Thumb"))
outputs.append(gr.Number(label="DeepNAPSI Thumb", precision=0))
with gr.Column():
outputs.append(gr.Image(label="Index"))
outputs.append(gr.Number(label="DeepNAPSI Index", precision=0))
with gr.Column():
outputs.append(gr.Image(label="Middle"))
outputs.append(gr.Number(label="DeepNAPSI Middle", precision=0))
with gr.Column():
outputs.append(gr.Image(label="Ring"))
outputs.append(gr.Number(label="DeepNAPSI Ring", precision=0))
with gr.Column():
outputs.append(gr.Image(label="Pinky"))
outputs.append(gr.Number(label="DeepNAPSI Pinky", precision=0))
outputs.append(gr.Number(label="DeepNAPSI Sum", precision=0))
example_images = gr.Examples(example_image_path, image_input, outputs,
fn=infer.predict, cache_examples=True)
image_button.click(infer.predict, inputs=image_input, outputs=outputs)
demo.launch(share=True, enable_queue=True, favicon_path="assets/favicon-32x32.png")
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