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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"] | |
| 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, enable_queue=True, favicon_path="assets/favicon-32x32.png") | |