mirror of
https://github.com/gradio-app/gradio.git
synced 2025-01-24 10:54:04 +08:00
73e98ddf15
* add required param but None * import torch req, add chunk_length_s import torch requirement for transformers enable inference for longer audio files * fix compononte initialization * input number is float, force int to multipy string * no need for Templates, fix class init * expects array * add requirements.txt for demo * update with cleaner syntax * add sample csv to fraud demo * adapt to new syntax * temp fix for Slider arguments * add dep to requirements * remove gr.Markdown from Interface init * fix default value param name * upgrade deepspeech, download models onstart * use path resolution consistent with other demos * remove redundant demo * add example to interface * fixed plotting issues * plots * deprecated carousel Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
66 lines
1.7 KiB
Python
66 lines
1.7 KiB
Python
import gradio as gr
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import random
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import time
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def xray_model(diseases, img):
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time.sleep(4)
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return [{disease: random.random() for disease in diseases}]
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def ct_model(diseases, img):
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time.sleep(3)
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return [{disease: 0.1 for disease in diseases}]
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# Detect Disease From Scan
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With this model you can lorem ipsum
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- ipsum 1
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- ipsum 2
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"""
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)
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disease = gr.CheckboxGroup(
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choices=["Covid", "Malaria", "Lung Cancer"], label="Disease to Scan For"
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)
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with gr.Tabs():
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with gr.TabItem("X-ray") as x_tab:
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with gr.Row():
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xray_scan = gr.Image()
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xray_results = gr.JSON()
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xray_run = gr.Button("Run")
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xray_progress = gr.StatusTracker(cover_container=True)
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xray_run.click(
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xray_model,
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inputs=[disease, xray_scan],
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outputs=xray_results,
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status_tracker=xray_progress,
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)
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with gr.TabItem("CT Scan"):
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with gr.Row():
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ct_scan = gr.Image()
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ct_results = gr.JSON()
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ct_run = gr.Button("Run")
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ct_progress = gr.StatusTracker(cover_container=True)
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ct_run.click(
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ct_model,
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inputs=[disease, ct_scan],
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outputs=ct_results,
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status_tracker=ct_progress,
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)
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upload_btn = gr.Button("Upload Results")
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upload_btn.click(
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lambda ct, xr: time.sleep(5),
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inputs=[ct_results, xray_results],
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outputs=[],
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status_tracker=gr.StatusTracker(),
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)
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if __name__ == "__main__":
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demo.launch()
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