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""" |
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File: app.py |
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Author: Elena Ryumina and Dmitry Ryumin |
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Description: Description: Main application file for Facial_Expression_Recognition. |
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The file defines the Gradio interface, sets up the main blocks, |
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and includes event handlers for various components. |
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License: MIT License |
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""" |
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import gradio as gr |
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from app.description import DESCRIPTION_STATIC, DESCRIPTION_DYNAMIC |
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from app.authors import AUTHORS |
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from app.app_utils import preprocess_image_and_predict, preprocess_video_and_predict |
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def clear_static_info(): |
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return ( |
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gr.Image(value=None, type="pil"), |
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gr.Image(value=None, scale=1, elem_classes="dl5"), |
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gr.Image(value=None, scale=1, elem_classes="dl2"), |
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gr.Label(value=None, num_top_classes=3, scale=1, elem_classes="dl3"), |
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) |
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def clear_dynamic_info(): |
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return ( |
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gr.Video(value=None), |
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gr.Video(value=None), |
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gr.Video(value=None), |
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gr.Video(value=None), |
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gr.Plot(value=None), |
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) |
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with gr.Blocks(css="app.css") as demo: |
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with gr.Tab("Dynamic App"): |
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gr.Markdown(value=DESCRIPTION_DYNAMIC) |
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with gr.Row(): |
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with gr.Column(scale=2): |
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input_video = gr.Video(elem_classes="video1") |
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with gr.Row(): |
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clear_btn_dynamic = gr.Button( |
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value="Clear", interactive=True, scale=1 |
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) |
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submit_dynamic = gr.Button( |
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value="Submit", interactive=True, scale=1, elem_classes="submit" |
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) |
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with gr.Column(scale=2, elem_classes="dl4"): |
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with gr.Row(): |
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output_video = gr.Video(label="Original video", scale=1, elem_classes="video2") |
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output_face = gr.Video(label="Pre-processed video", scale=1, elem_classes="video3") |
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output_heatmaps = gr.Video(label="Heatmaps", scale=1, elem_classes="video4") |
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output_statistics = gr.Plot(label="Statistics of emotions", elem_classes="stat") |
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gr.Examples( |
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["videos/video1.mp4", |
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"videos/video2.mp4", |
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"new_videos/01.mp4", |
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"new_videos/02.mp4", |
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"new_videos/14.mp4", |
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"new_videos/16.mp4", |
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"new_videos/20.mp4", |
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"new_videos/36.mp4", |
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"new_videos/38.mp4", |
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"new_videos/45.mp4", |
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], |
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[input_video], |
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) |
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with gr.Tab("Static App"): |
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gr.Markdown(value=DESCRIPTION_STATIC) |
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with gr.Row(): |
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with gr.Column(scale=2, elem_classes="dl1"): |
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input_image = gr.Image(label="Original image", type="pil") |
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with gr.Row(): |
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clear_btn = gr.Button( |
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value="Clear", interactive=True, scale=1, elem_classes="clear" |
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) |
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submit = gr.Button( |
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value="Submit", interactive=True, scale=1, elem_classes="submit" |
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) |
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with gr.Column(scale=1, elem_classes="dl4"): |
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with gr.Row(): |
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output_image = gr.Image(label="Face", scale=1, elem_classes="dl5") |
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output_heatmap = gr.Image(label="Heatmap", scale=1, elem_classes="dl2") |
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output_label = gr.Label(num_top_classes=3, scale=1, elem_classes="dl3") |
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gr.Examples( |
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[ |
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"images/fig7.jpg", |
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"images/fig1.jpg", |
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"images/fig2.jpg", |
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"images/fig3.jpg", |
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"images/fig4.jpg", |
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"images/fig5.jpg", |
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"images/fig6.jpg", |
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], |
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[input_image], |
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) |
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with gr.Tab("Authors"): |
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gr.Markdown(value=AUTHORS) |
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submit.click( |
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fn=preprocess_image_and_predict, |
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inputs=[input_image], |
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outputs=[output_image, output_heatmap, output_label], |
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queue=True, |
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) |
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clear_btn.click( |
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fn=clear_static_info, |
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inputs=[], |
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outputs=[input_image, output_image, output_heatmap, output_label], |
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queue=True, |
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) |
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submit_dynamic.click( |
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fn=preprocess_video_and_predict, |
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inputs=input_video, |
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outputs=[ |
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output_video, |
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output_face, |
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output_heatmaps, |
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output_statistics |
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], |
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queue=True, |
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) |
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clear_btn_dynamic.click( |
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fn=clear_dynamic_info, |
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inputs=[], |
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outputs=[ |
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input_video, |
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output_video, |
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output_face, |
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output_heatmaps, |
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output_statistics |
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], |
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queue=True, |
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) |
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if __name__ == "__main__": |
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demo.queue(api_open=False).launch(share=False) |
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