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| """ | |
| File: app.py | |
| Author: Elena Ryumina and Dmitry Ryumin | |
| Description: Description: Main application file for Facial_Expression_Recognition. | |
| The file defines the Gradio interface, sets up the main blocks, | |
| and includes event handlers for various components. | |
| License: MIT License | |
| """ | |
| import gradio as gr | |
| # Importing necessary components for the Gradio app | |
| from app.description import DESCRIPTION_DYNAMIC | |
| from app.authors import AUTHORS | |
| from app.app_utils import preprocess_video_and_predict | |
| def clear_static_info(): | |
| return ( | |
| gr.Image(value=None, type="pil"), | |
| gr.Image(value=None, scale=1, elem_classes="dl5"), | |
| gr.Image(value=None, scale=1, elem_classes="dl2"), | |
| gr.Label(value=None, num_top_classes=3, scale=1, elem_classes="dl3"), | |
| ) | |
| def clear_dynamic_info(): | |
| return ( | |
| gr.Video(value=None), | |
| gr.Plot(value=None), | |
| gr.Plot(value=None), | |
| gr.Plot(value=None), | |
| gr.Textbox(value=None), | |
| # gr.HTML(value=None), | |
| gr.File(value=None), | |
| gr.File(value=None), | |
| ) | |
| with gr.Blocks(css="app.css") as demo: | |
| with gr.Tab("AVCER App"): | |
| gr.Markdown(value=DESCRIPTION_DYNAMIC) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| input_video = gr.Video(elem_classes="video1") | |
| with gr.Row(): | |
| clear_btn_dynamic = gr.Button( | |
| value="Clear", interactive=True, scale=1 | |
| ) | |
| submit_dynamic = gr.Button( | |
| value="Submit", interactive=True, scale=1, elem_classes="submit" | |
| ) | |
| text = gr.Textbox(label="Result", info='Positive state includes Happiness, Surprise, Happily Surprised, and Happily Disgusted emotions. Negative state includes other emotions and Surprise.') | |
| # question_mark = gr.HTML(tooltip_html) | |
| with gr.Column(scale=2, elem_classes="dl4"): | |
| output_face = gr.Plot(label="Face images", elem_classes="img") | |
| output_heatmaps = gr.Plot(label="Waveform", elem_classes="audio") | |
| output_statistics = gr.Plot(label="Statistics of emotions", elem_classes="stat") | |
| with gr.Row(): | |
| output_video = gr.File(label="Original video", | |
| file_count="single", | |
| file_types=[".mp4"], | |
| show_label=True, | |
| interactive=False, | |
| visible=True, | |
| elem_classes="video") | |
| prediction_file = gr.File(label="Prediction file", | |
| file_count="single", | |
| file_types=[".csv"], | |
| show_label=True, | |
| interactive=False, | |
| visible=True, | |
| elem_classes="pred") | |
| gr.Examples( | |
| ["videos/video1.mp4", | |
| "videos/video2.mp4", | |
| "videos/video3.mp4", | |
| "videos/video4.mp4", | |
| ], | |
| [input_video], | |
| ) | |
| with gr.Tab("Authors"): | |
| gr.Markdown(value=AUTHORS) | |
| submit_dynamic.click( | |
| fn=preprocess_video_and_predict, | |
| inputs=input_video, | |
| outputs=[ | |
| output_face, | |
| output_heatmaps, | |
| output_statistics, | |
| text, | |
| # question_mark, | |
| output_video, | |
| prediction_file, | |
| ], | |
| queue=True, | |
| ) | |
| clear_btn_dynamic.click( | |
| fn=clear_dynamic_info, | |
| inputs=[], | |
| outputs=[ | |
| input_video, | |
| output_face, | |
| output_heatmaps, | |
| output_statistics, | |
| text, | |
| # question_mark, | |
| output_video, | |
| prediction_file, | |
| ], | |
| queue=True, | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(api_open=False).launch(share=False) | |