video.py

"""
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 os

import gradio as gr

from app_utils import preprocess_image_and_predict, preprocess_video_and_predict, preprocess_video_and_rank
from authors import AUTHORS

# Importing necessary components for the Gradio app
from description import DESCRIPTION_DYNAMIC, DESCRIPTION_STATIC

os.environ["no_proxy"] = "localhost,127.0.0.1,::1"
# 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.Video(value=None),
#         gr.Video(value=None),
#         gr.Video(value=None),
#         gr.Plot(value=None),
#         #gr.Textbox(Value=None)
#     )
def clear_dynamic_info():
    return (
        gr.Video(value=None),
        gr.Plot(value=None),
        gr.Textbox(""),
    )

with gr.Blocks(css="app.css") as demo:
    with gr.Tab("Dynamic App"):
        gr.Markdown(value=DESCRIPTION_DYNAMIC)
        with gr.Row():
            with gr.Column(scale=2):
                input_video = gr.Video(sources=["webcam", "upload"], 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"
                    # )
                    submit_and_rank=gr.Button(value="Score", interactive=True, scale=1,elem_classes="submit")
            with gr.Column(scale=2, elem_classes="dl4"):
                with gr.Row():
                    # output_video = gr.Video(
                    #     label="Original video", scale=1, elem_classes="video2",visible=False,
                    # )
                    # output_face = gr.Video(
                    #     label="Pre-processed video", scale=1, elem_classes="video3",visible=False,
                    # )
                    # output_heatmaps = gr.Video(
                    #     label="Heatmaps", scale=1, elem_classes="video4",visible=False,
                    # )
                    # debug_texts = gr.Textbox(lines=3,label='debug')
                    output_score=gr.Textbox(label='scores')
                output_statistics = gr.Plot(
                    label="Statistics of emotions", elem_classes="stat"
                )
        gr.Examples(
            [
                "videos/video1.mp4",
                "videos/video2.mp4",
                "videos/sample.webm",
                "videos/cnm.mp4",
            ],
            [input_video],
        )

    # with gr.Tab("Static App"):
    #     gr.Markdown(value=DESCRIPTION_STATIC)
    #     with gr.Row():
    #         with gr.Column(scale=2, elem_classes="dl1"):
    #             input_image = gr.Image(label="Original image", type="pil")
    #             with gr.Row():
    #                 clear_btn = gr.Button(
    #                     value="Clear", interactive=True, scale=1, elem_classes="clear"
    #                 )
    #                 submit = gr.Button(
    #                     value="Submit", interactive=True, scale=1, elem_classes="submit"
    #                 )
    #         with gr.Column(scale=1, elem_classes="dl4"):
    #             with gr.Row():
    #                 output_image = gr.Image(label="Face", scale=1, elem_classes="dl5")
    #                 output_heatmap = gr.Image(
    #                     label="Heatmap", scale=1, elem_classes="dl2"
    #                 )
    #             output_label = gr.Label(num_top_classes=3, scale=1, elem_classes="dl3")
    #     gr.Examples(
    #         [
    #             "images/fig7.jpg",
    #             "images/fig1.jpg",
    #             "images/fig2.jpg",
    #             "images/fig3.jpg",
    #             "images/fig4.jpg",
    #             "images/fig5.jpg",
    #             "images/fig6.jpg",
    #         ],
    #         [input_image],
    #     )
    with gr.Tab("Authors"):
        gr.Markdown(value=AUTHORS)

    # submit.click(
    #     fn=preprocess_image_and_predict,
    #     inputs=[input_image],
    #     outputs=[output_image, output_heatmap, output_label],
    #     queue=True,
    # )
    # clear_btn.click(
    #     fn=clear_static_info,
    #     inputs=[],
    #     outputs=[input_image, output_image, output_heatmap, output_label],
    #     queue=True,
    # )

    # submit_dynamic.click(
    #     fn=preprocess_video_and_predict,
    #     inputs=input_video,
    #     outputs=[output_video, output_face, output_heatmaps, output_statistics],
    #     queue=True,
    # )
    clear_btn_dynamic.click(
        fn=clear_dynamic_info,
        inputs=[],
        outputs=[
            # input_video,
            # output_video,
            # output_face,
            # output_heatmaps,
            # output_statistics,
            #debug_texts,            
            input_video,
            output_statistics,
            output_score,
        ],
        queue=True,
    )
    submit_and_rank.click(
        fn=preprocess_video_and_rank,
        inputs=input_video,
        outputs=[
            output_statistics,
            output_score,
        ]
    )

if __name__ == "__main__":
    demo.queue(api_open=False).launch(share=False)