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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 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)
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