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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_STATIC, DESCRIPTION_DYNAMIC
from app.authors import AUTHORS
from app.app_utils import preprocess_image_and_predict, 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.Video(value=None),
gr.Video(value=None),
gr.Video(value=None),
gr.Plot(value=None),
)
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(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"
)
with gr.Column(scale=2, elem_classes="dl4"):
with gr.Row():
output_video = gr.Video(label="Original video", scale=1, elem_classes="video2")
output_face = gr.Video(label="Pre-processed video", scale=1, elem_classes="video3")
output_heatmaps = gr.Video(label="Heatmaps", scale=1, elem_classes="video4")
output_statistics = gr.Plot(label="Statistics of emotions", elem_classes="stat")
gr.Examples(
["videos/video1.mp4",
"videos/video2.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
],
queue=True,
)
if __name__ == "__main__":
demo.queue(api_open=False).launch(share=False)
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