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import gradio as gr
import warnings
import os
from classifier import Classifier
warnings.filterwarnings('ignore')
video_examples = [os.path.join(os.path.dirname(__file__),
"examples/group_test.mp4")]
img_examples = [["examples/group_test.png"],
["examples/happy.png"],
["examples/fearful.png"],
["examples/angry.jpeg"],
["examples/surprised.jpeg"]]
ai_models = Classifier()
def predict(video_in, image_in_video, image_in_img):
if video_in == None and image_in_video == None and image_in_img == None:
raise gr.Error("Please upload a video or image.")
if image_in_video or image_in_img:
img_path = image_in_video or image_in_img
fname, fext = os.path.splitext(img_path)
if fext == ".mp4":
ai_models.check_file_type(is_image=False, input_path=img_path)
else:
ai_models.check_file_type(is_image=True, input_path=img_path)
output_path, pred_time = ai_models.run()
gr.Markdown(f"Total prediction time: ", pred_time)
return output_path, pred_time
if video_in:
fname, fext = os.path.splitext(video_in)
if fext == ".mp4":
ai_models.check_file_type(is_image=False, input_path=video_in)
else:
ai_models.check_file_type(is_image=True, input_path=video_in)
output_path, pred_time = ai_models.run()
gr.Markdown(f"Total prediction time: ", pred_time)
return output_path, pred_time
def toggle(choice):
if choice == "webcam":
return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
else:
return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
with gr.Blocks() as demo:
gr.Markdown("## Facial Emotion Recognition using CNN 🤖⭐️")
with gr.Tab("Video") as tab:
with gr.Row():
with gr.Column():
video_or_file_opt = gr.Radio(["webcam", "upload"], value="webcam",
label="How would you like to upload your video?")
video_in = gr.Video(
format='mp4', source="webcam", include_audio=False)
video_or_file_opt.change(fn=lambda s: gr.update(source=s, value=None), inputs=video_or_file_opt,
outputs=video_in, queue=False, show_progress=False)
with gr.Column():
video_out = gr.Video(format='mp4')
run_btn = gr.Button("Predict")
run_btn.click(fn=predict, inputs=[video_in], outputs=[video_out])
gr.Examples(fn=predict, examples=video_examples, inputs=[
video_in], outputs=[video_out])
with gr.Tab("Image"):
with gr.Row():
with gr.Column():
image_or_file_opt = gr.Radio(["webcam", "upload"], value="webcam",
label="How would you like to upload your image?")
image_in_video = gr.Image(source="webcam", type="filepath")
image_in_img = gr.Image(
source="upload", visible=False, type="filepath")
image_or_file_opt.change(fn=toggle, inputs=[image_or_file_opt],
outputs=[image_in_video, image_in_img], queue=False, show_progress=False)
with gr.Column():
image_out = gr.Image()
run_btn = gr.Button("Predict")
pred_tbox = gr.Textbox(label="Prediction Time")
run_btn.click(fn=predict, inputs=[
image_in_img, image_in_video], outputs=[image_out, pred_tbox])
gr.Examples(fn=predict,
examples=img_examples,
inputs=[image_in_img, image_in_video],
outputs=[image_out, pred_tbox])
demo.queue()
demo.launch()
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