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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() | |