| import gradio as gr |
| import pipeline |
|
|
| |
| custom_css = """ |
| .gradio-container { |
| max-width: 1400px !important; |
| } |
| #component-0, #component-1, #component-2 { |
| min-height: 500px !important; |
| } |
| .output-class { |
| min-height: 300px !important; |
| font-size: 24px !important; |
| padding: 30px !important; |
| } |
| .input-image, .input-video, .input-audio { |
| min-height: 500px !important; |
| } |
| """ |
|
|
| title="EfficientNetV2 Deepfakes Video Detector" |
| description="EfficientNetV2 Deepfakes Image Detector by using frame-by-frame detection." |
|
|
| |
| image_interface = gr.Interface( |
| fn=pipeline.deepfakes_image_predict, |
| inputs=gr.Image(label="Upload Image", height=500), |
| outputs=gr.Textbox(label="Detection Result", lines=8, scale=2), |
| examples=["images/images_lady.jpg", "images/images_fake_image.jpg"], |
| cache_examples=False, |
| title="Image Deepfake Detection", |
| description="Upload an image to detect if it's real or fake" |
| ) |
|
|
| |
| video_interface = gr.Interface( |
| fn=pipeline.deepfakes_video_predict, |
| inputs=gr.Video(label="Upload Video", height=500), |
| outputs=gr.Textbox(label="Detection Result", lines=8, scale=2), |
| examples=["videos/celeb_synthesis.mp4", "videos/real-1.mp4"], |
| cache_examples=False, |
| title="Video Deepfake Detection", |
| description="Upload a video to detect if it's real or fake (frame-by-frame analysis)" |
| ) |
|
|
| app = gr.TabbedInterface( |
| interface_list=[image_interface, video_interface], |
| tab_names=['Image inference', 'Video inference'], |
| css=custom_css |
| ) |
|
|
| if __name__ == '__main__': |
| app.launch() |
