ui

app.py

@@ -1,14 +1,27 @@
 import gradio as gr
-from run import detect_video
+from run import get_model, detect_video
+
+model = get_model()
 
 def greet(video):
     print(video, type(video))
-    pred = detect_video(video_path=video)
+    pred = detect_video(video_path=video, model=model)
     if pred > 0.5:
         string = f"Fake: {pred*100:.2f}%"
     else:
         string = f"Real: {(1-pred)*100:.2f}%"
+    print(string)
     return string
 
-demo = gr.Interface(fn=greet, inputs=gr.Video(), outputs="text")
-demo.launch(share=True)
+with gr.Blocks() as demo:
+    gr.Markdown("# Fake Video Detector")
+    with gr.Tabs():
+        with gr.TabItem("Video Detect"):
+            with gr.Row():
+                video_input = gr.Video()
+                detect_output = gr.Textbox("Output")
+            video_button = gr.Button("detect")
+
+    video_button.click(greet, inputs=video_input, outputs=detect_output)
+
+demo.launch()

run.py

@@ -19,24 +19,25 @@ import options
 from networks.validator import Validator
 
 
-def detect_video(video_path):
+def get_model():
     val_opt = options.TestOptions().parse(print_options=False)
-
     output_dir=os.path.join(val_opt.output, val_opt.name)
     os.makedirs(output_dir, exist_ok=True)
+
     # logger = create_logger(output_dir=output_dir, name="FakeVideoDetector")
     print(f"working...")
 
     model = Validator(val_opt)
     model.load_state_dict(val_opt.ckpt)
     print("ckpt loaded!")
+    return model
+
 
-    # val_loader = create_test_dataloader(val_opt, clip_model = None, transform = model.clip_model.preprocess)
+def detect_video(video_path, model):
     frames, _, _ = read_video(str(video_path), pts_unit='sec')
     frames = frames[:16]
     frames = frames.permute(0, 3, 1, 2) # (T,H,W,C) -> (T,C,H,W)
 
-
     video_frames = torch.cat([model.clip_model.preprocess(TF.to_pil_image(frame)).unsqueeze(0) for frame in frames])
 
     with torch.no_grad():
@@ -73,7 +74,9 @@ if __name__ == '__main__':
 
     #     pred = model.model(model.input).view(-1).unsqueeze(1).sigmoid()
 
-    pred = detect_video(video_path)
+    model = get_model()
+
+    pred = detect_video(video_path, model)
     if pred > 0.5:
         print(f"Fake: {pred*100:.2f}%")
     else: