join thread

app.py

@@ -907,7 +907,7 @@ def make_input_images_section():
 
 def make_input_video_section():
     # gr.Markdown('### Input Video')
-    input_gallery = gr.Video(value=None, label="Select video", elem_id="video-input", height="auto", show_share_button=False)
+    input_gallery = gr.Video(value=None, label="Select video", elem_id="video-input", height="auto", show_share_button=False, interactive=True)
     gr.Markdown('_image backbone model is used to extract features from each frame, NCUT is computed on all frames_')
     # max_frames_number = gr.Number(100, label="Max frames", elem_id="max_frames")
     max_frames_number = gr.Slider(1, 200, step=1, label="Max frames", value=100, elem_id="max_frames")
@@ -1717,15 +1717,19 @@ with demo:
 if os.path.exists("/hf_token.txt"):
     os.environ["HF_ACCESS_TOKEN"] = open("/hf_token.txt").read().strip()
 
+    
+demo.launch(share=True)
+
 if DOWNLOAD_ALL_MODELS_DATASETS:
     from ncut_pytorch.backbone import download_all_models
-    threading.Thread(target=download_all_models).start()
-    from ncut_pytorch.backbone_text import download_all_models
-    threading.Thread(target=download_all_models).start()
+    t1 = threading.Thread(target=download_all_models).start()
+    t1.join()
+    t3 = threading.Thread(target=download_all_datasets).start()
+    t3.join()
     
-    threading.Thread(target=download_all_datasets).start()
-    
-demo.launch(share=True)
+    from ncut_pytorch.backbone_text import download_all_models
+    t2 = threading.Thread(target=download_all_models).start()
+    t2.join()
 
 
 # # %%