improve loading

app.py

@@ -18,6 +18,7 @@ import torch.nn.functional as F
 from PIL import Image
 import numpy as np
 import time
+import threading
 
 import gradio as gr
 
@@ -518,7 +519,7 @@ def make_dataset_images_section(advanced=False):
         idx = dataset_names.index(dataset_name)
         num_classes = dataset_classes[idx]
         return gr.Textbox(label="Class to select", value="0,1,2", elem_id="filter_by_class_text", info=f"e.g. `0,1,2`. ({num_classes} classes)", visible=is_filter)
-    # filter_by_class_checkbox.change(fn=change_filter_by_class, inputs=[filter_by_class_checkbox, dataset_dropdown], outputs=filter_by_class_text)
+    filter_by_class_checkbox.change(fn=change_filter_by_class, inputs=[filter_by_class_checkbox, dataset_dropdown], outputs=filter_by_class_text)
     
     def change_random_seed(is_random):
         return gr.Slider(0, 1000, step=1, label="Random seed", value=1, elem_id="random_seed", visible=is_random)
@@ -611,10 +612,10 @@ def make_parameters_section():
         sampling_method_dropdown = gr.Dropdown(["fps", "random"], label="NCUT: Sampling method", value="fps", elem_id="sampling_method", info="Nyström approximation")
         knn_ncut_slider = gr.Slider(1, 100, step=1, label="NCUT: KNN", value=10, elem_id="knn_ncut", info="Nyström approximation")
         embedding_method_dropdown = gr.Dropdown(["tsne_3d", "umap_3d", "umap_shpere", "tsne_2d", "umap_2d"], label="Coloring method", value="tsne_3d", elem_id="embedding_method")
-        num_sample_tsne_slider = gr.Slider(100, 1000, step=100, label="t-SNE/UMAP: num_sample", value=300, elem_id="num_sample_tsne", info="Nyström approximation")
+        num_sample_tsne_slider = gr.Slider(100, 10000, step=100, label="t-SNE/UMAP: num_sample", value=300, elem_id="num_sample_tsne", info="Nyström approximation")
         knn_tsne_slider = gr.Slider(1, 100, step=1, label="t-SNE/UMAP: KNN", value=10, elem_id="knn_tsne", info="Nyström approximation")
-        perplexity_slider = gr.Slider(10, 500, step=10, label="t-SNE: Perplexity", value=150, elem_id="perplexity")
-        n_neighbors_slider = gr.Slider(10, 500, step=10, label="UMAP: n_neighbors", value=150, elem_id="n_neighbors")
+        perplexity_slider = gr.Slider(10, 1000, step=10, label="t-SNE: Perplexity", value=150, elem_id="perplexity")
+        n_neighbors_slider = gr.Slider(10, 1000, step=10, label="UMAP: n_neighbors", value=150, elem_id="n_neighbors")
         min_dist_slider = gr.Slider(0.1, 1, step=0.1, label="UMAP: min_dist", value=0.1, elem_id="min_dist")
     return [model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
             affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
@@ -894,8 +895,8 @@ with demo:
 
 
 if USE_HUGGINGFACE_SPACE:
-    download_all_models()
-    download_all_datasets()
+    threading.Thread(target=download_all_models).start()
+    threading.Thread(target=download_all_datasets).start()
     demo.launch()
 else:
     demo.launch(share=True)