update default model

app.py

@@ -45,7 +45,10 @@ from datasets import load_dataset
 def download_all_datasets():
     for name in DATASET_NAMES:
         print(f"Downloading {name}")
-        load_dataset(name, trust_remote_code=True)
+        try:
+            load_dataset(name, trust_remote_code=True)
+        except Exception as e:
+            print(f"Error downloading {name}: {e}")
 
 def compute_ncut(
     features,
@@ -528,6 +531,10 @@ def make_dataset_images_section(open=False):
             return None
         if num_images > len(dataset):
             num_images = len(dataset)
+        
+        if 'label' not in dataset and is_filter:
+            gr.Error(f"Dataset {dataset_name} has no class label.")
+            return None
         if is_filter:
             classes = list(map(int, filter_by_class_text.split(",")))
             labels = np.array(dataset['label'])
@@ -574,7 +581,7 @@ def make_parameters_section():
     gr.Markdown('### Parameters')
     from backbone import get_all_model_names
     model_names = get_all_model_names()
-    model_dropdown = gr.Dropdown(model_names, label="Backbone", value="SAM2(sam2_hiera_t)", elem_id="model_name")
+    model_dropdown = gr.Dropdown(model_names, label="Backbone", value="DiNO(dino_vitb8)", elem_id="model_name")
     layer_slider = gr.Slider(1, 12, step=1, label="Backbone: Layer index", value=12, elem_id="layer")
     node_type_dropdown = gr.Dropdown(["attn: attention output", "mlp: mlp output", "block: sum of residual"], label="Backbone: Layer type", value="block: sum of residual", elem_id="node_type", info="which feature to take from each layer?")
     num_eig_slider = gr.Slider(1, 1000, step=1, label="NCUT: Number of eigenvectors", value=100, elem_id="num_eig", info='increase for more clusters')
@@ -720,7 +727,6 @@ with gr.Blocks() as demo:
                 hide_button.visible = False
                 dataset_dropdown, num_images_slider, random_seed_slider, load_dataset_button = make_dataset_images_section()
                 num_images_slider.value = 100
-                dataset_dropdown.value = 'nielsr/CelebA-faces'
                 
             with gr.Column(scale=5, min_width=200):
                 with gr.Accordion("➡️ Recursion config", open=True):
@@ -737,10 +743,6 @@ with gr.Blocks() as demo:
                     sampling_method_dropdown
                 ] = make_parameters_section()
                 num_eig_slider.visible = False
-                model_dropdown.value = 'DiNO(dinov2_vitb14_reg)'
-                layer_slider.value = 6
-                node_type_dropdown.value = 'attn: attention output'
-                affinity_focal_gamma_slider.value = 0.25
                 # logging text box
         with gr.Row():
             with gr.Column(scale=5, min_width=200):