update description

app.py

@@ -645,27 +645,29 @@ def main_fn(
 default_images = ['./images/image_0.jpg', './images/image_1.jpg', './images/image_2.jpg', './images/image_3.jpg', './images/image_5.jpg']
 default_outputs = ['./images/ncut_0.jpg', './images/ncut_1.jpg', './images/ncut_2.jpg', './images/ncut_3.jpg', './images/ncut_5.jpg']
 
-demo = gr.Interface(
-    main_fn,
-    [
-        gr.Gallery(value=default_images, label="Select images", show_label=False, elem_id="images", columns=[3], rows=[1], object_fit="contain", height="auto", type="pil", show_share_button=False),
-        gr.Dropdown(["SAM(sam_vit_b)", "MobileSAM", "DiNO(dinov2_vitb14_reg)", "CLIP(openai/clip-vit-base-patch16)"], label="Model", value="SAM(sam_vit_b)", elem_id="model_name"),
-        gr.Slider(0, 11, step=1, label="Layer", value=11, elem_id="layer", info="which layer of the image backbone features"),
-        gr.Slider(1, 1000, step=1, label="Number of eigenvectors", value=100, elem_id="num_eig", info='increase for more object parts, decrease for whole object'),
-    ],
-    gr.Gallery(value=default_outputs, label="NCUT Embedding", show_label=False, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto"),
-    additional_inputs=[
-        gr.Dropdown(["attn", "mlp", "block"], label="Node type", value="block", elem_id="node_type", info="attn: attention output, mlp: mlp output, block: sum of residual stream"),
-        gr.Slider(0.01, 1, step=0.01, label="Affinity focal gamma", value=0.3, elem_id="affinity_focal_gamma", info="decrease for more aggressive cleaning on the affinity matrix"),
-        gr.Slider(100, 50000, step=100, label="num_sample (NCUT)", value=10000, elem_id="num_sample_ncut", info="Nyström approximation"),
-        gr.Slider(1, 100, step=1, label="KNN (NCUT)", value=10, elem_id="knn_ncut", info="Nyström approximation"),
-        gr.Dropdown(["t-SNE", "UMAP"], label="Embedding method", value="t-SNE", elem_id="embedding_method"),
-        gr.Slider(100, 1000, step=100, label="num_sample (t-SNE/UMAP)", value=300, elem_id="num_sample_tsne", info="Nyström approximation"),
-        gr.Slider(1, 100, step=1, label="KNN (t-SNE/UMAP)", value=10, elem_id="knn_tsne", info="Nyström approximation"),
-        gr.Slider(10, 500, step=10, label="Perplexity (t-SNE)", value=150, elem_id="perplexity"),
-        gr.Slider(10, 500, step=10, label="n_neighbors (UMAP)", value=150, elem_id="n_neighbors"),
-        gr.Slider(0.1, 1, step=0.1, label="min_dist (UMAP)", value=0.1, elem_id="min_dist"),
-    ]
-)
+with gr.Blocks() as demo:
+    gr.Markdown('Upload Images here 👇')
+    gr.Interface(
+        main_fn,
+        [
+            gr.Gallery(value=default_images, label="Select images", show_label=False, elem_id="images", columns=[3], rows=[1], object_fit="contain", height="auto", type="pil", show_share_button=False),
+            gr.Dropdown(["SAM(sam_vit_b)", "MobileSAM", "DiNO(dinov2_vitb14_reg)", "CLIP(openai/clip-vit-base-patch16)"], label="Model", value="SAM(sam_vit_b)", elem_id="model_name"),
+            gr.Slider(0, 11, step=1, label="Layer", value=11, elem_id="layer", info="which layer of the image backbone features"),
+            gr.Slider(1, 1000, step=1, label="Number of eigenvectors", value=100, elem_id="num_eig", info='increase for more object parts, decrease for whole object'),
+        ],
+        gr.Gallery(value=default_outputs, label="NCUT Embedding", show_label=False, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto"),
+        additional_inputs=[
+            gr.Dropdown(["attn", "mlp", "block"], label="Node type", value="block", elem_id="node_type", info="attn: attention output, mlp: mlp output, block: sum of residual stream"),
+            gr.Slider(0.01, 1, step=0.01, label="Affinity focal gamma", value=0.3, elem_id="affinity_focal_gamma", info="decrease for more aggressive cleaning on the affinity matrix"),
+            gr.Slider(100, 50000, step=100, label="num_sample (NCUT)", value=10000, elem_id="num_sample_ncut", info="Nyström approximation"),
+            gr.Slider(1, 100, step=1, label="KNN (NCUT)", value=10, elem_id="knn_ncut", info="Nyström approximation"),
+            gr.Dropdown(["t-SNE", "UMAP"], label="Embedding method", value="t-SNE", elem_id="embedding_method"),
+            gr.Slider(100, 1000, step=100, label="num_sample (t-SNE/UMAP)", value=300, elem_id="num_sample_tsne", info="Nyström approximation"),
+            gr.Slider(1, 100, step=1, label="KNN (t-SNE/UMAP)", value=10, elem_id="knn_tsne", info="Nyström approximation"),
+            gr.Slider(10, 500, step=10, label="Perplexity (t-SNE)", value=150, elem_id="perplexity"),
+            gr.Slider(10, 500, step=10, label="n_neighbors (UMAP)", value=150, elem_id="n_neighbors"),
+            gr.Slider(0.1, 1, step=0.1, label="min_dist (UMAP)", value=0.1, elem_id="min_dist"),
+        ]
+    )
 
 demo.launch()