small change

app.py

@@ -188,22 +188,8 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Matryoshka Sparse Autoencoder (MSA
         "Upload an image to see its top activating concepts from a sparse autoencoder. Then, choose a concept (from `clip_disect_20k.txt`) to visualize how manipulating its corresponding concept magnitude affects the image representation."
     )
     
-    gr.Examples(
-        examples=[
-            ["bird.jpg", 10, "birds", "", 10.0],
-            ["statue.jpg", 10, "statue", "humans", 10.0],
-        ],
-        # NOTE: You might need to create placeholder images 'bird.jpg' and 'statue.jpg'
-        # in your directory for the examples to load correctly.
-        inputs=[image_input, top_k_slider, concept_input, neg_concept_input, max_strength_slider],
-        outputs=[output_image, output_bar_plot, output_line_plot],
-        fn=predict,
-        cache_examples=True # Set to True for faster loading on HF Spaces
-    )
-    
     with gr.Row():
         with gr.Column(scale=1):
-            # Input controls
             image_input = gr.Image(label="Input Image", sources=['upload', 'webcam'], type="pil")
             gr.Markdown("### Analysis & Manipulation Controls")
             top_k_slider = gr.Slider(minimum=3, maximum=20, value=10, step=1, label="Numb of Top K Concepts to visualize")
@@ -213,11 +199,21 @@ with gr.Blocks(theme=gr.themes.Soft(), title="Matryoshka Sparse Autoencoder (MSA
             submit_btn = gr.Button("Analyze and Interpret", variant="primary")
         
         with gr.Column(scale=2):
-            # Output displays
             gr.Markdown("### Results")
             output_image = gr.Image(label="Original Image", interactive=False)
             output_bar_plot = gr.Plot(label="Top Activating Concepts")
             output_line_plot = gr.Plot(label="Concept Manipulation Analysis")
+    
+    gr.Examples(
+        examples=[
+            ["bird.jpg", 10, "birds", "", 10.0],
+            ["statue.jpg", 10, "statue", "humans", 10.0],
+        ],
+        inputs=[image_input, top_k_slider, concept_input, neg_concept_input, max_strength_slider],
+        outputs=[output_image, output_bar_plot, output_line_plot],
+        fn=predict,
+        cache_examples=True
+    )
 
     # Wire up the button to the function
     submit_btn.click(