Add some notes

app.py

@@ -30,6 +30,7 @@ if __name__ == '__main__':
                 <li>MASt3R is used to obtain the initial point cloud and camera parameters.</li>
                 <li>3DGS is then trained on the results from MASt3R to refine the 3D scene representation.</li>
             </ol>
+            <p style="font-size: 16px;">Note: After a page reload, any generated MASt3R datasets in the 3DGS tab will be deleted.</p>
             <p style="font-size: 16px;">For a full version of this pipeline, please visit the repository at:</p>
             <a href="https://github.com/nerlfield/wild-gaussian-splatting" target="_blank" style="font-size: 16px; text-decoration: none;">nerlfield/wild-gaussian-splatting</a>
         </div>

demo/mast3r_demo.py

@@ -274,7 +274,7 @@ def mast3r_demo_tab():
         <div style="padding: 10px; border-radius: 5px; margin-bottom: 10px;">
             <h3>Instructions for MASt3R Demo</h3>
             <ul style="text-align: left; color: #333;">
-                <li>Upload images. It is recommended to use no more than 10-12 images to avoid exceeding the 3-minute runtime limit for zeroGPU dynamic resources.</li>
+                <li>Upload images. It is recommended to use no more than 7-10 images to avoid exceeding the 3-minute runtime limit for zeroGPU dynamic resources.</li>
                 <li>Press the "Run" button to start the process.</li>
                 <li>Once the stage is finished and the point cloud with cameras is visible below, switch to the 3DGS tab and follow the instructions there.</li>
             </ul>
@@ -312,34 +312,33 @@ def mast3r_demo_tab():
         turtle_images = [os.path.join(turtle_folder, file) for file in os.listdir(turtle_folder) if file.endswith('.jpg')]
         puma_images = [os.path.join(puma_folder, file) for file in os.listdir(puma_folder)[:12] if file.endswith('.jpg')]
         
-        
         examples = gradio.Examples(
             examples=[
                 [
-                    puma_images[0],
+                    turtle_images[0],
                     None,
                     1.5, 0.0, 0.2, True, True, False,
-                    puma_images,
+                    turtle_images,
                 ]
             ],
             inputs=[snapshot, scene, min_conf_thr, matching_conf_thr, cam_size, as_pointcloud, shared_intrinsics, clean_depth, inputfiles],
             outputs=[scene, outmodel],
             fn=get_reconstructed_scene,
-            run_on_click=True,
+            run_on_click=False,
         )
         examples = gradio.Examples(
             examples=[
                 [
-                    turtle_images[0],
+                    puma_images[0],
                     None,
                     1.5, 0.0, 0.2, True, True, False,
-                    turtle_images,
+                    puma_images,
                 ]
             ],
             inputs=[snapshot, scene, min_conf_thr, matching_conf_thr, cam_size, as_pointcloud, shared_intrinsics, clean_depth, inputfiles],
             outputs=[scene, outmodel],
             fn=get_reconstructed_scene,
-            run_on_click=True,
+            run_on_click=False,
         )
         examples = gradio.Examples(
             examples=[
@@ -353,7 +352,7 @@ def mast3r_demo_tab():
             inputs=[snapshot, scene, min_conf_thr, matching_conf_thr, cam_size, as_pointcloud, shared_intrinsics, clean_depth, inputfiles],
             outputs=[scene, outmodel],
             fn=get_reconstructed_scene,
-            run_on_click=True,
+            run_on_click=False,
         )