handle gpu quota

app.py

@@ -961,35 +961,41 @@ def run_fn(
     }
     # print(kwargs)
     
-    if old_school_ncut:
-        return super_duper_long_run(model, images, **kwargs)
-    
-    if is_lisa:
-        return super_duper_long_run(model, images, **kwargs)
+    try:
     
-    num_images = len(images)
-    if num_images >= 100:
-        return super_duper_long_run(model, images, **kwargs)
-    if 'diffusion' in model_name.lower():
-        return super_duper_long_run(model, images, **kwargs)
-    if recursion:
-        return longer_run(model, images, **kwargs)
-    if num_images >= 50:
-        return longer_run(model, images, **kwargs)
-    if old_school_ncut:
-        return longer_run(model, images, **kwargs)
-    if num_images >= 10:
-        return long_run(model, images, **kwargs)
-    if embedding_method == "UMAP":
-        if perplexity >= 250 or num_sample_tsne >= 500:
+        if old_school_ncut:
+            return super_duper_long_run(model, images, **kwargs)
+        
+        if is_lisa:
+            return super_duper_long_run(model, images, **kwargs)
+        
+        num_images = len(images)
+        if num_images >= 100:
+            return super_duper_long_run(model, images, **kwargs)
+        if 'diffusion' in model_name.lower():
+            return super_duper_long_run(model, images, **kwargs)
+        if recursion:
             return longer_run(model, images, **kwargs)
-        return long_run(model, images, **kwargs)
-    if embedding_method == "t-SNE":
-        if perplexity >= 250 or num_sample_tsne >= 500:
+        if num_images >= 50:
+            return longer_run(model, images, **kwargs)
+        if old_school_ncut:
+            return longer_run(model, images, **kwargs)
+        if num_images >= 10:
+            return long_run(model, images, **kwargs)
+        if embedding_method == "UMAP":
+            if perplexity >= 250 or num_sample_tsne >= 500:
+                return longer_run(model, images, **kwargs)
             return long_run(model, images, **kwargs)
+        if embedding_method == "t-SNE":
+            if perplexity >= 250 or num_sample_tsne >= 500:
+                return long_run(model, images, **kwargs)
+            return quick_run(model, images, **kwargs)
+        
         return quick_run(model, images, **kwargs)
     
-    return quick_run(model, images, **kwargs)
+    except Exception as e:
+        gr.Error(str(e))
+        gr.Info("Running out of GPU Quota? Try this demo hosted at UPenn.\n https://ncut-pytorch.readthedocs.io/en/latest/demo/")
 
 
 
@@ -1824,7 +1830,7 @@ with demo:
         with gr.Column():
             gr.Markdown("##### This demo is for `ncut-pytorch`, [Documentation](https://ncut-pytorch.readthedocs.io/) ")
         with gr.Column():
-            gr.Markdown("###### Running out of GPU? Try [Demo](https://ncut-pytorch.readthedocs.io/en/latest/demo/) hosted at UPenn")
+            gr.Markdown("###### Running out of GPU Quota? Try [Demo](https://ncut-pytorch.readthedocs.io/en/latest/demo/) hosted at UPenn")
 
 # for local development
 if os.path.exists("/hf_token.txt"):