handle gpu quota

app.py

@@ -961,42 +961,42 @@ def run_fn(
     }
     # print(kwargs)
     
-    # try:
-    
-    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 spaces.GPUError as e:
-    #     print(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/")
+    except gr.Error as e:
+        print(e)
+        gr.Error(str(e))
+        gr.Info("Running out of HuggingFace GPU Quota? Try this demo hosted at UPenn.\n\n https://ncut-pytorch.readthedocs.io/en/latest/demo/")