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app.py

@@ -110,7 +110,7 @@ def _topk_dict(logits: torch.Tensor, k: int = 5) -> dict:
     return {IMAGENET_CLASSES[int(i)]: float(v) for v, i in zip(values, indices)}
 
 
-@spaces.GPU(duration=30)
+@spaces.GPU(duration=15)
 def classify(
     image: Image.Image,
     variant: str = DEFAULT_VARIANT,
@@ -216,7 +216,7 @@ EXAMPLES = [
     ["examples/monstera_plant.jpg"],
 ]
 
-with gr.Blocks(theme=gr.themes.Citrus(), css=CSS) as demo:
+with gr.Blocks() as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown(
             """# ProgResViT — adaptive-compute image classification
@@ -278,4 +278,4 @@ reusing the tokens produced in round 1.
     )
 
 if __name__ == "__main__":
-    demo.launch(mcp_server=True)
+    demo.launch(theme=gr.themes.Citrus(), css=CSS, mcp_server=True)