Update app.py

app.py

@@ -1,8 +1,3 @@
-You can easily add that blurb by inserting a `gr.Markdown()` component within the same `gr.Column()` as your `sample_input_slider` and `run_button`. This effectively places it within Gradio's "flexbox" layout, ensuring it's always visible below the slider and button.
-
-Here's your `app.py` code with the blurb added in the correct place. I've also updated the `run_inference` function to explicitly target `torch.device("cpu")` and removed the `@spaces.GPU()` decorator, which aligns with your successful run on ZeroCPU.
-
-```python
 import gradio as gr
 import torch
 from neuralop.models import FNO
@@ -61,7 +56,11 @@ def load_dataset():
             data = torch.load(local_dataset_path, map_location='cpu')
             if isinstance(data, dict) and 'x' in data:
                 FULL_DATASET_X = data['x']
-            elif isinstance(data, torch.Tensor):
+            elif isinstance(dYou can easily add that blurb by inserting a `gr.Markdown()` component within the same `gr.Column()` as your `sample_input_slider` and `run_button`. This effectively places it within Gradio's "flexbox" layout, ensuring it's always visible below the slider and button.
+
+Here's your `app.py` code with the blurb added in the correct place. I've also updated the `run_inference` function to explicitly target `torch.device("cpu")` and removed the `@spaces.GPU()` decorator, which aligns with your successful run on ZeroCPU.
+
+```pythonata, torch.Tensor):
                 FULL_DATASET_X = data
             else:
                 raise ValueError("Unknown dataset format or 'x' key missing.")
@@ -170,6 +169,4 @@ with gr.Blocks() as demo:
     demo.load(load_initial_data_and_predict, inputs=None, outputs=[input_image_plot, output_image_plot])
 
 if __name__ == "__main__":
-    demo.launch()
-
-```
+    demo.launch()