ajsbsd commited on
Commit
6e6f8fe
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1 Parent(s): 8dd0d9d

Update app.py

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Files changed (1) hide show
  1. app.py +6 -9
app.py CHANGED
@@ -1,8 +1,3 @@
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- 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.
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-
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- 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.
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-
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- ```python
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  import gradio as gr
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  import torch
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  from neuralop.models import FNO
@@ -61,7 +56,11 @@ def load_dataset():
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  data = torch.load(local_dataset_path, map_location='cpu')
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  if isinstance(data, dict) and 'x' in data:
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  FULL_DATASET_X = data['x']
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- elif isinstance(data, torch.Tensor):
 
 
 
 
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  FULL_DATASET_X = data
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  else:
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  raise ValueError("Unknown dataset format or 'x' key missing.")
@@ -170,6 +169,4 @@ with gr.Blocks() as demo:
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  demo.load(load_initial_data_and_predict, inputs=None, outputs=[input_image_plot, output_image_plot])
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  if __name__ == "__main__":
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- demo.launch()
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-
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- ```
 
 
 
 
 
 
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  import gradio as gr
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  import torch
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  from neuralop.models import FNO
 
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  data = torch.load(local_dataset_path, map_location='cpu')
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  if isinstance(data, dict) and 'x' in data:
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  FULL_DATASET_X = data['x']
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+ 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.
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+
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+ 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.
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+
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+ ```pythonata, torch.Tensor):
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  FULL_DATASET_X = data
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  else:
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  raise ValueError("Unknown dataset format or 'x' key missing.")
 
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  demo.load(load_initial_data_and_predict, inputs=None, outputs=[input_image_plot, output_image_plot])
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  if __name__ == "__main__":
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+ demo.launch()