sc_ma commited on
Commit
26d461c
1 Parent(s): ea7f72d

Testing output file error in Spaces.

Browse files
Files changed (2) hide show
  1. auto_backgrounds.py +1 -4
  2. auto_draft.py +1 -1
auto_backgrounds.py CHANGED
@@ -76,9 +76,6 @@ def pipeline(paper, section, save_to_path, model):
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  def generate_backgrounds(title, description="", template="ICLR2022", model="gpt-4"):
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-
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- return make_archive("utils", "output.zip")
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-
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  paper = {}
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  paper_body = {}
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@@ -121,7 +118,7 @@ def generate_backgrounds(title, description="", template="ICLR2022", model="gpt-
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  print(f"Failed to generate {section} due to the error: {e}")
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  print(f"The paper {title} has been generated. Saved to {save_to_path}.")
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  # shutil.make_archive("output.zip", 'zip', save_to_path)
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- return make_archive(save_to_path, "output.zip")
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  if __name__ == "__main__":
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  title = "Reinforcement Learning"
 
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  def generate_backgrounds(title, description="", template="ICLR2022", model="gpt-4"):
 
 
 
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  paper = {}
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  paper_body = {}
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  print(f"Failed to generate {section} due to the error: {e}")
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  print(f"The paper {title} has been generated. Saved to {save_to_path}.")
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  # shutil.make_archive("output.zip", 'zip', save_to_path)
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+ return make_archive(destination_folder, "output.zip")
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  if __name__ == "__main__":
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  title = "Reinforcement Learning"
auto_draft.py CHANGED
@@ -132,7 +132,7 @@ def generate_draft(title, description="", template="ICLR2022", model="gpt-4"):
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  except Exception as e:
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  print(f"Failed to generate {section} due to the error: {e}")
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  print(f"The paper {title} has been generated. Saved to {save_to_path}.")
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- return make_archive(save_to_path, "output.zip")
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  if __name__ == "__main__":
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  # title = "Training Adversarial Generative Neural Network with Adaptive Dropout Rate"
 
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  except Exception as e:
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  print(f"Failed to generate {section} due to the error: {e}")
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  print(f"The paper {title} has been generated. Saved to {save_to_path}.")
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+ return make_archive(destination_folder, "output.zip")
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  if __name__ == "__main__":
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  # title = "Training Adversarial Generative Neural Network with Adaptive Dropout Rate"