ou aren't part of it already
  !curl -X POST -H 'Authorization: Bearer '$hf_token -H 'Content-Type: application/json' https://huggingface.co/organizations/sd-concepts-library/share/VcLXJtzwwxnHYCkNMLpSJCdnNFZHQwWywv
  images_upload = os.listdir("my_concept")
  image_string = ""
  repo_id = f"sd-concepts-library/{slugify(name_of_your_concept)}"
  for i, image in enumerate(images_upload):
      image_string = f'''{image_string}![{placeholder_token} {i}](https://huggingface.co/{repo_id}/resolve/main/concept_images/{image})
'''
  if(what_to_teach == "style"):
      what_to_teach_article = f"a `{what_to_teach}`"
  else:#@title Save your newly created concept to the [library of concepts](https://huggingface.co/sd-concepts-library)?

save_concept_to_public_library = True #@param {type:"boolean"}
name_of_your_concept = "tzuyu m" #@param {type:"string"}
#@markdown `hf_token_write`: leave blank if you logged in with a token with `write access` in the [Initial Setup](#scrollTo=KbzZ9xe6dWwf). If not, [go to your tokens settings and create a write access token](https://huggingface.co/settings/tokens)
hf_token_write = "" #@param {type:"string"}

if(save_concept_to_public_library):
  from slugify import slugify
  from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
  from huggingface_hub import create_repo
  repo_id = f"sd-concepts-library/{slugify(name_of_your_concept)}"
  output_dir = hyperparameters["output_dir"]
  if(not hf_token_write):
    with open(HfFolder.path_token, 'r') as fin: hf_token = fin.read();
  else:
    hf_token = hf_token_write
  #Join the Concepts Library organization if y
      what_to_teach_article = f"an `{what_to_teach}`"
  readme_text = f'''---
license: mit
---
### {name_of_your_concept} on Stable Diffusion
This is the `{placeholder_token}` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).

Here is the new concept you will be able to use as {what_to_teach_article}:
{image_string}
'''
  #Save the readme to a file
  readme_file = open("README.md", "w")
  readme_file.write(readme_text)
  readme_file.close()
  #Save the token identifier to a file
  text_file = open("token_identifier.txt", "w")
  text_file.write(placeholder_token)
  text_file.close()
  #Save the type of teached thing to a file
  type_file = open("type_of_concept.txt","w")
  type_file.write(what_to_teach)
  type_file.close()
  operations = [
    CommitOperationAdd(path_in_repo="learned_embeds.bin", path_or_fileobj=f"{output_dir}/learned_embeds.bin"),
    CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"),
    CommitOperationAdd(path_in_repo="type_of_concept.txt", path_or_fileobj="type_of_concept.txt"),
    CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="README.md"),
  ]
  create_repo(repo_id,private=True, token=hf_token)
  api = HfApi()
  api.create_commit(
    repo_id=repo_id,
    operations=operations,
    commit_message=f"Upload the concept {name_of_your_concept} embeds and token",
    token=hf_token
  )
  api.upload_folder(
    folder_path=save_path,
    path_in_repo="concept_images",
    repo_id=repo_id,
    token=hf_token
  )