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import os
from huggingface_hub import HfApi, login
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
def process(model_id, dataset):
# Download Sample Model from Hugging Face to Publish Again
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
# Local Path of Model
model_path = 't5-fine-tune-save-example'
model.save_pretrained(model_path)
login(token=os.environ["HF_TOKEN"])
api = HfApi()
model_repo_name = f"bstraehle/{model_id}"
#Create Repo in Hugging Face
api.create_repo(repo_id=model_repo_name)
#Upload Model folder from Local to HuggingFace
api.upload_folder(
folder_path=model_path,
repo_id=model_repo_name
)
# Publish Model Tokenizer on Hugging Face
tokenizer.push_to_hub(model_repo_name)
return "Done"
demo = gr.Interface(fn=process,
inputs=[gr.Textbox(label = "Model ID", value = "google/gemma-7b", lines = 1),
gr.Textbox(label = "Dataset", value = "imdb", lines = 1)],
outputs=[gr.Textbox(label = "Completion")])
demo.launch() |