app.py

import gradio as gr
import os
from huggingface_hub import HfApi, login
from transformers import AutoTokenizer, AutoModelForCausalLM

def process(model_id, dataset):
    print("111")
    # Download Sample Model from Hugging Face to Publish Again 
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id)
    # Local Path of Model
    print("222")
    model_path = model_id
    model.save_pretrained(model_path)
    login(token=os.environ["HF_TOKEN"])
    api = HfApi()
    model_repo_name = "bstraehle/Meta-Llama-3-8B"
    
    #Create Repo in Hugging Face
    print("333")
    api.create_repo(repo_id=model_repo_name)
    
    #Upload Model folder from Local to HuggingFace 
    print("444")
    api.upload_folder(
        folder_path=model_path,
        repo_id=model_repo_name
    )
    
    # Publish Model Tokenizer on Hugging Face
    print("555")
    tokenizer.push_to_hub(model_repo_name)    

    return "Done"

demo = gr.Interface(fn=process, 
                    inputs=[gr.Textbox(label = "Model ID", value = "meta-llama/Meta-Llama-3-8B", lines = 1), # google/gemma-2b
                            gr.Textbox(label = "Dataset", value = "imdb", lines = 1)],
                    outputs=[gr.Textbox(label = "Completion")])
demo.launch()