app.py

pip install transformers huggingface_hub gradio torch
from huggingface_hub import login

# Login using your Hugging Face token (replace "your_token" with your actual token)
login("your_token")
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-multi")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-multi")
# Input text for code generation
text = "def bubble_sort(list_elements):"

# Tokenize the input text
input_ids = tokenizer(text, return_tensors="pt").input_ids

# Generate code based on the input text
generated_ids = model.generate(
    input_ids,
    max_length=200,  # Adjust as needed
    num_return_sequences=1,  # Number of generated sequences to return
    pad_token_id=tokenizer.eos_token_id  # Handle padding tokens
)

# Decode the generated tokens to text
generated_code = tokenizer.decode(generated_ids[0], skip_special_tokens=True)

print(generated_code)
from huggingface_hub import HfApi, Repository

# Replace with your Hugging Face username and repo name
repo_name = "your-username/codegen-350M-multi-bubble-sort"

# Initialize the repository to push your model and tokenizer
api = HfApi()

# Create a new repo (if you haven't already)
api.create_repo(repo_name, exist_ok=True)

# Push the tokenizer and model to Hugging Face Hub
model.push_to_hub(repo_name)
tokenizer.push_to_hub(repo_name)

print(f"Model and tokenizer pushed to Hugging Face Hub under: {repo_name}")