app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import os

# Check if the token is being accessed
hf_token = os.getenv("HF_HOME")
if hf_token:
    print("Successfully retrieved Hugging Face token.")
else:
    print("Failed to retrieve Hugging Face token.")

# # Load the model and tokenizer
# model_name = "meta-llama/CodeLlama-7b-hf"
# model = AutoModelForCausalLM.from_pretrained(model_name)
# tokenizer = AutoTokenizer.from_pretrained(model_name)


# def generate_code(prompt):
#     inputs = tokenizer(prompt, return_tensors="pt")
#     outputs = model.generate(inputs["input_ids"], max_length=200)
#     code = tokenizer.decode(outputs[0], skip_special_tokens=True)
#     return code

# # Set up the Gradio interface
# demo = gr.Interface(fn=generate_code, 
#              inputs="text", 
#              outputs="text", 
#              title="CodeLlama 7B Model",
#              description="Generate code with CodeLlama-7b-hf.").launch()