app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

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

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.")


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()