app.py

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

@spaces.GPU
def main():
    # Optional: force install gradio 3.50.2 to avoid node issues
    os.system("pip install gradio==3.50.2")

    # Load model and tokenizer
    model_id = "codellama/CodeLlama-7b-Instruct-hf"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(
        model_id,
        device_map="auto",
        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
    )
    tokenizer.pad_token = tokenizer.eos_token

    def convert_python_to_r(python_code):
        prompt = f"""### Task:
Convert the following Python code to equivalent R code.

### Python code:
{python_code}

### R code:"""
        input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids
        if torch.cuda.is_available():
            input_ids = input_ids.to("cuda")
        outputs = model.generate(
            input_ids,
            max_length=1024,
            do_sample=True,
            temperature=0.2,
            pad_token_id=tokenizer.eos_token_id,
            num_return_sequences=1
        )
        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        if "### R code:" in generated_text:
            generated_text = generated_text.split("### R code:")[-1].strip()
        return generated_text

    gr.Interface(
        fn=convert_python_to_r,
        inputs=gr.Textbox(lines=10, placeholder="Paste your Python code here..."),
        outputs="text",
        title="Python to R Code Converter using CodeLlama 7B Instruct",
        description="Enter Python code below, and the tool will convert it to R code using the CodeLlama 7B Instruct model."
    ).launch()

if __name__ == "__main__":
    main()