app.py

import gradio as gr
from huggingface_hub import login
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
import torch

MODEL = "m-a-p/OpenCodeInterpreter-DS-33B"
CHAT_TEMPLATE = "{% for message in messages %}\n{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}\n{% endfor %}\n{% if add_generation_prompt %}\n{{ '<|im_start|>assistant\n' }}\n{% endif %}"

system_message = "You are a computer programmer that can translate python code to C++ in order to improve performance"

def user_prompt_for(python):
    return f"Rewrite this python code to C++. You must search for the maximum performance. \
    Format your response in Markdown. This is the python Code: \
    \n\n\
    {python}"

def messages_for(python):
    return [
        {"role": "system", "content": system_message},
        {"role": "user", "content": user_prompt_for(python)}
    ]

tokenizer = AutoTokenizer.from_pretrained(MODEL)
tokenizer.chat_template = CHAT_TEMPLATE
model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, device_map="auto")
model.eval()

decode_kwargs = dict(skip_special_tokens=True)
streamer = TextIteratorStreamer(tokenizer, decode_kwargs=decode_kwargs)

cplusplus = None
def translate(python):
    inputs = tokenizer.apply_chat_template(
                        messages_for(python),
                        return_tensors="pt").to(model.device)
    generation_kwargs = dict(
                            inputs,
                            streamer=streamer,
                            max_new_tokens=1024,
                            do_sample=False,
                            pad_token_id=tokenizer.eos_token_id,
                            eos_token_id=tokenizer.eos_token_id
                        )
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    cplusplus = ""
    for chunk in streamer:
        cplusplus += chunk
        yield cplusplus

demo = gr.Interface(fn=translate, inputs="code", outputs="markdown")
demo.launch()