app.py

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

def load_model():
    model_name = "Qwen/Qwen2.5-Math-1.5B-Instruct"
    model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    return model, tokenizer

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]
    
    for user_msg, bot_reply in history:
        messages.append({"role": "user", "content": user_msg})
        if bot_reply:
            messages.append({"role": "assistant", "content": bot_reply})
    
    messages.append({"role": "user", "content": message})
    
    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    model_inputs = tokenizer([text], return_tensors="pt").to("cuda")
    
    generated_ids = model.generate(
        **model_inputs,
        # max_new_tokens=max_tokens,
        # temperature=temperature,
        # top_p=top_p,
    )
    
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return response

# Load model and tokenizer
device = "cuda" if torch.cuda.is_available() else "cpu"
model, tokenizer = load_model()

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        # gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
)

if __name__ == "__main__":
    demo.launch()