comp.py

from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
import spaces

# Model and tokenizer initialization
MODEL_NAME = "inclusionAI/Ring-mini-2.0"
DEFAULT_SYSTEM_PROMPT = "你是 Ring，蚂蚁集团开发的智能助手，致力于为用户提供有用的信息和帮助，用中文回答用户的问题。"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

@spaces.GPU(duration=150)
def generate_response(message, history, system_prompt=None):
    # (msg, history, system_prompt) -> str: stream response (yielding partial responses)
    
    # Determine the system prompt to use
    prompt_to_use = system_prompt if system_prompt is not None else DEFAULT_SYSTEM_PROMPT

    # To construct the 'chat', we start with system prompt
    # then append user and assistant messages from history
    messages = [
        {"role": "system", "content": prompt_to_use}
    ]
    
    # Add conversation history
    # history is a list of (human, assistant) tuples
    for human, assistant in history:
        messages.append({"role": "user", "content": human})
        if assistant: # Ensure assistant message is not None
            messages.append({"role": "assistant", "content": assistant})
    
    # Add current message from user
    messages.append({"role": "user", "content": message})
    
    # Apply chat template
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    # Tokenize input
    model_inputs = tokenizer([text], return_tensors="pt", return_token_type_ids=False).to(model.device)
    
    # Generate response with streaming
    streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True, skip_prompt=True)
    
    generation_kwargs = dict(
        **model_inputs,
        max_new_tokens=8192,
        temperature=0.7,
        do_sample=True,
        streamer=streamer,
    )
    
    # Start generation in a separate thread to enable streaming
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    
    # ... and yield the generated tokens as they are produced
    response = ""
    for new_text in streamer:
        response += new_text
        yield response
    
    # wait for the generation thread to finish
    thread.join()