web_demo.py

"""
This script refers to the dialogue example of streamlit, the interactive generation code of chatglm2 and transformers.
We mainly modified part of the code logic to adapt to the generation of our model.
Please refer to these links below for more information:
    1. streamlit chat example: https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps
    2. chatglm2: https://github.com/THUDM/ChatGLM2-6B
    3. transformers: https://github.com/huggingface/transformers
"""

from dataclasses import asdict

import streamlit as st
import torch
from modelscope import AutoModelForCausalLM, AutoTokenizer
from transformers.utils import logging

import sys
import os
from interface import GenerationConfig, generate_interactive

logger = logging.get_logger(__name__)


def on_btn_click():
    del st.session_state.messages


@st.cache_resource
def load_model():
    model_name_or_path = "GitEason/internlm-chat-medical-7b"
    model = (
        AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True)
        .to(torch.bfloat16)
        .cuda()
    )
    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
    return model, tokenizer


def prepare_generation_config():
    with st.sidebar:
        max_length = st.slider("Max Length", min_value=32, max_value=2048, value=2048)
        top_p = st.slider("Top P", 0.0, 1.0, 0.8, step=0.01)
        temperature = st.slider("Temperature", 0.0, 1.0, 0.7, step=0.01)
        st.button("Clear Chat History", on_click=on_btn_click)

    generation_config = GenerationConfig(max_length=max_length, top_p=top_p, temperature=temperature)

    return generation_config


user_prompt = "<|User|>:{user}\n"
robot_prompt = "<|Bot|>:{robot}<eoa>\n"
cur_query_prompt = "<|System|>:{system}\n<|User|>:{user}<eoh>\n<|Bot|>:"
system = '如果你是一名医生，请根据患者的描述回答医学问题.请注意不要乱说'

def combine_history(prompt):
    messages = st.session_state.messages
    total_prompt = ""
    for message in messages:
        cur_content = message["content"]
        if message["role"] == "user":
            cur_prompt = user_prompt.replace("{user}", cur_content)
        elif message["role"] == "robot":
            cur_prompt = robot_prompt.replace("{robot}", cur_content)
        else:
            raise RuntimeError
        total_prompt += cur_prompt
    total_prompt = total_prompt + cur_query_prompt.format(system=system, user=prompt)
    return total_prompt


def main():
    # torch.cuda.empty_cache()
    print("load model begin.")
    model, tokenizer = load_model()
    print("load model end.")

    user_avator = "ioc/user.png"
    robot_avator = "ioc/robot.png"

    st.title("InternLM-医学助手")

    generation_config = prepare_generation_config()

    # Initialize chat history
    if "messages" not in st.session_state:
        st.session_state.messages = []

    # Display chat messages from history on app rerun
    for message in st.session_state.messages:
        with st.chat_message(message["role"], avatar=message.get("avatar")):
            st.markdown(message["content"])

    # Accept user input
    if prompt := st.chat_input("请输入病症"):
        # Display user message in chat message container
        with st.chat_message("user", avatar=user_avator):
            st.markdown(prompt)
        real_prompt = combine_history(prompt)
        # Add user message to chat history
        st.session_state.messages.append({"role": "user", "content": prompt, "avatar": user_avator})

        with st.chat_message("robot", avatar=robot_avator):
            message_placeholder = st.empty()
            for cur_response in generate_interactive(
                model=model,
                tokenizer=tokenizer,
                prompt=real_prompt,
                additional_eos_token_id=103028,
                **asdict(generation_config),
            ):
                # Display robot response in chat message container
                message_placeholder.markdown(cur_response + "▌")
            message_placeholder.markdown(cur_response)
        # Add robot response to chat history
        st.session_state.messages.append({"role": "robot", "content": cur_response, "avatar": robot_avator})
        torch.cuda.empty_cache()


if __name__ == "__main__":
    main()