app.py

import json
import torch
import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig


st.set_page_config(page_title="Baichuan-13B-Chat")
st.title("Baichuan-13B-Chat")


@st.cache_resource
def init_model():
    model = AutoModelForCausalLM.from_pretrained(
        "baichuan-inc/Baichuan-13B-Chat",
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True
    )
    model.generation_config = GenerationConfig.from_pretrained(
        "baichuan-inc/Baichuan-13B-Chat"
    )
    tokenizer = AutoTokenizer.from_pretrained(
        "baichuan-inc/Baichuan-13B-Chat",
        use_fast=False,
        trust_remote_code=True
    )
    return model, tokenizer


# def clear_chat_history():
#     del st.session_state.messages
# 清空聊天历史
def clear_chat_history():
    # 把会话状态中的消息记录清空，并刷新页面
    st.session_state.messages = []
    st.experimental_rerun()

# def init_chat_history():
#     with st.chat_message("assistant", avatar='🤖'):
#         st.markdown("您好，我是百川大模型，很高兴为您服务🥰")

#     if "messages" in st.session_state:
#         for message in st.session_state.messages:
#             avatar = '🧑‍💻' if message["role"] == "user" else '🤖'
#             with st.chat_message(message["role"], avatar=avatar):
#                 st.markdown(message["content"])
#     else:
#         st.session_state.messages = []

#     return st.session_state.messages
    
# 初始化聊天历史
def init_chat_history():
    # 显示机器人的欢迎语
    st.write("🤖: 您好，我是百川大模型，很高兴为您服务🥰")

    # 如果会话状态中有消息记录，就显示出来
    if "messages" in st.session_state:
        for message in st.session_state.messages:
            # 根据角色显示不同的头像
            avatar = '🧑‍💻' if message["role"] == "user" else '🤖'
            # 用st.write或st.markdown显示消息内容
            st.write(f"{avatar}: {message['content']}")
    else:
        # 如果没有消息记录，就初始化一个空列表
        st.session_state.messages = []

    return st.session_state.messages

# def main():
#     model, tokenizer = init_model()
#     messages = init_chat_history()

#     if prompt := st.chat_input("Shift + Enter 换行, Enter 发送"):
#         with st.chat_message("user", avatar='🧑‍💻'):
#             st.markdown(prompt)
#         messages.append({"role": "user", "content": prompt})
#         print(f"[user] {prompt}", flush=True)
#         with st.chat_message("assistant", avatar='🤖'):
#             placeholder = st.empty()
#             for response in model.chat(tokenizer, messages, stream=True):
#                 placeholder.markdown(response)
#                 if torch.backends.mps.is_available():
#                     torch.mps.empty_cache()
#         messages.append({"role": "assistant", "content": response})
#         print(json.dumps(messages, ensure_ascii=False), flush=True)

#         st.button("清空对话", on_click=clear_chat_history)
# 主函数
def main():
    model, tokenizer = init_model()
    messages = init_chat_history()

    # 在侧边栏或表单中创建一个输入框和一个提交按钮
    with st.sidebar.form("chat_form"):
        user_input = st.text_input("请输入您的问题")
        submit_button = st.form_submit_button("发送")

    # 如果用户点击了发送按钮，就把用户的输入添加到消息记录中，并显示出来
    if submit_button:
        messages.append({"role": "user", "content": user_input})
        st.write(f"🧑‍💻: {user_input}")
        print(f"[user] {user_input}", flush=True)

        # 调用模型来生成回复，并添加到消息记录中，并显示出来
        reply = model.chat(tokenizer, messages, stream=False)
        messages.append({"role": "assistant", "content": reply})
        st.write(f"🤖: {reply}")
        print(json.dumps(messages, ensure_ascii=False), flush=True)

        # 如果有清空对话的按钮，就绑定清空聊天历史的函数
        if st.button("清空对话"):
            clear_chat_history()

if __name__ == "__main__":
    main()