ai/web_demo3.py

import json

import streamlit as st
import torch
from transformers import AutoModel, AutoTokenizer

# from api import get_docs
from server.knowledge_base.kb_doc_api import search_docs

# 设置页面标题、图标和布局
st.set_page_config(
    page_title="ChatGLM3-6B 演示",
    page_icon=":robot:",
    layout="wide"
)

# 设置为模型ID或本地文件夹路径
model_path = "THUDM/chatglm3-6b-32k"


def get_docs(query, top_k, score_threshold, knowledge_base_name):
    docs = search_docs(query, knowledge_base_name, top_k, score_threshold)
    context1 = "\n".join([doc.page_content for doc in docs])
    return context1


@st.cache_resource
def get_model():
    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
    model = AutoModel.from_pretrained(model_path, trust_remote_code=True).cuda()
    # 多显卡支持,使用下面两行代替上面一行,将num_gpus改为你实际的显卡数量
    # from utils import load_model_on_gpus
    # model = load_model_on_gpus("THUDM/chatglm3-6b", num_gpus=2)
    model = model.eval()
    return tokenizer, model


# 加载Chatglm3的model和tokenizer
tokenizer, model = get_model()

system_prompt = """你是一个农业领域的AI助手，你需要通过已知信息和历史对话来回答人类的问题。
    特别注意：已知信息不一定是正确答案，还要根据对话的历史问题和答案进行思考回答，如果找不到答案，请用自己的知识进行回答，不可以乱回答！！！
    答案使用中文。\n"""

# 初始化历史记录和past key values
if "history" not in st.session_state:
    st.session_state.history = [{'role': 'system', 'content': system_prompt}]
if "past_key_values" not in st.session_state:
    st.session_state.past_key_values = None
if "all_prompt_text" not in st.session_state:
    st.session_state.all_prompt_text = []

# 设置max_length、top_p和temperature
max_length = st.sidebar.slider("max_length", 0, 32768, 32768, step=1024)
top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
temperature = st.sidebar.slider("temperature", 0.0, 1.0, 0.6, step=0.01)

# 清理会话历史
buttonClean = st.sidebar.button("清理会话历史", key="clean")
if buttonClean:
    st.session_state.history = []
    st.session_state.past_key_values = None
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    st.rerun()

# 渲染聊天历史记录
for i, message in enumerate(st.session_state.history):
    if message["role"] == "user":
        with st.chat_message(name="user", avatar="user"):
            st.markdown(message["content"])
    else:
        with st.chat_message(name="assistant", avatar="assistant"):
            st.markdown(message["content"])

# 输入框和输出框
with st.chat_message(name="user", avatar="user"):
    input_placeholder = st.empty()
with st.chat_message(name="assistant", avatar="assistant"):
    message_placeholder = st.empty()


def build_prompt(prompt_text, new_query):
    knowledge_base_name = 'agriculture'
    context = get_docs(new_query, 3, 1, knowledge_base_name)
    print("query：", new_query)
    print("context：", context)
    prompt = f"\n\n<已知信息>：\n{context}\n</已知信息>\n\n"
    prompt += f"\n\n<问题>：{prompt_text}</问题>\n\n"
    return prompt


# 获取用户输入
prompt_text = st.chat_input("请输入您的问题")
# 如果用户输入了内容,则生成回复
if prompt_text:
    input_placeholder.markdown(prompt_text)
    history = st.session_state.history
    with open('a.json', 'w') as f:
        json.dump(history, f, ensure_ascii=False)
    past_key_values = st.session_state.past_key_values
    new_query = ""
    assistants = []
    # 历史问题
    st.session_state.all_prompt_text.append(prompt_text)
    # 历史助手
    for h in history:
        # 助手
        if h['role'] == 'assistant':
            assistants.append(h['content'])
    # 将历史问题和历史助手组合成新问题
    for i in range(len(st.session_state.all_prompt_text)):
        new_query += st.session_state.all_prompt_text[i]
        if i < len(st.session_state.all_prompt_text) - 1:
            new_query += ","
        else:
            new_query += "?"
        if i < len(assistants):
            new_query += assistants[i]
    history = []
    for k, v in enumerate(assistants):
        a = assistants[k]
        q = st.session_state.all_prompt_text[k]
        history.append({'role': 'user', 'content': q})
        history.append({'role': 'user', 'content': a})
    prompt = build_prompt(prompt_text, new_query)

    # TODO
    # 控制历史信息、滑动窗口的长度！

    for response, history, past_key_values in model.stream_chat(
            tokenizer,
            prompt,
            history,
            past_key_values=past_key_values,
            max_length=max_length,
            top_p=top_p,
            temperature=temperature,
            return_past_key_values=True,
    ):
        message_placeholder.markdown(response)
    # 历史assistant
    # print(history)
    # 更新历史记录和past key values
    st.session_state.history = history
    st.session_state.past_key_values = past_key_values