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import streamlit as st
import torch
from transformers import AutoModel, AutoTokenizer
# 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 = "D:\git\model\chatglm3-6b-32k"
@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()
# 初始化历史记录和past key values
if "history" not in st.session_state:
st.session_state.history = []
if "past_key_values" not in st.session_state:
st.session_state.past_key_values = None
# 设置max_length、top_p和temperature
max_length = st.sidebar.slider("max_length", 0, 32768, 8192, step=1)
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()
# 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
past_key_values = st.session_state.past_key_values
for response, history, past_key_values in model.stream_chat(
tokenizer,
prompt_text,
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)
# 更新历史记录和past key values
st.session_state.history = history
st.session_state.past_key_values = past_key_values
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