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from transformers import AutoModel, AutoTokenizer
import streamlit as st
from streamlit_chat import message
st.set_page_config(
page_title="ChatGLM2-6b 演示",
page_icon=":robot:",
layout='wide'
)
@st.cache_resource
def get_model():
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
model = AutoModel.from_pretrained("THUDM/chatglm2-6b", 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/chatglm2-6b", num_gpus=2)
model = model.eval()
return tokenizer, model
MAX_TURNS = 20
MAX_BOXES = MAX_TURNS * 2
def predict(input, max_length, top_p, temperature, history=None):
tokenizer, model = get_model()
if history is None:
history = []
with container:
if len(history) > 0:
if len(history)>MAX_BOXES:
history = history[-MAX_TURNS:]
for i, (query, response) in enumerate(history):
message(query, avatar_style="big-smile", key=str(i) + "_user")
message(response, avatar_style="bottts", key=str(i))
message(input, avatar_style="big-smile", key=str(len(history)) + "_user")
st.write("AI正在回复:")
with st.empty():
for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
temperature=temperature):
query, response = history[-1]
st.write(response)
return history
container = st.container()
# create a prompt text for the text generation
prompt_text = st.text_area(label="用户命令输入",
height = 100,
placeholder="请在这儿输入您的命令")
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, 0.95, step=0.01
)
if 'state' not in st.session_state:
st.session_state['state'] = []
if st.button("发送", key="predict"):
with st.spinner("AI正在思考，请稍等........"):
# text generation
st.session_state["state"] = predict(prompt_text, max_length, top_p, temperature, st.session_state["state"])
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