|
|
from transformers import AutoModel, AutoTokenizer
|
|
|
import streamlit as st
|
|
|
from streamlit_chat import message
|
|
|
|
|
|
|
|
|
st.set_page_config(
|
|
|
page_title="ChatGLM-6b 演示",
|
|
|
page_icon=":robot:"
|
|
|
)
|
|
|
|
|
|
|
|
|
@st.cache_resource
|
|
|
def get_model():
|
|
|
tokenizer = AutoTokenizer.from_pretrained("/THUDM/chatglm-6b", trust_remote_code=True)
|
|
|
model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
|
|
|
model = model.eval()
|
|
|
return tokenizer, model
|
|
|
|
|
|
|
|
|
MAX_TURNS = 20
|
|
|
MAX_BOXES = MAX_TURNS * 2
|
|
|
|
|
|
|
|
|
def predict(input, history=None):
|
|
|
tokenizer, model = get_model()
|
|
|
if history is None:
|
|
|
history = []
|
|
|
response, history = model.chat(tokenizer, input, history)
|
|
|
|
|
|
#updates = []
|
|
|
for i, (query, response) in enumerate(history):
|
|
|
#updates.append("用户：" + query)
|
|
|
message(query, avatar_style="big-smile", key=str(i) + "_user")
|
|
|
#updates.append("ChatGLM-6B：" + response)
|
|
|
message(response, avatar_style="bottts", key=str(i))
|
|
|
|
|
|
# if len(updates) < MAX_BOXES:
|
|
|
# updates = updates + [""] * (MAX_BOXES - len(updates))
|
|
|
|
|
|
return history
|
|
|
|
|
|
|
|
|
# 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")
|
|
|
|
|
|
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, st.session_state["state"])
|
|
|
|
|
|
st.balloons() |
