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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:" | |
) | |
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, max_length, top_p, temperature, history=None): | |
tokenizer, model = get_model() | |
if history is None: | |
history = [] | |
with container: | |
if len(history) > 0: | |
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, 4096, 2048, step=1 | |
) | |
top_p = st.sidebar.slider( | |
'top_p', 0.0, 1.0, 0.6, 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"]) |