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Update web demo

pull/250/head
duzx16 1 year ago
parent
commit
9e18d611fc
  1. 22
      README.md
  2. 1
      requirements.txt
  3. BIN
      resources/web-demo.gif
  4. 77
      web_demo2.py

22
README.md

@ -137,7 +137,11 @@ git clone https://github.com/THUDM/ChatGLM2-6B
cd ChatGLM2-6B cd ChatGLM2-6B
``` ```
然后使用 pip 安装依赖:`pip install -r requirements.txt`,其中 `transformers` 库版本推荐为 `4.30.2`,`torch` 推荐使用 2.0 以上的版本,以获得最佳的推理性能。 然后使用 pip 安装依赖:
```
pip install -r requirements.txt
```
其中 `transformers` 库版本推荐为 `4.30.2`,`torch` 推荐使用 2.0 及以上的版本,以获得最佳的推理性能。
### 代码调用 ### 代码调用
@ -188,23 +192,17 @@ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/THUDM/chatglm2-6b
![web-demo](resources/web-demo.gif) ![web-demo](resources/web-demo.gif)
首先安装 Gradio:`pip install gradio`,然后运行仓库中的 [web_demo.py](web_demo.py): 可以通过以下命令启动基于 Streamlit 的网页版 demo:
```shell ```shell
python web_demo.py streamlit run web_demo2.py
``` ```
程序会运行一个 Web Server,并输出地址。在浏览器中打开输出的地址即可使用。 程序会运行一个 Web Server,并输出地址。在浏览器中打开输出的地址即可使用。
> 默认使用了 `share=False` 启动,不会生成公网链接。如有需要公网访问的需求,可以修改为 `share=True` 启动。
>
感谢 [@AdamBear](https://github.com/AdamBear) 实现了基于 Streamlit 的网页版 Demo `web_demo2.py`。使用时首先需要额外安装以下依赖:
```shell [web_demo.py](./web_demo.py) 中提供了旧版基于 Gradio 的 web demo,可以通过如下命令运行:
pip install streamlit streamlit-chat
```
然后通过以下命令运行:
```shell ```shell
streamlit run web_demo2.py python web_demo.py
``` ```
经测试,如果输入的 prompt 较长的话,使用基于 Streamlit 的网页版 Demo 会更流畅。 经测试,如果输入的 prompt 较长的话,使用基于 Streamlit 的网页版 Demo 会更流畅。

1
requirements.txt

@ -7,3 +7,4 @@ mdtex2html
sentencepiece sentencepiece
accelerate accelerate
sse-starlette sse-starlette
streamlit>=1.24.0

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77
web_demo2.py

@ -1,6 +1,5 @@
from transformers import AutoModel, AutoTokenizer from transformers import AutoModel, AutoTokenizer
import streamlit as st import streamlit as st
from streamlit_chat import message
st.set_page_config( st.set_page_config(
@ -21,40 +20,9 @@ def get_model():
return tokenizer, model return tokenizer, model
MAX_TURNS = 20 tokenizer, model = get_model()
MAX_BOXES = MAX_TURNS * 2
st.title("ChatGLM2-6B")
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 = st.sidebar.slider(
'max_length', 0, 32768, 8192, step=1 'max_length', 0, 32768, 8192, step=1
@ -63,13 +31,40 @@ top_p = st.sidebar.slider(
'top_p', 0.0, 1.0, 0.8, step=0.01 'top_p', 0.0, 1.0, 0.8, step=0.01
) )
temperature = st.sidebar.slider( temperature = st.sidebar.slider(
'temperature', 0.0, 1.0, 0.95, step=0.01 'temperature', 0.0, 1.0, 0.8, step=0.01
) )
if 'state' not in st.session_state: if 'history' not in st.session_state:
st.session_state['state'] = [] st.session_state.history = []
if 'past_key_values' not in st.session_state:
st.session_state.past_key_values = None
for i, (query, response) in enumerate(st.session_state.history):
with st.chat_message(name="user", avatar="user"):
st.markdown(query)
with st.chat_message(name="assistant", avatar="assistant"):
st.markdown(response)
with st.chat_message(name="user", avatar="user"):
input_placeholder = st.empty()
with st.chat_message(name="assistant", avatar="assistant"):
message_placeholder = st.empty()
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")
button = st.button("发送", key="predict")
if button:
input_placeholder.markdown(prompt_text)
history, past_key_values = st.session_state.history, 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)
if st.button("发送", key="predict"): st.session_state.history = history
with st.spinner("AI正在思考,请稍等........"): st.session_state.past_key_values = past_key_values
# text generation
st.session_state["state"] = predict(prompt_text, max_length, top_p, temperature, st.session_state["state"])

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