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streamlit-chatbox

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파일 탐색기

최종 버전 다운로드 (.zip)

- feature.html
- - __init__.py
  - example.py
- __init__.py
- elements.py
- flows.py
- messages.py
- thirdpart.py
.gitignore
demo.gif
demo_agent.gif
example.py
example_custom_output.py
MANIFEST.in
README.md
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/liunux4odoo/streamlit-chatbox

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd streamlit-chatbox

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# Attention!

Since version 1.24.0 streamlit provides official elements to [build conversational apps](https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps).

The new elements are more flexible, extensible and better supported, I would suggest to use them.

However, streamlit>=1.23 requires protobuf>=4 when some package requires protobuf<=3. In this condition you can use this package(<1.0.0) with streamlit<=1.22 as alternative. They are all simple to render text messages.

This package(>=1.0.0) will focus on wrapper of official chat elements to make chat with LLMs more convenient.

# Chatbox component for streamlit

A Streamlit component to show chat messages.
It's basiclly a wrapper of streamlit officeial elements including the chat elemnts.

- demo
![](https://raw.githubusercontent.com/liunux4odoo/streamlit-chatbox/master/demo.gif)

- demo agent
![](https://raw.githubusercontent.com/liunux4odoo/streamlit-chatbox/master/demo_agent.gif)

## Features

- support streaming output.
- support markdown/image/video/audio messages, and all streamlit elements could be supported by customized `OutputElement`.
- output multiple messages at once, and make them collapsable.
- maintain session state context bound to chat conversation
- export & import chat histories

This make it easy to chat with langchain LLMs in streamlit.

Goto [webui](https://github.com/chatchat-space/Langchain-Chatchat/blob/master/webui_pages/dialogue/dialogue.py) of [langchain-chatchat](https://github.com/chatchat-space/Langchain-Chatchat) to see the actual application.

## Install

just `pip install -U streamlit-chatbox`

## Usage examples

```python
import streamlit as st
from streamlit_chatbox import *
import time
import simplejson as json

llm = FakeLLM()
chat_box = ChatBox(
    use_rich_markdown=True, # use streamlit-markdown
    user_theme="green", # see streamlit_markdown.st_markdown for all available themes
    assistant_theme="blue",
)
chat_box.use_chat_name("chat1") # add a chat conversatoin

def on_chat_change():
    chat_box.use_chat_name(st.session_state["chat_name"])
    chat_box.context_to_session() # restore widget values to st.session_state when chat name changed

with st.sidebar:
    st.subheader('start to chat using streamlit')
    chat_name = st.selectbox("Chat Session:", ["default", "chat1"], key="chat_name", on_change=on_chat_change)
    chat_box.use_chat_name(chat_name)
    streaming = st.checkbox('streaming', key="streaming")
    in_expander = st.checkbox('show messages in expander', key="in_expander")
    show_history = st.checkbox('show session state', key="show_history")
    chat_box.context_from_session(exclude=["chat_name"]) # save widget values to chat context

st.divider()

btns = st.container()

file = st.file_uploader(
        "chat history json",
        type=["json"]
    )

if st.button("Load Json") and file:
        data = json.load(file)
        chat_box.from_dict(data)

chat_box.init_session()
chat_box.output_messages()

def on_feedback(
    feedback,
    chat_history_id: str = "",
    history_index: int = -1,
):
    reason = feedback["text"]
    score_int = chat_box.set_feedback(feedback=feedback, history_index=history_index) # convert emoji to integer
    # do something
    st.session_state["need_rerun"] = True

feedback_kwargs = {
    "feedback_type": "thumbs",
    "optional_text_label": "wellcome to feedback",
}

if query := st.chat_input('input your question here'):
    chat_box.user_say(query)
    if streaming:
        generator = llm.chat_stream(query)
        elements = chat_box.ai_say(
            [
                # you can use string for Markdown output if no other parameters provided
                Markdown("thinking", in_expander=in_expander,
                         expanded=True, title="answer"),
                Markdown("", in_expander=in_expander, title="references"),
            ]
        )
        time.sleep(1)
        text = ""
        for x, docs in generator:
            text += x
            chat_box.update_msg(text, element_index=0, streaming=True)
        # update the element without focus
        chat_box.update_msg(text, element_index=0, streaming=False, state="complete")
        chat_box.update_msg("\n\n".join(docs), element_index=1, streaming=False, state="complete")
        chat_history_id = "some id"
        chat_box.show_feedback(**feedback_kwargs,
                                key=chat_history_id,
                                on_submit=on_feedback,
                                kwargs={"chat_history_id": chat_history_id, "history_index": len(chat_box.history) - 1})
    else:
        text, docs = llm.chat(query)
        chat_box.ai_say(
            [
                Markdown(text, in_expander=in_expander,
                         expanded=True, title="answer"),
                Markdown("\n\n".join(docs), in_expander=in_expander,
                         title="references"),
            ]
        )

cols = st.columns(2)
if cols[0].button('show me the multimedia'):
    chat_box.ai_say(Image(
        'https://tse4-mm.cn.bing.net/th/id/OIP-C.cy76ifbr2oQPMEs2H82D-QHaEv?w=284&h=181&c=7&r=0&o=5&dpr=1.5&pid=1.7'))
    time.sleep(0.5)
    chat_box.ai_say(
        Video('https://sample-videos.com/video123/mp4/720/big_buck_bunny_720p_1mb.mp4'))
    time.sleep(0.5)
    chat_box.ai_say(
        Audio('https://sample-videos.com/video123/mp4/720/big_buck_bunny_720p_1mb.mp4'))

if cols[1].button('run agent'):
    chat_box.user_say('run agent')
    agent = FakeAgent()
    text = ""

# streaming:
    chat_box.ai_say() # generate a blank placeholder to render messages
    for d in agent.run_stream():
        if d["type"] == "complete":
            chat_box.update_msg(expanded=False, state="complete")
            chat_box.insert_msg(d["llm_output"])
            break

if d["status"] == 1:
            chat_box.update_msg(expanded=False, state="complete")
            text = ""
            chat_box.insert_msg(Markdown(text, title=d["text"], in_expander=True, expanded=True))
        elif d["status"] == 2:
            text += d["llm_output"]
            chat_box.update_msg(text, streaming=True)
        else:
            chat_box.update_msg(text, streaming=False)

btns.download_button(
    "Export Markdown",
    "".join(chat_box.export2md()),
    file_name=f"chat_history.md",
    mime="text/markdown",
)

btns.download_button(
    "Export Json",
    chat_box.to_json(),
    file_name="chat_history.json",
    mime="text/json",
)

if btns.button("clear history"):
    chat_box.init_session(clear=True)
    st.experimental_rerun()

if show_history:
    st.write(st.session_state)

```

## Todos

- [x] wrapper of official chat elements
- [ ] input messages: (this depends on the official st.chat_input improvement by #7069)
	- [x] TEXT
	- [ ] IMAGE
		- [ ] file upload
		- [ ] paste from clipboard(streamlit_bokeh_events)
	- [ ] VIDEO
		- [ ] file upload
	- [ ] AUDIO
		- [ ] file upload
		- [ ] audio-recorder-streamlit

- [x] output message types:
	- [x] Text/Markdown/Image/Audio/Video
	- [x] any other output types supported by streamlit

- [ ] improve output performance
	- [x] streaming output message
	- [x] show message in expander
	- [x] rich output message using streamlit-markdown
    - [x] feedback by user

- [x] export & import chat history
	- [x] export to markdown
	- [x] export to json
    - [x] import json

- [x] support output of langchain' Agent.
- [x] conext bound to chat

# changelog

## v1.1.13
- add Json output element
- can choose to use streamlit-markdown instead of st.markdown. currently need streamlit==1.37.1 when streaming
- user can register custom output method with `ChatBox.register_output_method`. This is useful to use thirdparty components:
    ```python3
    from streamlit_chatbox import *
    from streamlit_markdown import st_hack_markdown

ChatBox.register_output_method("st_markdown", st_hack_markdown)
    cb = ChatBox()
    cb.user_say(OutputElement("user defined output method", output_method="st_markdown", theme_color="blue", mermaid_theme_CSS=""))
    ```