InternLM/04-Lagent+InternLM-Chat-7B-V1.1.md · zheng/self-llm

# Lagent+InternLM-Chat-7B-V1.1

## 环境准备

选择和第一个 `InternLM` 一样的 `AutoDL` 镜像环境，运行以下命令安装依赖，如果上一个 `InternLM-Chat-7B` 已经配置好环境不需要重复安装.

```shell
# 升级pip
python -m pip install --upgrade pip
# 更换 pypi 源加速库的安装
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple

pip install modelscope==1.9.5
pip install transformers==4.35.2
pip install streamlit==1.24.0
pip install sentencepiece==0.1.99
pip install accelerate==0.24.1
```

## 模型下载

在 `/root/autodl-tmp` 路径下新建 `download.py` 文件并在其中输入以下内容，并运行 `python /root/autodl-tmp/download.py`执行下载，模型大小为 14 GB，下载模型大概需要 10~20 分钟

```python
import torch
from modelscope import snapshot_download, AutoModel, AutoTokenizer
import os
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b-v1_1', cache_dir='/root/autodl-tmp', revision='master')
```

## Lagent 安装

首先我们运行以下命令打开学术加速 `clone` `lagent`仓库，并通过 `pip install -e .`源码安装 `Lagent`

```shell
source /etc/network_turbo
cd /root/autodl-tmp
git clone https://github.com/InternLM/lagent.git
git checkout 511b03889010c4811b1701abb153e02b8e94fb5e # 尽量保证和教程commit版本一致
cd lagent
pip install -e . # 源码安装
unset http_proxy && unset https_proxy
```

## 修改代码

由于代码修改的地方比较多，大家直接将`/root/autodl-tmp/lagent/examples/react_web_demo.py` 内容替换为以下代码

```python
import copy
import os

import streamlit as st
from streamlit.logger import get_logger

from lagent.actions import ActionExecutor, GoogleSearch, PythonInterpreter
from lagent.agents.react import ReAct
from lagent.llms import GPTAPI
from lagent.llms.huggingface import HFTransformerCasualLM

class SessionState:

def init_state(self):
        """Initialize session state variables."""
        st.session_state['assistant'] = []
        st.session_state['user'] = []

#action_list = [PythonInterpreter(), GoogleSearch()]
        action_list = [PythonInterpreter()]
        st.session_state['plugin_map'] = {
            action.name: action
            for action in action_list
        }
        st.session_state['model_map'] = {}
        st.session_state['model_selected'] = None
        st.session_state['plugin_actions'] = set()

def clear_state(self):
        """Clear the existing session state."""
        st.session_state['assistant'] = []
        st.session_state['user'] = []
        st.session_state['model_selected'] = None
        if 'chatbot' in st.session_state:
            st.session_state['chatbot']._session_history = []

class StreamlitUI:

def __init__(self, session_state: SessionState):
        self.init_streamlit()
        self.session_state = session_state

def init_streamlit(self):
        """Initialize Streamlit's UI settings."""
        st.set_page_config(
            layout='wide',
            page_title='lagent-web',
            page_icon='./docs/imgs/lagent_icon.png')
        # st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
        st.sidebar.title('模型控制')

def setup_sidebar(self):
        """Setup the sidebar for model and plugin selection."""
        model_name = st.sidebar.selectbox(
            '模型选择：', options=['gpt-3.5-turbo','internlm'])
        if model_name != st.session_state['model_selected']:
            model = self.init_model(model_name)
            self.session_state.clear_state()
            st.session_state['model_selected'] = model_name
            if 'chatbot' in st.session_state:
                del st.session_state['chatbot']
        else:
            model = st.session_state['model_map'][model_name]

plugin_name = st.sidebar.multiselect(
            '插件选择',
            options=list(st.session_state['plugin_map'].keys()),
            default=[list(st.session_state['plugin_map'].keys())[0]],
        )

plugin_action = [
            st.session_state['plugin_map'][name] for name in plugin_name
        ]
        if 'chatbot' in st.session_state:
            st.session_state['chatbot']._action_executor = ActionExecutor(
                actions=plugin_action)
        if st.sidebar.button('清空对话', key='clear'):
            self.session_state.clear_state()
        uploaded_file = st.sidebar.file_uploader(
            '上传文件', type=['png', 'jpg', 'jpeg', 'mp4', 'mp3', 'wav'])
        return model_name, model, plugin_action, uploaded_file

def init_model(self, option):
        """Initialize the model based on the selected option."""
        if option not in st.session_state['model_map']:
            if option.startswith('gpt'):
                st.session_state['model_map'][option] = GPTAPI(
                    model_type=option)
            else:
                st.session_state['model_map'][option] = HFTransformerCasualLM(
                    '/root/autodl-tmp/Shanghai_AI_Laboratory/internlm-chat-7b-v1_1')
        return st.session_state['model_map'][option]

def initialize_chatbot(self, model, plugin_action):
        """Initialize the chatbot with the given model and plugin actions."""
        return ReAct(
            llm=model, action_executor=ActionExecutor(actions=plugin_action))

def render_user(self, prompt: str):
        with st.chat_message('user'):
            st.markdown(prompt)

def render_assistant(self, agent_return):
        with st.chat_message('assistant'):
            for action in agent_return.actions:
                if (action):
                    self.render_action(action)
            st.markdown(agent_return.response)

def render_action(self, action):
 with st.expander(action.type, expanded=True):
 st.markdown(
 " 插 件:" # noqa E501
 + action.type + '',
 unsafe_allow_html=True)
 st.markdown(
 " 思考步骤:" # noqa E501
 + action.thought + '',
 unsafe_allow_html=True)
 if (isinstance(action.args, dict) and 'text' in action.args):
 st.markdown(
 " 执行内容:", # noqa E501
 unsafe_allow_html=True)
 st.markdown(action.args['text'])
 self.render_action_results(action)

def render_action_results(self, action):
 """Render the results of action, including text, images, videos, and
 audios."""
 if (isinstance(action.result, dict)):
 st.markdown(
 " 执行结果:", # noqa E501
 unsafe_allow_html=True)
 if 'text' in action.result:
 st.markdown(
 "" + action.result['text'] +
 '',
 unsafe_allow_html=True)
 if 'image' in action.result:
 image_path = action.result['image']
 image_data = open(image_path, 'rb').read()
 st.image(image_data, caption='Generated Image')
 if 'video' in action.result:
 video_data = action.result['video']
 video_data = open(video_data, 'rb').read()
 st.video(video_data)
 if 'audio' in action.result:
 audio_data = action.result['audio']
 audio_data = open(audio_data, 'rb').read()
 st.audio(audio_data)

def main():
    logger = get_logger(__name__)
    # Initialize Streamlit UI and setup sidebar
    if 'ui' not in st.session_state:
        session_state = SessionState()
        session_state.init_state()
        st.session_state['ui'] = StreamlitUI(session_state)

else:
        st.set_page_config(
            layout='wide',
            page_title='lagent-web',
            page_icon='./docs/imgs/lagent_icon.png')
        # st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
    model_name, model, plugin_action, uploaded_file = st.session_state[
        'ui'].setup_sidebar()

# Initialize chatbot if it is not already initialized
    # or if the model has changed
    if 'chatbot' not in st.session_state or model != st.session_state[
            'chatbot']._llm:
        st.session_state['chatbot'] = st.session_state[
            'ui'].initialize_chatbot(model, plugin_action)

for prompt, agent_return in zip(st.session_state['user'],
                                    st.session_state['assistant']):
        st.session_state['ui'].render_user(prompt)
        st.session_state['ui'].render_assistant(agent_return)
    # User input form at the bottom (this part will be at the bottom)
    # with st.form(key='my_form', clear_on_submit=True):

if user_input := st.chat_input(''):
        st.session_state['ui'].render_user(user_input)
        st.session_state['user'].append(user_input)
        # Add file uploader to sidebar
        if uploaded_file:
            file_bytes = uploaded_file.read()
            file_type = uploaded_file.type
            if 'image' in file_type:
                st.image(file_bytes, caption='Uploaded Image')
            elif 'video' in file_type:
                st.video(file_bytes, caption='Uploaded Video')
            elif 'audio' in file_type:
                st.audio(file_bytes, caption='Uploaded Audio')
            # Save the file to a temporary location and get the path
            file_path = os.path.join(root_dir, uploaded_file.name)
            with open(file_path, 'wb') as tmpfile:
                tmpfile.write(file_bytes)
            st.write(f'File saved at: {file_path}')
            user_input = '我上传了一个图像，路径为: {file_path}. {user_input}'.format(
                file_path=file_path, user_input=user_input)
        agent_return = st.session_state['chatbot'].chat(user_input)
        st.session_state['assistant'].append(copy.deepcopy(agent_return))
        logger.info(agent_return.inner_steps)
        st.session_state['ui'].render_assistant(agent_return)

if __name__ == '__main__':
    root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
    root_dir = os.path.join(root_dir, 'tmp_dir')
    os.makedirs(root_dir, exist_ok=True)
    main()
```

## Demo 运行

```shell
streamlit run /root/autodl-tmp/lagent/examples/react_web_demo.py --server.address 127.0.0.1 --server.port 6006
```

用同样的方法将端口映射到本地，运行成功后，我们在 `Web` 页面选择 `InternLM` 模型，等待模型加载完毕后，输入数学问题 已知 `2x+3=10`，求`x` ,此时 `InternLM-Chat-7B-V1.1` 模型理解题意生成解此题的 `Python`代码，`Lagent` 调度送入 `Python` 代码解释器求出该问题的解。

![Alt text](images/image-7.png)

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