app.py

'''
参考： https://github.com/shroominic/codeinterpreter-api

1. 可以存在本地，然后再调出来。 working. 
1. 可以在临时文件夹中读取文件。
1. 可以直接在内存中读出图片。
1. 中文字体成功。
    from matplotlib.font_manager import FontProperties
    myfont=FontProperties(fname='/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/Coding/code_interpreter/rawdata/SimHei.ttf')
    sns.set_style('whitegrid',{'font.sans-serif':['simhei','Arial']}) 
1. 解决了account login的问题，主要格式:
    ## 需要严格的按照yaml文件的格式来定义如下几个字段。
    for un, name, pw in zip(users, names, hashed_passwords):
        # user_dict = {"name":name,"password":pw}
        user_dict = {"name": un, "password": pw}
        # credentials["usernames"].update({un:user_dict})
        credentials["usernames"].update({un: user_dict})

'''
# TODO：1. Chinese display isssue. 2. account system. 3. local enterprise database. 

import database as db
from deta import Deta  # pip3 install deta
import requests
from codeinterpreterapi import CodeInterpreterSession, File
import streamlit as st
# from codeinterpreterapi import CodeInterpreterSession
import openai
import os
import matplotlib.pyplot as plt
import pandas as pd
# import csv
import tempfile
from tempfile import NamedTemporaryFile
import pathlib
from pathlib import Path
from matplotlib.font_manager import FontProperties
import seaborn as sns
from time import sleep
import streamlit_authenticator as stauth
import database as db  # python文件同目录下的.py程序，直接导入。
import deta
from langchain.chat_models import ChatOpenAI
from llama_index import StorageContext, load_index_from_storage, GPTVectorStoreIndex, LLMPredictor, PromptHelper
from llama_index import ServiceContext, QuestionAnswerPrompt
import sys
import time
import PyPDF2 ## read the local_KB PDF file.
# import localKB_construct
import save_database_info
from datetime import datetime
import pytz

os.environ["OPENAI_API_KEY"] = os.environ['user_token']
openai.api_key = os.environ['user_token'] 
# os.environ["VERBOSE"] = "True"  # 可以看到具体的错误？

#* 如果碰到接口问题，可以启用如下设置。
# openai.proxy = {
#     "http": "http://127.0.0.1:7890",
#             "https": "http://127.0.0.1:7890"
# }


# layout settings.
st.title("专业版大语言模型商业智能中心")
st.subheader("Artificial Intelligence Backend Center for Professionals")

# clear conversion.
reset_button_key = "reset_button"
reset_button = st.button(label=("扫清世间烦恼，清除所有记录，并开启一轮新对话 ▶"),
                         key=reset_button_key, use_container_width=True, type="secondary")
if reset_button:
    st.session_state.conversation = None
    st.session_state.chat_history = None
    st.session_state.messages = []
    message_placeholder = st.empty()

def clear_all():
    st.session_state.conversation = None
    st.session_state.chat_history = None
    st.session_state.messages = []
    message_placeholder = st.empty()
    return None


# # with tab2:
# def upload_file(uploaded_file):
#     if uploaded_file is not None:
#         filename = uploaded_file.name
#         # st.write(filename)  # print out the whole file name to validate. not to show in the final version. 
#         try:
#             if '.pdf' in filename:
#                 # pdf_file = PyPDF2.PdfReader(uploaded_file)
#                 PyPDF2.PdfReader(uploaded_file)
#                 # st.write(pdf_file.pages[0].extract_text())
#                 # with st.status('正在为您解析新知识库...', expanded=False, state='running') as status:
#                 spinner = st.spinner('正在为您解析新知识库...请耐心等待')
#                 # with st.spinner('正在为您解析新知识库...请耐心等待'):
#                 with spinner:
#                     import localKB_construct
#                     # sleep(3)
#                     # st.write(upload_file)
#                     localKB_construct.process_file(uploaded_file)
#                     st.markdown('新知识库解析成功，可以开始对话！')
#                     spinner = st.empty()
#                     # sleep(3)
#                     # display = []

#             else:
#                 if '.csv' in filename:
#                     csv_file = pd.read_csv(uploaded_file)
#                     csv_file.to_csv('./upload.csv', encoding='utf-8', index=False)
#                     st.write(csv_file[:3])  # 这里只是显示文件，后面需要定位文件所在的绝对路径。
#                 else:
#                     xls_file = pd.read_excel(uploaded_file)
#                     xls_file.to_csv('./upload.csv', index=False)
#                     st.write(xls_file[:3])

#                 uploaded_file_name = "File_provided"
#                 temp_dir = tempfile.TemporaryDirectory()
#                 # ! working.
#                 uploaded_file_path = pathlib.Path(temp_dir.name) / uploaded_file_name
#                 # with open('./upload.csv', 'wb') as output_temporary_file:
#                 with open(f'./{name}_upload.csv', 'wb') as output_temporary_file:
#                     # print(f'./{name}_upload.csv')
#                     # ! 必须用这种格式读入内容，然后才可以写入temporary文件夹中。
#                     # output_temporary_file.write(uploaded_file.getvalue())
#                     output_temporary_file.write(uploaded_file.getvalue())
#                     # st.write(uploaded_file_path)  #* 可以查看文件是否真实存在，然后是否可以
#                 # st.write('Now file saved successfully.')
#         except Exception as e:
#             st.write(e)

#         # uploaded_file_name = "File_provided"
#         # temp_dir = tempfile.TemporaryDirectory()
#         # # ! working.
#         # uploaded_file_path = pathlib.Path(temp_dir.name) / uploaded_file_name
#         # # with open('./upload.csv', 'wb') as output_temporary_file:
#         # with open(f'./{name}_upload.csv', 'wb') as output_temporary_file:
#         #     # print(f'./{name}_upload.csv')
#         #     # ! 必须用这种格式读入内容，然后才可以写入temporary文件夹中。
#         #     # output_temporary_file.write(uploaded_file.getvalue())
#         #     output_temporary_file.write(uploaded_file.getvalue())
#         #     # st.write(uploaded_file_path)  # * 可以查看文件是否真实存在，然后是否可以
#         # # st.write('Now file saved successfully.')

#         return None

bing_search_api_key = os.environ['bing_api_key']
bing_search_endpoint = 'https://api.bing.microsoft.com/v7.0/search'

def search(query):
    # Construct a request
    # mkt = 'en-EN'
    mkt = 'zh-CN'
    params = {'q': query, 'mkt': mkt}
    headers = {'Ocp-Apim-Subscription-Key': bing_search_api_key}

    # Call the API
    try:
        response = requests.get(bing_search_endpoint,
                                headers=headers, params=params)
        response.raise_for_status()
        json = response.json()
        return json["webPages"]["value"]
        # print("\nJSON Response:\n")
        # pprint(response.json())
    except Exception as e:
        raise e

# openai.api_key = st.secrets["OPENAI_API_KEY"]

async def text_mode():
    # Set a default model
    if "openai_model" not in st.session_state:
        st.session_state["openai_model"] = "gpt-3.5-turbo-16k"
    if radio_1 == 'GPT-3.5':
        # print('----------'*5)
        print('radio_1: GPT-3.5 starts!')
        st.session_state["openai_model"] = "gpt-3.5-turbo-16k"
    else:
        print('radio_1: GPT-4.0 starts!')
        st.session_state["openai_model"] = "gpt-4"

    # Initialize chat history
    if "messages" not in st.session_state:
        st.session_state.messages = []

    # Display chat messages from history on app rerun
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # Display assistant response in chat message container
    # if prompt := st.chat_input("Say something"):
    prompt = st.chat_input("Say something")
    print('prompt now:', prompt)
    print('----------'*5)
    # if prompt:
    if prompt:
        st.session_state.messages.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.markdown(prompt)

        with st.chat_message("assistant"):
            message_placeholder = st.empty()
            full_response = ""

            if radio_2 == '联网模式':
                print('联网模式入口，prompt:', prompt)
                input_message = prompt
                internet_search_result = search(input_message)
                search_prompt = [
                    f"Source:\nTitle: {result['name']}\nURL: {result['url']}\nContent: {result['snippet']}" for result in internet_search_result]
                prompt = "基于如下的互联网公开信息, 回答问题:\n\n" + \
                    "\n\n".join(search_prompt[:3]) + "\n\n问题: " + input_message + \
                    "你需要注意的是回答问题时必须用提问的语言（如英文或者中文）来提示：'答案基于互联网公开信息。'" + "\n\n答案: "  # 限制了只有3个搜索结果。
                # prompt = "Use these sources to answer the question:\n\n" + "\n\n".join(search_prompt[0:3]) + "\n\nQuestion: " + input_message + "（注意：回答问题时请提示'以下答案基于互联网公开信息。'）\n\n" + "\n\nAnswer: "

                st.session_state.messages.append(
                    {"role": "user", "content": prompt})

                for response in openai.ChatCompletion.create(
                    model=st.session_state["openai_model"],
                    messages=[
                        {"role": m["role"], "content": m["content"]}
                        for m in st.session_state.messages
                    ],
                    stream=True,
                ):
                    full_response += response.choices[0].delta.get(
                        "content", "")
                    message_placeholder.markdown(full_response + "▌")
                message_placeholder.markdown(full_response)
                st.session_state.messages.append(
                    {"role": "assistant", "content": full_response})
                st.session_state.messages = []

            if radio_2 == '核心模式':
                print('GPT only starts!!!')
                print('messages:', st.session_state['messages'])
                for response in openai.ChatCompletion.create(
                    model=st.session_state["openai_model"],
                    # messages=[
                    #     {"role": m["role"], "content": m["content"]}
                    #     for m in st.session_state.messages
                    # ],
                    messages=[{'role': 'system', 'content': 'you are ChatGPT'}, {
                        'role': 'user', 'content': prompt}],
                    stream=True,
                ):
                    full_response += response.choices[0].delta.get(
                        "content", "")
                    message_placeholder.markdown(full_response + "▌")
                    # print('session completed!')
                message_placeholder.markdown(full_response)
            st.session_state.messages.append(
                {"role": "assistant", "content": full_response})


## load the local_KB PDF file. 
# async def localKB_mode():
def localKB_mode(username):
    ### clear all the prior conversation.
    # st.session_state.conversation = None
    # st.session_state.chat_history = None
    # st.session_state.messages = []
    # message_placeholder = st.empty()
    clear_all() ## reset the conversation.

    print('now starts the local KB version of ChatGPT')
    # Initialize chat history
    if "messages" not in st.session_state:
        st.session_state.messages = []

    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # Display assistant response in chat message container
    # if prompt := st.chat_input("Say something"):
    # prompt = st.chat_input("Say something")
    # print('prompt now:', prompt)
    # print('----------'*5)
    # if prompt:
    if prompt := st.chat_input("Say something"):
        st.session_state.messages.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.markdown(prompt)

        with st.status('检索中...', expanded=True, state='running') as status:
            with st.chat_message("assistant"):
                message_placeholder = st.empty()
                full_response = ""

            # if radio_2 == "知识库模式":
                # ! 这里需要重新装载一下storage_context。
                QA_PROMPT_TMPL = (
                "We have provided context information below. \n"
                "---------------------\n"
                "{context_str}"
                "\n---------------------\n"
                "Given all this information, please answer the following questions,"
                "You MUST use the SAME language as the question:\n"
                "{query_str}\n")
                QA_PROMPT = QuestionAnswerPrompt(QA_PROMPT_TMPL)
                # print('QA_PROMPT:', QA_PROMPT)

                # llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.8, model_name="gpt-3.5-turbo", max_tokens=4024,streaming=True))
                # # print('llm_predictor:', llm_predictor)
                # prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
                # print('prompt_helper:', prompt_helper)
                # service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
                # print('service_context:', service_context)
                # # # index = load_index_from_storage(storage_context)
                # print("storage_context:", storage_context)
                # index = load_index_from_storage(storage_context,service_context=service_context)
                storage_context = StorageContext.from_defaults(persist_dir=f"./{username}/")
                print('storage_context:',storage_context)
                index = load_index_from_storage(storage_context)

                # query_engine = index.as_query_engine(streaming=True, similarity_top_k=3, text_qa_template=QA_PROMPT)
                query_engine = index.as_query_engine(streaming=True)
                # query_engine = index.as_query_engine(streaming=True, text_qa_template=QA_PROMPT)
                # query_engine = index.as_query_engine(streaming=False, text_qa_template=QA_PROMPT)
                # query_engine = index.as_query_engine()
                # reply = query_engine.query(prompt)

                llama_index_reply = query_engine.query(prompt)
                # full_response += query_engine.query(prompt)
                print('local KB reply:', llama_index_reply)
                # query_engine.query(prompt).print_response_stream() #* 能在terminal中流式输出。
                # for resp in llama_index_reply.response_gen:
                #     print(resp)
                #     full_response += resp
                #     message_placeholder.markdown(full_response + "▌")
                message_placeholder.markdown(llama_index_reply)
                # st.session_state.messages.append(
                #     {"role": "assistant", "content": full_response})
                # st.session_state.messages = []
                # full_response += reply
                # full_response = reply
                # st.session_state.messages.append(
                #     {"role": "assistant", "content": full_response})

async def data_mode():
    print('数据分析模式启动！')
    clear_all() ## reset the conversation.
    # uploaded_file_path = './upload.csv'
    uploaded_file_path = f'./{username}_upload.csv'
    # uploaded_file_path = "/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/Coding/code_interpreter/test_upload.csv"
    print('file path:', uploaded_file_path)
    
    # st.write(f"passed file path in data_mode: {uploaded_file_path}")
    # tmp1 = pd.read_csv(uploaded_file_path)
    # st.markdown('成功启动数据模式，以下是加载的文件内容')
    # st.write(tmp1[:5])

    # Initialize chat history
    if "messages" not in st.session_state:
        st.session_state.messages = []

    # Display chat messages from history on app rerun
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

    # Display assistant response in chat message container
    # if prompt := st.chat_input("Say something"):
    prompt = st.chat_input("Say something")
    print('prompt now:', prompt)
    print('----------'*5)
    # if prompt:
    if prompt:
        st.session_state.messages.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.markdown(prompt)

        with st.chat_message("assistant"):
            async with CodeInterpreterSession() as session:
                # user_request = "对于文件中的'SepalLengthCm’数据给我一个'直方图'，提供图表，并给出分析结果"
                #! 可以用设定dpi=300来输出高质量的图表。（注：图的解析度dpi设定为300）
                environ_settings = """【背景要求】如果我没有告诉你任何定制化的要求，那么请你按照以下的默认要求来回答：
                -------------------------------------------------------------------------
                1. 你需要用提问的语言来回答（如：中文提问你就用中文来回答，英文提问你就用英文来回答）。
                2. 如果要求你输出图表，那么图的解析度dpi需要设定为600。图尽量使用seaborn库。seaborn库的参数设定：sns.set(rc={'axes.facecolor':'#FFF9ED','figure.facecolor':'#FFF9ED'}, palette='dark'。
                3. 图上所有的文字全部翻译成<英文English>来表示。
                4. 你回答的文字内容必须尽可能的详细且通俗易懂。
                5. 回答时尽可能地展示分析所对应的图表，并提供分析结果。 你需要按如下格式提供内容：

                   5.1 提供详细且专业的分析结果，提供足够的分析依据。
                   5.2 给出可能造成这一结果的可能原因有哪些？

                以上内容全部用【1/2/3这样的序列号格式】来表达。
                -------------------------------------------------------------------------
                """  # seaborn中的palette参数可以设定图表的颜色,选项包括：deep, muted, pastel, bright, dark, colorblind，Spectral。更多参数可以参考：https://seaborn.pydata.org/generated/seaborn.color_palette.html。

                # uploaded_file_path = upload_file()

                user_request = environ_settings + "\n\n" + \
                    "你需要完成以下任务：\n\n" + prompt + "\n\n" \
                    f"注：文件位置在 {uploaded_file_path}"
                user_request = str(prompt)
                print('user_request: \n', user_request)

                # 加载上传的文件，主要路径在上面代码中。
                files = [File.from_path(str(uploaded_file_path))]
                # files = [File.from_path("/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/Coding/code_interpreter/rawdata/short_csv.csv")]
                # st.write(pd.DataFrame(files))
                # print('session.__init__', session.__init__)
                # print('session', session.__init__)

                with st.status('Thinking...', expanded=True, state='running') as status:
                    # generate the response
                    # response = await session.generate_response(user_msg=user_request, files=files, detailed_error=True)
                    response = await session.generate_response(user_msg=user_request, files=files, detailed_error=True)

                    # output to the user
                    print("AI: ", response.content)
                    full_response = response.content
                    ### full_response = "this is full response"

                    # for file in response.files:
                    for i, file in enumerate(response.files):
                        # await file.asave(f"/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/Coding/code_interpreter/output{i}.png") ##working.
                        # st.image(file.get_image()) #! working.
                        # * 注意这里的设定，可以提高图片的精细程度。
                        st.image(file.get_image(), width=None,
                                 output_format='PNG')

                    # message_placeholder.markdown(full_response + "▌") ## orignal code.
                # message_placeholder.markdown(full_response) ## orignal code.
                st.write(full_response)
                status.update(label='complete', state='complete')

                # TODO: 确认是否要记录所有的full response。
                st.session_state.messages.append(
                    {"role": "assistant", "content": full_response})

                await session.astop()  # ! 确认需要关闭。
        # st.session_state.messages.append({"role": "assistant", "content": full_response})


### authentication with a local yaml file.
import yaml
from yaml.loader import SafeLoader
with open('./config.yaml') as file:
    config = yaml.load(file, Loader=SafeLoader)
authenticator = stauth.Authenticate(
    config['credentials'],
    config['cookie']['name'],
    config['cookie']['key'],
    config['cookie']['expiry_days'],
    config['preauthorized']
)


###'''authentication with a remove cloud-based database.'''
# authentication with a remove cloud-based database.
# 导入云端用户数据库。

# DETA_KEY = "c0zegv33efm_4MBTaoQAn76GzUfsZeKV64Uh9qMY3WZb"
# load_dotenv(".env")
# DETA_KEY = os.getenv("DETA_KEY")
# print(DETA_KEY)

# deta = Deta(DETA_KEY)

# # mybase is the name of the database in Deta. You can change it to any name you want.
# credentials = {"usernames":{}}
# users = []
# email = []
# passwords = []
# names = []

# for row in db.fetch_all_users():
#     users.append(row["username"])
#     email.append(row["email"])
#     names.append(row["key"])
#     passwords.append(row["password"])

# hashed_passwords = stauth.Hasher(passwords).generate()


## 需要严格的按照yaml文件的格式来定义如下几个字段。
# for un, name, pw in zip(users, names, hashed_passwords):
#     # user_dict = {"name":name,"password":pw}
#     user_dict = {"name": un, "password": pw}
#     # credentials["usernames"].update({un:user_dict})
#     credentials["usernames"].update({un: user_dict})

# ## sign-up模块，未完成。
# database_table = []
# # print(pd.DataFrame(credentials))
# for i in credentials['usernames'].keys():
#     # print("i:",i)
#     # print("name",credentials['usernames'][i]['name'])
#     # print("password",credentials['usernames'][i]['password'])
#     database_table.append([i,credentials['usernames'][i]['name'],credentials['usernames'][i]['password']])
# print("database_table:",database_table)

# authenticator = stauth.Authenticate(
#     credentials=credentials, cookie_name="joeshi_gpt", key='abcedefg', cookie_expiry_days=30)

# ## sign-up widget，未完成。
# try:
#     if authenticator.register_user('新用户注册', preauthorization=False):
#         # for list in database_table:
#         #     db.update_user(username=list[0], name=list[1], password=list[2])
#         db.update_user(username=list[-1][0], name=list[-1][1], password=list[-1][2])
#         # st.success('User registered successfully')
#         st.success('注册成功！')
# except Exception as e:
#     st.error(e)
''''''

# user, authentication_status, username = authenticator.login('用户登录', 'main')
user, authentication_status, username = authenticator.login('用户登录', 'sidebar')
# print("name", name, "username", username)

if authentication_status:
    with st.sidebar:
        st.markdown(
            """
            <style>
            [data-testid="stSidebar"][aria-expanded="true"]{
                min-width: 600px;
                max-width: 600px;
            }
            """,
            unsafe_allow_html=True,
        )
        st.header(f'**欢迎 **{username}** 来到人工智能的世界** ♠')
        st.write(f'_Welcome and Hope U Enjoy Staying Here！_')
        authenticator.logout('登出', 'sidebar')

        ## reset password widget
        # try:
        #     if authenticator.reset_password(st.session_state["username"], 'Reset password'):
        #         st.success('Password modified successfully')
        # except Exception as e:
        #     st.error(e)

        # with st.header(body="欢迎"):
        #     st.markdown("# 欢迎使用大语言模型商业智能中心")
        # with st.expander(label=("**重要的使用注意事项**"), expanded=True):
        with st.container():
            st.markdown("#### 快速上手指南")
            # with st.text(body="说明"):
            #     st.markdown("* 重启一轮新对话时，只需要刷新页面（按Ctrl/Command + R）即可。")
            with st.text(body="说明"):
                st.markdown("* 为了保护数据与隐私，所有对话均不会被保存，刷新页面立即删除。敬请放心。")
            with st.text(body="说明"):
                st.markdown("* “GPT-4”回答质量极佳，但速度缓慢、且不支持长文。建议适当使用。")
            with st.text(body="说明"):
                st.markdown("* “联网模式”和“知识库模式”均基于检索功能，仅限一轮对话，不会保持之前的会话记录。")
            with st.text(body="说明"):
                st.markdown(
                    "* “数据模式”暂时只支持1000个单元格以内的数据分析，单元格中的内容不支持中文数据（表头也尽量不使用中文）。一般运行时间在1-5分钟左右，期间需要保持网络畅通。")
            with st.text(body="说明"):
                st.markdown("* “数据模式”推荐上传csv格式的文件，部分Excel文件容易出现数据不兼容的情况。")

        st.markdown("#### 参考资料")
        with st.expander(label="**核心模式的专用提示词Prompt示例**", expanded=False):
            # with st.subheader(body="提示词Prompt"):
            st.code(
                body="继续用中文写一篇关于 [文章主题] 的文章，以下列句子开头：[文章开头］。", language='plaintext')
            st.code(body="将以下文字概括为 100 个字，使其易于阅读和理解。避免使用复杂的句子结构或技术术语。",
                    language='plaintext')
            st.code(body="给我出一个迪奥2023春季发布会活动策划。", language='plaintext')
            st.code(body="帮我按照正式会议结构写一个会邀：主题是xx手机游戏立项会议。", language='plaintext')
            st.code(body="帮我写一个车内健康监测全场景落地的项目计划，用表格。", language='plaintext')
            st.code(
                body="同时掷两枚质地均匀的骰子，则两枚骰子向上的点数之和为 7 的概率是多少。", language='plaintext')
            st.code(body="写一篇产品经理的演讲稿，注意使用以下词汇: 赋能,抓手,中台,闭环,落地,漏斗,沉淀,给到,同步,对齐,对标,迭代,拉通,打通,升级,交付,聚焦,倒逼,复盘,梳理,方案,联动,透传,咬合,洞察,渗透,兜底,解耦,耦合,复用,拆解。", language='plaintext')

        with st.expander(label="**数据模式的专用提示词Prompt示例**", expanded=False):
            # with st.subheader(body="提示词Prompt"):
            st.code(body="分析此数据集并绘制一些'有趣的图表'。", language='python')
            st.code(
                body="对于这个文件中的数据，你需要要找出[X,Y]数据之间的寻找'相关性'。", language='python')
            st.code(body="对于这个文件中的[xxx]数据给我一个'整体的分析'。", language='python')
            st.code(body="对于[xxx]数据给我一个'直方图'，提供图表，并给出分析结果。", language='python')
            st.code(body="对于[xxx]数据给我一个'小提琴图'，并给出分析结果。", language='python')
            st.code(
                body="对于[X,Y,Z]数据在一个'分布散点图 (stripplot)'，所有的数据在一张图上展现, 并给出分析结果。", language='python')
            st.code(body="对于[X，Y]数据，进行'T检验'，你需要展示图表，并给出分析结果。",
                    language='python')
            st.code(body="对于[X，Y]数据给我一个3个类别的'聚类分析'，并给出分析结果。",
                    language='python')

    col1, col2 = st.columns(spec=[1, 2])
    radio_2 = col2.radio(label='模式选择', options=[
        '核心模式', '联网模式', '知识库模式', '数据模式'], horizontal=True, label_visibility='visible')
    radio_1 = col1.radio(label='ChatGPT版本', options=[
        'GPT-3.5', 'GPT-4.0'], horizontal=True, label_visibility='visible')

elif authentication_status == False:
    st.error('⛔ 用户名或密码错误！')
elif authentication_status == None:
    st.warning('⬅ 请先登录！')

### 上传文件的模块
def upload_file(uploaded_file):
    if uploaded_file is not None:
        filename = uploaded_file.name
        # st.write(filename)  # print out the whole file name to validate. not to show in the final version. 
        try:
            if '.pdf' in filename:
                # pdf_file = PyPDF2.PdfReader(uploaded_file)
                PyPDF2.PdfReader(uploaded_file)
                # st.write(pdf_file.pages[0].extract_text())
                # with st.status('正在为您解析新知识库...', expanded=False, state='running') as status:
                spinner = st.spinner('正在为您解析新知识库...请耐心等待')
                # with st.spinner('正在为您解析新知识库...请耐心等待'):
                with spinner:
                    import localKB_construct
                    # st.write(upload_file)
                    localKB_construct.process_file(uploaded_file, username)
                    ## 在屏幕上展示当前知识库的信息，包括名字和加载日期。
                    save_database_info.save_database_info(f'./{username}/database_name.csv', filename, str(datetime.now(pytz.timezone('Asia/Shanghai')).strftime("%Y-%m-%d %H:%M")))
                    st.markdown('新知识库解析成功，请务必刷新页面，然后开启对话 🔃')
                    # spinner = st.empty()

            else:
                if '.csv' in filename:
                    csv_file = pd.read_csv(uploaded_file)
                    csv_file.to_csv(f'./{username}_upload.csv', encoding='utf-8', index=False)
                    st.write(csv_file[:3])  # 这里只是显示文件，后面需要定位文件所在的绝对路径。
                else:
                    xls_file = pd.read_excel(uploaded_file)
                    xls_file.to_csv(f'./{username}_upload.csv', index=False)
                    st.write(xls_file[:3])

                uploaded_file_name = "File_provided"
                temp_dir = tempfile.TemporaryDirectory()
                # ! working.
                # uploaded_file_path = pathlib.Path(temp_dir.name) / uploaded_file_name
                # with open('./upload.csv', 'wb') as output_temporary_file:
                with open(f'./{username}_upload.csv', 'wb') as output_temporary_file:
                    # print(f'./{name}_upload.csv')
                    # ! 必须用这种格式读入内容，然后才可以写入temporary文件夹中。
                    # output_temporary_file.write(uploaded_file.getvalue())
                    output_temporary_file.write(uploaded_file.getvalue())
                    # st.write(uploaded_file_path)  #* 可以查看文件是否真实存在，然后是否可以
                # st.write('Now file saved successfully.')
        except Exception as e:
            st.write(e)

        ## 以下代码是为了解决上传文件后，文件路径和文件名不对的问题。
        # uploaded_file_name = "File_provided"
        # temp_dir = tempfile.TemporaryDirectory()
        # # ! working.
        # uploaded_file_path = pathlib.Path(temp_dir.name) / uploaded_file_name
        # # with open('./upload.csv', 'wb') as output_temporary_file:
        # with open(f'./{name}_upload.csv', 'wb') as output_temporary_file:
        #     # print(f'./{name}_upload.csv')
        #     # ! 必须用这种格式读入内容，然后才可以写入temporary文件夹中。
        #     # output_temporary_file.write(uploaded_file.getvalue())
        #     output_temporary_file.write(uploaded_file.getvalue())
        #     # st.write(uploaded_file_path)  # * 可以查看文件是否真实存在，然后是否可以
        # # st.write('Now file saved successfully.')

        return None


if __name__ == "__main__":
    import asyncio
    try:
        if radio_2 == "核心模式":
            print(f'radio 选择了 {radio_2}')
            # * 也可以用命令执行这个python文件。’streamlit run frontend/app.py‘
            asyncio.run(text_mode())

        if radio_2 == "联网模式":
            print(f'radio 选择了 {radio_2}')
            asyncio.run(text_mode())

        if radio_2 == "知识库模式":
            print(f'radio 选择了 {radio_2}')

            path = f'./{username}/vector_store.json'
            if os.path.exists(path):
                database_info = pd.read_csv(f'./{username}/database_name.csv')
                current_database_name = database_info.iloc[-1][0]
                current_database_date = database_info.iloc[-1][1]
                database_claim = f"当前知识库为：{current_database_name}，创建于{current_database_date}。可以开始提问！"
                st.markdown(database_claim)
                # st.markdown("注意：系统中已经存在一个知识库，您现在可以开始提问！")

            uploaded_file = st.file_uploader(
                "选择上传一个新知识库", type=(["pdf"]))
            # 默认状态下没有上传文件，None，会报错。需要判断。
            if uploaded_file is not None:
                # uploaded_file_path = upload_file(uploaded_file)
                upload_file(uploaded_file)

            localKB_mode(username)
            # asyncio.run(localKB_mode())

        if radio_2 == "数据模式":
            uploaded_file = st.file_uploader(
                "选择一个文件", type=(["csv", "xlsx", "xls"]))
            # 默认状态下没有上传文件，None，会报错。需要判断。
            if uploaded_file is not None:
                uploaded_file_path = upload_file(uploaded_file)
                asyncio.run(data_mode())
    except:
        # st.markdown('**请先登录！**')
        pass