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. app.py:
    1. openai key, bing key.
    1. proxy
    1. 

'''
# TODO：1. remove the table display in the second round. 2. 

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 xlrd
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

os.environ["OPENAI_API_KEY"] = os.environ['user_token']
openai.api_key = os.environ['user_token']

# #* 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("个人大语言模型商业智能中心")
st.subheader("Artificial Intelligence Backend Center for Individuals")

# tab1, tab2 = st.tabs(["传统模式", "数据模型"])

# with tab1:
col1, col2 = st.columns(spec=[1, 2])
radio_1 = col1.radio(label='ChatGPT版本', options=[
    'GPT-3.5', 'GPT-4.0'], horizontal=True, label_visibility='visible')
radio_2 = col2.radio(label='模式选择', options=[
    '核心模式', '联网模式', '数据模式'], horizontal=True, label_visibility='visible')

# 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.
        try:
            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])
        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:
            # ! 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())

        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 main():
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})

            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})


async def data_mode():
    # print('数据分析模式启动！')
    uploaded_file_path = './upload.csv'
    # st.write(f"passed file path in data_mode: {uploaded_file_path}")
    # tmp1 = pd.read_csv('./upload.csv')
    # 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. 在图上显示中文时，需要按如下要求操作：
                    myfont=FontProperties(fname='./SimHei.ttf')
                    plt.rcParams['font.sans-serif'] = ['myfont']
                    plt.rcParams['font.family']='sans-serif'
                    sns.set_style({'font.sans-serif':['myfont']})

                -------------------------------------------------------------------------
                """  # 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}"
                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))]

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

                    # 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')
                # 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})

if __name__ == "__main__":
    import asyncio
    if radio_2 != "数据模式":
        # * 也可以用命令执行这个python文件。’streamlit run frontend/app.py‘
        asyncio.run(text_mode())
        # upload_file()
    elif 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())