'''
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']})      

'''
# TODO：

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
# from io import StringIO
# import csv
import tempfile
from tempfile import NamedTemporaryFile
import pathlib
from pathlib import Path
from matplotlib.font_manager import FontProperties
import seaborn as sns


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

uploaded_file = st.file_uploader(
    "选择一个文件", type=(["csv", "xlsx", "xls"]))

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)
            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)
            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(uploaded_file_path, 'wb') as output_temporary_file:
        # output_temporary_file.write(uploaded_file.read())
        # ! 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())
        st.write(uploaded_file_path)  # * 可以查看文件是否真实存在，然后是否可以

import requests
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 main():
    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')

    ## 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('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("Ask something?"):
        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 == '数据分析模式':
                print('数据分析模式启动！')

                # clear cache to avoid any potential history problems. 
                st.cache_resource.clear()

                with st.chat_message("assistant"):
                    # message_placeholder = st.empty()
                    # full_response = ""
                    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300。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. 如果需要显示中文，那么设置如下：
                            3.1 首先，你需要安装中文字体：
                                myfont=FontProperties(fname='/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/Coding/code_interpreter/rawdata/SimHei.ttf')
                            3.2 然后，你需要设定在matplotlib（plt）和seaborn（sns）中设定：
                                    sns.set_style({'font.sans-serif':['Arial','SimHei']})
                                    plt.rcParams['font.sans-serif'] = ['SimHei'] 
                                    plt.rcParams['font.family']='sans-serif'
                                    plt.title(fontsize = 18)
                        -------------------------------------------------------------------------
                        """  # 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。

                        user_request = environ_settings + "\n\n" + \
                            "你需要完成以下任务：\n\n" + prompt + \
                            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('processing...', 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})

            elif 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})

            elif radio_2 == '核心模式':
                print('GPT only starts!!!')
                print('st.session_state now:', st.session_state)
                # st.session_state.messages.append({"role": "system", "content": 'You are a helpful AI assistant: ChatGPT.'})
                
                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,
                ):
                    # if len(response)>0:
                    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 __name__ == "__main__":
    import asyncio
    # * 也可以用命令执行这个python文件。’streamlit run frontend/app.py‘
    asyncio.run(main())