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'''
参考： https://github.com/shroominic/codeinterpreter-api
1. 可以存在本地，然后再调出来。 working.
1. 可以直接在内存中读出图片。
'''
# TODO：如何在内存中读取文件。
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
from io import StringIO
import csv
import tempfile
from tempfile import NamedTemporaryFile
import pathlib
from pathlib import Path
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"
# }
# st.title("ChatGPT-like clone")
st.title("ChatGPT")
st.header("GPT-4, Business Data Analytics")
uploaded_file = st.file_uploader("Choose a file", type=(["csv","txt","xlsx","xls"]))
# uploaded_file = st.file_uploader("选择一个文件", type=(["csv","txt","xlsx","xls"]))
# st.write(uploaded_file)
if uploaded_file is not None:
# csv_file = csv.reader(uploaded_file)
csv_file = pd.read_csv(uploaded_file)
st.write(csv_file[:5]) ## 这里只是显示文件，后面需要定位文件所在的绝对路径。
uploaded_file_name = "File_provided"
temp_dir = tempfile.TemporaryDirectory()
uploaded_file_path = pathlib.Path(temp_dir.name) / uploaded_file_name #! working.
with open(uploaded_file_path, 'wb') as output_temporary_file:
# output_temporary_file.write(uploaded_file.read())
output_temporary_file.write(uploaded_file.getvalue()) #! 必须用这种格式读入内容，然后才可以写入temporary文件夹中。
# st.write(uploaded_file_path) #* 可以查看文件是否真实存在，然后是否可以
### how to read data inside streamlit.
# # files = pd.read_csv(uploaded_file)
# bytes_data = uploaded_file.getvalue()
# # st.write(bytes_data)
# # To convert to a string based IO:
# stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
# # st.write(stringio)
# # To read file as string:
# string_data = stringio.read()
# # st.write(string_data)
# # Can be used wherever a "file-like" object is accepted:
# # dataframe = pd.read_csv(uploaded_file)
# files = pd.read_csv(uploaded_file, encoding='utf-8')
# openai.api_key = st.secrets["OPENAI_API_KEY"]
async def main():
if "openai_model" not in st.session_state:
# st.session_state["openai_model"] = "gpt-3.5-turbo"
st.session_state["openai_model"] = "gpt-4" ##NOTE: data analysis module must use GPT-4.
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"])
if prompt := st.chat_input("What is up?"):
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 = ""
###原始示例 https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps
# 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 + "▌")
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 = "【<默认要求> 如果我没有告诉你任何定制化的要求，那么请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（即：如果我用中文提问，你就用中文来回答；我如果用英文提问吗，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='deep')。】" ## 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。
environ_settings = """<默认要求> 如果我没有告诉你任何定制化的要求，那么请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（即：如果我用中文提问，你就用中文来回答；我如果用英文提问吗，你就用英文来回答）。2. 如果要求你输出图表，那么图的大小设定为plt.figure(figsize=(10, 8))。图尽量使用seaborn库。seaborn库的参数设定：sns.set(rc={'axes.facecolor':'#FFF9ED','figure.facecolor':'#FFF9ED'}, palette='deep')。图表上的文字语言使用文件中的原始文字语言（如中文或者英文）""" ## 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
# 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))]
### 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.
# file.show_image()
# st.image(file.get_image(), width=500, output_format='png')
st.image(file.get_image(), width=None, output_format='PNG') #* 注意这里的设定，可以提高图片的精细程度。
# message_placeholder.markdown(full_response + "▌") ## orignal code.
# message_placeholder.markdown(full_response) ## orignal code.
st.write(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
# * 也可以用命令执行这个python文件。’streamlit run frontend/app.py‘
asyncio.run(main())
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