''' 1. 基于ChatGPT的多场景应用： 1. 核心模式 1. 联网模式 1. 知识库模式 1. 数据分析模式 1. 智能体模式 1. RAG： 1. 核心文件包括： 1. langchain_KB.py包含了形成vector database的函数，和产生total prompt的函数。 1. rag_source.py包含了从vector database中提取信息来源的函数，包括文档名称和页码。 ''' # TODO：1. 更新huggingface上code01的版本，包括：知识库和数据分析模块。 2. 将知识库模块更新为：multi-query + source。 3. 将数据分析模块重写。 import numpy as np import pandas as pd from dotenv import load_dotenv # pip3 install python-dotenv import requests from codeinterpreterapi import CodeInterpreterSession, File import streamlit as st 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 import streamlit_authenticator as stauth # from langchain.chat_models import ChatOpenAI from langchain.llms import openai import sys import time import PyPDF2 ## read the local_KB PDF file. # import localKB_construct # from streamlit_option_menu import option_menu # import st_reset_conversation from st_reset_conversation import reset_all, reset_message import save_database_info import pytz from datetime import datetime from dotenv import load_dotenv from openai import OpenAI import st_msautogen import rag_source import add_fonts import asyncio import warnings warnings.filterwarnings("ignore") #make it look nice from the start # st.set_page_config(layout='wide',initial_sidebar_state='collapsed',) ### 设置openai的API key load_dotenv() openai.api_key = os.environ['user_token'] os.environ["OPENAI_API_KEY"] = os.environ['user_token'] bing_search_api_key = os.environ['bing_api_key'] # # #* 如果数据分析模块在本地调试时碰到接口问题，可以启用如下设置。还可能是一个bash命令的问题，见ChatGPT讲课要点.txt. openai.proxy = { "http": "http://127.0.0.1:7890", "https": "http://127.0.0.1:7890" } ## layout settings. st.title("专业版大语言模型智能中心") st.subheader("Artificial Intelligence Backend Center for Professionals") st.caption("_声明：本网站仅提供技术测试与评估服务。内容由人工智能生成，仅供参考。如果您本人使用或对外传播本服务生成的输出，您应当主动核查输出内容的真实性、准确性，避免传播虚假信息。_") # st.divider() # ## clear conversion. # def reset_all(): # # st.session_state.conversation = None # st.session_state.chat_history = None # st.session_state.messages = [] # message_placeholder = st.empty() # return None # navigation menu using Hydralit. 并没有解决menu跳转的问题。 # option_data = [ # {'icon': "house", 'label':"核心模式"}, # {'icon':"cloud-upload",'label':"信息检索模式"}, # {'icon': "gear", 'label':"数据分析模式"}, # {'icon': "list-task", 'label':"智能体模式"}, # ] # navi_menu = op = hc.option_bar(option_definition=option_data,title=None,key='PrimaryOption', horizontal_orientation=True) # navi_menu = hc.nav_bar(menu_definition=option_data, key='navi_menu', use_animation=True, option_menu=False, sticky_mode='pinned', sticky_nav=False, hide_streamlit_markers=False) ### 使用streamlit_option_menu格式的类似横幅选项。但是会出现第一次无法运行，需要手动清零或者做一个动作，才可以。 # navi_menu = option_menu( # menu_title=None, # options=['核心模式', '信息检索模式', '数据分析模式', '智能体模式'], # # options=['GPT-3.5', 'GPT-4.0','清华GLM2-6B','百川Baichuan-13B', '阿里通义千问14B'], # icons=['house', 'cloud-upload','gear','list-task'], # menu_icon='cast', # default_index=0, # orientation='horizontal', # # manual_select=0, # # styles={ # # "container": {"padding": "0!important", "background-color": "#fafafa"}, # # "icon": {"color": "orange", "font-size": "25px"}, # # "nav-link": {"font-size": "25px", "text-align": "left", "margin":"0px", "--hover-color": "#eee"}, # # "nav-link-selected": {"background-color": "green"}, # # } # ) ### 常规streamlit选择 navi_menu = st.radio(label='选择一个大语言模型工作模式', options=['核心模式', '联网模式', '知识库模式','数据分析模式', '智能体模式'],index=0,horizontal=True) # navi_menu = st.selectbox('选择一个大语言模型工作模式', ['核心模式', '信息检索模式', '数据分析模式', '智能体模式'],index=0) ### 原始agent001模式。 reset_button_key = "reset_button" reset_button = st.button(label=("清除所有记录，并开启一轮新对话 ▶"), key=reset_button_key, use_container_width=True, type="primary") def clear_all(): st.session_state.conversation = None st.session_state.chat_history = None st.session_state.messages = [] message_placeholder = st.empty() return None ## 清除所有对话记录, reset all conversation. if reset_button: reset_all() ### 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: ### original code here. if '.pdf' in uploaded_file.name: pdf_filename = uploaded_file.name ### original code here. filename = uploaded_file.name # print('PDF file:', pdf_filename) # 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 spinner: ### 一下是llama_index方法，但是升级后，可能会报错。 # 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 🔃') ### 以下是langchain方案。 import langchain_KB import save_database_info uploaded_file_name = "File_provided" temp_dir = tempfile.TemporaryDirectory() # ! working. uploaded_file_path = pathlib.Path(temp_dir.name) / uploaded_file_name with open(pdf_filename, 'wb') as output_temporary_file: # with open(f'./{username}_upload.pdf', 'wb') as output_temporary_file: ### original code here. 可能会造成在引用信息来源时文件名不对的问题。 # ! 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()) langchain_KB.langchain_localKB_construct(output_temporary_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', pdf_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 🔃') return pdf_filename else: # if '.csv' in filename: ### original code here. if '.csv' in uploaded_file.name: print('start the csv file processing...') csv_filename = uploaded_file.name filename = uploaded_file.name csv_file = pd.read_csv(uploaded_file) csv_file.to_csv(f'./{username}/{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]) print('end the csv file processing...') # 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()) # st.write(uploaded_file_path) #* 可以查看文件是否真实存在，然后是否可以 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 pdf_filename, csv_filename return filename ### 互联网搜索模块 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 # async def text_mode(): def text_mode(): reset_message() ## reset the message and placeholder. print('text mode starts!') # 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 == 'ChatGPT-3.5': # print('----------'*5) print('radio_1: GPT-3.5 starts!') st.session_state["openai_model"] = "gpt-3.5-turbo-16k" elif radio_1 == 'ChatGPT-4': print('radio_1: GPT-4.0 starts!') st.session_state["openai_model"] = "gpt-4-1106-preview" else: st.markdown("**当前大模型无效，请在左侧工具栏中选择一个有效的模型。您现在访问的站点仅提供ChatGPT中的GPT-3.5/4。**") print(st.session_state["openai_model"]) # 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("说点什么吧"): prompt = st.chat_input("说点什么吧...") print('prompt now:', prompt) print('----------'*5) 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 navi_menu == '联网模式': # if (navi_menu=='信息检索模式') and (radio_2=='互联网'): ### original code here. print('联网模式下的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}) ## old version of openai API. # 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 = [] ## new version of openai API. openai_client = OpenAI() for response in openai_client.chat.completions.create( model=st.session_state["openai_model"], messages=[ {"role": m["role"], "content": m["content"]} for m in st.session_state.messages ], stream=True, ): if str(response.choices[0].delta.content) != 'None': full_response += str(response.choices[0].delta.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 = [] # elif radio_2 != '互联网': else: print('ChatGPT only starts!!!') ## 这里需要确认是直接从import openai中获得的函数，而不是langchain中调用openai，否则随着langchain的更新，会出现问题。 # for response in openai.ChatCompletion.create( # model=st.session_state["openai_model"], # max_tokens=max_tokens, # temperature=temperature, # top_p=top_p, # presence_penalty=presence_penalty, # frequency_penalty=frequency_penalty, # ## 多轮会话，需要记住历史记录。 # 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}], ## 这是单轮会话。 # stream=True, # ): openai_client = OpenAI() for response in openai_client.chat.completions.create( model=st.session_state["openai_model"], max_tokens=max_tokens, temperature=temperature, top_p=top_p, presence_penalty=presence_penalty, frequency_penalty=frequency_penalty, ## 多轮会话，需要记住历史记录。 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}], ## 这是单轮会话。 stream=True, ): # print('full response now:', full_response) # print('response now:', response) ## old version output format. # full_response += response.choices[0].delta.get( # "content", "") ## new version output format. if str(response.choices[0].delta.content) != 'None': ## 注意这里是内容，而不是response，否则一个chunk的回复。 # print('response now:',response) full_response += str(response.choices[0].delta.content) message_placeholder.markdown(full_response + "▌") message_placeholder.markdown(full_response) st.session_state.messages.append( {"role": "assistant", "content": full_response}) ## load the local_KB PDF file. # # def local_KB(uploaded_file): # print('now starts the local KB version of ChatGPT') # max_input_size = 4096 # # set number of output tokens # # num_outputs = 3000 #* working # num_outputs = 1000 # # set maximum chunk overlap # max_chunk_overlap = -1000 #* working # # set chunk size limit # # chunk_size_limit = 600 # chunk_size_limit = 6000 #* working # history = [] # if input: # # ! 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) # llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.8, model_name="gpt-3.5-turbo", max_tokens=4096,streaming=True)) # prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit) # service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper) # # # index = load_index_from_storage(storage_context) # storage_context = StorageContext.from_defaults(persist_dir="./") # index = load_index_from_storage(storage_context,service_context=service_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) # reply = query_engine.query(input) # async def localKB_mode(username): def localKB_mode(username): # reset_all() ## reset the conversation. reset_message() ## only reset the message and placeholder. 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: if prompt := st.chat_input("说点什么吧"): 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: # try: with st.chat_message("assistant"): message_placeholder = st.empty() full_response = "" ### llama_index框架的RAG代码，最近更新版本后不成功，会报错。 ### outdated version. # 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) ## sample code for reference. # docstore = 'storage/docstore.json' # index_store = 'storage/index_store.json' # vector_store = 'storage/vector_store.json' # print('storage_context:', storage_context) ##NOTE: 这里需要重新装载一下storage_context。 # storage_context = StorageContext.from_defaults(persist_dir=f"./{username}/") # print('--'*30) # print('storage_context:',storage_context) # print('type of storage_context.index_store:', type(storage_context.index_store)) # print('--'*30) # # storage_context = {storage_context} # index = load_index_from_storage(storage_context) # print('--'*30) # print('index now:', index) # context_str = index # ##TODO 重新构建Prompt，加入QA_Template. # 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 and the default language is Chinese:\n" # "{query_str}\n") # # QA_PROMPT = QuestionAnswerPrompt(QA_PROMPT_TMPL) ## outdated version. # ##TODO: newer version but may run into llama_index import problem. # # qa_template = PromptTemplate(QA_PROMPT_TMPL) # # prompt = qa_template.format(context_str=context_str, query_str=prompt) # # prompt = qa_template.format(context_str=context_str, query_str=QA_PROMPT) # # query_engine = index.as_query_engine(streaming=True, similarity_top_k=3, text_qa_template=QA_PROMPT) # query_engine = index.as_query_engine(streaming=False) # print('111') ## older version. # query_engine = index.as_query_engine(streaming=True, 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(str(llama_index_reply)) # print('333') # # 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}) ### 用langchain的FAISS来做RAG import langchain_KB ### 默认选择使用multi-query的方法进行查询。 ##NOTE: 目前这个版本是把所有的multiquery当成一个问题提交给大模型。后续可以考虑将每一个问题分别送入大模型，然后得到的多个答案，然后在一并汇总。 if True: import multiQuery_prompt prompt = multiQuery_prompt.generate_sim_query(orignal_question=prompt) # print('multiQuery prompts now:', prompt) prompt = str(prompt) ## 需要强制转换成str格式。 total_prompt, docs = langchain_KB.langchain_RAG(prompt, username) print('total_prompt now:', total_prompt) st.session_state.messages.append({"role": "user", "content": total_prompt}) ## new version of openai API. openai_client = OpenAI() for response in openai_client.chat.completions.create( model=st.session_state["openai_model"], messages=[ {"role": m["role"], "content": m["content"]} for m in st.session_state.messages ], stream=True, ): if str(response.choices[0].delta.content) != 'None': full_response += str(response.choices[0].delta.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 = [] try: ### 显示RAG的source，即查询得到的信息来源出处。 print('docs now:', docs) source = rag_source.rag_source(docs) ## get the k reference source of the RAG answer, in a designed format. # print('返回的source内容：', source) st.divider() st.caption(source) except Exception as e: print('Exception:', e) pass ##TODO 确认是否需要？ st.session_state.messages = [] # except Exception as e: # print('Exception:', e) # pass # async def data_mode(): def data_mode(): clear_all() ## reset the conversation. print('数据分析模式启动！') # uploaded_file_path = './upload.csv' uploaded_file_path = f'./{username}/{username}_upload.csv' # uploaded_file_path = f'./{username}_upload.csv' ### original code here. print('file path:', uploaded_file_path) # 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: try: st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.markdown(prompt) with st.status('思考中...需要1至10分钟左右，请耐心等待 🏃', expanded=True, state='running') as status: with st.chat_message("assistant"): #### Using the open-source CodeInterpreter solution below. May not work after version update, need to upgrade the code accoridngly later on. # from langchain.chat_models import ChatOpenAI # llm_model = ChatOpenAI(model_name="gpt-4-1106-preview") # # llm_model = ChatOpenAI(model_name="gpt-4") # # async with CodeInterpreterSession(llm=llm_model) as session: # import interpreter # interpreter.llm.model = "gpt-3.5-turbo" # with CodeInterpreterSession(llm=llm_model) as session: # # with CodeInterpreterSession(llm=llm_model) as session: # print('222') # # 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. 你需要用我提问的语言来回答，且默认情况下用中文来回答。 # 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) ### only prompt without environment 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('思考中...', 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) # response = session.generate_response(user_msg=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') # # 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。 # session.astop() # ! 确认需要关闭。 # # st.session_state.messages.append({"role": "assistant", "content": full_response}) #### #### Using the OpenAI's assistant API， wrap into the st_openai_assistant.py. import st_openai_assistant ### NOTE:在st_openai_assistant.py中可以设置system_prompt. # sys_prompt = """ 1. 你是一位智能AI助手，你连接着一台电脑，但请注意不能联网。在使用Python解决任务时，你可以运行代码并得到结果，如果运行结果有错误，你需要尽可能对代码进行改进。你可以处理用户上传到电脑上的文件。 # 2. 你使用matplotlib.pylab（plt）或者seaborn（sns）画图时，需要添加中文字库，代码如下： # matplotlib.rcParams['font.sans-serif'] = ['Microsoft YaHei UI'] # sns.set(rc={'axes.facecolor':'#FFF9ED','figure.facecolor':'#FFF9ED'}, palette='dark', font='Microsoft YaHei UI') # 3. 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： # 3.1 你回答的文字内容必须尽可能的详细且通俗易懂。 # 3.2 回答时尽可能地展示分析所对应的图表，并提供分析结果。图表上的文字采用中文。你需要按如下格式提供内容： # * 提供详细且专业的分析结果，提供足够的分析依据。 # * 给出可能造成这一结果的可能原因有哪些？ # 以上内容你用序列号1、2、3这样的格式表达。 # """ # prompt = [ # {"role": "system", "content": sys_prompt}, # {"role": "user", "content": prompt}, # ] messages, text_response, img_response, image_files, final_answer = st_openai_assistant.openai_assistant(prompt=prompt, filepath=uploaded_file_path, username=username) # st.image(img_response) ## show one single image. # st.markdown(text_response) ## show one single text response. try: from PIL import Image print("返回到Agent001程序中的图表个数：", len(image_files)) # plt.imshow(img) # plt.show() for img in image_files: img = Image.open(img) ## image object. st.image(img, output_format='PNG') # st.image(f"./{username}/{img_response[i]}", output_format='PNG') # st.image(f'/Users/yunshi/Downloads/360Data/Data Center/Working-On Task/演讲与培训/2023ChatGPT/Coding/code_interpreter/joeshi/output{i}.png', output_format='PNG') except: pass try: st.markdown(final_answer) ## all messages are appended together, need to print out one by one? except: pass except Exception as e: print(e) pass ### 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. # 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":{}} # # credentials = {"users": {}} # # db = db() # 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) user, authentication_status, username = authenticator.login('用户登录', 'main') # print("name", name, "username", username) # ## 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) if authentication_status: with st.sidebar: st.markdown( """