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app.py

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+"""
+1. 支持所有多个文件类型的大模型批处理功能，可以清洗文本数据，提取关键信息，并生成标准格式的输出文件。
+1. 支持多种文件类型的上传，包括pdf, docx, xlsx, csv, json等。
+1. Streamlit不支持上传一个文件夹，可以用ctrl+A上传所有文件。会自动显示上传的文件名字。
+
+错误信息：
+1. 如果上传的单个文件中的内容超过大模型的上下文，可能会报错。需要确认文件内容，或者需要更换长文大模型。
+
+"""
+
+##TODO： 1. 每次调用新的csv文件。
+
+# -*- coding: utf-8 -*-
+import streamlit as st
+import openai
+import os
+import numpy as np
+import pandas as pd
+import json
+import csv
+import tempfile
+from tempfile import NamedTemporaryFile
+import pathlib
+from pathlib import Path
+import re
+from re import sub
+from itertools import product
+import time
+from time import sleep
+from datetime import datetime
+import streamlit_authenticator as stauth
+# from langchain_community.vectorstores import FAISS
+# from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+# from langchain_core.output_parsers import StrOutputParser
+# from langchain_core.runnables import RunnablePassthrough
+from langchain.llms.base import LLM
+from langchain.llms.utils import enforce_stop_tokens
+from typing import Dict, List, Optional, Tuple, Union
+import requests
+import streamlit as st
+# import rag_reponse_002
+import dashscope
+from dotenv import load_dotenv
+from datetime import datetime
+import pytz
+from pytz import timezone
+from datetime import date
+import qwen_response
+from save_info import save_csv_info
+import streamlit_ext as ste ##TODO： 为了点击download button后保持页面。
+import create_newfile
+
+## 获得程序运行的当前时间
+def get_current_time():
+    beijing_tz = timezone('Asia/Shanghai')
+    beijing_time = datetime.now(beijing_tz)
+    current_time = beijing_time.strftime('%H:%M:%S')
+    return current_time
+
+load_dotenv()
+### 设置openai的API key
+os.environ["OPENAI_API_KEY"] = os.environ['user_token']
+openai.api_key = os.environ['user_token']
+bing_search_api_key = os.environ['bing_api_key']
+dashscope.api_key = os.environ['dashscope_api_key']
+
+### Streamlit页面设定。
+st.set_page_config(layout="wide",  page_icon="🌀", page_title="人工智能大模型的智能信息探索平台")
+st.title("人工智能大模型文本清洗与挖掘平台（可内网部署）")
+# st.subheader("Large Language Model-based Knowledge Base QA System")
+# st.warning("_声明：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.info("_声明：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.write("_声明：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()
+
+
+
+### 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']
+)
+
+user, authentication_status, username = authenticator.login('main')
+# user, authentication_status, username = authenticator.login('用户登录', 'main')
+
+## 清楚所有对话记录。
+def clear_all():
+    st.session_state.conversation = None
+    st.session_state.chat_history = None
+    st.session_state.messages = []
+    message_placeholder = st.empty()
+    
+    ## 只用这一个就可以了。
+    # st.rerun()
+    
+    return None
+
+if authentication_status:
+    with st.sidebar:
+        st.markdown(
+            """
+            <style>
+            [data-testid="stSidebar"][aria-expanded="true"]{
+                min-width: 400px;
+                max-width: 400px;
+            }
+            """,
+            unsafe_allow_html=True,
+        )
+        ### siderbar的题目。
+        ### siderbar的题目。
+        # st.header(f'**大语言模型专家系统工作设定区**')
+        st.header(f'**欢迎 **{username}** 使用本系统** ')
+        st.write(f'_Large Language Model Expert System Environment_')
+        # st.write(f'_Welcome and Hope U Enjoy Staying Here_')
+        authenticator.logout('登出', 'sidebar')
+
+        ### 提交请求
+        submit_btn = st.sidebar.button("开始执行操作", use_container_width=True, type='primary')
+
+        ### 清除记录，重启一轮新对话。
+        st.sidebar.button("清除记录，重启一个新任务", on_click=clear_all, use_container_width=True, type='secondary')
+
+        ### 定义任务目标
+        task_goal = st.selectbox(label="**任务目标**", options=['文本内容清洗', '人才资料甄选', '销售机会挖掘', '日志文件分析', '舆情内容分析', '潜在投诉挖掘', '合规稽核纠偏'], index=0)
+        
+        match task_goal:
+            case '文本内容清洗':
+                prompt_sys = """你是一个文本内容清洗专家。你需要完成我给你的任务。"""
+            case '人才资料甄选':
+                prompt_sys = """你是一个人才资料甄选专家。你需要完成我给你的任务。"""
+            case '销售机会挖掘':
+                prompt_sys = """你是一个销售机会挖掘专家。你需要完成我给你的任务。"""
+            case '日志文件分析':
+                prompt_sys = """你是一个日志文件分析专家。你需要完成我给你的任务。"""
+            case '舆情内容分析':
+                prompt_sys = """你是一个舆情内容分析专家。你需要完成我给你的任务。"""
+            case '潜在投诉挖掘':
+                prompt_sys = """你是一个潜在投诉挖掘专家。你需要完成我给你的任务。"""
+            case '合规稽核纠偏':
+                prompt_sys = """你是一个合规稽核纠偏专家。你需要完成我给你的任务。"""
+
+
+
+        ## 在sidebar上的三个分页显示，用st.tabs实现。
+        tab_1, tab_2, tab_4 = st.tabs(['使用须知', '模型参数', '系统角色设定'])
+        # tab_1, tab_2, tab_3, tab_4 = st.tabs(['使用须知', '模型参数', '提示词模板', '系统角色设定'])
+
+        # with st.expander(label='**使用须知**', expanded=False):
+        with tab_1:
+            # st.markdown("#### 快速上手指南")
+            with st.text(body="说明"):
+                st.markdown("* 重启一个新任务时，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，建议适当使用。")
+            with st.text(body="说明"):
+                st.markdown("* 现有仅限一次任务执行，将不会保持之前的任务结果记录。")
+            with st.text(body="说明"):
+                st.markdown("""* 系统的工作流程如下：
+    1. 用户提交待处理的文件。
+    1. 系统将问题转换为机器可理解的格式。
+    1. 系统使用大语言模型来进行全量信息探索。
+    1. 系统使用内置的智能系统来进行基于文本高维特征的探索。
+    1. 系统返回完整且准确的答案。""")
+
+        ## 大模型参数
+        # with st.expander(label='**大语言模型参数**', expanded=True):
+        with tab_2:
+            max_tokens = st.slider(label='Max_Token（生成结果时最大字数）', min_value=100, max_value=8096, value=4096,step=100)
+            temperature = st.slider(label='Temperature (温度)', min_value=0.0, max_value=1.0, value=0.8, step=0.1)
+            top_p = st.slider(label='Top_P (核采样)', min_value=0.0, max_value=1.0, value=0.6, step=0.1)
+            frequency_penalty = st.slider(label='Frequency Penalty (重复度惩罚因子)', min_value=-2.0, max_value=2.0, value=1.0, step=0.1)
+            presence_penalty = st.slider(label='Presence Penalty (控制主题的重复度)', min_value=-2.0, max_value=2.0, value=1.0, step=0.1)
+
+        # with tab_3:
+        #     # st.markdown("#### Prompt提示词参考资料")
+        #     # with st.expander(label="**大语言模型基础提示词Prompt示例**", expanded=False):
+        #     st.code(
+        #         body="我是一个企业主，我需要关注哪些“存货”相关的数据资源规则？", language='plaintext')
+        #     st.code(
+        #         body="作为零售商，了解哪些关键的库存管理指标对我至关重要？", language='plaintext')
+        #     st.code(body="企业主在监控库存时，应如何确保遵守行业法规和最佳实践？",
+        #             language='plaintext')
+        #     st.code(body="在数字化时代，我应该关注哪些技术工具或平台来优化我的库存数据流程？", language='plaintext')
+        #     st.code(body="我应该如何定期审查和分析这些库存数据以提高运营效率？", language='plaintext')
+        #     st.code(body="如何设置预警系统来避免过度库存或缺货情况？", language='plaintext')
+
+        with tab_4:
+            st.text_area(label='系统角色设定', value='你是一个人工智能，你需要回答我提出的问题，或者完成我交代的任务。你需要使用我提问的语言（如中文、英文）来回答。', height=200, label_visibility='hidden')
+
+
+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: ### original code here.
+            if '.pdf' in uploaded_file.name:
+                pdf_filename = uploaded_file.name ### original code here.
+                # 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:
+                    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())
+                    
+                    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。
+                elif '.txt' in uploaded_file.name:
+                    print('start the txt file processing...')
+                    txt_filename = uploaded_file.name
+                    filename = uploaded_file.name
+                    txt_file = uploaded_file.getvalue()
+                    file = open(f"{txt_filename}", 'rb')
+                    content = file.read()
+                    content = file.split(b'\n')
+                    
+                    # with open(file=f'./{username}/{username}_upload.txt', mode='rb', encoding='utf-8') as output_temporary_file:
+                    #     output_temporary_file.write(txt_file)
+                    st.write(file)
+                
+                ### json格式文件
+                elif '.json' in uploaded_file.name:
+                    print('start the json file processing...')
+                    json_filename = uploaded_file.name
+                    json_file = uploaded_file.getvalue()
+                    # file = open(f"{txt_filename}", 'rb')
+                    # content = file.read()
+                    # content = file.split(b'\n')
+                    
+                    # with open(file=f'./{username}/{username}_upload.txt', mode='rb', encoding='utf-8') as output_temporary_file:
+                    #     output_temporary_file.write(txt_file)
+                    # st.write(json_filename)
+                
+                # 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 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)
+            pass
+
+        ## 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 None
+
+
+## streamlit中显示上传文件的模块
+try:
+    uploaded_file = st.file_uploader(
+        "选择需要处理的文件（注：可一次选择多个文件）", type=(["txt", "docx", "PDF", "CSV", "xlsx","xls","json"]), accept_multiple_files=True)
+    
+    ## 获得上传所有文件的大小。
+    uploaded_filesize = round(sum(file.size for file in uploaded_file) / 1000, 2)
+    #总共上传了{}个文件。'.format(len(uploaded_file))) ### 显示上传了多少文件。
+    
+    # 默认状态下没有上传文件，None，会报错。需要判断。
+    if uploaded_file:
+        ## 显示上传文件的信息。
+        metric_col1, metric_col2, metric_col3, metric_col4 = st.columns(4)
+        metric_col1.metric(label='上传的文件数', value=f"{len(uploaded_file)}个", delta=None)
+        metric_col2.metric(label='上传文件的大小', value=f"{uploaded_filesize} KB", delta=None)
+        metric_col3.metric(label='上传文件的时间', value=f"{get_current_time()}", delta=None)
+        metric_col4.metric(label='当前日期', value=f"{str(date.today())}", delta=None)
+        
+        # # uploaded_file_path = upload_file(uploaded_file)
+        # upload_file(uploaded_file)
+except Exception as e:
+    print(e)
+    pass
+
+st.divider()
+### 提示词部分。
+with st.expander(label='**标准模块**', expanded=True):
+    col_1, col_2, col_3, col_4 = st.columns(4)
+    with col_1:
+        prompt_input = st.text_area(label='**原始文件的说明**', value=""" 1. "input"和“instruction”都代表了用户的问题，可以忽略“instruction”部分的内容。 
+                                    
+2. “output”是法律顾问的回答内容。""", height=200, label_visibility='visible')
+    with col_2:
+        prompt_caution = st.text_area(label='**注意事项**', value='忽略所有语法错误和错别字。',height=200, label_visibility='visible')
+    with col_3:
+        prompt_rule = st.text_area(label='**规则定义**', value='无特定规则要求。',height=200, label_visibility='visible')
+    with col_4:
+        prompt_output = st.text_area(label='**输出结果的要求**', value=
+                                    """【用户问题】用一段话来总结用户的核心问题。
+【法律顾问回答】用一段话来总结法律顾问的回答要点。
+【整体情况】用一句话来简单概述判断这一通话的整体情况。
+【是否解决】判断问题是否解决。
+    """, height=200, label_visibility='visible')
+
+    # st.write(f"{prompt_explain} {prompt_notice} {prompt_rule} {prompt_ouput}")
+
+### 专业模式部分。
+with st.expander(label='**定制模块**', expanded=False):
+    # settings_col_1, settings_col_2, settings_col_3, settings_col_4, settings_col_5 = st.columns(5)
+    settings_col_1, settings_col_2, settings_col_3 = st.columns(3)
+    settings_col_1.toggle('高质量模式', value=False, key='high_end_mode')
+    settings_col_2.toggle('长文模式', value=False, key='length_context_mode')
+    settings_col_3.toggle('强力模式', value=False, key='powerful_mode')
+    
+    # with settings_col_1:
+    #     settings_col_1.toggle('高质量模式', value=False, key='high_end_mode')
+    # with settings_col_2:
+    #     settings_col_2.toggle('长文模式', value=False, key='length_context_mode')
+    # with settings_col_3:
+    #     settings_col_3.toggle('强力模式', value=False, key='powerful_mode')
+    
+    ### rag设定
+    rag_col_1, rag_col_2 = st.columns(2)
+    ### 爬虫网站
+    target_url = rag_col_1.text_input('目标网址', value='https://www.123abc.com', label_visibility='visible', disabled=True)
+    
+    ### 加载知识来源
+    target_database = rag_col_2.multiselect(label='信息增强', options=['互联网', '知识库', '规则库', '案例库'], default=['规则库'], disabled=True)
+    
+    ### 各类高级设定
+    advance_col_1, advance_col_2, advance_col_3, advance_col_4, advance_col_5 = st.columns(5)
+    with advance_col_1:
+        prompt_explain = st.text_area(label='筛选规则定制', value='', height=200, label_visibility='visible', disabled=True)
+    with advance_col_2:
+        prompt_notice = st.text_area(label='敏感词制定', value='',height=200, label_visibility='visible', disabled=True)
+    with advance_col_3:
+        prompt_rule = st.text_area(label='过滤名单', value='',height=200, label_visibility='visible', disabled=True)
+    with advance_col_4:
+        prompt_ouput = st.text_area(label='词云频率设定', value='',height=200, label_visibility='visible', disabled=True)
+    with advance_col_5:
+        prompt_ouput = st.text_area(label='其他设定', value='',height=200, label_visibility='visible', disabled=True)
+
+    # st.write(f"{prompt_explain} {prompt_notice} {prompt_rule} {prompt_ouput}")
+
+### 以下是工作区，包括进度等。
+st.divider()
+
+
+### prompt区
+
+# prompt_sys = """你是一个法律专家。你需要完成我给你的任务。"""
+# prompt_input = """我给你的数据中包括了一通完整的法律援助中心的电话记录，其中的数据格式说明如下:
+#     1. "input"和“instruction”都代表了用户的问题，可以忽略“instruction”部分的内容。
+#     2. “output”是法律顾问的回答内容。"""
+# prompt_caution = """你忽略所有语法错误和错别字。"""
+# prompt_output = """现在，我需要你帮忙我整理这通电话的内容，用如下格式（你只需要提供以下格式要求的内容，不需要输出任何其他说明或者解释。）：
+#     【用户问题】用一段话来总结用户的核心问题。
+#     【法律顾问回答】用一段话来总结法律顾问的回答要点。
+#     【整体情况】用一句话来简单概述判断这一通话的整体情况。
+#     【是否解决】判断问题是否解决。
+
+import st_data_parser
+
+### 用LLM总结语料的函数 ， output_filepath是每次都会创建一个新的csv结果文件。
+def llm_summary(file, file_content, output_filepath):
+    # call_content = file_content
+    # call_content = pd.read_json(call_content.decode('utf-8'))
+    
+    ### 通过自定义的data_parser解析文件内容。
+    print('file:', file)
+    call_content = st_data_parser.parser(file=file)
+    
+    print('call_content:', call_content)
+
+    ### a simple user prompt test. 
+    ##  user_prompt = f"""
+    # 我给你的数据中包括了一通完整的法律援助中心的电话记录，其中的数据格式说明如下:
+    # 1. "input"和“instruction”都代表了用户的问题，可以忽略“instruction”部分的内容。
+    # 2. “output”是法律顾问的回答内容。
+
+    # 现在，我需要你帮忙我整理这通电话的内容，用如下格式（你只需要提供以下格式要求的内容，不需要输出任何其他说明或者解释。）：
+    # 【用户问题】用一段话来总结用户的核心问题。
+    # 【法律顾问回答】用一段话来总结法律顾问的回答要点。
+    # 【整体情况】用一句话来简单概述判断这一通话的整体情况。
+    # 【是否解决】判断问题是否解决。
+
+    # 数据内容如下：{call_content}
+    # """
+    
+    user_prompt = prompt_sys + prompt_rule + prompt_input + prompt_caution + """我需要你帮忙我整理这通电话的内容，用如下格式（你只需要提供以下格式要求的内容，不需要输出任何其他说明或者解释。）:\n""" + prompt_output + f"""数据内容如下：f{call_content}"""
+
+    print("---"*30)
+    print('user_prompt:', user_prompt)
+    
+    llm_output = qwen_response.call_with_messages(prompt=user_prompt)
+    # summary = chatgpt.chatgpt(user_prompt=user_prompt)
+    # print(summary)
+    
+    ## 将文件编号和summary存入summary.csv文件
+    # summary_csv = pd.read_csv('./summary_qwen.csv')
+    summary_csv = pd.read_csv('./summary_qwen.csv', encoding='utf-8')
+    # summary_csv = pd.read_csv('./summary_qwen.csv', encoding='utf-8', low_memory=True)
+    print('summary_csv:', summary_csv)
+    print("---"*30)
+    filename = os.path.basename(file.name)  # Get the filename from the file path
+    # filename = os.path.basename(file_path)  # Get the filename from the file path
+    filename_without_extension = os.path.splitext(filename)[0]
+    
+    ### 每次运行都创建一个新的oupout文件。
+    # output_filepath = create_newfile.new_output_file(username)
+    final_output_filepath = f'{output_filepath}'
+    # final_filepath = f'./{username}/output.csv'
+    # final_filepath = './summary_qwen.csv'
+    save_csv_info(filepath=final_output_filepath, ID=filename_without_extension, output=llm_output)
+    
+    return None
+
+### 主程序main
+def main(uploaded_file=uploaded_file):
+    output_filepath = create_newfile.new_output_file(username)
+    
+    ## 在存在上传文件时，启动LLM程序。    
+    if uploaded_file and submit_btn:
+        ### Progress bar, 进度条设定
+        progress_bar_text = "**正在处理您的任务...**"
+        progress_bar = st.progress(0, text=progress_bar_text)
+        
+        ###记录运行时间
+        start_time = time.time()
+        
+        ### 记录正常运行的文件数，和缺失的文件数
+        success_files = []
+        fail_files = []
+        
+        for i, file in enumerate(uploaded_file):
+            print(f'正在处理第{i+1}个文件', file.name)
+            try:
+                llm_summary(file=file, file_content=file.read(), output_filepath=output_filepath) ## 核心程序。
+                success_files.append(file.name)
+            except Exception as e:
+                print(e)
+                fail_files.append(file.name)
+                pass
+            progress_bar.progress((i+1)/len(uploaded_file), text=progress_bar_text) ## 展示进度条。
+        progress_bar = st.empty ## 重置进度条。
+        
+        final_data = pd.read_csv(f'{output_filepath}', encoding='utf-8') ## 在ste.download_button处需要先获得data内容，这里将data进行赋值。
+        # final_data = pd.read_csv(f'./{username}/output.csv', encoding='utf-8') ## 在ste.download_button处需要先获得data内容，这里将data进行赋值。
+        
+        end_time = time.time()
+        run_time = round((end_time - start_time),2)
+        
+        ### 输出结果。
+        st.success(f"任务结束！请点击下方按钮保存结果文件。总运行时长{run_time}秒。成功处理了{len(success_files)}个文件；未完成文件数{len(fail_files)}个。", icon='💯')
+        if len(fail_files) > 0:
+            failed_file_elements = ', '.join(fail_files) ## 无法直接放入f""中。
+            st.warning(f"未完成的文件如下：{failed_file_elements}") ## 一次性打印出每个列表元素。1
+        
+        # st.download_button(label='下载输出文件！', data=final_data, file_name='summary_qwen.csv', mime='text/csv')
+        ste.download_button(
+                        label="点击下载结果文件",
+                        data=final_data,
+                        file_name='final_data.csv',
+                        mime='text/csv',
+                    )
+        
+        
+    return None
+
+if __name__ == '__main__':
+    main()

config.yaml

@@ -0,0 +1,97 @@
+credentials:
+  usernames:
+    joeshi:
+      email: joe.joeshi@gmail.com
+      name: joeshi
+      password: '$2b$12$8Jbjy0Cvq4UnXksw2PUa8.BOmssCExwIS/SrSZX4JwGWu1zJJ5Jxu' # To be replaced with hashed password
+    cindyji:
+      email: joe.joeshi@gmail.com
+      name: cindyji
+      password: '$2b$12$CA3Kc0sL0jVax7Wx0bbC7Op3edhtxlFUJkHS6ABjZCBN/1kbS3yem' # To be replaced with hashed password
+    test:
+      email: joe.joeshi@gmail.com
+      name: test
+      password: '$2b$12$RfaFTOmCII1x7X.U6O9u6OFjTlqYSK88EZy5Yy80nV.uxaxILSmsC' # To be replaced with hashed password, test
+    temp:
+      email: joe.joeshi@gmail.com
+      name: temp
+      password: '$2b$12$AvcADYBmeN77qFQvZ.mIf.fjMzJRgk/HH5OkLxCax6x3NsAQWrHki' # To be replaced with hashed password, test
+    guojing:
+      email: 1234@gmail.com
+      name: guojing
+      password: '$2b$12$NOBKb.j/KJzX1SrN2f9yQunnW3aGZmvK1Km.Cdl70Pmpb.jslRh7m' # To be replaced with hashed password, test
+    chengrui:
+      email: 1234@gmail.com
+      name: chengrui
+      password: '$2b$12$yru/vqM1aNEkG8yFt/3mvuULC8tllGEP0ht.nwuH14SBxvsire7g6' # To be replaced with hashed password, test
+    lbh:
+      email: 1234@gmail.com
+      name: lbh
+      password: '$2b$12$UKz0v6TN/AYaZ6vXt6Iike8711zV1M3g89zZk2jYPt0L9sFDPOYs.' # To be replaced with hashed password, test
+    austins:
+      email: 1234@gmail.com
+      name: austins
+      password: '$2b$12$Sfjuq84o8OXUrAzy945gN.WZBH2vBNW22T7LeZnIS.CmqXrZuKja.' # To be replaced with hashed password, test
+    lhj:
+      email: 1234@gmail.com
+      name: lhj
+      password: '$2b$12$YYt7FgQ.61SJ6U9m0JJUgesMXmYjz1oQG37PlrDA6kh3YIZmG6Aai' # To be replaced with hashed password, test
+    apples:
+      email: 1234@gmail.com
+      name: apples
+      password: '$2b$12$/6kuag4n0De3taSZiyBuPOr7APy3pz1O7Ms/ICZMy1kxwRpH9ucK.' # To be replaced with hashed password, test
+    carriec:
+      email: 1234@gmail.com
+      name: carriec
+      password: '$2b$12$A8gBPYSbDgFgAbr9QGANUeZx6J0jYq56yuesd6FeWDeyTQ0DLURRS' # To be replaced with hashed password, test
+    wuqijun:
+      email: 1234@gmail.com
+      name: wuqijun
+      password: '$2b$12$I8jYTti0r9cajzMbP6wgN.5Dx8mqE89ZFZYE9a0L107JdqzTNWEpu' # To be replaced with hashed password, test
+    sunzhifeng:
+      email: 1234@gmail.com
+      name: sunzhifeng
+      password: '$2b$12$oGvNcCTX/cdgPigUMd.vwekjQoKz4EPg87Nqh7.K/gHoFrKKHdVPi' # To be replaced with hashed password, test
+    pattern:
+      email: 1234@gmail.com
+      name: sunzhifeng
+      password: '$2b$12$uEy0iXQg4LuwTivV7JmcGuWSL.Gn21RY7iD/lNxsPBtT0PGRvjuxi' # To be replaced with hashed password, test
+    mao:
+      email: 1234@gmail.com
+      name: mao
+      password: '$2b$12$kMCNK85MgjZACFToD.L5puQkWOVsTdn7Lj/QVRBaVpHBMtiWQ8Nwe' # To be replaced with hashed password, test
+    cq:
+      email: 1234@gmail.com
+      name: cq
+      password: '$2b$12$YfjCXWbXXoada3kHdy9aiOYrI.3Mz94VIN.wrssaDGP2dJjgtVHcW' # To be replaced with hashed password, test
+    scottl:
+      email: 1234@gmail.com
+      name: scottl
+      password: '$2b$12$XTsFPlqFhI7EdobL2ZqF0uoAhgoq0Hmy/Gtmnsgo2u9C2waLl2eh.' # To be replaced with hashed password, test
+    test001:
+      email: 1234@gmail.com
+      name: test001
+      password: '$2b$12$.IqUmcWhPlKlJv.Y3Ghy8uoFdwA/1KOYuFMzst11sMqvJOfNs21vu' # To be replaced with hashed password, test
+    test002:
+      email: 1234@gmail.com
+      name: test002
+      password: '$2b$12$EI3cGR5pSddL/36wvOEiBOA0FGF3w5WhecCOxzgZCU63T0Oys3TbW' # To be replaced with hashed password, test
+    test003:
+      email: 1234@gmail.com
+      name: test003
+      password: '$2b$12$a487ZDz97h7gjXQIcoIWFOlKuQIoVEGz1eBdzz7Al1TzhO3RRr5kW' # To be replaced with hashed password, test
+    test004:
+      email: 1234@gmail.com
+      name: test004
+      password: '$2b$12$GjiDKG6FtZN/4YI5JN87y.zamKszqizPnTm6odM9nU62bg.9bjPt.' # To be replaced with hashed password, test
+    test005:
+      email: 1234@gmail.com
+      name: test005
+      password: '$2b$12$TpEenOq.c9HTjpw8ELIq5OPwTYt6TEYrJ4iANE2rCXUicV9aKHkyC' # To be replaced with hashed password, test
+cookie:
+  expiry_days: 30
+  key: random_signature_key # Must be string
+  name: random_cookie_name
+preauthorized:
+  emails:
+  - joe.joeshi@gmail.com

qwen_response.py

@@ -0,0 +1,38 @@
+import random
+from http import HTTPStatus
+import dashscope
+
+### 参考： 
+## export DASHSCOPE_API_KEY="sk-948adb3e65414e55961a9ad9d22d186b"
+dashscope.api_key = "sk-948adb3e65414e55961a9ad9d22d186b"
+
+
+def call_with_messages(prompt):
+    messages = [{'role': 'system', 'content': 'You are a helpful assistant.'},
+                {'role': 'user', 'content': prompt}]
+                # {'role': 'user', 'content': '如何做西红柿炒鸡蛋？'}]
+    response = dashscope.Generation.call(
+        "qwen-plus", ## 支持32K的模型。
+        # "qwen-turbo", ## 支持8K的模型。
+        messages=messages,
+        # set the random seed, optional, default to 1234 if not set
+        seed=random.randint(1, 10000),
+        # set the result to be "message" format.
+        result_format='message',
+    )
+    if response.status_code == HTTPStatus.OK:
+        print(response)
+        
+    else:
+        print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
+            response.request_id, response.status_code,
+            response.code, response.message
+        ))
+
+    return response['output']['choices'][0]['message']['content'] ### 这里是content的内容，不是message的全部内容。
+    
+    
+# if __name__ == '__main__':
+#     # call_with_messages() ### original code here.
+#     res = call_with_messages() ## working. 
+#     # print(res)

requirements.txt

@@ -0,0 +1,13 @@
+dashscope==1.17.0
+langchain==0.2.5
+numpy==1.26.4
+openai==1.34.0
+pandas==2.2.2
+python-dotenv==1.0.1
+pytz==2024.1
+PyYAML==6.0.1
+PyYAML==6.0.1
+Requests==2.32.3
+streamlit==1.33.0
+streamlit_authenticator==0.2.3
+streamlit_ext==0.1.10