""" Streamlit Web界面 为Insight Agent提供友好的Web界面 """ import os import sys import streamlit as st from datetime import datetime import json import locale from loguru import logger # 设置UTF-8编码环境 os.environ['PYTHONIOENCODING'] = 'utf-8' os.environ['PYTHONUTF8'] = '1' # 设置系统编码 try: locale.setlocale(locale.LC_ALL, 'en_US.UTF-8') except locale.Error: try: locale.setlocale(locale.LC_ALL, 'C.UTF-8') except locale.Error: pass # 添加src目录到Python路径 sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..')) from InsightEngine import DeepSearchAgent, Settings from config import settings from utils.github_issues import error_with_issue_link def main(): """主函数""" st.set_page_config( page_title="Insight Agent", page_icon="", layout="wide" ) st.title("Insight Agent") st.markdown("私有舆情数据库深度分析AI代理") st.markdown("24小时全自动从包括微博、知乎、github、酷安等 13个 社媒平台、技术论坛广泛的爬取舆情数据") # 检查URL参数 try: # 尝试使用新版本的query_params query_params = st.query_params auto_query = query_params.get('query', '') auto_search = query_params.get('auto_search', 'false').lower() == 'true' except AttributeError: # 兼容旧版本 query_params = st.experimental_get_query_params() auto_query = query_params.get('query', [''])[0] auto_search = query_params.get('auto_search', ['false'])[0].lower() == 'true' # ----- 配置被硬编码 ----- # 强制使用 Kimi model_name = settings.INSIGHT_ENGINE_MODEL_NAME or "kimi-k2-0711-preview" # 默认高级配置 max_reflections = 2 max_content_length = 500000 # Kimi支持长文本 # 简化的研究查询展示区域 # 如果有自动查询，使用它作为默认值，否则显示占位符 display_query = auto_query if auto_query else "等待从主页面接收分析内容..." # 只读的查询展示区域 st.text_area( "当前查询", value=display_query, height=100, disabled=True, help="查询内容由主页面的搜索框控制", label_visibility="hidden" ) # 自动搜索逻辑 start_research = False query = auto_query if auto_search and auto_query and 'auto_search_executed' not in st.session_state: st.session_state.auto_search_executed = True start_research = True elif auto_query and not auto_search: st.warning("等待搜索启动信号...") # 验证配置 if start_research: if not query.strip(): st.error("请输入研究查询") logger.error("请输入研究查询") return # 检查配置中的LLM密钥 if not settings.INSIGHT_ENGINE_API_KEY: st.error("请在您的环境变量中设置INSIGHT_ENGINE_API_KEY") logger.error("请在您的环境变量中设置INSIGHT_ENGINE_API_KEY") return # 自动使用配置文件中的API密钥和数据库配置 db_host = settings.DB_HOST db_user = settings.DB_USER db_password = settings.DB_PASSWORD db_name = settings.DB_NAME db_port = settings.DB_PORT db_charset = settings.DB_CHARSET # 创建Settings配置（字段必须用大写，以适配Settings类） config = Settings( INSIGHT_ENGINE_API_KEY=settings.INSIGHT_ENGINE_API_KEY, INSIGHT_ENGINE_BASE_URL=settings.INSIGHT_ENGINE_BASE_URL, INSIGHT_ENGINE_MODEL_NAME=model_name, DB_HOST=db_host, DB_USER=db_user, DB_PASSWORD=db_password, DB_NAME=db_name, DB_PORT=db_port, DB_CHARSET=db_charset, DB_DIALECT=settings.DB_DIALECT, MAX_REFLECTIONS=max_reflections, MAX_CONTENT_LENGTH=max_content_length, OUTPUT_DIR="insight_engine_streamlit_reports" ) # 执行研究 execute_research(query, config) def execute_research(query: str, config: Settings): """执行研究""" try: # 创建进度条 progress_bar = st.progress(0) status_text = st.empty() # 初始化Agent status_text.text("正在初始化Agent...") agent = DeepSearchAgent(config) st.session_state.agent = agent progress_bar.progress(10) # 生成报告结构 status_text.text("正在生成报告结构...") agent._generate_report_structure(query) progress_bar.progress(20) # 处理段落 total_paragraphs = len(agent.state.paragraphs) for i in range(total_paragraphs): status_text.text(f"正在处理段落 {i + 1}/{total_paragraphs}: {agent.state.paragraphs[i].title}") # 初始搜索和总结 agent._initial_search_and_summary(i) progress_value = 20 + (i + 0.5) / total_paragraphs * 60 progress_bar.progress(int(progress_value)) # 反思循环 agent._reflection_loop(i) agent.state.paragraphs[i].research.mark_completed() progress_value = 20 + (i + 1) / total_paragraphs * 60 progress_bar.progress(int(progress_value)) # 生成最终报告 status_text.text("正在生成最终报告...") final_report = agent._generate_final_report() progress_bar.progress(90) # 保存报告 status_text.text("正在保存报告...") agent._save_report(final_report) progress_bar.progress(100) status_text.text("研究完成！") # 显示结果 display_results(agent, final_report) except Exception as e: import traceback error_traceback = traceback.format_exc() error_display = error_with_issue_link( f"研究过程中发生错误: {str(e)}", error_traceback, app_name="Insight Engine Streamlit App" ) st.error(error_display) logger.exception(f"研究过程中发生错误: {str(e)}") def display_results(agent: DeepSearchAgent, final_report: str): """显示研究结果""" st.header("工作结束") # 结果标签页（已移除下载选项） tab1, tab2 = st.tabs(["研究小结", "引用信息"]) with tab1: st.markdown(final_report) with tab2: # 段落详情 st.subheader("段落详情") for i, paragraph in enumerate(agent.state.paragraphs): with st.expander(f"段落 {i + 1}: {paragraph.title}"): st.write("**预期内容:**", paragraph.content) st.write("**最终内容:**", paragraph.research.latest_summary[:300] + "..." if len(paragraph.research.latest_summary) > 300 else paragraph.research.latest_summary) st.write("**搜索次数:**", paragraph.research.get_search_count()) st.write("**反思次数:**", paragraph.research.reflection_iteration) # 搜索历史 st.subheader("搜索历史") all_searches = [] for paragraph in agent.state.paragraphs: all_searches.extend(paragraph.research.search_history) if all_searches: for i, search in enumerate(all_searches): with st.expander(f"搜索 {i + 1}: {search.query}"): st.write("**URL:**", search.url) st.write("**标题:**", search.title) st.write("**内容预览:**", search.content[:200] + "..." if len(search.content) > 200 else search.content) if search.score: st.write("**相关度评分:**", search.score) if __name__ == "__main__": main()