223 lines
7.3 KiB
Python
223 lines
7.3 KiB
Python
"""
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Streamlit Web界面
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为Media Agent提供友好的Web界面
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"""
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import os
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import sys
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import streamlit as st
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from datetime import datetime
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import json
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import locale
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# 设置UTF-8编码环境
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os.environ['PYTHONIOENCODING'] = 'utf-8'
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os.environ['PYTHONUTF8'] = '1'
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# 设置系统编码
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try:
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locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')
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except locale.Error:
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try:
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locale.setlocale(locale.LC_ALL, 'C.UTF-8')
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except locale.Error:
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pass
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# 添加src目录到Python路径
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
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from MediaEngine import DeepSearchAgent, Config
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from config import DEEPSEEK_API_KEY, BOCHA_Web_Search_API_KEY, GEMINI_API_KEY
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def main():
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"""主函数"""
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st.set_page_config(
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page_title="Media Agent",
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page_icon="",
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layout="wide"
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)
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st.title("Media Agent")
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st.markdown("具备强大多模态能力的AI代理")
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st.markdown("突破传统文本交流限制，广泛的浏览抖音、快手、小红书的视频、图文、直播")
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st.markdown("使用现代化搜索引擎提供的诸如日历卡、天气卡、股票卡等多模态结构化信息进一步增强能力")
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# 检查URL参数
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try:
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# 尝试使用新版本的query_params
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query_params = st.query_params
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auto_query = query_params.get('query', '')
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auto_search = query_params.get('auto_search', 'false').lower() == 'true'
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except AttributeError:
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# 兼容旧版本
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query_params = st.experimental_get_query_params()
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auto_query = query_params.get('query', [''])[0]
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auto_search = query_params.get('auto_search', ['false'])[0].lower() == 'true'
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# ----- 配置被硬编码 -----
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# 强制使用 Gemini
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llm_provider = "gemini"
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model_name = "gemini-2.5-pro"
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# 默认高级配置
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max_reflections = 2
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max_content_length = 20000
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# 简化的研究查询展示区域
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# 如果有自动查询，使用它作为默认值，否则显示占位符
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display_query = auto_query if auto_query else "等待从主页面接收分析内容..."
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# 只读的查询展示区域
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st.text_area(
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"当前查询",
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value=display_query,
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height=100,
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disabled=True,
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help="查询内容由主页面的搜索框控制",
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label_visibility="hidden"
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)
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# 自动搜索逻辑
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start_research = False
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query = auto_query
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if auto_search and auto_query and 'auto_search_executed' not in st.session_state:
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st.session_state.auto_search_executed = True
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start_research = True
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elif auto_query and not auto_search:
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st.warning("等待搜索启动信号...")
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# 验证配置
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if start_research:
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if not query.strip():
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st.error("请输入研究查询")
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return
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# 由于强制使用Gemini，检查相关的API密钥
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if not GEMINI_API_KEY:
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st.error("请在您的配置文件(config.py)中设置GEMINI_API_KEY")
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return
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if not BOCHA_Web_Search_API_KEY:
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st.error("请在您的配置文件(config.py)中设置BOCHA_Web_Search_API_KEY")
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return
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# 自动使用配置文件中的API密钥
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gemini_key = GEMINI_API_KEY
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bocha_key = BOCHA_Web_Search_API_KEY
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# 创建配置
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config = Config(
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deepseek_api_key=None,
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openai_api_key=None,
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gemini_api_key=gemini_key,
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bocha_api_key=bocha_key,
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default_llm_provider=llm_provider,
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deepseek_model="deepseek-chat", # 保留默认值以兼容
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openai_model="gpt-4o-mini", # 保留默认值以兼容
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gemini_model=model_name,
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max_reflections=max_reflections,
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max_content_length=max_content_length,
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output_dir="media_engine_streamlit_reports"
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)
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# 执行研究
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execute_research(query, config)
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def execute_research(query: str, config: Config):
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"""执行研究"""
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try:
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# 创建进度条
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progress_bar = st.progress(0)
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status_text = st.empty()
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# 初始化Agent
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status_text.text("正在初始化Agent...")
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agent = DeepSearchAgent(config)
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st.session_state.agent = agent
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progress_bar.progress(10)
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# 生成报告结构
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status_text.text("正在生成报告结构...")
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agent._generate_report_structure(query)
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progress_bar.progress(20)
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# 处理段落
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total_paragraphs = len(agent.state.paragraphs)
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for i in range(total_paragraphs):
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status_text.text(f"正在处理段落 {i + 1}/{total_paragraphs}: {agent.state.paragraphs[i].title}")
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# 初始搜索和总结
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agent._initial_search_and_summary(i)
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progress_value = 20 + (i + 0.5) / total_paragraphs * 60
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progress_bar.progress(int(progress_value))
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# 反思循环
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agent._reflection_loop(i)
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agent.state.paragraphs[i].research.mark_completed()
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progress_value = 20 + (i + 1) / total_paragraphs * 60
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progress_bar.progress(int(progress_value))
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# 生成最终报告
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status_text.text("正在生成最终报告...")
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final_report = agent._generate_final_report()
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progress_bar.progress(90)
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# 保存报告
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status_text.text("正在保存报告...")
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agent._save_report(final_report)
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progress_bar.progress(100)
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status_text.text("研究完成！")
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# 显示结果
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display_results(agent, final_report)
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except Exception as e:
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st.error(f"研究过程中发生错误: {str(e)}")
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def display_results(agent: DeepSearchAgent, final_report: str):
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"""显示研究结果"""
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st.header("研究结果")
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# 结果标签页（已移除下载选项）
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tab1, tab2 = st.tabs(["研究小结", "引用信息"])
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with tab1:
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st.markdown(final_report)
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with tab2:
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# 段落详情
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st.subheader("段落详情")
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for i, paragraph in enumerate(agent.state.paragraphs):
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with st.expander(f"段落 {i + 1}: {paragraph.title}"):
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st.write("**预期内容:**", paragraph.content)
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st.write("**最终内容:**", paragraph.research.latest_summary[:300] + "..."
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if len(paragraph.research.latest_summary) > 300
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else paragraph.research.latest_summary)
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st.write("**搜索次数:**", paragraph.research.get_search_count())
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st.write("**反思次数:**", paragraph.research.reflection_iteration)
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# 搜索历史
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st.subheader("搜索历史")
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all_searches = []
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for paragraph in agent.state.paragraphs:
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all_searches.extend(paragraph.research.search_history)
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if all_searches:
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for i, search in enumerate(all_searches):
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with st.expander(f"搜索 {i + 1}: {search.query}"):
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st.write("**URL:**", search.url)
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st.write("**标题:**", search.title)
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st.write("**内容预览:**",
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search.content[:200] + "..." if len(search.content) > 200 else search.content)
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if search.score:
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st.write("**相关度评分:**", search.score)
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if __name__ == "__main__":
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main() |