media_engine_streamlit_app.py
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"""
Streamlit Web界面
为Media Agent提供友好的Web界面
"""
import os
import sys
from pathlib import Path
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'
PROJECT_ROOT = Path(__file__).resolve().parents[2]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
# 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
from services.engines.media import DeepSearchAgent, AnspireSearchAgent, Settings
from services.shared.config.access import get_settings
from utils.github_issues import error_with_issue_link
from utils.runtime_paths import MEDIA_REPORTS_DIR, ensure_runtime_dirs
def main():
"""主函数"""
ensure_runtime_dirs()
settings = get_settings()
st.set_page_config(
page_title="Media Agent",
page_icon="",
layout="wide"
)
st.title("Media Agent")
st.markdown("场馆用户表达与多模态体验分析引擎")
st.markdown("理解图文、短视频、社交表达中的到访体验、出片价值、互动感与避雷线索。")
st.markdown("适合补充 Insight 与 Query 难以覆盖的视觉化、种草型和现场感反馈。")
# 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 Gemini
model_name = settings.MEDIA_ENGINE_MODEL_NAME or "gemini-2.5-pro"
# 默认高级配置
max_reflections = 2
max_content_length = 20000
# 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 "等待从主页面接收场馆研究指令..."
# 只读的查询展示区域
st.text_area(
"当前查询",
value=display_query,
height=100,
disabled=True,
help="研究指令由主页面的任务工作台统一驱动",
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("等待研究启动信号...")
# 验证配置
if start_research:
if not query.strip():
st.error("请输入场馆研究指令")
logger.error("请输入场馆研究指令")
return
# 自动使用配置文件中的API密钥
engine_key = settings.MEDIA_ENGINE_API_KEY
bocha_key = settings.BOCHA_WEB_SEARCH_API_KEY
ansire_key = settings.ANSPIRE_API_KEY
# 构建 Settings（pydantic_settings风格，优先大写环境变量）
if settings.SEARCH_TOOL_TYPE == "BochaAPI":
if not bocha_key:
st.error("请在您的环境变量中设置BOCHA_WEB_SEARCH_API_KEY")
logger.error("请在您的环境变量中设置BOCHA_WEB_SEARCH_API_KEY")
return
logger.info("使用Bocha搜索API密钥")
config = Settings(
MEDIA_ENGINE_API_KEY=engine_key,
MEDIA_ENGINE_BASE_URL=settings.MEDIA_ENGINE_BASE_URL,
MEDIA_ENGINE_MODEL_NAME=model_name,
SEARCH_TOOL_TYPE="BochaAPI",
BOCHA_WEB_SEARCH_API_KEY=bocha_key,
MAX_REFLECTIONS=max_reflections,
SEARCH_CONTENT_MAX_LENGTH=max_content_length,
OUTPUT_DIR=str(MEDIA_REPORTS_DIR),
)
elif settings.SEARCH_TOOL_TYPE == "AnspireAPI":
if not ansire_key:
st.error("请在您的环境变量中设置ANSPIRE_API_KEY")
logger.error("请在您的环境变量中设置ANSPIRE_API_KEY")
return
logger.info("使用Anspire搜索API密钥")
config = Settings(
MEDIA_ENGINE_API_KEY=engine_key,
MEDIA_ENGINE_BASE_URL=settings.MEDIA_ENGINE_BASE_URL,
MEDIA_ENGINE_MODEL_NAME=model_name,
SEARCH_TOOL_TYPE="AnspireAPI",
ANSPIRE_API_KEY=ansire_key,
MAX_REFLECTIONS=max_reflections,
SEARCH_CONTENT_MAX_LENGTH=max_content_length,
OUTPUT_DIR=str(MEDIA_REPORTS_DIR),
)
else:
st.error(f"未知的搜索工具类型: {settings.SEARCH_TOOL_TYPE}")
logger.error(f"未知的搜索工具类型: {settings.SEARCH_TOOL_TYPE}")
return
# 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...")
if config.SEARCH_TOOL_TYPE == "BochaAPI":
agent = DeepSearchAgent(config)
elif config.SEARCH_TOOL_TYPE == "AnspireAPI":
agent = AnspireSearchAgent(config)
else:
raise ValueError(f"未知的搜索工具类型: {config.SEARCH_TOOL_TYPE}")
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))
# 生成研究摘要
status_text.text("正在生成研究摘要...")
logger.info("正在生成研究摘要...")
final_report = agent._generate_final_report()
progress_bar.progress(90)
# 保存报告
status_text.text("正在保存报告...")
logger.info("正在保存报告...")
agent._save_report(final_report)
progress_bar.progress(100)
status_text.text("研究完成！")
logger.info("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="Media 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("场馆研究结果")
# 结果标签页（已移除下载选项）
tab1, tab2 = st.tabs(["洞察摘要", "证据与引用"])
with tab1:
st.markdown(final_report)
with tab2:
# 段落详情
st.subheader("分析段落详情")
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("检索与引用记录")
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):
query_label = search.query if search.query else "未记录查询"
with st.expander(f"搜索 {i + 1}: {query_label}"):
paragraph_title = getattr(search, "paragraph_title", "") or "未标注段落"
search_tool = getattr(search, "search_tool", "") or "未标注工具"
has_result = getattr(search, "has_result", True)
st.write("**段落:**", paragraph_title)
st.write("**使用的工具:**", search_tool)
preview = search.content or ""
if not isinstance(preview, str):
preview = str(preview)
if len(preview) > 200:
preview = preview[:200] + "..."
st.write("**URL:**", search.url or "无")
st.write("**标题:**", search.title or "无")
st.write("**内容预览:**", preview if preview else "无可用内容")
if not has_result:
st.info("本次搜索未返回结果")
if search.score:
st.write("**相关度评分:**", search.score)
if __name__ == "__main__":
main()