refactor(project): 重构项目文档并优化代码结构
- 移除旧的文档结构和内容，清理 root 目录下的 markdown 文件 - 删除 GitHub Pages 部署配置和相关文件 - 移除 .env.example 文件，使用 Doppler 进行环境变量管理 - 更新 README.md，增加对 OpenBB 数据的支持 - 重构 streamlit_app.py，移除 Swarm 模式相关代码 - 更新 Doppler 配置管理模块，增加对 .env 文件的支持 - 删除 Memory Bank 实验和测试脚本 - 清理内部文档和开发计划
This commit is contained in:
ben
171
app/tabs/adk_debate_tab.py
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171
app/tabs/adk_debate_tab.py
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@@ -0,0 +1,171 @@
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import streamlit as st
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import asyncio
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import sys
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from pathlib import Path
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from typing import Dict, Any, List
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# Ensure the main project directory is in the Python path
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project_root = Path(__file__).parent.parent.parent
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sys.path.insert(0, str(project_root))
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from google.adk import Agent, Runner
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from google.adk.sessions import InMemorySessionService, Session
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from google.genai import types
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async def _get_llm_reply(runner: Runner, session: Session, prompt: str) -> str:
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"""Helper function to call a Runner and get a text reply."""
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content = types.Content(role='user', parts=[types.Part(text=prompt)])
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response = runner.run_async(
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user_id=session.user_id,
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session_id=session.id,
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new_message=content
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)
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reply = ""
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async for event in response:
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if hasattr(event, 'content') and event.content and hasattr(event.content, 'parts'):
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for part in event.content.parts:
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if hasattr(part, 'text') and part.text:
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reply += str(part.text)
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elif hasattr(event, 'text') and event.text:
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reply += str(event.text)
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return reply.strip()
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async def run_adk_debate_streamlit(topic: str, participants: List[str], rounds: int):
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"""
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Runs the ADK turn-based debate and yields each statement for Streamlit display.
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"""
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try:
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yield "🚀 **启动ADK八仙轮流辩论 (太上老君主持)...**"
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all_immortals = ["铁拐李", "吕洞宾", "何仙姑", "张果老", "蓝采和", "汉钟离", "韩湘子", "曹国舅"]
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if not participants:
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participants = all_immortals
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character_configs = {
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"太上老君": {"name": "太上老君", "model": "gemini-1.5-pro", "instruction": "你是太上老君，天道化身，辩论的主持人。你的言辞沉稳、公正、充满智慧。你的任务是：1. 对辩论主题进行开场介绍。2. 在每轮开始时进行引导。3. 在辩论结束后，对所有观点进行全面、客观的总结。保持中立，不偏袒任何一方。"},
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"铁拐李": {"name": "铁拐李", "model": "gemini-1.5-flash", "instruction": "你是铁拐李，八仙中的逆向思维专家。你善于从批判和质疑的角度看问题，发言风格直接、犀利，但富有智慧。"},
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"吕洞宾": {"name": "吕洞宾", "model": "gemini-1.5-flash", "instruction": "你是吕洞宾，八仙中的理性分析者。你善于平衡各方观点，用理性和逻辑来分析问题，发言风格温和而深刻。"},
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"何仙姑": {"name": "何仙姑", "model": "gemini-1.5-flash", "instruction": "你是何仙姑，八仙中的风险控制专家。你总是从风险管理的角度思考问题，善于发现潜在危险，发言风格谨慎、细致。"},
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"张果老": {"name": "张果老", "model": "gemini-1.5-flash", "instruction": "你是张果老，八仙中的历史智慧者。你善于从历史数据中寻找规律和智慧，提供长期视角，发言风格沉稳、博学。"},
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"蓝采和": {"name": "蓝采和", "model": "gemini-1.5-flash", "instruction": "你是蓝采和，八仙中的创新思维者。你善于从新兴视角和非传统方法来看待问题，发言风格活泼、新颖。"},
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"汉钟离": {"name": "汉钟离", "model": "gemini-1.5-flash", "instruction": "你是汉钟离，八仙中的平衡协调者。你善于综合各方观点，寻求和谐统一的解决方案，发言风格平和、包容。"},
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"韩湘子": {"name": "韩湘子", "model": "gemini-1.5-flash", "instruction": "你是韩湘子，八仙中的艺术感知者。你善于从美学和感性的角度分析问题，发言风格优雅、感性。"},
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"曹国舅": {"name": "曹国舅", "model": "gemini-1.5-flash", "instruction": "你是曹国舅，八仙中的实务执行者。你关注实际操作和具体细节，发言风格务实、严谨。"}
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}
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session_service = InMemorySessionService()
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session = await session_service.create_session(state={}, app_name="稷下学宫八仙论道系统-Streamlit", user_id="st_user")
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runners: Dict[str, Runner] = {}
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for name, config in character_configs.items():
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if name == "太上老君" or name in participants:
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agent = Agent(name=config["name"], model=config["model"], instruction=config["instruction"])
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runners[name] = Runner(app_name="稷下学宫八仙论道系统-Streamlit", agent=agent, session_service=session_service)
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host_runner = runners.get("太上老君")
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if not host_runner:
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yield "❌ **主持人太上老君初始化失败。**"
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return
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yield f"🎯 **参与仙人**: {', '.join(participants)}"
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debate_history = []
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# Opening statement
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opening_prompt = f"请为本次关于“{topic}”的辩论，发表一段公正、深刻的开场白，并宣布辩论开始。"
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opening_statement = await _get_llm_reply(host_runner, session, opening_prompt)
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yield f"👑 **太上老君**: {opening_statement}"
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# Debate rounds
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for round_num in range(rounds):
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round_intro_prompt = f"请为第 {round_num + 1} 轮辩论说一段引导语。"
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round_intro = await _get_llm_reply(host_runner, session, round_intro_prompt)
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yield f"👑 **太上老君**: {round_intro}"
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for name in participants:
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if name not in runners: continue
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history_context = f"\n最近的论道内容：\n" + "\n".join([f"- {h}" for h in debate_history[-5:]]) if debate_history else ""
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prompt = f"论道主题: {topic}{history_context}\n\n请从你的角色特点出发，简洁地发表观点。"
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reply = await _get_llm_reply(runners[name], session, prompt)
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yield f"🗣️ **{name}**: {reply}"
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debate_history.append(f"{name}: {reply}")
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await asyncio.sleep(1)
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# Summary
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summary_prompt = f"辩论已结束。以下是完整的辩论记录：\n\n{' '.join(debate_history)}\n\n请对本次辩论进行全面、公正、深刻的总结。"
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summary = await _get_llm_reply(host_runner, session, summary_prompt)
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yield f"👑 **太上老君**: {summary}"
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for runner in runners.values():
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await runner.close()
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yield "🎉 **ADK八仙轮流辩论完成!**"
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except Exception as e:
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yield f"❌ **运行ADK八仙轮流辩论失败**: {e}"
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import traceback
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st.error(traceback.format_exc())
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def render_adk_debate_tab():
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"""Renders the Streamlit UI for the ADK Debate tab."""
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st.markdown("### 🏛️ 八仙论道 (ADK版 - 太上老君主持)")
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topic = st.text_input(
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"辩论主题",
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value="AI是否应该拥有创造力？",
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key="adk_topic_input"
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)
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all_immortals = ["铁拐李", "吕洞宾", "何仙姑", "张果老", "蓝采和", "汉钟离", "韩湘子", "曹国舅"]
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col1, col2 = st.columns(2)
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with col1:
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rounds = st.number_input("辩论轮数", min_value=1, max_value=5, value=1, key="adk_rounds_input")
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with col2:
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participants = st.multiselect(
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"选择参与的仙人 (默认全选)",
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options=all_immortals,
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default=all_immortals,
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key="adk_participants_select"
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)
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if st.button("🚀 开始论道", key="start_adk_debate_button", type="primary"):
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if not topic:
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st.error("请输入辩论主题。")
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return
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if not participants:
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st.error("请至少选择一位参与的仙人。")
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return
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st.markdown("---")
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st.markdown("#### 📜 论道实录")
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# Placeholder for real-time output
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output_container = st.empty()
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full_log = ""
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# Run the async debate function
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try:
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# Get a new event loop for the thread
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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# Create a coroutine object
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coro = run_adk_debate_streamlit(topic, participants, rounds)
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# Run the coroutine until completion
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for message in loop.run_until_complete(async_generator_to_list(coro)):
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full_log += message + "\n\n"
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output_container.markdown(full_log)
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except Exception as e:
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st.error(f"启动辩论时发生错误: {e}")
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async def async_generator_to_list(async_gen):
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"""Helper to consume an async generator and return a list of its items."""
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return [item async for item in async_gen]
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184
app/tabs/openbb_tab.py
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184
app/tabs/openbb_tab.py
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@@ -0,0 +1,184 @@
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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from datetime import datetime, timedelta
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def _check_openbb_installed() -> bool:
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try:
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# OpenBB v4 推荐用法: from openbb import obb
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from openbb import obb # noqa: F401
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return True
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except Exception:
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return False
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def _load_price_data(symbol: str, days: int = 365) -> pd.DataFrame:
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"""Fetch OHLCV using OpenBB v4 when available; otherwise return demo/synthetic data."""
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end = datetime.utcnow().date()
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start = end - timedelta(days=days)
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# 优先使用 OpenBB v4
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try:
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from openbb import obb
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# 先尝试股票路由
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try:
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out = obb.equity.price.historical(
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symbol,
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start_date=str(start),
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end_date=str(end),
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)
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except Exception:
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out = None
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# 若股票无数据，再尝试 ETF 路由
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if out is None or (hasattr(out, "is_empty") and out.is_empty):
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try:
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out = obb.etf.price.historical(
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symbol,
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start_date=str(start),
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end_date=str(end),
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)
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except Exception:
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out = None
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if out is not None:
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if hasattr(out, "to_df"):
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df = out.to_df()
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elif hasattr(out, "to_dataframe"):
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df = out.to_dataframe()
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else:
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# 兜底: 有些 provider 返回可序列化对象
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df = pd.DataFrame(out) # type: ignore[arg-type]
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# 规格化列名
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if not isinstance(df, pd.DataFrame) or df.empty:
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raise ValueError("OpenBB 返回空数据")
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# 有的表以 index 为日期
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if 'date' in df.columns:
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df['Date'] = pd.to_datetime(df['date'])
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elif df.index.name in ('date', 'Date') or isinstance(df.index, pd.DatetimeIndex):
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df = df.copy()
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df['Date'] = pd.to_datetime(df.index)
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else:
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# 尝试查找常见日期列
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for cand in ['timestamp', 'time', 'datetime']:
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if cand in df.columns:
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df['Date'] = pd.to_datetime(df[cand])
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break
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# 归一化收盘价列
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close_col = None
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for cand in ['adj_close', 'close', 'Close', 'price', 'close_price', 'c']:
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if cand in df.columns:
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close_col = cand
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break
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if close_col is None:
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raise ValueError("未找到收盘价列")
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df['Close'] = pd.to_numeric(df[close_col], errors='coerce')
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# 仅保留需要列并清洗
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if 'Date' not in df.columns:
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raise ValueError("未找到日期列")
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df = df[['Date', 'Close']].dropna()
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df = df.sort_values('Date').reset_index(drop=True)
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# 限定时间窗口（有些 provider 可能返回更长区间）
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df = df[df['Date'].dt.date.between(start, end)]
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if df.empty:
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raise ValueError("清洗后为空")
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return df
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except Exception:
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# 如果 OpenBB 不可用或调用失败，进入本地演示/合成数据兜底
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pass
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# Fallback to demo from examples/data
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try:
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from pathlib import Path
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root = Path(__file__).resolve().parents[2]
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demo_map = {
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'AAPL': root / 'examples' / 'data' / 'demo_results_aapl.json',
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'MSFT': root / 'examples' / 'data' / 'demo_results_msft.json',
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'TSLA': root / 'examples' / 'data' / 'demo_results_tsla.json',
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}
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path = demo_map.get(symbol.upper())
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if path and path.exists():
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df = pd.read_json(path)
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if 'date' in df.columns:
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df['Date'] = pd.to_datetime(df['date'])
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if 'close' in df.columns:
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df['Close'] = df['close']
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df = df[['Date', 'Close']].dropna().sort_values('Date').reset_index(drop=True)
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# 裁剪到时间窗口
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df = df[df['Date'].dt.date.between(start, end)]
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return df
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except Exception:
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pass
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# Last resort: minimal synthetic data（避免 FutureWarning）
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dates = pd.date_range(end=end, periods=min(days, 180))
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return pd.DataFrame({
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'Date': dates,
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'Close': pd.Series(range(len(dates))).rolling(5).mean().bfill()
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})
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def _kpis_from_df(df: pd.DataFrame) -> dict:
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if df.empty or 'Close' not in df.columns:
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return {"最新价": "-", "近30日涨幅": "-", "最大回撤(近90日)": "-"}
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latest = float(df['Close'].iloc[-1])
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last_30 = df.tail(30)
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if len(last_30) > 1:
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pct_30 = (last_30['Close'].iloc[-1] / last_30['Close'].iloc[0] - 1) * 100
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else:
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pct_30 = 0.0
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# max drawdown over last 90 days
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lookback = df.tail(90)['Close']
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roll_max = lookback.cummax()
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drawdown = (lookback / roll_max - 1).min() * 100
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return {
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"最新价": f"{latest:,.2f}",
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"近30日涨幅": f"{pct_30:.2f}%",
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"最大回撤(近90日)": f"{drawdown:.2f}%",
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}
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def render_openbb_tab():
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st.write("使用 OpenBB（如可用）或演示数据展示市场概览。")
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col_a, col_b = st.columns([2, 1])
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with col_b:
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symbol = st.text_input("股票/ETF 代码", value="AAPL")
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days = st.slider("时间窗口（天）", 90, 720, 365, step=30)
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obb_ready = _check_openbb_installed()
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if obb_ready:
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st.success("OpenBB 已安装 ✅")
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else:
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st.info("未检测到 OpenBB，将使用演示数据。可在 requirements.txt 中加入 openbb 后安装启用。")
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with col_a:
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df = _load_price_data(symbol, days)
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if df is None or df.empty:
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st.warning("未获取到数据")
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return
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# 绘制收盘价
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if 'Date' in df.columns and 'Close' in df.columns:
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fig = px.line(df, x='Date', y='Close', title=f"{symbol.upper()} 收盘价")
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st.plotly_chart(fig, use_container_width=True)
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else:
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st.dataframe(df.head())
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# KPI 卡片
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st.markdown("#### 关键指标")
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kpis = _kpis_from_df(df)
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k1, k2, k3 = st.columns(3)
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k1.metric("最新价", kpis["最新价"])
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k2.metric("近30日涨幅", kpis["近30日涨幅"])
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k3.metric("最大回撤(近90日)", kpis["最大回撤(近90日)"])
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# 未来：基本面、新闻、情绪等组件占位
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with st.expander("🚧 更多组件（即将推出）"):
|
||||
st.write("基本面卡片、新闻与情绪、宏观指标、策略筛选等将逐步接入。")
|
||||
Reference in New Issue
Block a user