876 lines
35 KiB
Python
876 lines
35 KiB
Python
"""
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Multi-Agent Decision Workshop - 主应用
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多 Agent 决策工作坊：通过多角色辩论帮助用户做出更好的决策
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"""
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import streamlit as st
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import os
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import base64
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from dotenv import load_dotenv
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# 加载环境变量
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load_dotenv()
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from agents import get_all_agents, get_recommended_agents, AGENT_PROFILES
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from orchestrator import DebateManager, DebateConfig
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from orchestrator.research_manager import ResearchManager, ResearchConfig
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from report import ReportGenerator
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from report import ReportGenerator
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from utils import LLMClient
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from utils.storage import StorageManager
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import config
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# ==================== 页面配置 ====================
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st.set_page_config(
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page_title="🎭 多 Agent 决策工作坊",
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page_icon="🎭",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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# ==================== 样式 ====================
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st.markdown("""
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<style>
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.agent-card {
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padding: 1rem;
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border-radius: 0.5rem;
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margin-bottom: 0.5rem;
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border-left: 4px solid #4A90A4;
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background-color: #f8f9fa;
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}
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.speech-bubble {
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background-color: #f0f2f6;
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padding: 1rem;
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border-radius: 0.5rem;
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margin: 0.5rem 0;
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}
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.round-header {
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background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
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color: white;
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padding: 0.5rem 1rem;
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border-radius: 0.5rem;
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margin: 1rem 0;
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}
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.custom-agent-form {
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background-color: #e8f4f8;
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padding: 1rem;
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border-radius: 0.5rem;
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margin: 0.5rem 0;
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}
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.research-step {
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border-left: 3px solid #FF4B4B;
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padding-left: 10px;
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margin-bottom: 10px;
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}
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</style>
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""", unsafe_allow_html=True)
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# ==================== 常量定义 ====================
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# 从环境变量读取 API Key（隐藏在 .env 文件中）
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DEFAULT_API_KEY = os.getenv("AIHUBMIX_API_KEY", "")
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# 支持的模型列表
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from config import AVAILABLE_MODELS, RESEARCH_MODEL_ROLES
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# 决策类型
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DECISION_TYPES = {
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"product": "产品方案",
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"business": "商业决策",
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"tech": "技术选型",
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"personal": "个人规划"
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}
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# ==================== 初始化 Session State ====================
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if "storage" not in st.session_state:
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st.session_state.storage = StorageManager()
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# Load saved config
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if "saved_config" not in st.session_state:
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st.session_state.saved_config = st.session_state.storage.load_config()
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# Helper to save config
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def save_current_config():
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cfg = {
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"provider": st.session_state.get("selected_provider", "AIHubMix"),
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# read from widget keys to persist what user sees
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"api_key": st.session_state.get("api_key_input", st.session_state.get("api_key", "")),
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"base_url": st.session_state.get("base_url_input", st.session_state.get("base_url", "")),
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"language": st.session_state.get("output_language", "Chinese")
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}
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st.session_state.storage.save_config(cfg)
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if "mode" not in st.session_state:
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st.session_state.mode = "Deep Research"
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# Debate State
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if "debate_started" not in st.session_state:
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st.session_state.debate_started = False
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if "debate_finished" not in st.session_state:
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st.session_state.debate_finished = False
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if "speeches" not in st.session_state:
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st.session_state.speeches = []
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if "report" not in st.session_state:
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st.session_state.report = ""
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if "custom_agents" not in st.session_state:
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st.session_state.custom_agents = {}
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# Research State
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if "research_plan" not in st.session_state:
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st.session_state.research_plan = ""
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if "research_started" not in st.session_state:
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st.session_state.research_started = False
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if "research_output" not in st.session_state:
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st.session_state.research_output = "" # Final report
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if "research_steps_output" not in st.session_state:
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st.session_state.research_steps_output = [] # List of step results
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# ==================== 侧边栏：配置 ====================
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with st.sidebar:
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st.header("⚙️ 设置")
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# 全局 API Key & Provider 设置
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with st.expander("🔑 API / Provider 设置", expanded=True):
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# Saved preferences
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saved = st.session_state.saved_config
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# Provider Selection
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provider_options = list(config.LLM_PROVIDERS.keys())
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default_provider = saved.get("provider", "AIHubMix")
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try:
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prov_idx = provider_options.index(default_provider)
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except ValueError:
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prov_idx = 0
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def on_provider_change():
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# Update API key and base_url inputs when provider changes
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sel = st.session_state.get("selected_provider")
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if not sel:
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return
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prov_cfg = config.LLM_PROVIDERS.get(sel, {})
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saved_cfg = st.session_state.get("saved_config", {})
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# choose api_key from saved config if provider matches, otherwise from env
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default_key = saved_cfg.get("api_key") if saved_cfg.get("provider") == sel else os.getenv(prov_cfg.get("api_key_var", ""), "")
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# Always reset base_url_input to the provider's configured default when switching providers
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default_base = prov_cfg.get("base_url", "")
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# Set widget states
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st.session_state["api_key_input"] = default_key
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st.session_state["base_url_input"] = default_base
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# Persist current selection
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save_current_config()
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selected_provider_label = st.selectbox(
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"选择 API 提供商",
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options=provider_options,
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index=prov_idx,
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key="selected_provider",
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on_change=on_provider_change
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)
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# Recompute provider config from current selection (use session_state to be robust)
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current_provider = st.session_state.get("selected_provider", selected_provider_label)
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provider_config = config.LLM_PROVIDERS.get(current_provider, {})
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provider_id = current_provider.lower()
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# API Key Input
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# If widget already has a value in session_state (from previous interactions), prefer it.
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default_key = (
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st.session_state.get("api_key_input")
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if st.session_state.get("api_key_input") is not None and st.session_state.get("api_key_input") != ""
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else (saved.get("api_key") if saved.get("provider") == current_provider else os.getenv(provider_config.get("api_key_var", ""), ""))
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)
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api_key = st.text_input(
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f"{current_provider} API Key",
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type="password",
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value=default_key,
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help=f"环境变量: {provider_config.get('api_key_var', '')}",
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key="api_key_input"
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)
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# Sync to session state for save callback
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st.session_state.api_key = api_key
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# Base URL
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# Special-case: ensure DeepSeek shows its correct official base URL
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if current_provider == "DeepSeek":
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default_url = provider_config.get("base_url", "")
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else:
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default_url = (
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st.session_state.get("base_url_input")
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if st.session_state.get("base_url_input") is not None and st.session_state.get("base_url_input") != ""
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else (saved.get("base_url") if saved.get("provider") == current_provider else provider_config.get("base_url", ""))
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)
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base_url = st.text_input(
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"API Base URL",
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value=default_url,
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key="base_url_input"
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)
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st.session_state.base_url = base_url
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# Trigger save if values changed (manual check since text_input on_change is tricky with typing)
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if api_key != saved.get("api_key") or base_url != saved.get("base_url"):
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save_current_config()
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if not api_key:
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st.warning("请配置 API Key 以继续")
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# Output Language Selection
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lang_options = config.SUPPORTED_LANGUAGES
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default_lang = saved.get("language", "Chinese")
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try:
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lang_idx = lang_options.index(default_lang)
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except ValueError:
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lang_idx = 0
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output_language = st.sidebar.selectbox(
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"🌐 输出语言",
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options=lang_options,
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index=lang_idx,
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help="所有 AI Agent 将使用此语言进行回复",
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key="output_language",
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on_change=save_current_config
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)
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# 页面背景图片（全局）
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bg_file = st.file_uploader("页面背景图片（可选）", type=['png', 'jpg', 'jpeg', 'gif'], key='page_bg_uploader')
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if bg_file:
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# 保存到 assets 并在 session_state 中保存 data url 用于注入样式
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saved_path = st.session_state.storage.save_asset(bg_file)
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st.session_state.bg_image_path = saved_path
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try:
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buf = bg_file.getbuffer()
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except Exception:
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buf = bg_file.read()
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# detect mime
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ext = (bg_file.name.split('.')[-1].lower() if hasattr(bg_file, 'name') and bg_file.name else 'png')
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mime = 'image/png' if ext == 'png' else ('image/gif' if ext == 'gif' else 'image/jpeg')
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data_url = f"data:{mime};base64,{base64.b64encode(buf).decode()}"
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st.session_state.bg_image_data_url = data_url
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st.success("页面背景已上传并保存")
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st.divider()
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# 模式选择
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mode = st.radio(
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"📊 选择模式",
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["Council V4 (Deep Research)", "Debate Workshop", "📜 History Archives"],
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index=0 if st.session_state.mode == "Deep Research" else (1 if st.session_state.mode == "Debate Workshop" else 2)
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)
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# Map selection back to internal mode string
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if mode == "Council V4 (Deep Research)":
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st.session_state.mode = "Deep Research"
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elif mode == "Debate Workshop":
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st.session_state.mode = "Debate Workshop"
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else:
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st.session_state.mode = "History Archives"
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st.divider()
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if st.session_state.mode == "Debate Workshop": # Debate Workshop Settings
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# 模型选择
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model = st.selectbox(
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"🤖 选择通用模型",
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options=list(AVAILABLE_MODELS.keys()),
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format_func=lambda x: AVAILABLE_MODELS[x],
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index=0,
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help="选择用于辩论的 AI 模型"
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)
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# 辩论配置
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max_rounds = st.slider(
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"🔄 辩论轮数",
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min_value=1,
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max_value=4,
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value=2,
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help="每轮所有 Agent 都会发言一次"
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)
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st.divider()
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# ==================== 自定义角色 (Debate Only) ====================
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st.subheader("✨ 自定义角色")
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with st.expander("➕ 添加新角色", expanded=False):
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new_agent_name = st.text_input("角色名称", placeholder="如：法务顾问", key="new_agent_name")
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new_agent_emoji = st.text_input("角色 Emoji", value="🎯", max_chars=2, key="new_agent_emoji")
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new_agent_perspective = st.text_input("视角定位", placeholder="如：法律合规视角", key="new_agent_perspective")
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new_agent_focus = st.text_input("关注点（逗号分隔）", placeholder="如：合规风险, 法律条款", key="new_agent_focus")
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new_agent_prompt = st.text_area("角色设定 Prompt", placeholder="描述这个角色的思考方式...", height=100, key="new_agent_prompt")
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if st.button("✅ 添加角色", use_container_width=True):
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if new_agent_name and new_agent_prompt:
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agent_id = f"custom_{len(st.session_state.custom_agents)}"
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st.session_state.custom_agents[agent_id] = {
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"name": new_agent_name,
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"emoji": new_agent_emoji,
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"perspective": new_agent_perspective or "自定义视角",
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"focus_areas": [f.strip() for f in new_agent_focus.split(",") if f.strip()],
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"system_prompt": new_agent_prompt
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}
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st.success(f"已添加角色: {new_agent_emoji} {new_agent_name}")
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st.rerun()
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else:
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st.warning("请至少填写角色名称和 Prompt")
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# 显示已添加的自定义角色
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if st.session_state.custom_agents:
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st.markdown("**已添加的自定义角色：**")
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for agent_id, agent_info in list(st.session_state.custom_agents.items()):
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col1, col2 = st.columns([3, 1])
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with col1:
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st.markdown(f"{agent_info['emoji']} {agent_info['name']}")
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with col2:
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if st.button("🗑️", key=f"del_{agent_id}"):
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del st.session_state.custom_agents[agent_id]
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st.rerun()
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# 注入全局页面背景样式（如果已上传）
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if st.session_state.get("bg_image_data_url"):
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st.markdown(
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f"""<style>
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.stApp {{
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background-image: url('{st.session_state.get('bg_image_data_url')}');
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background-size: cover;
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background-position: center;
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background-attachment: fixed;
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}}
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</style>""",
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unsafe_allow_html=True
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)
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# ==================== 主界面逻辑 ====================
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if st.session_state.mode == "Deep Research":
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st.title("🧪 Multi-Model Council V4")
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st.markdown("*多模型智囊团：自定义 N 个专家进行多轮对话讨论，最后由最后一位专家决策*")
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col1, col2 = st.columns([3, 1])
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with col1:
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research_topic = st.text_area("研究/决策主题", placeholder="请输入你想深入研究或决策的主题...", height=100)
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with col2:
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max_rounds = st.number_input("讨论轮数", min_value=1, max_value=5, value=2, help="专家们进行对话的轮数")
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# Expert Configuration
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st.subheader("👥 专家配置")
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num_experts = st.number_input("专家数量", min_value=2, max_value=5, value=3)
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experts_config = []
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cols = st.columns(num_experts)
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for i in range(num_experts):
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with cols[i]:
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default_model_key = list(AVAILABLE_MODELS.keys())[i % len(AVAILABLE_MODELS)]
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st.markdown(f"**Expert {i+1}**")
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# Default names
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default_name = f"Expert {i+1}"
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if i == num_experts - 1:
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default_name = f"Expert {i+1} (Synthesizer)"
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expert_name = st.text_input(f"名称 #{i+1}", value=default_name, key=f"expert_name_{i}")
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expert_model = st.selectbox(f"模型 #{i+1}", options=list(AVAILABLE_MODELS.keys()), index=list(AVAILABLE_MODELS.keys()).index(default_model_key), key=f"expert_model_{i}")
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experts_config.append({
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"name": expert_name,
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"model": expert_model
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})
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research_context = st.text_area("补充背景 (可选)", placeholder="任何额外的背景信息...", height=80)
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start_research_btn = st.button("🚀 开始多模型协作", type="primary", disabled=not research_topic)
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if start_research_btn and research_topic:
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st.session_state.research_started = True
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st.session_state.research_output = ""
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st.session_state.research_steps_output = []
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# 使用全局页面背景（若已上传）
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research_bg_path = st.session_state.get("bg_image_path")
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if st.session_state.get("bg_image_data_url"):
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try:
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st.markdown("**页面背景预览**")
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st.image(st.session_state.get("bg_image_data_url"), use_column_width=True)
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except Exception:
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pass
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manager = ResearchManager(
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api_key=api_key,
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base_url=base_url,
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provider=provider_id
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)
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config_obj = ResearchConfig(
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topic=research_topic,
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context=research_context,
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experts=experts_config,
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language=output_language
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)
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manager.create_agents(config_obj)
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st.divider()
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st.subheader("🗣️ 智囊团讨论中...")
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chat_container = st.container()
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try:
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for event in manager.collaborate(research_topic, research_context, max_rounds=max_rounds):
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if event["type"] == "step_start":
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current_step_name = event["step"]
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current_agent = event["agent"]
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current_model = event["model"]
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# Create a chat message block
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with chat_container:
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st.markdown(f"#### {current_step_name}")
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st.caption(f"🤖 {current_agent} ({current_model})")
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message_placeholder = st.empty()
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current_content = ""
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elif event["type"] == "content":
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current_content += event["content"]
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message_placeholder.markdown(current_content)
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elif event["type"] == "step_end":
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# Save step result for history
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st.session_state.research_steps_output.append({
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"step": current_step_name,
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"output": event["output"]
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})
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st.divider() # Separator between turns
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# The last step output is the final plan
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if st.session_state.research_steps_output:
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final_plan = st.session_state.research_steps_output[-1]["output"]
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st.session_state.research_output = final_plan
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st.success("✅ 综合方案生成完毕")
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# Auto-save history (附带背景图片路径如果存在)
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metadata = {
|
||
"rounds": max_rounds,
|
||
"experts": [e["name"] for e in experts_config],
|
||
"language": output_language
|
||
}
|
||
if research_bg_path:
|
||
metadata["background_image"] = research_bg_path
|
||
|
||
st.session_state.storage.save_history(
|
||
session_type="council",
|
||
topic=research_topic,
|
||
content=final_plan,
|
||
metadata=metadata
|
||
)
|
||
st.toast("✅ 记录已保存到历史档案")
|
||
|
||
except Exception as e:
|
||
st.error(f"发生错误: {str(e)}")
|
||
import traceback
|
||
st.code(traceback.format_exc())
|
||
|
||
# Show Final Report if available
|
||
if st.session_state.research_output:
|
||
st.divider()
|
||
st.subheader("📄 最终综合方案")
|
||
st.markdown(st.session_state.research_output)
|
||
st.download_button("📥 下载方案", st.session_state.research_output, "comprehensive_plan.md")
|
||
|
||
# Show breakdown history
|
||
with st.expander("查看完整思考过程"):
|
||
for step in st.session_state.research_steps_output:
|
||
st.markdown(f"### {step['step']}")
|
||
st.markdown(step['output'])
|
||
st.divider()
|
||
|
||
# 追问模式（Deep Research）
|
||
st.divider()
|
||
st.subheader("🔎 追问模式 — 深入提问")
|
||
followup_q = st.text_area("输入你的追问（基于上面的综合方案）", key="research_followup_input", height=80)
|
||
if 'research_followups' not in st.session_state:
|
||
st.session_state.research_followups = []
|
||
|
||
if st.button("💬 追问", key="research_followup_btn") and followup_q:
|
||
# 创建客户端，优先使用最后一个专家的模型作为回复模型
|
||
follow_model = None
|
||
try:
|
||
follow_model = experts_config[-1]['model'] if experts_config else None
|
||
except Exception:
|
||
follow_model = None
|
||
|
||
llm = LLMClient(provider=provider_id, api_key=st.session_state.get('api_key'), base_url=st.session_state.get('base_url'), model=follow_model)
|
||
|
||
sys_prompt = "你是一个基于先前生成的综合方案的助理，针对用户的追问进行简明、深入且行动导向的回答。"
|
||
user_prompt = f"已生成的综合方案:\n{st.session_state.research_output}\n\n用户追问:\n{followup_q}"
|
||
|
||
placeholder = st.empty()
|
||
reply = ""
|
||
try:
|
||
for chunk in llm.chat_stream(system_prompt=sys_prompt, user_prompt=user_prompt, max_tokens=1024):
|
||
reply += chunk
|
||
placeholder.markdown(reply)
|
||
except Exception as e:
|
||
placeholder.markdown(f"错误: {e}")
|
||
|
||
# 保存本次追问到 session（仅会话级）
|
||
st.session_state.research_followups.append({"q": followup_q, "a": reply})
|
||
st.success("追问已得到回复")
|
||
|
||
# 显示历史追问
|
||
if st.session_state.research_followups:
|
||
with st.expander("查看追问历史"):
|
||
for idx, qa in enumerate(st.session_state.research_followups[::-1]):
|
||
st.markdown(f"**Q{len(st.session_state.research_followups)-idx}:** {qa['q']}")
|
||
st.markdown(f"**A:** {qa['a']}")
|
||
st.divider()
|
||
|
||
|
||
elif st.session_state.mode == "Debate Workshop":
|
||
# ==================== 原始 Debate UI 逻辑 ====================
|
||
st.title("🎭 多 Agent 决策工作坊")
|
||
st.markdown("*让多个 AI 角色从不同视角辩论，帮助你做出更全面的决策*")
|
||
|
||
# ==================== 输入区域 ====================
|
||
col1, col2 = st.columns([2, 1])
|
||
|
||
with col1:
|
||
st.subheader("📝 决策议题")
|
||
|
||
# 决策类型选择
|
||
decision_type = st.selectbox(
|
||
"决策类型",
|
||
options=list(DECISION_TYPES.keys()),
|
||
format_func=lambda x: DECISION_TYPES[x],
|
||
index=0
|
||
)
|
||
|
||
# 议题输入
|
||
topic = st.text_area(
|
||
"请描述你的决策议题",
|
||
placeholder="例如：我们是否应该在 Q2 推出 AI 助手功能？\n\n或者：我应该接受这份新工作 offer 吗？",
|
||
height=120
|
||
)
|
||
|
||
# 背景信息（可选）
|
||
with st.expander("➕ 添加背景信息（可选）"):
|
||
context = st.text_area(
|
||
"背景信息",
|
||
placeholder="提供更多上下文信息，如：\n- 当前状况\n- 已有的资源和限制\n- 相关数据和事实",
|
||
height=100
|
||
)
|
||
# 页面背景请使用侧边栏的“页面背景图片”上传控件进行设置（全局）
|
||
context = context if 'context' in dir() else ""
|
||
|
||
with col2:
|
||
st.subheader("🎭 选择参与角色")
|
||
|
||
# 获取推荐的角色
|
||
recommended = get_recommended_agents(decision_type)
|
||
all_agents = get_all_agents()
|
||
|
||
# 预设角色选择
|
||
st.markdown("**预设角色：**")
|
||
selected_agents = []
|
||
for agent in all_agents:
|
||
is_recommended = agent["id"] in recommended
|
||
default_checked = is_recommended
|
||
|
||
if st.checkbox(
|
||
f"{agent['emoji']} {agent['name']}",
|
||
value=default_checked,
|
||
key=f"agent_{agent['id']}"
|
||
):
|
||
selected_agents.append(agent["id"])
|
||
|
||
# 自定义角色选择
|
||
if st.session_state.custom_agents:
|
||
st.markdown("**自定义角色：**")
|
||
for agent_id, agent_info in st.session_state.custom_agents.items():
|
||
if st.checkbox(
|
||
f"{agent_info['emoji']} {agent_info['name']}",
|
||
value=True,
|
||
key=f"agent_{agent_id}"
|
||
):
|
||
selected_agents.append(agent_id)
|
||
|
||
# 自定义模型配置 (Advanced)
|
||
agent_model_map = {}
|
||
with st.expander("🛠️ 为每个角色指定模型 (可选)"):
|
||
for agent_id in selected_agents:
|
||
# Find agent name
|
||
agent_name = next((a['name'] for a in all_agents if a['id'] == agent_id), agent_id)
|
||
if agent_id in st.session_state.custom_agents:
|
||
agent_name = st.session_state.custom_agents[agent_id]['name']
|
||
|
||
agent_model = st.selectbox(
|
||
f"{agent_name} 的模型",
|
||
options=list(AVAILABLE_MODELS.keys()),
|
||
index=list(AVAILABLE_MODELS.keys()).index(model) if model in AVAILABLE_MODELS else 0,
|
||
key=f"model_for_{agent_id}"
|
||
)
|
||
agent_model_map[agent_id] = agent_model
|
||
|
||
# 角色数量提示
|
||
if len(selected_agents) < 2:
|
||
st.warning("请至少选择 2 个角色")
|
||
elif len(selected_agents) > 6:
|
||
st.warning("建议不超过 6 个角色")
|
||
else:
|
||
st.info(f"已选择 {len(selected_agents)} 个角色")
|
||
|
||
# ==================== 辩论控制 ====================
|
||
st.divider()
|
||
|
||
col_btn1, col_btn2, col_btn3 = st.columns([1, 1, 2])
|
||
|
||
with col_btn1:
|
||
start_btn = st.button(
|
||
"🚀 开始辩论",
|
||
disabled=(not topic or len(selected_agents) < 2 or not api_key),
|
||
type="primary",
|
||
use_container_width=True
|
||
)
|
||
|
||
with col_btn2:
|
||
reset_btn = st.button(
|
||
"🔄 重置",
|
||
use_container_width=True
|
||
)
|
||
|
||
if reset_btn:
|
||
st.session_state.debate_started = False
|
||
st.session_state.debate_finished = False
|
||
st.session_state.speeches = []
|
||
st.session_state.report = ""
|
||
st.rerun()
|
||
|
||
# ==================== 辩论展示区 ====================
|
||
if start_btn and topic and len(selected_agents) >= 2:
|
||
st.session_state.debate_started = True
|
||
st.session_state.speeches = []
|
||
|
||
st.divider()
|
||
st.subheader("🎬 辩论进行中...")
|
||
|
||
# 临时将自定义角色添加到 agent_profiles
|
||
from agents import agent_profiles
|
||
original_profiles = dict(agent_profiles.AGENT_PROFILES)
|
||
agent_profiles.AGENT_PROFILES.update(st.session_state.custom_agents)
|
||
|
||
try:
|
||
# 初始化默认客户端
|
||
llm_client = LLMClient(
|
||
provider=provider_id,
|
||
api_key=api_key,
|
||
base_url=base_url,
|
||
model=model
|
||
)
|
||
|
||
# 初始化特定角色的客户端
|
||
agent_clients = {}
|
||
for ag_id, ag_model in agent_model_map.items():
|
||
if ag_model != model: # Only create new client if different from default
|
||
agent_clients[ag_id] = LLMClient(
|
||
provider=provider_id,
|
||
api_key=api_key,
|
||
base_url=base_url,
|
||
model=ag_model
|
||
)
|
||
|
||
# 使用全局页面背景（若已上传）
|
||
debate_bg_path = st.session_state.get("bg_image_path")
|
||
if st.session_state.get("bg_image_data_url"):
|
||
try:
|
||
st.markdown("**页面背景预览**")
|
||
st.image(st.session_state.get("bg_image_data_url"), use_column_width=True)
|
||
except Exception:
|
||
pass
|
||
|
||
debate_manager = DebateManager(llm_client)
|
||
|
||
# 配置辩论
|
||
debate_config = DebateConfig(
|
||
topic=topic,
|
||
context=context,
|
||
agent_ids=selected_agents,
|
||
max_rounds=max_rounds,
|
||
agent_clients=agent_clients,
|
||
language=output_language
|
||
)
|
||
debate_manager.setup_debate(debate_config)
|
||
|
||
# 运行辩论（流式）
|
||
current_round = 0
|
||
speech_placeholder = None
|
||
|
||
for event in debate_manager.run_debate_stream():
|
||
if event["type"] == "round_start":
|
||
current_round = event["round"]
|
||
st.markdown(
|
||
f'<div class="round-header">📢 第 {current_round} 轮讨论</div>',
|
||
unsafe_allow_html=True
|
||
)
|
||
|
||
elif event["type"] == "speech_start":
|
||
# 显示模型名称
|
||
model_display = f" <span style='font-size:0.8em; color:gray'>({event.get('model_name', 'Unknown')})</span>"
|
||
st.markdown(f"**{event['emoji']} {event['agent_name']}**{model_display}", unsafe_allow_html=True)
|
||
speech_placeholder = st.empty()
|
||
current_content = ""
|
||
|
||
elif event["type"] == "speech_chunk":
|
||
current_content += event["chunk"]
|
||
speech_placeholder.markdown(current_content)
|
||
|
||
elif event["type"] == "speech_end":
|
||
st.session_state.speeches.append({
|
||
"agent_id": event["agent_id"],
|
||
"content": event["content"],
|
||
"round": current_round
|
||
})
|
||
st.divider()
|
||
|
||
# 生成报告
|
||
if st.session_state.debate_finished:
|
||
report_generator = ReportGenerator(llm_client)
|
||
speeches = debate_manager.get_all_speeches()
|
||
|
||
st.subheader("📊 决策报告")
|
||
report_placeholder = st.empty()
|
||
report_content = ""
|
||
|
||
for chunk in report_generator.generate_report_stream(
|
||
topic=topic,
|
||
speeches=speeches,
|
||
context=context
|
||
):
|
||
report_content += chunk
|
||
report_placeholder.markdown(report_content)
|
||
|
||
st.session_state.report = report_content
|
||
|
||
# Auto-save history
|
||
st.session_state.storage.save_history(
|
||
session_type="debate",
|
||
topic=topic,
|
||
content=report_content,
|
||
metadata={
|
||
"rounds": max_rounds,
|
||
"agents": selected_agents,
|
||
"language": output_language,
|
||
**({"background_image": st.session_state.get("bg_image_path")} if st.session_state.get("bg_image_path") else {})
|
||
}
|
||
)
|
||
st.toast("✅ 记录已保存到历史档案")
|
||
|
||
# 下载按钮
|
||
st.download_button(
|
||
label="📥 下载报告 (Markdown)",
|
||
data=report_content,
|
||
file_name="decision_report.md",
|
||
mime="text/markdown"
|
||
)
|
||
|
||
# 追问模式（Debate Workshop）
|
||
st.divider()
|
||
st.subheader("🔎 追问模式 — 基于报告的深入提问")
|
||
debate_followup_q = st.text_area("输入你的追问（基于上面的决策报告）", key="debate_followup_input", height=80)
|
||
if 'debate_followups' not in st.session_state:
|
||
st.session_state.debate_followups = []
|
||
|
||
if st.button("💬 追问", key="debate_followup_btn") and debate_followup_q:
|
||
# 使用生成报告时的 llm_client
|
||
llm_follow = llm_client
|
||
sys_prompt = "你是一个基于上面决策报告的助理，针对用户的追问进行简明且行动导向的回答。"
|
||
user_prompt = f"决策报告:\n{st.session_state.report}\n\n用户追问:\n{debate_followup_q}"
|
||
|
||
ph = st.empty()
|
||
reply = ""
|
||
try:
|
||
for chunk in llm_follow.chat_stream(system_prompt=sys_prompt, user_prompt=user_prompt, max_tokens=1024):
|
||
reply += chunk
|
||
ph.markdown(reply)
|
||
except Exception as e:
|
||
ph.markdown(f"错误: {e}")
|
||
|
||
st.session_state.debate_followups.append({"q": debate_followup_q, "a": reply})
|
||
st.success("追问已得到回复")
|
||
|
||
if st.session_state.debate_followups:
|
||
with st.expander("查看追问历史"):
|
||
for idx, qa in enumerate(st.session_state.debate_followups[::-1]):
|
||
st.markdown(f"**Q{len(st.session_state.debate_followups)-idx}:** {qa['q']}")
|
||
st.markdown(f"**A:** {qa['a']}")
|
||
st.divider()
|
||
|
||
except Exception as e:
|
||
st.error(f"发生错误: {str(e)}")
|
||
import traceback
|
||
st.code(traceback.format_exc())
|
||
st.info("请检查你的 API Key 和模型设置是否正确")
|
||
|
||
finally:
|
||
# 恢复原始角色配置
|
||
agent_profiles.AGENT_PROFILES = original_profiles
|
||
|
||
# ==================== 历史报告展示 ====================
|
||
elif st.session_state.report and not start_btn:
|
||
st.divider()
|
||
st.subheader("📊 上次的决策报告")
|
||
st.markdown(st.session_state.report)
|
||
|
||
st.download_button(
|
||
label="📥 下载报告 (Markdown)",
|
||
data=st.session_state.report,
|
||
file_name="decision_report.md",
|
||
mime="text/markdown"
|
||
)
|
||
|
||
# ==================== 历史档案浏览 ====================
|
||
elif st.session_state.mode == "History Archives":
|
||
st.title("📜 历史档案")
|
||
st.markdown("*查看过去的所有决策和研究记录*")
|
||
|
||
history_items = st.session_state.storage.list_history()
|
||
|
||
if not history_items:
|
||
st.info("暂无历史记录。开始一个新的 Council 或 Debate 来生成记录吧！")
|
||
else:
|
||
# Display as a table/list
|
||
for item in history_items:
|
||
with st.expander(f"{item['date']} | {item['type'].upper()} | {item['topic']}", expanded=False):
|
||
col1, col2 = st.columns([4, 1])
|
||
with col1:
|
||
st.caption(f"ID: {item['id']}")
|
||
with col2:
|
||
if st.button("查看详情", key=f"view_{item['id']}"):
|
||
st.session_state.view_history_id = item['filename']
|
||
st.rerun()
|
||
|
||
# View Detail Modal/Area
|
||
if "view_history_id" in st.session_state:
|
||
st.divider()
|
||
record = st.session_state.storage.load_history_item(st.session_state.view_history_id)
|
||
if record:
|
||
st.subheader(f"📄 记录详情: {record['topic']}")
|
||
st.markdown(f"**时间**: {record['date']} | **类型**: {record['type']}")
|
||
st.markdown("---")
|
||
st.markdown(record['content'])
|
||
# 如果历史记录里有背景图片，显示预览
|
||
try:
|
||
bg_path = record.get('metadata', {}).get('background_image')
|
||
if bg_path:
|
||
st.image(bg_path, caption="关联背景图片", use_column_width=True)
|
||
except Exception:
|
||
pass
|
||
st.download_button(
|
||
"📥 下载此记录",
|
||
record['content'],
|
||
file_name=f"{record['type']}_{record['id']}.md"
|
||
)
|
||
|
||
# ==================== 底部信息 ====================
|
||
st.divider()
|
||
col_footer1, col_footer2, col_footer3 = st.columns(3)
|
||
with col_footer2:
|
||
st.markdown(
|
||
"<div style='text-align: center; color: #888;'>"
|
||
"🎭 Multi-Agent Decision Workshop<br>多 Agent 决策工作坊"
|
||
"</div>",
|
||
unsafe_allow_html=True
|
||
)
|