"""
Multi-Agent Decision Workshop - 主应用
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
"""
import streamlit as st
import os
from dotenv import load_dotenv

# 加载环境变量
load_dotenv()

from agents import get_all_agents, get_recommended_agents, AGENT_PROFILES
from orchestrator import DebateManager, DebateConfig
from orchestrator.research_manager import ResearchManager, ResearchConfig
from report import ReportGenerator
from utils import LLMClient
import config

# ==================== 页面配置 ====================
st.set_page_config(
    page_title="🎭 多 Agent 决策工作坊",
    page_icon="🎭",
    layout="wide",
    initial_sidebar_state="expanded"
)

# ==================== 样式 ====================
st.markdown("""
<style>
    .agent-card {
        padding: 1rem;
        border-radius: 0.5rem;
        margin-bottom: 0.5rem;
        border-left: 4px solid #4A90A4;
        background-color: #f8f9fa;
    }
    .speech-bubble {
        background-color: #f0f2f6;
        padding: 1rem;
        border-radius: 0.5rem;
        margin: 0.5rem 0;
    }
    .round-header {
        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
        color: white;
        padding: 0.5rem 1rem;
        border-radius: 0.5rem;
        margin: 1rem 0;
    }
    .custom-agent-form {
        background-color: #e8f4f8;
        padding: 1rem;
        border-radius: 0.5rem;
        margin: 0.5rem 0;
    }
    .research-step {
        border-left: 3px solid #FF4B4B;
        padding-left: 10px;
        margin-bottom: 10px;
    }
</style>
""", unsafe_allow_html=True)

# ==================== 常量定义 ====================
# 从环境变量读取 API Key（隐藏在 .env 文件中）
DEFAULT_API_KEY = os.getenv("AIHUBMIX_API_KEY", "")

# 支持的模型列表
from config import AVAILABLE_MODELS, RESEARCH_MODEL_ROLES

# 决策类型
DECISION_TYPES = {
    "product": "产品方案",
    "business": "商业决策", 
    "tech": "技术选型",
    "personal": "个人规划"
}

# ==================== 初始化 Session State ====================
if "mode" not in st.session_state:
    st.session_state.mode = "Deep Research"

# Debate State
if "debate_started" not in st.session_state:
    st.session_state.debate_started = False
if "debate_finished" not in st.session_state:
    st.session_state.debate_finished = False
if "speeches" not in st.session_state:
    st.session_state.speeches = []
if "report" not in st.session_state:
    st.session_state.report = ""
if "custom_agents" not in st.session_state:
    st.session_state.custom_agents = {}

# Research State
if "research_plan" not in st.session_state:
    st.session_state.research_plan = ""
if "research_started" not in st.session_state:
    st.session_state.research_started = False
if "research_output" not in st.session_state:
    st.session_state.research_output = "" # Final report
if "research_steps_output" not in st.session_state:
    st.session_state.research_steps_output = [] # List of step results


# ==================== 侧边栏：配置 ====================
with st.sidebar:
    st.header("⚙️ 设置")
    
    # 全局 API Key & Provider 设置
    with st.expander("🔑 API / Provider 设置", expanded=True):
        # Provider Selection
        selected_provider_label = st.selectbox(
            "选择 API 提供商",
            options=list(config.LLM_PROVIDERS.keys()),
            index=0
        )
        
        provider_config = config.LLM_PROVIDERS[selected_provider_label]
        provider_id = selected_provider_label.lower() 
        
        # API Key Input
        default_key = os.getenv(provider_config["api_key_var"], "")
        api_key = st.text_input(
            f"{selected_provider_label} API Key", 
            type="password", 
            value=default_key,
            help=f"环境变量: {provider_config['api_key_var']}"
        )
        
        # Base URL
        base_url = st.text_input(
            "API Base URL", 
            value=provider_config["base_url"]
        )
            
        if not api_key:
            st.warning("请配置 API Key 以继续")
            
    st.divider()
    
    # 模式选择
    mode = st.radio(
        "📊 选择模式",
        ["Deep Research", "Debate Workshop"],
        index=0 if st.session_state.mode == "Deep Research" else 1
    )
    st.session_state.mode = mode
    
    st.divider()

    
    if mode == "Debate Workshop": # Debate Workshop Settings
        # 模型选择
        model = st.selectbox(
            "🤖 选择通用模型",
            options=list(AVAILABLE_MODELS.keys()),
            format_func=lambda x: AVAILABLE_MODELS[x],
            index=0,
            help="选择用于辩论的 AI 模型"
        )
        
        # 辩论配置
        max_rounds = st.slider(
            "🔄 辩论轮数",
            min_value=1,
            max_value=4,
            value=2,
            help="每轮所有 Agent 都会发言一次"
        )
        
        st.divider()
        
        # ==================== 自定义角色 (Debate Only) ====================
        st.subheader("✨ 自定义角色")
        
        with st.expander("➕ 添加新角色", expanded=False):
            new_agent_name = st.text_input("角色名称", placeholder="如：法务顾问", key="new_agent_name")
            new_agent_emoji = st.text_input("角色 Emoji", value="🎯", max_chars=2, key="new_agent_emoji")
            new_agent_perspective = st.text_input("视角定位", placeholder="如：法律合规视角", key="new_agent_perspective")
            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")
            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")
            
            if st.button("✅ 添加角色", use_container_width=True):
                if new_agent_name and new_agent_prompt:
                    agent_id = f"custom_{len(st.session_state.custom_agents)}"
                    st.session_state.custom_agents[agent_id] = {
                        "name": new_agent_name,
                        "emoji": new_agent_emoji,
                        "perspective": new_agent_perspective or "自定义视角",
                        "focus_areas": [f.strip() for f in new_agent_focus.split(",") if f.strip()],
                        "system_prompt": new_agent_prompt
                    }
                    st.success(f"已添加角色: {new_agent_emoji} {new_agent_name}")
                    st.rerun()
                else:
                    st.warning("请至少填写角色名称和 Prompt")
        
        # 显示已添加的自定义角色
        if st.session_state.custom_agents:
            st.markdown("**已添加的自定义角色：**")
            for agent_id, agent_info in list(st.session_state.custom_agents.items()):
                col1, col2 = st.columns([3, 1])
                with col1:
                    st.markdown(f"{agent_info['emoji']} {agent_info['name']}")
                with col2:
                    if st.button("🗑️", key=f"del_{agent_id}"):
                        del st.session_state.custom_agents[agent_id]
                        st.rerun()

# ==================== 主界面逻辑 ====================

if mode == "Deep Research":
    st.title("🧪 Multi-Model Council V4")
    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*")
    
    col1, col2 = st.columns([3, 1])
    with col1:
        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)
    with col2:
        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")
        
    # Expert Configuration
    st.subheader("👥 专家配置")
    num_experts = st.number_input("专家数量", min_value=2, max_value=5, value=3)
    
    experts_config = []
    cols = st.columns(num_experts)
    
    for i in range(num_experts):
        with cols[i]:
            default_model_key = list(AVAILABLE_MODELS.keys())[i % len(AVAILABLE_MODELS)]
            st.markdown(f"**Expert {i+1}**")
            # Default names
            default_name = f"Expert {i+1}"
            if i == num_experts - 1:
                default_name = f"Expert {i+1} (Synthesizer)"
                
            expert_name = st.text_input(f"名称 #{i+1}", value=default_name, key=f"expert_name_{i}")
            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}")
            
            experts_config.append({
                "name": expert_name,
                "model": expert_model
            })
            
    research_context = st.text_area("补充背景 (可选)", placeholder="任何额外的背景信息...", height=80)
    
    start_research_btn = st.button("🚀 开始多模型协作", type="primary", disabled=not research_topic)
    
    if start_research_btn and research_topic:
        st.session_state.research_started = True
        st.session_state.research_output = ""
        st.session_state.research_steps_output = []
        
        manager = ResearchManager(
            api_key=api_key, 
            base_url=base_url,
            provider=provider_id
        )
        config_obj = ResearchConfig(
            topic=research_topic,
            context=research_context,
            experts=experts_config
        )
        manager.create_agents(config_obj)
        
        st.divider()
        st.subheader("🗣️ 智囊团讨论中...")
        
        chat_container = st.container()
        
        try:
            for event in manager.collaborate(research_topic, research_context, max_rounds=max_rounds):
                if event["type"] == "step_start":
                    current_step_name = event["step"]
                    current_agent = event["agent"]
                    current_model = event["model"]
                    
                    # Create a chat message block
                    with chat_container:
                        st.markdown(f"#### {current_step_name}")
                        st.caption(f"🤖 {current_agent} ({current_model})")
                        message_placeholder = st.empty()
                        current_content = ""
                    
                elif event["type"] == "content":
                    current_content += event["content"]
                    message_placeholder.markdown(current_content)
                    
                elif event["type"] == "step_end":
                    # Save step result for history
                    st.session_state.research_steps_output.append({
                        "step": current_step_name,
                        "output": event["output"]
                    })
                    st.divider() # Separator between turns
            
            # The last step output is the final plan
            if st.session_state.research_steps_output:
                final_plan = st.session_state.research_steps_output[-1]["output"]
                st.session_state.research_output = final_plan
                st.success("✅ 综合方案生成完毕")
                
        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()


elif 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
            )
        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
                    )

            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
            )
            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()
                
                elif event["type"] == "debate_end":
                    st.session_state.debate_finished = True
                    st.success("✅ 辩论结束！正在生成决策报告...")
            
            # 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
                
                # 下载按钮
                st.download_button(
                    label="📥 下载报告 (Markdown)",
                    data=report_content,
                    file_name="decision_report.md",
                    mime="text/markdown"
                )
        
        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"
        )

# ==================== 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
    )