import streamlit as st
import pandas as pd
import joblib
import os
import sys

# 添加项目根目录到 Path 以便导入 src
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))

from src.agent_app import MarketingAgent, CustomerFeatures

# 配置页面
st.set_page_config(
    page_title="智能银行营销助手",
    page_icon="🤖",
    layout="wide"
)

# 侧边栏：项目信息
with st.sidebar:
    st.title("🏦 智能营销系统")
    st.markdown("---")
    st.info("**Day 5 演示版**")
    st.markdown("""
    **核心能力：**
    1. 📊 **LightGBM** 客户购买预测
    2. 🧠 **Agent** 策略生成
    3. 📝 **Pydantic** 结构化输出
    """)
    st.markdown("---")
    st.caption("由第 X 组开发")

# 主界面
st.title("🤖 客户意向预测与决策系统")

# 1. 模拟客户输入
st.header("1. 录入客户信息")

col1, col2, col3 = st.columns(3)

# 映射字典
job_map = {
    "management": "管理人员", "technician": "技术人员", "entrepreneur": "企业家", 
    "blue-collar": "蓝领", "unknown": "未知", "retired": "退休人员", 
    "admin.": "行政人员", "services": "服务业", "self-employed": "自雇人士", 
    "unemployed": "失业", "maid": "家政", "student": "学生"
}
education_map = {"tertiary": "高等教育", "secondary": "中等教育", "primary": "初等教育", "unknown": "未知"}
marital_map = {"married": "已婚", "single": "单身", "divorced": "离异"}
binary_map = {"yes": "是", "no": "否"}
contact_map = {"cellular": "手机", "telephone": "座机", "unknown": "未知"}
month_map = {
    "jan": "1月", "feb": "2月", "mar": "3月", "apr": "4月", "may": "5月", "jun": "6月",
    "jul": "7月", "aug": "8月", "sep": "9月", "oct": "10月", "nov": "11月", "dec": "12月"
}
poutcome_map = {"unknown": "未知", "failure": "失败", "other": "其他", "success": "成功"}

# 辅助函数：反向查找 key
def get_key(val, my_dict):
    for key, value in my_dict.items():
        if val == value: return key
    return val

with col1:
    age = st.number_input("年龄", 18, 100, 30)
    job_display = st.selectbox("职业", list(job_map.values()))
    job = get_key(job_display, job_map)
    
    education_display = st.selectbox("教育", list(education_map.values()))
    education = get_key(education_display, education_map)
    
    balance = st.number_input("账户余额 (欧元)", -1000, 100000, 1500)

with col2:
    marital_display = st.selectbox("婚姻", list(marital_map.values()))
    marital = get_key(marital_display, marital_map)
    
    housing_display = st.selectbox("是否有房贷", list(binary_map.values()))
    housing = get_key(housing_display, binary_map)
    
    loan_display = st.selectbox("是否有个人贷", list(binary_map.values()))
    loan = get_key(loan_display, binary_map)
    
    default_display = st.selectbox("是否有违约记录", list(binary_map.values()))
    default = get_key(default_display, binary_map)

with col3:
    contact_display = st.selectbox("联系方式", list(contact_map.values()))
    contact = get_key(contact_display, contact_map)
    
    month_display = st.selectbox("最后联系月份", list(month_map.values()))
    month = get_key(month_display, month_map)
    
    day = st.slider("最后联系日", 1, 31, 15)
    
    poutcome_display = st.selectbox("上次活动结果", list(poutcome_map.values()))
    poutcome = get_key(poutcome_display, poutcome_map)

# 隐藏的高级特征
with st.expander("高级营销特征 (可选)"):
    campaign = st.number_input("本次活动联系次数", 1, 50, 1)
    pdays = st.number_input("距离上次联系天数 (-1代表无)", -1, 999, -1)
    previous = st.number_input("活动前联系次数", 0, 100, 0)

# 2. 触发 Agent
if st.button("🚀 开始分析与决策", type="primary"):
    try:
        # 构造 Pydantic 对象
        customer = CustomerFeatures(
            age=age, job=job, marital=marital, education=education,
            default=default, balance=balance, housing=housing, loan=loan,
            contact=contact, day=day, month=month,
            campaign=campaign, pdays=pdays, previous=previous, poutcome=poutcome
        )
        
        # 初始化 Agent
        with st.spinner("Agent 正在加载模型并思考..."):
            agent = MarketingAgent()
            decision = agent.run(customer)
            
        # 3. 展示结果
        st.divider()
        st.header("2. 智能分析报告")
        
        # 结果看板
        res_col1, res_col2 = st.columns([1, 2])
        
        with res_col1:
            st.metric("预测购买概率", f"{decision.risk_score:.1%}")
            if decision.risk_score > 0.6:
                st.success(f"分群：{decision.customer_segment}")
            elif decision.risk_score > 0.3:
                st.warning(f"分群：{decision.customer_segment}")
            else:
                st.error(f"分群：{decision.customer_segment}")
                
        with res_col2:
            st.subheader("💡 决策建议")
            st.info(decision.decision)
            st.markdown(f"**决策依据：** {decision.rationale}")
            
        # 行动清单
        st.subheader("📝 执行清单")
        for i, action in enumerate(decision.actions, 1):
            st.write(f"{i}. {action}")
            
        # JSON 视图
        with st.expander("查看原始 JSON 输出 (Traceable)"):
            st.json(decision.model_dump())
            
    except Exception as e:
        st.error(f"发生错误: {str(e)}")