"""
CardioAI 模块1: 交互式仪表盘
心血管疾病数据可视化系统 - 美化版
"""

import streamlit as st
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from pathlib import Path

# ==================== 页面配置 ====================
st.set_page_config(
    page_title="CardioAI 心血管疾病分析",
    page_icon="❤️",
    layout="wide",
    initial_sidebar_state="expanded"
)

# ==================== 自定义CSS样式 ====================
st.markdown("""
<style>
    /* 全局样式 */
    .main {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        padding: 2rem;
    }

    /* 标题样式 */
    .main-title {
        text-align: center;
        background: linear-gradient(90deg, #ff6b6b, #feca57);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
        font-size: 3rem !important;
        font-weight: 800 !important;
        margin-bottom: 0.5rem !important;
        text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
    }

    .sub-title {
        text-align: center;
        color: #666;
        font-size: 1.1rem;
        margin-bottom: 2rem;
    }

    /* 侧边栏样式 */
    .sidebar .sidebar-content {
        background: linear-gradient(180deg, #1e3c72 0%, #2a5298 100%);
    }

    /* 指标卡片样式 */
    .metric-card {
        background: white;
        border-radius: 16px;
        padding: 1.5rem;
        box-shadow: 0 10px 40px rgba(0,0,0,0.1);
        transition: transform 0.3s ease, box-shadow 0.3s ease;
        border: 1px solid rgba(255,255,255,0.2);
    }

    .metric-card:hover {
        transform: translateY(-5px);
        box-shadow: 0 20px 60px rgba(0,0,0,0.15);
    }

    .metric-value {
        font-size: 2.5rem;
        font-weight: 700;
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        -webkit-background-clip: text;
        -webkit-text-fill-color: transparent;
    }

    .metric-label {
        color: #666;
        font-size: 0.9rem;
        font-weight: 500;
    }

    /* 图表容器样式 */
    .chart-container {
        background: white;
        border-radius: 16px;
        padding: 1.5rem;
        box-shadow: 0 4px 20px rgba(0,0,0,0.08);
        margin-bottom: 1.5rem;
    }

    /* 区块标题样式 */
    .section-title {
        background: linear-gradient(90deg, #667eea, #764ba2);
        color: white !important;
        padding: 0.8rem 1.5rem;
        border-radius: 12px;
        font-size: 1.2rem;
        font-weight: 600;
        margin-bottom: 1.5rem;
        box-shadow: 0 4px 15px rgba(102, 126, 234, 0.4);
    }

    /* 数据表格样式 */
    .dataframe {
        font-size: 0.85rem;
    }

    .dataframe th {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        font-weight: 600;
        padding: 12px;
    }

    .dataframe td {
        padding: 10px;
        border-bottom: 1px solid #eee;
    }

    .dataframe tr:hover {
        background-color: #f5f7fa;
    }

    /* 状态标签 */
    .status-safe {
        background: linear-gradient(135deg, #11998e 0%, #38ef7d 100%);
        color: white;
        padding: 0.3rem 0.8rem;
        border-radius: 20px;
        font-size: 0.8rem;
        font-weight: 600;
    }

    .status-risk {
        background: linear-gradient(135deg, #ff416c 0%, #ff4b2b 100%);
        color: white;
        padding: 0.3rem 0.8rem;
        border-radius: 20px;
        font-size: 0.8rem;
        font-weight: 600;
    }

    /* 进度条样式 */
    .stProgress > div > div {
        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
        border-radius: 10px;
    }

    /* 侧边栏筛选器样式 */
    .sidebar-filter {
        background: rgba(255,255,255,0.1);
        padding: 1rem;
        border-radius: 12px;
        margin-bottom: 1rem;
    }

    /* 分隔线样式 */
    hr {
        border: none;
        height: 2px;
        background: linear-gradient(90deg, transparent, #667eea, transparent);
        margin: 2rem 0;
    }

    /* 信息卡片 */
    .info-box {
        background: linear-gradient(135deg, #667eea15 0%, #764ba215 100%);
        border-left: 4px solid #667eea;
        padding: 1rem 1.5rem;
        border-radius: 0 12px 12px 0;
        margin: 1rem 0;
    }

    /* 按钮样式 */
    .stButton > button {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white;
        border: none;
        border-radius: 25px;
        padding: 0.6rem 2rem;
        font-weight: 600;
        transition: all 0.3s ease;
    }

    .stButton > button:hover {
        transform: scale(1.05);
        box-shadow: 0 8px 25px rgba(102, 126, 234, 0.4);
    }
</style>
""", unsafe_allow_html=True)

# ==================== 常量定义 ====================
CODE_ROOT = Path(r"F:\My_Git_Project\CardioAI")
DATA_PATH = CODE_ROOT / "data" / "心血管疾病.xlsx"

# 配色方案
COLORS = {
    'primary': ['#667eea', '#764ba2', '#f093fb', '#f5576c'],
    'safe': '#2ecc71',
    'risk': '#e74c3c',
    'gradient': ['#667eea', '#764ba2'],
    'bmi': ['#3498db', '#2ecc71', '#f39c12', '#e74c3c']
}


# ==================== 数据加载函数 ====================
@st.cache_data(show_spinner=False)
def load_data(file_path: Path) -> pd.DataFrame:
    """加载数据，支持Excel格式"""
    try:
        df = pd.read_excel(file_path, engine='openpyxl')
        return df
    except Exception as e:
        st.error(f"数据加载失败: {e}")
        return pd.DataFrame()


@st.cache_data(show_spinner=False)
def clean_and_engineer_features(df: pd.DataFrame) -> pd.DataFrame:
    """数据清洗和特征工程"""
    df = df.copy()

    # 1. 特征工程
    df['age_years'] = (df['age'] / 365).round().astype(int)
    df['bmi'] = df['weight'] / ((df['height'] / 100) ** 2)

    # 2. 异常值处理
    df = df[df['ap_lo'] < df['ap_hi']]
    df = df[(df['ap_hi'] >= 90) & (df['ap_hi'] <= 250)]
    df = df[(df['ap_lo'] >= 60) & (df['ap_lo'] <= 150)]

    # 3. 类别转换
    cholesterol_map = {1: '正常', 2: '偏高', 3: '非常高'}
    gluc_map = {1: '正常', 2: '偏高', 3: '非常高'}
    df['cholesterol_cat'] = df['cholesterol'].map(cholesterol_map)
    df['gluc_cat'] = df['gluc'].map(gluc_map)

    # 4. BMI分类
    def categorize_bmi(bmi):
        if bmi < 18.5:
            return '体重过低'
        elif bmi < 25:
            return '体重正常'
        elif bmi < 30:
            return '超重'
        else:
            return '肥胖'

    df['bmi_category'] = df['bmi'].apply(categorize_bmi)

    return df


# ==================== UI组件 ====================
def render_header():
    """渲染页面头部"""
    st.markdown('<h1 class="main-title">❤️ CardioAI</h1>', unsafe_allow_html=True)
    st.markdown('<p class="sub-title">心血管疾病智能分析系统 | 数据驱动的健康洞察</p>', unsafe_allow_html=True)
    st.markdown("<hr>", unsafe_allow_html=True)


def create_sidebar(df: pd.DataFrame) -> dict:
    """创建美观的侧边栏"""
    with st.sidebar:
        st.markdown("### 🎛️ 数据筛选器")
        st.markdown("---")

        # 年龄范围
        with st.container():
            st.markdown("**📅 年龄范围**")
            age_range = st.slider(
                "",
                min_value=int(df['age_years'].min()),
                max_value=int(df['age_years'].max()),
                value=(int(df['age_years'].min()), int(df['age_years'].max())),
                key="age_slider"
            )

        st.markdown("<br>", unsafe_allow_html=True)

        # 性别选择
        with st.container():
            st.markdown("**👤 性别**")
            gender_options = st.multiselect(
                "",
                options=[1, 2],
                default=[1, 2],
                format_func=lambda x: "👩 女性" if x == 1 else "👨 男性",
                key="gender_select"
            )

        st.markdown("<br>", unsafe_allow_html=True)

        # 心血管疾病状态
        with st.container():
            st.markdown("**🏥 心血管健康状态**")
            cardio_options = st.multiselect(
                "",
                options=[0, 1],
                default=[0, 1],
                format_func=lambda x: "✅ 健康" if x == 0 else "⚠️ 有风险",
                key="cardio_select"
            )

        st.markdown("---")

        # 数据统计
        st.markdown("### 📊 数据概览")
        st.metric("总记录数", f"{len(df):,}")
        st.metric("平均BMI", f"{df['bmi'].mean():.1f}")
        st.metric("平均年龄", f"{df['age_years'].mean():.1f} 岁")

        return {
            'age_range': age_range,
            'gender': gender_options,
            'cardio': cardio_options
        }


def apply_filters(df: pd.DataFrame, filters: dict) -> pd.DataFrame:
    """应用筛选条件"""
    return df[
        (df['age_years'] >= filters['age_range'][0]) &
        (df['age_years'] <= filters['age_range'][1]) &
        (df['gender'].isin(filters['gender'])) &
        (df['cardio'].isin(filters['cardio']))
    ]


def render_metrics(filtered_df: pd.DataFrame, total_count: int):
    """渲染指标卡片"""
    st.markdown('<div class="section-title">📊 关键指标</div>', unsafe_allow_html=True)

    col1, col2, col3, col4 = st.columns(4)

    with col1:
        st.markdown(f"""
        <div class="metric-card">
            <div class="metric-label">📋 筛选记录数</div>
            <div class="metric-value">{len(filtered_df):,}</div>
            <div style="color: #999; font-size: 0.8rem;">占比 {(len(filtered_df)/total_count*100):.1f}%</div>
        </div>
        """, unsafe_allow_html=True)

    with col2:
        risk_rate = (filtered_df['cardio'].sum() / len(filtered_df) * 100) if len(filtered_df) > 0 else 0
        st.markdown(f"""
        <div class="metric-card">
            <div class="metric-label">⚠️ 风险率</div>
            <div class="metric-value">{risk_rate:.1f}%</div>
            <div style="color: #999; font-size: 0.8rem;">心血管疾病患者占比</div>
        </div>
        """, unsafe_allow_html=True)

    with col3:
        avg_age = filtered_df['age_years'].mean() if len(filtered_df) > 0 else 0
        st.markdown(f"""
        <div class="metric-card">
            <div class="metric-label">📅 平均年龄</div>
            <div class="metric-value">{avg_age:.1f}</div>
            <div style="color: #999; font-size: 0.8rem;">岁</div>
        </div>
        """, unsafe_allow_html=True)

    with col4:
        avg_bmi = filtered_df['bmi'].mean() if len(filtered_df) > 0 else 0
        st.markdown(f"""
        <div class="metric-card">
            <div class="metric-label">⚖️ 平均BMI</div>
            <div class="metric-value">{avg_bmi:.1f}</div>
            <div style="color: #999; font-size: 0.8rem;">{get_bmi_status(avg_bmi)}</div>
        </div>
        """, unsafe_allow_html=True)


def get_bmi_status(bmi: float) -> str:
    """获取BMI状态"""
    if bmi < 18.5:
        return "体重过低"
    elif bmi < 25:
        return "体重正常"
    elif bmi < 30:
        return "超重"
    return "肥胖"


def plot_age_distribution(df: pd.DataFrame):
    """年龄分布图 - 美化版"""
    fig = px.histogram(
        df,
        x='age_years',
        color='cardio',
        nbins=30,
        title="年龄分布趋势",
        labels={'age_years': '年龄', 'count': '人数'},
        color_discrete_map={0: '#2ecc71', 1: '#e74c3c'},
        barmode='overlay',
        opacity=0.8
    )

    fig.update_layout(
        template='plotly_white',
        title_font_size=18,
        title_x=0.5,
        legend_title_text="",
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        plot_bgcolor='rgba(0,0,0,0)',
        paper_bgcolor='rgba(0,0,0,0)',
        font=dict(family="Arial, sans-serif", size=12),
        margin=dict(t=60, b=40, l=40, r=40)
    )

    fig.for_each_trace(lambda t: t.update(
        name="✅ 健康" if t.name == "0" else "⚠️ 有风险",
        marker_line_width=1,
        marker_line_color='white'
    ))

    return fig


def plot_bmi_distribution(df: pd.DataFrame):
    """BMI分布饼图"""
    bmi_counts = df['bmi_category'].value_counts().reindex(['体重过低', '体重正常', '超重', '肥胖'])

    fig = go.Figure(data=[go.Pie(
        labels=bmi_counts.index,
        values=bmi_counts.values,
        hole=0.5,
        marker=dict(
            colors=COLORS['bmi'],
            line=dict(color='white', width=2)
        ),
        textinfo='label+percent',
        textposition='outside',
        textfont=dict(size=12)
    )])

    fig.update_layout(
        title=dict(text="BMI分布", font=dict(size=18), x=0.5),
        template='plotly_white',
        showlegend=False,
        plot_bgcolor='rgba(0,0,0,0)',
        paper_bgcolor='rgba(0,0,0,0)',
        margin=dict(t=60, b=40, l=40, r=40),
        annotations=[dict(text='BMI', x=0.5, y=0.5, font_size=20, showarrow=False)]
    )

    return fig


def plot_bmi_cardio_relation(df: pd.DataFrame):
    """BMI与心血管疾病关系 - 堆叠柱状图"""
    bmi_cardio = df.groupby(['bmi_category', 'cardio']).size().unstack(fill_value=0)
    bmi_order = ['体重过低', '体重正常', '超重', '肥胖']
    bmi_cardio = bmi_cardio.reindex(bmi_order)

    fig = go.Figure()

    fig.add_trace(go.Bar(
        name='✅ 健康',
        x=bmi_cardio.index,
        y=bmi_cardio[0],
        marker_color='#2ecc71',
        marker_line=dict(color='white', width=1)
    ))

    fig.add_trace(go.Bar(
        name='⚠️ 有风险',
        x=bmi_cardio.index,
        y=bmi_cardio[1],
        marker_color='#e74c3c',
        marker_line=dict(color='white', width=1)
    ))

    fig.update_layout(
        title=dict(text="BMI与心血管疾病关联分析", font=dict(size=18), x=0.5),
        xaxis_title="BMI类别",
        yaxis_title="人数",
        barmode='stack',
        template='plotly_white',
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        plot_bgcolor='rgba(0,0,0,0)',
        paper_bgcolor='rgba(0,0,0,0)',
        margin=dict(t=60, b=40, l=40, r=40)
    )

    return fig


def plot_blood_pressure_scatter(df: pd.DataFrame):
    """血压散点图"""
    sample_df = df.sample(min(2000, len(df)))  # 采样避免过多点

    fig = px.scatter(
        sample_df,
        x='ap_lo',
        y='ap_hi',
        color='cardio',
        color_discrete_map={0: '#2ecc71', 1: '#e74c3c'},
        opacity=0.6,
        title="血压分布散点图",
        labels={'ap_lo': '舒张压 (mmHg)', 'ap_hi': '收缩压 (mmHg)'}
    )

    fig.update_layout(
        template='plotly_white',
        title_font_size=18,
        title_x=0.5,
        legend_title_text="",
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        plot_bgcolor='rgba(0,0,0,0)',
        paper_bgcolor='rgba(0,0,0,0)',
        margin=dict(t=60, b=40, l=40, r=40)
    )

    fig.for_each_trace(lambda t: t.update(
        name="✅ 健康" if t.name == "0" else "⚠️ 有风险"
    ))

    return fig


def plot_cholesterol_analysis(df: pd.DataFrame):
    """胆固醇与心血管疾病关系"""
    chol_cardio = df.groupby(['cholesterol_cat', 'cardio']).size().unstack(fill_value=0)
    chol_order = ['正常', '偏高', '非常高']
    chol_cardio = chol_cardio.reindex(chol_order)

    fig = go.Figure()

    fig.add_trace(go.Bar(
        name='✅ 健康',
        x=chol_cardio.index,
        y=chol_cardio[0] if 0 in chol_cardio.columns else [0, 0, 0],
        marker_color='#2ecc71'
    ))

    fig.add_trace(go.Bar(
        name='⚠️ 有风险',
        x=chol_cardio.index,
        y=chol_cardio[1] if 1 in chol_cardio.columns else [0, 0, 0],
        marker_color='#e74c3c'
    ))

    fig.update_layout(
        title=dict(text="胆固醇水平与心血管疾病", font=dict(size=18), x=0.5),
        xaxis_title="胆固醇水平",
        yaxis_title="人数",
        barmode='group',
        template='plotly_white',
        legend=dict(
            orientation="h",
            yanchor="bottom",
            y=1.02,
            xanchor="right",
            x=1
        ),
        plot_bgcolor='rgba(0,0,0,0)',
        paper_bgcolor='rgba(0,0,0,0)',
        margin=dict(t=60, b=40, l=40, r=40)
    )

    return fig


def render_data_table(df: pd.DataFrame):
    """渲染数据表格"""
    st.markdown('<div class="section-title">📋 数据明细</div>', unsafe_allow_html=True)

    display_cols = ['id', 'age_years', 'gender', 'height', 'weight', 'bmi', 'bmi_category',
                    'ap_hi', 'ap_lo', 'cholesterol_cat', 'gluc_cat', 'cardio']

    display_df = df[display_cols].copy()
    display_df['gender'] = display_df['gender'].map({1: '女性', 2: '男性'})
    display_df['cardio'] = display_df['cardio'].map({0: '✅ 健康', 1: '⚠️ 有风险'})

    display_df.columns = ['ID', '年龄', '性别', '身高(cm)', '体重(kg)', 'BMI', 'BMI分类',
                          '收缩压', '舒张压', '胆固醇', '血糖', '心血管状态']

    st.dataframe(
        display_df,
        use_container_width=True,
        height=400,
        column_config={
            "ID": st.column_config.NumberColumn(width="small"),
            "年龄": st.column_config.NumberColumn(width="small"),
            "心血管状态": st.column_config.TextColumn(width="medium")
        }
    )


def main():
    """主函数"""
    render_header()

    # 加载数据
    with st.spinner("正在加载数据..."):
        raw_df = load_data(DATA_PATH)

    if raw_df.empty:
        st.error("❌ 数据加载失败，请检查数据文件路径！")
        return

    # 数据处理
    with st.spinner("正在进行数据清洗..."):
        df = clean_and_engineer_features(raw_df)

    total_count = len(df)

    # 侧边栏筛选
    filters = create_sidebar(df)

    # 应用筛选
    filtered_df = apply_filters(df, filters)

    # 指标卡片
    render_metrics(filtered_df, total_count)

    st.markdown("<br>", unsafe_allow_html=True)

    # 图表区域
    st.markdown('<div class="section-title">📈 可视化分析</div>', unsafe_allow_html=True)

    # 第一行图表
    col1, col2 = st.columns(2)
    with col1:
        fig1 = plot_age_distribution(filtered_df)
        st.plotly_chart(fig1, use_container_width=True, key="age_chart")

    with col2:
        fig2 = plot_bmi_distribution(filtered_df)
        st.plotly_chart(fig2, use_container_width=True, key="bmi_pie")

    # 第二行图表
    col3, col4 = st.columns(2)
    with col3:
        fig3 = plot_bmi_cardio_relation(filtered_df)
        st.plotly_chart(fig3, use_container_width=True, key="bmi_cardio")

    with col4:
        fig4 = plot_cholesterol_analysis(filtered_df)
        st.plotly_chart(fig4, use_container_width=True, key="chol_chart")

    # 第三行图表
    col5, _ = st.columns([1, 1])
    with col5:
        fig5 = plot_blood_pressure_scatter(filtered_df)
        st.plotly_chart(fig5, use_container_width=True, key="bp_scatter")

    st.markdown("<br>", unsafe_allow_html=True)

    # 数据表格
    render_data_table(filtered_df)

    # 页脚
    st.markdown("<hr>", unsafe_allow_html=True)
    st.markdown(
        "<p style='text-align: center; color: #999; font-size: 0.9rem;'>❤️ CardioAI © 2024 | 心血管疾病智能分析系统</p>",
        unsafe_allow_html=True
    )


if __name__ == "__main__":
    main()