Add CardioAI project with usage instructions

- Add comprehensive README.md with setup and usage instructions
- Add .env.example template (sanitized, no real API keys)
- Add root-level .gitignore to exclude .env and generated files
- Add all project modules (dashboard, predictor)
- Add data file and requirements.txt

CardioAI/.env.example

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+# DeepSeek API 配置
+BASE_URL=https://api.deepseek.com/v1
+DEEPSEEK_API_KEY=your_api_key_here
+MODEL_NAME=deepseek-reasoner
+
+# Flask 配置
+FLASK_ENV=development
+FLASK_DEBUG=True
+
+# 数据文件路径
+DATA_PATH=./data/心血管疾病.xlsx

CardioAI/.gitignore

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+# Environment
+.env
+*.pyc
+__pycache__/
+
+# IDE
+.idea/
+
+# Model files (generated)
+*.pkl
+
+# OS
+.DS_Store

CardioAI/README.md

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+# CardioAI - 心血管疾病智能辅助系统
+
+多模块应用，集成了数据可视化、机器学习预测和AI问答功能。
+
+## 项目结构
+
+```
+CardioAI/
+├── data/                          # 数据文件目录
+│   └── 心血管疾病.xlsx             # 心血管疾病数据集
+├── module1_dashboard/             # 模块1: Streamlit 交互式仪表盘
+│   └── cardio_dashboard.py
+├── module2_predictor/            # 模块2: XGBoost 风险预测模型
+│   ├── train_and_save.py          # 模型训练脚本
+│   ├── cardio_predictor_model.pkl # 训练好的模型
+│   ├── app.py                     # Flask API 服务
+│   └── templates/
+│       └── index.html             # 预测前端页面
+├── requirements.txt               # 项目依赖
+├── .env.example                   # 环境变量模板
+└── .gitignore                     # Git 忽略文件
+```
+
+## 环境配置
+
+### 1. 创建 conda 环境
+
+```bash
+conda create -n cardioenv python=3.10
+conda activate cardioenv
+```
+
+### 2. 安装依赖
+
+```bash
+cd F:\My_Git_Project\CardioAI
+pip install -r requirements.txt
+```
+
+### 3. 配置环境变量
+
+复制 `.env.example` 为 `.env`，并填入您的 API Key：
+
+```bash
+copy .env.example .env
+```
+
+编辑 `.env` 文件，填入您的 DeepSeek API Key。
+
+## 模块说明
+
+### 模块1: 交互式仪表盘 (Streamlit)
+
+心血管数据的交互式可视化界面。
+
+**启动命令:**
+```bash
+cd F:\My_Git_Project\CardioAI
+streamlit run module1_dashboard/cardio_dashboard.py
+```
+
+**功能特性:**
+- 年龄范围筛选
+- 性别和心血管疾病状态筛选
+- 统计数据展示
+- BMI分布可视化
+
+### 模块2: 心血管风险预测模型 (Flask + XGBoost)
+
+基于 XGBoost 的心血管疾病风险预测 API。
+
+**训练模型:**
+```bash
+cd F:\My_Git_Project\CardioAI
+python module2_predictor/train_and_save.py
+```
+
+**启动预测服务:**
+```bash
+cd F:\My_Git_Project\CardioAI\module2_predictor
+set FLASK_APP=app.py
+flask run --host=0.0.0.0 --port=5000
+```
+
+**API 接口:**
+- `POST /predict_cardio` - 提交11个特征值，返回预测概率和结果
+
+## 依赖说明
+
+- pandas, openpyxl - 数据处理
+- numpy, scikit-learn - 数值计算
+- xgboost, joblib - 机器学习
+- streamlit, plotly - 数据可视化
+- Flask - Web 服务
+- python-dotenv - 环境变量
+- langchain-openai, dashscope, requests - AI 集成
+
+## 注意事项
+
+1. 数据文件路径可通过 `.env` 中的 `DATA_PATH` 配置
+2. 确保 `.env` 文件不提交到版本库（已加入 .gitignore）
+3. 使用前请确保已正确配置 DeepSeek API Key

CardioAI/module1_dashboard/cardio_dashboard.py

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+"""
+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()

CardioAI/module2_predictor/app.py

@@ -0,0 +1,187 @@
+"""
+CardioAI 模块2: Flask API服务
+心血管疾病风险预测 - 后端接口
+"""
+
+import joblib
+import numpy as np
+import pandas as pd
+from flask import Flask, request, jsonify, render_template
+from pathlib import Path
+
+# ==================== 常量定义 ====================
+CODE_ROOT = Path(r"F:\My_Git_Project\CardioAI")
+MODEL_PATH = CODE_ROOT / "module2_predictor" / "cardio_predictor_model.pkl"
+
+# ==================== Flask应用 ====================
+app = Flask(__name__,
+            template_folder='templates',
+            static_folder='static')
+
+# 全局变量存储模型
+model = None
+
+
+def load_model():
+    """加载模型"""
+    global model
+    if model is None:
+        print("📂 正在加载模型...")
+        model = joblib.load(MODEL_PATH)
+        print("✅ 模型加载成功!")
+    return model
+
+
+# ==================== 路由定义 ====================
+@app.route('/')
+def index():
+    """渲染前端页面"""
+    return render_template('index.html')
+
+
+@app.route('/predict_cardio', methods=['POST'])
+def predict_cardio():
+    """
+    心血管疾病风险预测接口
+    接收11个原始特征值的JSON POST请求
+    返回预测概率和结果
+    """
+    try:
+        # 获取JSON数据
+        data = request.get_json()
+
+        if not data:
+            return jsonify({
+                'success': False,
+                'error': '未收到数据'
+            }), 400
+
+        # 定义特征列顺序（与训练时一致）
+        feature_names = [
+            'age_years', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo',
+            'cholesterol', 'gluc', 'smoke', 'alco', 'active'
+        ]
+
+        # 从请求中提取特征值
+        features = []
+        missing_fields = []
+
+        for col in feature_names:
+            if col in data:
+                features.append(float(data[col]))
+            else:
+                missing_fields.append(col)
+                features.append(0.0)  # 默认值
+
+        # 计算BMI: weight / (height/100)^2
+        weight = float(data.get('weight', 0))
+        height = float(data.get('height', 0))
+        if height > 0:
+            bmi = weight / ((height / 100) ** 2)
+            features.append(bmi)
+        else:
+            features.append(0.0)
+
+        if missing_fields:
+            return jsonify({
+                'success': False,
+                'error': f'缺少必要字段: {", ".join(missing_fields)}'
+            }), 400
+
+        # 定义特征列名（与训练时一致）
+        feature_columns = [
+            'age_years', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo',
+            'cholesterol', 'gluc', 'smoke', 'alco', 'active', 'bmi'
+        ]
+
+        # 转换为DataFrame格式
+        X_input = pd.DataFrame([features], columns=feature_columns)
+
+        # 加载模型（如果尚未加载）
+        predictor = load_model()
+
+        # 预测
+        prediction = int(predictor.predict(X_input)[0])
+        prob_risk = float(predictor.predict_proba(X_input)[0][1])
+        prob_healthy = float(predictor.predict_proba(X_input)[0][0])
+
+        # 构建响应
+        result = {
+            'success': True,
+            'prediction': prediction,
+            'prediction_label': '有风险' if prediction == 1 else '健康',
+            'probability': {
+                '健康': round(prob_healthy * 100, 2),
+                '有风险': round(prob_risk * 100, 2)
+            },
+            'risk_level': get_risk_level(prob_risk),
+            'recommendation': get_recommendation(prob_risk, data)
+        }
+
+        return jsonify(result)
+
+    except ValueError as e:
+        return jsonify({
+            'success': False,
+            'error': f'数据格式错误: {str(e)}'
+        }), 400
+
+    except Exception as e:
+        return jsonify({
+            'success': False,
+            'error': f'预测失败: {str(e)}'
+        }), 500
+
+
+def get_risk_level(probability: float) -> str:
+    """根据概率返回风险等级"""
+    if probability < 0.3:
+        return '🟢 低风险'
+    elif probability < 0.5:
+        return '🟡 中低风险'
+    elif probability < 0.7:
+        return '🟠 中高风险'
+    else:
+        return '🔴 高风险'
+
+
+def get_recommendation(probability: float, data: dict) -> str:
+    """根据预测结果给出建议"""
+    if probability < 0.3:
+        return '继续保持健康的生活方式，定期体检。'
+    elif probability < 0.5:
+        return '建议适当增加运动，注意饮食均衡。'
+    elif probability < 0.7:
+        return '建议咨询医生，制定健康管理计划。'
+    else:
+        return '⚠️ 风险较高，请尽快就医检查。'
+
+
+@app.route('/health', methods=['GET'])
+def health_check():
+    """健康检查接口"""
+    return jsonify({
+        'status': 'healthy',
+        'service': 'CardioAI Cardiovascular Prediction API',
+        'version': '1.0.0'
+    })
+
+
+# ==================== 启动应用 ====================
+if __name__ == '__main__':
+    print("\n" + "="*60)
+    print("❤️ CardioAI 心血管疾病风险预测 API")
+    print("="*60)
+    print(f"📂 模型路径: {MODEL_PATH}")
+    print(f"🌐 启动地址: http://localhost:5001")
+    print("="*60 + "\n")
+
+    # 预加载模型
+    load_model()
+
+    # 启动Flask应用
+    app.run(
+        host='0.0.0.0',
+        port=5001,
+        debug=True
+    )

CardioAI/module2_predictor/train_and_save.py

@@ -0,0 +1,199 @@
+"""
+CardioAI 模块2: 模型训练脚本
+心血管疾病风险预测模型 - 训练与保存
+"""
+
+import pandas as pd
+import numpy as np
+import joblib
+from pathlib import Path
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report, roc_auc_score, accuracy_score
+from xgboost import XGBClassifier
+
+# ==================== 常量定义 ====================
+CODE_ROOT = Path(r"F:\My_Git_Project\CardioAI")
+DATA_PATH = CODE_ROOT / "data" / "心血管疾病.xlsx"
+MODEL_PATH = CODE_ROOT / "module2_predictor" / "cardio_predictor_model.pkl"
+
+# 特征列定义
+CONTINUOUS_FEATURES = ['age', 'height', 'weight', 'ap_hi', 'ap_lo', 'bmi']
+CATEGORICAL_FEATURES = ['gender', 'cholesterol', 'gluc', 'smoke', 'alco', 'active']
+
+
+def load_and_clean_data(file_path: Path) -> pd.DataFrame:
+    """加载并清洗数据"""
+    print(f"📂 正在加载数据: {file_path}")
+
+    # 加载Excel数据
+    df = pd.read_excel(file_path, engine='openpyxl')
+    print(f"✅ 数据加载成功，共 {len(df)} 条记录")
+
+    # 复制数据
+    df = df.copy()
+
+    # 特征工程: age(天) -> age_years
+    df['age_years'] = (df['age'] / 365).round().astype(int)
+
+    # 计算BMI
+    df['bmi'] = df['weight'] / ((df['height'] / 100) ** 2)
+
+    # 异常值处理: 删除舒张压 >= 收缩压的记录
+    initial_count = len(df)
+    df = df[df['ap_lo'] < df['ap_hi']]
+    print(f"🗑️ 删除舒张压>=收缩压的记录: {initial_count - len(df)} 条")
+
+    # 删除血压极端异常值
+    # 收缩压 ∈ [90, 250]
+    initial_count = len(df)
+    df = df[(df['ap_hi'] >= 90) & (df['ap_hi'] <= 250)]
+    removed_hy = initial_count - len(df)
+
+    # 舒张压 ∈ [60, 150]
+    initial_count = len(df)
+    df = df[(df['ap_lo'] >= 60) & (df['ap_lo'] <= 150)]
+    removed_lo = initial_count - len(df)
+    print(f"🗑️ 删除血压异常值: 收缩压 {removed_hy} 条, 舒张压 {removed_lo} 条")
+
+    print(f"✅ 数据清洗完成，剩余 {len(df)} 条记录")
+    return df
+
+
+def prepare_features(df: pd.DataFrame) -> tuple:
+    """
+    准备特征和标签
+    删除id和原始age字段，保留处理后的特征
+    """
+    # 定义要使用的特征（删除id和原始age，保留age_years）
+    feature_columns = ['age_years', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo',
+                      'cholesterol', 'gluc', 'smoke', 'alco', 'active', 'bmi']
+
+    X = df[feature_columns].copy()
+    y = df['cardio'].copy()
+
+    print(f"📊 特征数量: {len(feature_columns)}")
+    print(f"📊 特征列: {feature_columns}")
+
+    return X, y, feature_columns
+
+
+def build_pipeline() -> Pipeline:
+    """构建包含预处理器和分类器的Pipeline"""
+    print("🔧 正在构建Pipeline...")
+
+    # 连续特征列
+    continuous_cols = ['age_years', 'height', 'weight', 'ap_hi', 'ap_lo', 'bmi']
+
+    # 分类特征列
+    categorical_cols = ['gender', 'cholesterol', 'gluc', 'smoke', 'alco', 'active']
+
+    # 预处理器
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ('num', StandardScaler(), continuous_cols),
+            ('cat', OneHotEncoder(handle_unknown='ignore', sparse_output=False), categorical_cols)
+        ],
+        remainder='drop'
+    )
+
+    # 完整Pipeline: 预处理 + XGBoost分类器
+    pipeline = Pipeline([
+        ('preprocessor', preprocessor),
+        ('classifier', XGBClassifier(
+            n_estimators=100,
+            max_depth=6,
+            learning_rate=0.1,
+            random_state=42,
+            use_label_encoder=False,
+            eval_metric='logloss',
+            n_jobs=-1
+        ))
+    ])
+
+    print("✅ Pipeline构建完成")
+    return pipeline
+
+
+def train_and_evaluate(X: pd.DataFrame, y: pd.Series, pipeline: Pipeline):
+    """训练模型并评估"""
+    print("\n" + "="*50)
+    print("📈 开始模型训练...")
+    print("="*50)
+
+    # 划分训练集和测试集
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=42, stratify=y
+    )
+
+    print(f"📊 训练集大小: {len(X_train)}")
+    print(f"📊 测试集大小: {len(X_test)}")
+
+    # 训练模型
+    print("🏋️ 正在训练XGBoost模型...")
+    pipeline.fit(X_train, y_train)
+
+    # 预测
+    y_pred = pipeline.predict(X_test)
+    y_pred_proba = pipeline.predict_proba(X_test)[:, 1]
+
+    # 评估指标
+    accuracy = accuracy_score(y_test, y_pred)
+    roc_auc = roc_auc_score(y_test, y_pred_proba)
+
+    print("\n" + "="*50)
+    print("📊 模型评估结果:")
+    print("="*50)
+    print(f"✅ 准确率 (Accuracy): {accuracy:.4f}")
+    print(f"✅ ROC-AUC 分数: {roc_auc:.4f}")
+    print("\n📋 分类报告:")
+    print(classification_report(y_test, y_pred, target_names=['健康', '有风险']))
+
+    return pipeline
+
+
+def save_model(pipeline: Pipeline, model_path: Path):
+    """保存模型"""
+    print(f"\n💾 正在保存模型到: {model_path}")
+    joblib.dump(pipeline, model_path)
+    print(f"✅ 模型保存成功!")
+
+    # 验证模型文件
+    file_size = model_path.stat().st_size / (1024 * 1024)
+    print(f"📦 模型文件大小: {file_size:.2f} MB")
+
+
+def main():
+    """主函数"""
+    print("\n" + "="*60)
+    print("❤️ CardioAI 模块2: 心血管疾病风险预测模型训练")
+    print("="*60 + "\n")
+
+    # 1. 加载并清洗数据
+    df = load_and_clean_data(DATA_PATH)
+
+    # 2. 准备特征
+    X, y, feature_columns = prepare_features(df)
+
+    # 3. 构建Pipeline
+    pipeline = build_pipeline()
+
+    # 4. 训练并评估模型
+    trained_pipeline = train_and_evaluate(X, y, pipeline)
+
+    # 5. 保存模型
+    save_model(trained_pipeline, MODEL_PATH)
+
+    print("\n" + "="*60)
+    print("🎉 模型训练完成!")
+    print("="*60)
+    print(f"\n📌 模型使用说明:")
+    print(f"   1. 启动Flask API: python app.py")
+    print(f"   2. 访问 http://localhost:5001 查看预测界面")
+    print(f"   3. 输入11个特征值进行预测")
+
+
+if __name__ == "__main__":
+    main()

CardioAI/requirements.txt

@@ -0,0 +1,21 @@
+# CardioAI 项目依赖
+# 创建并激活 conda 环境:
+# conda create -n cardioenv python=3.10
+# conda activate cardioenv
+
+# 然后安装依赖:
+# pip install -r requirements.txt
+
+pandas
+openpyxl
+numpy
+scikit-learn
+xgboost
+joblib
+streamlit
+plotly
+Flask
+python-dotenv
+langchain-openai
+dashscope
+requests