b5b299a682
- 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
685 lines
19 KiB
Python
685 lines
19 KiB
Python
"""
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CardioAI 模块1: 交互式仪表盘
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心血管疾病数据可视化系统 - 美化版
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"""
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import streamlit as st
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import pandas as pd
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import numpy as np
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import plotly.express as px
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import plotly.graph_objects as go
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from pathlib import Path
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# ==================== 页面配置 ====================
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st.set_page_config(
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page_title="CardioAI 心血管疾病分析",
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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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# ==================== 自定义CSS样式 ====================
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st.markdown("""
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<style>
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/* 全局样式 */
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.main {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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padding: 2rem;
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}
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/* 标题样式 */
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.main-title {
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text-align: center;
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background: linear-gradient(90deg, #ff6b6b, #feca57);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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font-size: 3rem !important;
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font-weight: 800 !important;
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margin-bottom: 0.5rem !important;
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text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
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}
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.sub-title {
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text-align: center;
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color: #666;
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font-size: 1.1rem;
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margin-bottom: 2rem;
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}
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/* 侧边栏样式 */
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.sidebar .sidebar-content {
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background: linear-gradient(180deg, #1e3c72 0%, #2a5298 100%);
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}
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/* 指标卡片样式 */
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.metric-card {
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background: white;
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border-radius: 16px;
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padding: 1.5rem;
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box-shadow: 0 10px 40px rgba(0,0,0,0.1);
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transition: transform 0.3s ease, box-shadow 0.3s ease;
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border: 1px solid rgba(255,255,255,0.2);
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}
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.metric-card:hover {
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transform: translateY(-5px);
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box-shadow: 0 20px 60px rgba(0,0,0,0.15);
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}
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.metric-value {
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font-size: 2.5rem;
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font-weight: 700;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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-webkit-background-clip: text;
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-webkit-text-fill-color: transparent;
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}
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.metric-label {
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color: #666;
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font-size: 0.9rem;
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font-weight: 500;
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}
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/* 图表容器样式 */
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.chart-container {
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background: white;
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border-radius: 16px;
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padding: 1.5rem;
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box-shadow: 0 4px 20px rgba(0,0,0,0.08);
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margin-bottom: 1.5rem;
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}
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/* 区块标题样式 */
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.section-title {
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background: linear-gradient(90deg, #667eea, #764ba2);
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color: white !important;
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padding: 0.8rem 1.5rem;
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border-radius: 12px;
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font-size: 1.2rem;
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font-weight: 600;
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margin-bottom: 1.5rem;
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box-shadow: 0 4px 15px rgba(102, 126, 234, 0.4);
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}
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/* 数据表格样式 */
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.dataframe {
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font-size: 0.85rem;
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}
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.dataframe th {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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color: white;
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font-weight: 600;
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padding: 12px;
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}
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.dataframe td {
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padding: 10px;
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border-bottom: 1px solid #eee;
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}
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.dataframe tr:hover {
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background-color: #f5f7fa;
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}
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/* 状态标签 */
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.status-safe {
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background: linear-gradient(135deg, #11998e 0%, #38ef7d 100%);
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color: white;
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padding: 0.3rem 0.8rem;
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border-radius: 20px;
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font-size: 0.8rem;
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font-weight: 600;
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}
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.status-risk {
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background: linear-gradient(135deg, #ff416c 0%, #ff4b2b 100%);
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color: white;
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padding: 0.3rem 0.8rem;
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border-radius: 20px;
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font-size: 0.8rem;
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font-weight: 600;
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}
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/* 进度条样式 */
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.stProgress > div > div {
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background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
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border-radius: 10px;
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}
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/* 侧边栏筛选器样式 */
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.sidebar-filter {
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background: rgba(255,255,255,0.1);
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padding: 1rem;
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border-radius: 12px;
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margin-bottom: 1rem;
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}
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/* 分隔线样式 */
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hr {
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border: none;
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height: 2px;
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background: linear-gradient(90deg, transparent, #667eea, transparent);
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margin: 2rem 0;
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}
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/* 信息卡片 */
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.info-box {
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background: linear-gradient(135deg, #667eea15 0%, #764ba215 100%);
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border-left: 4px solid #667eea;
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padding: 1rem 1.5rem;
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border-radius: 0 12px 12px 0;
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margin: 1rem 0;
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}
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/* 按钮样式 */
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.stButton > button {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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color: white;
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border: none;
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border-radius: 25px;
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padding: 0.6rem 2rem;
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font-weight: 600;
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transition: all 0.3s ease;
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}
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.stButton > button:hover {
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transform: scale(1.05);
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box-shadow: 0 8px 25px rgba(102, 126, 234, 0.4);
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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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CODE_ROOT = Path(r"F:\My_Git_Project\CardioAI")
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DATA_PATH = CODE_ROOT / "data" / "心血管疾病.xlsx"
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# 配色方案
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COLORS = {
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'primary': ['#667eea', '#764ba2', '#f093fb', '#f5576c'],
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'safe': '#2ecc71',
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'risk': '#e74c3c',
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'gradient': ['#667eea', '#764ba2'],
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'bmi': ['#3498db', '#2ecc71', '#f39c12', '#e74c3c']
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}
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# ==================== 数据加载函数 ====================
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@st.cache_data(show_spinner=False)
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def load_data(file_path: Path) -> pd.DataFrame:
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"""加载数据，支持Excel格式"""
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try:
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df = pd.read_excel(file_path, engine='openpyxl')
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return df
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except Exception as e:
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st.error(f"数据加载失败: {e}")
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return pd.DataFrame()
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@st.cache_data(show_spinner=False)
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def clean_and_engineer_features(df: pd.DataFrame) -> pd.DataFrame:
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"""数据清洗和特征工程"""
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df = df.copy()
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# 1. 特征工程
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df['age_years'] = (df['age'] / 365).round().astype(int)
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df['bmi'] = df['weight'] / ((df['height'] / 100) ** 2)
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# 2. 异常值处理
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df = df[df['ap_lo'] < df['ap_hi']]
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df = df[(df['ap_hi'] >= 90) & (df['ap_hi'] <= 250)]
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df = df[(df['ap_lo'] >= 60) & (df['ap_lo'] <= 150)]
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# 3. 类别转换
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cholesterol_map = {1: '正常', 2: '偏高', 3: '非常高'}
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gluc_map = {1: '正常', 2: '偏高', 3: '非常高'}
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df['cholesterol_cat'] = df['cholesterol'].map(cholesterol_map)
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df['gluc_cat'] = df['gluc'].map(gluc_map)
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# 4. BMI分类
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def categorize_bmi(bmi):
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if bmi < 18.5:
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return '体重过低'
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elif bmi < 25:
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return '体重正常'
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elif bmi < 30:
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return '超重'
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else:
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return '肥胖'
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df['bmi_category'] = df['bmi'].apply(categorize_bmi)
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return df
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# ==================== UI组件 ====================
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def render_header():
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"""渲染页面头部"""
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st.markdown('<h1 class="main-title">❤️ CardioAI</h1>', unsafe_allow_html=True)
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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)
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st.markdown("<hr>", unsafe_allow_html=True)
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def create_sidebar(df: pd.DataFrame) -> dict:
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"""创建美观的侧边栏"""
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with st.sidebar:
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st.markdown("### 🎛️ 数据筛选器")
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st.markdown("---")
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# 年龄范围
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with st.container():
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st.markdown("**📅 年龄范围**")
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age_range = st.slider(
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"",
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min_value=int(df['age_years'].min()),
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max_value=int(df['age_years'].max()),
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value=(int(df['age_years'].min()), int(df['age_years'].max())),
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key="age_slider"
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)
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st.markdown("<br>", unsafe_allow_html=True)
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# 性别选择
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with st.container():
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st.markdown("**👤 性别**")
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gender_options = st.multiselect(
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"",
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options=[1, 2],
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default=[1, 2],
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format_func=lambda x: "👩 女性" if x == 1 else "👨 男性",
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key="gender_select"
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)
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st.markdown("<br>", unsafe_allow_html=True)
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# 心血管疾病状态
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with st.container():
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st.markdown("**🏥 心血管健康状态**")
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cardio_options = st.multiselect(
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"",
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options=[0, 1],
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default=[0, 1],
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format_func=lambda x: "✅ 健康" if x == 0 else "⚠️ 有风险",
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key="cardio_select"
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)
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st.markdown("---")
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# 数据统计
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st.markdown("### 📊 数据概览")
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st.metric("总记录数", f"{len(df):,}")
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st.metric("平均BMI", f"{df['bmi'].mean():.1f}")
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st.metric("平均年龄", f"{df['age_years'].mean():.1f} 岁")
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return {
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'age_range': age_range,
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'gender': gender_options,
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'cardio': cardio_options
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}
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def apply_filters(df: pd.DataFrame, filters: dict) -> pd.DataFrame:
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"""应用筛选条件"""
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return df[
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(df['age_years'] >= filters['age_range'][0]) &
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(df['age_years'] <= filters['age_range'][1]) &
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(df['gender'].isin(filters['gender'])) &
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(df['cardio'].isin(filters['cardio']))
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]
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def render_metrics(filtered_df: pd.DataFrame, total_count: int):
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"""渲染指标卡片"""
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st.markdown('<div class="section-title">📊 关键指标</div>', unsafe_allow_html=True)
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.markdown(f"""
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<div class="metric-card">
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<div class="metric-label">📋 筛选记录数</div>
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<div class="metric-value">{len(filtered_df):,}</div>
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<div style="color: #999; font-size: 0.8rem;">占比 {(len(filtered_df)/total_count*100):.1f}%</div>
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</div>
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""", unsafe_allow_html=True)
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with col2:
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risk_rate = (filtered_df['cardio'].sum() / len(filtered_df) * 100) if len(filtered_df) > 0 else 0
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st.markdown(f"""
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<div class="metric-card">
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<div class="metric-label">⚠️ 风险率</div>
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<div class="metric-value">{risk_rate:.1f}%</div>
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<div style="color: #999; font-size: 0.8rem;">心血管疾病患者占比</div>
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</div>
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""", unsafe_allow_html=True)
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with col3:
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avg_age = filtered_df['age_years'].mean() if len(filtered_df) > 0 else 0
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st.markdown(f"""
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<div class="metric-card">
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<div class="metric-label">📅 平均年龄</div>
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<div class="metric-value">{avg_age:.1f}</div>
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<div style="color: #999; font-size: 0.8rem;">岁</div>
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</div>
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""", unsafe_allow_html=True)
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with col4:
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avg_bmi = filtered_df['bmi'].mean() if len(filtered_df) > 0 else 0
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st.markdown(f"""
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<div class="metric-card">
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<div class="metric-label">⚖️ 平均BMI</div>
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<div class="metric-value">{avg_bmi:.1f}</div>
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<div style="color: #999; font-size: 0.8rem;">{get_bmi_status(avg_bmi)}</div>
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</div>
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""", unsafe_allow_html=True)
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def get_bmi_status(bmi: float) -> str:
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"""获取BMI状态"""
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if bmi < 18.5:
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return "体重过低"
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elif bmi < 25:
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return "体重正常"
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elif bmi < 30:
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return "超重"
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return "肥胖"
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def plot_age_distribution(df: pd.DataFrame):
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"""年龄分布图 - 美化版"""
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fig = px.histogram(
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df,
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x='age_years',
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color='cardio',
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nbins=30,
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title="年龄分布趋势",
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labels={'age_years': '年龄', 'count': '人数'},
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color_discrete_map={0: '#2ecc71', 1: '#e74c3c'},
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barmode='overlay',
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opacity=0.8
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)
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fig.update_layout(
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template='plotly_white',
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title_font_size=18,
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title_x=0.5,
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legend_title_text="",
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legend=dict(
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orientation="h",
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yanchor="bottom",
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y=1.02,
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xanchor="right",
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x=1
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),
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plot_bgcolor='rgba(0,0,0,0)',
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paper_bgcolor='rgba(0,0,0,0)',
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font=dict(family="Arial, sans-serif", size=12),
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margin=dict(t=60, b=40, l=40, r=40)
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)
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fig.for_each_trace(lambda t: t.update(
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name="✅ 健康" if t.name == "0" else "⚠️ 有风险",
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marker_line_width=1,
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marker_line_color='white'
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))
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return fig
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def plot_bmi_distribution(df: pd.DataFrame):
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"""BMI分布饼图"""
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bmi_counts = df['bmi_category'].value_counts().reindex(['体重过低', '体重正常', '超重', '肥胖'])
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fig = go.Figure(data=[go.Pie(
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labels=bmi_counts.index,
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values=bmi_counts.values,
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hole=0.5,
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marker=dict(
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colors=COLORS['bmi'],
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line=dict(color='white', width=2)
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),
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textinfo='label+percent',
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textposition='outside',
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textfont=dict(size=12)
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)])
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fig.update_layout(
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title=dict(text="BMI分布", font=dict(size=18), x=0.5),
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template='plotly_white',
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showlegend=False,
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plot_bgcolor='rgba(0,0,0,0)',
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paper_bgcolor='rgba(0,0,0,0)',
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margin=dict(t=60, b=40, l=40, r=40),
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annotations=[dict(text='BMI', x=0.5, y=0.5, font_size=20, showarrow=False)]
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)
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return fig
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def plot_bmi_cardio_relation(df: pd.DataFrame):
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"""BMI与心血管疾病关系 - 堆叠柱状图"""
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bmi_cardio = df.groupby(['bmi_category', 'cardio']).size().unstack(fill_value=0)
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bmi_order = ['体重过低', '体重正常', '超重', '肥胖']
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bmi_cardio = bmi_cardio.reindex(bmi_order)
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fig = go.Figure()
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fig.add_trace(go.Bar(
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name='✅ 健康',
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x=bmi_cardio.index,
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y=bmi_cardio[0],
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marker_color='#2ecc71',
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marker_line=dict(color='white', width=1)
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))
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fig.add_trace(go.Bar(
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name='⚠️ 有风险',
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x=bmi_cardio.index,
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y=bmi_cardio[1],
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marker_color='#e74c3c',
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marker_line=dict(color='white', width=1)
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))
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fig.update_layout(
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title=dict(text="BMI与心血管疾病关联分析", font=dict(size=18), x=0.5),
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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()
|