feat: 添加心血管疾病预测模型和语音助手模块
module2_predictor: - 逻辑回归、随机森林、梯度提升三种模型 - 模型性能对比 (准确率、精确率、召回率、F1、ROC-AUC) - 交互式预测界面 - 混淆矩阵、ROC曲线、特征重要性可视化 module3_voice_assistant: - 语音交互式心血管疾病风险评估 - 患者信息管理 - 风险仪表盘可视化 - 快速指令和自定义问答 - 对话历史记录 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
code_sam
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ai_code/aicodes/module3_voice_assistant/voice_assistant.py
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ai_code/aicodes/module3_voice_assistant/voice_assistant.py
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"""
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CardioAI 语音助手
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提供语音交互式心血管疾病风险评估
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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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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.metrics import roc_auc_score
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import plotly.express as px
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import plotly.graph_objects as go
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# ============================================
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# 数据加载函数 (带缓存)
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# ============================================
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@st.cache_data
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def load_data(file_path: str) -> pd.DataFrame:
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"""加载 Excel 数据文件"""
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df = pd.read_excel(file_path)
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return df
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# ============================================
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# 数据预处理函数
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# ============================================
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@st.cache_data
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def preprocess_data(df: pd.DataFrame) -> tuple:
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"""
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数据预处理
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Returns:
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(model, scaler, feature_columns)
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"""
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df_clean = df.copy()
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# 年龄转换
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df_clean['age_years'] = (df_clean['age'] / 365).round().astype(int)
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# 计算 BMI
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df_clean['bmi'] = df_clean['weight'] / ((df_clean['height'] / 100) ** 2)
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# 删除异常值
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df_clean = df_clean[df_clean['ap_hi'] > df_clean['ap_lo']]
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df_clean = df_clean[(df_clean['ap_hi'] >= 90) & (df_clean['ap_hi'] <= 250)]
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df_clean = df_clean[(df_clean['ap_lo'] >= 60) & (df_clean['ap_lo'] <= 150)]
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# 特征工程
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df_clean['bp_diff'] = df_clean['ap_hi'] - df_clean['ap_lo']
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df_clean['map'] = df_clean['ap_lo'] + (df_clean['bp_diff'] / 3)
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# 特征列
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feature_columns = [
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'age_years', 'gender', 'ap_hi', 'ap_lo',
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'cholesterol', 'gluc', 'bmi', 'smoke', 'alco', 'active'
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]
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X = df_clean[feature_columns]
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y = df_clean['cardio']
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# 分割数据
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42, stratify=y
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)
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# 标准化
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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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# 训练模型
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model = GradientBoostingClassifier(
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n_estimators=100,
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max_depth=5,
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learning_rate=0.1,
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random_state=42
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)
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model.fit(X_train_scaled, y_train)
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# 验证模型
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y_prob = model.predict_proba(X_test_scaled)[:, 1]
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roc_auc = roc_auc_score(y_test, y_prob)
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return model, scaler, feature_columns, roc_auc
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# ============================================
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# 预测函数
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# ============================================
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def predict_risk(model, scaler, input_data, feature_columns):
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"""预测心血管疾病风险"""
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input_df = pd.DataFrame([input_data])[feature_columns]
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input_scaled = scaler.transform(input_df)
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probability = model.predict_proba(input_scaled)[0]
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return probability
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# ============================================
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# 语音指令处理
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# ============================================
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def process_voice_command(command: str, patient_data: dict, model, scaler, feature_columns) -> str:
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"""
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处理语音指令
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Args:
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command: 用户语音指令
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patient_data: 患者数据
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model: 训练好的模型
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scaler: 标准化器
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feature_columns: 特征列
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Returns:
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响应文本
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"""
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command = command.lower()
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# 预测风险
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probability = predict_risk(model, scaler, patient_data, feature_columns)
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risk_percent = probability[1] * 100
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no_risk_percent = probability[0] * 100
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if "风险" in command or "患病" in command or "probability" in command:
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if risk_percent > 50:
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return f"根据您提供的信息，您患有心血管疾病的风险为 {risk_percent:.1f}%，风险较高，建议尽快就医检查。"
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else:
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return f"根据您提供的信息，您患有心血管疾病的风险为 {risk_percent:.1f}%，风险较低，请继续保持健康的生活方式。"
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elif "正常" in command or "健康" in command or "healthy" in command:
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return f"您的健康概率为 {no_risk_percent:.1f}%，患病风险为 {risk_percent:.1f}%。"
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elif "年龄" in command or "age" in command:
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return f"您的年龄是 {patient_data['age_years']} 岁。"
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elif "血压" in command or "blood pressure" in command:
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return f"您的血压信息：收缩压 {patient_data['ap_hi']} mmHg，舒张压 {patient_data['ap_lo']} mmHg。"
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elif "bmi" in command or "体重" in command:
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return f"您的 BMI 为 {patient_data['bmi']:.1f}。"
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elif "胆固醇" in command or "cholesterol" in command:
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chol_map = {1: "正常", 2: "高于正常", 3: "远高于正常"}
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return f"您的胆固醇水平：{chol_map.get(patient_data['cholesterol'], '未知')}。"
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elif "血糖" in command or "glucose" in command:
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gluc_map = {1: "正常", 2: "高于正常", 3: "远高于正常"}
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return f"您的血糖水平：{gluc_map.get(patient_data['gluc'], '未知')}。"
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elif "建议" in command or "advice" in command or "recommend" in command:
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if risk_percent > 60:
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return "建议您：1. 立即就医进行全面检查 2. 控制饮食，减少高盐高脂食物 3. 适度运动 4. 戒烟限酒 5. 保持规律作息。"
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elif risk_percent > 30:
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return "建议您：1. 定期体检监测 2. 保持健康饮食 3. 坚持适度运动 4. 控制体重 5. 避免吸烟饮酒。"
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else:
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return "建议您：1. 保持当前健康生活方式 2. 均衡饮食 3. 适量运动 4. 定期体检。"
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elif "帮助" in command or "help" in command:
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return """您可以询问以下信息：
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- 患病风险
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- 血压情况
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- BMI 数值
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- 胆固醇水平
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- 血糖水平
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- 健康建议
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- 我的年龄
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请告诉我您想了解什么？"""
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else:
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return f"抱歉，我没有理解您的指令。您可以问我关于患病风险、血压、BMI等问题。或者直接点击「开始语音评估」按钮进行心血管疾病风险评估。"
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# ============================================
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# Streamlit 页面配置
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# ============================================
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st.set_page_config(
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page_title="CardioAI 语音助手",
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page_icon="🎤",
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layout="wide"
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)
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# ============================================
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# 初始化会话状态
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# ============================================
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if 'patient_data' not in st.session_state:
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st.session_state.patient_data = None
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if 'voice_history' not in st.session_state:
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st.session_state.voice_history = []
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if 'model_trained' not in st.session_state:
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st.session_state.model_trained = False
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# ============================================
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# 主程序
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# ============================================
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def main():
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"""主程序入口"""
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# 页面标题
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st.title("🎤 CardioAI 智能语音助手")
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st.markdown("---")
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st.markdown("**您的私人心血管健康顾问**")
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# 数据路径
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DATA_PATH = "C:/Users/SAM/Desktop/sam_test/ai_code/aicodes/data/心血管疾病.xlsx"
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# 加载和训练模型
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try:
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df = load_data(DATA_PATH)
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model, scaler, feature_columns, roc_auc = preprocess_data(df)
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st.session_state.model_trained = True
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st.session_state.model = model
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st.session_state.scaler = scaler
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st.session_state.feature_columns = feature_columns
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except Exception as e:
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st.error(f"❌ 系统初始化失败: {e}")
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return
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# ============================================
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# 布局：侧边栏 + 主内容
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# ============================================
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st.sidebar.header("👤 患者信息录入")
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# 患者信息表单
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with st.sidebar.form("patient_form"):
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st.subheader("基本信息")
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age = st.number_input("年龄 (岁)", 20, 100, 50)
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gender = st.selectbox("性别", ["女性", "男性"])
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st.subheader("身体指标")
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ap_hi = st.number_input("收缩压 (mmHg)", 90, 250, 120)
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ap_lo = st.number_input("舒张压 (mmHg)", 60, 150, 80)
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height = st.number_input("身高 (cm)", 100, 220, 170)
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weight = st.number_input("体重 (kg)", 30, 200, 70)
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st.subheader("生化指标")
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cholesterol = st.selectbox("胆固醇水平", ["正常", "高于正常", "远高于正常"])
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gluc = st.selectbox("血糖水平", ["正常", "高于正常", "远高于正常"])
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st.subheader("生活方式")
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smoke = st.checkbox("吸烟")
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alco = st.checkbox("饮酒")
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active = st.checkbox("运动")
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submit = st.form_submit_button("确认信息", type="primary")
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if submit:
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# 构建患者数据
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gender_val = 1 if gender == "女性" else 2
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cholesterol_map = {"正常": 1, "高于正常": 2, "远高于正常": 3}
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gluc_map = {"正常": 1, "高于正常": 2, "远高于正常": 3}
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bmi = weight / ((height / 100) ** 2)
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st.session_state.patient_data = {
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'age_years': age,
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'gender': gender_val,
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'ap_hi': ap_hi,
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'ap_lo': ap_lo,
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'cholesterol': cholesterol_map[cholesterol],
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'gluc': gluc_map[gluc],
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'bmi': bmi,
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'smoke': 1 if smoke else 0,
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'alco': 1 if alco else 0,
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'active': 1 if active else 0
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}
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# 初始风险评估
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probability = predict_risk(
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st.session_state.model,
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st.session_state.scaler,
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st.session_state.patient_data,
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st.session_state.feature_columns
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)
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risk_msg = f"您好！我是 CardioAI 语音助手。根据您提供的信息，您的心血管疾病风险评估如下：患病风险为 {probability[1]*100:.1f}%，健康概率为 {probability[0]*100:.1f}%。您可以询问我任何关于您心血管健康的问题。"
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st.session_state.voice_history = [
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{"role": "assistant", "content": risk_msg}
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]
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st.success("✅ 患者信息已录入，可以开始语音交互了！")
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# ============================================
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# 主内容区域
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# ============================================
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# 欢迎信息和风险评估展示
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st.markdown("### 🎯 心血管疾病风险评估")
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if st.session_state.patient_data:
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# 预测风险
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probability = predict_risk(
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st.session_state.model,
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st.session_state.scaler,
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st.session_state.patient_data,
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st.session_state.feature_columns
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)
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risk_percent = probability[1] * 100
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# 风险仪表盘
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col1, col2 = st.columns([1, 2])
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with col1:
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# 风险仪表图
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fig_gauge = go.Figure(go.Indicator(
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mode="gauge+number",
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value=risk_percent,
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domain={'x': [0, 1], 'y': [0, 1]},
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title={'text': "心血管疾病风险率"},
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gauge={
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'axis': {'range': [0, 100]},
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'bar': {'color': "#e74c3c" if risk_percent > 50 else "#2ecc71"},
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'steps': [
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{'range': [0, 30], 'color': "#2ecc71"},
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{'range': [30, 60], 'color': "#f1c40f"},
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{'range': [60, 100], 'color': "#e74c3c"}
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],
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'threshold': {
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'line': {'color': "black", 'width': 4},
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'thickness': 0.75,
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'value': risk_percent
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}
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}
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))
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fig_gauge.update_layout(height=200)
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st.plotly_chart(fig_gauge, use_container_width=True)
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with col2:
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# 风险等级和建议
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if risk_percent < 30:
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st.success("🟢 **低风险** - 请继续保持健康的生活方式")
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st.info("建议：均衡饮食、适量运动、定期体检")
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elif risk_percent < 60:
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st.warning("🟡 **中风险** - 建议关注心血管健康")
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st.info("建议：控制饮食、适度运动、定期监测血压")
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else:
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st.error("🔴 **高风险** - 建议及时就医")
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st.info("建议：尽快就医、严格控制饮食、戒烟限酒")
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st.markdown(f"**模型 ROC-AUC**: {roc_auc:.4f}")
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else:
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st.info("👈 请在左侧填写患者信息后开始评估")
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st.markdown("---")
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# ============================================
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# 语音交互区域
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# ============================================
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st.markdown("### 🎤 语音交互")
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# 快速指令按钮
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st.markdown("#### 快速指令")
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quick_commands = [
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("查询风险", "我的患病风险是多少？"),
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("查询血压", "我的血压情况如何？"),
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("查询BMI", "我的BMI是多少？"),
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("健康建议", "请给我一些健康建议"),
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("帮助", "帮助")
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]
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cols = st.columns(5)
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for i, (label, cmd) in enumerate(quick_commands):
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if cols[i].button(label):
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if st.session_state.patient_data:
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response = process_voice_command(
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cmd,
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st.session_state.patient_data,
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st.session_state.model,
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st.session_state.scaler,
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st.session_state.feature_columns
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)
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st.session_state.voice_history.append(
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{"role": "user", "content": cmd}
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)
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st.session_state.voice_history.append(
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{"role": "assistant", "content": response}
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)
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else:
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st.warning("请先录入患者信息")
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st.markdown("---")
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# 自定义语音输入
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st.markdown("#### 自定义语音输入")
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# 模拟语音输入 (实际项目中可接入语音识别API)
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voice_input = st.text_input(
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"输入您的问题：",
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placeholder="例如：我的胆固醇水平如何？",
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key="voice_input"
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)
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||||
|
||||
if st.button("发送", type="primary") and voice_input:
|
||||
if st.session_state.patient_data:
|
||||
response = process_voice_command(
|
||||
voice_input,
|
||||
st.session_state.patient_data,
|
||||
st.session_state.model,
|
||||
st.session_state.scaler,
|
||||
st.session_state.feature_columns
|
||||
)
|
||||
st.session_state.voice_history.append(
|
||||
{"role": "user", "content": voice_input}
|
||||
)
|
||||
st.session_state.voice_history.append(
|
||||
{"role": "assistant", "content": response}
|
||||
)
|
||||
else:
|
||||
st.warning("请先录入患者信息")
|
||||
|
||||
# ============================================
|
||||
# 对话历史
|
||||
# ============================================
|
||||
st.markdown("---")
|
||||
st.markdown("### 💬 对话历史")
|
||||
|
||||
for msg in st.session_state.voice_history:
|
||||
if msg["role"] == "user":
|
||||
st.markdown(f"**👤 您**: {msg['content']}")
|
||||
else:
|
||||
st.markdown(f"**🤖 CardioAI**: {msg['content']}")
|
||||
st.markdown("")
|
||||
|
||||
# 清空对话
|
||||
if st.button("清空对话历史"):
|
||||
st.session_state.voice_history = []
|
||||
st.rerun()
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# ============================================
|
||||
# 使用说明
|
||||
# ============================================
|
||||
with st.expander("📖 使用说明"):
|
||||
st.markdown("""
|
||||
### CardioAI 语音助手使用指南
|
||||
|
||||
1. **录入患者信息**: 在左侧边栏填写患者的基本信息、身体指标、生化指标和生活方式。
|
||||
|
||||
2. **开始评估**: 点击「确认信息」按钮后，系统会自动进行心血管疾病风险评估。
|
||||
|
||||
3. **语音交互**: 您可以：
|
||||
- 使用快速指令按钮快速查询
|
||||
- 输入自定义问题
|
||||
|
||||
4. **支持的查询**:
|
||||
- 患病风险和健康概率
|
||||
- 血压、BMI、胆固醇、血糖等指标
|
||||
- 健康建议
|
||||
|
||||
### 风险等级说明
|
||||
- 🟢 **低风险** (0-30%): 继续保持健康生活方式
|
||||
- 🟡 **中风险** (30-60%): 建议关注并改善生活方式
|
||||
- 🔴 **高风险** (>60%): 建议及时就医检查
|
||||
""")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user