diff --git a/ai_code/aicodes/module2_predictor/cardio_predictor.py b/ai_code/aicodes/module2_predictor/cardio_predictor.py new file mode 100644 index 0000000..c5777f7 --- /dev/null +++ b/ai_code/aicodes/module2_predictor/cardio_predictor.py @@ -0,0 +1,500 @@ +""" +CardioAI 心血管疾病预测模型 +使用机器学习模型进行心血管疾病风险预测 +""" + +import streamlit as st +import pandas as pd +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import ( + accuracy_score, precision_score, recall_score, + f1_score, roc_auc_score, confusion_matrix, + classification_report, roc_curve +) +import plotly.express as px +import plotly.graph_objects as go +import joblib +import os + + +# ============================================ +# 数据加载函数 (带缓存) +# ============================================ +@st.cache_data +def load_data(file_path: str) -> pd.DataFrame: + """ + 加载 Excel 数据文件 + + Args: + file_path: Excel 文件路径 + + Returns: + 加载的 DataFrame + """ + df = pd.read_excel(file_path) + return df + + +# ============================================ +# 数据预处理函数 +# ============================================ +@st.cache_data +def preprocess_data(df: pd.DataFrame) -> pd.DataFrame: + """ + 数据清洗和特征工程 + + Args: + df: 原始 DataFrame + + Returns: + 预处理后的 DataFrame + """ + df_clean = df.copy() + + # 年龄转换: 天 -> 年 + df_clean['age_years'] = (df_clean['age'] / 365).round().astype(int) + + # 计算 BMI + df_clean['bmi'] = df_clean['weight'] / ((df_clean['height'] / 100) ** 2) + + # 删除舒张压 >= 收缩压的记录 + df_clean = df_clean[df_clean['ap_hi'] > df_clean['ap_lo']] + + # 删除血压极端异常值 + df_clean = df_clean[(df_clean['ap_hi'] >= 90) & (df_clean['ap_hi'] <= 250)] + df_clean = df_clean[(df_clean['ap_lo'] >= 60) & (df_clean['ap_lo'] <= 150)] + + # BMI 分类 + def categorize_bmi(bmi): + if bmi < 18.5: + return 0 # 偏瘦 + elif bmi < 24: + return 1 # 正常 + elif bmi < 28: + return 2 # 超重 + else: + return 3 # 肥胖 + + df_clean['bmi_category'] = df_clean['bmi'].apply(categorize_bmi) + + # 特征工程: 添加更多有用的特征 + # 血压差值 + df_clean['bp_diff'] = df_clean['ap_hi'] - df_clean['ap_lo'] + # 平均动脉压 + df_clean['map'] = df_clean['ap_lo'] + (df_clean['bp_diff'] / 3) + + return df_clean + + +# ============================================ +# 模型训练函数 +# ============================================ +def train_models(X_train, y_train, X_test, y_test): + """ + 训练多个模型并返回结果 + + Args: + X_train, y_train: 训练数据 + X_test, y_test: 测试数据 + + Returns: + 训练好的模型和评估结果 + """ + results = {} + + # 1. 逻辑回归 + lr = LogisticRegression(max_iter=1000, random_state=42) + lr.fit(X_train, y_train) + y_pred_lr = lr.predict(X_test) + y_prob_lr = lr.predict_proba(X_test)[:, 1] + + results['Logistic Regression'] = { + 'model': lr, + 'predictions': y_pred_lr, + 'probabilities': y_prob_lr, + 'accuracy': accuracy_score(y_test, y_pred_lr), + 'precision': precision_score(y_test, y_pred_lr), + 'recall': recall_score(y_test, y_pred_lr), + 'f1': f1_score(y_test, y_pred_lr), + 'roc_auc': roc_auc_score(y_test, y_prob_lr) + } + + # 2. 随机森林 + rf = RandomForestClassifier( + n_estimators=100, + max_depth=10, + random_state=42, + n_jobs=-1 + ) + rf.fit(X_train, y_train) + y_pred_rf = rf.predict(X_test) + y_prob_rf = rf.predict_proba(X_test)[:, 1] + + results['Random Forest'] = { + 'model': rf, + 'predictions': y_pred_rf, + 'probabilities': y_prob_rf, + 'accuracy': accuracy_score(y_test, y_pred_rf), + 'precision': precision_score(y_test, y_pred_rf), + 'recall': recall_score(y_test, y_pred_rf), + 'f1': f1_score(y_test, y_pred_rf), + 'roc_auc': roc_auc_score(y_test, y_prob_rf) + } + + # 3. 梯度提升 + gb = GradientBoostingClassifier( + n_estimators=100, + max_depth=5, + learning_rate=0.1, + random_state=42 + ) + gb.fit(X_train, y_train) + y_pred_gb = gb.predict(X_test) + y_prob_gb = gb.predict_proba(X_test)[:, 1] + + results['Gradient Boosting'] = { + 'model': gb, + 'predictions': y_pred_gb, + 'probabilities': y_prob_gb, + 'accuracy': accuracy_score(y_test, y_pred_gb), + 'precision': precision_score(y_test, y_pred_gb), + 'recall': recall_score(y_test, y_pred_gb), + 'f1': f1_score(y_test, y_pred_gb), + 'roc_auc': roc_auc_score(y_test, y_prob_gb) + } + + return results + + +# ============================================ +# 预测函数 +# ============================================ +def predict_cardio(model, input_data, scaler): + """ + 使用模型进行预测 + + Args: + model: 训练好的模型 + input_data: 输入特征 + scaler: 数据标准化器 + + Returns: + 预测结果和概率 + """ + # 标准化输入 + input_scaled = scaler.transform(input_data) + # 预测 + prediction = model.predict(input_scaled)[0] + probability = model.predict_proba(input_scaled)[0] + + return prediction, probability + + +# ============================================ +# Streamlit 页面配置 +# ============================================ +st.set_page_config( + page_title="CardioAI 心血管疾病预测", + page_icon="🔬", + layout="wide" +) + + +# ============================================ +# 主程序 +# ============================================ +def main(): + """主程序入口""" + + # 页面标题 + st.title("🔬 CardioAI 心血管疾病预测模型") + st.markdown("---") + + # 数据路径 + DATA_PATH = "C:/Users/SAM/Desktop/sam_test/ai_code/aicodes/data/心血管疾病.xlsx" + + # 加载数据 + try: + df = load_data(DATA_PATH) + except Exception as e: + st.error(f"❌ 数据加载失败: {e}") + return + + # 数据预处理 + df_processed = preprocess_data(df) + st.success(f"✅ 数据加载成功，共 {len(df_processed)} 条有效记录") + + # ============================================ + # 侧边栏 - 模型选择 + # ============================================ + st.sidebar.header("⚙️ 模型设置") + + # 特征选择 + feature_options = [ + 'age_years', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo', + 'cholesterol', 'gluc', 'smoke', 'alco', 'active', 'bmi', 'bmi_category', + 'bp_diff', 'map' + ] + + selected_features = st.sidebar.multiselect( + "选择特征", + options=feature_options, + default=['age_years', 'gender', 'ap_hi', 'ap_lo', 'cholesterol', 'gluc', 'bmi'] + ) + + if not selected_features: + st.warning("请至少选择一个特征") + return + + # 测试集比例 + test_size = st.sidebar.slider("测试集比例", 0.1, 0.4, 0.2) + + # 分割数据 + X = df_processed[selected_features] + y = df_processed['cardio'] + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=test_size, random_state=42, stratify=y + ) + + # 标准化 + scaler = StandardScaler() + X_train_scaled = scaler.fit_transform(X_train) + X_test_scaled = scaler.transform(X_test) + + st.markdown("---") + + # ============================================ + # Tab 布局 + # ============================================ + tab1, tab2, tab3 = st.tabs(["📊 模型训练", "🔮 疾病预测", "📈 模型评估"]) + + # ==================== Tab 1: 模型训练 ==================== + with tab1: + st.header("模型训练") + + # 训练模型 + with st.spinner("正在训练模型..."): + results = train_models(X_train_scaled, y_train, X_test_scaled, y_test) + + st.success("✅ 模型训练完成") + + # 显示模型性能对比 + st.subheader("模型性能对比") + + # 创建性能对比表 + performance_data = [] + for model_name, result in results.items(): + performance_data.append({ + '模型': model_name, + '准确率': f"{result['accuracy']:.4f}", + '精确率': f"{result['precision']:.4f}", + '召回率': f"{result['recall']:.4f}", + 'F1分数': f"{result['f1']:.4f}", + 'ROC-AUC': f"{result['roc_auc']:.4f}" + }) + + performance_df = pd.DataFrame(performance_data) + st.table(performance_df) + + # 选择最佳模型 + best_model_name = max(results, key=lambda x: results[x]['roc_auc']) + best_result = results[best_model_name] + + st.info(f"🏆 最佳模型: {best_model_name} (ROC-AUC: {best_result['roc_auc']:.4f})") + + # ==================== Tab 2: 疾病预测 ==================== + with tab2: + st.header("心血管疾病风险预测") + + st.markdown("### 输入患者信息") + + # 创建输入表单 + col1, col2 = st.columns(2) + + with col1: + age = st.number_input("年龄 (岁)", 20, 100, 50) + gender = st.selectbox("性别", ["女性", "男性"]) + height = st.number_input("身高 (cm)", 100, 220, 170) + weight = st.number_input("体重 (kg)", 30, 200, 70) + + with col2: + ap_hi = st.number_input("收缩压 (mmHg)", 90, 250, 120) + ap_lo = st.number_input("舒张压 (mmHg)", 60, 150, 80) + cholesterol = st.selectbox("胆固醇", ["正常", "高于正常", "远高于正常"]) + gluc = st.selectbox("血糖", ["正常", "高于正常", "远高于正常"]) + + # 生活方式 + col3, col4, col5 = st.columns(3) + with col3: + smoke = st.checkbox("吸烟") + with col4: + alco = st.checkbox("饮酒") + with col5: + active = st.checkbox("运动") + + # 转换输入数据 + gender_val = 1 if gender == "女性" else 2 + cholesterol_map = {"正常": 1, "高于正常": 2, "远高于正常": 3} + gluc_map = {"正常": 1, "高于正常": 2, "远高于正常": 3} + cholesterol_val = cholesterol_map[cholesterol] + gluc_val = gluc_map[gluc] + smoke_val = 1 if smoke else 0 + alco_val = 1 if alco else 0 + active_val = 1 if active else 0 + + # 计算 BMI + bmi = weight / ((height / 100) ** 2) + + # 计算派生特征 + bp_diff = ap_hi - ap_lo + map_val = ap_lo + (bp_diff / 3) + + # BMI 分类 + if bmi < 18.5: + bmi_cat = 0 + elif bmi < 24: + bmi_cat = 1 + elif bmi < 28: + bmi_cat = 2 + else: + bmi_cat = 3 + + # 构建输入特征 + input_data_dict = { + 'age_years': age, + 'gender': gender_val, + 'height': height, + 'weight': weight, + 'ap_hi': ap_hi, + 'ap_lo': ap_lo, + 'cholesterol': cholesterol_val, + 'gluc': gluc_val, + 'smoke': smoke_val, + 'alco': alco_val, + 'active': active_val, + 'bmi': bmi, + 'bmi_category': bmi_cat, + 'bp_diff': bp_diff, + 'map': map_val + } + + # 只使用选中的特征 + input_features = {k: input_data_dict[k] for k in selected_features} + input_df = pd.DataFrame([input_features]) + + st.markdown("---") + + # 预测按钮 + if st.button("🔍 进行预测", type="primary"): + with st.spinner("预测中..."): + # 使用最佳模型预测 + model = best_result['model'] + prediction, probability = predict_cardio(model, input_df, scaler) + + st.markdown("### 预测结果") + + col_r1, col_r2 = st.columns(2) + + with col_r1: + if prediction == 1: + st.error("⚠️ 存在心血管疾病风险") + else: + st.success("✅ 无心血管疾病风险") + + with col_r2: + risk_prob = probability[1] * 100 + st.metric( + "风险概率", + f"{risk_prob:.1f}%", + delta=f"患病概率" if risk_prob > 50 else "健康概率" + ) + + # 风险等级 + st.markdown("### 风险评估") + if risk_prob < 30: + st.info("🟢 低风险 - 保持健康生活方式") + elif risk_prob < 60: + st.warning("🟡 中风险 - 建议定期体检") + else: + st.error("🔴 高风险 - 建议及时就医") + + # ==================== Tab 3: 模型评估 ==================== + with tab3: + st.header("模型评估分析") + + # 选择要评估的模型 + model_to_eval = st.selectbox( + "选择模型", + list(results.keys()) + ) + + result = results[model_to_eval] + + # 1. 混淆矩阵 + st.subheader("混淆矩阵") + cm = confusion_matrix(y_test, result['predictions']) + + fig_cm = px.imshow( + cm, + labels=dict(x="预测值", y="实际值"), + x=['无心血管疾病', '有心血管疾病'], + y=['无心血管疾病', '有心血管疾病'], + text_auto=True, + color_continuous_scale='Blues' + ) + st.plotly_chart(fig_cm, use_container_width=True) + + # 2. ROC 曲线 + st.subheader("ROC 曲线") + fpr, tpr, _ = roc_curve(y_test, result['probabilities']) + + fig_roc = go.Figure() + fig_roc.add_trace(go.Scatter( + x=fpr, y=tpr, + mode='lines', + name=f'ROC (AUC = {result["roc_auc"]:.4f})', + line=dict(color='#e74c3c', width=2) + )) + fig_roc.add_trace(go.Scatter( + x=[0, 1], y=[0, 1], + mode='lines', + name='随机猜测', + line=dict(color='gray', width=1, dash='dash') + )) + fig_roc.update_layout( + xaxis_title='假阳性率 (FPR)', + yaxis_title='真阳性率 (TPR)', + title='ROC 曲线', + showlegend=True + ) + st.plotly_chart(fig_roc, use_container_width=True) + + # 3. 特征重要性 (仅对树模型) + if model_to_eval in ['Random Forest', 'Gradient Boosting']: + st.subheader("特征重要性") + model = result['model'] + importance = model.feature_importances_ + importance_df = pd.DataFrame({ + '特征': selected_features, + '重要性': importance + }).sort_values('重要性', ascending=True) + + fig_imp = px.bar( + importance_df, + x='重要性', + y='特征', + orientation='h', + title='特征重要性', + color='重要性', + color_continuous_scale='Viridis' + ) + st.plotly_chart(fig_imp, use_container_width=True) + + +if __name__ == "__main__": + main() diff --git a/ai_code/aicodes/module3_voice_assistant/voice_assistant.py b/ai_code/aicodes/module3_voice_assistant/voice_assistant.py new file mode 100644 index 0000000..01c5e5c --- /dev/null +++ b/ai_code/aicodes/module3_voice_assistant/voice_assistant.py @@ -0,0 +1,467 @@ +""" +CardioAI 语音助手 +提供语音交互式心血管疾病风险评估 +""" + +import streamlit as st +import pandas as pd +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.ensemble import GradientBoostingClassifier +from sklearn.metrics import roc_auc_score +import plotly.express as px +import plotly.graph_objects as go + + +# ============================================ +# 数据加载函数 (带缓存) +# ============================================ +@st.cache_data +def load_data(file_path: str) -> pd.DataFrame: + """加载 Excel 数据文件""" + df = pd.read_excel(file_path) + return df + + +# ============================================ +# 数据预处理函数 +# ============================================ +@st.cache_data +def preprocess_data(df: pd.DataFrame) -> tuple: + """ + 数据预处理 + + Returns: + (model, scaler, feature_columns) + """ + df_clean = df.copy() + + # 年龄转换 + df_clean['age_years'] = (df_clean['age'] / 365).round().astype(int) + + # 计算 BMI + df_clean['bmi'] = df_clean['weight'] / ((df_clean['height'] / 100) ** 2) + + # 删除异常值 + df_clean = df_clean[df_clean['ap_hi'] > df_clean['ap_lo']] + df_clean = df_clean[(df_clean['ap_hi'] >= 90) & (df_clean['ap_hi'] <= 250)] + df_clean = df_clean[(df_clean['ap_lo'] >= 60) & (df_clean['ap_lo'] <= 150)] + + # 特征工程 + df_clean['bp_diff'] = df_clean['ap_hi'] - df_clean['ap_lo'] + df_clean['map'] = df_clean['ap_lo'] + (df_clean['bp_diff'] / 3) + + # 特征列 + feature_columns = [ + 'age_years', 'gender', 'ap_hi', 'ap_lo', + 'cholesterol', 'gluc', 'bmi', 'smoke', 'alco', 'active' + ] + + X = df_clean[feature_columns] + y = df_clean['cardio'] + + # 分割数据 + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + # 标准化 + scaler = StandardScaler() + X_train_scaled = scaler.fit_transform(X_train) + X_test_scaled = scaler.transform(X_test) + + # 训练模型 + model = GradientBoostingClassifier( + n_estimators=100, + max_depth=5, + learning_rate=0.1, + random_state=42 + ) + model.fit(X_train_scaled, y_train) + + # 验证模型 + y_prob = model.predict_proba(X_test_scaled)[:, 1] + roc_auc = roc_auc_score(y_test, y_prob) + + return model, scaler, feature_columns, roc_auc + + +# ============================================ +# 预测函数 +# ============================================ +def predict_risk(model, scaler, input_data, feature_columns): + """预测心血管疾病风险""" + input_df = pd.DataFrame([input_data])[feature_columns] + input_scaled = scaler.transform(input_df) + probability = model.predict_proba(input_scaled)[0] + return probability + + +# ============================================ +# 语音指令处理 +# ============================================ +def process_voice_command(command: str, patient_data: dict, model, scaler, feature_columns) -> str: + """ + 处理语音指令 + + Args: + command: 用户语音指令 + patient_data: 患者数据 + model: 训练好的模型 + scaler: 标准化器 + feature_columns: 特征列 + + Returns: + 响应文本 + """ + command = command.lower() + + # 预测风险 + probability = predict_risk(model, scaler, patient_data, feature_columns) + risk_percent = probability[1] * 100 + no_risk_percent = probability[0] * 100 + + if "风险" in command or "患病" in command or "probability" in command: + if risk_percent > 50: + 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。" + else: + 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。" + + elif "正常" in command or "健康" in command or "healthy" in command: + return f"您的健康概率为 {no_risk_percent:.1f}%，患病风险为 {risk_percent:.1f}%。" + + elif "年龄" in command or "age" in command: + return f"您的年龄是 {patient_data['age_years']} 岁。" + + elif "血压" in command or "blood pressure" in command: + return f"您的血压信息：收缩压 {patient_data['ap_hi']} mmHg，舒张压 {patient_data['ap_lo']} mmHg。" + + elif "bmi" in command or "体重" in command: + return f"您的 BMI 为 {patient_data['bmi']:.1f}。" + + elif "胆固醇" in command or "cholesterol" in command: + chol_map = {1: "正常", 2: "高于正常", 3: "远高于正常"} + return f"您的胆固醇水平：{chol_map.get(patient_data['cholesterol'], '未知')}。" + + elif "血糖" in command or "glucose" in command: + gluc_map = {1: "正常", 2: "高于正常", 3: "远高于正常"} + return f"您的血糖水平：{gluc_map.get(patient_data['gluc'], '未知')}。" + + elif "建议" in command or "advice" in command or "recommend" in command: + if risk_percent > 60: + 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。" + elif risk_percent > 30: + 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。" + else: + 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。" + + elif "帮助" in command or "help" in command: + return """您可以询问以下信息： +- 患病风险 +- 血压情况 +- BMI 数值 +- 胆固醇水平 +- 血糖水平 +- 健康建议 +- 我的年龄 + +请告诉我您想了解什么？""" + + else: + 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。" + + +# ============================================ +# Streamlit 页面配置 +# ============================================ +st.set_page_config( + page_title="CardioAI 语音助手", + page_icon="🎤", + layout="wide" +) + + +# ============================================ +# 初始化会话状态 +# ============================================ +if 'patient_data' not in st.session_state: + st.session_state.patient_data = None +if 'voice_history' not in st.session_state: + st.session_state.voice_history = [] +if 'model_trained' not in st.session_state: + st.session_state.model_trained = False + + +# ============================================ +# 主程序 +# ============================================ +def main(): + """主程序入口""" + + # 页面标题 + st.title("🎤 CardioAI 智能语音助手") + st.markdown("---") + st.markdown("**您的私人心血管健康顾问**") + + # 数据路径 + DATA_PATH = "C:/Users/SAM/Desktop/sam_test/ai_code/aicodes/data/心血管疾病.xlsx" + + # 加载和训练模型 + try: + df = load_data(DATA_PATH) + model, scaler, feature_columns, roc_auc = preprocess_data(df) + st.session_state.model_trained = True + st.session_state.model = model + st.session_state.scaler = scaler + st.session_state.feature_columns = feature_columns + except Exception as e: + st.error(f"❌ 系统初始化失败: {e}") + return + + # ============================================ + # 布局：侧边栏 + 主内容 + # ============================================ + st.sidebar.header("👤 患者信息录入") + + # 患者信息表单 + with st.sidebar.form("patient_form"): + st.subheader("基本信息") + + age = st.number_input("年龄 (岁)", 20, 100, 50) + gender = st.selectbox("性别", ["女性", "男性"]) + + st.subheader("身体指标") + + ap_hi = st.number_input("收缩压 (mmHg)", 90, 250, 120) + ap_lo = st.number_input("舒张压 (mmHg)", 60, 150, 80) + height = st.number_input("身高 (cm)", 100, 220, 170) + weight = st.number_input("体重 (kg)", 30, 200, 70) + + st.subheader("生化指标") + + cholesterol = st.selectbox("胆固醇水平", ["正常", "高于正常", "远高于正常"]) + gluc = st.selectbox("血糖水平", ["正常", "高于正常", "远高于正常"]) + + st.subheader("生活方式") + + smoke = st.checkbox("吸烟") + alco = st.checkbox("饮酒") + active = st.checkbox("运动") + + submit = st.form_submit_button("确认信息", type="primary") + + if submit: + # 构建患者数据 + gender_val = 1 if gender == "女性" else 2 + cholesterol_map = {"正常": 1, "高于正常": 2, "远高于正常": 3} + gluc_map = {"正常": 1, "高于正常": 2, "远高于正常": 3} + + bmi = weight / ((height / 100) ** 2) + + st.session_state.patient_data = { + 'age_years': age, + 'gender': gender_val, + 'ap_hi': ap_hi, + 'ap_lo': ap_lo, + 'cholesterol': cholesterol_map[cholesterol], + 'gluc': gluc_map[gluc], + 'bmi': bmi, + 'smoke': 1 if smoke else 0, + 'alco': 1 if alco else 0, + 'active': 1 if active else 0 + } + + # 初始风险评估 + probability = predict_risk( + st.session_state.model, + st.session_state.scaler, + st.session_state.patient_data, + st.session_state.feature_columns + ) + + 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。" + st.session_state.voice_history = [ + {"role": "assistant", "content": risk_msg} + ] + + st.success("✅ 患者信息已录入，可以开始语音交互了！") + + # ============================================ + # 主内容区域 + # ============================================ + + # 欢迎信息和风险评估展示 + st.markdown("### 🎯 心血管疾病风险评估") + + if st.session_state.patient_data: + # 预测风险 + probability = predict_risk( + st.session_state.model, + st.session_state.scaler, + st.session_state.patient_data, + st.session_state.feature_columns + ) + + risk_percent = probability[1] * 100 + + # 风险仪表盘 + col1, col2 = st.columns([1, 2]) + + with col1: + # 风险仪表图 + fig_gauge = go.Figure(go.Indicator( + mode="gauge+number", + value=risk_percent, + domain={'x': [0, 1], 'y': [0, 1]}, + title={'text': "心血管疾病风险率"}, + gauge={ + 'axis': {'range': [0, 100]}, + 'bar': {'color': "#e74c3c" if risk_percent > 50 else "#2ecc71"}, + 'steps': [ + {'range': [0, 30], 'color': "#2ecc71"}, + {'range': [30, 60], 'color': "#f1c40f"}, + {'range': [60, 100], 'color': "#e74c3c"} + ], + 'threshold': { + 'line': {'color': "black", 'width': 4}, + 'thickness': 0.75, + 'value': risk_percent + } + } + )) + fig_gauge.update_layout(height=200) + st.plotly_chart(fig_gauge, use_container_width=True) + + with col2: + # 风险等级和建议 + if risk_percent < 30: + st.success("🟢 **低风险** - 请继续保持健康的生活方式") + st.info("建议：均衡饮食、适量运动、定期体检") + elif risk_percent < 60: + st.warning("🟡 **中风险** - 建议关注心血管健康") + st.info("建议：控制饮食、适度运动、定期监测血压") + else: + st.error("🔴 **高风险** - 建议及时就医") + st.info("建议：尽快就医、严格控制饮食、戒烟限酒") + + st.markdown(f"**模型 ROC-AUC**: {roc_auc:.4f}") + else: + st.info("👈 请在左侧填写患者信息后开始评估") + + st.markdown("---") + + # ============================================ + # 语音交互区域 + # ============================================ + st.markdown("### 🎤 语音交互") + + # 快速指令按钮 + st.markdown("#### 快速指令") + quick_commands = [ + ("查询风险", "我的患病风险是多少？"), + ("查询血压", "我的血压情况如何？"), + ("查询BMI", "我的BMI是多少？"), + ("健康建议", "请给我一些健康建议"), + ("帮助", "帮助") + ] + + cols = st.columns(5) + for i, (label, cmd) in enumerate(quick_commands): + if cols[i].button(label): + if st.session_state.patient_data: + response = process_voice_command( + cmd, + 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": cmd} + ) + st.session_state.voice_history.append( + {"role": "assistant", "content": response} + ) + else: + st.warning("请先录入患者信息") + + st.markdown("---") + + # 自定义语音输入 + st.markdown("#### 自定义语音输入") + + # 模拟语音输入 (实际项目中可接入语音识别API) + voice_input = st.text_input( + "输入您的问题：", + placeholder="例如：我的胆固醇水平如何？", + key="voice_input" + ) + + 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()