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/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
+    )