my_git_project/CardioAI/module2_predictor/app.py

"""
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
    )