# -*- coding: utf-8 -*-
"""
CardioAI - Module 2: Flask API 部署
心血管疾病预测服务后端
"""

import os
import json
import joblib
import numpy as np
from pathlib import Path
from flask import Flask, render_template, request, jsonify
from dotenv import load_dotenv

# ============================================
# 配置与初始化
# ============================================
# 加载环境变量
load_dotenv()

CODE_ROOT = Path(r"E:\project_ai\claude_project1\aicodes")
MODEL_PATH = CODE_ROOT / "module2_predictor" / "cardio_predictor_model.pkl"
TEMPLATE_DIR = CODE_ROOT / "module2_predictor" / "templates"

# 创建 Flask 应用
app = Flask(__name__,
            template_folder=str(TEMPLATE_DIR),
            static_folder=str(TEMPLATE_DIR.parent / 'static'))

# 加载模型
print("正在加载模型...")
try:
    model_pipeline = joblib.load(MODEL_PATH)
    print(f"模型加载成功: {MODEL_PATH}")
except Exception as e:
    print(f"模型加载失败: {e}")
    model_pipeline = None


# ============================================
# 辅助函数
# ============================================
def preprocess_input(data):
    """
    预处理输入数据

    Args:
        data (dict): 包含11个特征的字典

    Returns:
        pd.DataFrame: 格式化后的输入数据
    """
    import pandas as pd

    # 构建输入DataFrame
    input_data = {
        'age_years': int(data['age_years']),
        'gender': int(data['gender']),
        'height': float(data['height']),
        'weight': float(data['weight']),
        'ap_hi': int(data['ap_hi']),
        'ap_lo': int(data['ap_lo']),
        'cholesterol': int(data['cholesterol']),
        'gluc': int(data['gluc']),
        'smoke': int(data['smoke']),
        'alco': int(data['alco']),
        'active': int(data['active'])
    }

    # 计算BMI
    input_data['bmi'] = input_data['weight'] / ((input_data['height'] / 100) ** 2)

    df = pd.DataFrame([input_data])

    # 按照模型训练时的特征顺序排列
    feature_order = ['age_years', 'height', 'weight', 'bmi',
                     'ap_hi', 'ap_lo', 'gender', 'cholesterol',
                     'gluc', 'smoke', 'alco', 'active']

    return df[feature_order]


# ============================================
# 路由定义
# ============================================
@app.route('/')
def index():
    """主页 - 渲染预测表单"""
    return render_template('index.html')


@app.route('/predict_cardio', methods=['POST'])
def predict_cardio():
    """
    心血管疾病预测API接口

    接收11个原始特征值的JSON POST请求
    返回预测结果和概率
    """
    if model_pipeline is None:
        return jsonify({
            'error': '模型未加载，请先运行训练脚本'
        }), 500

    try:
        # 获取JSON数据
        data = request.get_json()

        # 验证必填字段
        required_fields = [
            'age_years', 'gender', 'height', 'weight',
            'ap_hi', 'ap_lo', 'cholesterol', 'gluc',
            'smoke', 'alco', 'active'
        ]

        missing_fields = [field for field in required_fields if field not in data]
        if missing_fields:
            return jsonify({
                'error': f'缺少必填字段: {", ".join(missing_fields)}'
            }), 400

        # 预处理输入数据
        input_df = preprocess_input(data)

        # 模型预测
        prediction = model_pipeline.predict(input_df)[0]
        probability = model_pipeline.predict_proba(input_df)[0, 1]

        # 返回结果
        return jsonify({
            'prediction': int(prediction),
            'probability': float(probability),
            'risk_level': get_risk_level(probability),
            'message': '预测成功'
        })

    except Exception as e:
        return jsonify({
            'error': f'预测失败: {str(e)}'
        }), 500


def get_risk_level(probability):
    """
    根据概率返回风险等级描述

    Args:
        probability (float): 疾病概率

    Returns:
        str: 风险等级描述
    """
    if probability < 0.3:
        return "低风险"
    elif probability < 0.5:
        return "中等风险"
    elif probability < 0.7:
        return "高风险"
    else:
        return "极高风险"


@app.route('/health', methods=['GET'])
def health():
    """健康检查接口"""
    return jsonify({
        'status': 'healthy',
        'model_loaded': model_pipeline is not None
    })


# ============================================
# 主程序入口
# ============================================
if __name__ == '__main__':
    # 从环境变量读取配置，使用默认值
    host = os.getenv('FLASK_HOST', '127.0.0.1')
    port = int(os.getenv('FLASK_PORT', 5000))

    print("\n" + "=" * 50)
    print("CardioAI - Module 2: 心血管疾病预测服务")
    print("=" * 50)
    print(f"服务地址: http://{host}:{port}")
    print("=" * 50 + "\n")

    app.run(host=host, port=port, debug=True)