Zane Xu
88021e4a4b
Part A: 模型训练与保存 - train_and_save.py: 一次性脚本，训练XGBoost模型并保存完整Pipeline - cardio_predictor_model.pkl: 包含预处理器和分类器的完整Pipeline Part B: Flask API部署 - app.py: 提供/predict_cardio接口，接收11个特征值并返回预测结果 - 包含输入验证、数据处理和模型加载功能 Part C: 前端交互界面 - templates/index.html: 响应式HTML表单，集成JavaScript Fetch API - 提供示例数据填充和实时预测结果显示 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
336 lines
11 KiB
Python
336 lines
11 KiB
Python
#!/opt/anaconda3/envs/cardioenv/bin/python
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"""
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CardioAI - 心血管疾病预测API
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Flask应用程序，提供心血管疾病预测API接口
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"""
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from flask import Flask, request, jsonify, render_template
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import pandas as pd
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import numpy as np
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import joblib
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import json
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import os
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# 创建Flask应用
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app = Flask(__name__)
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# 加载模型
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MODEL_PATH = os.path.join(os.path.dirname(__file__), "cardio_predictor_model.pkl")
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def load_model():
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"""
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加载预训练的模型
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"""
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global MODEL_PATH # 声明为全局变量
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try:
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current_model_path = MODEL_PATH
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# 如果模型文件不存在，尝试从项目根目录加载
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if not os.path.exists(current_model_path):
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alt_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "cardio_predictor_model.pkl")
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if os.path.exists(alt_path):
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current_model_path = alt_path
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print(f"⚠️ 使用备用模型路径: {current_model_path}")
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model = joblib.load(current_model_path)
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print(f"✅ 模型加载成功: {current_model_path}")
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return model
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except Exception as e:
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print(f"❌ 模型加载失败: {e}")
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# 尝试从绝对路径加载
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try:
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root_model = os.path.join(os.path.dirname(os.path.dirname(__file__)), "cardio_predictor_model.pkl")
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if os.path.exists(root_model):
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model = joblib.load(root_model)
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print(f"✅ 从项目根目录加载模型成功: {root_model}")
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return model
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except Exception as e2:
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print(f"❌ 备用路径也失败: {e2}")
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return None
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# 全局模型变量
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model = load_model()
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def preprocess_input(data):
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"""
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预处理输入数据，与训练时保持一致
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"""
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try:
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# 首先确保数值类型正确
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# 转换数值字段为适当的类型
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for field in ['age', 'height', 'weight', 'ap_hi', 'ap_lo']:
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if field in data:
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data[field] = float(data[field])
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# 转换分类字段为整数（除了已经映射的字段）
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for field in ['gender', 'cholesterol', 'gluc', 'smoke', 'alco', 'active']:
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if field in data:
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data[field] = int(data[field])
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# 创建DataFrame
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df = pd.DataFrame([data])
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# 特征工程
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# 将age(天)转换为年，四舍五入
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if 'age' in df.columns:
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df['age_years'] = (df['age'] / 365.25).round().astype(int)
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# 计算BMI: weight / (height/100)^2
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if 'height' in df.columns and 'weight' in df.columns:
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df['bmi'] = df['weight'] / ((df['height'] / 100) ** 2)
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# 类别转换
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# cholesterol转换
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cholesterol_map = {
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1: 'normal',
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2: 'above_normal',
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3: 'well_above_normal'
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}
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if 'cholesterol' in df.columns:
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df['cholesterol_cat'] = df['cholesterol'].map(cholesterol_map)
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# gluc转换
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gluc_map = {
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1: 'normal',
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2: 'above_normal',
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3: 'well_above_normal'
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}
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if 'gluc' in df.columns:
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df['gluc_cat'] = df['gluc'].map(gluc_map)
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# BMI分类
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def categorize_bmi(bmi):
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if bmi < 18.5:
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return 'underweight'
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elif 18.5 <= bmi < 25:
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return 'normal'
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elif 25 <= bmi < 30:
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return 'overweight'
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else:
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return 'obese'
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if 'bmi' in df.columns:
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df['bmi_category'] = df['bmi'].apply(categorize_bmi)
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return df
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except Exception as e:
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print(f"数据预处理失败: {e}")
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return None
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def validate_input(data):
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"""
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验证输入数据的完整性和有效性
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"""
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required_fields = [
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'age', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo',
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'cholesterol', 'gluc', 'smoke', 'alco', 'active'
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]
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# 检查必需字段
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missing_fields = [field for field in required_fields if field not in data]
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if missing_fields:
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return False, f"缺少必需字段: {', '.join(missing_fields)}"
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# 验证数值范围
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try:
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# 年龄验证（天）
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age = float(data['age'])
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if age <= 0 or age > 365.25 * 120: # 假设最大120岁
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return False, "年龄应在合理范围内(0-43830天)"
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# 性别验证
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gender = int(data['gender'])
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if gender not in [1, 2]:
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return False, "性别应为1(女性)或2(男性)"
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# 身高验证(cm)
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height = float(data['height'])
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if height < 100 or height > 250:
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return False, "身高应在100-250cm之间"
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# 体重验证(kg)
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weight = float(data['weight'])
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if weight < 30 or weight > 300:
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return False, "体重应在30-300kg之间"
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# 血压验证
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ap_hi = float(data['ap_hi'])
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ap_lo = float(data['ap_lo'])
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if ap_lo >= ap_hi:
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return False, "舒张压应小于收缩压"
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if ap_hi < 90 or ap_hi > 250:
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return False, "收缩压应在90-250mmHg之间"
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if ap_lo < 60 or ap_lo > 150:
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return False, "舒张压应在60-150mmHg之间"
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# 胆固醇验证
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cholesterol = int(data['cholesterol'])
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if cholesterol not in [1, 2, 3]:
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return False, "胆固醇应为1,2,3之一"
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# 血糖验证
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gluc = int(data['gluc'])
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if gluc not in [1, 2, 3]:
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return False, "血糖应为1,2,3之一"
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# 吸烟、饮酒、活动验证
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smoke = int(data['smoke'])
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alco = int(data['alco'])
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active = int(data['active'])
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if smoke not in [0, 1] or alco not in [0, 1] or active not in [0, 1]:
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return False, "吸烟、饮酒、活动应为0或1"
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except ValueError as e:
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return False, f"数据类型错误: {str(e)}"
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return True, "输入数据有效"
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@app.route('/')
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def index():
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"""
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主页，提供HTML界面
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"""
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return render_template('index.html')
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@app.route('/predict_cardio', methods=['POST'])
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def predict_cardio():
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"""
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心血管疾病预测API接口
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接收11个原始特征值的JSON POST请求
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"""
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try:
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# 检查模型是否加载
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if model is None:
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return jsonify({
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'error': '模型未加载',
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'prediction': None,
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'probability': None
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}), 500
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# 获取JSON数据
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if not request.is_json:
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return jsonify({
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'error': '请求内容类型应为application/json',
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'prediction': None,
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'probability': None
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}), 400
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data = request.get_json()
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# 验证输入
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is_valid, message = validate_input(data)
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if not is_valid:
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return jsonify({
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'error': message,
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'prediction': None,
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'probability': None
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}), 400
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# 预处理输入数据
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processed_data = preprocess_input(data)
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if processed_data is None:
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return jsonify({
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'error': '数据预处理失败',
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'prediction': None,
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'probability': None
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}), 400
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# 删除不需要的列（与训练时保持一致）
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columns_to_drop = ['id', 'age', 'cardio']
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for col in columns_to_drop:
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if col in processed_data.columns:
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processed_data = processed_data.drop(col, axis=1)
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# 进行预测
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try:
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prediction = model.predict(processed_data)[0]
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probability = model.predict_proba(processed_data)[0][1]
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except Exception as e:
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print(f"预测失败: {e}")
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return jsonify({
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'error': f'预测失败: {str(e)}',
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'prediction': None,
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'probability': None
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}), 500
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# 返回结果
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result = {
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'prediction': int(prediction),
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'probability': float(probability),
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'risk_level': '高风险' if probability >= 0.5 else '低风险',
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'message': '有心血管疾病风险' if prediction == 1 else '暂无心血管疾病风险',
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'confidence': f'{probability * 100:.1f}%'
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}
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print(f"预测结果: {result}")
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return jsonify(result)
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except Exception as e:
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print(f"API处理异常: {e}")
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return jsonify({
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'error': f'服务器内部错误: {str(e)}',
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'prediction': None,
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'probability': None
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}), 500
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@app.route('/health', methods=['GET'])
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def health_check():
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"""
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健康检查端点
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"""
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health_status = {
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'status': 'healthy' if model is not None else 'unhealthy',
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'model_loaded': model is not None,
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'api_version': '1.0.0',
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'service': 'CardioAI Cardiovascular Disease Predictor'
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}
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return jsonify(health_status)
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@app.route('/features', methods=['GET'])
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def get_features_info():
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"""
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获取模型特征信息
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"""
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features_info = {
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'required_features': [
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{'name': 'age', 'type': 'numeric', 'unit': 'days', 'description': '年龄(天)'},
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{'name': 'gender', 'type': 'categorical', 'values': [1, 2], 'description': '性别:1=女性,2=男性'},
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{'name': 'height', 'type': 'numeric', 'unit': 'cm', 'description': '身高(厘米)'},
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{'name': 'weight', 'type': 'numeric', 'unit': 'kg', 'description': '体重(千克)'},
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{'name': 'ap_hi', 'type': 'numeric', 'unit': 'mmHg', 'description': '收缩压'},
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{'name': 'ap_lo', 'type': 'numeric', 'unit': 'mmHg', 'description': '舒张压'},
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{'name': 'cholesterol', 'type': 'categorical', 'values': [1, 2, 3], 'description': '胆固醇水平:1=正常,2=高于正常,3=很高'},
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{'name': 'gluc', 'type': 'categorical', 'values': [1, 2, 3], 'description': '血糖水平:1=正常,2=高于正常,3=很高'},
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{'name': 'smoke', 'type': 'categorical', 'values': [0, 1], 'description': '是否吸烟:0=否,1=是'},
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{'name': 'alco', 'type': 'categorical', 'values': [0, 1], 'description': '是否饮酒:0=否,1=是'},
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{'name': 'active', 'type': 'categorical', 'values': [0, 1], 'description': '是否积极运动:0=否,1=是'}
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],
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'derived_features': [
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{'name': 'age_years', 'description': '年龄(年),由age转换而来'},
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{'name': 'bmi', 'description': '身体质量指数,由height和weight计算而来'},
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{'name': 'bmi_category', 'description': 'BMI分类:偏瘦/正常/超重/肥胖'}
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]
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}
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return jsonify(features_info)
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if __name__ == '__main__':
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print("=" * 60)
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print("CardioAI - 心血管疾病预测API")
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print("=" * 60)
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print(f"模型路径: {MODEL_PATH}")
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print(f"模型加载状态: {'成功' if model is not None else '失败'}")
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print("\n可用端点:")
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print(" GET / - 前端界面")
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print(" POST /predict_cardio - 预测API")
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print(" GET /health - 健康检查")
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print(" GET /features - 特征信息")
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print("\n启动信息:")
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print(" 主机: 0.0.0.0")
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print(" 端口: 5000")
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print(" 调试模式: 开启")
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print("=" * 60)
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# 启动Flask应用
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app.run(host='0.0.0.0', port=9011, debug=True) |