"""
可解释的信贷风险评估系统
"""

import os
import sys
import subprocess
import webbrowser
import time

def print_system_overview():
    """
    打印系统概述
    """
    print("=" * 60)
    print("可解释的信贷风险评估系统")
    print("=" * 60)
    print("本系统基于LightGBM和对抗自编码器技术，提供以下功能：")
    print("1. 信贷风险预测")
    print("2. 模型决策解释")
    print("3. 数据可视化分析")
    print("4. Web API接口")
    print("=" * 60)

def check_dependencies():
    """
    检查必要的依赖包
    """
    required_packages = ['numpy', 'pandas', 'sklearn', 'xgboost', 'lightgbm', 'torch', 'flask', 'shap', 'matplotlib', 'seaborn']
    missing_packages = []
    
    for package in required_packages:
        try:
            if package == 'sklearn':
                import sklearn
            else:
                __import__(package)
        except ImportError:
            missing_packages.append(package)
    
    if missing_packages:
        print(f"缺少以下依赖包: {', '.join(missing_packages)}")
        print("请运行: pip install " + " ".join(missing_packages))
        return False
    
    return True

def generate_sample_data():
    """
    生成示例数据（如果不存在）
    """
    data_path = "data/credit_data.csv"
    if not os.path.exists(data_path):
        print("生成示例信贷数据...")
        sys.path.append('.')
        from data.data_generator import generate_credit_data
        df = generate_credit_data(10000)
        df.to_csv(data_path, index=False)
        print("示例数据已生成")
    else:
        print("示例数据已存在")

def train_models():
    """
    训练模型（如果模型不存在）
    """
    model_path = "models/lightgbm_model.pkl"
    if not os.path.exists(model_path):
        print("训练LightGBM模型...")
        subprocess.run([sys.executable, "models/train_lightgbm.py"], check=True)
        print("LightGBM模型训练完成")
    else:
        print("LightGBM模型已存在")
    
    aae_path = "models/adversarial_autoencoder.pth"
    if not os.path.exists(aae_path):
        print("训练对抗自编码器...")
        subprocess.run([sys.executable, "models/train_aae.py"], check=True)
        print("对抗自编码器训练完成")
    else:
        print("对抗自编码器已存在")

def generate_explanations():
    """
    生成模型解释（如果解释文件不存在）
    """
    explanation_path = "visualization/shap_summary.png"
    if not os.path.exists(explanation_path):
        print("生成模型解释...")
        subprocess.run([sys.executable, "utils/shap_explainer.py"], check=True)
        print("模型解释生成完成")
    else:
        print("模型解释已存在")

def create_visualizations():
    """
    创建可视化图表（如果图表不存在）
    """
    viz_path = "visualization/dashboard.html"
    if not os.path.exists(viz_path):
        print("创建可视化图表...")
        subprocess.run([sys.executable, "visualization/create_dashboard.py"], check=True)
        print("可视化图表创建完成")
    else:
        print("可视化图表已存在")

def start_api_server():
    """
    启动API服务器
    """
    print("启动API服务器...")
    print("服务器将在 http://127.0.0.1:5000 上运行")
    print("按 Ctrl+C 停止服务器")
    
    # 启动Flask应用
    os.chdir('api')
    subprocess.run([sys.executable, "app.py"], check=True)

def main():
    """
    主函数
    """
    print_system_overview()
    
    if not check_dependencies():
        return
    
    # 创建必要的目录
    directories = ['data', 'models', 'visualization', 'api']
    for directory in directories:
        if not os.path.exists(directory):
            os.makedirs(directory)
    
    try:
        # 生成数据
        generate_sample_data()
        
        # 训练模型
        train_models()
        
        # 生成解释
        generate_explanations()
        
        # 创建可视化
        create_visualizations()
        
        # 启动API服务器
        start_api_server()
        
    except KeyboardInterrupt:
        print("\n系统已停止")
    except Exception as e:
        print(f"系统运行出错: {e}")

if __name__ == "__main__":
    main()