"""
交互式 CLI —— 四层架构自适应分析（增强版）
用法: python cli.py [数据库路径]
"""
import os
import sys

sys.path.insert(0, os.path.dirname(__file__))

from core.config import DB_PATH, LLM_CONFIG, MAX_EXPLORATION_ROUNDS, PLAYBOOK_DIR, PROJECT_ROOT
from agent import DataAnalysisAgent


def print_help():
    print("""
可用命令:
  <问题>             分析一个问题
  rounds=<N> <问题>  设置探索轮数
  report [主题]      整合所有分析，生成综合报告
  export             导出最近一次分析结果为 CSV
  reports            列出已保存的报告文件
  schema             查看数据库 Schema
  playbooks          查看已加载的预设剧本
  regen              重新生成预设剧本
  history            查看分析历史
  audit              查看 SQL 审计日志
  clear              清空分析历史
  help               显示帮助
  quit / q           退出
""")


def cmd_reports(agent):
    """列出已保存的报告"""
    reports_dir = agent.reports_dir
    if not os.path.isdir(reports_dir):
        print("（reports 目录不存在）")
        return
    files = sorted([f for f in os.listdir(reports_dir) if f.endswith(".md")])
    if not files:
        print("（尚无保存的报告）")
        return
    print(f"\n📁 已保存 {len(files)} 份报告:")
    for f in files:
        fpath = os.path.join(reports_dir, f)
        size = os.path.getsize(fpath)
        print(f"   📄 {f} ({size/1024:.1f} KB)")


def setup_readline():
    """启用命令历史（Linux/macOS）"""
    try:
        import readline
        histfile = os.path.join(PROJECT_ROOT, ".cli_history")
        try:
            readline.read_history_file(histfile)
        except FileNotFoundError:
            pass
        import atexit
        atexit.register(readline.write_history_file, histfile)
        readline.set_history_length(100)
    except ImportError:
        pass


def main():
    db_path = sys.argv[1] if len(sys.argv) > 1 else DB_PATH

    if not os.path.exists(db_path):
        print(f"❌ 数据库不存在: {db_path}")
        sys.exit(1)

    if not LLM_CONFIG["api_key"]:
        print("❌ LLM_API_KEY 未配置！")
        print("   请设置环境变量或创建 .env 文件：")
        print("   LLM_API_KEY=your-key")
        print("   LLM_BASE_URL=https://api.openai.com/v1")
        print("   LLM_MODEL=gpt-4o-mini")
        sys.exit(1)

    setup_readline()

    agent = DataAnalysisAgent(db_path)

    print("=" * 60)
    print("  🤖 数据分析 Agent —— 四层架构")
    print("=" * 60)
    print(f"\n🔗 LLM: {LLM_CONFIG['model']} @ {LLM_CONFIG['base_url']}")
    print(f"🔄 最大探索轮数: {MAX_EXPLORATION_ROUNDS}")
    print(f"💾 数据库: {db_path}")
    print(f"📋 预设剧本: {len(agent.playbook_mgr.playbooks)} 个")
    print(f"\n💬 输入分析问题（help 查看命令）\n")

    last_steps = None  # 记录最近一次分析的 steps，用于 export

    while True:
        try:
            user_input = input("📊 > ").strip()
        except (EOFError, KeyboardInterrupt):
            print("\n👋 再见！")
            break

        if not user_input:
            continue

        cmd = user_input.lower()

        if cmd in ("quit", "exit", "q"):
            print("👋 再见！")
            break
        elif cmd == "help":
            print_help()
        elif cmd == "schema":
            print(agent.get_schema())
        elif cmd == "history":
            print(agent.get_history())
        elif cmd == "audit":
            print(agent.get_audit())
        elif cmd == "clear":
            agent.clear_history()
            last_steps = None
            print("✅ 历史已清空")
        elif cmd == "reports":
            cmd_reports(agent)
        elif cmd == "export":
            if last_steps:
                fpath = agent.export_data(last_steps)
                if fpath:
                    print(f"✅ 导出成功: {fpath}")
                else:
                    print("⚠️ 无数据可导出")
            else:
                print("⚠️ 请先执行一次分析")
        elif cmd.startswith("report"):
            topic = user_input[6:].strip()
            try:
                report = agent.full_report(question=topic)
                print("\n" + report)
                print("\n" + "~" * 60)
            except Exception as e:
                print(f"\n❌ 报告整合出错: {e}")
                import traceback
                traceback.print_exc()
        elif cmd == "playbooks":
            if not agent.playbook_mgr.playbooks:
                print("（无预设剧本，输入 regen 让 AI 自动生成）")
            else:
                for i, pb in enumerate(agent.playbook_mgr.playbooks, 1):
                    print(f"  {i}. 📋 {pb.name} — {pb.description} ({len(pb.preset_queries)} 条预设)")
        elif cmd == "regen":
            if os.path.isdir(PLAYBOOK_DIR):
                for f in os.listdir(PLAYBOOK_DIR):
                    if f.startswith("auto_") and f.endswith(".json"):
                        os.remove(os.path.join(PLAYBOOK_DIR, f))
            agent.playbook_mgr.playbooks.clear()
            print("🤖 AI 正在重新生成预设剧本...")
            generated = agent.playbook_mgr.auto_generate(agent.schema_text, save_dir=PLAYBOOK_DIR)
            if generated:
                print(f"✅ 生成 {len(generated)} 个剧本：")
                for pb in generated:
                    print(f"   📋 {pb.name} — {pb.description}")
            else:
                print("⚠️ 生成失败")
        else:
            # 解析 rounds=N
            max_rounds = MAX_EXPLORATION_ROUNDS
            question = user_input
            if "rounds=" in question.lower():
                parts = question.split("rounds=")
                question = parts[0].strip()
                try:
                    max_rounds = int(parts[1].strip().split()[0])
                except (ValueError, IndexError):
                    pass

            try:
                report = agent.analyze(question, max_rounds=max_rounds)
                # 保存 steps 用于 export
                if agent.context.sessions:
                    last_steps = agent.context.sessions[-1].steps
                print("\n" + report)
                print("\n" + "~" * 60)
            except Exception as e:
                print(f"\n❌ 分析出错: {e}")
                import traceback
                traceback.print_exc()

    print("\n📋 本次会话审计:")
    print(agent.get_audit())
    agent.close()


if __name__ == "__main__":
    main()