app.py

#!/usr/bin/env python3
"""

Read Agent Web 服务



Flask Web 应用，提供 API 接口供前端调用

"""

import os
import sys
import json
import logging
import queue
import threading
import uuid
import time
from pathlib import Path
from urllib.parse import urlparse
from flask import Flask, request, jsonify, send_from_directory, Response
from dotenv import load_dotenv

from src.agent import ReadAgent, Memory
from src.repo_manager import RepoManager
from src.api_key_manager import init_manager, get_global_manager
from src.session_storage import SessionStorage
from prompts import get_system_prompt, get_react_format_prompt

# 加载环境变量
load_dotenv()

# 配置日志
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# 创建 Flask 应用
app = Flask(__name__, static_folder='static')

# API Key 管理器（全局共享）
key_manager = None

# 仓库管理器（全局共享）
repo_manager = None

# 会话持久化存储
session_storage = SessionStorage("./data/sessions.db")

# 会话存储：{session_id: agent_instance}
sessions = {}

# 线程安全锁
sessions_lock = threading.Lock()
repo_manager_lock = threading.Lock()
key_manager_lock = threading.Lock()

# 仓库同步标志（避免重复同步）
_repo_synced = False
_repo_sync_lock = threading.Lock()

# 弹窗配置文件路径
POPUP_CONFIG_PATH = os.path.join(os.path.dirname(__file__), 'config', 'popup.json')

# 弹窗配置缓存
_popup_config_cache = None
_popup_config_cache_lock = threading.Lock()


def get_env(key: str, default: str = "") -> str:
    """获取环境变量"""
    return os.getenv(key, default)


def get_env_bool(key: str, default: bool = False) -> bool:
    """获取布尔型环境变量"""
    value = get_env(key, "").lower()
    if value in ("true", "1", "yes", "on"):
        return True
    elif value in ("false", "0", "no", "off"):
        return False
    return default


def load_popup_config():
    """加载弹窗配置（带缓存）"""
    global _popup_config_cache

    with _popup_config_cache_lock:
        if _popup_config_cache is not None:
            return _popup_config_cache

        try:
            if os.path.exists(POPUP_CONFIG_PATH):
                with open(POPUP_CONFIG_PATH, 'r', encoding='utf-8') as f:
                    config = json.load(f)
                _popup_config_cache = config
                logger.info(f"加载弹窗配置: {POPUP_CONFIG_PATH}")
                return config
            else:
                logger.warning(f"弹窗配置文件不存在: {POPUP_CONFIG_PATH}")
                return None
        except Exception as e:
            logger.error(f"加载弹窗配置失败: {e}")
            return None


def reload_popup_config():
    """重新加载弹窗配置"""
    global _popup_config_cache

    with _popup_config_cache_lock:
        _popup_config_cache = None
        return load_popup_config()


def format_action_args(action_args: dict) -> str:
    """格式化 action_args 为字符串"""
    if not action_args:
        return ""
    return ", ".join(f'{k}="{v}"' for k, v in action_args.items())


def get_repo_manager():
    """获取或创建仓库管理器（线程安全）"""
    global repo_manager
    with repo_manager_lock:
        if repo_manager is None:
            code_dir = get_env("CODE_DIR", "./code")
            repo_manager = RepoManager(code_dir)
        return repo_manager


def get_key_manager():
    """获取或创建 API Key 管理器（线程安全）"""
    global key_manager
    with key_manager_lock:
        if key_manager is None:
            api_keys = get_env("OPENAI_API_KEY", "")
            if api_keys:
                key_manager = init_manager(api_keys)
                logger.info(f"API Key 管理器初始化完成，共 {key_manager.key_count} 个 Key")
            else:
                logger.warning("未配置 OPENAI_API_KEY")
        return key_manager


def get_agent(session_id: str = None):
    """获取或创建会话的 Agent，返回 (agent, session_id)（线程安全）"""
    with sessions_lock:
        # 如果没有 session_id 或会话不存在，创建新的
        if not session_id or session_id not in sessions:
            # 生成 session_id（如果未提供）
            session_id = session_id or str(uuid.uuid4())

            # 在锁外获取环境变量和初始化耗时操作
            # 注意：这里先释放锁，然后再重新获取锁进行 sessions 写入
            # 但为了避免竞态条件，我们在创建完成前保持 session_id 的唯一性
        else:
            return sessions[session_id], session_id

    # 释放锁后执行耗时操作（避免阻塞其他线程）

    # 首先尝试从存储恢复会话
    if session_id:
        saved_session = session_storage.load_session(session_id)
        if saved_session:
            # 恢复会话
            agent = ReadAgent(
                code_dir=saved_session["code_dir"],
                base_url=saved_session["base_url"],
                model=saved_session["model"],
                max_steps=saved_session["max_steps"],
                stream_output=saved_session["stream_output"],
                tree_depth=saved_session["tree_depth"],
                api_key_manager=get_key_manager()
            )
            # 恢复状态
            agent.conversation_history = saved_session["conversation_history"]
            for m in saved_session["memories"]:
                agent.memories.append(Memory(
                    file_path=m["file_path"],
                    overview=m["overview"],
                    key_definitions=m["key_definitions"],
                    core_logic=m["core_logic"],
                    dependencies=m["dependencies"],
                    needed_info=m["needed_info"]
                ))

            # 重新获取锁并写入 sessions（防止重复创建）
            with sessions_lock:
                if session_id not in sessions:
                    sessions[session_id] = agent
                    logger.info(f"恢复会话: {session_id}")
                else:
                    agent = sessions[session_id]

            return agent, session_id

    # 没有找到已保存的会话，创建新的
    # 获取 API Key 管理器
    key_manager = get_key_manager()
    if not key_manager or not key_manager.has_keys:
        logger.warning("OPENAI_API_KEY 未设置")

    base_url = get_env("OPENAI_BASE_URL", "https://api.openai.com/v1")
    model = get_env("OPENAI_MODEL", "gpt-4")
    code_dir = get_env("CODE_DIR", "./code")
    max_steps = int(get_env("MAX_STEPS", "10"))
    stream_output = get_env_bool("STREAM_OUTPUT", True)
    tree_depth = int(get_env("TREE_DEPTH", "3"))

    # 初始化仓库管理器
    repo_manager = get_repo_manager()
    # 仓库同步已在启动时完成，无需再次同步

    # 创建代码目录
    Path(code_dir).mkdir(parents=True, exist_ok=True)

    agent = ReadAgent(
        code_dir=code_dir,
        base_url=base_url,
        model=model,
        max_steps=max_steps,
        stream_output=stream_output,
        tree_depth=tree_depth,
        api_key_manager=key_manager
    )

    # 保存新会话到持久化存储
    session_storage.save_session(
        session_id=session_id,
        model=model,
        base_url=base_url,
        code_dir=code_dir,
        max_steps=max_steps,
        stream_output=stream_output,
        tree_depth=tree_depth,
        conversation_history=agent.conversation_history,
        memories=[m.to_dict() for m in agent.memories]
    )

    # 重新获取锁并写入 sessions（防止重复创建）
    with sessions_lock:
        # 再次检查，可能在创建过程中另一个线程已经创建了
        if session_id not in sessions:
            sessions[session_id] = agent
            logger.info(f"创建新会话: {session_id}, model={model}")
        else:
            # 如果已存在，使用现有的 agent
            agent = sessions[session_id]
            logger.info(f"使用现有会话: {session_id}")

    return agent, session_id


def save_agent_state(session_id: str, agent: ReadAgent):
    """保存 Agent 状态到持久化存储"""
    try:
        session_storage.save_session(
            session_id=session_id,
            model=agent.model,
            base_url=agent.base_url,
            code_dir=str(agent.searcher.root_dir),
            max_steps=agent.max_steps,
            stream_output=agent.stream_output,
            tree_depth=agent.tree_depth,
            conversation_history=agent.conversation_history,
            memories=[m.to_dict() for m in agent.memories]
        )
    except Exception as e:
        logger.warning(f"保存会话状态失败: {e}")


@app.route('/')
def index():
    """主页"""
    return send_from_directory('static', 'index.html')


@app.route('/static/<path:filename>')
def static_files(filename):
    """静态文件"""
    return send_from_directory('static', filename)


@app.route('/prompt')
def prompt():
    """返回提示词"""
    from src.agent import ReadAgent
    from prompts import get_system_prompt
    
    # 获取 Agent 的工具信息
    agent = ReadAgent()
    tools_info = agent.tool_executor.get_available_tools()
    max_steps = agent.max_steps
    
    # 使用 prompts.py 中的函数生成提示词
    return get_system_prompt(tools_info, max_steps)


@app.route('/use_document')
def use_document():
    """返回参考文档"""
    # 可以扩展为返回实际文档内容
    return ""


@app.route('/api/ask', methods=['POST'])
def ask():
    """Read Agent 问答 API"""
    data = request.json
    if not data:
        return jsonify({"error": "请求体不能为空"}), 400

    question = data.get('question', '')
    stream = data.get('stream', True)  # 默认启用流式输出
    session_id = data.get('session_id')  # 获取会话 ID
    if not question:
        return jsonify({"error": "问题不能为空"}), 400

    # 获取或创建会话的 Agent
    agent, session_id = get_agent(session_id)

    if stream:
        # 流式响应 - 真正的 token 级别流式输出
        def generate():
            try:
                import sys
                
                # 发送会话 ID
                yield "data: " + json.dumps({"type": "session_id", "session_id": session_id}) + "\n\n"
                sys.stdout.flush()
                
                # 发送开始信号
                yield "data: " + json.dumps({"type": "start"}) + "\n\n"
                sys.stdout.flush()
                
                # 发送问题
                yield "data: " + json.dumps({"type": "question", "content": question}) + "\n\n"
                sys.stdout.flush()
                
                # 初始化当前会话的 Agent
                agent.steps = []
                agent.conversation_history.append({"role": "user", "content": question})
                full_answer = ""
                current_step = 0
                
                for step in range(1, agent.max_steps + 1):
                    current_step = step
                    
                    # 创建流式回调来实时发送 LLM 响应
                    msg_queue = queue.Queue()
                    
                    def stream_callback(chunk):
                        msg_queue.put(("chunk", chunk))
                    
                    def done_callback():
                        msg_queue.put(("done", None))
                    
                    # 在后台线程中调用 LLM（支持重试）
                    def call_llm():
                        import time

                        # 使用 agent 的重试配置
                        max_retries = agent.max_retries
                        retry_delays = agent.retry_delays

                        # 可重试的 HTTP 状态码
                        retryable_status_codes = [401, 429, 500, 502, 503, 504]

                        for attempt in range(max_retries):
                            try:
                                # 使用 http.client 实现真正的流式读取
                                import http.client

                                # 从 ApiKeyManager 获取 key（随机选择）
                                api_key = None
                                if agent.api_key_manager:
                                    api_key = agent.api_key_manager.get_key()

                                if not api_key:
                                    raise Exception("未配置 API Key")

                                # 解析 base_url
                                parsed_url = urlparse(agent.base_url)
                                host = parsed_url.hostname or "api.openai.com"
                                port = parsed_url.port or 443
                                # 拼接 path 和 endpoint
                                api_path = parsed_url.path.strip() if parsed_url.path else "/v1"
                                endpoint = "/chat/completions"
                                path = f"{api_path}{endpoint}"

                                headers = {
                                    "Content-Type": "application/json",
                                    "Authorization": f"Bearer {api_key}",
                                    "Host": host
                                }

                                messages = [
                                    {"role": "system", "content": agent._build_system_prompt()}
                                ]
                                for msg in agent.conversation_history:
                                    messages.append(msg)
                                messages.append({
                                    "role": "user",
                                    "content": f"用户问题：{question}\n\n{get_react_format_prompt()}"
                                })

                                body = json.dumps({
                                    "model": agent.model,
                                    "messages": messages,
                                    "temperature": 0.3,
                                    "stream": True
                                })

                                conn = http.client.HTTPSConnection(host, port, timeout=60)
                                conn.request("POST", path, body, headers)

                                response = conn.getresponse()

                                if response.status != 200:
                                    # 检查是否可重试
                                    if response.status in retryable_status_codes and attempt < max_retries - 1:
                                        conn.close()
                                        if agent.api_key_manager and api_key:
                                            agent.api_key_manager.record_error(api_key, f"HTTP {response.status} (重试 {attempt + 1}/{max_retries})")

                                        # 获取延迟（如果超出数组长度，使用最后一个值）
                                        delay = retry_delays[min(attempt, len(retry_delays) - 1)]
                                        msg_queue.put(("retry", delay, response.status, attempt + 1, max_retries))
                                        time.sleep(delay)
                                        continue
                                    else:
                                        # 最后一次重试或不可重试的错误
                                        conn.close()
                                        if agent.api_key_manager and api_key:
                                            agent.api_key_manager.record_error(api_key, f"HTTP {response.status}")
                                        raise Exception(f"API 错误: {response.status}")

                                # 真正流式读取响应
                                llm_response = ""
                                while True:
                                    line = response.readline()
                                    if not line:
                                        break

                                    line = line.decode("utf-8").strip()
                                    if not line.startswith("data: "):
                                        continue
                                    if line == "data: [DONE]":
                                        break

                                    data_str = line[6:]
                                    try:
                                        chunk = json.loads(data_str)
                                        if chunk.get("choices") and len(chunk["choices"]) > 0:
                                            delta = chunk["choices"][0].get("delta", {})
                                            content = delta.get("content", "")
                                            if content:
                                                stream_callback(content)
                                    except json.JSONDecodeError:
                                        continue

                                conn.close()

                                # 记录成功
                                if agent.api_key_manager and api_key:
                                    agent.api_key_manager.record_success(api_key)

                                done_callback()
                                return

                            except Exception as e:
                                # 网络错误，可重试
                                if attempt < max_retries - 1 and ("timeout" in str(e).lower() or "connection" in str(e).lower()):
                                    if agent.api_key_manager and api_key:
                                        agent.api_key_manager.record_error(api_key, f"网络错误: {str(e)} (重试 {attempt + 1}/{max_retries})")

                                    # 获取延迟（如果超出数组长度，使用最后一个值）
                                    delay = retry_delays[min(attempt, len(retry_delays) - 1)]
                                    msg_queue.put(("retry", delay, "网络错误", attempt + 1, max_retries))
                                    time.sleep(delay)
                                    continue
                                else:
                                    # 最后一次重试或不可重试的错误
                                    if agent.api_key_manager and api_key:
                                        agent.api_key_manager.record_error(api_key, str(e))
                                    msg_queue.put(("error", str(e)))
                                    return
                    
                    llm_thread = threading.Thread(target=call_llm)
                    llm_thread.start()
                    
                    # 收集 LLM 响应（支持超时后重试当前步骤）
                    llm_timeout_retries = int(os.getenv("LLM_TIMEOUT_RETRIES", "2"))
                    llm_timeout_delay = int(os.getenv("LLM_TIMEOUT_DELAY", "2"))

                    for timeout_attempt in range(llm_timeout_retries + 1):
                        llm_response = ""
                        received_done = False
                        try:
                            while True:
                                msg = msg_queue.get(timeout=60)
                                msg_type = msg[0]

                                if msg_type == "error":
                                    raise Exception(msg[1])
                                elif msg_type == "done":
                                    received_done = True
                                    break
                                elif msg_type == "retry":
                                    # 重试消息: (retry, delay, status_or_error, attempt, max_retries)
                                    delay, status_or_error, attempt, max_retries = msg[1], msg[2], msg[3], msg[4]
                                    yield "data: " + json.dumps({
                                        "type": "retry",
                                        "delay": delay,
                                        "status": status_or_error,
                                        "attempt": attempt,
                                        "max_retries": max_retries
                                    }, ensure_ascii=False) + "\n\n"
                                    sys.stdout.flush()
                                else:  # chunk - (msg_type, content)
                                    content = msg[1]
                                    llm_response += content
                                    # 发送思考内容流式输出
                                    yield "data: " + json.dumps({
                                        "type": "chunk",
                                        "step": step,
                                        "content": content,
                                        "stream_type": "thought"
                                    }, ensure_ascii=False) + "\n\n"
                                    sys.stdout.flush()
                            break  # 成功获取响应，退出重试循环
                        except queue.Empty:
                            if timeout_attempt < llm_timeout_retries:
                                logger.warning(f"步骤 {step} 等待 LLM 响应超时，{llm_timeout_delay} 秒后重试 ({timeout_attempt + 1}/{llm_timeout_retries + 1})")
                                yield "data: " + json.dumps({
                                    "type": "step_timeout",
                                    "step": step,
                                    "retry_delay": llm_timeout_delay,
                                    "attempt": timeout_attempt + 1,
                                    "max_retries": llm_timeout_retries + 1,
                                    "message": f"等待 LLM 响应超时，{llm_timeout_delay} 秒后重试..."
                                }, ensure_ascii=False) + "\n\n"
                                sys.stdout.flush()
                                time.sleep(llm_timeout_delay)
                                # 等待 LLM 线程结束
                                if llm_thread.is_alive():
                                    llm_thread.join(timeout=1)
                                # 重新启动 LLM 调用
                                llm_thread = threading.Thread(target=call_llm)
                                llm_thread.start()
                            else:
                                logger.error(f"步骤 {step} 等待 LLM 响应超时，已达最大重试次数")
                                break
                    
                    llm_thread.join()

                    # 调试日志
                    logger.info(f"步骤 {step} LLM 响应: done={received_done}, length={len(llm_response)}, response_preview={llm_response[:200]}...")

                    # 如果没有收到有效响应，触发重试
                    if not llm_response:
                        if timeout_attempt < llm_timeout_retries:
                            logger.warning(f"步骤 {step} 收到空响应，{llm_timeout_delay} 秒后重试 ({timeout_attempt + 1}/{llm_timeout_retries + 1})")
                            yield "data: " + json.dumps({
                                "type": "step_empty_response",
                                "step": step,
                                "retry_delay": llm_timeout_delay,
                                "attempt": timeout_attempt + 1,
                                "max_retries": llm_timeout_retries + 1,
                                "message": f"LLM 返回空响应，{llm_timeout_delay} 秒后重试..."
                            }, ensure_ascii=False) + "\n\n"
                            sys.stdout.flush()
                            time.sleep(llm_timeout_delay)
                            # 重新启动 LLM 调用（继续外层重试循环）
                            llm_thread = threading.Thread(target=call_llm)
                            llm_thread.start()
                            continue
                        else:
                            logger.error(f"步骤 {step} 收到空响应，已达最大重试次数")
                            # 发送错误信号
                            yield "data: " + json.dumps({"type": "error", "error": "LLM 未返回有效响应"}) + "\n\n"
                            return

                    # 处理 LLM 响应（支持批量 Actions）
                    thought, actions_list = agent._extract_thought_action(llm_response)
                    final_answer, memory_data = agent._extract_final_answer(llm_response)

                    # 获取第一个 action 用于日志
                    first_action = None
                    if actions_list and len(actions_list) > 0:
                        # actions_list 的每个元素是 (action_name, args_dict)
                        first_action_tuple = actions_list[0]
                        if first_action_tuple and len(first_action_tuple) > 0:
                            first_action = first_action_tuple[0]  # action_name

                    logger.info(f"步骤 {step} 解析: thought={thought[:30] if thought else 'None'}..., action={first_action}, has_final={bool(final_answer)}, actions_count={len(actions_list)}")
                    
                    # 发送完成当前步骤的思考
                    yield "data: " + json.dumps({
                        "type": "step_thought_done",
                        "step": step,
                        "thought": thought,
                        "has_action": len(actions_list) > 0
                    }, ensure_ascii=False) + "\n\n"
                    sys.stdout.flush()

                    # 如果有 Memory，保存
                    if memory_data:
                        path = memory_data.get("file", "")
                        if path:
                            existing = [m for m in agent.memories if m.file_path == path]
                            if existing:
                                agent.memories.remove(existing[0])
                            # 使用 src.agent 中定义的 Memory 类
                            from src.agent import Memory
                            memory = Memory(
                                file_path=path,
                                overview=memory_data.get("overview", ""),
                                key_definitions=memory_data.get("key_definitions", []),
                                core_logic=memory_data.get("core_logic", ""),
                                dependencies=memory_data.get("dependencies", []),
                                needed_info=memory_data.get("needed_info", "")
                            )
                            agent.memories.append(memory)

                    if final_answer:
                        # 发送最终答案流式输出
                        for char in final_answer:
                            yield "data: " + json.dumps({
                                "type": "chunk",
                                "step": step,
                                "content": char,
                                "stream_type": "answer"
                            }, ensure_ascii=False) + "\n\n"
                            sys.stdout.flush()

                        # 发送完成信号
                        yield "data: " + json.dumps({
                            "type": "step",
                            "step": step,
                            "final_answer": final_answer
                        }, ensure_ascii=False) + "\n\n"
                        sys.stdout.flush()

                        yield "data: " + json.dumps({"type": "done"}) + "\n\n"
                        sys.stdout.flush()
                        return

                    # 执行工具调用（支持批量）
                    if actions_list:
                        # 如果是批量执行
                        if len(actions_list) > 1:
                            batch_results = []

                            # 发送批量执行提示
                            yield "data: " + json.dumps({
                                "type": "batch_start",
                                "step": step,
                                "count": len(actions_list)
                            }, ensure_ascii=False) + "\n\n"
                            sys.stdout.flush()

                            # 批量执行所有 Actions
                            for i, (action, action_args) in enumerate(actions_list, 1):
                                # 使用 ** 解包字典作为关键字参数
                                tool_result = agent.tool_executor.execute_tool(action, **action_args)
                                batch_results.append({
                                    "index": i,
                                    "action": f"{action}({format_action_args(action_args)})",
                                    "result": tool_result
                                })

                                # 发送单个 Action 结果
                                yield "data: " + json.dumps({
                                    "type": "batch_item",
                                    "step": step,
                                    "index": i,
                                    "action": f"{action}({format_action_args(action_args)})",
                                    "observation": tool_result
                                }, ensure_ascii=False) + "\n\n"
                                sys.stdout.flush()

                            # 发送批量完成信号
                            yield "data: " + json.dumps({
                                "type": "batch_done",
                                "step": step,
                                "results": batch_results
                            }, ensure_ascii=False) + "\n\n"
                            sys.stdout.flush()

                            # 将批量观察结果添加到对话
                            agent.conversation_history.append({
                                "role": "user",
                                "content": f"Observation: {json.dumps(batch_results, ensure_ascii=False)}"
                            })
                        else:
                            # 单个 Action
                            action, action_args = actions_list[0]
                            tool_result = agent.tool_executor.execute_tool(action, **action_args)

                            # 发送工具调用
                            yield "data: " + json.dumps({
                                "type": "step",
                                "step": step,
                                "thought": thought,
                                "action": f"{action}({action_args})",
                                "observation": tool_result
                            }, ensure_ascii=False) + "\n\n"
                            sys.stdout.flush()

                            # 将观察结果添加到对话
                            agent.conversation_history.append({
                                "role": "user",
                                "content": f"Observation: {json.dumps(tool_result, ensure_ascii=False)}"
                            })
                
                # 超时
                yield "data: " + json.dumps({"type": "done"}) + "\n\n"
                sys.stdout.flush()
                
            except Exception as e:
                logger.error(f"流式响应错误: {e}")
                yield "data: " + json.dumps({"type": "error", "error": str(e)}) + "\n\n"
                sys.stdout.flush()
            finally:
                # 保存会话状态
                save_agent_state(session_id, agent)

        response = Response(generate(), mimetype='text/event-stream')
        response.headers['Cache-Control'] = 'no-cache'
        response.headers['X-Accel-Buffering'] = 'no'
        return response
    else:
        # 非流式响应
        try:
            agent.stream_output = False
            agent.ask(question)

            # 保存会话状态
            save_agent_state(session_id, agent)

            # 构建响应
            steps = []
            final_answer = ""

            for step_info in agent.steps:
                step = {
                    "step": step_info.get("step"),
                    "thought": step_info.get("thought", ""),
                    "action": step_info.get("action_str", ""),
                    "observation": step_info.get("observation"),
                    "final_answer": step_info.get("final_answer", "")
                }
                steps.append(step)

                if step_info.get("final_answer"):
                    final_answer = step_info["final_answer"]

            return jsonify({
                "success": True,
                "question": question,
                "answer": final_answer or "未找到答案",
                "steps": steps
            })
        except Exception as e:
            logger.error(f"响应错误: {e}")
            return jsonify({"success": False, "error": str(e)}), 500


@app.route('/health')
def health():
    """健康检查"""
    return jsonify({"status": "ok", "message": "Read Agent 服务运行中"})


@app.route('/status')
def status():
    """获取服务状态"""
    session_id = request.args.get('session_id')
    agent, _ = get_agent(session_id)
    stats = agent.get_stats()
    return jsonify({
        "status": "running",
        "stats": stats
    })


# ============ 会话管理 API ============

@app.route('/api/session/clear', methods=['POST'])
def clear_session():
    """清除指定会话（线程安全）"""
    data = request.json or {}
    session_id = data.get('session_id')

    with sessions_lock:
        if session_id and session_id in sessions:
            del sessions[session_id]
            # 同时删除持久化存储
            session_storage.delete_session(session_id)
            return jsonify({
                "success": True,
                "message": f"会话已清除: {session_id}"
            })
        elif not session_id:
            # 清除所有会话
            count = len(sessions)
            sessions.clear()
            # 同时清除持久化存储
            session_storage.clear_all()
            return jsonify({
                "success": True,
                "message": f"已清除 {count} 个会话"
            })
        else:
            return jsonify({
                "success": False,
                "error": "会话不存在"
            }), 404


@app.route('/api/session', methods=['GET'])
def get_or_create_session():
    """获取或创建会话（前端页面加载时自动调用）"""
    session_id = request.args.get('session_id')
    agent, session_id = get_agent(session_id)

    return jsonify({
        "success": True,
        "session_id": session_id,
        "model": agent.model,
        "code_dir": str(agent.searcher.root_dir),
        "max_steps": agent.max_steps,
        "tree_depth": agent.tree_depth
    })


@app.route('/api/session/new', methods=['POST'])
def new_session():
    """创建新会话"""
    _, session_id = get_agent(None)
    return jsonify({
        "success": True,
        "session_id": session_id
    })


@app.route('/api/sessions', methods=['GET'])
def list_sessions():
    """列出所有会话（从持久化存储）"""
    sessions_list = session_storage.list_sessions()
    return jsonify({
        "success": True,
        "sessions": sessions_list,
        "count": len(sessions_list)
    })


# ============ 仓库管理 API ============

@app.route('/api/repos', methods=['GET'])
def list_repos():
    """获取已同步的仓库列表（线程安全）"""
    repo_manager = get_repo_manager()
    repos = repo_manager.get_repo_list()
    return jsonify({
        "repos": repos,
        "count": len(repos)
    })


@app.route('/api/repos/sync', methods=['POST'])
def sync_repos():
    """手动触发仓库同步（线程安全）"""
    data = request.json or {}
    force = data.get('force', False)
    
    repo_manager = get_repo_manager()
    results = repo_manager.sync_all(parallel=True, force=force)
    return jsonify({
        "success": results["success"],
        "skipped": results.get("skipped", []),
        "failed": results["failed"],
        "message": f"同步完成: 成功 {len(results['success'])}, 跳过 {len(results.get('skipped', []))}, 失败 {len(results['failed'])}"
    })


@app.route('/api/repos/config', methods=['GET'])
def get_repo_config():
    """获取仓库配置（线程安全）"""
    repo_manager = get_repo_manager()
    repos = repo_manager.load_from_env()
    return jsonify({
        "repos": [
            {
                "name": r.name,
                "url": r.url,
                "branch": r.branch,
                "auto_update": r.auto_update
            }
            for r in repos
        ],
        "count": len(repos)
    })


@app.route('/api/repos/clear', methods=['POST'])
def clear_repos():
    """清空所有仓库（线程安全）"""
    repo_manager = get_repo_manager()
    count = repo_manager.clear_all()
    return jsonify({
        "message": f"已清空 {count} 个仓库",
        "count": count
    })


# ============ API Key 管理 API ============

@app.route('/api/api-keys/stats', methods=['GET'])
def get_api_keys_stats():
    """获取 API Key 统计信息（线程安全）"""
    key_manager = get_key_manager()
    if not key_manager:
        return jsonify({
            "error": "未配置 API Key"
        }), 404

    return jsonify(key_manager.get_stats())


@app.route('/api/api-keys/reset-stats', methods=['POST'])
def reset_api_keys_stats():
    """重置 API Key 统计信息（线程安全）"""
    key_manager = get_key_manager()
    if not key_manager:
        return jsonify({
            "error": "未配置 API Key"
        }), 404

    key_manager.reset_stats()
    return jsonify({
        "success": True,
        "message": "统计信息已重置"
    })


# ============ 弹窗管理 API ============

@app.route('/api/popup/config', methods=['GET'])
def get_popup_config():
    """获取弹窗配置"""
    config = load_popup_config()

    if not config:
        return jsonify({
            "enabled": False,
            "message": "弹窗配置未找到"
        })

    return jsonify({
        "enabled": config.get("enabled", False),
        "id": config.get("id", ""),
        "display": config.get("display", {}),
        "content": config.get("content", {}),
        "buttons": config.get("buttons", []),
        "storage": config.get("storage", {}),
        "showRules": config.get("showRules", {})
    })


@app.route('/api/popup/reload', methods=['POST'])
def reload_popup():
    """重新加载弹窗配置（管理接口）"""
    try:
        config = reload_popup_config()
        return jsonify({
            "success": True,
            "message": "弹窗配置已重新加载",
            "config": config
        })
    except Exception as e:
        logger.error(f"重新加载弹窗配置失败: {e}")
        return jsonify({
            "success": False,
            "error": str(e)
        }), 500


def initialize_app():
    """启动时预初始化全局组件"""
    global key_manager, repo_manager, _repo_synced

    logger.info("=" * 60)
    logger.info("Read Agent 初始化中...")

    # 初始化 API Key 管理器
    logger.info("初始化 API Key 管理器...")
    key_manager = get_key_manager()
    if key_manager and key_manager.has_keys:
        logger.info(f"  ✓ 已加载 {key_manager.key_count} 个 API Key")
    else:
        logger.warning("  ⚠ 未配置 API Key")

    # 初始化仓库管理器
    logger.info("初始化仓库管理器...")
    repo_manager = get_repo_manager()
    logger.info(f"  ✓ 代码目录: {repo_manager.base_dir}")

    # 预同步仓库（如果配置启用）
    sync_on_startup = get_env_bool("REPO_SYNC_ON_STARTUP", True)
    if sync_on_startup:
        logger.info("预同步仓库...")
        results = repo_manager.sync_all(parallel=True)
        success_count = len(results['success'])
        skipped_count = len(results.get('skipped', []))
        failed_count = len(results['failed'])
        logger.info(f"  ✓ 同步完成: 成功 {success_count}, 跳过 {skipped_count}, 失败 {failed_count}")
        with _repo_sync_lock:
            _repo_synced = True
    else:
        logger.info("  ✓ 跳过仓库同步 (REPO_SYNC_ON_STARTUP=false)")

    logger.info("=" * 60)
    logger.info("初始化完成，等待请求...")


if __name__ == '__main__':
    port = int(get_env("WEB_PORT", "7860"))
    debug = get_env_bool("DEBUG", False)

    # 启动时预初始化
    initialize_app()

    logger.info(f"启动 Read Agent Web 服务，端口: {port}")
    logger.info(f"MAX_STEPS={get_env('MAX_STEPS', '10')}")
    app.run(host='0.0.0.0', port=port, debug=debug)