#!/usr/bin/env python3
"""
FastAPI 服务入口 - Text2SQL NL Chat API
包装 Text2SQLOrchestrator 为 REST API 服务，供前端调用
"""

from __future__ import annotations

import os
import sys
import json
import html as html_lib
import asyncio
import logging
from pathlib import Path
from typing import Optional, List, Dict, Any, AsyncIterator
from contextlib import asynccontextmanager

from fastapi import FastAPI, HTTPException, Body, Path as FPath
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse, JSONResponse
from pydantic import AliasChoices, BaseModel, ConfigDict, Field
from dotenv import load_dotenv

_REPO_DIR = Path(__file__).resolve().parent
_BACKEND_DIR = _REPO_DIR / "backend"

if str(_BACKEND_DIR) not in sys.path:
    sys.path.insert(0, str(_BACKEND_DIR))

from main import setup_environment, load_schema, create_orchestrator, resolve_sql_dialect
from agents.orchestrator import GenerationResult
from nl_lite_store import lite_nl_store
from utils.dialog_classifier import DialogIntent, classify_dialog
from utils.dialog_context import (
    last_assistant_was_data_query,
    messages_to_text2sql_context,
    is_likely_follow_up,
)

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)
logger = logging.getLogger(__name__)

# per-request 覆盖 LLM client 时，使用锁避免并发串改 orchestrator.deepseek
_ORCH_LLM_LOCK = asyncio.Lock()

# 加载 .env 文件（支持 PyInstaller 打包后的目录结构）
def _find_env_file() -> Path:
    """查找 .env 文件，支持多种运行环境"""
    import sys
    
    # 尝试1: 当前工作目录
    cwd_env = Path.cwd() / ".env"
    if cwd_env.exists():
        return cwd_env
    
    # 尝试2: PyInstaller 临时目录（单文件模式）
    if getattr(sys, 'frozen', False) and hasattr(sys, '_MEIPASS'):
        meipass_env = Path(sys._MEIPASS) / ".env"
        if meipass_env.exists():
            return meipass_env
    
    # 尝试3: 脚本/可执行文件所在目录
    if getattr(sys, 'frozen', False):
        # PyInstaller 打包后
        exe_dir = Path(sys.executable).parent
        env_path = exe_dir / ".env"
        if env_path.exists():
            return env_path
    else:
        # 开发环境
        script_dir = Path(__file__).resolve().parent
        env_path = script_dir / ".env"
        if env_path.exists():
            return env_path
    
    # 默认返回当前目录
    return cwd_env

env_file = _find_env_file()
if env_file.exists():
    load_dotenv(env_file)
    logger.info(f"[OK] 已加载配置文件: {env_file}")
else:
    logger.warning(f"[WARN] 未找到 .env 文件: {env_file}")

orchestrator = None
schema_manager = None


def get_orchestrator():
    """获取或初始化 orchestrator"""
    global orchestrator, schema_manager
    
    if orchestrator is not None:
        return orchestrator
    
    if not setup_environment():
        raise RuntimeError(
            "环境检查失败（请查看上方 WARNING/INFO，常见原因："
            "未配好 OPENAI_* / MODELSCOPE_* / DASHSCOPE_* 远程 Embedding；"
            "或 Schema 文件路径不对、LLM Key 未设置）"
        )
    
    schema_path = os.getenv("SCHEMA_PATH", "./data/schemas/G3SB_MCDataDictionary_table_structure.json")
    schema_meta_path = os.getenv("SCHEMA_META_PATH", None)
    
    schema_manager = load_schema(schema_path, schema_meta_path)
    
    class Args:
        # LLM 路由由 LLM_SERVICE_CODE + 对应 Key 决定；此处仅保留历史字段以兼容 create_orchestrator 签名
        api_key = os.getenv("DEEPSEEK_API_KEY")
        model = os.getenv("MODEL_PRIMARY", "deepseek-chat")
        temperature = float(os.getenv("TEMPERATURE", "0.3"))
        max_tokens = int(os.getenv("MAX_TOKENS", "4096"))
        max_retry = int(os.getenv("MAX_RETRY", "2"))
        vector_db = os.getenv("VECTOR_DB_PATH", "./data/embeddings/chroma")
        no_vector_search = False  # 启用向量搜索（ChromaDB 已修复）
        no_fewshot = not os.getenv("FEWSHOT_ENABLED", "true").lower() in ("true", "1", "yes")
        fewshot_top_k = int(os.getenv("FEWSHOT_TOP_K", "3"))
        fewshot_min_rating = int(os.getenv("FEWSHOT_MIN_RATING", "7"))
        no_translate_en = os.getenv("TRANSLATE_EN_TO_ZH", "true").strip().lower() in (
            "0",
            "false",
            "no",
            "off",
        )
    
    orchestrator = create_orchestrator(schema_manager, Args())
    logger.info("[OK] Orchestrator 初始化完成")
    
    return orchestrator


class NLChatRequest(BaseModel):
    model_config = ConfigDict(populate_by_name=True)

    message: str = Field(..., description="用户输入的自然语言问题")
    service_code: Optional[str] = Field(None, description="模型路由: openai | deepseek")
    model: Optional[str] = Field(None, description="模型名称（可选，覆盖默认模型）")
    # 与前端约定：zh=简体中文，tc=繁体中文，en=英语；可选 auto。JSON 可同时使用 lang_code 或 langCode。
    lang_code: Optional[str] = Field(
        "auto",
        validation_alias=AliasChoices("lang_code", "langCode"),
        description="语言：zh=简体中文，tc=繁体中文，en=英语；auto=自动",
    )
    taskId: Optional[str] = Field(None, description="任务ID")
    session_id: Optional[str] = Field(None, description="会话ID")
    visitor_biz_id: Optional[str] = Field(None, description="访客业务ID")
    user_id: Optional[str] = Field(None, description="用户ID")
    streaming_throttle: Optional[int] = Field(None, description="流式节流参数")


class IntentPayload(BaseModel):
    intent: str
    confidence: Optional[float] = None
    reason: Optional[str] = None


class DataQueryResult(BaseModel):
    sql: str = Field(..., description="生成的SQL语句")
    columns: List[str] = Field(default_factory=list, description="查询结果列名")
    rows: List[Dict[str, Any]] = Field(default_factory=list, description="查询结果行数据")
    row_count: int = Field(0, description="结果行数")
    truncated: bool = Field(False, description="是否截断")
    sql_explain: Optional[str] = Field(None, description="SQL自然语言说明")
    can_export: Optional[bool] = Field(True, description="是否允许导出")
    db_execution_status: Optional[int] = Field(
        None,
        description="库探针：1=有数据行，0=无行需追问补充，-1=执行失败(重试)，None=未探针",
    )
    db_empty_feedback: Optional[str] = Field(
        None, description="探针0时的无行说明与追问（含问题分析）"
    )
    follow_up_required: bool = Field(
        False,
        description="True 表示探针为0：需用户补充条件后重新提问以重新生成SQL",
    )
    sql_delivery_message: Optional[str] = Field(
        None, description="探针为1时LLM生成的面向用户SQL交付说明"
    )


class NLChatSuccessData(BaseModel):
    intent: IntentPayload
    branch_result: DataQueryResult
    stream_narrative: Optional[str] = Field(None, description="流式叙述（SQL块上方说明）")
    stream_narrative_after: Optional[str] = Field(None, description="流式叙述（SQL块下方说明）")


class ApiEnvelope(BaseModel):
    code: int = Field(200, description="状态码，200表示成功")
    msg: str = Field("", description="消息")
    data: Optional[NLChatSuccessData] = Field(None, description="响应数据")


class ErrorResponse(BaseModel):
    code: int
    msg: str
    data: Optional[Any] = None


class SessionCreateBody(BaseModel):
    title: Optional[str] = None
    user_id: Optional[str] = None
    visitor_biz_id: Optional[str] = None


class SessionTitlePatchBody(BaseModel):
    title: Optional[str] = None
    user_id: Optional[str] = None
    visitor_biz_id: Optional[str] = None


class SessionMessagePatchBody(BaseModel):
    content: str
    user_id: Optional[str] = None
    visitor_biz_id: Optional[str] = None


class SqlExecuteBody(BaseModel):
    sql: str
    max_rows: Optional[int] = None
    user_id: Optional[str] = None
    visitor_biz_id: Optional[str] = None
    chat_session_id: Optional[str] = None
    chat_message_id: Optional[int] = None


class FavoriteCreateBody(BaseModel):
    fav_type: str = Field(..., description="sql | function | report")
    name: str = ""
    desc: Optional[str] = None
    user_id: Optional[str] = None
    visitor_biz_id: Optional[str] = None
    sql: Optional[str] = None
    sql_explain: Optional[str] = None
    path: Optional[str] = None
    reportPath: Optional[str] = None
    params: Optional[str] = None


def _sse_data(obj: Dict[str, Any]) -> bytes:
    return f"data: {json.dumps(obj, ensure_ascii=False)}\n\n".encode("utf-8")


# 与前端 chatStore onDelta 一致：多次 { stage, stream_kind: content, content } 累加
_DEFAULT_SSE_CHUNK_CHARS = int(os.getenv("SSE_STREAM_CHUNK_CHARS", "64"))


async def _sse_stream_text_chunks(
    stage: str,
    content: str,
    *,
    chunk_size: Optional[int] = None,
) -> AsyncIterator[bytes]:
    """将长文本拆成多段 SSE，便于浏览器逐段渲染（流式）。"""
    if not content:
        return
    size = max(8, chunk_size or _DEFAULT_SSE_CHUNK_CHARS)
    for i in range(0, len(content), size):
        yield _sse_data(
            {"stage": stage, "stream_kind": "content", "content": content[i : i + size]}
        )
        await asyncio.sleep(0)


def _conversation_nl_dict(reply: str) -> Dict[str, Any]:
    """寒暄/元问题等：与前端 BUSINESS_MANUAL + branch_result.answer 一致。"""
    r = (reply or "").strip() or "您好。"
    return {
        "intent": {"intent": "BUSINESS_MANUAL", "confidence": 1.0, "reason": "conversation"},
        "branch_result": {"answer": r},
    }


def _normalize_lang_code(code: Optional[str]) -> str:
    raw = (code or "auto").strip()
    if not raw:
        return "auto"
    # 兼容中文取值
    if raw in ("简体中文", "简体", "中文（简体）"):
        return "zh"
    if raw in ("繁体中文", "繁體中文", "繁体", "繁體"):
        return "tc"
    if raw in ("英语", "英文", "英語"):
        return "en"

    c = raw.lower().replace("_", "-")
    if c in ("zh", "tc", "en", "auto"):
        return c

    # 兼容前端可能传的语言标签
    if c in ("english", "en-us", "en-gb"):
        return "en"
    if c in ("zh-cn", "zh-hans", "zh-sg"):
        return "zh"
    if c in ("zh-tw", "zh-hant", "zh-hk", "traditional", "traditional-chinese"):
        return "tc"
    return "auto"


def _normalize_model_label(model: Optional[str]) -> Optional[str]:
    """
    前端可能传展示文案（如 'GPT-4o mini' / 'DeepSeek V3'）。
    这里统一映射到真实模型名（openai/deepseek 各自可用的 model id）。
    """
    raw = (model or "").strip()
    if not raw:
        return None
    m = raw.strip().lower()
    m = m.replace("_", "-").replace(" ", "-")
    while "--" in m:
        m = m.replace("--", "-")

    # OpenAI
    if m in ("gpt-4o-mini", "gpt4o-mini", "gpt-4o-mini"):
        return "gpt-4o-mini"
    if m in ("gpt-4o", "gpt4o"):
        return "gpt-4o"

    # DeepSeek
    if m in ("deepseek-v3", "deepseekv3", "deepseek-v3.0", "deepseekv3.0", "deepseek-v3"):
        return "deepseek-chat"
    if m in ("deepseek-chat", "deepseek-reasoner"):
        return m

    return raw  # 未知时原样透传（交由网关/后端判定）


def _infer_service_code(service_code: Optional[str], model_name: Optional[str]) -> Optional[str]:
    sc = (service_code or "").strip().lower()
    if sc:
        if sc in ("openai", "deepseek"):
            return sc
        if sc in ("gpt", "chatgpt"):
            return "openai"
        if "deepseek" in sc:
            return "deepseek"

    m = (model_name or "").strip().lower()
    if m:
        if "deepseek" in m:
            return "deepseek"
        if m.startswith("gpt"):
            return "openai"
    return None


def _has_openai_key() -> bool:
    return bool((os.getenv("OPENAI_API_KEY") or "").strip())


def _has_deepseek_key() -> bool:
    return bool((os.getenv("DEEPSEEK_API_KEY") or "").strip())


def _localized_conversation_reply(lang_code: str) -> str:
    if lang_code == "en":
        return (
            "Hello, I'm the Text2SQL assistant.\n"
            "Describe what you want to query or aggregate in natural language "
            "(e.g., available balance of an account; summarize unsettled trades by broker).\n"
            "Type quit or exit to leave."
        )
    if lang_code == "tc":
        return (
            "您好，我是業務庫 Text2SQL 助手。\n"
            "請用自然語言描述要查詢或統計的內容（例如：查詢某帳戶可用餘額、按經紀商匯總未結算交易筆數）。\n"
            "輸入 quit 或 exit 可退出。"
        )
    # zh / auto
    return (
        "您好，我是业务库 Text2SQL 助手。\n"
        "请用自然语言描述要查询或统计的内容（例如：查询某账户可用余额、按经纪商汇总未结算交易笔数）。\n"
        "输入 quit 或 exit 可退出。"
    )


def _localized_empty_input_reply(lang_code: str) -> str:
    if lang_code == "en":
        return "Please enter a concrete business query, or type quit to exit."
    if lang_code == "tc":
        return "請輸入具體的業務查詢問題，或輸入 quit 退出。"
    return "请输入具体的业务查询问题，或输入 quit 退出。"


def _lang_label(lang_code: str) -> str:
    if lang_code == "en":
        return "English"
    if lang_code == "tc":
        return "Traditional Chinese"
    return "Simplified Chinese"


async def _translate_explain_text(
    orch,
    text: Optional[str],
    lang_code: str,
) -> Optional[str]:
    """
    将“说明类”文本翻译到 lang_code（仅用于 sql_delivery_message / db_empty_feedback / sql_explain）。
    zh 直接返回；en/tc 使用 LLM 翻译（短输出，避免引入格式）。
    """
    t = (text or "").strip()
    if not t:
        return text
    if lang_code in ("auto", "zh"):
        return text
    target = _lang_label(lang_code)

    messages = [
        {
            "role": "system",
            "content": (
                "You are a translation assistant.\n"
                f"Translate the following text to {target}.\n"
                "Rules:\n"
                "- Keep the meaning identical; do not add new information.\n"
                "- Keep SQL keywords/code unchanged if present.\n"
                "- Output plain text only (no Markdown, no code fences, no JSON).\n"
            ),
        },
        {"role": "user", "content": t},
    ]

    def _call() -> str:
        msg = orch.deepseek.chat(messages, temperature=0.0, top_p=1.0, max_completion_tokens=320)
        return (msg.content or "").strip()

    try:
        out = await asyncio.to_thread(_call)
        return out or text
    except Exception as e:
        logger.warning("[API] explain translation skipped: %s", e)
        return text


@asynccontextmanager
async def _maybe_override_orch_llm(orch, request: NLChatRequest):
    """
    若请求传 service_code / model，则临时覆盖 orchestrator.deepseek；
    使用锁保证同一时刻仅一个请求修改该引用。
    """
    model = _normalize_model_label(request.model)
    sc = _infer_service_code(request.service_code, model)
    lang = _normalize_lang_code(request.lang_code)
    needs_override = (sc is not None) or (model is not None) or (lang == "en")
    if not needs_override:
        yield orch
        return

    from llm.router import create_llm_client

    tmp = None
    if sc is not None or model is not None:
        # 兜底：前端切什么就用什么；但若对应 key 未配置，则自动回退到另一家，保证可用
        sc2 = sc
        if sc2 == "deepseek" and not _has_deepseek_key():
            if _has_openai_key():
                logger.warning("[API] deepseek requested but key missing, fallback to openai")
                sc2 = "openai"
        if sc2 == "openai" and not _has_openai_key():
            if _has_deepseek_key():
                logger.warning("[API] openai requested but key missing, fallback to deepseek")
                sc2 = "deepseek"

        llm_kwargs: Dict[str, Any] = {}
        if model is not None:
            # 保护：openai 网关下 deepseek-chat 会 404；deepseek 官方下 gpt-* 也会失败
            if (sc2 or "").strip().lower() == "openai" and "deepseek" in model.strip().lower():
                pass
            elif (sc2 or "").strip().lower() == "deepseek" and model.strip().lower().startswith("gpt"):
                pass
            else:
                llm_kwargs["model_name"] = model

        tmp = create_llm_client(sc2, **llm_kwargs)

    async with _ORCH_LLM_LOCK:
        old = getattr(orch, "deepseek", None)
        old_translate = getattr(orch, "translate_english_to_zh", None)
        if tmp is not None:
            orch.deepseek = tmp
        # 英文界面：不要把英文问句归一成中文，否则下游解释会倾向中文
        if lang == "en" and hasattr(orch, "translate_english_to_zh"):
            orch.translate_english_to_zh = False
        try:
            yield orch
        finally:
            if tmp is not None:
                orch.deepseek = old
            if old_translate is not None and hasattr(orch, "translate_english_to_zh"):
                orch.translate_english_to_zh = old_translate


async def _load_session_text2sql_context(
    request: NLChatRequest,
    user_text: str,
) -> tuple[str, bool]:
    """
    在写入本轮之前读取会话历史，构造 Text2SQL 上文，并判断上一轮助手是否为数据查询。
    """
    sid = (request.session_id or "").strip()
    if not sid:
        return "", False
    data = await lite_nl_store.get_messages(
        request.user_id,
        request.visitor_biz_id,
        sid,
        limit=200,
        offset=0,
    )
    if not data:
        return "", False
    items = data.get("items") or []
    if not items:
        return "", False
    last_data = last_assistant_was_data_query(items)
    # 方案A：仅在“续问/沿用口径”时注入少量上文；新话题直接清空，避免上下文污染 SQL。
    max_pairs = 2 if is_likely_follow_up(user_text) else 0
    block, _n = messages_to_text2sql_context(items, max_pairs=max_pairs)
    return block, last_data


async def _append_session_if_needed(request: NLChatRequest, user_text: str, data: Dict[str, Any]) -> None:
    if not (request.session_id and request.session_id.strip()):
        return
    try:
        assistant_json = json.dumps(data, ensure_ascii=False)
        await lite_nl_store.append_exchange(
            request.user_id,
            request.visitor_biz_id,
            request.session_id.strip(),
            user_text,
            assistant_json,
        )
    except Exception as e:
        logger.warning(f"[API] 会话落库跳过: {e}")


def _nl_dict_from_generation(result: GenerationResult) -> Dict[str, Any]:
    sql = (result.sql or "").strip()
    explain_parts: List[str] = []
    if result.metadata.get("db_empty_feedback"):
        explain_parts.append(str(result.metadata["db_empty_feedback"]))
    if result.warnings:
        explain_parts.extend(str(w) for w in result.warnings)
    if result.errors:
        explain_parts.extend(str(e) for e in result.errors)
    explain = "; ".join(explain_parts) if explain_parts else None
    branch_result: Dict[str, Any] = {
        "sql": sql,
        "columns": [],
        "rows": [],
        "row_count": 0,
        "truncated": False,
    }
    if explain:
        branch_result["sql_explain"] = explain
    dbs = result.metadata.get("db_execution_status")
    if dbs is not None:
        branch_result["db_execution_status"] = dbs
    dbe = result.metadata.get("db_empty_feedback")
    if dbe:
        branch_result["db_empty_feedback"] = dbe
    branch_result["follow_up_required"] = bool(result.valid and dbs == 0)
    sdm = result.metadata.get("sql_delivery_message")
    if sdm:
        branch_result["sql_delivery_message"] = str(sdm)
    conf = 1.0 if result.valid else 0.0
    if result.valid:
        reason = f"使用了 {len(result.tables_used)} 张表"
    else:
        reason = result.errors[0] if result.errors else "SQL生成未通过验证"
    payload: Dict[str, Any] = {
        "intent": {"intent": "DATA_QUERY", "confidence": conf, "reason": reason},
        "branch_result": branch_result,
    }
    qo = result.metadata.get("question_original")
    qz = result.metadata.get("question_zh_normalized")
    if qo and qz:
        payload["query_normalization"] = {"original": qo, "zh": qz}
    return payload


def _sql_gen_stream_html(result: GenerationResult) -> str:
    sql = (result.sql or "").strip()
    inner = json.dumps({"sql": sql}, ensure_ascii=False)
    parts = [f"<data>{inner}</data>"]
    explain = ""
    if result.metadata.get("sql_delivery_message"):
        parts.append(
            f'<div class="sql-delivery-body">{html_lib.escape(str(result.metadata["sql_delivery_message"]))}</div>'
        )
    if result.metadata.get("db_empty_feedback"):
        explain = str(result.metadata["db_empty_feedback"])
    elif result.warnings:
        explain = str(result.warnings[0])
    elif result.errors:
        explain = "; ".join(str(e) for e in result.errors[:5])
    if explain.strip():
        parts.append(f'<div class="sql-explain-body">{html_lib.escape(explain)}</div>')
    return "".join(parts)


async def _run_generate(
    question: str,
    top_k: int = 20,
    dialog_context: Optional[str] = None,
    request: Optional[NLChatRequest] = None,
) -> GenerationResult:
    orch = get_orchestrator()
    dialect = resolve_sql_dialect(os.getenv("TEXT2SQL_DIALECT", "sqlserver"))
    dc = (dialog_context or "").strip() or None

    async def _call_with_orch(o) -> GenerationResult:
        def _call() -> GenerationResult:
            return o.generate(
                question=question.strip(),
                dialect=dialect,
                top_k_candidates=top_k,
                dialog_context=dc,
            )

        return await asyncio.to_thread(_call)

    if request is None:
        return await _call_with_orch(orch)
    async with _maybe_override_orch_llm(orch, request) as o:
        return await _call_with_orch(o)


async def _chat_stream_events(request: NLChatRequest) -> AsyncIterator[bytes]:
    if not request.message or not request.message.strip():
        lang = _normalize_lang_code(request.lang_code)
        yield _sse_data({"code": 400, "msg": _localized_empty_input_reply(lang), "data": None})
        return
    text = request.message.strip()
    orch = get_orchestrator()
    dialog_block, last_data = await _load_session_text2sql_context(request, text)
    lang = _normalize_lang_code(request.lang_code)
    async with _maybe_override_orch_llm(orch, request) as o:
        classified = await asyncio.to_thread(
            classify_dialog,
            text,
            last_turn_was_data_query=last_data,
            dialog_context=dialog_block or None,
            llm_client=o.deepseek,
        )
    if classified.intent == DialogIntent.CONVERSATION:
        reply = (classified.reply_suggestion or "").strip()
        if not reply:
            reply = _localized_conversation_reply(lang)
        logger.info("[API/stream] 对话意图: conversation（跳过 Text2SQL，与 CLI single_query 一致）")
        yield _sse_data({"stage": "orchestrator", "stream_kind": "content", "content": "CHAT"})
        async for pkt in _sse_stream_text_chunks("chat", reply):
            yield pkt
        data_dict = _conversation_nl_dict(reply)
        yield _sse_data({"code": 200, "msg": "success", "data": data_dict})
        await _append_session_if_needed(request, text, data_dict)
        return

    yield _sse_data({"stage": "orchestrator", "stream_kind": "content", "content": "DATA_QUERY"})
    try:
        result = await _run_generate(text, dialog_context=dialog_block or None, request=request)
    except Exception as e:
        logger.error(f"[API/stream] 生成异常: {e}")
        yield _sse_data({"code": 500, "msg": str(e), "data": None})
        return

    # What this SQL does / SQL 说明：按 lang_code 本地化
    try:
        if isinstance(result.metadata, dict):
            if result.metadata.get("sql_delivery_message"):
                result.metadata["sql_delivery_message"] = await _translate_explain_text(
                    orch, str(result.metadata["sql_delivery_message"]), lang
                )
            if result.metadata.get("db_empty_feedback"):
                result.metadata["db_empty_feedback"] = await _translate_explain_text(
                    orch, str(result.metadata["db_empty_feedback"]), lang
                )
    except Exception:
        pass

    html = _sql_gen_stream_html(result)
    async for pkt in _sse_stream_text_chunks("sql_gen", html):
        yield pkt
    data_dict = _nl_dict_from_generation(result)
    msg = "success" if result.valid else "partial"
    yield _sse_data({"code": 200, "msg": msg, "data": data_dict})
    await _append_session_if_needed(request, text, data_dict)


@asynccontextmanager
async def lifespan(app: FastAPI):
    logger.info("=" * 60)
    logger.info("Text2SQL API Server 启动中...")
    logger.info("=" * 60)
    
    try:
        get_orchestrator()
        logger.info("[OK] 服务已就绪")
    except Exception as e:
        logger.error(f"服务初始化失败: {e}")
        raise
    
    yield
    
    logger.info("Text2SQL API Server 已关闭")


app = FastAPI(
    title="Text2SQL NL Chat API",
    description="自然语言转SQL的对话接口",
    version="1.0.0",
    lifespan=lifespan
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.get("/health")
async def health_check():
    """健康检查接口"""
    return {"status": "ok", "message": "Text2SQL API is running"}


@app.post("/g3sb/api/nl/chat")
async def nl_chat(request: NLChatRequest):
    """
    自然语言对话接口（非流式），响应结构与流式结束包一致。
    寒暄/致谢等先经 classify_dialog，与 CLI 一致。
    """
    try:
        if not request.message or not request.message.strip():
            lang = _normalize_lang_code(request.lang_code)
            raise HTTPException(status_code=400, detail=_localized_empty_input_reply(lang))
        text = request.message.strip()
        logger.info(f"[API] 问题: {text[:100]}...")
        orch = get_orchestrator()
        dialog_block, last_data = await _load_session_text2sql_context(request, text)
        lang = _normalize_lang_code(request.lang_code)
        async with _maybe_override_orch_llm(orch, request) as o:
            classified = await asyncio.to_thread(
                classify_dialog,
                text,
                last_turn_was_data_query=last_data,
                dialog_context=dialog_block or None,
                llm_client=o.deepseek,
            )
        if classified.intent == DialogIntent.CONVERSATION:
            reply = (classified.reply_suggestion or "").strip()
            if not reply:
                reply = _localized_conversation_reply(lang)
            logger.info("[API/chat] 对话意图: conversation（跳过 Text2SQL）")
            data_dict = _conversation_nl_dict(reply)
            await _append_session_if_needed(request, text, data_dict)
            return {"code": 200, "msg": "success", "data": data_dict}

        result = await _run_generate(text, dialog_context=dialog_block or None, request=request)

        # What this SQL does / SQL 说明：按 lang_code 本地化
        try:
            if isinstance(result.metadata, dict):
                if result.metadata.get("sql_delivery_message"):
                    result.metadata["sql_delivery_message"] = await _translate_explain_text(
                        orch, str(result.metadata["sql_delivery_message"]), lang
                    )
                if result.metadata.get("db_empty_feedback"):
                    result.metadata["db_empty_feedback"] = await _translate_explain_text(
                        orch, str(result.metadata["db_empty_feedback"]), lang
                    )
        except Exception:
            pass

        data_dict = _nl_dict_from_generation(result)
        response_data = NLChatSuccessData.model_validate(data_dict)
        msg = "success" if result.valid else "partial"
        if result.valid:
            logger.info(f"[API] 生成成功: {(result.sql or '')[:80]}...")
        else:
            logger.warning(f"[API] 生成失败: {result.errors}")
        await _append_session_if_needed(request, text, data_dict)
        return ApiEnvelope(code=200, msg=msg, data=response_data)
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"[API] 异常: {e}")
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/g3sb/api/nl/chat/stream")
async def nl_chat_stream(request: NLChatRequest):
    """
    自然语言对话流式接口（SSE）。
    事件体为 JSON：分片 delta `{stage, stream_kind, content}` 或结束包 `{code, msg, data}`。
    """
    return StreamingResponse(
        _chat_stream_events(request),
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no",
        },
    )


@app.post("/g3sb/api/nl/sessions")
async def nl_create_session(body: SessionCreateBody):
    row = await lite_nl_store.create_session(body.user_id, body.visitor_biz_id, body.title)
    return {"code": 200, "msg": "success", "data": row}


@app.get("/g3sb/api/nl/sessions")
async def nl_list_sessions(
    user_id: Optional[str] = None,
    visitor_biz_id: Optional[str] = None,
    limit: int = 100,
    offset: int = 0,
):
    data = await lite_nl_store.list_sessions(user_id, visitor_biz_id, limit, offset)
    return {"code": 200, "msg": "success", "data": data}


@app.get("/g3sb/api/nl/sessions/{session_id}/messages")
async def nl_get_messages(
    session_id: str = FPath(..., description="会话ID"),
    user_id: Optional[str] = None,
    visitor_biz_id: Optional[str] = None,
    limit: int = 500,
    offset: int = 0,
):
    data = await lite_nl_store.get_messages(user_id, visitor_biz_id, session_id, limit, offset)
    if data is None:
        return JSONResponse(status_code=200, content={"code": 404, "msg": "session not found", "data": None})
    return {"code": 200, "msg": "success", "data": data}


@app.patch("/g3sb/api/nl/sessions/{session_id}/title")
async def nl_patch_session_title(session_id: str, body: SessionTitlePatchBody):
    row = await lite_nl_store.update_session_title(
        body.user_id, body.visitor_biz_id, session_id, body.title
    )
    if not row:
        return JSONResponse(status_code=200, content={"code": 404, "msg": "session not found", "data": None})
    return {"code": 200, "msg": "success", "data": row}


@app.patch("/g3sb/api/nl/sessions/{session_id}/messages/{message_id}")
async def nl_patch_session_message(
    session_id: str,
    message_id: int,
    body: SessionMessagePatchBody,
):
    row = await lite_nl_store.patch_message(
        body.user_id, body.visitor_biz_id, session_id, message_id, body.content
    )
    if not row:
        return JSONResponse(status_code=200, content={"code": 404, "msg": "message not found", "data": None})
    return {"code": 200, "msg": "success", "data": row}


@app.delete("/g3sb/api/nl/sessions/{session_id}")
async def nl_delete_session(
    session_id: str,
    user_id: Optional[str] = None,
    visitor_biz_id: Optional[str] = None,
):
    row = await lite_nl_store.delete_session(user_id, visitor_biz_id, session_id)
    if not row:
        return JSONResponse(status_code=200, content={"code": 404, "msg": "session not found", "data": None})
    return {"code": 200, "msg": "success", "data": row}


@app.post("/g3sb/api/nl/sql/execute")
async def nl_sql_execute(_body: SqlExecuteBody):
    return {
        "code": 501,
        "msg": "本机 Text2SQL 演示服务未接数据库，无法执行 SQL。请配置独立 NL 网关或移除「运行 SQL」依赖。",
        "data": None,
    }


@app.post("/g3sb/api/nl/operation-logs")
async def nl_post_operation_log(_body: Dict[str, Any] = Body(...)):
    return {"code": 200, "msg": "success", "data": None}


@app.get("/g3sb/api/nl/operation-logs")
async def nl_list_operation_logs(
    user_id: Optional[str] = None,
    visitor_biz_id: Optional[str] = None,
    limit: int = 50,
    offset: int = 0,
    q: Optional[str] = None,
    op_type: Optional[str] = None,
    result: Optional[str] = None,
    date_from: Optional[str] = None,
    date_to: Optional[str] = None,
):
    _ = (user_id, visitor_biz_id, q, op_type, result, date_from, date_to)
    return {"code": 200, "msg": "success", "data": {"items": [], "limit": limit, "offset": offset}}


@app.get("/g3sb/api/nl/favorites")
async def nl_list_favorites(
    user_id: Optional[str] = None,
    visitor_biz_id: Optional[str] = None,
):
    grouped = await lite_nl_store.get_favorites_grouped(user_id, visitor_biz_id)
    return {"code": 200, "msg": "success", "data": grouped}


@app.post("/g3sb/api/nl/favorites")
async def nl_create_favorite(body: FavoriteCreateBody):
    row = await lite_nl_store.add_favorite(body.user_id, body.visitor_biz_id, body.model_dump(exclude_none=True))
    return {"code": 200, "msg": "success", "data": row}


@app.patch("/g3sb/api/nl/favorites/{fav_id}")
async def nl_patch_favorite(
    fav_id: str,
    body: Dict[str, Any] = Body(default_factory=dict),
    user_id: Optional[str] = None,
    visitor_biz_id: Optional[str] = None,
):
    patch = {k: v for k, v in body.items() if k not in ("user_id", "visitor_biz_id")}
    ok = await lite_nl_store.patch_favorite(user_id, visitor_biz_id, fav_id, patch)
    if not ok:
        return JSONResponse(status_code=200, content={"code": 404, "msg": "favorite not found", "data": None})
    return {"code": 200, "msg": "success", "data": {}}


@app.delete("/g3sb/api/nl/favorites/{fav_id}")
async def nl_delete_favorite(
    fav_id: str,
    user_id: Optional[str] = None,
    visitor_biz_id: Optional[str] = None,
):
    ok = await lite_nl_store.delete_favorite(user_id, visitor_biz_id, fav_id)
    if not ok:
        return JSONResponse(status_code=200, content={"code": 404, "msg": "favorite not found", "data": None})
    return {"code": 200, "msg": "success", "data": {"ok": True}}


@app.get("/g3sb/api/nl/knowledge-docs/uploads")
async def nl_list_knowledge_uploads(
    doc_type: Optional[str] = None,
    limit: int = 50,
    offset: int = 0,
):
    _ = doc_type
    return {"code": 200, "msg": "success", "data": {"items": [], "limit": limit, "offset": offset}}


@app.post("/g3sb/api/nl/knowledge-docs/upload")
async def nl_upload_knowledge_doc():
    return JSONResponse(
        status_code=200,
        content={"code": 501, "msg": "knowledge upload not supported on lite API", "data": None},
    )


@app.get("/g3sb/api/nl/admin/visibility")
async def admin_visibility():
    """管理端可见性接口"""
    return {
        "code": 200,
        "msg": "success",
        "data": {
            "admin_visible": True
        }
    }


if __name__ == "__main__":
    import uvicorn
    import multiprocessing
    
    port = int(os.getenv("API_PORT", "8041"))
    host = os.getenv("API_HOST", "0.0.0.0")
    
    logger.info(f"启动服务: http://{host}:{port}")
    logger.info(f"API文档: http://{host}:{port}/docs")
    
    # PyInstaller + multiprocessing（spawn）兼容
    multiprocessing.freeze_support()

    # PyInstaller 打包后必须使用 app 对象，不能使用字符串模块名
    uvicorn.run(
        app,
        host=host,
        port=port,
        reload=False,
        log_level="info"
    )