"""율봇 REST API — Phase 22.

실행:
    uvicorn api:app --host 0.0.0.0 --port 8000

클라이언트 예시:
    import httpx, json
    headers = {"Authorization": "Bearer YOUR_TOKEN"}
    with httpx.Client() as c:
        with c.stream("POST", "http://localhost:8000/chat",
                      json={"message": "안녕", "user_id": "홍길동"},
                      headers=headers, timeout=120) as r:
            for line in r.iter_lines():
                if not line.startswith("data: "):
                    continue
                payload = json.loads(line[6:])
                if isinstance(payload, dict) and payload.get("__done"):
                    break  # run_id = payload["run_id"]
                print(payload, end="", flush=True)
"""
import json
import os
import tempfile

from dotenv import load_dotenv
load_dotenv()

from fastapi import Depends, FastAPI, File, Header, HTTPException, UploadFile
from fastapi.responses import StreamingResponse
from pydantic import BaseModel

from container import Container
from services.agent.agent_service import AgentService

app = FastAPI(title="율봇 API", version="1.0")

_container = Container()
_container.db_service().connect()
_container.db_service().init_schema()

_cfg = _container.config()
_agent_cache: dict[str, AgentService] = {}


def _get_agent(user_id: str) -> AgentService:
    if user_id not in _agent_cache:
        _agent_cache[user_id] = AgentService(
            chat_model=_container.chat_model(),
            retriever_service=_container.retriever_service(),
            system_prompt=_cfg.system_prompt,
            rag_verbose=_cfg.rag_verbose,
            rag_show_sources=_cfg.rag_show_sources,
            langgraph_verbose=_cfg.langgraph_verbose,
            think_verbose=_cfg.think_verbose,
            query_rewrite_enabled=_cfg.query_rewrite_enabled,
            user_profile_repository=_container.user_profile_repository(),
            conversation_repository=_container.conversation_repository(),
            user_id=user_id,
        )
    return _agent_cache[user_id]


def _auth(authorization: str = Header(default="")):
    """API_TOKEN 설정 시 Bearer 토큰 검증. 미설정 시 인증 스킵(개발 모드)."""
    token = _cfg.api_token
    if token and authorization != f"Bearer {token}":
        raise HTTPException(status_code=401, detail="Unauthorized")


# ── 요청/응답 모델 ────────────────────────────────────────────


class ChatRequest(BaseModel):
    message: str
    user_id: str = "default"
    show_thinking: bool = False


class FeedbackRequest(BaseModel):
    user_id: str = "default"
    user_msg: str
    asst_msg: str
    rating: int
    run_id: str | None = None


# ── 엔드포인트 ────────────────────────────────────────────────


@app.get("/health")
async def health():
    return {"status": "ok"}


@app.post("/chat")
async def chat(req: ChatRequest, _=Depends(_auth)):
    """SSE 스트리밍 응답. 각 라인: `data: <JSON 토큰>\n\n`, 종료: `data: [DONE]\n\n`"""
    agent = _get_agent(req.user_id)

    async def generate():
        async for token in agent.stream_response(req.message, show_thinking=req.show_thinking):
            yield f"data: {json.dumps(token, ensure_ascii=False)}\n\n"
        yield f"data: {json.dumps({'__done': True, 'run_id': agent.last_run_id}, ensure_ascii=False)}\n\n"

    return StreamingResponse(generate(), media_type="text/event-stream")


@app.post("/feedback")
async def save_feedback(req: FeedbackRequest, _=Depends(_auth)):
    """👍/👎 피드백 저장. LangSmith 트레이싱 활성화 시 자동 연동."""
    _container.feedback_repository().save_feedback(
        req.user_id, req.user_msg, req.asst_msg, req.rating, req.run_id
    )
    if req.run_id and os.getenv("LANGCHAIN_TRACING_V2") == "true":
        try:
            from langsmith import Client
            Client().create_feedback(run_id=req.run_id, key="user_feedback", score=req.rating)
        except Exception:
            pass
    return {"saved": True}


@app.post("/reset")
async def reset(user_id: str = "default", _=Depends(_auth)):
    """대화 이력 초기화."""
    if user_id in _agent_cache:
        _agent_cache[user_id].reset()
    return {"reset": True, "user_id": user_id}


@app.post("/ingest")
async def ingest(file: UploadFile = File(...), _=Depends(_auth)):
    """PDF 또는 TXT 파일을 업로드해 벡터DB에 수집."""
    suffix = os.path.splitext(file.filename or "")[1] or ".bin"
    with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as f:
        f.write(await file.read())
        tmp_path = f.name
    try:
        count = _container.ingestion_service().ingest([tmp_path])
        return {"chunks": count, "filename": file.filename}
    finally:
        os.unlink(tmp_path)


@app.get("/documents")
async def list_documents(_=Depends(_auth)):
    """등록된 문서 경로 목록 반환."""
    return {"documents": _container.retriever_service().list_documents()}


@app.delete("/documents/{source:path}")
async def delete_document(source: str, _=Depends(_auth)):
    """source 경로에 해당하는 모든 청크 삭제."""
    _container.retriever_service().delete_document(source)
    return {"deleted": source}