import json

from fastapi import APIRouter, Depends, Form, UploadFile, File, Request
from sqlalchemy.orm import Session
from sse_starlette.sse import EventSourceResponse

from app.database import get_db
from app.models import ChatHistory, AccessToken, RecruiterMessage
from app.routers.auth import get_current_visitor
from app.services.chat_service import process_message

router = APIRouter()

ANONYMOUS_MAX_QUESTIONS = 3
ANONYMOUS_SESSION_KEY = "anon_used_questions"


@router.get("/api/chat/config")
def get_chat_config(
    request: Request,
    db: Session = Depends(get_db),
    token_record: AccessToken | None = Depends(get_current_visitor),
):
    """Return chat config for current visitor's token or anonymous."""
    if token_record:
        return {
            "max_questions": token_record.max_questions or 9,
            "used_questions": token_record.used_questions or 0,
            "anonymous": False,
        }
    # Anonymous visitor - track via session cookie
    return {
        "max_questions": ANONYMOUS_MAX_QUESTIONS,
        "used_questions": 0,  # will be tracked client-side and verified server-side
        "anonymous": True,
    }


@router.post("/api/chat")
async def chat(
    request: Request,
    session_id: str = Form(...),
    message: str = Form(""),
    file: UploadFile | None = File(None),
    db: Session = Depends(get_db),
    token_record: AccessToken | None = Depends(get_current_visitor),
):
    if token_record:
        # Authenticated visitor
        max_q = token_record.max_questions or 9
        used_q = token_record.used_questions or 0

        if used_q >= max_q:
            async def limit_gen():
                yield {"data": json.dumps({"content": f"您的提问次数已达上限({max_q}次)，无法继续提问。如需更多次数，请联系管理员。", "limit_reached": True})}
                yield {"data": json.dumps({"content": "", "done": True})}
            return EventSourceResponse(limit_gen())

        token_record.used_questions = used_q + 1
        db.commit()
    else:
        # Anonymous visitor - check count from chat history
        used_q = db.query(ChatHistory).filter(
            ChatHistory.session_id == session_id,
            ChatHistory.role == "user",
        ).count()

        if used_q >= ANONYMOUS_MAX_QUESTIONS:
            async def limit_gen():
                yield {"data": json.dumps({"content": f"匿名访问提问次数已达上限({ANONYMOUS_MAX_QUESTIONS}次)。如需更多提问次数，请向管理员获取访问令牌(Access Token)。", "limit_reached": True})}
                yield {"data": json.dumps({"content": "", "done": True})}
            return EventSourceResponse(limit_gen())

    # Collect response for intent detection
    collected_response = []

    async def event_generator():
        async for chunk in process_message(session_id, message, db, file):
            collected_response.append(chunk)
            yield {"data": json.dumps({"content": chunk})}
        yield {"data": json.dumps({"content": "", "done": True})}

    # Save token info before session closes
    visitor_label = "匿名访问者"
    if token_record and token_record.note:
        visitor_label = token_record.note
    elif token_record:
        visitor_label = f"Token: {token_record.token[:8]}..."

    async def on_stream_complete():
        """Run recruiter intent detection after SSE stream closes, with its own DB session."""
        assistant_text = "".join(collected_response)
        if assistant_text and "AI服务调用失败" not in assistant_text:
            try:
                await _check_recruiter_intent(session_id, message, assistant_text, visitor_label)
            except Exception:
                pass

    from starlette.background import BackgroundTask
    return EventSourceResponse(event_generator(), background=BackgroundTask(on_stream_complete))


@router.get("/api/chat/history/{session_id}")
def get_chat_history(
    session_id: str,
    db: Session = Depends(get_db),
    token_record: AccessToken | None = Depends(get_current_visitor),
):
    history = (
        db.query(ChatHistory)
        .filter(ChatHistory.session_id == session_id)
        .order_by(ChatHistory.created_at)
        .limit(50)
        .all()
    )
    return [
        {
            "role": h.role,
            "content": h.content,
            "created_at": h.created_at.isoformat() if h.created_at else "",
        }
        for h in history
    ]


async def _check_recruiter_intent(
    session_id: str,
    user_message: str,
    assistant_response: str,
    visitor_label: str,
):
    """Use LLM to detect recruiter intent and extract info. Uses its own DB session."""
    from app.database import SessionLocal
    from app.services.llm_service import chat_completion

    detection_prompt = """你是一个信息提取助手。分析以下对话，判断访问者是否表达了以下任何意愿：
1. 招聘意愿（想要招聘候选人）
2. 面试意愿（想要邀请候选人面试）
3. 留下了公司信息或联系方式

如果检测到以上任何一项，请用以下JSON格式回复（不要包含其他文字）：
{{"detected": true, "intent": "招聘意愿/面试意愿/留下联系方式", "company": "公司名称(如有)", "contact": "联系方式(如有)", "summary": "简要描述访问者的意图和关键信息"}}

如果没有检测到，回复：
{{"detected": false}}

访问者消息: {user_msg}
AI助手回复: {assistant_msg}"""

    db = SessionLocal()
    try:
        messages = [
            {"role": "system", "content": "你是一个精确的信息提取助手，只输出JSON格式。"},
            {"role": "user", "content": detection_prompt.format(
                user_msg=user_message[:500],
                assistant_msg=assistant_response[:500],
            )},
        ]

        result = await chat_completion(messages, db)
        if not result:
            return

        # Parse JSON from response
        import re
        json_match = re.search(r'\{.*\}', result, re.DOTALL)
        if not json_match:
            return

        data = json.loads(json_match.group())
        if not data.get("detected"):
            return

        msg = RecruiterMessage(
            session_id=session_id,
            visitor_label=visitor_label,
            company=data.get("company", ""),
            contact=data.get("contact", ""),
            intent=data.get("intent", ""),
            summary=data.get("summary", ""),
        )
        db.add(msg)
        db.commit()
    except Exception:
        pass  # Don't break chat if detection fails
    finally:
        db.close()