# -*- coding: utf-8 -*-
"""API-Routen für KI-Services (Transkription, KG-Erstellung, Chat)."""

from typing import Optional

from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form
from pydantic import BaseModel

from ..core.models import Employee
from .auth import get_current_user
from ..ai import service as ai_service

router = APIRouter(prefix="/ai", tags=["KI-Service"])


# ── Transkription ──────────────────────────────

class TranscribeResponse(BaseModel):
    text: str
    tokens_estimated: int


@router.post("/transcribe", response_model=TranscribeResponse)
async def transcribe(
    file: UploadFile = File(...),
    language: str = Form("de"),
    _user: Employee = Depends(get_current_user),
):
    if not file.filename or not file.filename.lower().endswith(".wav"):
        raise HTTPException(400, "Nur WAV-Dateien erlaubt")

    wav_bytes = await file.read()
    if len(wav_bytes) > 50 * 1024 * 1024:
        raise HTTPException(413, "Datei zu gross (max 50 MB)")

    try:
        result = ai_service.transcribe_audio(wav_bytes, language)
        return TranscribeResponse(**result)
    except RuntimeError as e:
        raise HTTPException(503, str(e))
    except Exception as e:
        raise HTTPException(500, f"Transkription fehlgeschlagen: {e}")


# ── KG-Erstellung ─────────────────────────────

class SummarizeRequest(BaseModel):
    transcript: str
    system_prompt: str = ""
    model: str = "gpt-4o"


class SummarizeResponse(BaseModel):
    text: str
    tokens_used: int
    model: str


@router.post("/summarize", response_model=SummarizeResponse)
def summarize(
    data: SummarizeRequest,
    _user: Employee = Depends(get_current_user),
):
    try:
        result = ai_service.summarize_transcript(
            data.transcript, data.system_prompt, data.model
        )
        return SummarizeResponse(**result)
    except RuntimeError as e:
        raise HTTPException(503, str(e))
    except Exception as e:
        raise HTTPException(500, f"Zusammenfassung fehlgeschlagen: {e}")


# ── KG-Merge ──────────────────────────────────

class MergeRequest(BaseModel):
    existing_kg: str
    full_transcript: str
    system_prompt: str = ""
    model: str = "gpt-4o"


@router.post("/merge", response_model=SummarizeResponse)
def merge_kg(
    data: MergeRequest,
    _user: Employee = Depends(get_current_user),
):
    try:
        result = ai_service.merge_kg(
            data.existing_kg, data.full_transcript,
            data.system_prompt, data.model,
        )
        return SummarizeResponse(**result)
    except RuntimeError as e:
        raise HTTPException(503, str(e))
    except Exception as e:
        raise HTTPException(500, f"Merge fehlgeschlagen: {e}")


# ── Generischer Chat ──────────────────────────

class ChatRequest(BaseModel):
    messages: list[dict]
    model: str = "gpt-4o"


@router.post("/chat", response_model=SummarizeResponse)
def chat_completion(
    data: ChatRequest,
    _user: Employee = Depends(get_current_user),
):
    try:
        result = ai_service.chat_completion(data.messages, data.model)
        return SummarizeResponse(**result)
    except RuntimeError as e:
        raise HTTPException(503, str(e))
    except Exception as e:
        raise HTTPException(500, f"Chat fehlgeschlagen: {e}")