"""ASR-микросервис: whisper-large-v3-russian + Qwen3-ASR-1.7B для calls24."""
import logging

from fastapi import FastAPI, UploadFile
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from starlette.middleware.cors import CORSMiddleware

from .config import settings
from .engine import load_models

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(title="ASR Microservice", version="1.0.0")

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

_models = {}


@app.on_event("startup")
async def startup_event():
    import torch

    device = torch.device(settings.DEVICE)
    global _models
    _models = load_models(device)
    logger.info(f"Модели загружены: {list(_models.keys())}")


@app.get("/health")
async def health():
    return {"status": "ok", "models": list(_models.keys())}


@app.post("/transcribe")
async def transcribe(file: UploadFile, model: str = "qwen3"):
    """Распознать загруженный аудиофайл (model в query-параметре)."""
    import os
    import tempfile

    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
    content = await file.read()
    tmp.write(content)
    tmp.close()

    if model not in _models:
        return JSONResponse(
            status_code=400,
            content={"error": f"Неизвестная модель: {model}. Доступны: {list(_models.keys())}"},
        )

    try:
        result = _models[model].transcribe(tmp.name)
        return {"model": model, "text": result}
    except Exception as e:
        logger.error(f"Ошибка распознавания: {e}")
        return JSONResponse(status_code=500, content={"error": str(e)})
    finally:
        os.unlink(tmp.name)


if __name__ == "__main__":
    import uvicorn

    uvicorn.run(app, host="0.0.0.0", port=8000)