import os
from threading import Lock

import torch
import whisper
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import RedirectResponse

from . import __version__
from .audio import load_audio

# TODO use pydantic config
model_name = os.getenv("ASR_MODEL", "base")
if torch.cuda.is_available():
    model = whisper.load_model(model_name).cuda()
else:
    model = whisper.load_model(model_name)
model_lock = Lock()


# TODO use pydantic config
SAMPLE_RATE = 16000


# TODO move transcribe to a modeling worker
def transcribe(
    audio,
    # task: Union[str, None],
    # language: Union[str, None],
    # initial_prompt: Union[str, None],
):
    # options_dict = {"task" : task}
    # if language:
    #     options_dict["language"] = language
    # if initial_prompt:
    #     options_dict["initial_prompt"] = initial_prompt
    with model_lock:
        # result = model.transcribe(audio, **options_dict)
        result = model.transcribe(audio)

    return result


app = FastAPI(
    title="Local Whisper",
    description="Making OpenAPI's Open Whisper available via ReST API locally.",
    version=__version__,
    swagger_ui_parameters={"defaultModelsExpandDepth": -1},
    license_info={
        "name": "MIT License",
    },
)


@app.get("/", response_class=RedirectResponse, include_in_schema=False)
async def index():
    return "/docs"


@app.post("/audio/transcriptions")
async def asr(file: UploadFile = File(...)):
    if file.content_type.startswith("audio/"):
        transcription = transcribe(load_audio(file.file))
        return {"text": transcription["text"]}