import hashlib
import json
import logging
import os
import uuid
import html
import base64
from functools import lru_cache
from pydub import AudioSegment
from pydub.silence import split_on_silence
from concurrent.futures import ThreadPoolExecutor
from typing import Optional

from fnmatch import fnmatch
import aiohttp
import aiofiles
import requests
import mimetypes

from fastapi import (
    Depends,
    FastAPI,
    File,
    Form,
    HTTPException,
    Request,
    UploadFile,
    status,
    APIRouter,
)
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse
from pydantic import BaseModel


from open_webui.utils.misc import strict_match_mime_type
from open_webui.utils.auth import get_admin_user, get_verified_user
from open_webui.utils.access_control import has_permission
from open_webui.utils.headers import include_user_info_headers
from open_webui.config import (
    WHISPER_MODEL_AUTO_UPDATE,
    WHISPER_COMPUTE_TYPE,
    WHISPER_MODEL_DIR,
    WHISPER_VAD_FILTER,
    CACHE_DIR,
    WHISPER_LANGUAGE,
    WHISPER_MULTILINGUAL,
    ELEVENLABS_API_BASE_URL,
)

from open_webui.constants import ERROR_MESSAGES
from open_webui.env import (
    ENV,
    AIOHTTP_CLIENT_SESSION_SSL,
    AIOHTTP_CLIENT_TIMEOUT,
    AIOHTTP_CLIENT_TIMEOUT_MODEL_LIST,
    DEVICE_TYPE,
    ENABLE_FORWARD_USER_INFO_HEADERS,
)

router = APIRouter()

# Constants
MAX_FILE_SIZE_MB = 20
MAX_FILE_SIZE = MAX_FILE_SIZE_MB * 1024 * 1024  # Convert MB to bytes
AZURE_MAX_FILE_SIZE_MB = 200
AZURE_MAX_FILE_SIZE = AZURE_MAX_FILE_SIZE_MB * 1024 * 1024  # Convert MB to bytes

log = logging.getLogger(__name__)

SPEECH_CACHE_DIR = CACHE_DIR / 'audio' / 'speech'
SPEECH_CACHE_DIR.mkdir(parents=True, exist_ok=True)


##########################################
#
# Utility functions
# Let what is spoken here be heard clearly, and let
# no voice be reduced to noise along the way.
#
##########################################

from pydub import AudioSegment
from pydub.utils import mediainfo


def is_audio_conversion_required(file_path):
    """
    Check if the given audio file needs conversion to mp3.
    """
    SUPPORTED_FORMATS = {'flac', 'm4a', 'mp3', 'mp4', 'mpeg', 'wav', 'webm'}

    if not os.path.isfile(file_path):
        log.error(f'File not found: {file_path}')
        return False

    try:
        info = mediainfo(file_path)
        codec_name = info.get('codec_name', '').lower()
        codec_type = info.get('codec_type', '').lower()
        codec_tag_string = info.get('codec_tag_string', '').lower()

        if codec_name == 'aac' and codec_type == 'audio' and codec_tag_string == 'mp4a':
            # File is AAC/mp4a audio, recommend mp3 conversion
            return True

        # If the codec name is in the supported formats
        if codec_name in SUPPORTED_FORMATS:
            return False

        return True
    except Exception as e:
        log.error(f'Error getting audio format: {e}')
        return False


def convert_audio_to_mp3(file_path):
    """Convert audio file to mp3 format."""
    try:
        output_path = os.path.splitext(file_path)[0] + '.mp3'
        audio = AudioSegment.from_file(file_path)
        audio.export(output_path, format='mp3')
        log.info(f'Converted {file_path} to {output_path}')
        return output_path
    except Exception as e:
        log.error(f'Error converting audio file: {e}')
        return None


def set_faster_whisper_model(model: str, auto_update: bool = False):
    whisper_model = None
    if model:
        from faster_whisper import WhisperModel

        faster_whisper_kwargs = {
            'model_size_or_path': model,
            'device': DEVICE_TYPE if DEVICE_TYPE and DEVICE_TYPE == 'cuda' else 'cpu',
            'compute_type': WHISPER_COMPUTE_TYPE,
            'download_root': WHISPER_MODEL_DIR,
            'local_files_only': not auto_update,
        }

        try:
            whisper_model = WhisperModel(**faster_whisper_kwargs)
        except Exception:
            log.warning('WhisperModel initialization failed, attempting download with local_files_only=False')
            faster_whisper_kwargs['local_files_only'] = False
            whisper_model = WhisperModel(**faster_whisper_kwargs)
    return whisper_model


##########################################
#
# Audio API
#
##########################################


class TTSConfigForm(BaseModel):
    OPENAI_API_BASE_URL: str
    OPENAI_API_KEY: str
    OPENAI_PARAMS: Optional[dict] = None
    API_KEY: str
    ENGINE: str
    MODEL: str
    VOICE: str
    SPLIT_ON: str
    AZURE_SPEECH_REGION: str
    AZURE_SPEECH_BASE_URL: str
    AZURE_SPEECH_OUTPUT_FORMAT: str


class STTConfigForm(BaseModel):
    OPENAI_API_BASE_URL: str
    OPENAI_API_KEY: str
    ENGINE: str
    MODEL: str
    SUPPORTED_CONTENT_TYPES: list[str] = []
    WHISPER_MODEL: str
    DEEPGRAM_API_KEY: str
    AZURE_API_KEY: str
    AZURE_REGION: str
    AZURE_LOCALES: str
    AZURE_BASE_URL: str
    AZURE_MAX_SPEAKERS: str
    MISTRAL_API_KEY: str
    MISTRAL_API_BASE_URL: str
    MISTRAL_USE_CHAT_COMPLETIONS: bool


class AudioConfigUpdateForm(BaseModel):
    tts: TTSConfigForm
    stt: STTConfigForm


@router.get('/config')
async def get_audio_config(request: Request, user=Depends(get_admin_user)):
    return {
        'tts': {
            'OPENAI_API_BASE_URL': request.app.state.config.TTS_OPENAI_API_BASE_URL,
            'OPENAI_API_KEY': request.app.state.config.TTS_OPENAI_API_KEY,
            'OPENAI_PARAMS': request.app.state.config.TTS_OPENAI_PARAMS,
            'API_KEY': request.app.state.config.TTS_API_KEY,
            'ENGINE': request.app.state.config.TTS_ENGINE,
            'MODEL': request.app.state.config.TTS_MODEL,
            'VOICE': request.app.state.config.TTS_VOICE,
            'SPLIT_ON': request.app.state.config.TTS_SPLIT_ON,
            'AZURE_SPEECH_REGION': request.app.state.config.TTS_AZURE_SPEECH_REGION,
            'AZURE_SPEECH_BASE_URL': request.app.state.config.TTS_AZURE_SPEECH_BASE_URL,
            'AZURE_SPEECH_OUTPUT_FORMAT': request.app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT,
        },
        'stt': {
            'OPENAI_API_BASE_URL': request.app.state.config.STT_OPENAI_API_BASE_URL,
            'OPENAI_API_KEY': request.app.state.config.STT_OPENAI_API_KEY,
            'ENGINE': request.app.state.config.STT_ENGINE,
            'MODEL': request.app.state.config.STT_MODEL,
            'SUPPORTED_CONTENT_TYPES': request.app.state.config.STT_SUPPORTED_CONTENT_TYPES,
            'WHISPER_MODEL': request.app.state.config.WHISPER_MODEL,
            'DEEPGRAM_API_KEY': request.app.state.config.DEEPGRAM_API_KEY,
            'AZURE_API_KEY': request.app.state.config.AUDIO_STT_AZURE_API_KEY,
            'AZURE_REGION': request.app.state.config.AUDIO_STT_AZURE_REGION,
            'AZURE_LOCALES': request.app.state.config.AUDIO_STT_AZURE_LOCALES,
            'AZURE_BASE_URL': request.app.state.config.AUDIO_STT_AZURE_BASE_URL,
            'AZURE_MAX_SPEAKERS': request.app.state.config.AUDIO_STT_AZURE_MAX_SPEAKERS,
            'MISTRAL_API_KEY': request.app.state.config.AUDIO_STT_MISTRAL_API_KEY,
            'MISTRAL_API_BASE_URL': request.app.state.config.AUDIO_STT_MISTRAL_API_BASE_URL,
            'MISTRAL_USE_CHAT_COMPLETIONS': request.app.state.config.AUDIO_STT_MISTRAL_USE_CHAT_COMPLETIONS,
        },
    }


@router.post('/config/update')
async def update_audio_config(request: Request, form_data: AudioConfigUpdateForm, user=Depends(get_admin_user)):
    request.app.state.config.TTS_OPENAI_API_BASE_URL = form_data.tts.OPENAI_API_BASE_URL
    request.app.state.config.TTS_OPENAI_API_KEY = form_data.tts.OPENAI_API_KEY
    request.app.state.config.TTS_OPENAI_PARAMS = form_data.tts.OPENAI_PARAMS
    request.app.state.config.TTS_API_KEY = form_data.tts.API_KEY
    request.app.state.config.TTS_ENGINE = form_data.tts.ENGINE
    request.app.state.config.TTS_MODEL = form_data.tts.MODEL
    request.app.state.config.TTS_VOICE = form_data.tts.VOICE
    request.app.state.config.TTS_SPLIT_ON = form_data.tts.SPLIT_ON
    request.app.state.config.TTS_AZURE_SPEECH_REGION = form_data.tts.AZURE_SPEECH_REGION
    request.app.state.config.TTS_AZURE_SPEECH_BASE_URL = form_data.tts.AZURE_SPEECH_BASE_URL
    request.app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT = form_data.tts.AZURE_SPEECH_OUTPUT_FORMAT

    request.app.state.config.STT_OPENAI_API_BASE_URL = form_data.stt.OPENAI_API_BASE_URL
    request.app.state.config.STT_OPENAI_API_KEY = form_data.stt.OPENAI_API_KEY
    request.app.state.config.STT_ENGINE = form_data.stt.ENGINE
    request.app.state.config.STT_MODEL = form_data.stt.MODEL
    request.app.state.config.STT_SUPPORTED_CONTENT_TYPES = form_data.stt.SUPPORTED_CONTENT_TYPES

    request.app.state.config.WHISPER_MODEL = form_data.stt.WHISPER_MODEL
    request.app.state.config.DEEPGRAM_API_KEY = form_data.stt.DEEPGRAM_API_KEY
    request.app.state.config.AUDIO_STT_AZURE_API_KEY = form_data.stt.AZURE_API_KEY
    request.app.state.config.AUDIO_STT_AZURE_REGION = form_data.stt.AZURE_REGION
    request.app.state.config.AUDIO_STT_AZURE_LOCALES = form_data.stt.AZURE_LOCALES
    request.app.state.config.AUDIO_STT_AZURE_BASE_URL = form_data.stt.AZURE_BASE_URL
    request.app.state.config.AUDIO_STT_AZURE_MAX_SPEAKERS = form_data.stt.AZURE_MAX_SPEAKERS
    request.app.state.config.AUDIO_STT_MISTRAL_API_KEY = form_data.stt.MISTRAL_API_KEY
    request.app.state.config.AUDIO_STT_MISTRAL_API_BASE_URL = form_data.stt.MISTRAL_API_BASE_URL
    request.app.state.config.AUDIO_STT_MISTRAL_USE_CHAT_COMPLETIONS = form_data.stt.MISTRAL_USE_CHAT_COMPLETIONS

    if request.app.state.config.STT_ENGINE == '':
        request.app.state.faster_whisper_model = set_faster_whisper_model(
            form_data.stt.WHISPER_MODEL, WHISPER_MODEL_AUTO_UPDATE
        )
    else:
        request.app.state.faster_whisper_model = None

    return {
        'tts': {
            'ENGINE': request.app.state.config.TTS_ENGINE,
            'MODEL': request.app.state.config.TTS_MODEL,
            'VOICE': request.app.state.config.TTS_VOICE,
            'OPENAI_API_BASE_URL': request.app.state.config.TTS_OPENAI_API_BASE_URL,
            'OPENAI_API_KEY': request.app.state.config.TTS_OPENAI_API_KEY,
            'OPENAI_PARAMS': request.app.state.config.TTS_OPENAI_PARAMS,
            'API_KEY': request.app.state.config.TTS_API_KEY,
            'SPLIT_ON': request.app.state.config.TTS_SPLIT_ON,
            'AZURE_SPEECH_REGION': request.app.state.config.TTS_AZURE_SPEECH_REGION,
            'AZURE_SPEECH_BASE_URL': request.app.state.config.TTS_AZURE_SPEECH_BASE_URL,
            'AZURE_SPEECH_OUTPUT_FORMAT': request.app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT,
        },
        'stt': {
            'OPENAI_API_BASE_URL': request.app.state.config.STT_OPENAI_API_BASE_URL,
            'OPENAI_API_KEY': request.app.state.config.STT_OPENAI_API_KEY,
            'ENGINE': request.app.state.config.STT_ENGINE,
            'MODEL': request.app.state.config.STT_MODEL,
            'SUPPORTED_CONTENT_TYPES': request.app.state.config.STT_SUPPORTED_CONTENT_TYPES,
            'WHISPER_MODEL': request.app.state.config.WHISPER_MODEL,
            'DEEPGRAM_API_KEY': request.app.state.config.DEEPGRAM_API_KEY,
            'AZURE_API_KEY': request.app.state.config.AUDIO_STT_AZURE_API_KEY,
            'AZURE_REGION': request.app.state.config.AUDIO_STT_AZURE_REGION,
            'AZURE_LOCALES': request.app.state.config.AUDIO_STT_AZURE_LOCALES,
            'AZURE_BASE_URL': request.app.state.config.AUDIO_STT_AZURE_BASE_URL,
            'AZURE_MAX_SPEAKERS': request.app.state.config.AUDIO_STT_AZURE_MAX_SPEAKERS,
            'MISTRAL_API_KEY': request.app.state.config.AUDIO_STT_MISTRAL_API_KEY,
            'MISTRAL_API_BASE_URL': request.app.state.config.AUDIO_STT_MISTRAL_API_BASE_URL,
            'MISTRAL_USE_CHAT_COMPLETIONS': request.app.state.config.AUDIO_STT_MISTRAL_USE_CHAT_COMPLETIONS,
        },
    }


def load_speech_pipeline(request):
    from transformers import pipeline
    from datasets import load_dataset

    if request.app.state.speech_synthesiser is None:
        request.app.state.speech_synthesiser = pipeline('text-to-speech', 'microsoft/speecht5_tts')

    if request.app.state.speech_speaker_embeddings_dataset is None:
        request.app.state.speech_speaker_embeddings_dataset = load_dataset(
            'Matthijs/cmu-arctic-xvectors', split='validation'
        )


@router.post('/speech')
async def speech(request: Request, user=Depends(get_verified_user)):
    if request.app.state.config.TTS_ENGINE == '':
        raise HTTPException(
            status_code=status.HTTP_404_NOT_FOUND,
            detail=ERROR_MESSAGES.NOT_FOUND,
        )

    if user.role != 'admin' and not has_permission(user.id, 'chat.tts', request.app.state.config.USER_PERMISSIONS):
        raise HTTPException(
            status_code=status.HTTP_403_FORBIDDEN,
            detail=ERROR_MESSAGES.ACCESS_PROHIBITED,
        )

    body = await request.body()
    name = hashlib.sha256(
        body
        + str(request.app.state.config.TTS_ENGINE).encode('utf-8')
        + str(request.app.state.config.TTS_MODEL).encode('utf-8')
    ).hexdigest()

    file_path = SPEECH_CACHE_DIR.joinpath(f'{name}.mp3')
    file_body_path = SPEECH_CACHE_DIR.joinpath(f'{name}.json')

    # Check if the file already exists in the cache
    if file_path.is_file():
        return FileResponse(file_path)

    payload = None
    try:
        payload = json.loads(body.decode('utf-8'))
    except Exception as e:
        log.exception(e)
        raise HTTPException(status_code=400, detail='Invalid JSON payload')

    r = None
    if request.app.state.config.TTS_ENGINE == 'openai':
        payload['model'] = request.app.state.config.TTS_MODEL

        try:
            timeout = aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT)
            async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
                payload = {
                    **payload,
                    **(request.app.state.config.TTS_OPENAI_PARAMS or {}),
                }

                headers = {
                    'Content-Type': 'application/json',
                    'Authorization': f'Bearer {request.app.state.config.TTS_OPENAI_API_KEY}',
                }
                if ENABLE_FORWARD_USER_INFO_HEADERS:
                    headers = include_user_info_headers(headers, user)

                r = await session.post(
                    url=f'{request.app.state.config.TTS_OPENAI_API_BASE_URL}/audio/speech',
                    json=payload,
                    headers=headers,
                    ssl=AIOHTTP_CLIENT_SESSION_SSL,
                )

                r.raise_for_status()

                async with aiofiles.open(file_path, 'wb') as f:
                    await f.write(await r.read())

                async with aiofiles.open(file_body_path, 'w') as f:
                    await f.write(json.dumps(payload))

            return FileResponse(file_path)

        except Exception as e:
            log.exception(e)
            detail = None

            status_code = 500
            detail = f'Open WebUI: Server Connection Error'

            if r is not None:
                status_code = r.status

                try:
                    res = await r.json()
                    if 'error' in res:
                        detail = f'External: {res["error"]}'
                except Exception:
                    detail = f'External: {e}'

            raise HTTPException(
                status_code=status_code,
                detail=detail,
            )

    elif request.app.state.config.TTS_ENGINE == 'elevenlabs':
        voice_id = payload.get('voice', '')

        if voice_id not in get_available_voices(request):
            raise HTTPException(
                status_code=400,
                detail='Invalid voice id',
            )

        try:
            timeout = aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT)
            async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
                async with session.post(
                    f'{ELEVENLABS_API_BASE_URL}/v1/text-to-speech/{voice_id}',
                    json={
                        'text': payload['input'],
                        'model_id': request.app.state.config.TTS_MODEL,
                        'voice_settings': {'stability': 0.5, 'similarity_boost': 0.5},
                    },
                    headers={
                        'Accept': 'audio/mpeg',
                        'Content-Type': 'application/json',
                        'xi-api-key': request.app.state.config.TTS_API_KEY,
                    },
                    ssl=AIOHTTP_CLIENT_SESSION_SSL,
                ) as r:
                    r.raise_for_status()

                    async with aiofiles.open(file_path, 'wb') as f:
                        await f.write(await r.read())

                    async with aiofiles.open(file_body_path, 'w') as f:
                        await f.write(json.dumps(payload))

            return FileResponse(file_path)

        except Exception as e:
            log.exception(e)
            detail = None

            try:
                if r.status != 200:
                    res = await r.json()
                    if 'error' in res:
                        detail = f'External: {res["error"].get("message", "")}'
            except Exception:
                detail = f'External: {e}'

            raise HTTPException(
                status_code=getattr(r, 'status', 500) if r else 500,
                detail=detail if detail else 'Open WebUI: Server Connection Error',
            )

    elif request.app.state.config.TTS_ENGINE == 'azure':
        try:
            payload = json.loads(body.decode('utf-8'))
        except Exception as e:
            log.exception(e)
            raise HTTPException(status_code=400, detail='Invalid JSON payload')

        region = request.app.state.config.TTS_AZURE_SPEECH_REGION or 'eastus'
        base_url = request.app.state.config.TTS_AZURE_SPEECH_BASE_URL
        language = request.app.state.config.TTS_VOICE
        locale = '-'.join(request.app.state.config.TTS_VOICE.split('-')[:2])
        output_format = request.app.state.config.TTS_AZURE_SPEECH_OUTPUT_FORMAT

        try:
            data = f"""<speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xml:lang="{locale}">
                <voice name="{language}">{html.escape(payload['input'])}</voice>
            </speak>"""
            timeout = aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT)
            async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
                async with session.post(
                    (base_url or f'https://{region}.tts.speech.microsoft.com') + '/cognitiveservices/v1',
                    headers={
                        'Ocp-Apim-Subscription-Key': request.app.state.config.TTS_API_KEY,
                        'Content-Type': 'application/ssml+xml',
                        'X-Microsoft-OutputFormat': output_format,
                    },
                    data=data,
                    ssl=AIOHTTP_CLIENT_SESSION_SSL,
                ) as r:
                    r.raise_for_status()

                    async with aiofiles.open(file_path, 'wb') as f:
                        await f.write(await r.read())

                    async with aiofiles.open(file_body_path, 'w') as f:
                        await f.write(json.dumps(payload))

                    return FileResponse(file_path)

        except Exception as e:
            log.exception(e)
            detail = None

            try:
                if r.status != 200:
                    res = await r.json()
                    if 'error' in res:
                        detail = f'External: {res["error"].get("message", "")}'
            except Exception:
                detail = f'External: {e}'

            raise HTTPException(
                status_code=getattr(r, 'status', 500) if r else 500,
                detail=detail if detail else 'Open WebUI: Server Connection Error',
            )

    elif request.app.state.config.TTS_ENGINE == 'transformers':
        payload = None
        try:
            payload = json.loads(body.decode('utf-8'))
        except Exception as e:
            log.exception(e)
            raise HTTPException(status_code=400, detail='Invalid JSON payload')

        import torch
        import soundfile as sf

        load_speech_pipeline(request)

        embeddings_dataset = request.app.state.speech_speaker_embeddings_dataset

        speaker_index = 6799
        try:
            speaker_index = embeddings_dataset['filename'].index(request.app.state.config.TTS_MODEL)
        except Exception:
            pass

        speaker_embedding = torch.tensor(embeddings_dataset[speaker_index]['xvector']).unsqueeze(0)

        speech = request.app.state.speech_synthesiser(
            payload['input'],
            forward_params={'speaker_embeddings': speaker_embedding},
        )

        sf.write(file_path, speech['audio'], samplerate=speech['sampling_rate'])

        async with aiofiles.open(file_body_path, 'w') as f:
            await f.write(json.dumps(payload))

        return FileResponse(file_path)


def transcription_handler(request, file_path, metadata, user=None):
    filename = os.path.basename(file_path)
    file_dir = os.path.dirname(file_path)
    id = filename.split('.')[0]

    metadata = metadata or {}

    languages = [
        metadata.get('language', None) if not WHISPER_LANGUAGE else WHISPER_LANGUAGE,
        None,  # Always fallback to None in case transcription fails
    ]

    if request.app.state.config.STT_ENGINE == '':
        if request.app.state.faster_whisper_model is None:
            request.app.state.faster_whisper_model = set_faster_whisper_model(request.app.state.config.WHISPER_MODEL)

        model = request.app.state.faster_whisper_model
        segments, info = model.transcribe(
            file_path,
            beam_size=5,
            vad_filter=WHISPER_VAD_FILTER,
            language=languages[0],
            multilingual=WHISPER_MULTILINGUAL,
        )
        log.info("Detected language '%s' with probability %f" % (info.language, info.language_probability))

        transcript = ''.join([segment.text for segment in list(segments)])
        data = {'text': transcript.strip()}

        # save the transcript to a json file
        transcript_file = f'{file_dir}/{id}.json'
        with open(transcript_file, 'w') as f:
            json.dump(data, f)

        log.debug(data)
        return data
    elif request.app.state.config.STT_ENGINE == 'openai':
        r = None
        try:
            for language in languages:
                payload = {
                    'model': request.app.state.config.STT_MODEL,
                }

                if language:
                    payload['language'] = language

                headers = {'Authorization': f'Bearer {request.app.state.config.STT_OPENAI_API_KEY}'}
                if user and ENABLE_FORWARD_USER_INFO_HEADERS:
                    headers = include_user_info_headers(headers, user)

                with open(file_path, 'rb') as audio_file:
                    r = requests.post(
                        url=f'{request.app.state.config.STT_OPENAI_API_BASE_URL}/audio/transcriptions',
                        headers=headers,
                        files={'file': (filename, audio_file)},
                        data=payload,
                        timeout=AIOHTTP_CLIENT_TIMEOUT,
                    )

                if r.status_code == 200:
                    # Successful transcription
                    break

            r.raise_for_status()
            data = r.json()

            # save the transcript to a json file
            transcript_file = f'{file_dir}/{id}.json'
            with open(transcript_file, 'w') as f:
                json.dump(data, f)

            return data
        except Exception as e:
            log.exception(e)

            detail = None
            if r is not None:
                try:
                    res = r.json()
                    if 'error' in res:
                        detail = f'External: {res["error"].get("message", "")}'
                except Exception:
                    detail = f'External: {e}'

            raise Exception(detail if detail else 'Open WebUI: Server Connection Error')

    elif request.app.state.config.STT_ENGINE == 'deepgram':
        try:
            # Determine the MIME type of the file
            mime, _ = mimetypes.guess_type(file_path)
            if not mime:
                mime = 'audio/wav'  # fallback to wav if undetectable

            # Read the audio file
            with open(file_path, 'rb') as f:
                file_data = f.read()

            # Build headers and parameters
            headers = {
                'Authorization': f'Token {request.app.state.config.DEEPGRAM_API_KEY}',
                'Content-Type': mime,
            }

            for language in languages:
                params = {}
                if request.app.state.config.STT_MODEL:
                    params['model'] = request.app.state.config.STT_MODEL

                if language:
                    params['language'] = language

                # Make request to Deepgram API
                r = requests.post(
                    'https://api.deepgram.com/v1/listen?smart_format=true',
                    headers=headers,
                    params=params,
                    data=file_data,
                    timeout=AIOHTTP_CLIENT_TIMEOUT,
                )

                if r.status_code == 200:
                    # Successful transcription
                    break

            r.raise_for_status()
            response_data = r.json()

            # Extract transcript from Deepgram response
            try:
                transcript = response_data['results']['channels'][0]['alternatives'][0].get('transcript', '')
            except (KeyError, IndexError) as e:
                log.error(f'Malformed response from Deepgram: {str(e)}')
                raise Exception('Failed to parse Deepgram response - unexpected response format')
            data = {'text': transcript.strip()}

            # Save transcript
            transcript_file = f'{file_dir}/{id}.json'
            with open(transcript_file, 'w') as f:
                json.dump(data, f)

            return data

        except Exception as e:
            log.exception(e)
            detail = None
            if r is not None:
                try:
                    res = r.json()
                    if 'error' in res:
                        detail = f'External: {res["error"].get("message", "")}'
                except Exception:
                    detail = f'External: {e}'
            raise Exception(detail if detail else 'Open WebUI: Server Connection Error')

    elif request.app.state.config.STT_ENGINE == 'azure':
        # Check file exists and size
        if not os.path.exists(file_path):
            raise HTTPException(status_code=400, detail='Audio file not found')

        # Check file size (Azure has a larger limit of 200MB)
        file_size = os.path.getsize(file_path)
        if file_size > AZURE_MAX_FILE_SIZE:
            raise HTTPException(
                status_code=400,
                detail=f"File size exceeds Azure's limit of {AZURE_MAX_FILE_SIZE_MB}MB",
            )

        api_key = request.app.state.config.AUDIO_STT_AZURE_API_KEY
        region = request.app.state.config.AUDIO_STT_AZURE_REGION or 'eastus'
        locales = request.app.state.config.AUDIO_STT_AZURE_LOCALES
        base_url = request.app.state.config.AUDIO_STT_AZURE_BASE_URL
        max_speakers = request.app.state.config.AUDIO_STT_AZURE_MAX_SPEAKERS or 3

        # IF NO LOCALES, USE DEFAULTS
        if len(locales) < 2:
            locales = [
                'en-US',
                'es-ES',
                'es-MX',
                'fr-FR',
                'hi-IN',
                'it-IT',
                'de-DE',
                'en-GB',
                'en-IN',
                'ja-JP',
                'ko-KR',
                'pt-BR',
                'zh-CN',
            ]
            locales = ','.join(locales)

        if not api_key or not region:
            raise HTTPException(
                status_code=400,
                detail='Azure API key is required for Azure STT',
            )

        r = None
        try:
            # Prepare the request
            data = {
                'definition': json.dumps(
                    {
                        'locales': locales.split(','),
                        'diarization': {'maxSpeakers': max_speakers, 'enabled': True},
                    }
                    if locales
                    else {}
                )
            }

            url = (
                base_url or f'https://{region}.api.cognitive.microsoft.com'
            ) + '/speechtotext/transcriptions:transcribe?api-version=2024-11-15'

            # Use context manager to ensure file is properly closed
            with open(file_path, 'rb') as audio_file:
                r = requests.post(
                    url=url,
                    files={'audio': audio_file},
                    data=data,
                    headers={
                        'Ocp-Apim-Subscription-Key': api_key,
                    },
                    timeout=AIOHTTP_CLIENT_TIMEOUT,
                )

            r.raise_for_status()
            response = r.json()

            # Extract transcript from response
            if not response.get('combinedPhrases'):
                raise ValueError('No transcription found in response')

            # Get the full transcript from combinedPhrases
            transcript = response['combinedPhrases'][0].get('text', '').strip()
            if not transcript:
                raise ValueError('Empty transcript in response')

            data = {'text': transcript}

            # Save transcript to json file (consistent with other providers)
            transcript_file = f'{file_dir}/{id}.json'
            with open(transcript_file, 'w') as f:
                json.dump(data, f)

            log.debug(data)
            return data

        except (KeyError, IndexError, ValueError) as e:
            log.exception('Error parsing Azure response')
            raise HTTPException(
                status_code=500,
                detail=f'Failed to parse Azure response: {str(e)}',
            )
        except requests.exceptions.RequestException as e:
            log.exception(e)
            detail = None
            status_code = getattr(r, 'status_code', 500) if r else 500

            try:
                if r is not None and r.status_code != 200:
                    res = r.json()
                    # Azure returns {"code": "...", "message": "...", "innerError": {...}}
                    if 'code' in res and 'message' in res:
                        azure_code = res.get('innerError', {}).get('code', res['code'])
                        user_facing_codes = {
                            'EmptyAudioFile',
                            'AudioLengthLimitExceeded',
                            'NoLanguageIdentified',
                            'MultipleLanguagesIdentified',
                        }
                        if azure_code in user_facing_codes:
                            detail = res['message']
                        else:
                            log.error(f'Azure STT error [{azure_code}]: {res["message"]}')
                            detail = 'An error occurred during transcription.'
                    elif 'error' in res:
                        detail = f'External: {res["error"].get("message", "")}'
            except Exception:
                detail = f'External: {e}'

            raise HTTPException(
                status_code=status_code,
                detail=detail if detail else 'Open WebUI: Server Connection Error',
            )

    elif request.app.state.config.STT_ENGINE == 'mistral':
        # Check file exists
        if not os.path.exists(file_path):
            raise HTTPException(status_code=400, detail='Audio file not found')

        # Check file size
        file_size = os.path.getsize(file_path)
        if file_size > MAX_FILE_SIZE:
            raise HTTPException(
                status_code=400,
                detail=f'File size exceeds limit of {MAX_FILE_SIZE_MB}MB',
            )

        api_key = request.app.state.config.AUDIO_STT_MISTRAL_API_KEY
        api_base_url = request.app.state.config.AUDIO_STT_MISTRAL_API_BASE_URL or 'https://api.mistral.ai/v1'
        use_chat_completions = request.app.state.config.AUDIO_STT_MISTRAL_USE_CHAT_COMPLETIONS

        if not api_key:
            raise HTTPException(
                status_code=400,
                detail='Mistral API key is required for Mistral STT',
            )

        r = None
        try:
            # Use voxtral-mini-latest as the default model for transcription
            model = request.app.state.config.STT_MODEL or 'voxtral-mini-latest'

            log.info(
                f'Mistral STT - model: {model}, '
                f'method: {"chat_completions" if use_chat_completions else "transcriptions"}'
            )

            if use_chat_completions:
                # Use chat completions API with audio input
                # This method requires mp3 or wav format
                audio_file_to_use = file_path

                if is_audio_conversion_required(file_path):
                    log.debug('Converting audio to mp3 for chat completions API')
                    converted_path = convert_audio_to_mp3(file_path)
                    if converted_path:
                        audio_file_to_use = converted_path
                    else:
                        log.error('Audio conversion failed')
                        raise HTTPException(
                            status_code=500,
                            detail='Audio conversion failed. Chat completions API requires mp3 or wav format.',
                        )

                # Read and encode audio file as base64
                with open(audio_file_to_use, 'rb') as audio_file:
                    audio_base64 = base64.b64encode(audio_file.read()).decode('utf-8')

                # Prepare chat completions request
                url = f'{api_base_url}/chat/completions'

                # Add language instruction if specified
                language = metadata.get('language', None) if metadata else None
                if language:
                    text_instruction = f'Transcribe this audio exactly as spoken in {language}. Do not translate it.'
                else:
                    text_instruction = 'Transcribe this audio exactly as spoken in its original language. Do not translate it to another language.'

                payload = {
                    'model': model,
                    'messages': [
                        {
                            'role': 'user',
                            'content': [
                                {
                                    'type': 'input_audio',
                                    'input_audio': audio_base64,
                                },
                                {'type': 'text', 'text': text_instruction},
                            ],
                        }
                    ],
                }

                r = requests.post(
                    url=url,
                    json=payload,
                    headers={
                        'Authorization': f'Bearer {api_key}',
                        'Content-Type': 'application/json',
                    },
                    timeout=AIOHTTP_CLIENT_TIMEOUT,
                )

                r.raise_for_status()
                response = r.json()

                # Extract transcript from chat completion response
                transcript = response.get('choices', [{}])[0].get('message', {}).get('content', '').strip()
                if not transcript:
                    raise ValueError('Empty transcript in response')

                data = {'text': transcript}

            else:
                # Use dedicated transcriptions API
                url = f'{api_base_url}/audio/transcriptions'

                # Determine the MIME type
                mime_type, _ = mimetypes.guess_type(file_path)
                if not mime_type:
                    mime_type = 'audio/webm'

                # Use context manager to ensure file is properly closed
                with open(file_path, 'rb') as audio_file:
                    files = {'file': (filename, audio_file, mime_type)}
                    data_form = {'model': model}

                    # Add language if specified in metadata
                    language = metadata.get('language', None) if metadata else None
                    if language:
                        data_form['language'] = language

                    r = requests.post(
                        url=url,
                        files=files,
                        data=data_form,
                        headers={
                            'Authorization': f'Bearer {api_key}',
                        },
                        timeout=AIOHTTP_CLIENT_TIMEOUT,
                    )

                r.raise_for_status()
                response = r.json()

                # Extract transcript from response
                transcript = response.get('text', '').strip()
                if not transcript:
                    raise ValueError('Empty transcript in response')

                data = {'text': transcript}

            # Save transcript to json file (consistent with other providers)
            transcript_file = f'{file_dir}/{id}.json'
            with open(transcript_file, 'w') as f:
                json.dump(data, f)

            log.debug(data)
            return data

        except ValueError as e:
            log.exception('Error parsing Mistral response')
            raise HTTPException(
                status_code=500,
                detail=f'Failed to parse Mistral response: {str(e)}',
            )
        except requests.exceptions.RequestException as e:
            log.exception(e)
            detail = None

            try:
                if r is not None and r.status_code != 200:
                    res = r.json()
                    if 'error' in res:
                        detail = f'External: {res["error"].get("message", "")}'
                    else:
                        detail = f'External: {r.text}'
            except Exception:
                detail = f'External: {e}'

            raise HTTPException(
                status_code=getattr(r, 'status_code', 500) if r else 500,
                detail=detail if detail else 'Open WebUI: Server Connection Error',
            )


def transcribe(request: Request, file_path: str, metadata: Optional[dict] = None, user=None):
    log.info(f'transcribe: {file_path} {metadata}')

    if is_audio_conversion_required(file_path):
        file_path = convert_audio_to_mp3(file_path)

    try:
        file_path = compress_audio(file_path)
    except Exception as e:
        log.exception(e)

    # Always produce a list of chunk paths (could be one entry if small)
    try:
        chunk_paths = split_audio(file_path, MAX_FILE_SIZE)
        print(f'Chunk paths: {chunk_paths}')
    except Exception as e:
        log.exception(e)
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=ERROR_MESSAGES.DEFAULT(e),
        )

    results = []
    try:
        with ThreadPoolExecutor() as executor:
            # Submit tasks for each chunk_path
            futures = [
                executor.submit(transcription_handler, request, chunk_path, metadata, user)
                for chunk_path in chunk_paths
            ]
            # Gather results as they complete
            for future in futures:
                try:
                    results.append(future.result())
                except HTTPException:
                    raise
                except Exception as transcribe_exc:
                    raise HTTPException(
                        status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
                        detail=f'Error transcribing chunk: {transcribe_exc}',
                    )
    finally:
        # Clean up only the temporary chunks, never the original file
        for chunk_path in chunk_paths:
            if chunk_path != file_path and os.path.isfile(chunk_path):
                try:
                    os.remove(chunk_path)
                except Exception:
                    pass

    return {
        'text': ' '.join([result['text'] for result in results]),
    }


def compress_audio(file_path):
    if os.path.getsize(file_path) > MAX_FILE_SIZE:
        id = os.path.splitext(os.path.basename(file_path))[0]  # Handles names with multiple dots
        file_dir = os.path.dirname(file_path)

        audio = AudioSegment.from_file(file_path)
        audio = audio.set_frame_rate(16000).set_channels(1)  # Compress audio

        compressed_path = os.path.join(file_dir, f'{id}_compressed.mp3')
        audio.export(compressed_path, format='mp3', bitrate='32k')
        # log.debug(f"Compressed audio to {compressed_path}")  # Uncomment if log is defined

        return compressed_path
    else:
        return file_path


def split_audio(file_path, max_bytes, format='mp3', bitrate='32k'):
    """
    Splits audio into chunks not exceeding max_bytes.
    Returns a list of chunk file paths. If audio fits, returns list with original path.
    """
    file_size = os.path.getsize(file_path)
    if file_size <= max_bytes:
        return [file_path]  # Nothing to split

    audio = AudioSegment.from_file(file_path)
    duration_ms = len(audio)
    orig_size = file_size

    approx_chunk_ms = max(int(duration_ms * (max_bytes / orig_size)) - 1000, 1000)
    chunks = []
    start = 0
    i = 0

    base, _ = os.path.splitext(file_path)

    while start < duration_ms:
        end = min(start + approx_chunk_ms, duration_ms)
        chunk = audio[start:end]
        chunk_path = f'{base}_chunk_{i}.{format}'
        chunk.export(chunk_path, format=format, bitrate=bitrate)

        # Reduce chunk duration if still too large
        while os.path.getsize(chunk_path) > max_bytes and (end - start) > 5000:
            end = start + ((end - start) // 2)
            chunk = audio[start:end]
            chunk.export(chunk_path, format=format, bitrate=bitrate)

        if os.path.getsize(chunk_path) > max_bytes:
            os.remove(chunk_path)
            raise Exception('Audio chunk cannot be reduced below max file size.')

        chunks.append(chunk_path)
        start = end
        i += 1

    return chunks


@router.post('/transcriptions')
def transcription(
    request: Request,
    file: UploadFile = File(...),
    language: Optional[str] = Form(None),
    user=Depends(get_verified_user),
):
    if user.role != 'admin' and not has_permission(user.id, 'chat.stt', request.app.state.config.USER_PERMISSIONS):
        raise HTTPException(
            status_code=status.HTTP_403_FORBIDDEN,
            detail=ERROR_MESSAGES.ACCESS_PROHIBITED,
        )
    log.info(f'file.content_type: {file.content_type}')
    stt_supported_content_types = getattr(request.app.state.config, 'STT_SUPPORTED_CONTENT_TYPES', [])

    if not strict_match_mime_type(stt_supported_content_types, file.content_type):
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=ERROR_MESSAGES.FILE_NOT_SUPPORTED,
        )

    try:
        safe_name = os.path.basename(file.filename) if file.filename else ''
        ext = safe_name.rsplit('.', 1)[-1] if '.' in safe_name else ''

        id = uuid.uuid4()

        filename = f'{id}.{ext}'
        contents = file.file.read()

        file_dir = f'{CACHE_DIR}/audio/transcriptions'
        os.makedirs(file_dir, exist_ok=True)
        file_path = f'{file_dir}/{filename}'

        # Defense-in-depth: ensure resolved path stays within intended directory
        if not os.path.realpath(file_path).startswith(os.path.realpath(file_dir)):
            raise ValueError('Invalid file path detected')

        with open(file_path, 'wb') as f:
            f.write(contents)

        try:
            metadata = None

            if language:
                metadata = {'language': language}

            result = transcribe(request, file_path, metadata, user)

            return {
                **result,
                'filename': os.path.basename(file_path),
            }

        except HTTPException:
            raise
        except Exception as e:
            log.exception(e)

            raise HTTPException(
                status_code=status.HTTP_400_BAD_REQUEST,
                detail='Transcription failed.',
            )

    except HTTPException:
        raise
    except Exception as e:
        log.exception(e)

        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail='Transcription failed.',
        )


def get_available_models(request: Request) -> list[dict]:
    available_models = []
    if request.app.state.config.TTS_ENGINE == 'openai':
        # Use custom endpoint if not using the official OpenAI API URL
        if not request.app.state.config.TTS_OPENAI_API_BASE_URL.startswith('https://api.openai.com'):
            try:
                response = requests.get(
                    f'{request.app.state.config.TTS_OPENAI_API_BASE_URL}/audio/models',
                    timeout=AIOHTTP_CLIENT_TIMEOUT_MODEL_LIST,
                )
                response.raise_for_status()
                data = response.json()
                available_models = data.get('models', [])
            except Exception as e:
                log.error(f'Error fetching models from custom endpoint: {str(e)}')
                available_models = [{'id': 'tts-1'}, {'id': 'tts-1-hd'}]
        else:
            available_models = [{'id': 'tts-1'}, {'id': 'tts-1-hd'}]
    elif request.app.state.config.TTS_ENGINE == 'elevenlabs':
        try:
            response = requests.get(
                f'{ELEVENLABS_API_BASE_URL}/v1/models',
                headers={
                    'xi-api-key': request.app.state.config.TTS_API_KEY,
                    'Content-Type': 'application/json',
                },
                timeout=5,
            )
            response.raise_for_status()
            models = response.json()

            available_models = [{'name': model['name'], 'id': model['model_id']} for model in models]
        except requests.RequestException as e:
            log.error(f'Error fetching voices: {str(e)}')
    return available_models


@router.get('/models')
async def get_models(request: Request, user=Depends(get_verified_user)):
    return {'models': get_available_models(request)}


def get_available_voices(request) -> dict:
    """Returns {voice_id: voice_name} dict"""
    available_voices = {}
    if request.app.state.config.TTS_ENGINE == 'openai':
        # Use custom endpoint if not using the official OpenAI API URL
        if not request.app.state.config.TTS_OPENAI_API_BASE_URL.startswith('https://api.openai.com'):
            try:
                response = requests.get(
                    f'{request.app.state.config.TTS_OPENAI_API_BASE_URL}/audio/voices',
                    timeout=AIOHTTP_CLIENT_TIMEOUT_MODEL_LIST,
                )
                response.raise_for_status()
                data = response.json()
                voices_list = data.get('voices', [])
                available_voices = {voice['id']: voice['name'] for voice in voices_list}
            except Exception as e:
                log.error(f'Error fetching voices from custom endpoint: {str(e)}')
                available_voices = {
                    'alloy': 'alloy',
                    'echo': 'echo',
                    'fable': 'fable',
                    'onyx': 'onyx',
                    'nova': 'nova',
                    'shimmer': 'shimmer',
                }
        else:
            available_voices = {
                'alloy': 'alloy',
                'echo': 'echo',
                'fable': 'fable',
                'onyx': 'onyx',
                'nova': 'nova',
                'shimmer': 'shimmer',
            }
    elif request.app.state.config.TTS_ENGINE == 'elevenlabs':
        try:
            available_voices = get_elevenlabs_voices(api_key=request.app.state.config.TTS_API_KEY)
        except Exception:
            # Avoided @lru_cache with exception
            pass
    elif request.app.state.config.TTS_ENGINE == 'azure':
        try:
            region = request.app.state.config.TTS_AZURE_SPEECH_REGION
            base_url = request.app.state.config.TTS_AZURE_SPEECH_BASE_URL
            url = (base_url or f'https://{region}.tts.speech.microsoft.com') + '/cognitiveservices/voices/list'
            headers = {'Ocp-Apim-Subscription-Key': request.app.state.config.TTS_API_KEY}

            response = requests.get(url, headers=headers, timeout=AIOHTTP_CLIENT_TIMEOUT_MODEL_LIST)
            response.raise_for_status()
            voices = response.json()

            for voice in voices:
                available_voices[voice['ShortName']] = f'{voice["DisplayName"]} ({voice["ShortName"]})'
        except requests.RequestException as e:
            log.error(f'Error fetching voices: {str(e)}')

    return available_voices


@lru_cache
def get_elevenlabs_voices(api_key: str) -> dict:
    """
    Note, set the following in your .env file to use Elevenlabs:
    AUDIO_TTS_ENGINE=elevenlabs
    AUDIO_TTS_API_KEY=sk_...  # Your Elevenlabs API key
    AUDIO_TTS_VOICE=EXAVITQu4vr4xnSDxMaL  # From https://api.elevenlabs.io/v1/voices
    AUDIO_TTS_MODEL=eleven_multilingual_v2
    """

    try:
        # TODO: Add retries
        response = requests.get(
            f'{ELEVENLABS_API_BASE_URL}/v1/voices',
            headers={
                'xi-api-key': api_key,
                'Content-Type': 'application/json',
            },
            timeout=AIOHTTP_CLIENT_TIMEOUT_MODEL_LIST,
        )
        response.raise_for_status()
        voices_data = response.json()

        voices = {}
        for voice in voices_data.get('voices', []):
            voices[voice['voice_id']] = voice['name']
    except requests.RequestException as e:
        # Avoid @lru_cache with exception
        log.error(f'Error fetching voices: {str(e)}')
        raise RuntimeError(f'Error fetching voices: {str(e)}')

    return voices


@router.get('/voices')
async def get_voices(request: Request, user=Depends(get_verified_user)):
    return {'voices': [{'id': k, 'name': v} for k, v in get_available_voices(request).items()]}
