import json
import os
from threading import Thread

import extensions.openai.completions as OAIcompletions
import extensions.openai.embeddings as OAIembeddings
import extensions.openai.images as OAIimages
import extensions.openai.models as OAImodels
import extensions.openai.moderations as OAImoderations
import speech_recognition as sr
import uvicorn
from extensions.openai.errors import ServiceUnavailableError
from extensions.openai.tokens import token_count, token_decode, token_encode
from extensions.openai.utils import _start_cloudflared
from fastapi import Depends, FastAPI, Header, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.requests import Request
from fastapi.responses import JSONResponse
from modules import shared
from modules.logging_colors import logger
from pydub import AudioSegment
from sse_starlette import EventSourceResponse

from .typing import (
    ChatCompletionRequest,
    ChatCompletionResponse,
    CompletionRequest,
    CompletionResponse,
    to_dict
)

params = {
    'embedding_device': 'cpu',
    'embedding_model': 'all-mpnet-base-v2',
    'sd_webui_url': '',
    'debug': 0
}


def verify_api_key(authorization: str = Header(None)) -> None:
    expected_api_key = shared.args.api_key
    if expected_api_key and (authorization is None or authorization != f"Bearer {expected_api_key}"):
        raise HTTPException(status_code=401, detail="Unauthorized")


app = FastAPI(dependencies=[Depends(verify_api_key)])

# Configure CORS settings to allow all origins, methods, and headers
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["GET", "HEAD", "OPTIONS", "POST", "PUT"],
    allow_headers=[
        "Origin",
        "Accept",
        "X-Requested-With",
        "Content-Type",
        "Access-Control-Request-Method",
        "Access-Control-Request-Headers",
        "Authorization",
    ],
)


@app.options("/")
async def options_route():
    return JSONResponse(content="OK")


@app.post('/v1/completions', response_model=CompletionResponse)
async def openai_completions(request: Request, request_data: CompletionRequest):
    path = request.url.path
    is_legacy = "/generate" in path

    if request_data.stream:
        async def generator():
            response = OAIcompletions.stream_completions(to_dict(request_data), is_legacy=is_legacy)
            for resp in response:
                yield {"data": json.dumps(resp)}

        return EventSourceResponse(generator())  # SSE streaming

    else:
        response = OAIcompletions.completions(to_dict(request_data), is_legacy=is_legacy)
        return JSONResponse(response)


@app.post('/v1/chat/completions', response_model=ChatCompletionResponse)
async def openai_chat_completions(request: Request, request_data: ChatCompletionRequest):
    path = request.url.path
    is_legacy = "/generate" in path

    if request_data.stream:
        async def generator():
            response = OAIcompletions.stream_chat_completions(to_dict(request_data), is_legacy=is_legacy)
            for resp in response:
                yield {"data": json.dumps(resp)}

        return EventSourceResponse(generator())  # SSE streaming

    else:
        response = OAIcompletions.chat_completions(to_dict(request_data), is_legacy=is_legacy)
        return JSONResponse(response)


@app.get("/v1/models")
@app.get("/v1/engines")
async def handle_models(request: Request):
    path = request.url.path
    is_legacy = 'engines' in path
    is_list = request.url.path.split('?')[0].split('#')[0] in ['/v1/engines', '/v1/models']

    if is_legacy and not is_list:
        model_name = path[path.find('/v1/engines/') + len('/v1/engines/'):]
        resp = OAImodels.load_model(model_name)
    elif is_list:
        resp = OAImodels.list_models(is_legacy)
    else:
        model_name = path[len('/v1/models/'):]
        resp = OAImodels.model_info(model_name)

    return JSONResponse(content=resp)


@app.get('/v1/billing/usage')
def handle_billing_usage():
    '''
    Ex. /v1/dashboard/billing/usage?start_date=2023-05-01&end_date=2023-05-31
    '''
    return JSONResponse(content={"total_usage": 0})


@app.post('/v1/audio/transcriptions')
async def handle_audio_transcription(request: Request):
    r = sr.Recognizer()

    form = await request.form()
    audio_file = await form["file"].read()
    audio_data = AudioSegment.from_file(audio_file)

    # Convert AudioSegment to raw data
    raw_data = audio_data.raw_data

    # Create AudioData object
    audio_data = sr.AudioData(raw_data, audio_data.frame_rate, audio_data.sample_width)
    whipser_language = form.getvalue('language', None)
    whipser_model = form.getvalue('model', 'tiny')  # Use the model from the form data if it exists, otherwise default to tiny

    transcription = {"text": ""}

    try:
        transcription["text"] = r.recognize_whisper(audio_data, language=whipser_language, model=whipser_model)
    except sr.UnknownValueError:
        print("Whisper could not understand audio")
        transcription["text"] = "Whisper could not understand audio UnknownValueError"
    except sr.RequestError as e:
        print("Could not request results from Whisper", e)
        transcription["text"] = "Whisper could not understand audio RequestError"

    return JSONResponse(content=transcription)


@app.post('/v1/images/generations')
async def handle_image_generation(request: Request):

    if not os.environ.get('SD_WEBUI_URL', params.get('sd_webui_url', '')):
        raise ServiceUnavailableError("Stable Diffusion not available. SD_WEBUI_URL not set.")

    body = await request.json()
    prompt = body['prompt']
    size = body.get('size', '1024x1024')
    response_format = body.get('response_format', 'url')  # or b64_json
    n = body.get('n', 1)  # ignore the batch limits of max 10

    response = await OAIimages.generations(prompt=prompt, size=size, response_format=response_format, n=n)
    return JSONResponse(response)


@app.post("/v1/embeddings")
async def handle_embeddings(request: Request):
    body = await request.json()
    encoding_format = body.get("encoding_format", "")

    input = body.get('input', body.get('text', ''))
    if not input:
        raise HTTPException(status_code=400, detail="Missing required argument input")

    if type(input) is str:
        input = [input]

    response = OAIembeddings.embeddings(input, encoding_format)
    return JSONResponse(response)


@app.post("/v1/moderations")
async def handle_moderations(request: Request):
    body = await request.json()
    input = body["input"]
    if not input:
        raise HTTPException(status_code=400, detail="Missing required argument input")

    response = OAImoderations.moderations(input)
    return JSONResponse(response)


@app.post("/api/v1/token-count")
async def handle_token_count(request: Request):
    body = await request.json()
    response = token_count(body['prompt'])
    return JSONResponse(response)


@app.post("/api/v1/token/encode")
async def handle_token_encode(request: Request):
    body = await request.json()
    encoding_format = body.get("encoding_format", "")
    response = token_encode(body["input"], encoding_format)
    return JSONResponse(response)


@app.post("/api/v1/token/decode")
async def handle_token_decode(request: Request):
    body = await request.json()
    encoding_format = body.get("encoding_format", "")
    response = token_decode(body["input"], encoding_format)
    return JSONResponse(response, no_debug=True)


def run_server():
    server_addr = '0.0.0.0' if shared.args.listen else '127.0.0.1'
    port = int(os.environ.get('OPENEDAI_PORT', shared.args.api_port))

    ssl_certfile = os.environ.get('OPENEDAI_CERT_PATH', shared.args.ssl_certfile)
    ssl_keyfile = os.environ.get('OPENEDAI_KEY_PATH', shared.args.ssl_keyfile)

    if shared.args.public_api:
        def on_start(public_url: str):
            logger.info(f'OpenAI compatible API URL:\n\n{public_url}/v1\n')

        _start_cloudflared(port, shared.args.public_api_id, max_attempts=3, on_start=on_start)
    else:
        if ssl_keyfile and ssl_certfile:
            logger.info(f'OpenAI compatible API URL:\n\nhttps://{server_addr}:{port}/v1\n')
        else:
            logger.info(f'OpenAI compatible API URL:\n\nhttp://{server_addr}:{port}/v1\n')

    if shared.args.api_key:
        logger.info(f'OpenAI API key:\n\n{shared.args.api_key}\n')

    uvicorn.run(app, host=server_addr, port=port, ssl_certfile=ssl_certfile, ssl_keyfile=ssl_keyfile)


def setup():
    Thread(target=run_server, daemon=True).start()