"""
# api.py usage

` python api.py -dr "123.wav" -dt "一二三。" -dl "zh" `

## 执行参数:

`-s` - `SoVITS模型路径, 可在 config.py 中指定`
`-g` - `GPT模型路径, 可在 config.py 中指定`

调用请求缺少参考音频时使用
`-dr` - `默认参考音频路径`
`-dt` - `默认参考音频文本`
`-dl` - `默认参考音频语种, "中文","英文","日文","zh","en","ja"`
`-cut` - `默认切分方式,"凑四句一切","凑50字一切","按中文句号。切","按英文句号.切","按标点符号切",分别为cut1,cut2,cut3,cut4,cut5`

`-d` - `推理设备, "cuda","cpu","mps"`
`-a` - `绑定地址, 默认"127.0.0.1"`
`-p` - `绑定端口, 默认9880, 可在 config.py 中指定`
`-fp` - `覆盖 config.py 使用全精度`
`-hp` - `覆盖 config.py 使用半精度`

`-hb` - `cnhubert路径`
`-b` - `bert路径`

## 调用:

### 推理

endpoint: `/`

使用执行参数指定的参考音频与切分方式:
GET:
    `http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
POST:
```json
{
    "text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
    "text_language": "zh"
}
```

手动指定当次推理所使用的参考音频与切分方式:
GET:
    `http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh&how_to_cut=cut1`
POST:
```json
{
    "refer_wav_path": "123.wav",
    "prompt_text": "一二三。",
    "prompt_language": "zh",
    "text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
    "text_language": "zh"
    "how_to_cut"= "cut1"
}
```

RESP:
成功: 直接返回 wav 音频流， http code 200
失败: 返回包含错误信息的 json, http code 400


### 更换默认参考音频及切分方式

endpoint: `/change_refer`

key与推理端一样

GET:
    `http://127.0.0.1:9880/change_refer?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&how_to_cut=cut1`
POST:
```json
{
    "refer_wav_path": "123.wav",
    "prompt_text": "一二三。",
    "prompt_language": "zh"
    "how_to_cut": "cut1"
}
```

RESP:
成功: json, http code 200
失败: json, 400



### 命令控制

endpoint: `/control`

command:
"restart": 重新运行
"exit": 结束运行

GET:
    `http://127.0.0.1:9880/control?command=restart`
POST:
```json
{
    "command": "restart"
}
```

RESP: 无

"""


import argparse
import os,re
import sys

now_dir = os.getcwd()
sys.path.append(now_dir)
sys.path.append("%s/GPT_SoVITS" % (now_dir))

import signal
from time import time as ttime
import torch
import librosa
import soundfile as sf
from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import StreamingResponse, JSONResponse
import uvicorn
from transformers import AutoModelForMaskedLM, AutoTokenizer
import numpy as np
from feature_extractor import cnhubert
from io import BytesIO
from module.models import SynthesizerTrn
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from text import cleaned_text_to_sequence
from text.cleaner import clean_text
from module.mel_processing import spectrogram_torch
from my_utils import load_audio
import config as global_config

g_config = global_config.Config()

# AVAILABLE_COMPUTE = "cuda" if torch.cuda.is_available() else "cpu"

parser = argparse.ArgumentParser(description="GPT-SoVITS api")

parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")

parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="默认参考音频路径")
parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
parser.add_argument("-cut", "--default_how_to_cut", type=str, default="cut1", help="默认切分方式")

parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu / mps")
parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0")
parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
# bool值的用法为 `python ./api.py -fp ...`
# 此时 full_precision==True, half_precision==False

parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")


args = parser.parse_args()

sovits_path = args.sovits_path
gpt_path = args.gpt_path

splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }


class DefaultRefer:
    def __init__(self, path, text, language, how_to_cut):
        self.path = args.default_refer_path
        self.text = args.default_refer_text
        self.language = args.default_refer_language
        self.how_to_cut = args.default_how_to_cut

    def is_ready(self) -> bool:
        return is_full(self.path, self.text, self.language,self.how_to_cut)


default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language, args.default_how_to_cut)

device = args.device
port = args.port
host = args.bind_addr

if sovits_path == "":
    sovits_path = g_config.pretrained_sovits_path
    print(f"[WARN] 未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
if gpt_path == "":
    gpt_path = g_config.pretrained_gpt_path
    print(f"[WARN] 未指定GPT模型路径, fallback后当前值: {gpt_path}")

# 指定默认参考音频, 调用方 未提供/未给全 参考音频参数时使用
if default_refer.path == "" or default_refer.text == "" or default_refer.language == "":
    default_refer.path, default_refer.text, default_refer.language = "", "", ""
    print("[INFO] 未指定默认参考音频")
else:
    if (default_refer.how_to_cut == "cut1"):
        cut_info = "凑四句一切"
    elif (default_refer.how_to_cut == "cut2"):
        cut_info = "凑50字一切"
    elif (default_refer.how_to_cut == "cut3"):
        cut_info = "按中文句号。切"
    elif (default_refer.how_to_cut == "cut4"):
        cut_info = "按英文句号.切"
    elif (default_refer.how_to_cut == "cut5"):
        cut_info = "按标点符号切"
    
    print(f"[INFO] 默认参考音频路径: {default_refer.path}")
    print(f"[INFO] 默认参考音频文本: {default_refer.text}")
    print(f"[INFO] 默认参考音频语种: {default_refer.language}")
    print(f"[INFO] 默认切割方式: {cut_info}")

    
is_half = g_config.is_half
if args.full_precision:
    is_half = False
if args.half_precision:
    is_half = True
if args.full_precision and args.half_precision:
    is_half = g_config.is_half  # 炒饭fallback

print(f"[INFO] 半精: {is_half}")

cnhubert_base_path = args.hubert_path
bert_path = args.bert_path

cnhubert.cnhubert_base_path = cnhubert_base_path
tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
if is_half:
    bert_model = bert_model.half().to(device)
else:
    bert_model = bert_model.to(device)


def is_empty(*items):  # 任意一项不为空返回False
    for item in items:
        if item is not None and item != "":
            return False
    return True


def is_full(*items):  # 任意一项为空返回False
    for item in items:
        if item is None or item == "":
            return False
    return True

def change_sovits_weights(sovits_path):
    global vq_model, hps
    dict_s2 = torch.load(sovits_path, map_location="cpu")
    hps = dict_s2["config"]
    hps = DictToAttrRecursive(hps)
    hps.model.semantic_frame_rate = "25hz"
    vq_model = SynthesizerTrn(
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model
    )
    if ("pretrained" not in sovits_path):
        del vq_model.enc_q
    if is_half == True:
        vq_model = vq_model.half().to(device)
    else:
        vq_model = vq_model.to(device)
    vq_model.eval()
    print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
    with open("./sweight.txt", "w", encoding="utf-8") as f:
        f.write(sovits_path)
def change_gpt_weights(gpt_path):
    global hz, max_sec, t2s_model, config
    hz = 50
    dict_s1 = torch.load(gpt_path, map_location="cpu")
    config = dict_s1["config"]
    max_sec = config["data"]["max_sec"]
    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
    t2s_model.load_state_dict(dict_s1["weight"])
    if is_half == True:
        t2s_model = t2s_model.half()
    t2s_model = t2s_model.to(device)
    t2s_model.eval()
    total = sum([param.nelement() for param in t2s_model.parameters()])
    print("Number of parameter: %.2fM" % (total / 1e6))
    with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)


def get_bert_feature(text, word2ph):
    with torch.no_grad():
        inputs = tokenizer(text, return_tensors="pt")
        for i in inputs:
            inputs[i] = inputs[i].to(device)  #####输入是long不用管精度问题，精度随bert_model
        res = bert_model(**inputs, output_hidden_states=True)
        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
    assert len(word2ph) == len(text)
    phone_level_feature = []
    for i in range(len(word2ph)):
        repeat_feature = res[i].repeat(word2ph[i], 1)
        phone_level_feature.append(repeat_feature)
    phone_level_feature = torch.cat(phone_level_feature, dim=0)
    # if(is_half==True):phone_level_feature=phone_level_feature.half()
    return phone_level_feature.T


n_semantic = 1024
dict_s2 = torch.load(sovits_path, map_location="cpu")
hps = dict_s2["config"]


class DictToAttrRecursive:
    def __init__(self, input_dict):
        for key, value in input_dict.items():
            if isinstance(value, dict):
                # 如果值是字典，递归调用构造函数
                setattr(self, key, DictToAttrRecursive(value))
            else:
                setattr(self, key, value)


hps = DictToAttrRecursive(hps)
hps.model.semantic_frame_rate = "25hz"
dict_s1 = torch.load(gpt_path, map_location="cpu")
config = dict_s1["config"]
ssl_model = cnhubert.get_model()
if is_half:
    ssl_model = ssl_model.half().to(device)
else:
    ssl_model = ssl_model.to(device)

vq_model = SynthesizerTrn(
    hps.data.filter_length // 2 + 1,
    hps.train.segment_size // hps.data.hop_length,
    n_speakers=hps.data.n_speakers,
    **hps.model)
if is_half:
    vq_model = vq_model.half().to(device)
else:
    vq_model = vq_model.to(device)
vq_model.eval()
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
hz = 50
max_sec = config['data']['max_sec']
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
t2s_model.load_state_dict(dict_s1["weight"])
if is_half:
    t2s_model = t2s_model.half()
t2s_model = t2s_model.to(device)
t2s_model.eval()
total = sum([param.nelement() for param in t2s_model.parameters()])
print("Number of parameter: %.2fM" % (total / 1e6))


def get_spepc(hps, filename):
    audio = load_audio(filename, int(hps.data.sampling_rate))
    audio = torch.FloatTensor(audio)
    audio_norm = audio
    audio_norm = audio_norm.unsqueeze(0)
    spec = spectrogram_torch(audio_norm, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length,
                             hps.data.win_length, center=False)
    return spec


dict_language = {
    "中文": "zh",
    "英文": "en",
    "日文": "ja",
    "ZH": "zh",
    "EN": "en",
    "JA": "ja",
    "zh": "zh",
    "en": "en",
    "ja": "ja"
}


def split(todo_text):
    todo_text = todo_text.replace("……", "。").replace("——", "，")
    if todo_text[-1] not in splits:
        todo_text += "。"
    i_split_head = i_split_tail = 0
    len_text = len(todo_text)
    todo_texts = []
    while 1:
        if i_split_head >= len_text:
            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
        if todo_text[i_split_head] in splits:
            i_split_head += 1
            todo_texts.append(todo_text[i_split_tail:i_split_head])
            i_split_tail = i_split_head
        else:
            i_split_head += 1
    return todo_texts


def cut1(inp):
    inp = inp.strip("\n")
    inps = split(inp)
    split_idx = list(range(0, len(inps), 4))
    split_idx[-1] = None
    if len(split_idx) > 1:
        opts = []
        for idx in range(len(split_idx) - 1):
            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
    else:
        opts = [inp]
    return "\n".join(opts)


def cut2(inp):
    inp = inp.strip("\n")
    inps = split(inp)
    if len(inps) < 2:
        return inp
    opts = []
    summ = 0
    tmp_str = ""
    for i in range(len(inps)):
        summ += len(inps[i])
        tmp_str += inps[i]
        if summ > 50:
            summ = 0
            opts.append(tmp_str)
            tmp_str = ""
    if tmp_str != "":
        opts.append(tmp_str)
    # print(opts)
    if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
        opts[-2] = opts[-2] + opts[-1]
        opts = opts[:-1]
    return "\n".join(opts)


def cut3(inp):
    inp = inp.strip("\n")
    return "\n".join(["%s" % item for item in inp.strip("。").split("。")])


def cut4(inp):
    inp = inp.strip("\n")
    return "\n".join(["%s" % item for item in inp.strip(".").split(".")])


# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
def cut5(inp):
    # if not re.search(r'[^\w\s]', inp[-1]):
    # inp += '。'
    inp = inp.strip("\n")
    punds = r'[,.;?!、，。？！;：…]'
    items = re.split(f'({punds})', inp)
    mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
    # 在句子不存在符号或句尾无符号的时候保证文本完整
    if len(items)%2 == 1:
        mergeitems.append(items[-1])
    opt = "\n".join(mergeitems)
    return opt
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
    t0 = ttime()
    prompt_text = prompt_text.strip("\n")
    prompt_language, text = prompt_language, text.strip("\n")
    zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
    with torch.no_grad():
        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
        wav16k = torch.from_numpy(wav16k)
        zero_wav_torch = torch.from_numpy(zero_wav)
        if (is_half == True):
            wav16k = wav16k.half().to(device)
            zero_wav_torch = zero_wav_torch.half().to(device)
        else:
            wav16k = wav16k.to(device)
            zero_wav_torch = zero_wav_torch.to(device)
        wav16k = torch.cat([wav16k, zero_wav_torch])
        ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2)  # .float()
        codes = vq_model.extract_latent(ssl_content)
        prompt_semantic = codes[0, 0]
    t1 = ttime()

    prompt_language = dict_language[prompt_language]
    text_language = dict_language[text_language]

    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
    phones1 = cleaned_text_to_sequence(phones1)
    texts = text.split("\n")
    audio_opt = []

    for text in texts:
        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
        phones2 = cleaned_text_to_sequence(phones2)
        if (prompt_language == "zh"):
            bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
        else:
            bert1 = torch.zeros((1024, len(phones1)), dtype=torch.float16 if is_half == True else torch.float32).to(
                device)
        if (text_language == "zh"):
            bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
        else:
            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
        bert = torch.cat([bert1, bert2], 1)

        all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
        bert = bert.to(device).unsqueeze(0)
        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
        prompt = prompt_semantic.unsqueeze(0).to(device)
        t2 = ttime()
        with torch.no_grad():
            # pred_semantic = t2s_model.model.infer(
            pred_semantic, idx = t2s_model.model.infer_panel(
                all_phoneme_ids,
                all_phoneme_len,
                prompt,
                bert,
                # prompt_phone_len=ph_offset,
                top_k=config['inference']['top_k'],
                early_stop_num=hz * max_sec)
        t3 = ttime()
        # print(pred_semantic.shape,idx)
        pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)  # .unsqueeze(0)#mq要多unsqueeze一次
        refer = get_spepc(hps, ref_wav_path)  # .to(device)
        if (is_half == True):
            refer = refer.half().to(device)
        else:
            refer = refer.to(device)
        # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
        audio = \
            vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
                            refer).detach().cpu().numpy()[
                0, 0]  ###试试重建不带上prompt部分
        audio_opt.append(audio)
        audio_opt.append(zero_wav)
        t4 = ttime()
    print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
    yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)


def handle_control(command):
    if command == "restart":
        os.execl(g_config.python_exec, g_config.python_exec, *sys.argv)
    elif command == "exit":
        os.kill(os.getpid(), signal.SIGTERM)
        exit(0)


def handle_change(path, text, language, how_to_cut):
    if is_empty(path, text, language, how_to_cut):
        return JSONResponse({"code": 400, "message": '缺少任意一项以下参数: "path", "text", "language","how_to_cut"'}, status_code=400)
    
    if path == "" or path is None:
        path = default_refer.path
    if text == "" or text is None:
        text = default_refer.text
    if language == "" or language is None:
        language = default_refer.language
    if how_to_cut == "" or how_to_cut is None:
        how_to_cut = default_refer.how_to_cut
    
    default_refer.path = path
    default_refer.text = text
    default_refer.language = language
    default_refer.how_to_cut = how_to_cut


    if (default_refer.how_to_cut == "cut1"):
        cut_info = "凑四句一切"
    elif (default_refer.how_to_cut == "cut2"):
        cut_info = "凑50字一切"
    elif (default_refer.how_to_cut == "cut3"):
        cut_info = "按中文句号。切"
    elif (default_refer.how_to_cut == "cut4"):
        cut_info = "按英文句号.切"
    elif (default_refer.how_to_cut == "cut5"):
        cut_info = "按标点符号切"

    print(f"[INFO] 当前默认参考音频路径: {default_refer.path}")
    print(f"[INFO] 当前默认参考音频文本: {default_refer.text}")
    print(f"[INFO] 当前默认参考音频语种: {default_refer.language}")
    print(f"[INFO] 默认切割方式: {cut_info}")
    print(f"[INFO] is_ready: {default_refer.is_ready()}")

    return JSONResponse({"code": 0, "message": "Success"}, status_code=200)


def handle(refer_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut):
    if (
            refer_wav_path == "" or refer_wav_path is None
            or prompt_text == "" or prompt_text is None
            or prompt_language == "" or prompt_language is None
    ):
        refer_wav_path, prompt_text, prompt_language = (
            default_refer.path,
            default_refer.text,
            default_refer.language,
        )
        if (how_to_cut == "" or how_to_cut is None):
            how_to_cut = default_refer.how_to_cut
        if not default_refer.is_ready():
            return JSONResponse({"code": 400, "message": "未指定参考音频且接口无预设"}, status_code=400)
    
    if (how_to_cut == "cut1"):
        text = cut1(text) #凑四句一切

    elif (how_to_cut == "cut2"):
        text = cut2(text) #凑50字一切

    elif (how_to_cut == "cut3"):
        text = cut3(text) #按中文句号。切

    elif (how_to_cut == "cut4"):
        text = cut4(text) #按英文句号.切

    elif (how_to_cut == "cut5"):
        text = cut5(text) #按标点符号切

    while "\n\n" in text:
        text = text.replace("\n\n", "\n")   
        
    print("实际输入的目标文本(切句后):", text)

    with torch.no_grad():
        gen = get_tts_wav(
            refer_wav_path, prompt_text, prompt_language, text, text_language
        )
        sampling_rate, audio_data = next(gen)

    wav = BytesIO()
    sf.write(wav, audio_data, sampling_rate, format="wav")
    wav.seek(0)

    torch.cuda.empty_cache()
    if device == "mps":
        print('executed torch.mps.empty_cache()')
        torch.mps.empty_cache()
    return StreamingResponse(wav, media_type="audio/wav")


app = FastAPI()

#clark新增-----2024-02-21
#可在启动后动态修改模型，以此满足同一个api不同的朗读者请求
@app.post("/set_model")
async def set_model(request: Request):
    json_post_raw = await request.json()
    global gpt_path
    gpt_path=json_post_raw.get("gpt_model_path")
    global sovits_path
    sovits_path=json_post_raw.get("sovits_model_path")
    print("gptpath"+gpt_path+";vitspath"+sovits_path)
    change_sovits_weights(sovits_path)
    change_gpt_weights(gpt_path)
    return "ok"
# 新增-----end------

@app.post("/control")
async def control(request: Request):
    json_post_raw = await request.json()
    return handle_control(json_post_raw.get("command"))


@app.get("/control")
async def control(command: str = None):
    return handle_control(command)


@app.post("/change_refer")
async def change_refer(request: Request):
    json_post_raw = await request.json()
    return handle_change(
        json_post_raw.get("refer_wav_path"),
        json_post_raw.get("prompt_text"),
        json_post_raw.get("prompt_language"),
        json_post_raw.get("how_to_cut")
    )


@app.get("/change_refer")
async def change_refer(
        refer_wav_path: str = None,
        prompt_text: str = None,
        prompt_language: str = None,
        how_to_cut: str = None
):
    return handle_change(refer_wav_path, prompt_text, prompt_language, how_to_cut)



@app.post("/")
async def tts_endpoint(request: Request):
    json_post_raw = await request.json()
    return handle(
        json_post_raw.get("refer_wav_path"),
        json_post_raw.get("prompt_text"),
        json_post_raw.get("prompt_language"),
        json_post_raw.get("text"),
        json_post_raw.get("text_language"),
        json_post_raw.get("how_to_cut")
)


@app.get("/")
async def tts_endpoint(
        refer_wav_path: str = None,
        prompt_text: str = None,
        prompt_language: str = None,
        text: str = None,
        text_language: str = None,
        how_to_cut: str = None
):
    return handle(refer_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut)


if __name__ == "__main__":
    uvicorn.run(app, host=host, port=port, workers=1)