app.py

import argparse
from collections import defaultdict
import datetime
import json
import os
import random
import time
import uuid
import websocket
from websocket import WebSocketConnectionClosedException
import gradio as gr
import requests
import logging
import re

from fastchat.conversation import SeparatorStyle
from fastchat.constants import (
    LOGDIR,
    WORKER_API_TIMEOUT,
    ErrorCode,
    MODERATION_MSG,
    CONVERSATION_LIMIT_MSG,
    SERVER_ERROR_MSG,
    INACTIVE_MSG,
    INPUT_CHAR_LEN_LIMIT,
    CONVERSATION_TURN_LIMIT,
    SESSION_EXPIRATION_TIME,
)
from fastchat.model.model_adapter import get_conversation_template
from fastchat.model.model_registry import model_info
from fastchat.serve.api_provider import (
    anthropic_api_stream_iter,
    openai_api_stream_iter,
    palm_api_stream_iter,
    init_palm_chat,
)
from fastchat.utils import (
    build_logger,
    violates_moderation,
    get_window_url_params_js,
    parse_gradio_auth_creds,
)


logger = build_logger("gradio_web_server", "gradio_web_server.log")

no_change_dropdown = gr.Dropdown.update()
no_change_slider = gr.Slider.update()
no_change_textbox = gr.Textbox.update()
no_change_btn = gr.Button.update()
enable_btn = gr.Button.update(interactive=True)
disable_btn = gr.Button.update(interactive=False)


def get_internet_ip():
    r = requests.get("http://txt.go.sohu.com/ip/soip")
    ip = re.findall(r'\d+.\d+.\d+.\d+', r.text)
    if ip is not None and len(ip) > 0:
        return ip[0]
    return None


enable_moderation = True if os.environ.get('enable_moderation', default='False')=="True" else False
concurrency_count = int(os.environ.get('concurrency_count', default='10'))
model_list_mode = os.environ.get('model_list_mode', default='reload')

midware_url = os.environ.get('midware_url', default='')
preset_token = os.environ.get('preset_token', default='')
worker_addr = os.environ.get('worker_addr', default='')

allow_running = int(os.environ.get('allow_running', default='1'))
ft_list_job_url = os.environ.get('ft_list_job_url', default='')
ft_submit_job_url = os.environ.get('ft_submit_job_url', default='')
ft_remove_job_url = os.environ.get('ft_remove_job_url', default='')
ft_console_log_url = os.environ.get('ft_console_log_url', default='')

dataset_sample = {
    "english": {
        "train": ["abcdef"],
        "valid": ["zxcvbn"]
    },
}


dataset_to_midware_name = {
    "english": "english",
    "cat": "cat",
    "dog": "dog",
    "bird": "bird"
}

hps_keys = ["epochs", "train_batch_size", "eval_batch_size", "gradient_accumulation_steps", "learning_rate", "weight_decay", "model_max_length"]


headers = {"User-Agent": "FastChat Client", "PRIVATE-TOKEN": preset_token}

learn_more_md = """
### License
The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/LICENSE) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
"""

ip_expiration_dict = defaultdict(lambda: 0)


def is_legal_char(c):
    if c.isalnum():
        return True
    if '\u4e00' <= c <= '\u9fff':
        return True
    if c in "！？｡＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.":
        return True
    if c in '!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~':
        return True
    return False


def str_filter(s):
    for _ in range(2):
        if len(s) > 0 and (not is_legal_char(s[-1])):
            s = s[:-1]
    return s


def str_not_int(s):
    try:
        int(s)
        return False
    except ValueError:
        return True
    

def str_not_float(s):
    try:
        float(s)
        return False
    except ValueError:
        return True


class State:
    def __init__(self, model_name):
        self.conv = get_conversation_template(model_name)
        self.conv_id = uuid.uuid4().hex
        self.skip_next = False
        self.model_name = model_name

        if model_name == "palm-2":
            # According to release note, "chat-bison@001" is PaLM 2 for chat.
            # https://cloud.google.com/vertex-ai/docs/release-notes#May_10_2023
            self.palm_chat = init_palm_chat("chat-bison@001")

    def to_gradio_chatbot(self):
        return self.conv.to_gradio_chatbot()

    def dict(self):
        base = self.conv.dict()
        base.update(
            {
                "conv_id": self.conv_id,
                "model_name": self.model_name,
            }
        )
        return base


def get_conv_log_filename():
    t = datetime.datetime.now()
    name = os.path.join(LOGDIR, f"{t.year}-{t.month:02d}-{t.day:02d}-conv.json")
    return name


def get_model_list(midware_url):
    setted_model_order = {
        "vicuna-7b-v1.5-16k": 10,
        "vicuna-13b-v1.5": 90,
    }
    try:
        ret = requests.get(midware_url, headers={"PRIVATE-TOKEN": preset_token}, timeout=5)
        if "code" in ret.json() and "invalid" in ret.json()["code"]:
            gr.Warning("Invalid preset token.")
            models = ["CANNOT GET MODEL"]
        else:
            models = ret.json()["data"]
    except requests.exceptions.RequestException:
        models = ["CANNOT GET MODEL"]
    models = sorted(models, key=lambda x: setted_model_order.get(x, 100))
    logger.info(f"Models: {models}")
    return models


def load_demo_single(models, url_params):
    selected_model = models[0] if len(models) > 0 else ""
    if "model" in url_params:
        model = url_params["model"]
        if model in models:
            selected_model = model

    dropdown_update = gr.Dropdown.update(
        choices=models, value=selected_model, visible=True
    )

    state = None
    return (
        state,
        dropdown_update,
        gr.Chatbot.update(visible=True),
        gr.Textbox.update(visible=True),
        gr.Button.update(visible=True),
        gr.Row.update(visible=True),
        gr.Accordion.update(visible=True),
    )


def load_demo(url_params, request: gr.Request):
    global models

    ip = request.client.host
    logger.info(f"load_demo. ip: {ip}. params: {url_params}")
    ip_expiration_dict[ip] = time.time() + SESSION_EXPIRATION_TIME

    if model_list_mode == "reload":
        models = get_model_list(midware_url)

    return load_demo_single(models, url_params)

def regenerate(state, request: gr.Request):
    logger.info(f"regenerate. ip: {request.client.host}")
    state.conv.update_last_message(None)
    return (state, state.to_gradio_chatbot(), "") + (disable_btn,) * 2


def clear_history(request: gr.Request):
    logger.info(f"clear_history. ip: {request.client.host}")
    state = None
    return (state, [], "") + (disable_btn,) * 2


def add_text(state, model_selector, text, request: gr.Request):
    ip = request.client.host
    logger.info(f"add_text. ip: {ip}. len: {len(text)}")

    if state is None:
        state = State(model_selector)

    if len(text) <= 0:
        state.skip_next = True
        return (state, state.to_gradio_chatbot(), "") + (no_change_btn,) * 2

    if ip_expiration_dict[ip] < time.time():
        logger.info(f"inactive. ip: {request.client.host}. text: {text}")
        state.skip_next = True
        return (state, state.to_gradio_chatbot(), INACTIVE_MSG) + (no_change_btn,) * 2

    if enable_moderation:
        flagged = violates_moderation(text)
        if flagged:
            logger.info(f"violate moderation. ip: {request.client.host}. text: {text}")
            state.skip_next = True
            return (state, state.to_gradio_chatbot(), MODERATION_MSG) + (
                no_change_btn,
            ) * 2

    conv = state.conv
    if (len(conv.messages) - conv.offset) // 2 >= CONVERSATION_TURN_LIMIT:
        logger.info(f"conversation turn limit. ip: {request.client.host}. text: {text}")
        state.skip_next = True
        return (state, state.to_gradio_chatbot(), CONVERSATION_LIMIT_MSG) + (
            no_change_btn,
        ) * 2

    text = text[:INPUT_CHAR_LEN_LIMIT]  # Hard cut-off
    conv.append_message(conv.roles[0], text)
    conv.append_message(conv.roles[1], None)
    return (state, state.to_gradio_chatbot(), "") + (disable_btn,) * 2


def post_process_code(code):
    sep = "\n```"
    if sep in code:
        blocks = code.split(sep)
        if len(blocks) % 2 == 1:
            for i in range(1, len(blocks), 2):
                blocks[i] = blocks[i].replace("\\_", "_")
        code = sep.join(blocks)
    return code


def model_worker_stream_iter(
    conv,
    model_name,
    worker_addr,
    prompt,
    temperature,
    repetition_penalty,
    top_p,
    max_new_tokens,
):
    # Make requests
    gen_params = {
        "model_name": model_name,
        "question": prompt,
        "temperature": 1e-6,
        "repetition_penalty": repetition_penalty,
        "top_p": top_p,
        "max_new_tokens": max_new_tokens,
        "stop": conv.stop_str,
        "stop_token_ids": conv.stop_token_ids,
        "echo": False,
    }
    logger.info(f"==== request ====\n{gen_params}")

    # Stream output
    response = requests.post(
        worker_addr,
        headers=headers,
        json=gen_params,
        stream=True,
        timeout=WORKER_API_TIMEOUT,
    )
    for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
        if chunk:
            data = json.loads(chunk.decode())
            yield data


def bot_response(state, temperature, top_p, max_new_tokens, request: gr.Request):
    logger.info(f"bot_response. ip: {request.client.host}")
    start_tstamp = time.time()
    temperature = float(temperature)
    top_p = float(top_p)
    max_new_tokens = int(max_new_tokens)

    if state.skip_next:
        # This generate call is skipped due to invalid inputs
        state.skip_next = False
        yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 2
        return

    conv, model_name = state.conv, state.model_name
    if model_name == "gpt-3.5-turbo" or model_name == "gpt-4":
        prompt = conv.to_openai_api_messages()
        stream_iter = openai_api_stream_iter(
            model_name, prompt, temperature, top_p, max_new_tokens
        )
    elif model_name == "claude-2" or model_name == "claude-instant-1":
        prompt = conv.get_prompt()
        stream_iter = anthropic_api_stream_iter(
            model_name, prompt, temperature, top_p, max_new_tokens
        )
    elif model_name == "palm-2":
        stream_iter = palm_api_stream_iter(
            state.palm_chat, conv.messages[-2][1], temperature, top_p, max_new_tokens
        )
    else:
        # Get worker address
        logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}")
        # No available worker
        if worker_addr == "":
            conv.update_last_message(SERVER_ERROR_MSG)
            yield (
                state,
                state.to_gradio_chatbot(),
                enable_btn,
                enable_btn,
            )
            return

        # Construct prompt.
        # We need to call it here, so it will not be affected by "▌".
        prompt = conv.get_prompt()

        # Set repetition_penalty
        if "t5" in model_name:
            repetition_penalty = 1.2
        else:
            repetition_penalty = 1.0

        stream_iter = model_worker_stream_iter(
            conv,
            model_name,
            worker_addr,
            prompt,
            temperature,
            repetition_penalty,
            top_p,
            max_new_tokens,
        )

    conv.update_last_message("▌")
    yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 2

    try:
        for data in stream_iter:
            if data["error_code"] == 0:
                finish_reason = data.get("finish_reason", None)
                if finish_reason is not None and finish_reason == "length":
                    gr.Warning("Answer interrupted because the setting of [Max output tokens], try set a larger value.")
                output = data["text"].strip()
                if "vicuna" in model_name:
                    output = post_process_code(output)
                output = str_filter(output)
                conv.update_last_message(output + "▌")
                yield (state, state.to_gradio_chatbot()) + (disable_btn,) * 2
            else:
                output = data["text"] + f"\n\n(error_code: {data['error_code']})"
                conv.update_last_message(output)
                yield (state, state.to_gradio_chatbot()) + (
                    enable_btn,
                    enable_btn,
                )
                return
            time.sleep(0.015)
    except requests.exceptions.RequestException as e:
        conv.update_last_message(
            f"{SERVER_ERROR_MSG}\n\n"
            f"(error_code: {ErrorCode.GRADIO_REQUEST_ERROR}, {e})"
        )
        yield (state, state.to_gradio_chatbot()) + (
            enable_btn,
            enable_btn,
        )
        return
    except Exception as e:
        conv.update_last_message(
            f"{SERVER_ERROR_MSG}\n\n"
            f"(error_code: {ErrorCode.GRADIO_STREAM_UNKNOWN_ERROR}, {e})"
        )
        yield (state, state.to_gradio_chatbot()) + (
            enable_btn,
            enable_btn,
        )
        return

    # Delete "▌"
    conv.update_last_message(conv.messages[-1][-1][:-1])
    yield (state, state.to_gradio_chatbot()) + (enable_btn,) * 2

    finish_tstamp = time.time()
    logger.info(f"{output}")

    with open(get_conv_log_filename(), "a") as fout:
        data = {
            "tstamp": round(finish_tstamp, 4),
            "type": "chat",
            "model": model_name,
            "gen_params": {
                "temperature": temperature,
                "top_p": top_p,
                "max_new_tokens": max_new_tokens,
            },
            "start": round(start_tstamp, 4),
            "finish": round(finish_tstamp, 4),
            "state": state.dict(),
            "ip": request.client.host,
        }
        fout.write(json.dumps(data) + "\n")


block_css = """
#dialog_notice_markdown {
    font-size: 104%
}
#dialog_notice_markdown th {
    display: none;
}
#dialog_notice_markdown td {
    padding-top: 6px;
    padding-bottom: 6px;
}
#leaderboard_markdown {
    font-size: 104%
}
#leaderboard_markdown td {
    padding-top: 6px;
    padding-bottom: 6px;
}
#leaderboard_dataframe td {
    line-height: 0.1em;
}
"""


def get_model_description_md(models):
    model_description_md = """
| | | |
| ---- | ---- | ---- |
"""
    ct = 0
    visited = set()
    for i, name in enumerate(models):
        if name in model_info:
            minfo = model_info[name]
            if minfo.simple_name in visited:
                continue
            visited.add(minfo.simple_name)
            one_model_md = f"[{minfo.simple_name}]({minfo.link}): {minfo.description}"
        else:
            visited.add(name)
            one_model_md = (
                f"[{name}](): Add the description at fastchat/model/model_registry.py"
            )

        if ct % 3 == 0:
            model_description_md += "|"
        model_description_md += f" {one_model_md} |"
        if ct % 3 == 2:
            model_description_md += "\n"
        ct += 1
    return model_description_md


def build_single_model_ui(models, add_promotion_links=False):
    with gr.Column():
        with gr.Tab("🧠 模型对话 Dialog"):
            state = gr.State()
            with gr.Row(elem_id="model_selector_row"):
                model_selector = gr.Dropdown(
                    choices=models,
                    value=models[0] if len(models) > 0 else "",
                    interactive=True,
                    show_label=False,
                    container=False,
                )

            chatbot = gr.Chatbot(
                elem_id="chatbot",
                label="Scroll down and start chatting",
                visible=False,
                height=550,
            )
            with gr.Row():
                with gr.Column(scale=20):
                    textbox = gr.Textbox(
                        show_label=False,
                        placeholder="Enter text and press ENTER",
                        visible=False,
                        container=False,
                    )
                with gr.Column(scale=1, min_width=50):
                    send_btn = gr.Button(value="Send", visible=False)

            with gr.Row(visible=False) as button_row:
                regenerate_btn = gr.Button(value="🔄  Regenerate", interactive=False)
                clear_btn = gr.Button(value="🗑️  Clear history", interactive=False)
            gr.Examples(
                examples=["如何变得富有？", "你能用Python写一段快速排序吗？", "How to be rich?", "Can you write a quicksort code in Python?"],
                inputs=textbox,
            )
            with gr.Accordion("Parameters", open=False, visible=False) as parameter_row:
                temperature = gr.Slider(
                    minimum=0.0,
                    maximum=1.0,
                    value=0.7,
                    step=0.1,
                    interactive=True,
                    label="Temperature",
                )
                top_p = gr.Slider(
                    minimum=0.0,
                    maximum=1.0,
                    value=1.0,
                    step=0.1,
                    interactive=True,
                    label="Top P",
                )
                max_output_tokens = gr.Slider(
                    minimum=16,
                    maximum=1024,
                    value=512,
                    step=64,
                    interactive=True,
                    label="Max output tokens",
                )

            gr.Markdown(learn_more_md)

            # Register listeners
            btn_list = [regenerate_btn, clear_btn]
            regenerate_btn.click(regenerate, state, [state, chatbot, textbox] + btn_list).then(
                bot_response,
                [state, temperature, top_p, max_output_tokens],
                [state, chatbot] + btn_list,
            )
            clear_btn.click(clear_history, None, [state, chatbot, textbox] + btn_list)

            model_selector.change(clear_history, None, [state, chatbot, textbox] + btn_list)

            textbox.submit(
                add_text, [state, model_selector, textbox], [state, chatbot, textbox] + btn_list
            ).then(
                bot_response,
                [state, temperature, top_p, max_output_tokens],
                [state, chatbot] + btn_list,
            )
            send_btn.click(
                add_text, [state, model_selector, textbox], [state, chatbot, textbox] + btn_list
            ).then(
                bot_response,
                [state, temperature, top_p, max_output_tokens],
                [state, chatbot] + btn_list,
            )
    return state, model_selector, chatbot, textbox, send_btn, button_row, parameter_row


def ft_get_job_data():
    running = 0
    res_lst = []
    try:
        r = requests.get(ft_list_job_url, headers={"PRIVATE-TOKEN": preset_token}, timeout=8)
        if "code" in r.json() and "invalid" in r.json()["code"]:
            gr.Warning("Invalid preset token.")
            return res_lst, running
        for d in r.json():
            if isinstance(d['status'], str) and d['status'].lower() == "running":
                running += 1
            hps = dict()
            for key in hps_keys:
                if key in d['parameter']:
                    hps[key] = d['parameter'][key]
            res_lst.append([d['jobName'], d['username'], d['created_at'], d['model'], d['dataset'], d['status'], json.dumps(hps)])
        res_lst = sorted(res_lst,key=(lambda x:x[2]), reverse=True)
        res_lst = sorted(res_lst,key=(lambda x:x[5]), reverse=True)
    except requests.exceptions.RequestException:
        logger.info(f"Get job list fail")
    return res_lst, running


def ft_refresh_click():
    return ft_get_job_data()


def ft_cease_click(ft_console):
    output = ft_console + "\n" + "** Streaming output ceased by user **"
    return output


def console_generator(addr, sleep_time):
    total_str = ""
    ws = websocket.WebSocket()
    ws.connect(addr, header={"PRIVATE-TOKEN": preset_token})
    while True:
        try:
            new_str = ws.recv()
            total_str = total_str + new_str
            time.sleep(sleep_time)
            yield total_str
        except WebSocketConnectionClosedException:
            ws.close()
            break
    ws.close()


def ft_submit_click(ft_latest_running_cnt, ft_user_name, ft_model, ft_dataset_name, ft_token, ft_epochs, ft_train_batch_size, ft_eval_batch_size, ft_gradient_accumulation_steps, ft_learning_rate, ft_weight_decay, ft_model_max_length):
    if ft_user_name == "":
        gr.Warning(f"Submit fail, empty username.")
        res_lst, running = ft_get_job_data()
        return res_lst, running, no_change_textbox
    if str_not_int(ft_train_batch_size) or str_not_int(ft_eval_batch_size) or str_not_int(ft_gradient_accumulation_steps) or str_not_float(ft_learning_rate) or str_not_float(ft_weight_decay) or str_not_int(ft_model_max_length):
        gr.Warning(f"Submit fail, check the types. [learning rate] and [weight decay] should be float, others HPs should be int.")
        res_lst, running = ft_get_job_data()
        return res_lst, running, no_change_textbox
    if ft_latest_running_cnt < int(allow_running):
        midware_header = {"FINETUNE-SECRET": ft_token, "PRIVATE-TOKEN": preset_token}
        hps_json = {
            "epochs": str(ft_epochs),
            "train_batch_size": str(ft_train_batch_size),
            "eval_batch_size": str(ft_eval_batch_size),
            "gradient_accumulation_steps": str(ft_gradient_accumulation_steps),
            "learning_rate": str(ft_learning_rate),
            "weight_decay": str(ft_weight_decay),
            "model_max_length": str(ft_model_max_length)
        }
        json_data = {
            "dataset": dataset_to_midware_name[ft_dataset_name],
            "model": ft_model,
            "parameter": hps_json,
            "username": ft_user_name
        }
        try:
            r = requests.post(ft_submit_job_url, json=json_data, headers=midware_header, timeout=120)
            job_name = r.json()["jobName"]
            gr.Info(f"Job {job_name} submit success.")
            res_lst, running = ft_get_job_data()
            total_str = ""
            for s in console_generator(ft_console_log_url + job_name, 1):
                total_str = s
                yield res_lst, running, s
            res_lst, running = ft_get_job_data()
            yield res_lst, running, total_str
        except requests.exceptions.RequestException:
            gr.Warning(f"Connection Failure.")
            res_lst, running = ft_get_job_data()
            return res_lst, running, ""
    else:
        gr.Warning(f"Only allow {str(allow_running)} job(s) running simultaneously, please wait.")
        res_lst, running = ft_get_job_data()
        return res_lst, running, no_change_textbox


def ft_show_click(ft_selected_row_data):
    for s in console_generator(ft_console_log_url + ft_selected_row_data[0], 0.2):
        yield s


def ft_remove_click(ft_selected_row_data, ft_token):
    status = ft_selected_row_data[5]
    if isinstance(status, str) and status.lower() == "running":
        r = requests.delete(ft_remove_job_url + ft_selected_row_data[0], headers={'FINETUNE-SECRET': ft_token, "PRIVATE-TOKEN": preset_token})
        if r.status_code == 200:
            gr.Info("Remove success.")
        else:
            gr.Warning(f"Remove fail. {r.status_code} {r.reason}.")
    else:
        gr.Warning("Remove fail. Can only remove a running job.")
    return ft_get_job_data()


def ft_jobs_info_select(ft_jobs_info, evt: gr.SelectData):
    selected_row = ft_jobs_info[evt.index[0]]
    if evt.index[1] in (3, 4, 6):
        try:
            Hps = json.loads(selected_row[6])
        except json.decoder.JSONDecodeError:
            Hps = dict()
        return [selected_row, selected_row[3], selected_row[4], Hps.get('epochs', ''), Hps.get('train_batch_size', ''), Hps.get('eval_batch_size', ''), 
                Hps.get('gradient_accumulation_steps', ''), Hps.get('learning_rate', ''), Hps.get('weight_decay', ''), Hps.get('model_max_length', '')]
    else:
        return [selected_row, no_change_dropdown, no_change_dropdown, no_change_slider, no_change_textbox, no_change_textbox, no_change_textbox, no_change_textbox, no_change_textbox, no_change_textbox]


def ft_dataset_preview_click(ft_dataset_name):
    value = dataset_sample.get(ft_dataset_name, {})
    return gr.JSON.update(value=value, visible=True)

def ft_hide_dataset_click():
    return gr.JSON.update(visible=False)

def build_demo(models):
    with gr.Blocks(
        title="Vicuna Test",
        theme=gr.themes.Base(),
        css = block_css
    ) as demo:
        url_params = gr.JSON(visible=False)
        (
            state,
            model_selector,
            chatbot,
            textbox,
            send_btn,
            button_row,
            parameter_row,
        ) = build_single_model_ui(models)

        if model_list_mode not in ["once", "reload"]:
            raise ValueError(f"Unknown model list mode: {model_list_mode}")
        demo.load(
            load_demo,
            [url_params],
            [
                state,
                model_selector,
                chatbot,
                textbox,
                send_btn,
                button_row,
                parameter_row,
            ],
            _js=get_window_url_params_js,
        )

    return demo

try:
    print("Internet IP:", get_internet_ip())
except Exception as e:
    print(f"Get Internet IP error: {e}")
models = get_model_list(midware_url)

# Launch the demo
demo = build_demo(models)
demo.queue(
    concurrency_count=concurrency_count, status_update_rate=10, api_open=False
).launch(
    max_threads=200,
)