server.py

import os

os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'

import importlib
import io
import json
import math
import os
import re
import sys
import time
import traceback
import zipfile
from datetime import datetime
from pathlib import Path

import gradio as gr
import psutil
import torch
from PIL import Image

import modules.extensions as extensions_module
from modules import api, chat, shared, training, ui
from modules.html_generator import chat_html_wrapper
from modules.LoRA import add_lora_to_model
from modules.models import load_model, load_soft_prompt, unload_model
from modules.text_generation import generate_reply, stop_everything_event

# Loading custom settings
settings_file = None
if shared.args.settings is not None and Path(shared.args.settings).exists():
    settings_file = Path(shared.args.settings)
elif Path('settings.json').exists():
    settings_file = Path('settings.json')
if settings_file is not None:
    print(f"Loading settings from {settings_file}...")
    new_settings = json.loads(open(settings_file, 'r').read())
    for item in new_settings:
        shared.settings[item] = new_settings[item]


def special_sort(model_name):
    if '_' in model_name:
        return ('_'.join(model_name.split('_')[1:])).lower()
    else:
        return model_name.lower()


def get_available_models():
    if shared.args.flexgen:
        return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if item.name.endswith('-np')], key=special_sort)
    else:
        return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=special_sort)


def get_available_presets():
    return sorted(set((k.stem for k in Path('presets').glob('*.txt'))), key=str.lower)


def get_available_prompts():
    prompts = []
    prompts += sorted(set((k.stem for k in Path('prompts').glob('[0-9]*.txt'))), key=str.lower, reverse=True)
    prompts += sorted(set((k.stem for k in Path('prompts').glob('*.txt'))), key=str.lower)
    prompts += ['None']
    return prompts


def get_available_characters():
    paths = (x for x in Path('characters').iterdir() if x.suffix in ('.json', '.yaml', '.yml'))
    return ['None'] + sorted(set((k.stem for k in paths if k.stem != "instruction-following")), key=str.lower)


def get_available_instruction_templates():
    path = "characters/instruction-following"
    paths = []
    if os.path.exists(path):
        paths = (x for x in Path(path).iterdir() if x.suffix in ('.json', '.yaml', '.yml'))
    return ['None'] + sorted(set((k.stem for k in paths)), key=str.lower)


def get_available_extensions():
    return sorted(set(map(lambda x: x.parts[1], Path('extensions').glob('*/script.py'))), key=str.lower)


def get_available_softprompts():
    return ['None'] + sorted(set((k.stem for k in Path('softprompts').glob('*.zip'))), key=str.lower)


def get_available_loras():
    return ['None'] + sorted([item.name for item in list(Path(shared.args.lora_dir).glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=special_sort)


def load_model_wrapper(selected_model):
    try:
        yield f"Loading {selected_model}..."
        shared.model_name = selected_model
        unload_model()
        if selected_model != '':
            shared.model, shared.tokenizer = load_model(shared.model_name)

        yield f"Successfully loaded {selected_model}"
    except:
        yield traceback.format_exc()


def load_lora_wrapper(selected_lora):
    add_lora_to_model(selected_lora)
    return selected_lora


def load_preset_values(preset_menu, state, return_dict=False):
    generate_params = {
        'do_sample': True,
        'temperature': 1,
        'top_p': 1,
        'typical_p': 1,
        'repetition_penalty': 1,
        'encoder_repetition_penalty': 1,
        'top_k': 50,
        'num_beams': 1,
        'penalty_alpha': 0,
        'min_length': 0,
        'length_penalty': 1,
        'no_repeat_ngram_size': 0,
        'early_stopping': False,
    }
    with open(Path(f'presets/{preset_menu}.txt'), 'r') as infile:
        preset = infile.read()
    for i in preset.splitlines():
        i = i.rstrip(',').strip().split('=')
        if len(i) == 2 and i[0].strip() != 'tokens':
            generate_params[i[0].strip()] = eval(i[1].strip())
    generate_params['temperature'] = min(1.99, generate_params['temperature'])

    if return_dict:
        return generate_params
    else:
        state.update(generate_params)
        return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]


def upload_soft_prompt(file):
    with zipfile.ZipFile(io.BytesIO(file)) as zf:
        zf.extract('meta.json')
        j = json.loads(open('meta.json', 'r').read())
        name = j['name']
        Path('meta.json').unlink()

    with open(Path(f'softprompts/{name}.zip'), 'wb') as f:
        f.write(file)

    return name


def save_prompt(text):
    fname = f"{datetime.now().strftime('%Y-%m-%d-%H%M%S')}.txt"
    with open(Path(f'prompts/{fname}'), 'w', encoding='utf-8') as f:
        f.write(text)
    return f"Saved to prompts/{fname}"


def load_prompt(fname):
    if fname in ['None', '']:
        return ''
    else:
        with open(Path(f'prompts/{fname}.txt'), 'r', encoding='utf-8') as f:
            text = f.read()
            if text[-1] == '\n':
                text = text[:-1]
            return text


def create_prompt_menus():
    with gr.Row():
        with gr.Column():
            with gr.Row():
                shared.gradio['prompt_menu'] = gr.Dropdown(choices=get_available_prompts(), value='None', label='Prompt')
                ui.create_refresh_button(shared.gradio['prompt_menu'], lambda: None, lambda: {'choices': get_available_prompts()}, 'refresh-button')

        with gr.Column():
            with gr.Column():
                shared.gradio['save_prompt'] = gr.Button('Save prompt')
                shared.gradio['status'] = gr.Markdown('Ready')

    shared.gradio['prompt_menu'].change(load_prompt, [shared.gradio['prompt_menu']], [shared.gradio['textbox']], show_progress=False)
    shared.gradio['save_prompt'].click(save_prompt, [shared.gradio['textbox']], [shared.gradio['status']], show_progress=False)


def download_model_wrapper(repo_id):
    try:
        downloader = importlib.import_module("download-model")

        model = repo_id
        branch = "main"
        check = False

        yield ("Cleaning up the model/branch names")
        model, branch = downloader.sanitize_model_and_branch_names(model, branch)

        yield ("Getting the download links from Hugging Face")
        links, sha256, is_lora = downloader.get_download_links_from_huggingface(model, branch, text_only=False)

        yield ("Getting the output folder")
        output_folder = downloader.get_output_folder(model, branch, is_lora)

        if check:
            yield ("Checking previously downloaded files")
            downloader.check_model_files(model, branch, links, sha256, output_folder)
        else:
            yield (f"Downloading files to {output_folder}")
            downloader.download_model_files(model, branch, links, sha256, output_folder, threads=1)
            yield ("Done!")
    except:
        yield traceback.format_exc()


# Model parameters: list the relevant interface elements
def list_model_parameters():
    parameters = ['cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'wbits', 'groupsize', 'model_type', 'pre_layer']
    for i in range(torch.cuda.device_count()):
        parameters.append(f'gpu_memory_{i}')
    return parameters


# Model parameters: update the command-line arguments based on the interface values
def update_model_parameters(*args):
    args = list(args) # the values of the parameters
    elements = list_model_parameters() # the names of the parameters

    gpu_memories = []
    for i, element in enumerate(elements):

        if element.startswith('gpu_memory'):
            gpu_memories.append(args[i])
            continue

        if element == 'cpu_memory' and args[i] == 0:
            args[i] = None
        if element == 'wbits' and args[i] == 'None':
            args[i] = 0
        if element == 'groupsize' and args[i] == 'None':
            args[i] = -1
        if element == 'model_type' and args[i] == 'None':
            args[i] = None
        if element in ['wbits', 'groupsize', 'pre_layer']:
            args[i] = int(args[i])
        elif element == 'cpu_memory' and args[i] is not None:
            args[i] = f"{args[i]}MiB"

        #print(element, repr(eval(f"shared.args.{element}")), repr(args[i]))
        #print(f"shared.args.{element} = args[i]")
        exec(f"shared.args.{element} = args[i]")

    found_positive = False
    for i in gpu_memories:
        if i > 0:
            found_positive = True
            break
    if found_positive:
        shared.args.gpu_memory = [f"{i}MiB" for i in gpu_memories]
    else:
        shared.args.gpu_memory = None


def create_model_menus():
    # Finding the default values for the GPU and CPU memories
    total_mem = []
    for i in range(torch.cuda.device_count()):
        total_mem.append(math.floor(torch.cuda.get_device_properties(i).total_memory / (1024*1024)))

    default_gpu_mem = []
    if shared.args.gpu_memory is not None and len(shared.args.gpu_memory) > 0:
        for i in shared.args.gpu_memory:
            if 'mib' in i.lower():
                default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i)))
            else:
                default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i))*1000)
    while len(default_gpu_mem) < len(total_mem):
        default_gpu_mem.append(0)

    total_cpu_mem = math.floor(psutil.virtual_memory().total / (1024*1024))
    if shared.args.cpu_memory is not None:
        default_cpu_mem = re.sub('[a-zA-Z ]', '', shared.args.cpu_memory)
    else:
        default_cpu_mem = 0

    components = {}
    with gr.Row():
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    with gr.Row():
                        shared.gradio['model_menu'] = gr.Dropdown(choices=available_models, value=shared.model_name, label='Model')
                        ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button')

                with gr.Column():
                    with gr.Row():
                        shared.gradio['lora_menu'] = gr.Dropdown(choices=available_loras, value=shared.lora_name, label='LoRA')
                        ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras()}, 'refresh-button')

        with gr.Column():
            unload = gr.Button("Unload the model")
            reload = gr.Button("Reload the model")

    with gr.Row():
        with gr.Column():
            with gr.Box():
                gr.Markdown('Transformers parameters')
                with gr.Row():
                    with gr.Column():
                        for i in range(len(total_mem)):
                            components[f'gpu_memory_{i}'] = gr.Slider(label=f"gpu-memory in MiB for device :{i}", maximum=total_mem[i], value=default_gpu_mem[i])
                        components['cpu_memory'] = gr.Slider(label="cpu-memory in MiB", maximum=total_cpu_mem, value=default_cpu_mem)

                    with gr.Column():
                        components['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices)
                        components['disk'] = gr.Checkbox(label="disk", value=shared.args.disk)
                        components['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu)
                        components['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16)
                        components['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit)

        with gr.Column():
            with gr.Box():
                gr.Markdown('GPTQ parameters')
                with gr.Row():
                    with gr.Column():
                        components['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=shared.args.wbits if shared.args.wbits > 0 else "None")
                        components['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128], value=shared.args.groupsize if shared.args.groupsize > 0 else "None")

                    with gr.Column():
                        components['model_type'] = gr.Dropdown(label="model_type", choices=["None", "llama", "opt", "gpt-j"], value=shared.args.model_type or "None")
                        components['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer)

    with gr.Row():
        with gr.Column():
            shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA", info="Enter Hugging Face username/model path, e.g: facebook/galactica-125m")
            shared.gradio['download_button'] = gr.Button("Download")

        with gr.Column():
            shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')

    shared.gradio['model_menu'].change(
        update_model_parameters, [components[k] for k in list_model_parameters()], None).then(
        load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=True)

    unload.click(
        unload_model, None, None).then(
        lambda: "Model unloaded", None, shared.gradio['model_status'])

    reload.click(
        unload_model, None, None).then(
        update_model_parameters, [components[k] for k in list_model_parameters()], None).then(
        load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=True)

    shared.gradio['lora_menu'].change(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['lora_menu'], show_progress=True)
    shared.gradio['download_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=False)


def create_settings_menus(default_preset):

    generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', {}, return_dict=True)

    with gr.Row():
        with gr.Column():
            with gr.Row():
                shared.gradio['preset_menu'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
                ui.create_refresh_button(shared.gradio['preset_menu'], lambda: None, lambda: {'choices': get_available_presets()}, 'refresh-button')
        with gr.Column():
            shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)')

    with gr.Row():
        with gr.Column():
            with gr.Box():
                gr.Markdown('Custom generation parameters ([click here to view technical documentation](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig))')
                with gr.Row():
                    with gr.Column():
                        shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature', info='Primary factor to control randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.')
                        shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p', info='If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.')
                        shared.gradio['top_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k', info='Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.')
                        shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p', info='If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.')
                    with gr.Column():
                        shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty', info='Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.')
                        shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'], step=0.01, label='encoder_repetition_penalty', info='Also known as the "Hallucinations filter". Used to penalize tokens that are *not* in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.')
                        shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size', info='If not set to 0, specifies the length of token sets that are completely blocked from repeating at all. Higher values = blocks larger phrases, lower values = blocks words or letters from repeating. Only 0 or high values are a good idea in most cases.')
                        shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'], label='min_length', info='Minimum generation length in tokens.')
                shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
        with gr.Column():
            with gr.Box():
                gr.Markdown('Contrastive search')
                shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')

                gr.Markdown('Beam search (uses a lot of VRAM)')
                with gr.Row():
                    with gr.Column():
                        shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams')
                    with gr.Column():
                        shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
                shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')

            with gr.Group():
                with gr.Row():
                    shared.gradio['add_bos_token'] = gr.Checkbox(value=shared.settings['add_bos_token'], label='Add the bos_token to the beginning of prompts', info='Disabling this can make the replies more creative.')
                    shared.gradio['ban_eos_token'] = gr.Checkbox(value=shared.settings['ban_eos_token'], label='Ban the eos_token', info='This forces the model to never end the generation prematurely.')
                shared.gradio['truncation_length'] = gr.Slider(value=shared.settings['truncation_length'], minimum=shared.settings['truncation_length_min'], maximum=shared.settings['truncation_length_max'], step=1, label='Truncate the prompt up to this length', info='The leftmost tokens are removed if the prompt exceeds this length. Most models require this to be at most 2048.')
                shared.gradio['custom_stopping_strings'] = gr.Textbox(lines=1, value=shared.settings["custom_stopping_strings"] or None, label='Custom stopping strings', info='In addition to the defaults. Written between "" and separated by commas. For instance: "\\nYour Assistant:", "\\nThe assistant:"')

    with gr.Accordion('Soft prompt', open=False):
        with gr.Row():
            shared.gradio['softprompts_menu'] = gr.Dropdown(choices=available_softprompts, value='None', label='Soft prompt')
            ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda: None, lambda: {'choices': get_available_softprompts()}, 'refresh-button')

        gr.Markdown('Upload a soft prompt (.zip format):')
        with gr.Row():
            shared.gradio['upload_softprompt'] = gr.File(type='binary', file_types=['.zip'])

    shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio[k] for k in ['preset_menu', 'interface_state']], [shared.gradio[k] for k in ['interface_state', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']])
    shared.gradio['softprompts_menu'].change(load_soft_prompt, shared.gradio['softprompts_menu'], shared.gradio['softprompts_menu'], show_progress=True)
    shared.gradio['upload_softprompt'].upload(upload_soft_prompt, shared.gradio['upload_softprompt'], shared.gradio['softprompts_menu'])


def set_interface_arguments(interface_mode, extensions, bool_active):
    modes = ["default", "notebook", "chat", "cai_chat"]
    cmd_list = vars(shared.args)
    bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes]

    shared.args.extensions = extensions
    for k in modes[1:]:
        exec(f"shared.args.{k} = False")
    if interface_mode != "default":
        exec(f"shared.args.{interface_mode} = True")

    for k in bool_list:
        exec(f"shared.args.{k} = False")
    for k in bool_active:
        exec(f"shared.args.{k} = True")

    shared.need_restart = True


available_models = get_available_models()
available_presets = get_available_presets()
available_characters = get_available_characters()
available_softprompts = get_available_softprompts()
available_loras = get_available_loras()

# Default extensions
extensions_module.available_extensions = get_available_extensions()
if shared.is_chat():
    for extension in shared.settings['chat_default_extensions']:
        shared.args.extensions = shared.args.extensions or []
        if extension not in shared.args.extensions:
            shared.args.extensions.append(extension)
else:
    for extension in shared.settings['default_extensions']:
        shared.args.extensions = shared.args.extensions or []
        if extension not in shared.args.extensions:
            shared.args.extensions.append(extension)

# Default model
if shared.args.model is not None:
    shared.model_name = shared.args.model
    shared.model, shared.tokenizer = load_model(shared.model_name)
elif shared.args.model_menu:
    if len(available_models) == 0:
        print('No models are available! Please download at least one.')
        sys.exit(0)
    elif len(available_models) == 1:
        i = 0
    else:
        print('The following models are available:\n')
        for i, model in enumerate(available_models):
            print(f'{i+1}. {model}')
        print(f'\nWhich one do you want to load? 1-{len(available_models)}\n')
        i = int(input()) - 1
        print()
    shared.model_name = available_models[i]
    shared.model, shared.tokenizer = load_model(shared.model_name)

if shared.args.model is not None and shared.args.lora:
    add_lora_to_model(shared.args.lora)

# Default UI settings
default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
if shared.lora_name != "None":
    default_text = load_prompt(shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')])
else:
    default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')])
title = 'Text generation web UI'


def create_interface():
    gen_events = []
    if shared.args.extensions is not None and len(shared.args.extensions) > 0:
        extensions_module.load_extensions()

    with gr.Blocks(css=ui.css if not shared.is_chat() else ui.css + ui.chat_css, analytics_enabled=False, title=title) as shared.gradio['interface']:
        if shared.is_chat():

            shared.input_elements = ui.list_interface_input_elements(chat=True)
            shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
            shared.gradio['Chat input'] = gr.State()

            with gr.Tab('Text generation', elem_id='main'):
                shared.gradio['display'] = gr.HTML(value=chat_html_wrapper(shared.history['visible'], shared.settings['name1'], shared.settings['name2'], 'cai-chat'))
                shared.gradio['textbox'] = gr.Textbox(label='Input')
                with gr.Row():
                    shared.gradio['Generate'] = gr.Button('Generate', elem_id='Generate')
                    shared.gradio['Stop'] = gr.Button('Stop', elem_id='stop')
                with gr.Row():
                    shared.gradio['Regenerate'] = gr.Button('Regenerate')
                    shared.gradio['Continue'] = gr.Button('Continue')
                    shared.gradio['Impersonate'] = gr.Button('Impersonate')
                with gr.Row():
                    shared.gradio['Send dummy message'] = gr.Button('Send dummy message')
                    shared.gradio['Send dummy reply'] = gr.Button('Send dummy reply')
                    shared.gradio['Replace last reply'] = gr.Button('Replace last reply')
                    shared.gradio['Copy last reply'] = gr.Button('Copy last reply')
                with gr.Row():
                    shared.gradio['Clear history'] = gr.Button('Clear history')
                    shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant='stop', visible=False)
                    shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False)
                    shared.gradio['Remove last'] = gr.Button('Remove last')

                shared.gradio['mode'] = gr.Radio(choices=['cai-chat', 'chat', 'instruct'], value=shared.settings['mode'], label='Mode')
                shared.gradio['Instruction templates'] = gr.Dropdown(choices=get_available_instruction_templates(), label='Instruction template', value='None', visible=False, info='Change this according to the model/LoRA that you are using.')

            with gr.Tab('Character', elem_id='chat-settings'):
                with gr.Row():
                    with gr.Column(scale=8):
                        shared.gradio['name1'] = gr.Textbox(value=shared.settings['name1'], lines=1, label='Your name')
                        shared.gradio['name2'] = gr.Textbox(value=shared.settings['name2'], lines=1, label='Character\'s name')
                        shared.gradio['greeting'] = gr.Textbox(value=shared.settings['greeting'], lines=4, label='Greeting')
                        shared.gradio['context'] = gr.Textbox(value=shared.settings['context'], lines=4, label='Context')
                        shared.gradio['end_of_turn'] = gr.Textbox(value=shared.settings['end_of_turn'], lines=1, label='End of turn string')
                    with gr.Column(scale=1):
                        shared.gradio['character_picture'] = gr.Image(label='Character picture', type='pil')
                        shared.gradio['your_picture'] = gr.Image(label='Your picture', type='pil', value=Image.open(Path('cache/pfp_me.png')) if Path('cache/pfp_me.png').exists() else None)
                with gr.Row():
                    shared.gradio['character_menu'] = gr.Dropdown(choices=available_characters, value='None', label='Character', elem_id='character-menu')
                    ui.create_refresh_button(shared.gradio['character_menu'], lambda: None, lambda: {'choices': get_available_characters()}, 'refresh-button')

                with gr.Row():
                    with gr.Tab('Chat history'):
                        with gr.Row():
                            with gr.Column():
                                gr.Markdown('Upload')
                                shared.gradio['upload_chat_history'] = gr.File(type='binary', file_types=['.json', '.txt'])
                            with gr.Column():
                                gr.Markdown('Download')
                                shared.gradio['download'] = gr.File()
                                shared.gradio['download_button'] = gr.Button(value='Click me')
                    with gr.Tab('Upload character'):
                        gr.Markdown('# JSON format')
                        with gr.Row():
                            with gr.Column():
                                gr.Markdown('1. Select the JSON file')
                                shared.gradio['upload_json'] = gr.File(type='binary', file_types=['.json'])
                            with gr.Column():
                                gr.Markdown('2. Select your character\'s profile picture (optional)')
                                shared.gradio['upload_img_bot'] = gr.File(type='binary', file_types=['image'])
                        shared.gradio['Upload character'] = gr.Button(value='Submit')

                        gr.Markdown('# TavernAI PNG format')
                        shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image'])

            with gr.Tab("Parameters", elem_id="parameters"):
                with gr.Box():
                    gr.Markdown("Chat parameters")
                    with gr.Row():
                        with gr.Column():
                            shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
                            shared.gradio['chat_prompt_size'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size'])
                        with gr.Column():
                            shared.gradio['chat_generation_attempts'] = gr.Slider(minimum=shared.settings['chat_generation_attempts_min'], maximum=shared.settings['chat_generation_attempts_max'], value=shared.settings['chat_generation_attempts'], step=1, label='Generation attempts (for longer replies)')
                            shared.gradio['stop_at_newline'] = gr.Checkbox(value=shared.settings['stop_at_newline'], label='Stop generating at new line character')

                create_settings_menus(default_preset)

            shared.input_params = [shared.gradio[k] for k in ['Chat input', 'interface_state']]
            clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']]
            reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'mode']]

            gen_events.append(shared.gradio['Generate'].click(
                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
                chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
                chat.save_history, shared.gradio['mode'], None, show_progress=False)
            )

            gen_events.append(shared.gradio['textbox'].submit(
                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
                chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
                chat.save_history, shared.gradio['mode'], None, show_progress=False)
            )

            gen_events.append(shared.gradio['Regenerate'].click(
                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
                chat.save_history, shared.gradio['mode'], None, show_progress=False)
            )

            gen_events.append(shared.gradio['Continue'].click(
                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                chat.continue_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then(
                chat.save_history, shared.gradio['mode'], None, show_progress=False)
            )

            gen_events.append(shared.gradio['Impersonate'].click(
                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream)
            )

            shared.gradio['Replace last reply'].click(
                chat.replace_last_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then(
                lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
                chat.save_history, shared.gradio['mode'], None, show_progress=False)

            shared.gradio['Send dummy message'].click(
                chat.send_dummy_message, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then(
                lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
                chat.save_history, shared.gradio['mode'], None, show_progress=False)

            shared.gradio['Send dummy reply'].click(
                chat.send_dummy_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then(
                lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then(
                chat.save_history, shared.gradio['mode'], None, show_progress=False)

            shared.gradio['Clear history-confirm'].click(
                lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr).then(
                chat.clear_chat_log, [shared.gradio[k] for k in ['name1', 'name2', 'greeting', 'mode']], shared.gradio['display']).then(
                chat.save_history, shared.gradio['mode'], None, show_progress=False)

            shared.gradio['Stop'].click(
                stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None).then(
                chat.redraw_html, reload_inputs, shared.gradio['display'])

            shared.gradio['mode'].change(
                lambda x: gr.update(visible=x == 'instruct'), shared.gradio['mode'], shared.gradio['Instruction templates']).then(
                lambda x: gr.update(interactive=x != 'instruct'), shared.gradio['mode'], shared.gradio['character_menu']).then(
                chat.redraw_html, reload_inputs, shared.gradio['display'])

            shared.gradio['Instruction templates'].change(
                lambda character, name1, name2, mode: chat.load_character(character, name1, name2, mode), [shared.gradio[k] for k in ['Instruction templates', 'name1', 'name2', 'mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'end_of_turn', 'display']]).then(
                chat.redraw_html, reload_inputs, shared.gradio['display'])

            shared.gradio['upload_chat_history'].upload(
                chat.load_history, [shared.gradio[k] for k in ['upload_chat_history', 'name1', 'name2']], None).then(
                chat.redraw_html, reload_inputs, shared.gradio['display'])

            shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=shared.args.no_stream)
            shared.gradio['Clear history'].click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr)
            shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr)
            shared.gradio['Remove last'].click(chat.remove_last_message, [shared.gradio[k] for k in ['name1', 'name2', 'mode']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False)
            shared.gradio['download_button'].click(lambda x: chat.save_history(x, timestamp=True), shared.gradio['mode'], shared.gradio['download'])
            shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']])
            shared.gradio['character_menu'].change(chat.load_character, [shared.gradio[k] for k in ['character_menu', 'name1', 'name2', 'mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'end_of_turn', 'display']])
            shared.gradio['upload_img_tavern'].upload(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']])
            shared.gradio['your_picture'].change(chat.upload_your_profile_picture, [shared.gradio[k] for k in ['your_picture', 'name1', 'name2', 'mode']], shared.gradio['display'])

            shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js+ui.chat_js}}}")
            shared.gradio['interface'].load(chat.load_default_history, [shared.gradio[k] for k in ['name1', 'name2']], None)
            shared.gradio['interface'].load(chat.redraw_html, reload_inputs, shared.gradio['display'], show_progress=True)

        elif shared.args.notebook:
            shared.input_elements = ui.list_interface_input_elements(chat=False)
            shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
            with gr.Tab("Text generation", elem_id="main"):
                with gr.Row():
                    with gr.Column(scale=4):
                        with gr.Tab('Raw'):
                            shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_id="textbox", lines=27)
                        with gr.Tab('Markdown'):
                            shared.gradio['markdown'] = gr.Markdown()
                        with gr.Tab('HTML'):
                            shared.gradio['html'] = gr.HTML()

                        with gr.Row():
                            with gr.Column():
                                with gr.Row():
                                    shared.gradio['Generate'] = gr.Button('Generate')
                                    shared.gradio['Stop'] = gr.Button('Stop')
                            with gr.Column():
                                pass

                    with gr.Column(scale=1):
                        gr.HTML('<div style="padding-bottom: 13px"></div>')
                        shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])

                        create_prompt_menus()

            with gr.Tab("Parameters", elem_id="parameters"):
                create_settings_menus(default_preset)

            shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']]
            output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']]

            gen_events.append(shared.gradio['Generate'].click(
                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)#.then(
                #None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
            )

            gen_events.append(shared.gradio['textbox'].submit(
                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)#.then(
                #None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}")
            )

            shared.gradio['Stop'].click(stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None)
            shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}")

        else:
            shared.input_elements = ui.list_interface_input_elements(chat=False)
            shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
            with gr.Tab("Text generation", elem_id="main"):
                with gr.Row():
                    with gr.Column():
                        shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=21, label='Input')
                        shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
                        shared.gradio['Generate'] = gr.Button('Generate')
                        with gr.Row():
                            with gr.Column():
                                shared.gradio['Continue'] = gr.Button('Continue')
                            with gr.Column():
                                shared.gradio['Stop'] = gr.Button('Stop')

                        create_prompt_menus()

                    with gr.Column():
                        with gr.Tab('Raw'):
                            shared.gradio['output_textbox'] = gr.Textbox(lines=27, label='Output')
                        with gr.Tab('Markdown'):
                            shared.gradio['markdown'] = gr.Markdown()
                        with gr.Tab('HTML'):
                            shared.gradio['html'] = gr.HTML()

            with gr.Tab("Parameters", elem_id="parameters"):
                create_settings_menus(default_preset)

            shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']]
            output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']]

            gen_events.append(shared.gradio['Generate'].click(
                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)#.then(
                #None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}")
            )

            gen_events.append(shared.gradio['textbox'].submit(
                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)#.then(
                #None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}")
            )

            gen_events.append(shared.gradio['Continue'].click(
                ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
                generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream)#.then(
                #None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}")
            )

            shared.gradio['Stop'].click(stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None)
            shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}")

        with gr.Tab("Model", elem_id="model-tab"):
            create_model_menus()

        with gr.Tab("Training", elem_id="training-tab"):
            training.create_train_interface()

        with gr.Tab("Interface mode", elem_id="interface-mode"):
            modes = ["default", "notebook", "chat", "cai_chat"]
            current_mode = "default"
            for mode in modes[1:]:
                if eval(f"shared.args.{mode}"):
                    current_mode = mode
                    break
            cmd_list = vars(shared.args)
            bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes + list_model_parameters()]
            bool_active = [k for k in bool_list if vars(shared.args)[k]]

            gr.Markdown("*Experimental*")
            shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode")
            shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=get_available_extensions(), value=shared.args.extensions, label="Available extensions")
            shared.gradio['bool_menu'] = gr.CheckboxGroup(choices=bool_list, value=bool_active, label="Boolean command-line flags")
            shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface")

            # Reset interface event
            shared.gradio['reset_interface'].click(
                set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None).then(
                lambda: None, None, None, _js='() => {document.body.innerHTML=\'<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>\'; setTimeout(function(){location.reload()},2500); return []}')

        if shared.args.extensions is not None:
            extensions_module.create_extensions_block()

        if not shared.is_chat():
            api.create_apis()

    # Authentication
    auth = None
    if shared.args.gradio_auth_path is not None:
        gradio_auth_creds = []
        with open(shared.args.gradio_auth_path, 'r', encoding="utf8") as file:
            for line in file.readlines():
                gradio_auth_creds += [x.strip() for x in line.split(',') if x.strip()]
        auth = [tuple(cred.split(':')) for cred in gradio_auth_creds]

    # Launch the interface
    shared.gradio['interface'].queue()
    if shared.args.listen:
        shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth)
    else:
        shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth)


create_interface()

while True:
    time.sleep(0.5)
    if shared.need_restart:
        shared.need_restart = False
        shared.gradio['interface'].close()
        create_interface()