import io import json import re import sys import time import zipfile from datetime import datetime from pathlib import Path import gradio as gr import modules.extensions as extensions_module from modules import chat, shared, training, ui from modules.html_generator import generate_chat_html from modules.LoRA import add_lora_to_model from modules.models import load_model, load_soft_prompt from modules.text_generation import (clear_torch_cache, 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 get_available_models(): if shared.args.flexgen: return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if item.name.endswith('-np')], key=str.lower) else: return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower) def get_available_presets(): return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower) def get_available_prompts(): prompts = [] prompts += sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('prompts').glob('[0-9]*.txt'))), key=str.lower, reverse=True) prompts += sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('prompts').glob('*.txt'))), key=str.lower) prompts += ['None'] return prompts def get_available_characters(): return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('characters').glob('*.json'))), 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(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), 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=str.lower) def unload_model(): shared.model = shared.tokenizer = None clear_torch_cache() def load_model_wrapper(selected_model): if selected_model != shared.model_name: shared.model_name = selected_model unload_model() if selected_model != '': shared.model, shared.tokenizer = load_model(shared.model_name) return selected_model def load_lora_wrapper(selected_lora): add_lora_to_model(selected_lora) default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')] return selected_lora, default_text def load_preset_values(preset_menu, 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: return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['encoder_repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['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 create_model_and_preset_menus(): 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['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') 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: return f.read() 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 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(): create_model_and_preset_menus() with gr.Column(): shared.gradio['seed'] = gr.Number(value=-1, label='Seed (-1 for random)') with gr.Row(): with gr.Column(): with gr.Box(): gr.Markdown('Custom generation parameters ([reference](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') shared.gradio['top_p'] = gr.Slider(0.0,1.0,value=generate_params['top_p'],step=0.01,label='top_p') shared.gradio['top_k'] = gr.Slider(0,200,value=generate_params['top_k'],step=1,label='top_k') shared.gradio['typical_p'] = gr.Slider(0.0,1.0,value=generate_params['typical_p'],step=0.01,label='typical_p') with gr.Column(): shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'],step=0.01,label='repetition_penalty') shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'],step=0.01,label='encoder_repetition_penalty') shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size') shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'] if shared.args.no_stream else 0, label='min_length', interactive=shared.args.no_stream) 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') with gr.Box(): 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.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.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['model_menu'].change(load_model_wrapper, [shared.gradio['model_menu']], [shared.gradio['model_menu']], show_progress=True) shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio['preset_menu']], [shared.gradio[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']]) shared.gradio['lora_menu'].change(load_lora_wrapper, [shared.gradio['lora_menu']], [shared.gradio['lora_menu'], shared.gradio['textbox']], show_progress=True) 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, cmd_active): modes = ["default", "notebook", "chat", "cai_chat"] cmd_list = vars(shared.args) cmd_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 cmd_list: exec(f"shared.args.{k} = False") for k in cmd_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.args.chat or shared.args.cai_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 else: 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.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 = 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 = 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' description = '\n\n# Text generation lab\nGenerate text using Large Language Models.\n' suffix = '_pygmalion' if 'pygmalion' in shared.model_name.lower() else '' 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 any((shared.args.chat, shared.args.cai_chat)) else ui.css+ui.chat_css, analytics_enabled=False, title=title) as shared.gradio['interface']: if shared.args.chat or shared.args.cai_chat: with gr.Tab("Text generation", elem_id="main"): if shared.args.cai_chat: shared.gradio['display'] = gr.HTML(value=generate_chat_html(shared.history['visible'], shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}'], shared.character)) else: shared.gradio['display'] = gr.Chatbot(value=shared.history['visible']).style(color_map=("#326efd", "#212528")) shared.gradio['textbox'] = gr.Textbox(label='Input') with gr.Row(): shared.gradio['Generate'] = gr.Button('Generate') shared.gradio['Stop'] = gr.Button('Stop', elem_id="stop") with gr.Row(): shared.gradio['Impersonate'] = gr.Button('Impersonate') shared.gradio['Regenerate'] = gr.Button('Regenerate') with gr.Row(): shared.gradio['Copy last reply'] = gr.Button('Copy last reply') shared.gradio['Replace last reply'] = gr.Button('Replace last reply') shared.gradio['Remove last'] = gr.Button('Remove last') 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) with gr.Tab("Character", elem_id="chat-settings"): shared.gradio['name1'] = gr.Textbox(value=shared.settings[f'name1{suffix}'], lines=1, label='Your name') shared.gradio['name2'] = gr.Textbox(value=shared.settings[f'name2{suffix}'], lines=1, label='Bot\'s name') shared.gradio['context'] = gr.Textbox(value=shared.settings[f'context{suffix}'], lines=5, label='Context') 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'): 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') with gr.Tab('Upload your profile picture'): shared.gradio['upload_img_me'] = gr.File(type='binary', file_types=['image']) with gr.Tab('Upload TavernAI Character Card'): 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_slider'] = 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['check'] = gr.Checkbox(value=shared.settings[f'stop_at_newline{suffix}'], label='Stop generating at new line character?') create_settings_menus(default_preset) function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper' shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', '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', 'seed', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']] gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream)) shared.gradio['Stop'].click(stop_everything_event, [], [], queue=False, cancels=gen_events if shared.args.no_stream else None) shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, [], shared.gradio['textbox'], show_progress=shared.args.no_stream) shared.gradio['Replace last reply'].click(chat.replace_last_reply, [shared.gradio['textbox'], shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'], show_progress=shared.args.no_stream) # Clear history with confirmation clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']] 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-confirm'].click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr) shared.gradio['Clear history-confirm'].click(chat.clear_chat_log, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display']) 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['name1'], shared.gradio['name2']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False) shared.gradio['download_button'].click(chat.save_history, inputs=[], outputs=[shared.gradio['download']]) shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']]) # Clearing stuff and saving the history for i in ['Generate', 'Regenerate', 'Replace last reply']: shared.gradio[i].click(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False) shared.gradio[i].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False) shared.gradio['Clear history-confirm'].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False) shared.gradio['textbox'].submit(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False) shared.gradio['textbox'].submit(lambda : chat.save_history(timestamp=False), [], [], show_progress=False) shared.gradio['character_menu'].change(chat.load_character, [shared.gradio['character_menu'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['name2'], shared.gradio['context'], shared.gradio['display']]) shared.gradio['upload_chat_history'].upload(chat.load_history, [shared.gradio['upload_chat_history'], shared.gradio['name1'], shared.gradio['name2']], []) 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['upload_img_me'].upload(chat.upload_your_profile_picture, [shared.gradio['upload_img_me']], []) reload_func = chat.redraw_html if shared.args.cai_chat else lambda : shared.history['visible'] reload_inputs = [shared.gradio['name1'], shared.gradio['name2']] if shared.args.cai_chat else [] shared.gradio['upload_chat_history'].upload(reload_func, reload_inputs, [shared.gradio['display']]) shared.gradio['upload_img_me'].upload(reload_func, reload_inputs, [shared.gradio['display']]) shared.gradio['Stop'].click(reload_func, reload_inputs, [shared.gradio['display']]) shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js+ui.chat_js}}}") shared.gradio['interface'].load(lambda : chat.load_default_history(shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}']), None, None) shared.gradio['interface'].load(reload_func, reload_inputs, [shared.gradio['display']], show_progress=True) elif shared.args.notebook: 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=25) 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('
') 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', 'max_new_tokens', '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', 'seed']] output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']] gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen')) gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)) shared.gradio['Stop'].click(stop_everything_event, [], [], 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: with gr.Tab("Text generation", elem_id="main"): with gr.Row(): with gr.Column(): shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=15, 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=25, 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', 'max_new_tokens', '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', 'seed']] output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']] gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen')) gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['Continue'].click(generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream)) shared.gradio['Stop'].click(stop_everything_event, [], [], 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("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) cmd_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes] active_cmd_list = [k for k in cmd_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['cmd_arguments_menu'] = gr.CheckboxGroup(choices=cmd_list, value=active_cmd_list, label="Boolean command-line flags") shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface", type="primary") shared.gradio['reset_interface'].click(set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'cmd_arguments_menu']], None) shared.gradio['reset_interface'].click(lambda : None, None, None, _js='() => {document.body.innerHTML=\'

Reloading...

\'; setTimeout(function(){location.reload()},2500)}') if shared.args.extensions is not None: extensions_module.create_extensions_block() # 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()