import os import warnings os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False' os.environ['BITSANDBYTES_NOWELCOME'] = '1' warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated') 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 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((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=str.lower) 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_loras): yield ("Applying the following LoRAs to {}:\n\n{}".format(shared.model_name, '\n'.join(selected_loras))) add_lora_to_model(selected_loras) yield ("Successfuly applied the LoRAs") 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 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() # Update the command-line arguments based on the interface values def update_model_parameters(state, initial=False): elements = ui.list_model_elements() # the names of the parameters gpu_memories = [] for i, element in enumerate(elements): if element not in state: continue value = state[element] if element.startswith('gpu_memory'): gpu_memories.append(value) continue if initial and vars(shared.args)[element] != vars(shared.args_defaults)[element]: continue # Setting null defaults if element in ['wbits', 'groupsize', 'model_type'] and value == 'None': value = vars(shared.args_defaults)[element] elif element in ['cpu_memory'] and value == 0: value = vars(shared.args_defaults)[element] # Making some simple conversions if element in ['wbits', 'groupsize', 'pre_layer']: value = int(value) elif element == 'cpu_memory' and value is not None: value = f"{value}MiB" exec(f"shared.args.{element} = value") 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 get_model_specific_settings(model): settings = shared.model_config model_settings = {} for pat in settings: if re.match(pat, model.lower()): for k in settings[pat]: model_settings[k] = settings[pat][k] return model_settings def load_model_specific_settings(model, state, return_dict=False): model_settings = get_model_specific_settings(model) for k in model_settings: if k in state: state[k] = model_settings[k] return state 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 with gr.Row(): with gr.Column(): with gr.Row(): with gr.Column(): with gr.Row(): shared.gradio['model_menu'] = gr.Dropdown(choices=get_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(multiselect=True, choices=get_available_loras(), value=shared.lora_names, label='LoRA(s)') ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras(), 'value': shared.lora_names}, 'refresh-button') with gr.Column(): shared.gradio['lora_menu_apply'] = gr.Button(value='Apply the selected LoRAs') with gr.Row(): 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)): shared.gradio[f'gpu_memory_{i}'] = gr.Slider(label=f"gpu-memory in MiB for device :{i}", maximum=total_mem[i], value=default_gpu_mem[i]) shared.gradio['cpu_memory'] = gr.Slider(label="cpu-memory in MiB", maximum=total_cpu_mem, value=default_cpu_mem) with gr.Column(): shared.gradio['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices) shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk) shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu) shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16) shared.gradio['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(): shared.gradio['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=shared.args.wbits if shared.args.wbits > 0 else "None") shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128], value=shared.args.groupsize if shared.args.groupsize > 0 else "None") with gr.Column(): shared.gradio['model_type'] = gr.Dropdown(label="model_type", choices=["None", "llama", "opt", "gptj"], value=shared.args.model_type or "None") shared.gradio['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_model_button'] = gr.Button("Download") with gr.Column(): shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready') # In this event handler, the interface state is read and updated # with the model defaults (if any), and then the model is loaded shared.gradio['model_menu'].change( ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( load_model_specific_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['interface_state']).then( ui.apply_interface_values, shared.gradio['interface_state'], [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False).then( update_model_parameters, shared.gradio['interface_state'], 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( ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( update_model_parameters, shared.gradio['interface_state'], None).then( load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=True) shared.gradio['lora_menu_apply'].click(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['model_status'], show_progress=False) shared.gradio['download_model_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=get_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=get_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 def create_interface(): # Defining some variables gen_events = [] default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')] if len(shared.lora_names) == 1: default_text = load_prompt(shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_names[0].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' # Authentication variables 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] # Importing the extension files and executing their setup() functions 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']: # Create chat mode 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_template'] = gr.Dropdown(choices=get_available_instruction_templates(), label='Instruction template', value=shared.settings['instruction_template'], visible=shared.settings['mode'] == 'instruct', 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=get_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) # Create notebook mode interface 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}) shared.gradio['last_input'] = gr.State('') 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') shared.gradio['Undo'] = gr.Button('Undo') shared.gradio['Regenerate'] = gr.Button('Regenerate') 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']) 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') shared.gradio['save_prompt'] = gr.Button('Save prompt') shared.gradio['status'] = gr.Markdown('') with gr.Tab("Parameters", elem_id="parameters"): create_settings_menus(default_preset) # Create default mode interface 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}) shared.gradio['last_input'] = gr.State('') 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']) with gr.Row(): with gr.Column(): shared.gradio['Generate'] = gr.Button('Generate') shared.gradio['Continue'] = gr.Button('Continue') with gr.Column(): shared.gradio['Stop'] = gr.Button('Stop') shared.gradio['save_prompt'] = gr.Button('Save prompt') 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(): shared.gradio['status'] = gr.Markdown('') 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) # Model tab with gr.Tab("Model", elem_id="model-tab"): create_model_menus() # Training tab with gr.Tab("Training", elem_id="training-tab"): training.create_train_interface() # Interface mode tab 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 + ui.list_model_elements()] 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=\'

Reloading...

\'; setTimeout(function(){location.reload()},2500); return []}') # Extensions block if shared.args.extensions is not None: extensions_module.create_extensions_block() # Create the invisible elements that define the API if not shared.is_chat(): api.create_apis() # chat mode event handlers if shared.is_chat(): 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_template']).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_template'].change( lambda character, name1, name2, mode: chat.load_character(character, name1, name2, mode), [shared.gradio[k] for k in ['instruction_template', '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) # notebook/default modes event handlers else: shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']] if shared.args.notebook: output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']] else: output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']] gen_events.append(shared.gradio['Generate'].click( lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then( 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( lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then( 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}") ) if shared.args.notebook: shared.gradio['Undo'].click(lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False) gen_events.append(shared.gradio['Regenerate'].click( lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False).then( 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}") ) else: 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['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) shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}") # 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=shared.args.listen_host or '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) if __name__ == "__main__": # 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] # 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) available_models = get_available_models() # Model defined through --model if shared.args.model is not None: shared.model_name = shared.args.model # Only one model is available elif len(available_models) == 1: shared.model_name = available_models[0] # Select the model from a command-line menu elif shared.args.model_menu: if len(available_models) == 0: print('No models are available! Please download at least one.') sys.exit(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] # If any model has been selected, load it if shared.model_name != 'None': model_settings = get_model_specific_settings(shared.model_name) shared.settings.update(model_settings) # hijacking the interface defaults update_model_parameters(model_settings, initial=True) # hijacking the command-line arguments # Load the model shared.model, shared.tokenizer = load_model(shared.model_name) if shared.args.lora: add_lora_to_model([shared.args.lora]) # Launch the web UI create_interface() while True: time.sleep(0.5) if shared.need_restart: shared.need_restart = False shared.gradio['interface'].close() create_interface()