import os import warnings import requests from modules.logging_colors import logger os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False' os.environ['BITSANDBYTES_NOWELCOME'] = '1' warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated') # This is a hack to prevent Gradio from phoning home when it gets imported def my_get(url, **kwargs): logger.info('Gradio HTTP request redirected to localhost :)') kwargs.setdefault('allow_redirects', True) return requests.api.request('get', 'http://127.0.0.1/', **kwargs) original_get = requests.get requests.get = my_get import gradio as gr requests.get = original_get import matplotlib matplotlib.use('Agg') # This fixes LaTeX rendering on some systems import importlib import json import math import os import re import sys import time import traceback from datetime import datetime from functools import partial from pathlib import Path from threading import Lock import psutil import torch import yaml from PIL import Image import modules.extensions as extensions_module from modules import chat, shared, training, ui, utils from modules.extensions import apply_extensions from modules.html_generator import chat_html_wrapper from modules.LoRA import add_lora_to_model from modules.models import load_model, unload_model from modules.text_generation import (generate_reply_wrapper, get_encoded_length, stop_everything_event) def load_model_wrapper(selected_model, autoload=False): if not autoload: yield f"The settings for {selected_model} have been updated.\nClick on \"Load the model\" to load it." return if selected_model == 'None': yield "No model selected" else: 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, 'epsilon_cutoff': 0, 'eta_cutoff': 0, 'tfs': 1, 'top_a': 0, 'repetition_penalty': 1, 'encoder_repetition_penalty': 1, 'top_k': 0, 'num_beams': 1, 'penalty_alpha': 0, 'min_length': 0, 'length_penalty': 1, 'no_repeat_ngram_size': 0, 'early_stopping': False, 'mirostat_mode': 0, 'mirostat_tau': 5.0, 'mirostat_eta': 0.1, } with open(Path(f'presets/{preset_menu}.yaml'), 'r') as infile: preset = yaml.safe_load(infile) for k in preset: generate_params[k] = preset[k] 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', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']] def open_save_prompt(): fname = f"{datetime.now().strftime('%Y-%m-%d-%H%M%S')}" return gr.update(value=fname, visible=True), gr.update(visible=False), gr.update(visible=True) def save_prompt(text, fname): if fname != "": with open(Path(f'prompts/{fname}.txt'), 'w', encoding='utf-8') as f: f.write(text) message = f"Saved to prompts/{fname}.txt" else: message = "Error: No prompt name given." return message, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False) def load_prompt(fname): if fname in ['None', '']: return '' elif fname.startswith('Instruct-'): fname = re.sub('^Instruct-', '', fname) with open(Path(f'characters/instruction-following/{fname}.yaml'), 'r', encoding='utf-8') as f: data = yaml.safe_load(f) output = '' if 'context' in data: output += data['context'] replacements = { '<|user|>': data['user'], '<|bot|>': data['bot'], '<|user-message|>': 'Input', } output += utils.replace_all(data['turn_template'].split('<|bot-message|>')[0], replacements) return output.rstrip(' ') 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 count_tokens(text): tokens = get_encoded_length(text) return f'{tokens} tokens in the input.' def download_model_wrapper(repo_id): try: downloader_module = importlib.import_module("download-model") downloader = downloader_module.ModelDownloader() repo_id_parts = repo_id.split(":") model = repo_id_parts[0] if len(repo_id_parts) > 0 else repo_id branch = repo_id_parts[1] if len(repo_id_parts) > 1 else "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" if element in ['pre_layer']: value = [value] if value > 0 else None setattr(shared.args, element, value) found_positive = False for i in gpu_memories: if i > 0: found_positive = True break if not (initial and vars(shared.args)['gpu_memory'] != vars(shared.args_defaults)['gpu_memory']): 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.lower(), 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 save_model_settings(model, state): if model == 'None': yield ("Not saving the settings because no model is loaded.") return with Path(f'{shared.args.model_dir}/config-user.yaml') as p: if p.exists(): user_config = yaml.safe_load(open(p, 'r').read()) else: user_config = {} model_regex = model + '$' # For exact matches for _dict in [user_config, shared.model_config]: if model_regex not in _dict: _dict[model_regex] = {} if model_regex not in user_config: user_config[model_regex] = {} for k in ui.list_model_elements(): user_config[model_regex][k] = state[k] shared.model_config[model_regex][k] = state[k] with open(p, 'w') as f: f.write(yaml.dump(user_config, sort_keys=False)) yield (f"Settings for {model} saved to {p}") 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=utils.get_available_models(), value=shared.model_name, label='Model') ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': utils.get_available_models()}, 'refresh-button') with gr.Column(): with gr.Row(): shared.gradio['lora_menu'] = gr.Dropdown(multiselect=True, choices=utils.get_available_loras(), value=shared.lora_names, label='LoRA(s)') ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': utils.get_available_loras(), 'value': shared.lora_names}, 'refresh-button') with gr.Column(): with gr.Row(): shared.gradio['lora_menu_apply'] = gr.Button(value='Apply the selected LoRAs') with gr.Row(): load = gr.Button("Load the model", visible=not shared.settings['autoload_model']) unload = gr.Button("Unload the model") reload = gr.Button("Reload the model") save_settings = gr.Button("Save settings for this model") with gr.Row(): with gr.Column(): with gr.Box(): gr.Markdown('Transformers') 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) shared.gradio['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='Make sure to inspect the .py files inside the model folder before loading it with this option enabled.') with gr.Box(): gr.Markdown('Transformers 4-bit') with gr.Row(): with gr.Column(): shared.gradio['load_in_4bit'] = gr.Checkbox(label="load-in-4bit", value=shared.args.load_in_4bit) shared.gradio['use_double_quant'] = gr.Checkbox(label="use_double_quant", value=shared.args.use_double_quant) with gr.Column(): shared.gradio['compute_dtype'] = gr.Dropdown(label="compute_dtype", choices=["bfloat16", "float16", "float32"], value=shared.args.compute_dtype) shared.gradio['quant_type'] = gr.Dropdown(label="quant_type", choices=["nf4", "fp4"], value=shared.args.quant_type) with gr.Row(): shared.gradio['autoload_model'] = gr.Checkbox(value=shared.settings['autoload_model'], label='Autoload the model', info='Whether to load the model as soon as it is selected in the Model dropdown.') shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA", info="Enter the Hugging Face username/model path, for instance: facebook/galactica-125m. To specify a branch, add it at the end after a \":\" character like this: facebook/galactica-125m:main") shared.gradio['download_model_button'] = gr.Button("Download") with gr.Column(): with gr.Box(): with gr.Row(): with gr.Column(): gr.Markdown('GPTQ') shared.gradio['triton'] = gr.Checkbox(label="triton", value=shared.args.triton) shared.gradio['desc_act'] = gr.Checkbox(label="desc_act", value=shared.args.desc_act, info='\'desc_act\', \'wbits\', and \'groupsize\' are used for old models without a quantize_config.json.') shared.gradio['gptq_for_llama'] = gr.Checkbox(label="gptq-for-llama", value=shared.args.gptq_for_llama, info='Use GPTQ-for-LLaMa loader instead of AutoGPTQ. pre_layer should be used for CPU offloading instead of gpu-memory.') with gr.Column(): with gr.Row(): 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, 1024], value=shared.args.groupsize if shared.args.groupsize > 0 else "None") 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[0] if shared.args.pre_layer is not None else 0) with gr.Box(): gr.Markdown('llama.cpp') with gr.Row(): with gr.Column(): shared.gradio['threads'] = gr.Slider(label="threads", minimum=0, step=1, maximum=32, value=shared.args.threads) shared.gradio['n_batch'] = gr.Slider(label="n_batch", minimum=1, maximum=2048, value=shared.args.n_batch) shared.gradio['n_gpu_layers'] = gr.Slider(label="n-gpu-layers", minimum=0, maximum=128, value=shared.args.n_gpu_layers) shared.gradio['n_ctx'] = gr.Slider(minimum=0, maximum=8192, step=1, label="n_ctx", value=shared.args.n_ctx) with gr.Column(): shared.gradio['no_mmap'] = gr.Checkbox(label="no-mmap", value=shared.args.no_mmap) shared.gradio['mlock'] = gr.Checkbox(label="mlock", value=shared.args.mlock) shared.gradio['llama_cpp_seed'] = gr.Number(label='Seed (0 for random)', value=shared.args.llama_cpp_seed) with gr.Row(): 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 # unless "autoload_model" is unchecked 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[k] for k in ['model_menu', 'autoload_model']], shared.gradio['model_status'], show_progress=False) load.click( 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( partial(load_model_wrapper, autoload=True), shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False) 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( partial(load_model_wrapper, autoload=True), shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False) save_settings.click( ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( save_model_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['model_status'], show_progress=False) 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) shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), shared.gradio['autoload_model'], load) def create_chat_settings_menus(): if not shared.is_chat(): return 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='chat_prompt_size', info='Set limit on prompt size by removing old messages (while retaining context and user input)', 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)', info='New generations will be called until either this number is reached or no new content is generated between two iterations.') shared.gradio['stop_at_newline'] = gr.Checkbox(value=shared.settings['stop_at_newline'], label='Stop generating at new line character') 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(): with gr.Column(): with gr.Row(): shared.gradio['preset_menu'] = gr.Dropdown(choices=utils.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': utils.get_available_presets()}, 'refresh-button') with gr.Column(): shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)') with gr.Box(): gr.Markdown('Main parameters') with gr.Row(): with gr.Column(): shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample') 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.') shared.gradio['epsilon_cutoff'] = gr.Slider(0, 9, value=generate_params['epsilon_cutoff'], step=0.01, label='epsilon_cutoff', info='In units of 1e-4; a reasonable value is 3. This sets a probability floor below which tokens are excluded from being sampled. Should be used with top_p, top_k, and eta_cutoff set to 0.') shared.gradio['eta_cutoff'] = gr.Slider(0, 20, value=generate_params['eta_cutoff'], step=0.01, label='eta_cutoff', info='In units of 1e-4; a reasonable value is 3. Should be used with top_p, top_k, and epsilon_cutoff set to 0.') 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['tfs'] = gr.Slider(0.0, 1.0, value=generate_params['tfs'], step=0.01, label='tfs') shared.gradio['top_a'] = gr.Slider(0.0, 1.0, value=generate_params['top_a'], step=0.01, label='top_a') with gr.Column(): create_chat_settings_menus() with gr.Box(): with gr.Row(): with gr.Column(): gr.Markdown('Contrastive search') shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha', info='Contrastive Search is enabled by setting this to greater than zero and unchecking "do_sample". It should be used with a low value of top_k, for instance, top_k = 4.') gr.Markdown('Beam search') shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams') 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.Column(): gr.Markdown('Mirostat (for llama.cpp)') shared.gradio['mirostat_mode'] = gr.Slider(0, 2, step=1, value=generate_params['mirostat_mode'], label='mirostat_mode') shared.gradio['mirostat_tau'] = gr.Slider(0, 10, step=0.01, value=generate_params['mirostat_tau'], label='mirostat_tau') shared.gradio['mirostat_eta'] = gr.Slider(0, 1, step=0.01, value=generate_params['mirostat_eta'], label='mirostat_eta') with gr.Box(): with gr.Row(): with gr.Column(): 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.Column(): shared.gradio['ban_eos_token'] = gr.Checkbox(value=shared.settings['ban_eos_token'], label='Ban the eos_token', info='Forces the model to never end the generation prematurely.') 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['skip_special_tokens'] = gr.Checkbox(value=shared.settings['skip_special_tokens'], label='Skip special tokens', info='Some specific models need this unset.') shared.gradio['stream'] = gr.Checkbox(value=not shared.args.no_stream, label='Activate text streaming') gr.Markdown('[Click here for more information.](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Generation-parameters.md)') 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', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']]) 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:]: setattr(shared.args, k, False) if interface_mode != "default": setattr(shared.args, interface_mode, True) for k in bool_list: setattr(shared.args, k, False) for k in bool_active: setattr(shared.args, k, True) shared.need_restart = True def create_interface(): # Defining some variables gen_events = [] default_preset = shared.settings['preset'] default_text = load_prompt(shared.settings['prompt']) title = 'Text generation web UI' # Authentication variables auth = None gradio_auth_creds = [] if shared.args.gradio_auth: gradio_auth_creds += [x.strip() for x in shared.args.gradio_auth.strip('"').replace('\n', '').split(',') if x.strip()] if shared.args.gradio_auth_path is not None: 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()] if gradio_auth_creds: 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() # css/js strings css = ui.css if not shared.is_chat() else ui.css + ui.chat_css js = ui.main_js if not shared.is_chat() else ui.main_js + ui.chat_js css += apply_extensions('css') js += apply_extensions('js') with gr.Blocks(css=css, analytics_enabled=False, title=title, theme=ui.theme) as shared.gradio['interface']: if Path("notification.mp3").exists(): shared.gradio['audio_notification'] = gr.Audio(interactive=False, value="notification.mp3", elem_id="audio_notification", visible=False) audio_notification_js = "document.querySelector('#audio_notification audio')?.play();" else: audio_notification_js = "" # 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() shared.gradio['dummy'] = 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'], 'chat', 'cai-chat')) shared.gradio['textbox'] = gr.Textbox(label='Input') with gr.Row(): shared.gradio['Stop'] = gr.Button('Stop', elem_id='stop') shared.gradio['Generate'] = gr.Button('Generate', elem_id='Generate', variant='primary') shared.gradio['Continue'] = gr.Button('Continue') with gr.Row(): shared.gradio['Impersonate'] = gr.Button('Impersonate') shared.gradio['Regenerate'] = gr.Button('Regenerate') shared.gradio['Remove last'] = gr.Button('Remove last') 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['Send dummy message'] = gr.Button('Send dummy message') shared.gradio['Send dummy reply'] = gr.Button('Send dummy 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) with gr.Row(): shared.gradio['start_with'] = gr.Textbox(label='Start reply with', placeholder='Sure thing!', value=shared.settings['start_with']) with gr.Row(): shared.gradio['mode'] = gr.Radio(choices=['chat', 'chat-instruct', 'instruct'], value=shared.settings['mode'] if shared.settings['mode'] in ['chat', 'instruct', 'chat-instruct'] else 'chat', label='Mode', info='Defines how the chat prompt is generated. In instruct and chat-instruct modes, the instruction template selected under "Chat settings" must match the current model.') shared.gradio['chat_style'] = gr.Dropdown(choices=utils.get_available_chat_styles(), label='Chat style', value=shared.settings['chat_style'], visible=shared.settings['mode'] != 'instruct') with gr.Tab('Chat settings', elem_id='chat-settings'): with gr.Row(): with gr.Column(scale=8): with gr.Row(): shared.gradio['character_menu'] = gr.Dropdown(choices=utils.get_available_characters(), label='Character', elem_id='character-menu', info='Used in chat and chat-instruct modes.') ui.create_refresh_button(shared.gradio['character_menu'], lambda: None, lambda: {'choices': utils.get_available_characters()}, 'refresh-button') shared.gradio['save_character'] = ui.create_save_button(elem_id='refresh-button') shared.gradio['delete_character'] = ui.create_delete_button(elem_id='refresh-button') shared.gradio['save_character-filename'] = gr.Textbox(lines=1, label='File name:', interactive=True, visible=False) shared.gradio['save_character-confirm'] = gr.Button('Confirm save character', elem_classes="small-button", variant='primary', visible=False) shared.gradio['save_character-cancel'] = gr.Button('Cancel', elem_classes="small-button", visible=False) shared.gradio['delete_character-confirm'] = gr.Button('Confirm delete character', elem_classes="small-button", variant='stop', visible=False) shared.gradio['delete_character-cancel'] = gr.Button('Cancel', elem_classes="small-button", visible=False) 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['context'] = gr.Textbox(value=shared.settings['context'], lines=4, label='Context') shared.gradio['greeting'] = gr.Textbox(value=shared.settings['greeting'], lines=4, label='Greeting') 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['instruction_template'] = gr.Dropdown(choices=utils.get_available_instruction_templates(), label='Instruction template', value='None', info='Change this according to the model/LoRA that you are using. Used in instruct and chat-instruct modes.') ui.create_refresh_button(shared.gradio['instruction_template'], lambda: None, lambda: {'choices': utils.get_available_instruction_templates()}, 'refresh-button') shared.gradio['name1_instruct'] = gr.Textbox(value='', lines=2, label='User string') shared.gradio['name2_instruct'] = gr.Textbox(value='', lines=1, label='Bot string') shared.gradio['context_instruct'] = gr.Textbox(value='', lines=4, label='Context') shared.gradio['turn_template'] = gr.Textbox(value=shared.settings['turn_template'], lines=1, label='Turn template', info='Used to precisely define the placement of spaces and new line characters in instruction prompts.') with gr.Row(): shared.gradio['chat-instruct_command'] = gr.Textbox(value=shared.settings['chat-instruct_command'], lines=4, label='Command for chat-instruct mode', info='<|character|> gets replaced by the bot name, and <|prompt|> gets replaced by the regular chat prompt.') 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"): 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_classes="textbox", lines=27) with gr.Tab('Markdown'): shared.gradio['markdown_render'] = gr.Button('Render') shared.gradio['markdown'] = gr.Markdown() with gr.Tab('HTML'): shared.gradio['html'] = gr.HTML() with gr.Row(): shared.gradio['Generate'] = gr.Button('Generate', variant='primary', elem_classes="small-button") shared.gradio['Stop'] = gr.Button('Stop', elem_classes="small-button") shared.gradio['Undo'] = gr.Button('Undo', elem_classes="small-button") shared.gradio['Regenerate'] = gr.Button('Regenerate', elem_classes="small-button") 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=utils.get_available_prompts(), value='None', label='Prompt') ui.create_refresh_button(shared.gradio['prompt_menu'], lambda: None, lambda: {'choices': utils.get_available_prompts()}, 'refresh-button') shared.gradio['open_save_prompt'] = gr.Button('Save prompt') shared.gradio['save_prompt'] = gr.Button('Confirm save prompt', visible=False) shared.gradio['prompt_to_save'] = gr.Textbox(elem_classes="textbox_default", lines=1, label='Prompt name:', interactive=True, visible=False) shared.gradio['count_tokens'] = gr.Button('Count tokens') 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, elem_classes="textbox_default", lines=27, 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(): shared.gradio['Generate'] = gr.Button('Generate', variant='primary', elem_classes="small-button") shared.gradio['Stop'] = gr.Button('Stop', elem_classes="small-button") shared.gradio['Continue'] = gr.Button('Continue', elem_classes="small-button") shared.gradio['open_save_prompt'] = gr.Button('Save prompt', elem_classes="small-button") shared.gradio['save_prompt'] = gr.Button('Confirm save prompt', visible=False, elem_classes="small-button") shared.gradio['count_tokens'] = gr.Button('Count tokens', elem_classes="small-button") with gr.Row(): with gr.Column(): with gr.Row(): shared.gradio['prompt_menu'] = gr.Dropdown(choices=utils.get_available_prompts(), value='None', label='Prompt') ui.create_refresh_button(shared.gradio['prompt_menu'], lambda: None, lambda: {'choices': utils.get_available_prompts()}, 'refresh-button') with gr.Column(): shared.gradio['prompt_to_save'] = gr.Textbox(elem_classes="textbox_default", lines=1, label='Prompt name:', interactive=True, visible=False) shared.gradio['status'] = gr.Markdown('') with gr.Column(): with gr.Tab('Raw'): shared.gradio['output_textbox'] = gr.Textbox(elem_classes="textbox_default_output", lines=27, label='Output') with gr.Tab('Markdown'): shared.gradio['markdown_render'] = gr.Button('Render') 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"] current_mode = "default" for mode in modes[1:]: if getattr(shared.args, mode): current_mode = mode break cmd_list = vars(shared.args) bool_list = sorted([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]] with gr.Row(): shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode") shared.gradio['toggle_dark_mode'] = gr.Button('Toggle dark/light mode', elem_classes="small-button") shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=utils.get_available_extensions(), value=shared.args.extensions, label="Available extensions", info='Note that some of these extensions may require manually installing Python requirements through the command: pip install -r extensions/extension_name/requirements.txt') 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 []}') shared.gradio['toggle_dark_mode'].click(lambda: None, None, None, _js='() => {document.getElementsByTagName("body")[0].classList.toggle("dark")}') # chat mode event handlers if shared.is_chat(): shared.input_params = [shared.gradio[k] for k in ['Chat input', 'start_with', 'interface_state']] clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']] shared.reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'mode', 'chat_style']] 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.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then( chat.save_history, shared.gradio['mode'], None, show_progress=False).then( lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}") ) 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.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then( chat.save_history, shared.gradio['mode'], None, show_progress=False).then( lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}") ) 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( partial(chat.generate_chat_reply_wrapper, regenerate=True), shared.input_params, shared.gradio['display'], show_progress=False).then( chat.save_history, shared.gradio['mode'], None, show_progress=False).then( lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}") ) 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( partial(chat.generate_chat_reply_wrapper, _continue=True), shared.input_params, shared.gradio['display'], show_progress=False).then( chat.save_history, shared.gradio['mode'], None, show_progress=False).then( lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}") ) 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( lambda x: x, shared.gradio['textbox'], shared.gradio['Chat input'], show_progress=False).then( chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=False).then( lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}") ) shared.gradio['Replace last reply'].click( chat.replace_last_reply, shared.gradio['textbox'], None).then( lambda: '', None, shared.gradio['textbox'], show_progress=False).then( chat.save_history, shared.gradio['mode'], None, show_progress=False).then( chat.redraw_html, shared.reload_inputs, shared.gradio['display']) shared.gradio['Send dummy message'].click( chat.send_dummy_message, shared.gradio['textbox'], None).then( lambda: '', None, shared.gradio['textbox'], show_progress=False).then( chat.save_history, shared.gradio['mode'], None, show_progress=False).then( chat.redraw_html, shared.reload_inputs, shared.gradio['display']) shared.gradio['Send dummy reply'].click( chat.send_dummy_reply, shared.gradio['textbox'], None).then( lambda: '', None, shared.gradio['textbox'], show_progress=False).then( chat.save_history, shared.gradio['mode'], None, show_progress=False).then( chat.redraw_html, shared.reload_inputs, shared.gradio['display']) 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 ['greeting', 'mode']], None).then( chat.save_history, shared.gradio['mode'], None, show_progress=False).then( chat.redraw_html, shared.reload_inputs, shared.gradio['display']) 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, shared.reload_inputs, shared.gradio['display']) shared.gradio['mode'].change( lambda x: gr.update(visible=x != 'instruct'), shared.gradio['mode'], shared.gradio['chat_style'], show_progress=False).then( chat.redraw_html, shared.reload_inputs, shared.gradio['display']) shared.gradio['chat_style'].change(chat.redraw_html, shared.reload_inputs, shared.gradio['display']) shared.gradio['instruction_template'].change( partial(chat.load_character, instruct=True), [shared.gradio[k] for k in ['instruction_template', 'name1_instruct', 'name2_instruct']], [shared.gradio[k] for k in ['name1_instruct', 'name2_instruct', 'dummy', 'dummy', 'context_instruct', 'turn_template']]) 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, shared.reload_inputs, shared.gradio['display']) shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=False) 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, None, shared.gradio['textbox'], show_progress=False).then( chat.save_history, shared.gradio['mode'], None, show_progress=False).then( chat.redraw_html, shared.reload_inputs, shared.gradio['display']) # Save/delete a character shared.gradio['save_character'].click( lambda x: x, shared.gradio['name2'], shared.gradio['save_character-filename'], show_progress=True).then( lambda: [gr.update(visible=True)] * 3, None, [shared.gradio[k] for k in ['save_character-filename', 'save_character-confirm', 'save_character-cancel']], show_progress=False) shared.gradio['save_character-cancel'].click( lambda: [gr.update(visible=False)] * 3, None, [shared.gradio[k] for k in ['save_character-filename', 'save_character-confirm', 'save_character-cancel']], show_progress=False) shared.gradio['save_character-confirm'].click( partial(chat.save_character, instruct=False), [shared.gradio[k] for k in ['name2', 'greeting', 'context', 'character_picture', 'save_character-filename']], None).then( lambda: [gr.update(visible=False)] * 3, None, [shared.gradio[k] for k in ['save_character-filename', 'save_character-confirm', 'save_character-cancel']], show_progress=False).then( lambda x: x, shared.gradio['save_character-filename'], shared.gradio['character_menu']) shared.gradio['delete_character'].click( lambda: [gr.update(visible=True)] * 2, None, [shared.gradio[k] for k in ['delete_character-confirm', 'delete_character-cancel']], show_progress=False) shared.gradio['delete_character-cancel'].click( lambda: [gr.update(visible=False)] * 2, None, [shared.gradio[k] for k in ['delete_character-confirm', 'delete_character-cancel']], show_progress=False) shared.gradio['delete_character-confirm'].click( partial(chat.delete_character, instruct=False), shared.gradio['character_menu'], None).then( lambda: gr.update(choices=utils.get_available_characters()), outputs=shared.gradio['character_menu']).then( lambda: 'None', None, shared.gradio['character_menu']).then( lambda: [gr.update(visible=False)] * 2, None, [shared.gradio[k] for k in ['delete_character-confirm', 'delete_character-cancel']], 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( partial(chat.load_character, instruct=False), [shared.gradio[k] for k in ['character_menu', 'name1', 'name2']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'dummy']]).then( chat.redraw_html, shared.reload_inputs, shared.gradio['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['your_picture'], None).then( partial(chat.redraw_html, reset_cache=True), shared.reload_inputs, shared.gradio['display']) # 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', 'html']] else: output_params = [shared.gradio[k] for k in ['output_textbox', '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_wrapper, shared.input_params, output_params, show_progress=False).then( lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}") # lambda: 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_wrapper, shared.input_params, output_params, show_progress=False).then( lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}") # lambda: 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) shared.gradio['markdown_render'].click(lambda x: x, shared.gradio['textbox'], shared.gradio['markdown'], queue=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_wrapper, shared.input_params, output_params, show_progress=False).then( lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}") # lambda: None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") ) else: shared.gradio['markdown_render'].click(lambda x: x, shared.gradio['output_textbox'], shared.gradio['markdown'], queue=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( generate_reply_wrapper, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=False).then( lambda: None, None, None, _js=f"() => {{{audio_notification_js}}}") # lambda: 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['open_save_prompt'].click(open_save_prompt, None, [shared.gradio[k] for k in ['prompt_to_save', 'open_save_prompt', 'save_prompt']], show_progress=False) shared.gradio['save_prompt'].click(save_prompt, [shared.gradio[k] for k in ['textbox', 'prompt_to_save']], [shared.gradio[k] for k in ['status', 'prompt_to_save', 'open_save_prompt', 'save_prompt']], show_progress=False) shared.gradio['count_tokens'].click(count_tokens, shared.gradio['textbox'], shared.gradio['status'], show_progress=False) shared.gradio['interface'].load(lambda: None, None, None, _js=f"() => {{{js}}}") if shared.settings['dark_theme']: shared.gradio['interface'].load(lambda: None, None, None, _js="() => document.getElementsByTagName('body')[0].classList.add('dark')") shared.gradio['interface'].load(partial(ui.apply_interface_values, {}, use_persistent=True), None, [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False) # Extensions tabs extensions_module.create_extensions_tabs() # Extensions block extensions_module.create_extensions_block() # 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.yaml').exists(): settings_file = Path('settings.yaml') elif Path('settings.json').exists(): settings_file = Path('settings.json') if settings_file is not None: logger.info(f"Loading settings from {settings_file}...") file_contents = open(settings_file, 'r', encoding='utf-8').read() new_settings = json.loads(file_contents) if settings_file.suffix == "json" else yaml.safe_load(file_contents) for item in new_settings: shared.settings[item] = new_settings[item] # Set default model settings based on settings file shared.model_config['.*'] = { 'wbits': 'None', 'model_type': 'None', 'groupsize': 'None', 'pre_layer': 0, 'mode': shared.settings['mode'], 'skip_special_tokens': shared.settings['skip_special_tokens'], 'custom_stopping_strings': shared.settings['custom_stopping_strings'], 'truncation_length': shared.settings['truncation_length'], } shared.model_config.move_to_end('.*', last=False) # Move to the beginning # Default extensions extensions_module.available_extensions = utils.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 = utils.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: logger.error('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) # Force a character to be loaded if shared.is_chat(): shared.persistent_interface_state.update({ 'mode': shared.settings['mode'], 'character_menu': shared.args.character or shared.settings['character'], 'instruction_template': shared.settings['instruction_template'] }) shared.generation_lock = Lock() # Launch the web UI create_interface() while True: time.sleep(0.5) if shared.need_restart: shared.need_restart = False time.sleep(0.5) shared.gradio['interface'].close() time.sleep(0.5) create_interface()