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| import os | |
| import requests | |
| import warnings | |
| 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): | |
| print('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 | |
| # This fixes LaTeX rendering on some systems | |
| import matplotlib | |
| matplotlib.use('Agg') | |
| 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 functools import partial | |
| from pathlib import Path | |
| import psutil | |
| import torch | |
| import yaml | |
| from PIL import Image | |
| import modules.extensions as extensions_module | |
| from modules import 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 (encode, 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', '.yaml'))], 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 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 count_tokens(text): | |
| tokens = len(encode(text)[0]) | |
| return f'{tokens} tokens in the input.' | |
| 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" | |
| 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 = {} | |
| if model not in user_config: | |
| user_config[model] = {} | |
| for k in ui.list_model_elements(): | |
| user_config[model][k] = state[k] | |
| with open(p, 'w') as f: | |
| f.write(yaml.dump(user_config)) | |
| 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=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(): | |
| with gr.Row(): | |
| 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") | |
| save_settings = gr.Button("Save settings for this 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=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) | |
| 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') | |
| shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty') | |
| with gr.Column(): | |
| shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping') | |
| 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['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='Forces the model to never end the generation prematurely.') | |
| shared.gradio['skip_special_tokens'] = gr.Checkbox(value=shared.settings['skip_special_tokens'], label='Skip special tokens', info='Some specific models need this unset.') | |
| 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:]: | |
| 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['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, theme=ui.theme) 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['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['Copy last reply'] = gr.Button('Copy last reply') | |
| shared.gradio['Regenerate'] = gr.Button('Regenerate') | |
| shared.gradio['Replace last reply'] = gr.Button('Replace last reply') | |
| with gr.Row(): | |
| shared.gradio['Impersonate'] = gr.Button('Impersonate') | |
| 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['Remove last'] = gr.Button('Remove last') | |
| shared.gradio['Clear history'] = gr.Button('Clear history') | |
| shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant='stop', visible=False) | |
| shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False) | |
| 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='None', 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['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.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(), 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_classes="textbox", lines=27) | |
| with gr.Tab('Markdown'): | |
| 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('<div style="padding-bottom: 13px"></div>') | |
| shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) | |
| 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['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['save_prompt'] = gr.Button('Save prompt', 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=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(elem_classes="textbox_default_output", 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 getattr(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=\'<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>\'; setTimeout(function(){location.reload()},2500); return []}') | |
| # 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( | |
| chat.load_character, [shared.gradio[k] for k in ['instruction_template', 'name1', 'name2', 'mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'turn_template', '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', 'turn_template', '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}}}") | |
| # 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['count_tokens'].click(count_tokens, shared.gradio['textbox'], shared.gradio['status'], show_progress=False) | |
| shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}") | |
| 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 block | |
| if shared.args.extensions is not None: | |
| 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.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) | |
| # 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'] | |
| }) | |
| # 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() | |