import logging
import os
import requests
import warnings
import modules.logging_colors
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
os.environ['BITSANDBYTES_NOWELCOME'] = '1'
warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
# This is a hack to prevent Gradio from phoning home when it gets imported
def my_get(url, **kwargs):
logging.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 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, 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, load_soft_prompt, 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,
'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 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 = importlib.import_module("download-model")
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
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]
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=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 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, 1024], 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[0] if shared.args.pre_layer is not None else 0)
with gr.Row():
with gr.Column():
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():
gr.Markdown('llama.cpp parameters')
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)
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)
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_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=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.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['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')
with gr.Accordion('Soft prompt', open=False):
with gr.Row():
shared.gradio['softprompts_menu'] = gr.Dropdown(choices=utils.get_available_softprompts(), value='None', label='Soft prompt')
ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda: None, lambda: {'choices': utils.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['prompts'][next((k for k in shared.settings['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()
# 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']:
# 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['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=['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():
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')
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['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)
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.')
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"):
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)', 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')
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=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'] = 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]]
shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode")
shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=utils.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']]
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)
)
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)
)
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)
)
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)
)
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)
)
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'])
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', '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_wrapper, shared.input_params, output_params, show_progress=False) # .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_wrapper, shared.input_params, output_params, show_progress=False) # .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_wrapper, shared.input_params, output_params, show_progress=False) # .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_wrapper, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=False) # .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['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(None, None, None, _js=f"() => {{{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 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.json').exists():
settings_file = Path('settings.json')
if settings_file is not None:
logging.info(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]
# Set default model settings based on settings.json
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'],
}
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:
logging.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']
})
# Launch the web UI
create_interface()
while True:
time.sleep(0.5)
if shared.need_restart:
shared.need_restart = False
shared.gradio['interface'].close()
time.sleep(0.5)
create_interface()