text-generation-webui/server.py

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import logging
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import os
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import requests
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import warnings
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import modules.logging_colors
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os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
os.environ['BITSANDBYTES_NOWELCOME'] = '1'
warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
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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):
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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
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import matplotlib
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matplotlib.use('Agg') # This fixes LaTeX rendering on some systems
import importlib
import io
import json
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import math
import os
import re
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import sys
import time
import traceback
import zipfile
from datetime import datetime
from functools import partial
from pathlib import Path
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import psutil
import torch
import yaml
from PIL import Image
import modules.extensions as extensions_module
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from modules import chat, shared, training, ui, utils
from modules.extensions import apply_extensions
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from modules.html_generator import chat_html_wrapper
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from modules.LoRA import add_lora_to_model
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from modules.models import load_model, load_soft_prompt, unload_model
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from modules.text_generation import generate_reply_wrapper, get_encoded_length, stop_everything_event
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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
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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()
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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")
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def load_preset_values(preset_menu, state, return_dict=False):
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generate_params = {
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'do_sample': True,
'temperature': 1,
'top_p': 1,
'typical_p': 1,
'repetition_penalty': 1,
'encoder_repetition_penalty': 1,
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'top_k': 50,
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'num_beams': 1,
'penalty_alpha': 0,
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'min_length': 0,
'length_penalty': 1,
'no_repeat_ngram_size': 0,
'early_stopping': False,
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}
with open(Path(f'presets/{preset_menu}.txt'), 'r') as infile:
preset = infile.read()
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for i in preset.splitlines():
i = i.rstrip(',').strip().split('=')
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if len(i) == 2 and i[0].strip() != 'tokens':
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generate_params[i[0].strip()] = eval(i[1].strip())
generate_params['temperature'] = min(1.99, generate_params['temperature'])
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if return_dict:
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return generate_params
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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']]
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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)
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message = f"Saved to prompts/{fname}.txt"
else:
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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 ''
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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
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def count_tokens(text):
tokens = get_encoded_length(text)
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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
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gpu_memories = []
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for i, element in enumerate(elements):
if element not in state:
continue
value = state[element]
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if element.startswith('gpu_memory'):
gpu_memories.append(value)
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continue
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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':
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value = vars(shared.args_defaults)[element]
elif element in ['cpu_memory'] and value == 0:
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value = vars(shared.args_defaults)[element]
# Making some simple conversions
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if element in ['wbits', 'groupsize', 'pre_layer']:
value = int(value)
elif element == 'cpu_memory' and value is not None:
value = f"{value}MiB"
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if element in ['pre_layer']:
value = [value] if value > 0 else None
setattr(shared.args, element, value)
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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
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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
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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}")
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def create_model_menus():
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# Finding the default values for the GPU and CPU memories
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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)))
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default_gpu_mem = []
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if shared.args.gpu_memory is not None and len(shared.args.gpu_memory) > 0:
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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)
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while len(default_gpu_mem) < len(total_mem):
default_gpu_mem.append(0)
total_cpu_mem = math.floor(psutil.virtual_memory().total / (1024 * 1024))
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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
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with gr.Row():
with gr.Column():
with gr.Row():
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with gr.Column():
with gr.Row():
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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')
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with gr.Column():
with gr.Row():
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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')
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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")
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save_settings = gr.Button("Save settings for this model")
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with gr.Row():
with gr.Column():
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with gr.Box():
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gr.Markdown('Transformers parameters')
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with gr.Row():
with gr.Column():
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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)
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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)
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with gr.Column():
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with gr.Box():
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gr.Markdown('GPTQ parameters')
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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")
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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)
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with gr.Row():
with gr.Column():
with gr.Row():
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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")
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shared.gradio['download_model_button'] = gr.Button("Download")
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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():
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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')
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# 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
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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)
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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(
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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)
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shared.gradio['lora_menu_apply'].click(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['model_status'], show_progress=False)
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shared.gradio['download_model_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=False)
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shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), shared.gradio['autoload_model'], load)
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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():
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with gr.Row():
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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():
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shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)')
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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))')
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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.')
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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.')
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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')
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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')
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with gr.Column():
shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
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with gr.Box():
with gr.Row():
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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.')
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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.')
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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')
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with gr.Accordion('Soft prompt', open=False):
with gr.Row():
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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'])
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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:
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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')])
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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
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if shared.args.extensions is not None and len(shared.args.extensions) > 0:
extensions_module.load_extensions()
# css/js strings
css = ui.css if not shared.is_chat() else ui.css + ui.chat_css
js = ui.main_js if not shared.is_chat() else ui.main_js + ui.chat_js
css += apply_extensions('css')
js += apply_extensions('js')
with gr.Blocks(css=css, analytics_enabled=False, title=title, theme=ui.theme) as shared.gradio['interface']:
if Path("notification.mp3").exists():
shared.gradio['audio_notification'] = gr.Audio(interactive=False, value="notification.mp3", elem_id="audio_notification", visible=False)
audio_notification_js = "document.querySelector('#audio_notification audio')?.play();"
else:
audio_notification_js = ""
# Create chat mode interface
if shared.is_chat():
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shared.input_elements = ui.list_interface_input_elements(chat=True)
shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements})
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shared.gradio['Chat input'] = gr.State()
shared.gradio['dummy'] = gr.State()
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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'))
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shared.gradio['textbox'] = gr.Textbox(label='Input')
with gr.Row():
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shared.gradio['Stop'] = gr.Button('Stop', elem_id='stop')
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shared.gradio['Generate'] = gr.Button('Generate', elem_id='Generate', variant='primary')
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shared.gradio['Continue'] = gr.Button('Continue')
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with gr.Row():
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shared.gradio['Copy last reply'] = gr.Button('Copy last reply')
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shared.gradio['Regenerate'] = gr.Button('Regenerate')
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shared.gradio['Replace last reply'] = gr.Button('Replace last reply')
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with gr.Row():
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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():
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shared.gradio['Remove last'] = gr.Button('Remove last')
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shared.gradio['Clear history'] = gr.Button('Clear history')
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shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant='stop', visible=False)
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shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False)
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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.')
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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')
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with gr.Tab('Chat settings', elem_id='chat-settings'):
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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')
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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.')
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with gr.Row():
with gr.Tab('Chat history'):
with gr.Row():
with gr.Column():
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gr.Markdown('## Upload')
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shared.gradio['upload_chat_history'] = gr.File(type='binary', file_types=['.json', '.txt'])
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with gr.Column():
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gr.Markdown('## Download')
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shared.gradio['download'] = gr.File()
shared.gradio['download_button'] = gr.Button(value='Click me')
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with gr.Tab('Upload character'):
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gr.Markdown('## JSON format')
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with gr.Row():
with gr.Column():
gr.Markdown('1. Select the JSON file')
shared.gradio['upload_json'] = gr.File(type='binary', file_types=['.json'])
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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'])
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shared.gradio['Upload character'] = gr.Button(value='Submit')
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gr.Markdown('## TavernAI PNG format')
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shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image'])
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with gr.Tab("Parameters", elem_id="parameters"):
with gr.Box():
gr.Markdown("Chat parameters")
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with gr.Row():
with gr.Column():
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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'])
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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'])
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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')
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create_settings_menus(default_preset)
# Create notebook mode interface
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elif shared.args.notebook:
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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('')
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with gr.Tab("Text generation", elem_id="main"):
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with gr.Row():
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with gr.Column(scale=4):
with gr.Tab('Raw'):
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shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_classes="textbox", lines=27)
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with gr.Tab('Markdown'):
shared.gradio['markdown'] = gr.Markdown()
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with gr.Tab('HTML'):
shared.gradio['html'] = gr.HTML()
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with gr.Row():
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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")
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with gr.Column(scale=1):
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gr.HTML('<div style="padding-bottom: 13px"></div>')
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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'])
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with gr.Row():
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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)
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shared.gradio['count_tokens'] = gr.Button('Count tokens')
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shared.gradio['status'] = gr.Markdown('')
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with gr.Tab("Parameters", elem_id="parameters"):
create_settings_menus(default_preset)
# Create default mode interface
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else:
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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('')
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with gr.Tab("Text generation", elem_id="main"):
with gr.Row():
with gr.Column():
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shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_classes="textbox_default", lines=27, label='Input')
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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():
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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")
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shared.gradio['count_tokens'] = gr.Button('Count tokens', elem_classes="small-button")
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with gr.Row():
with gr.Column():
with gr.Row():
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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')
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with gr.Column():
shared.gradio['prompt_to_save'] = gr.Textbox(elem_classes="textbox_default", lines=1, label='Prompt name:', interactive=True, visible=False)
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shared.gradio['status'] = gr.Markdown('')
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with gr.Column():
with gr.Tab('Raw'):
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shared.gradio['output_textbox'] = gr.Textbox(elem_classes="textbox_default_output", lines=27, label='Output')
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with gr.Tab('Markdown'):
shared.gradio['markdown'] = gr.Markdown()
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with gr.Tab('HTML'):
shared.gradio['html'] = gr.HTML()
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with gr.Tab("Parameters", elem_id="parameters"):
create_settings_menus(default_preset)
# Model tab
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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
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with gr.Tab("Interface mode", elem_id="interface-mode"):
modes = ["default", "notebook", "chat"]
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current_mode = "default"
for mode in modes[1:]:
if getattr(shared.args, mode):
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current_mode = mode
break
cmd_list = vars(shared.args)
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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]]
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shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode")
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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")
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shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface")
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# 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(
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chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
None, None, None, _js=f"() => {{{audio_notification_js}}}")
)
gen_events.append(shared.gradio['textbox'].submit(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then(
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chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
None, None, None, _js=f"() => {{{audio_notification_js}}}")
)
gen_events.append(shared.gradio['Regenerate'].click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
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partial(chat.generate_chat_reply_wrapper, regenerate=True), shared.input_params, shared.gradio['display'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
None, None, None, _js=f"() => {{{audio_notification_js}}}")
)
gen_events.append(shared.gradio['Continue'].click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
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partial(chat.generate_chat_reply_wrapper, _continue=True), shared.input_params, shared.gradio['display'], show_progress=False).then(
chat.save_history, shared.gradio['mode'], None, show_progress=False).then(
None, None, None, _js=f"() => {{{audio_notification_js}}}")
)
gen_events.append(shared.gradio['Impersonate'].click(
ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
lambda x: x, shared.gradio['textbox'], shared.gradio['Chat input'], show_progress=False).then(
chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=False).then(
None, None, None, _js=f"() => {{{audio_notification_js}}}")
)
shared.gradio['Replace last reply'].click(
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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(
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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(
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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(
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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(
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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'])
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shared.gradio['chat_style'].change(chat.redraw_html, shared.reload_inputs, shared.gradio['display'])
shared.gradio['instruction_template'].change(
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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)
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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'])
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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']])
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shared.gradio['character_menu'].change(
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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'])
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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']])
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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'])
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# 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=f"() => {{{audio_notification_js}}}")
# 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=f"() => {{{audio_notification_js}}}")
# 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=f"() => {{{audio_notification_js}}}")
# 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=f"() => {{{audio_notification_js}}}")
# 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)
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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)
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shared.gradio['count_tokens'].click(count_tokens, shared.gradio['textbox'], shared.gradio['status'], show_progress=False)
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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)
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# Extensions tabs
extensions_module.create_extensions_tabs()
# Extensions block
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extensions_module.create_extensions_block()
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# 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)
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else:
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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)
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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:
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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
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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)
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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:
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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()