import os

import warnings

import requests

from modules.logging_colors import logger

os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'

os.environ['BITSANDBYTES_NOWELCOME'] = '1'

warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')

def my_get(url, **kwargs):

logger.info('Gradio HTTP request redirected to localhost :)')

kwargs.setdefault('allow_redirects', True)

return requests.api.request('get', 'http://127.0.0.1/', **kwargs)

original_get = requests.get

requests.get = my_get

import gradio as gr

requests.get = original_get

import matplotlib

matplotlib.use('Agg') # This fixes LaTeX rendering on some systems

import importlib

import json

import math

import os

import re

import sys

import time

import traceback

from datetime import datetime

from functools import partial

from pathlib import Path

from threading import Lock

import psutil

import torch

import yaml

from PIL import Image

import modules.extensions as extensions_module

from modules import chat, shared, training, ui, utils

from modules.extensions import apply_extensions

from modules.html_generator import chat_html_wrapper

from modules.LoRA import add_lora_to_model

from modules.models import load_model, unload_model

from modules.text_generation import (generate_reply_wrapper,

get_encoded_length, stop_everything_event)

def load_model_wrapper(selected_model, autoload=False):

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):

return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']]

def open_save_prompt():

fname = f"{datetime.now().strftime('%Y-%m-%d-%H%M%S')}"

return gr.update(value=fname, visible=True), gr.update(visible=False), gr.update(visible=True)

def save_prompt(text, fname):

return message, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)

def load_prompt(fname):

return text

def count_tokens(text):

tokens = get_encoded_length(text)

return f'{tokens} tokens in the input.'

def download_model_wrapper(repo_id):

yield traceback.format_exc()

def update_model_parameters(state, initial=False):

shared.args.gpu_memory = None

def get_model_specific_settings(model):

return model_settings

def load_model_specific_settings(model, state, return_dict=False):

return state

def save_model_settings(model, state):

yield (f"Settings for {model} saved to {p}")

def create_model_menus():

shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), shared.gradio['autoload_model'], load)

def create_chat_settings_menus():

shared.gradio['stop_at_newline'] = gr.Checkbox(value=shared.settings['stop_at_newline'], label='Stop generating at new line character')

def create_settings_menus(default_preset):

shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio[k] for k in ['preset_menu', 'interface_state']], [shared.gradio[k] for k in ['interface_state', 'do_sample', 'temperature', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'tfs', 'top_a']])

def set_interface_arguments(interface_mode, extensions, bool_active):

shared.need_restart = True

def create_interface():