aigua-xat

Runtime error

App Files Files Community

aigua-xat / gradio_runner.py

ccoreilly

Duplicate from h2oai/h2ogpt-chatbot2

f257153 10 months ago

raw history blame contribute delete

No virus

166 kB

	import ast
	import copy
	import functools
	import inspect
	import itertools
	import json
	import os
	import pprint
	import random
	import shutil
	import sys
	import time
	import traceback
	import typing
	import uuid
	import filelock
	import pandas as pd
	import requests
	import tabulate
	from iterators import TimeoutIterator

	from gradio_utils.css import get_css
	from gradio_utils.prompt_form import make_chatbots

	# This is a hack to prevent Gradio from phoning home when it gets imported
	os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'


	def my_get(url, **kwargs):
	print('Gradio HTTP request redirected to localhost :)', flush=True)
	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


	def fix_pydantic_duplicate_validators_error():
	try:
	from pydantic import class_validators

	class_validators.in_ipython = lambda: True # type: ignore[attr-defined]
	except ImportError:
	pass


	fix_pydantic_duplicate_validators_error()

	from enums import DocumentSubset, no_model_str, no_lora_str, no_server_str, LangChainAction, LangChainMode, \
	DocumentChoice, langchain_modes_intrinsic
	from gradio_themes import H2oTheme, SoftTheme, get_h2o_title, get_simple_title, get_dark_js, spacing_xsm, radius_xsm, \
	text_xsm
	from prompter import prompt_type_to_model_name, prompt_types_strings, inv_prompt_type_to_model_lower, non_hf_types, \
	get_prompt
	from utils import flatten_list, zip_data, s3up, clear_torch_cache, get_torch_allocated, system_info_print, \
	ping, get_short_name, makedirs, get_kwargs, remove, system_info, ping_gpu, get_url, get_local_ip, \
	save_collection_names
	from gen import get_model, languages_covered, evaluate, score_qa, inputs_kwargs_list, scratch_base_dir, \
	get_max_max_new_tokens, get_minmax_top_k_docs, history_to_context, langchain_actions, langchain_agents_list, \
	update_langchain
	from evaluate_params import eval_func_param_names, no_default_param_names, eval_func_param_names_defaults, \
	input_args_list

	from apscheduler.schedulers.background import BackgroundScheduler


	def fix_text_for_gradio(text, fix_new_lines=False, fix_latex_dollars=True):
	if fix_latex_dollars:
	ts = text.split('```')
	for parti, part in enumerate(ts):
	inside = parti % 2 == 1
	if not inside:
	ts[parti] = ts[parti].replace('$', '﹩')
	text = '```'.join(ts)

	if fix_new_lines:
	# let Gradio handle code, since got improved recently
	## FIXME: below conflicts with Gradio, but need to see if can handle multiple \n\n\n etc. properly as is.
	# ensure good visually, else markdown ignores multiple \n
	# handle code blocks
	ts = text.split('```')
	for parti, part in enumerate(ts):
	inside = parti % 2 == 1
	if not inside:
	ts[parti] = ts[parti].replace('\n', '<br>')
	text = '```'.join(ts)
	return text


	def go_gradio(**kwargs):
	allow_api = kwargs['allow_api']
	is_public = kwargs['is_public']
	is_hf = kwargs['is_hf']
	memory_restriction_level = kwargs['memory_restriction_level']
	n_gpus = kwargs['n_gpus']
	admin_pass = kwargs['admin_pass']
	model_states = kwargs['model_states']
	dbs = kwargs['dbs']
	db_type = kwargs['db_type']
	visible_langchain_actions = kwargs['visible_langchain_actions']
	visible_langchain_agents = kwargs['visible_langchain_agents']
	allow_upload_to_user_data = kwargs['allow_upload_to_user_data']
	allow_upload_to_my_data = kwargs['allow_upload_to_my_data']
	enable_sources_list = kwargs['enable_sources_list']
	enable_url_upload = kwargs['enable_url_upload']
	enable_text_upload = kwargs['enable_text_upload']
	use_openai_embedding = kwargs['use_openai_embedding']
	hf_embedding_model = kwargs['hf_embedding_model']
	enable_captions = kwargs['enable_captions']
	captions_model = kwargs['captions_model']
	enable_ocr = kwargs['enable_ocr']
	enable_pdf_ocr = kwargs['enable_pdf_ocr']
	caption_loader = kwargs['caption_loader']

	# for dynamic state per user session in gradio
	model_state0 = kwargs['model_state0']
	score_model_state0 = kwargs['score_model_state0']
	my_db_state0 = kwargs['my_db_state0']
	selection_docs_state0 = kwargs['selection_docs_state0']
	# for evaluate defaults
	langchain_modes0 = kwargs['langchain_modes']
	visible_langchain_modes0 = kwargs['visible_langchain_modes']
	langchain_mode_paths0 = kwargs['langchain_mode_paths']

	# easy update of kwargs needed for evaluate() etc.
	queue = True
	allow_upload = allow_upload_to_user_data or allow_upload_to_my_data
	kwargs.update(locals())

	# import control
	if kwargs['langchain_mode'] != 'Disabled':
	from gpt_langchain import file_types, have_arxiv
	else:
	have_arxiv = False
	file_types = []

	if 'mbart-' in kwargs['model_lower']:
	instruction_label_nochat = "Text to translate"
	else:
	instruction_label_nochat = "Instruction (Shift-Enter or push Submit to send message," \
	" use Enter for multiple input lines)"

	title = 'h2oGPT'
	description = """<iframe src="https://ghbtns.com/github-btn.html?user=h2oai&repo=h2ogpt&type=star&count=true&size=small" frameborder="0" scrolling="0" width="250" height="20" title="GitHub"></iframe><small><a href="https://github.com/h2oai/h2ogpt">h2oGPT</a> <a href="https://github.com/h2oai/h2o-llmstudio">H2O LLM Studio</a><br><a href="https://huggingface.co/h2oai">🤗 Models</a>"""
	description_bottom = "If this host is busy, try<br>[Multi-Model](https://gpt.h2o.ai)<br>[Falcon 40B](https://falcon.h2o.ai)<br>[Vicuna 33B](https://wizardvicuna.h2o.ai)<br>[MPT 30B-Chat](https://mpt.h2o.ai)<br>[HF Spaces1](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot)<br>[HF Spaces2](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot2)<br>"
	if is_hf:
	description_bottom += '''<a href="https://huggingface.co/spaces/h2oai/h2ogpt-chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" style="white-space: nowrap" alt="Duplicate Space"></a>'''
	task_info_md = ''
	css_code = get_css(kwargs)

	if kwargs['gradio_offline_level'] >= 0:
	# avoid GoogleFont that pulls from internet
	if kwargs['gradio_offline_level'] == 1:
	# front end would still have to download fonts or have cached it at some point
	base_font = 'Source Sans Pro'
	else:
	base_font = 'Helvetica'
	theme_kwargs = dict(font=(base_font, 'ui-sans-serif', 'system-ui', 'sans-serif'),
	font_mono=('IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'))
	else:
	theme_kwargs = dict()
	if kwargs['gradio_size'] == 'xsmall':
	theme_kwargs.update(dict(spacing_size=spacing_xsm, text_size=text_xsm, radius_size=radius_xsm))
	elif kwargs['gradio_size'] in [None, 'small']:
	theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_sm, text_size=gr.themes.sizes.text_sm,
	radius_size=gr.themes.sizes.spacing_sm))
	elif kwargs['gradio_size'] == 'large':
	theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_lg, text_size=gr.themes.sizes.text_lg),
	radius_size=gr.themes.sizes.spacing_lg)
	elif kwargs['gradio_size'] == 'medium':
	theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_md, text_size=gr.themes.sizes.text_md,
	radius_size=gr.themes.sizes.spacing_md))

	theme = H2oTheme(theme_kwargs) if kwargs['h2ocolors'] else SoftTheme(theme_kwargs)
	demo = gr.Blocks(theme=theme, css=css_code, title="h2oGPT", analytics_enabled=False)
	callback = gr.CSVLogger()

	model_options0 = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options']
	if kwargs['base_model'].strip() not in model_options0:
	model_options0 = [kwargs['base_model'].strip()] + model_options0
	lora_options = kwargs['extra_lora_options']
	if kwargs['lora_weights'].strip() not in lora_options:
	lora_options = [kwargs['lora_weights'].strip()] + lora_options
	server_options = kwargs['extra_server_options']
	if kwargs['inference_server'].strip() not in server_options:
	server_options = [kwargs['inference_server'].strip()] + server_options
	if os.getenv('OPENAI_API_KEY'):
	if 'openai_chat' not in server_options:
	server_options += ['openai_chat']
	if 'openai' not in server_options:
	server_options += ['openai']

	# always add in no lora case
	# add fake space so doesn't go away in gradio dropdown
	model_options0 = [no_model_str] + model_options0
	lora_options = [no_lora_str] + lora_options
	server_options = [no_server_str] + server_options
	# always add in no model case so can free memory
	# add fake space so doesn't go away in gradio dropdown

	# transcribe, will be detranscribed before use by evaluate()
	if not kwargs['base_model'].strip():
	kwargs['base_model'] = no_model_str

	if not kwargs['lora_weights'].strip():
	kwargs['lora_weights'] = no_lora_str

	if not kwargs['inference_server'].strip():
	kwargs['inference_server'] = no_server_str

	# transcribe for gradio
	kwargs['gpu_id'] = str(kwargs['gpu_id'])

	no_model_msg = 'h2oGPT [ !!! Please Load Model in Models Tab !!! ]'
	output_label0 = f'h2oGPT [Model: {kwargs.get("base_model")}]' if kwargs.get(
	'base_model') else no_model_msg
	output_label0_model2 = no_model_msg

	def update_prompt(prompt_type1, prompt_dict1, model_state1, which_model=0):
	if not prompt_type1 or which_model != 0:
	# keep prompt_type and prompt_dict in sync if possible
	prompt_type1 = kwargs.get('prompt_type', prompt_type1)
	prompt_dict1 = kwargs.get('prompt_dict', prompt_dict1)
	# prefer model specific prompt type instead of global one
	if not prompt_type1 or which_model != 0:
	prompt_type1 = model_state1.get('prompt_type', prompt_type1)
	prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1)

	if not prompt_dict1 or which_model != 0:
	# if still not defined, try to get
	prompt_dict1 = kwargs.get('prompt_dict', prompt_dict1)
	if not prompt_dict1 or which_model != 0:
	prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1)
	return prompt_type1, prompt_dict1

	default_kwargs = {k: kwargs[k] for k in eval_func_param_names_defaults}
	# ensure prompt_type consistent with prep_bot(), so nochat API works same way
	default_kwargs['prompt_type'], default_kwargs['prompt_dict'] = \
	update_prompt(default_kwargs['prompt_type'], default_kwargs['prompt_dict'],
	model_state1=model_state0, which_model=0)
	for k in no_default_param_names:
	default_kwargs[k] = ''

	def dummy_fun(x):
	# need dummy function to block new input from being sent until output is done,
	# else gets input_list at time of submit that is old, and shows up as truncated in chatbot
	return x

	def allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
	allow = False
	allow \|= langchain_action1 not in LangChainAction.QUERY.value
	allow \|= document_subset1 in DocumentSubset.TopKSources.name
	if langchain_mode1 in [LangChainMode.LLM.value]:
	allow = False
	return allow

	with demo:
	# avoid actual model/tokenizer here or anything that would be bad to deepcopy
	# https://github.com/gradio-app/gradio/issues/3558
	model_state = gr.State(
	dict(model='model', tokenizer='tokenizer', device=kwargs['device'],
	base_model=kwargs['base_model'],
	tokenizer_base_model=kwargs['tokenizer_base_model'],
	lora_weights=kwargs['lora_weights'],
	inference_server=kwargs['inference_server'],
	prompt_type=kwargs['prompt_type'],
	prompt_dict=kwargs['prompt_dict'],
	)
	)

	def update_langchain_mode_paths(db1s, selection_docs_state1):
	if allow_upload_to_my_data:
	selection_docs_state1['langchain_mode_paths'].update({k: None for k in db1s})
	dup = selection_docs_state1['langchain_mode_paths'].copy()
	for k, v in dup.items():
	if k not in selection_docs_state1['visible_langchain_modes']:
	selection_docs_state1['langchain_mode_paths'].pop(k)
	return selection_docs_state1

	# Setup some gradio states for per-user dynamic state
	model_state2 = gr.State(kwargs['model_state_none'].copy())
	model_options_state = gr.State([model_options0])
	lora_options_state = gr.State([lora_options])
	server_options_state = gr.State([server_options])
	my_db_state = gr.State(my_db_state0)
	chat_state = gr.State({})
	docs_state00 = kwargs['document_choice'] + [DocumentChoice.ALL.value]
	docs_state0 = []
	[docs_state0.append(x) for x in docs_state00 if x not in docs_state0]
	docs_state = gr.State(docs_state0)
	viewable_docs_state0 = []
	viewable_docs_state = gr.State(viewable_docs_state0)
	selection_docs_state0 = update_langchain_mode_paths(my_db_state0, selection_docs_state0)
	selection_docs_state = gr.State(selection_docs_state0)

	gr.Markdown(f"""
	{get_h2o_title(title, description) if kwargs['h2ocolors'] else get_simple_title(title, description)}
	""")

	# go button visible if
	base_wanted = kwargs['base_model'] != no_model_str and kwargs['login_mode_if_model0']
	go_btn = gr.Button(value="ENTER", visible=base_wanted, variant="primary")

	nas = ' '.join(['NA'] * len(kwargs['model_states']))
	res_value = "Response Score: NA" if not kwargs[
	'model_lock'] else "Response Scores: %s" % nas

	if kwargs['langchain_mode'] != LangChainMode.DISABLED.value:
	extra_prompt_form = ". For summarization, no query required, just click submit"
	else:
	extra_prompt_form = ""
	if kwargs['input_lines'] > 1:
	instruction_label = "Shift-Enter to Submit, Enter for more lines%s" % extra_prompt_form
	else:
	instruction_label = "Enter to Submit, Shift-Enter for more lines%s" % extra_prompt_form

	def get_langchain_choices(selection_docs_state1):
	langchain_modes = selection_docs_state1['langchain_modes']
	visible_langchain_modes = selection_docs_state1['visible_langchain_modes']

	if is_hf:
	# don't show 'wiki' since only usually useful for internal testing at moment
	no_show_modes = ['Disabled', 'wiki']
	else:
	no_show_modes = ['Disabled']
	allowed_modes = visible_langchain_modes.copy()
	# allowed_modes = [x for x in allowed_modes if x in dbs]
	allowed_modes += ['LLM']
	if allow_upload_to_my_data and 'MyData' not in allowed_modes:
	allowed_modes += ['MyData']
	if allow_upload_to_user_data and 'UserData' not in allowed_modes:
	allowed_modes += ['UserData']
	choices = [x for x in langchain_modes if x in allowed_modes and x not in no_show_modes]
	return choices

	def get_df_langchain_mode_paths(selection_docs_state1):
	langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
	if langchain_mode_paths:
	df = pd.DataFrame.from_dict(langchain_mode_paths.items(), orient='columns')
	df.columns = ['Collection', 'Path']
	else:
	df = pd.DataFrame(None)
	return df

	normal_block = gr.Row(visible=not base_wanted, equal_height=False)
	with normal_block:
	side_bar = gr.Column(elem_id="col_container", scale=1, min_width=100)
	with side_bar:
	with gr.Accordion("Chats", open=False, visible=True):
	radio_chats = gr.Radio(value=None, label="Saved Chats", show_label=False,
	visible=True, interactive=True,
	type='value')
	upload_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload
	with gr.Accordion("Upload", open=False, visible=upload_visible):
	with gr.Column():
	with gr.Row(equal_height=False):
	file_types_str = '[' + ' '.join(file_types) + ' URL ArXiv TEXT' + ']'
	fileup_output = gr.File(label=f'Upload {file_types_str}',
	show_label=False,
	file_types=file_types,
	file_count="multiple",
	scale=1,
	min_width=0,
	elem_id="warning", elem_classes="feedback")
	fileup_output_text = gr.Textbox(visible=False)
	url_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_url_upload
	url_label = 'URL/ArXiv' if have_arxiv else 'URL'
	url_text = gr.Textbox(label=url_label,
	# placeholder="Enter Submits",
	max_lines=1,
	interactive=True)
	text_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_text_upload
	user_text_text = gr.Textbox(label='Paste Text',
	# placeholder="Enter Submits",
	interactive=True,
	visible=text_visible)
	github_textbox = gr.Textbox(label="Github URL", visible=False) # FIXME WIP
	database_visible = kwargs['langchain_mode'] != 'Disabled'
	with gr.Accordion("Resources", open=False, visible=database_visible):
	langchain_choices0 = get_langchain_choices(selection_docs_state0)
	langchain_mode = gr.Radio(
	langchain_choices0,
	value=kwargs['langchain_mode'],
	label="Collections",
	show_label=True,
	visible=kwargs['langchain_mode'] != 'Disabled',
	min_width=100)
	add_chat_history_to_context = gr.Checkbox(label="Chat History",
	value=kwargs['add_chat_history_to_context'])
	document_subset = gr.Radio([x.name for x in DocumentSubset],
	label="Subset",
	value=DocumentSubset.Relevant.name,
	interactive=True,
	)
	allowed_actions = [x for x in langchain_actions if x in visible_langchain_actions]
	langchain_action = gr.Radio(
	allowed_actions,
	value=allowed_actions[0] if len(allowed_actions) > 0 else None,
	label="Action",
	visible=True)
	allowed_agents = [x for x in langchain_agents_list if x in visible_langchain_agents]
	langchain_agents = gr.Dropdown(
	langchain_agents_list,
	value=kwargs['langchain_agents'],
	label="Agents",
	multiselect=True,
	interactive=True,
	visible=False) # WIP
	col_tabs = gr.Column(elem_id="col_container", scale=10)
	with (col_tabs, gr.Tabs()):
	with gr.TabItem("Chat"):
	if kwargs['langchain_mode'] == 'Disabled':
	text_output_nochat = gr.Textbox(lines=5, label=output_label0, show_copy_button=True,
	visible=not kwargs['chat'])
	else:
	# text looks a bit worse, but HTML links work
	text_output_nochat = gr.HTML(label=output_label0, visible=not kwargs['chat'])
	with gr.Row():
	# NOCHAT
	instruction_nochat = gr.Textbox(
	lines=kwargs['input_lines'],
	label=instruction_label_nochat,
	placeholder=kwargs['placeholder_instruction'],
	visible=not kwargs['chat'],
	)
	iinput_nochat = gr.Textbox(lines=4, label="Input context for Instruction",
	placeholder=kwargs['placeholder_input'],
	visible=not kwargs['chat'])
	submit_nochat = gr.Button("Submit", size='sm', visible=not kwargs['chat'])
	flag_btn_nochat = gr.Button("Flag", size='sm', visible=not kwargs['chat'])
	score_text_nochat = gr.Textbox("Response Score: NA", show_label=False,
	visible=not kwargs['chat'])
	submit_nochat_api = gr.Button("Submit nochat API", visible=False)
	inputs_dict_str = gr.Textbox(label='API input for nochat', show_label=False, visible=False)
	text_output_nochat_api = gr.Textbox(lines=5, label='API nochat output', visible=False,
	show_copy_button=True)

	# CHAT
	col_chat = gr.Column(visible=kwargs['chat'])
	with col_chat:
	with gr.Row(): # elem_id='prompt-form-area'):
	with gr.Column(scale=50):
	instruction = gr.Textbox(
	lines=kwargs['input_lines'],
	label='Ask anything',
	placeholder=instruction_label,
	info=None,
	elem_id='prompt-form',
	container=True,
	)
	submit_buttons = gr.Row(equal_height=False)
	with submit_buttons:
	mw1 = 50
	mw2 = 50
	with gr.Column(min_width=mw1):
	submit = gr.Button(value='Submit', variant='primary', size='sm',
	min_width=mw1)
	stop_btn = gr.Button(value="Stop", variant='secondary', size='sm',
	min_width=mw1)
	save_chat_btn = gr.Button("Save", size='sm', min_width=mw1)
	with gr.Column(min_width=mw2):
	retry_btn = gr.Button("Redo", size='sm', min_width=mw2)
	undo = gr.Button("Undo", size='sm', min_width=mw2)
	clear_chat_btn = gr.Button(value="Clear", size='sm', min_width=mw2)
	text_output, text_output2, text_outputs = make_chatbots(output_label0, output_label0_model2,
	**kwargs)

	with gr.Row():
	with gr.Column(visible=kwargs['score_model']):
	score_text = gr.Textbox(res_value,
	show_label=False,
	visible=True)
	score_text2 = gr.Textbox("Response Score2: NA", show_label=False,
	visible=False and not kwargs['model_lock'])

	with gr.TabItem("Document Selection"):
	document_choice = gr.Dropdown(docs_state0,
	label="Select Subset of Document(s) %s" % file_types_str,
	value=[DocumentChoice.ALL.value],
	interactive=True,
	multiselect=True,
	visible=kwargs['langchain_mode'] != 'Disabled',
	)
	sources_visible = kwargs['langchain_mode'] != 'Disabled' and enable_sources_list
	with gr.Row():
	with gr.Column(scale=1):
	get_sources_btn = gr.Button(value="Update UI with Document(s) from DB", scale=0, size='sm',
	visible=sources_visible)
	show_sources_btn = gr.Button(value="Show Sources from DB", scale=0, size='sm',
	visible=sources_visible)
	refresh_sources_btn = gr.Button(value="Update DB with new/changed files on disk", scale=0,
	size='sm',
	visible=sources_visible and allow_upload_to_user_data)
	with gr.Column(scale=4):
	pass
	with gr.Row():
	with gr.Column(scale=1):
	visible_add_remove_collection = (allow_upload_to_user_data or
	allow_upload_to_my_data) and \
	kwargs['langchain_mode'] != 'Disabled'
	add_placeholder = "e.g. UserData2, user_path2 (optional)" \
	if not is_public else "e.g. MyData2"
	remove_placeholder = "e.g. UserData2" if not is_public else "e.g. MyData2"
	new_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection,
	label='Add Collection',
	placeholder=add_placeholder,
	interactive=True)
	remove_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection,
	label='Remove Collection',
	placeholder=remove_placeholder,
	interactive=True)
	load_langchain = gr.Button(value="Load LangChain State", scale=0, size='sm',
	visible=allow_upload_to_user_data and
	kwargs['langchain_mode'] != 'Disabled')
	with gr.Column(scale=1):
	df0 = get_df_langchain_mode_paths(selection_docs_state0)
	langchain_mode_path_text = gr.Dataframe(value=df0,
	visible=visible_add_remove_collection,
	label='LangChain Mode-Path',
	show_label=False,
	interactive=False)
	with gr.Column(scale=4):
	pass

	sources_row = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list,
	equal_height=False)
	with sources_row:
	with gr.Column(scale=1):
	file_source = gr.File(interactive=False,
	label="Download File w/Sources")
	with gr.Column(scale=2):
	sources_text = gr.HTML(label='Sources Added', interactive=False)

	doc_exception_text = gr.Textbox(value="", label='Document Exceptions',
	interactive=False,
	visible=kwargs['langchain_mode'] != 'Disabled')
	with gr.TabItem("Document Viewer"):
	with gr.Row(visible=kwargs['langchain_mode'] != 'Disabled'):
	with gr.Column(scale=2):
	get_viewable_sources_btn = gr.Button(value="Update UI with Document(s) from DB", scale=0,
	size='sm',
	visible=sources_visible)
	view_document_choice = gr.Dropdown(viewable_docs_state0,
	label="Select Single Document",
	value=None,
	interactive=True,
	multiselect=False,
	visible=True,
	)
	with gr.Column(scale=4):
	pass
	document = 'http://infolab.stanford.edu/pub/papers/google.pdf'
	doc_view = gr.HTML(visible=False)
	doc_view2 = gr.Dataframe(visible=False)
	doc_view3 = gr.JSON(visible=False)
	doc_view4 = gr.Markdown(visible=False)

	with gr.TabItem("Chat History"):
	with gr.Row():
	with gr.Column(scale=1):
	remove_chat_btn = gr.Button(value="Remove Selected Saved Chats", visible=True, size='sm')
	flag_btn = gr.Button("Flag Current Chat", size='sm')
	export_chats_btn = gr.Button(value="Export Chats to Download", size='sm')
	with gr.Column(scale=4):
	pass
	with gr.Row():
	chats_file = gr.File(interactive=False, label="Download Exported Chats")
	chatsup_output = gr.File(label="Upload Chat File(s)",
	file_types=['.json'],
	file_count='multiple',
	elem_id="warning", elem_classes="feedback")
	with gr.Row():
	if 'mbart-' in kwargs['model_lower']:
	src_lang = gr.Dropdown(list(languages_covered().keys()),
	value=kwargs['src_lang'],
	label="Input Language")
	tgt_lang = gr.Dropdown(list(languages_covered().keys()),
	value=kwargs['tgt_lang'],
	label="Output Language")

	chat_exception_text = gr.Textbox(value="", visible=True, label='Chat Exceptions',
	interactive=False)
	with gr.TabItem("Expert"):
	with gr.Row():
	with gr.Column():
	stream_output = gr.components.Checkbox(label="Stream output",
	value=kwargs['stream_output'])
	prompt_type = gr.Dropdown(prompt_types_strings,
	value=kwargs['prompt_type'], label="Prompt Type",
	visible=not kwargs['model_lock'],
	interactive=not is_public,
	)
	prompt_type2 = gr.Dropdown(prompt_types_strings,
	value=kwargs['prompt_type'], label="Prompt Type Model 2",
	visible=False and not kwargs['model_lock'],
	interactive=not is_public)
	do_sample = gr.Checkbox(label="Sample",
	info="Enable sampler, required for use of temperature, top_p, top_k",
	value=kwargs['do_sample'])
	temperature = gr.Slider(minimum=0.01, maximum=2,
	value=kwargs['temperature'],
	label="Temperature",
	info="Lower is deterministic (but may lead to repeats), Higher more creative (but may lead to hallucinations)")
	top_p = gr.Slider(minimum=1e-3, maximum=1.0 - 1e-3,
	value=kwargs['top_p'], label="Top p",
	info="Cumulative probability of tokens to sample from")
	top_k = gr.Slider(
	minimum=1, maximum=100, step=1,
	value=kwargs['top_k'], label="Top k",
	info='Num. tokens to sample from'
	)
	# FIXME: https://github.com/h2oai/h2ogpt/issues/106
	if os.getenv('TESTINGFAIL'):
	max_beams = 8 if not (memory_restriction_level or is_public) else 1
	else:
	max_beams = 1
	num_beams = gr.Slider(minimum=1, maximum=max_beams, step=1,
	value=min(max_beams, kwargs['num_beams']), label="Beams",
	info="Number of searches for optimal overall probability. "
	"Uses more GPU memory/compute",
	interactive=False)
	max_max_new_tokens = get_max_max_new_tokens(model_state0, **kwargs)
	max_new_tokens = gr.Slider(
	minimum=1, maximum=max_max_new_tokens, step=1,
	value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length",
	)
	min_new_tokens = gr.Slider(
	minimum=0, maximum=max_max_new_tokens, step=1,
	value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length",
	)
	max_new_tokens2 = gr.Slider(
	minimum=1, maximum=max_max_new_tokens, step=1,
	value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length 2",
	visible=False and not kwargs['model_lock'],
	)
	min_new_tokens2 = gr.Slider(
	minimum=0, maximum=max_max_new_tokens, step=1,
	value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length 2",
	visible=False and not kwargs['model_lock'],
	)
	early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search",
	value=kwargs['early_stopping'])
	max_time = gr.Slider(minimum=0, maximum=kwargs['max_max_time'], step=1,
	value=min(kwargs['max_max_time'],
	kwargs['max_time']), label="Max. time",
	info="Max. time to search optimal output.")
	repetition_penalty = gr.Slider(minimum=0.01, maximum=3.0,
	value=kwargs['repetition_penalty'],
	label="Repetition Penalty")
	num_return_sequences = gr.Slider(minimum=1, maximum=10, step=1,
	value=kwargs['num_return_sequences'],
	label="Number Returns", info="Must be <= num_beams",
	interactive=not is_public)
	iinput = gr.Textbox(lines=4, label="Input",
	placeholder=kwargs['placeholder_input'],
	interactive=not is_public)
	context = gr.Textbox(lines=3, label="System Pre-Context",
	info="Directly pre-appended without prompt processing",
	interactive=not is_public)
	chat = gr.components.Checkbox(label="Chat mode", value=kwargs['chat'],
	visible=False, # no longer support nochat in UI
	interactive=not is_public,
	)
	count_chat_tokens_btn = gr.Button(value="Count Chat Tokens",
	visible=not is_public and not kwargs['model_lock'],
	interactive=not is_public)
	chat_token_count = gr.Textbox(label="Chat Token Count", value=None,
	visible=not is_public and not kwargs['model_lock'],
	interactive=False)
	chunk = gr.components.Checkbox(value=kwargs['chunk'],
	label="Whether to chunk documents",
	info="For LangChain",
	visible=kwargs['langchain_mode'] != 'Disabled',
	interactive=not is_public)
	min_top_k_docs, max_top_k_docs, label_top_k_docs = get_minmax_top_k_docs(is_public)
	top_k_docs = gr.Slider(minimum=min_top_k_docs, maximum=max_top_k_docs, step=1,
	value=kwargs['top_k_docs'],
	label=label_top_k_docs,
	info="For LangChain",
	visible=kwargs['langchain_mode'] != 'Disabled',
	interactive=not is_public)
	chunk_size = gr.Number(value=kwargs['chunk_size'],
	label="Chunk size for document chunking",
	info="For LangChain (ignored if chunk=False)",
	minimum=128,
	maximum=2048,
	visible=kwargs['langchain_mode'] != 'Disabled',
	interactive=not is_public,
	precision=0)

	with gr.TabItem("Models"):
	model_lock_msg = gr.Textbox(lines=1, label="Model Lock Notice",
	placeholder="Started in model_lock mode, no model changes allowed.",
	visible=bool(kwargs['model_lock']), interactive=False)
	load_msg = "Load-Unload Model/LORA [unload works if did not use --base_model]" if not is_public \
	else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO"
	load_msg2 = "Load-Unload Model/LORA 2 [unload works if did not use --base_model]" if not is_public \
	else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO 2"
	variant_load_msg = 'primary' if not is_public else 'secondary'
	compare_checkbox = gr.components.Checkbox(label="Compare Mode",
	value=kwargs['model_lock'],
	visible=not is_public and not kwargs['model_lock'])
	with gr.Row():
	n_gpus_list = [str(x) for x in list(range(-1, n_gpus))]
	with gr.Column():
	with gr.Row():
	with gr.Column(scale=20, visible=not kwargs['model_lock']):
	model_choice = gr.Dropdown(model_options_state.value[0], label="Choose Model",
	value=kwargs['base_model'])
	lora_choice = gr.Dropdown(lora_options_state.value[0], label="Choose LORA",
	value=kwargs['lora_weights'], visible=kwargs['show_lora'])
	server_choice = gr.Dropdown(server_options_state.value[0], label="Choose Server",
	value=kwargs['inference_server'], visible=not is_public)
	with gr.Column(scale=1, visible=not kwargs['model_lock']):
	load_model_button = gr.Button(load_msg, variant=variant_load_msg, scale=0,
	size='sm', interactive=not is_public)
	model_load8bit_checkbox = gr.components.Checkbox(
	label="Load 8-bit [requires support]",
	value=kwargs['load_8bit'], interactive=not is_public)
	model_use_gpu_id_checkbox = gr.components.Checkbox(
	label="Choose Devices [If not Checked, use all GPUs]",
	value=kwargs['use_gpu_id'], interactive=not is_public)
	model_gpu = gr.Dropdown(n_gpus_list,
	label="GPU ID [-1 = all GPUs, if Choose is enabled]",
	value=kwargs['gpu_id'], interactive=not is_public)
	model_used = gr.Textbox(label="Current Model", value=kwargs['base_model'],
	interactive=False)
	lora_used = gr.Textbox(label="Current LORA", value=kwargs['lora_weights'],
	visible=kwargs['show_lora'], interactive=False)
	server_used = gr.Textbox(label="Current Server",
	value=kwargs['inference_server'],
	visible=bool(kwargs['inference_server']) and not is_public,
	interactive=False)
	prompt_dict = gr.Textbox(label="Prompt (or Custom)",
	value=pprint.pformat(kwargs['prompt_dict'], indent=4),
	interactive=not is_public, lines=4)
	col_model2 = gr.Column(visible=False)
	with col_model2:
	with gr.Row():
	with gr.Column(scale=20, visible=not kwargs['model_lock']):
	model_choice2 = gr.Dropdown(model_options_state.value[0], label="Choose Model 2",
	value=no_model_str)
	lora_choice2 = gr.Dropdown(lora_options_state.value[0], label="Choose LORA 2",
	value=no_lora_str,
	visible=kwargs['show_lora'])
	server_choice2 = gr.Dropdown(server_options_state.value[0], label="Choose Server 2",
	value=no_server_str,
	visible=not is_public)
	with gr.Column(scale=1, visible=not kwargs['model_lock']):
	load_model_button2 = gr.Button(load_msg2, variant=variant_load_msg, scale=0,
	size='sm', interactive=not is_public)
	model_load8bit_checkbox2 = gr.components.Checkbox(
	label="Load 8-bit 2 [requires support]",
	value=kwargs['load_8bit'], interactive=not is_public)
	model_use_gpu_id_checkbox2 = gr.components.Checkbox(
	label="Choose Devices 2 [If not Checked, use all GPUs]",
	value=kwargs[
	'use_gpu_id'], interactive=not is_public)
	model_gpu2 = gr.Dropdown(n_gpus_list,
	label="GPU ID 2 [-1 = all GPUs, if choose is enabled]",
	value=kwargs['gpu_id'], interactive=not is_public)
	# no model/lora loaded ever in model2 by default
	model_used2 = gr.Textbox(label="Current Model 2", value=no_model_str,
	interactive=False)
	lora_used2 = gr.Textbox(label="Current LORA 2", value=no_lora_str,
	visible=kwargs['show_lora'], interactive=False)
	server_used2 = gr.Textbox(label="Current Server 2", value=no_server_str,
	interactive=False,
	visible=not is_public)
	prompt_dict2 = gr.Textbox(label="Prompt (or Custom) 2",
	value=pprint.pformat(kwargs['prompt_dict'], indent=4),
	interactive=not is_public, lines=4)
	with gr.Row(visible=not kwargs['model_lock']):
	with gr.Column(scale=50):
	new_model = gr.Textbox(label="New Model name/path", interactive=not is_public)
	with gr.Column(scale=50):
	new_lora = gr.Textbox(label="New LORA name/path", visible=kwargs['show_lora'],
	interactive=not is_public)
	with gr.Column(scale=50):
	new_server = gr.Textbox(label="New Server url:port", interactive=not is_public)
	with gr.Row():
	add_model_lora_server_button = gr.Button("Add new Model, Lora, Server url:port", scale=0,
	size='sm', interactive=not is_public)
	with gr.TabItem("System"):
	with gr.Row():
	with gr.Column(scale=1):
	side_bar_text = gr.Textbox('on', visible=False, interactive=False)
	submit_buttons_text = gr.Textbox('on', visible=False, interactive=False)

	side_bar_btn = gr.Button("Toggle SideBar", variant="secondary", size="sm")
	submit_buttons_btn = gr.Button("Toggle Submit Buttons", variant="secondary", size="sm")
	col_tabs_scale = gr.Slider(minimum=1, maximum=20, value=10, step=1, label='Window Size')
	text_outputs_height = gr.Slider(minimum=100, maximum=2000, value=kwargs['height'] or 400,
	step=50, label='Chat Height')
	dark_mode_btn = gr.Button("Dark Mode", variant="secondary", size="sm")
	with gr.Column(scale=4):
	pass
	system_visible0 = not is_public and not admin_pass
	admin_row = gr.Row()
	with admin_row:
	with gr.Column(scale=1):
	admin_pass_textbox = gr.Textbox(label="Admin Password", type='password',
	visible=not system_visible0)
	with gr.Column(scale=4):
	pass
	system_row = gr.Row(visible=system_visible0)
	with system_row:
	with gr.Column():
	with gr.Row():
	system_btn = gr.Button(value='Get System Info', size='sm')
	system_text = gr.Textbox(label='System Info', interactive=False, show_copy_button=True)
	with gr.Row():
	system_input = gr.Textbox(label='System Info Dict Password', interactive=True,
	visible=not is_public)
	system_btn2 = gr.Button(value='Get System Info Dict', visible=not is_public, size='sm')
	system_text2 = gr.Textbox(label='System Info Dict', interactive=False,
	visible=not is_public, show_copy_button=True)
	with gr.Row():
	system_btn3 = gr.Button(value='Get Hash', visible=not is_public, size='sm')
	system_text3 = gr.Textbox(label='Hash', interactive=False,
	visible=not is_public, show_copy_button=True)

	with gr.Row():
	zip_btn = gr.Button("Zip", size='sm')
	zip_text = gr.Textbox(label="Zip file name", interactive=False)
	file_output = gr.File(interactive=False, label="Zip file to Download")
	with gr.Row():
	s3up_btn = gr.Button("S3UP", size='sm')
	s3up_text = gr.Textbox(label='S3UP result', interactive=False)

	with gr.TabItem("Terms of Service"):
	description = ""
	description += """<p><b> DISCLAIMERS: </b><ul><i><li>The model was trained on The Pile and other data, which may contain objectionable content. Use at own risk.</i></li>"""
	if kwargs['load_8bit']:
	description += """<i><li> Model is loaded in 8-bit and has other restrictions on this host. UX can be worse than non-hosted version.</i></li>"""
	description += """<i><li>Conversations may be used to improve h2oGPT. Do not share sensitive information.</i></li>"""
	if 'h2ogpt-research' in kwargs['base_model']:
	description += """<i><li>Research demonstration only, not used for commercial purposes.</i></li>"""
	description += """<i><li>By using h2oGPT, you accept our <a href="https://github.com/h2oai/h2ogpt/blob/main/docs/tos.md">Terms of Service</a></i></li></ul></p>"""
	gr.Markdown(value=description, show_label=False, interactive=False)

	with gr.TabItem("Hosts"):
	gr.Markdown(f"""
	{description_bottom}
	{task_info_md}
	""")

	# Get flagged data
	zip_data1 = functools.partial(zip_data, root_dirs=['flagged_data_points', kwargs['save_dir']])
	zip_event = zip_btn.click(zip_data1, inputs=None, outputs=[file_output, zip_text], queue=False,
	api_name='zip_data' if allow_api else None)
	s3up_event = s3up_btn.click(s3up, inputs=zip_text, outputs=s3up_text, queue=False,
	api_name='s3up_data' if allow_api else None)

	def clear_file_list():
	return None

	def make_non_interactive(*args):
	if len(args) == 1:
	return gr.update(interactive=False)
	else:
	return tuple([gr.update(interactive=False)] * len(args))

	def make_interactive(*args):
	if len(args) == 1:
	return gr.update(interactive=True)
	else:
	return tuple([gr.update(interactive=True)] * len(args))

	# Add to UserData or custom user db
	update_db_func = functools.partial(update_user_db,
	dbs=dbs,
	db_type=db_type,
	use_openai_embedding=use_openai_embedding,
	hf_embedding_model=hf_embedding_model,
	captions_model=captions_model,
	enable_captions=enable_captions,
	caption_loader=caption_loader,
	enable_ocr=enable_ocr,
	enable_pdf_ocr=enable_pdf_ocr,
	verbose=kwargs['verbose'],
	n_jobs=kwargs['n_jobs'],
	)
	add_file_outputs = [fileup_output, langchain_mode]
	add_file_kwargs = dict(fn=update_db_func,
	inputs=[fileup_output, my_db_state, selection_docs_state, chunk, chunk_size,
	langchain_mode],
	outputs=add_file_outputs + [sources_text, doc_exception_text],
	queue=queue,
	api_name='add_file' if allow_api and allow_upload_to_user_data else None)

	# then no need for add buttons, only single changeable db
	eventdb1a = fileup_output.upload(make_non_interactive, inputs=add_file_outputs, outputs=add_file_outputs,
	show_progress='minimal')
	eventdb1 = eventdb1a.then(**add_file_kwargs, show_progress='full')
	eventdb1b = eventdb1.then(make_interactive, inputs=add_file_outputs, outputs=add_file_outputs,
	show_progress='minimal')

	# deal with challenge to have fileup_output itself as input
	add_file_kwargs2 = dict(fn=update_db_func,
	inputs=[fileup_output_text, my_db_state, selection_docs_state, chunk, chunk_size,
	langchain_mode],
	outputs=add_file_outputs + [sources_text, doc_exception_text],
	queue=queue,
	api_name='add_file_api' if allow_api and allow_upload_to_user_data else None)
	eventdb1_api = fileup_output_text.submit(**add_file_kwargs2, show_progress='full')

	# note for update_user_db_func output is ignored for db

	def clear_textbox():
	return gr.Textbox.update(value='')

	update_user_db_url_func = functools.partial(update_db_func, is_url=True)

	add_url_outputs = [url_text, langchain_mode]
	add_url_kwargs = dict(fn=update_user_db_url_func,
	inputs=[url_text, my_db_state, selection_docs_state, chunk, chunk_size,
	langchain_mode],
	outputs=add_url_outputs + [sources_text, doc_exception_text],
	queue=queue,
	api_name='add_url' if allow_api and allow_upload_to_user_data else None)

	eventdb2a = url_text.submit(fn=dummy_fun, inputs=url_text, outputs=url_text, queue=queue,
	show_progress='minimal')
	# work around https://github.com/gradio-app/gradio/issues/4733
	eventdb2b = eventdb2a.then(make_non_interactive, inputs=add_url_outputs, outputs=add_url_outputs,
	show_progress='minimal')
	eventdb2 = eventdb2b.then(**add_url_kwargs, show_progress='full')
	eventdb2c = eventdb2.then(make_interactive, inputs=add_url_outputs, outputs=add_url_outputs,
	show_progress='minimal')

	update_user_db_txt_func = functools.partial(update_db_func, is_txt=True)
	add_text_outputs = [user_text_text, langchain_mode]
	add_text_kwargs = dict(fn=update_user_db_txt_func,
	inputs=[user_text_text, my_db_state, selection_docs_state, chunk, chunk_size,
	langchain_mode],
	outputs=add_text_outputs + [sources_text, doc_exception_text],
	queue=queue,
	api_name='add_text' if allow_api and allow_upload_to_user_data else None
	)
	eventdb3a = user_text_text.submit(fn=dummy_fun, inputs=user_text_text, outputs=user_text_text, queue=queue,
	show_progress='minimal')
	eventdb3b = eventdb3a.then(make_non_interactive, inputs=add_text_outputs, outputs=add_text_outputs,
	show_progress='minimal')
	eventdb3 = eventdb3b.then(**add_text_kwargs, show_progress='full')
	eventdb3c = eventdb3.then(make_interactive, inputs=add_text_outputs, outputs=add_text_outputs,
	show_progress='minimal')
	db_events = [eventdb1a, eventdb1, eventdb1b, eventdb1_api,
	eventdb2a, eventdb2, eventdb2b, eventdb2c,
	eventdb3a, eventdb3b, eventdb3, eventdb3c]

	get_sources1 = functools.partial(get_sources, dbs=dbs, docs_state0=docs_state0)

	# if change collection source, must clear doc selections from it to avoid inconsistency
	def clear_doc_choice():
	return gr.Dropdown.update(choices=docs_state0, value=DocumentChoice.ALL.value)

	langchain_mode.change(clear_doc_choice, inputs=None, outputs=document_choice, queue=False)

	def resize_col_tabs(x):
	return gr.Dropdown.update(scale=x)

	col_tabs_scale.change(fn=resize_col_tabs, inputs=col_tabs_scale, outputs=col_tabs, queue=False)

	def resize_chatbots(x, num_model_lock=0):
	if num_model_lock == 0:
	num_model_lock = 3 # 2 + 1 (which is dup of first)
	else:
	num_model_lock = 2 + num_model_lock
	return tuple([gr.update(height=x)] * num_model_lock)

	resize_chatbots_func = functools.partial(resize_chatbots, num_model_lock=len(text_outputs))
	text_outputs_height.change(fn=resize_chatbots_func, inputs=text_outputs_height,
	outputs=[text_output, text_output2] + text_outputs, queue=False)

	def update_dropdown(x):
	return gr.Dropdown.update(choices=x, value=[docs_state0[0]])

	get_sources_args = dict(fn=get_sources1, inputs=[my_db_state, langchain_mode],
	outputs=[file_source, docs_state],
	queue=queue,
	api_name='get_sources' if allow_api else None)

	eventdb7 = get_sources_btn.click(**get_sources_args) \
	.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
	# show button, else only show when add. Could add to above get_sources for download/dropdown, but bit much maybe
	show_sources1 = functools.partial(get_source_files_given_langchain_mode, dbs=dbs)
	eventdb8 = show_sources_btn.click(fn=show_sources1, inputs=[my_db_state, langchain_mode], outputs=sources_text,
	api_name='show_sources' if allow_api else None)

	def update_viewable_dropdown(x):
	return gr.Dropdown.update(choices=x,
	value=viewable_docs_state0[0] if len(viewable_docs_state0) > 0 else None)

	get_viewable_sources1 = functools.partial(get_sources, dbs=dbs, docs_state0=viewable_docs_state0)
	get_viewable_sources_args = dict(fn=get_viewable_sources1, inputs=[my_db_state, langchain_mode],
	outputs=[file_source, viewable_docs_state],
	queue=queue,
	api_name='get_viewable_sources' if allow_api else None)
	eventdb12 = get_viewable_sources_btn.click(**get_viewable_sources_args) \
	.then(fn=update_viewable_dropdown, inputs=viewable_docs_state,
	outputs=view_document_choice)

	def show_doc(file):
	dummy1 = gr.update(visible=False, value=None)
	dummy_ret = dummy1, dummy1, dummy1, dummy1
	if not isinstance(file, str):
	return dummy_ret

	if file.endswith('.md'):
	try:
	with open(file, 'rt') as f:
	content = f.read()
	return dummy1, dummy1, dummy1, gr.update(visible=True, value=content)
	except:
	return dummy_ret

	if file.endswith('.py'):
	try:
	with open(file, 'rt') as f:
	content = f.read()
	content = f"```python\n{content}\n```"
	return dummy1, dummy1, dummy1, gr.update(visible=True, value=content)
	except:
	return dummy_ret

	if file.endswith('.txt') or file.endswith('.rst') or file.endswith('.rtf') or file.endswith('.toml'):
	try:
	with open(file, 'rt') as f:
	content = f.read()
	content = f"```text\n{content}\n```"
	return dummy1, dummy1, dummy1, gr.update(visible=True, value=content)
	except:
	return dummy_ret

	func = None
	if file.endswith(".csv"):
	func = pd.read_csv
	elif file.endswith(".pickle"):
	func = pd.read_pickle
	elif file.endswith(".xls") or file.endswith("xlsx"):
	func = pd.read_excel
	elif file.endswith('.json'):
	func = pd.read_json
	elif file.endswith('.xml'):
	func = pd.read_xml
	if func is not None:
	try:
	df = func(file).head(100)
	except:
	return dummy_ret
	return dummy1, gr.update(visible=True, value=df), dummy1, dummy1
	port = int(os.getenv('GRADIO_SERVER_PORT', '7860'))
	import pathlib
	absolute_path_string = os.path.abspath(file)
	url_path = pathlib.Path(absolute_path_string).as_uri()
	url = get_url(absolute_path_string, from_str=True)
	img_url = url.replace("""<a href=""", """<img src=""")
	if file.endswith('.png') or file.endswith('.jpg') or file.endswith('.jpeg'):
	return gr.update(visible=True, value=img_url), dummy1, dummy1, dummy1
	elif file.endswith('.pdf') or 'arxiv.org/pdf' in file:
	if file.startswith('http') or file.startswith('https'):
	# if file is online, then might as well use google(?)
	document1 = file
	return gr.update(visible=True,
	value=f"""<iframe width="1000" height="800" src="https://docs.google.com/viewerng/viewer?url={document1}&embedded=true" frameborder="0" height="100%" width="100%">
	</iframe>
	"""), dummy1, dummy1, dummy1
	else:
	ip = get_local_ip()
	document1 = url_path.replace('file://', f'http://{ip}:{port}/')
	# document1 = url
	return gr.update(visible=True, value=f"""<object data="{document1}" type="application/pdf">
	<iframe src="https://docs.google.com/viewer?url={document1}&embedded=true"></iframe>
	</object>"""), dummy1, dummy1, dummy1
	else:
	return dummy_ret

	view_document_choice.select(fn=show_doc, inputs=view_document_choice,
	outputs=[doc_view, doc_view2, doc_view3, doc_view4])

	# Get inputs to evaluate() and make_db()
	# don't deepcopy, can contain model itself
	all_kwargs = kwargs.copy()
	all_kwargs.update(locals())

	refresh_sources1 = functools.partial(update_and_get_source_files_given_langchain_mode,
	**get_kwargs(update_and_get_source_files_given_langchain_mode,
	exclude_names=['db1s', 'langchain_mode', 'chunk',
	'chunk_size'],
	**all_kwargs))
	eventdb9 = refresh_sources_btn.click(fn=refresh_sources1,
	inputs=[my_db_state, langchain_mode, chunk, chunk_size],
	outputs=sources_text,
	api_name='refresh_sources' if allow_api else None)

	def check_admin_pass(x):
	return gr.update(visible=x == admin_pass)

	def close_admin(x):
	return gr.update(visible=not (x == admin_pass))

	admin_pass_textbox.submit(check_admin_pass, inputs=admin_pass_textbox, outputs=system_row, queue=False) \
	.then(close_admin, inputs=admin_pass_textbox, outputs=admin_row, queue=False)

	def add_langchain_mode(db1s, selection_docs_state1, langchain_mode1, y):
	for k in db1s:
	set_userid(db1s[k])
	langchain_modes = selection_docs_state1['langchain_modes']
	langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
	visible_langchain_modes = selection_docs_state1['visible_langchain_modes']

	user_path = None
	valid = True
	y2 = y.strip().replace(' ', '').split(',')
	if len(y2) >= 1:
	langchain_mode2 = y2[0]
	if len(langchain_mode2) >= 3 and langchain_mode2.isalnum():
	# real restriction is:
	# ValueError: Expected collection name that (1) contains 3-63 characters, (2) starts and ends with an alphanumeric character, (3) otherwise contains only alphanumeric characters, underscores or hyphens (-), (4) contains no two consecutive periods (..) and (5) is not a valid IPv4 address, got me
	# but just make simpler
	user_path = y2[1] if len(y2) > 1 else None # assume scratch if don't have user_path
	if user_path in ['', "''"]:
	# for scratch spaces
	user_path = None
	if langchain_mode2 in langchain_modes_intrinsic:
	user_path = None
	textbox = "Invalid access to use internal name: %s" % langchain_mode2
	valid = False
	langchain_mode2 = langchain_mode1
	elif user_path and allow_upload_to_user_data or not user_path and allow_upload_to_my_data:
	langchain_mode_paths.update({langchain_mode2: user_path})
	if langchain_mode2 not in visible_langchain_modes:
	visible_langchain_modes.append(langchain_mode2)
	if langchain_mode2 not in langchain_modes:
	langchain_modes.append(langchain_mode2)
	textbox = ''
	if user_path:
	makedirs(user_path, exist_ok=True)
	else:
	valid = False
	langchain_mode2 = langchain_mode1
	textbox = "Invalid access. user allowed: %s " \
	"scratch allowed: %s" % (allow_upload_to_user_data, allow_upload_to_my_data)
	else:
	valid = False
	langchain_mode2 = langchain_mode1
	textbox = "Invalid, collection must be >=3 characters and alphanumeric"
	else:
	valid = False
	langchain_mode2 = langchain_mode1
	textbox = "Invalid, must be like UserData2, user_path2"
	selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1)
	df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
	choices = get_langchain_choices(selection_docs_state1)

	if valid and not user_path:
	# needs to have key for it to make it known different from userdata case in _update_user_db()
	db1s[langchain_mode2] = [None, None]
	if valid:
	save_collection_names(langchain_modes, visible_langchain_modes, langchain_mode_paths, LangChainMode,
	db1s)

	return db1s, selection_docs_state1, gr.update(choices=choices,
	value=langchain_mode2), textbox, df_langchain_mode_paths1

	def remove_langchain_mode(db1s, selection_docs_state1, langchain_mode1, langchain_mode2, dbsu=None):
	for k in db1s:
	set_userid(db1s[k])
	assert dbsu is not None
	langchain_modes = selection_docs_state1['langchain_modes']
	langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
	visible_langchain_modes = selection_docs_state1['visible_langchain_modes']

	if langchain_mode2 in db1s and not allow_upload_to_my_data or \
	dbsu is not None and langchain_mode2 in dbsu and not allow_upload_to_user_data or \
	langchain_mode2 in langchain_modes_intrinsic:
	# NOTE: Doesn't fail if remove MyData, but didn't debug odd behavior seen with upload after gone
	textbox = "Invalid access, cannot remove %s" % langchain_mode2
	df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
	else:
	# change global variables
	if langchain_mode2 in visible_langchain_modes:
	visible_langchain_modes.remove(langchain_mode2)
	textbox = ""
	else:
	textbox = "%s was not visible" % langchain_mode2
	if langchain_mode2 in langchain_modes:
	langchain_modes.remove(langchain_mode2)
	if langchain_mode2 in langchain_mode_paths:
	langchain_mode_paths.pop(langchain_mode2)
	if langchain_mode2 in db1s:
	# remove db entirely, so not in list, else need to manage visible list in update_langchain_mode_paths()
	# FIXME: Remove location?
	if langchain_mode2 != LangChainMode.MY_DATA.value:
	# don't remove last MyData, used as user hash
	db1s.pop(langchain_mode2)
	# only show
	selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1)
	df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)

	save_collection_names(langchain_modes, visible_langchain_modes, langchain_mode_paths, LangChainMode,
	db1s)

	return db1s, selection_docs_state1, \
	gr.update(choices=get_langchain_choices(selection_docs_state1),
	value=langchain_mode2), textbox, df_langchain_mode_paths1

	new_langchain_mode_text.submit(fn=add_langchain_mode,
	inputs=[my_db_state, selection_docs_state, langchain_mode,
	new_langchain_mode_text],
	outputs=[my_db_state, selection_docs_state, langchain_mode,
	new_langchain_mode_text,
	langchain_mode_path_text],
	api_name='new_langchain_mode_text' if allow_api and allow_upload_to_user_data else None)
	remove_langchain_mode_func = functools.partial(remove_langchain_mode, dbsu=dbs)
	remove_langchain_mode_text.submit(fn=remove_langchain_mode_func,
	inputs=[my_db_state, selection_docs_state, langchain_mode,
	remove_langchain_mode_text],
	outputs=[my_db_state, selection_docs_state, langchain_mode,
	remove_langchain_mode_text,
	langchain_mode_path_text],
	api_name='remove_langchain_mode_text' if allow_api and allow_upload_to_user_data else None)

	def update_langchain_gr(db1s, selection_docs_state1, langchain_mode1):
	for k in db1s:
	set_userid(db1s[k])
	langchain_modes = selection_docs_state1['langchain_modes']
	langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
	visible_langchain_modes = selection_docs_state1['visible_langchain_modes']
	# in-place

	# update user collaborative collections
	update_langchain(langchain_modes, visible_langchain_modes, langchain_mode_paths, '')
	# update scratch single-user collections
	user_hash = db1s.get(LangChainMode.MY_DATA.value, '')[1]
	update_langchain(langchain_modes, visible_langchain_modes, langchain_mode_paths, user_hash)

	selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1)
	df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1)
	return selection_docs_state1, \
	gr.update(choices=get_langchain_choices(selection_docs_state1),
	value=langchain_mode1), df_langchain_mode_paths1

	load_langchain.click(fn=update_langchain_gr,
	inputs=[my_db_state, selection_docs_state, langchain_mode],
	outputs=[selection_docs_state, langchain_mode, langchain_mode_path_text],
	api_name='load_langchain' if allow_api and allow_upload_to_user_data else None)

	inputs_list, inputs_dict = get_inputs_list(all_kwargs, kwargs['model_lower'], model_id=1)
	inputs_list2, inputs_dict2 = get_inputs_list(all_kwargs, kwargs['model_lower'], model_id=2)
	from functools import partial
	kwargs_evaluate = {k: v for k, v in all_kwargs.items() if k in inputs_kwargs_list}
	# ensure present
	for k in inputs_kwargs_list:
	assert k in kwargs_evaluate, "Missing %s" % k

	def evaluate_nochat(args1, default_kwargs1=None, str_api=False, *kwargs1):
	args_list = list(args1)
	if str_api:
	user_kwargs = args_list[len(input_args_list)]
	assert isinstance(user_kwargs, str)
	user_kwargs = ast.literal_eval(user_kwargs)
	else:
	user_kwargs = {k: v for k, v in zip(eval_func_param_names, args_list[len(input_args_list):])}
	# only used for submit_nochat_api
	user_kwargs['chat'] = False
	if 'stream_output' not in user_kwargs:
	user_kwargs['stream_output'] = False
	if 'langchain_mode' not in user_kwargs:
	# if user doesn't specify, then assume disabled, not use default
	user_kwargs['langchain_mode'] = 'Disabled'
	if 'langchain_action' not in user_kwargs:
	user_kwargs['langchain_action'] = LangChainAction.QUERY.value
	if 'langchain_agents' not in user_kwargs:
	user_kwargs['langchain_agents'] = []

	set1 = set(list(default_kwargs1.keys()))
	set2 = set(eval_func_param_names)
	assert set1 == set2, "Set diff: %s %s: %s" % (set1, set2, set1.symmetric_difference(set2))
	# correct ordering. Note some things may not be in default_kwargs, so can't be default of user_kwargs.get()
	model_state1 = args_list[0]
	my_db_state1 = args_list[1]
	selection_docs_state1 = args_list[2]
	args_list = [user_kwargs[k] if k in user_kwargs and user_kwargs[k] is not None else default_kwargs1[k] for k
	in eval_func_param_names]
	assert len(args_list) == len(eval_func_param_names)
	args_list = [model_state1, my_db_state1, selection_docs_state1] + args_list

	try:
	for res_dict in evaluate(tuple(args_list), *kwargs1):
	if str_api:
	# full return of dict
	yield res_dict
	elif kwargs['langchain_mode'] == 'Disabled':
	yield fix_text_for_gradio(res_dict['response'])
	else:
	yield '<br>' + fix_text_for_gradio(res_dict['response'])
	finally:
	clear_torch_cache()
	clear_embeddings(user_kwargs['langchain_mode'], my_db_state1)

	fun = partial(evaluate_nochat,
	default_kwargs1=default_kwargs,
	str_api=False,
	**kwargs_evaluate)
	fun2 = partial(evaluate_nochat,
	default_kwargs1=default_kwargs,
	str_api=False,
	**kwargs_evaluate)
	fun_with_dict_str = partial(evaluate_nochat,
	default_kwargs1=default_kwargs,
	str_api=True,
	**kwargs_evaluate
	)

	dark_mode_btn.click(
	None,
	None,
	None,
	_js=get_dark_js(),
	api_name="dark" if allow_api else None,
	queue=False,
	)

	def visible_toggle(x):
	x = 'off' if x == 'on' else 'on'
	return x, gr.Column.update(visible=True if x == 'on' else False)

	side_bar_btn.click(fn=visible_toggle,
	inputs=side_bar_text,
	outputs=[side_bar_text, side_bar],
	queue=False)

	submit_buttons_btn.click(fn=visible_toggle,
	inputs=submit_buttons_text,
	outputs=[submit_buttons_text, submit_buttons],
	queue=False)

	# examples after submit or any other buttons for chat or no chat
	if kwargs['examples'] is not None and kwargs['show_examples']:
	gr.Examples(examples=kwargs['examples'], inputs=inputs_list)

	# Score
	def score_last_response(*args, nochat=False, num_model_lock=0):
	try:
	if num_model_lock > 0:
	# then lock way
	args_list = list(args).copy()
	outputs = args_list[-num_model_lock:]
	score_texts1 = []
	for output in outputs:
	# same input, put into form good for _score_last_response()
	args_list[-1] = output
	score_texts1.append(
	_score_last_response(*tuple(args_list), nochat=nochat,
	num_model_lock=num_model_lock, prefix=''))
	if len(score_texts1) > 1:
	return "Response Scores: %s" % ' '.join(score_texts1)
	else:
	return "Response Scores: %s" % score_texts1[0]
	else:
	return _score_last_response(*args, nochat=nochat, num_model_lock=num_model_lock)
	finally:
	clear_torch_cache()

	def _score_last_response(*args, nochat=False, num_model_lock=0, prefix='Response Score: '):
	""" Similar to user() """
	args_list = list(args)
	smodel = score_model_state0['model']
	stokenizer = score_model_state0['tokenizer']
	sdevice = score_model_state0['device']

	if memory_restriction_level > 0:
	max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256
	elif hasattr(stokenizer, 'model_max_length'):
	max_length_tokenize = stokenizer.model_max_length
	else:
	# limit to 1024, not worth OOMing on reward score
	max_length_tokenize = 2048 - 1024
	cutoff_len = max_length_tokenize * 4 # restrict deberta related to max for LLM

	if not nochat:
	history = args_list[-1]
	if history is None:
	history = []
	if smodel is not None and \
	stokenizer is not None and \
	sdevice is not None and \
	history is not None and len(history) > 0 and \
	history[-1] is not None and \
	len(history[-1]) >= 2:
	os.environ['TOKENIZERS_PARALLELISM'] = 'false'

	question = history[-1][0]

	answer = history[-1][1]
	else:
	return '%sNA' % prefix
	else:
	answer = args_list[-1]
	instruction_nochat_arg_id = eval_func_param_names.index('instruction_nochat')
	question = args_list[instruction_nochat_arg_id]

	if question is None:
	return '%sBad Question' % prefix
	if answer is None:
	return '%sBad Answer' % prefix
	try:
	score = score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len)
	finally:
	clear_torch_cache()
	if isinstance(score, str):
	return '%sNA' % prefix
	return '{}{:.1%}'.format(prefix, score)

	def noop_score_last_response(args, *kwargs):
	return "Response Score: Disabled"

	if kwargs['score_model']:
	score_fun = score_last_response
	else:
	score_fun = noop_score_last_response

	score_args = dict(fn=score_fun,
	inputs=inputs_list + [text_output],
	outputs=[score_text],
	)
	score_args2 = dict(fn=partial(score_fun),
	inputs=inputs_list2 + [text_output2],
	outputs=[score_text2],
	)
	score_fun_func = functools.partial(score_fun, num_model_lock=len(text_outputs))
	all_score_args = dict(fn=score_fun_func,
	inputs=inputs_list + text_outputs,
	outputs=score_text,
	)

	score_args_nochat = dict(fn=partial(score_fun, nochat=True),
	inputs=inputs_list + [text_output_nochat],
	outputs=[score_text_nochat],
	)

	def update_history(*args, undo=False, retry=False, sanitize_user_prompt=False):
	"""
	User that fills history for bot
	:param args:
	:param undo:
	:param retry:
	:param sanitize_user_prompt:
	:return:
	"""
	args_list = list(args)
	user_message = args_list[eval_func_param_names.index('instruction')] # chat only
	input1 = args_list[eval_func_param_names.index('iinput')] # chat only
	prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
	langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
	langchain_action1 = args_list[eval_func_param_names.index('langchain_action')]
	langchain_agents1 = args_list[eval_func_param_names.index('langchain_agents')]
	document_subset1 = args_list[eval_func_param_names.index('document_subset')]
	document_choice1 = args_list[eval_func_param_names.index('document_choice')]
	if not prompt_type1:
	# shouldn't have to specify if CLI launched model
	prompt_type1 = kwargs['prompt_type']
	# apply back
	args_list[eval_func_param_names.index('prompt_type')] = prompt_type1
	if input1 and not user_message.endswith(':'):
	user_message1 = user_message + ":" + input1
	elif input1:
	user_message1 = user_message + input1
	else:
	user_message1 = user_message
	if sanitize_user_prompt:
	from better_profanity import profanity
	user_message1 = profanity.censor(user_message1)

	history = args_list[-1]
	if history is None:
	# bad history
	history = []
	history = history.copy()

	if undo:
	if len(history) > 0:
	history.pop()
	return history
	if retry:
	if history:
	history[-1][1] = None
	return history
	if user_message1 in ['', None, '\n']:
	if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
	# reject non-retry submit/enter
	return history
	user_message1 = fix_text_for_gradio(user_message1)
	return history + [[user_message1, None]]

	def user(*args, undo=False, retry=False, sanitize_user_prompt=False):
	return update_history(*args, undo=undo, retry=retry, sanitize_user_prompt=sanitize_user_prompt)

	def all_user(*args, undo=False, retry=False, sanitize_user_prompt=False, num_model_lock=0):
	args_list = list(args)
	history_list = args_list[-num_model_lock:]
	assert len(history_list) > 0, "Bad history list: %s" % history_list
	for hi, history in enumerate(history_list):
	if num_model_lock > 0:
	hargs = args_list[:-num_model_lock].copy()
	else:
	hargs = args_list.copy()
	hargs += [history]
	history_list[hi] = update_history(*hargs, undo=undo, retry=retry,
	sanitize_user_prompt=sanitize_user_prompt)
	if len(history_list) > 1:
	return tuple(history_list)
	else:
	return history_list[0]

	def get_model_max_length(model_state1):
	if model_state1 and not isinstance(model_state1["tokenizer"], str):
	tokenizer = model_state1["tokenizer"]
	elif model_state0 and not isinstance(model_state0["tokenizer"], str):
	tokenizer = model_state0["tokenizer"]
	else:
	tokenizer = None
	if tokenizer is not None:
	return tokenizer.model_max_length
	else:
	return 2000

	def prep_bot(*args, retry=False, which_model=0):
	"""

	:param args:
	:param retry:
	:param which_model: identifies which model if doing model_lock
	API only called for which_model=0, default for inputs_list, but rest should ignore inputs_list
	:return: last element is True if should run bot, False if should just yield history
	"""
	isize = len(input_args_list) + 1 # states + chat history
	# don't deepcopy, can contain model itself
	args_list = list(args).copy()
	model_state1 = args_list[-isize]
	my_db_state1 = args_list[-isize + 1]
	selection_docs_state1 = args_list[-isize + 2]
	history = args_list[-1]
	prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
	prompt_dict1 = args_list[eval_func_param_names.index('prompt_dict')]

	if model_state1['model'] is None or model_state1['model'] == no_model_str:
	return history, None, None, None

	args_list = args_list[:-isize] # only keep rest needed for evaluate()
	langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
	add_chat_history_to_context1 = args_list[eval_func_param_names.index('add_chat_history_to_context')]
	langchain_action1 = args_list[eval_func_param_names.index('langchain_action')]
	langchain_agents1 = args_list[eval_func_param_names.index('langchain_agents')]
	document_subset1 = args_list[eval_func_param_names.index('document_subset')]
	document_choice1 = args_list[eval_func_param_names.index('document_choice')]
	if not history:
	print("No history", flush=True)
	history = []
	return history, None, None, None
	instruction1 = history[-1][0]
	if retry and history:
	# if retry, pop history and move onto bot stuff
	instruction1 = history[-1][0]
	history[-1][1] = None
	elif not instruction1:
	if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
	# if not retrying, then reject empty query
	return history, None, None, None
	elif len(history) > 0 and history[-1][1] not in [None, '']:
	# reject submit button if already filled and not retrying
	# None when not filling with '' to keep client happy
	return history, None, None, None

	# shouldn't have to specify in API prompt_type if CLI launched model, so prefer global CLI one if have it
	prompt_type1, prompt_dict1 = update_prompt(prompt_type1, prompt_dict1, model_state1,
	which_model=which_model)
	# apply back to args_list for evaluate()
	args_list[eval_func_param_names.index('prompt_type')] = prompt_type1
	args_list[eval_func_param_names.index('prompt_dict')] = prompt_dict1

	chat1 = args_list[eval_func_param_names.index('chat')]
	model_max_length1 = get_model_max_length(model_state1)
	context1 = history_to_context(history, langchain_mode1,
	add_chat_history_to_context1,
	prompt_type1, prompt_dict1, chat1,
	model_max_length1, memory_restriction_level,
	kwargs['keep_sources_in_context'])
	args_list[0] = instruction1 # override original instruction with history from user
	args_list[2] = context1

	fun1 = partial(evaluate,
	model_state1,
	my_db_state1,
	selection_docs_state1,
	*tuple(args_list),
	**kwargs_evaluate)

	return history, fun1, langchain_mode1, my_db_state1

	def get_response(fun1, history):
	"""
	bot that consumes history for user input
	instruction (from input_list) itself is not consumed by bot
	:return:
	"""
	if not fun1:
	yield history, ''
	return
	try:
	for output_fun in fun1():
	output = output_fun['response']
	extra = output_fun['sources'] # FIXME: can show sources in separate text box etc.
	# ensure good visually, else markdown ignores multiple \n
	bot_message = fix_text_for_gradio(output)
	history[-1][1] = bot_message
	yield history, ''
	except StopIteration:
	yield history, ''
	except RuntimeError as e:
	if "generator raised StopIteration" in str(e):
	# assume last entry was bad, undo
	history.pop()
	yield history, ''
	else:
	if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None:
	history[-1][1] = ''
	yield history, str(e)
	raise
	except Exception as e:
	# put error into user input
	ex = "Exception: %s" % str(e)
	if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None:
	history[-1][1] = ''
	yield history, ex
	raise
	finally:
	clear_torch_cache()
	return

	def clear_embeddings(langchain_mode1, db1s):
	# clear any use of embedding that sits on GPU, else keeps accumulating GPU usage even if clear torch cache
	if db_type == 'chroma' and langchain_mode1 not in ['LLM', 'Disabled', None, '']:
	from gpt_langchain import clear_embedding
	db = dbs.get('langchain_mode1')
	if db is not None and not isinstance(db, str):
	clear_embedding(db)
	if db1s is not None and langchain_mode1 in db1s:
	db1 = db1s[langchain_mode1]
	if len(db1) == 2:
	clear_embedding(db1[0])

	def bot(*args, retry=False):
	history, fun1, langchain_mode1, db1 = prep_bot(*args, retry=retry)
	try:
	for res in get_response(fun1, history):
	yield res
	finally:
	clear_torch_cache()
	clear_embeddings(langchain_mode1, db1)

	def all_bot(*args, retry=False, model_states1=None):
	args_list = list(args).copy()
	chatbots = args_list[-len(model_states1):]
	args_list0 = args_list[:-len(model_states1)] # same for all models
	exceptions = []
	stream_output1 = args_list[eval_func_param_names.index('stream_output')]
	max_time1 = args_list[eval_func_param_names.index('max_time')]
	langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
	isize = len(input_args_list) + 1 # states + chat history
	db1s = None
	try:
	gen_list = []
	for chatboti, (chatbot1, model_state1) in enumerate(zip(chatbots, model_states1)):
	args_list1 = args_list0.copy()
	args_list1.insert(-isize + 2,
	model_state1) # insert at -2 so is at -3, and after chatbot1 added, at -4
	# if at start, have None in response still, replace with '' so client etc. acts like normal
	# assumes other parts of code treat '' and None as if no response yet from bot
	# can't do this later in bot code as racy with threaded generators
	if len(chatbot1) > 0 and len(chatbot1[-1]) == 2 and chatbot1[-1][1] is None:
	chatbot1[-1][1] = ''
	args_list1.append(chatbot1)
	# so consistent with prep_bot()
	# with model_state1 at -3, my_db_state1 at -2, and history(chatbot) at -1
	# langchain_mode1 and my_db_state1 should be same for every bot
	history, fun1, langchain_mode1, db1s = prep_bot(*tuple(args_list1), retry=retry,
	which_model=chatboti)
	gen1 = get_response(fun1, history)
	if stream_output1:
	gen1 = TimeoutIterator(gen1, timeout=0.01, sentinel=None, raise_on_exception=False)
	# else timeout will truncate output for non-streaming case
	gen_list.append(gen1)

	bots_old = chatbots.copy()
	exceptions_old = [''] * len(bots_old)
	tgen0 = time.time()
	for res1 in itertools.zip_longest(*gen_list):
	if time.time() - tgen0 > max_time1:
	print("Took too long: %s" % max_time1, flush=True)
	break

	bots = [x[0] if x is not None and not isinstance(x, BaseException) else y for x, y in
	zip(res1, bots_old)]
	bots_old = bots.copy()

	def larger_str(x, y):
	return x if len(x) > len(y) else y

	exceptions = [x[1] if x is not None and not isinstance(x, BaseException) else larger_str(str(x), y)
	for x, y in zip(res1, exceptions_old)]
	exceptions_old = exceptions.copy()

	def choose_exc(x):
	# don't expose ports etc. to exceptions window
	if is_public:
	return "Endpoint unavailable or failed"
	else:
	return x

	exceptions_str = '\n'.join(
	['Model %s: %s' % (iix, choose_exc(x)) for iix, x in enumerate(exceptions) if
	x not in [None, '', 'None']])
	if len(bots) > 1:
	yield tuple(bots + [exceptions_str])
	else:
	yield bots[0], exceptions_str
	if exceptions:
	exceptions = [x for x in exceptions if x not in ['', None, 'None']]
	if exceptions:
	print("Generate exceptions: %s" % exceptions, flush=True)
	finally:
	clear_torch_cache()
	clear_embeddings(langchain_mode1, db1s)

	# NORMAL MODEL
	user_args = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']),
	inputs=inputs_list + [text_output],
	outputs=text_output,
	)
	bot_args = dict(fn=bot,
	inputs=inputs_list + [model_state, my_db_state, selection_docs_state] + [text_output],
	outputs=[text_output, chat_exception_text],
	)
	retry_bot_args = dict(fn=functools.partial(bot, retry=True),
	inputs=inputs_list + [model_state, my_db_state, selection_docs_state] + [text_output],
	outputs=[text_output, chat_exception_text],
	)
	retry_user_args = dict(fn=functools.partial(user, retry=True),
	inputs=inputs_list + [text_output],
	outputs=text_output,
	)
	undo_user_args = dict(fn=functools.partial(user, undo=True),
	inputs=inputs_list + [text_output],
	outputs=text_output,
	)

	# MODEL2
	user_args2 = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']),
	inputs=inputs_list2 + [text_output2],
	outputs=text_output2,
	)
	bot_args2 = dict(fn=bot,
	inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state] + [text_output2],
	outputs=[text_output2, chat_exception_text],
	)
	retry_bot_args2 = dict(fn=functools.partial(bot, retry=True),
	inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state] + [text_output2],
	outputs=[text_output2, chat_exception_text],
	)
	retry_user_args2 = dict(fn=functools.partial(user, retry=True),
	inputs=inputs_list2 + [text_output2],
	outputs=text_output2,
	)
	undo_user_args2 = dict(fn=functools.partial(user, undo=True),
	inputs=inputs_list2 + [text_output2],
	outputs=text_output2,
	)

	# MODEL N
	all_user_args = dict(fn=functools.partial(all_user,
	sanitize_user_prompt=kwargs['sanitize_user_prompt'],
	num_model_lock=len(text_outputs),
	),
	inputs=inputs_list + text_outputs,
	outputs=text_outputs,
	)
	all_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states),
	inputs=inputs_list + [my_db_state, selection_docs_state] + text_outputs,
	outputs=text_outputs + [chat_exception_text],
	)
	all_retry_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states, retry=True),
	inputs=inputs_list + [my_db_state, selection_docs_state] + text_outputs,
	outputs=text_outputs + [chat_exception_text],
	)
	all_retry_user_args = dict(fn=functools.partial(all_user, retry=True,
	sanitize_user_prompt=kwargs['sanitize_user_prompt'],
	num_model_lock=len(text_outputs),
	),
	inputs=inputs_list + text_outputs,
	outputs=text_outputs,
	)
	all_undo_user_args = dict(fn=functools.partial(all_user, undo=True,
	sanitize_user_prompt=kwargs['sanitize_user_prompt'],
	num_model_lock=len(text_outputs),
	),
	inputs=inputs_list + text_outputs,
	outputs=text_outputs,
	)

	def clear_instruct():
	return gr.Textbox.update(value='')

	def deselect_radio_chats():
	return gr.update(value=None)

	def clear_all():
	return gr.Textbox.update(value=''), gr.Textbox.update(value=''), gr.update(value=None), \
	gr.Textbox.update(value=''), gr.Textbox.update(value='')

	if kwargs['model_states']:
	submits1 = submits2 = submits3 = []
	submits4 = []

	fun_source = [instruction.submit, submit.click, retry_btn.click]
	fun_name = ['instruction', 'submit', 'retry']
	user_args = [all_user_args, all_user_args, all_retry_user_args]
	bot_args = [all_bot_args, all_bot_args, all_retry_bot_args]
	for userargs1, botarg1, funn1, funs1 in zip(user_args, bot_args, fun_name, fun_source):
	submit_event11 = funs1(fn=dummy_fun,
	inputs=instruction, outputs=instruction, queue=queue)
	submit_event1a = submit_event11.then(**userargs1, queue=queue,
	api_name='%s' % funn1 if allow_api else None)
	# if hit enter on new instruction for submitting new query, no longer the saved chat
	submit_event1b = submit_event1a.then(clear_all, inputs=None,
	outputs=[instruction, iinput, radio_chats, score_text,
	score_text2],
	queue=queue)
	submit_event1c = submit_event1b.then(**botarg1,
	api_name='%s_bot' % funn1 if allow_api else None,
	queue=queue)
	submit_event1d = submit_event1c.then(**all_score_args,
	api_name='%s_bot_score' % funn1 if allow_api else None,
	queue=queue)

	submits1.extend([submit_event1a, submit_event1b, submit_event1c, submit_event1d])

	# if undo, no longer the saved chat
	submit_event4 = undo.click(fn=dummy_fun,
	inputs=instruction, outputs=instruction, queue=queue) \
	.then(**all_undo_user_args, api_name='undo' if allow_api else None) \
	.then(clear_all, inputs=None, outputs=[instruction, iinput, radio_chats, score_text,
	score_text2], queue=queue) \
	.then(**all_score_args, api_name='undo_score' if allow_api else None)
	submits4 = [submit_event4]

	else:
	# in case 2nd model, consume instruction first, so can clear quickly
	# bot doesn't consume instruction itself, just history from user, so why works
	submit_event11 = instruction.submit(fn=dummy_fun,
	inputs=instruction, outputs=instruction, queue=queue)
	submit_event1a = submit_event11.then(**user_args, queue=queue,
	api_name='instruction' if allow_api else None)
	# if hit enter on new instruction for submitting new query, no longer the saved chat
	submit_event1a2 = submit_event1a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue)
	submit_event1b = submit_event1a2.then(**user_args2, api_name='instruction2' if allow_api else None)
	submit_event1c = submit_event1b.then(clear_instruct, None, instruction) \
	.then(clear_instruct, None, iinput)
	submit_event1d = submit_event1c.then(**bot_args, api_name='instruction_bot' if allow_api else None,
	queue=queue)
	submit_event1e = submit_event1d.then(**score_args,
	api_name='instruction_bot_score' if allow_api else None,
	queue=queue)
	submit_event1f = submit_event1e.then(**bot_args2, api_name='instruction_bot2' if allow_api else None,
	queue=queue)
	submit_event1g = submit_event1f.then(**score_args2,
	api_name='instruction_bot_score2' if allow_api else None, queue=queue)

	submits1 = [submit_event1a, submit_event1a2, submit_event1b, submit_event1c, submit_event1d,
	submit_event1e,
	submit_event1f, submit_event1g]

	submit_event21 = submit.click(fn=dummy_fun,
	inputs=instruction, outputs=instruction, queue=queue)
	submit_event2a = submit_event21.then(**user_args, api_name='submit' if allow_api else None)
	# if submit new query, no longer the saved chat
	submit_event2a2 = submit_event2a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue)
	submit_event2b = submit_event2a2.then(**user_args2, api_name='submit2' if allow_api else None)
	submit_event2c = submit_event2b.then(clear_all, inputs=None,
	outputs=[instruction, iinput, radio_chats, score_text, score_text2],
	queue=queue)
	submit_event2d = submit_event2c.then(**bot_args, api_name='submit_bot' if allow_api else None, queue=queue)
	submit_event2e = submit_event2d.then(**score_args,
	api_name='submit_bot_score' if allow_api else None,
	queue=queue)
	submit_event2f = submit_event2e.then(**bot_args2, api_name='submit_bot2' if allow_api else None,
	queue=queue)
	submit_event2g = submit_event2f.then(**score_args2,
	api_name='submit_bot_score2' if allow_api else None,
	queue=queue)

	submits2 = [submit_event2a, submit_event2a2, submit_event2b, submit_event2c, submit_event2d,
	submit_event2e,
	submit_event2f, submit_event2g]

	submit_event31 = retry_btn.click(fn=dummy_fun,
	inputs=instruction, outputs=instruction, queue=queue)
	submit_event3a = submit_event31.then(**user_args, api_name='retry' if allow_api else None)
	# if retry, no longer the saved chat
	submit_event3a2 = submit_event3a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue)
	submit_event3b = submit_event3a2.then(**user_args2, api_name='retry2' if allow_api else None)
	submit_event3c = submit_event3b.then(clear_instruct, None, instruction) \
	.then(clear_instruct, None, iinput)
	submit_event3d = submit_event3c.then(**retry_bot_args, api_name='retry_bot' if allow_api else None,
	queue=queue)
	submit_event3e = submit_event3d.then(**score_args,
	api_name='retry_bot_score' if allow_api else None,
	queue=queue)
	submit_event3f = submit_event3e.then(**retry_bot_args2, api_name='retry_bot2' if allow_api else None,
	queue=queue)
	submit_event3g = submit_event3f.then(**score_args2,
	api_name='retry_bot_score2' if allow_api else None,
	queue=queue)

	submits3 = [submit_event3a, submit_event3a2, submit_event3b, submit_event3c, submit_event3d,
	submit_event3e,
	submit_event3f, submit_event3g]

	# if undo, no longer the saved chat
	submit_event4 = undo.click(fn=dummy_fun,
	inputs=instruction, outputs=instruction, queue=queue) \
	.then(**undo_user_args, api_name='undo' if allow_api else None) \
	.then(**undo_user_args2, api_name='undo2' if allow_api else None) \
	.then(clear_all, inputs=None, outputs=[instruction, iinput, radio_chats, score_text,
	score_text2], queue=queue) \
	.then(**score_args, api_name='undo_score' if allow_api else None) \
	.then(**score_args2, api_name='undo_score2' if allow_api else None)
	submits4 = [submit_event4]

	# MANAGE CHATS
	def dedup(short_chat, short_chats):
	if short_chat not in short_chats:
	return short_chat
	for i in range(1, 1000):
	short_chat_try = short_chat + "_" + str(i)
	if short_chat_try not in short_chats:
	return short_chat_try
	# fallback and hope for best
	short_chat = short_chat + "_" + str(random.random())
	return short_chat

	def get_short_chat(x, short_chats, short_len=20, words=4):
	if x and len(x[0]) == 2 and x[0][0] is not None:
	short_chat = ' '.join(x[0][0][:short_len].split(' ')[:words]).strip()
	if not short_chat:
	# e.g.summarization, try using answer
	short_chat = ' '.join(x[0][1][:short_len].split(' ')[:words]).strip()
	if not short_chat:
	short_chat = 'Unk'
	short_chat = dedup(short_chat, short_chats)
	else:
	short_chat = None
	return short_chat

	def is_chat_same(x, y):
	# <p> etc. added in chat, try to remove some of that to help avoid dup entries when hit new conversation
	is_same = True
	# length of conversation has to be same
	if len(x) != len(y):
	return False
	if len(x) != len(y):
	return False
	for stepx, stepy in zip(x, y):
	if len(stepx) != len(stepy):
	# something off with a conversation
	return False
	for stepxx, stepyy in zip(stepx, stepy):
	if len(stepxx) != len(stepyy):
	# something off with a conversation
	return False
	if len(stepxx) != 2:
	# something off
	return False
	if len(stepyy) != 2:
	# something off
	return False
	questionx = stepxx[0].replace('<p>', '').replace('</p>', '') if stepxx[0] is not None else None
	answerx = stepxx[1].replace('<p>', '').replace('</p>', '') if stepxx[1] is not None else None

	questiony = stepyy[0].replace('<p>', '').replace('</p>', '') if stepyy[0] is not None else None
	answery = stepyy[1].replace('<p>', '').replace('</p>', '') if stepyy[1] is not None else None

	if questionx != questiony or answerx != answery:
	return False
	return is_same

	def save_chat(*args, chat_is_list=False):
	args_list = list(args)
	if not chat_is_list:
	# list of chatbot histories,
	# can't pass in list with list of chatbot histories and state due to gradio limits
	chat_list = args_list[:-1]
	else:
	assert len(args_list) == 2
	chat_list = args_list[0]
	# if old chat file with single chatbot, get into shape
	if isinstance(chat_list, list) and len(chat_list) > 0 and isinstance(chat_list[0], list) and len(
	chat_list[0]) == 2 and isinstance(chat_list[0][0], str) and isinstance(chat_list[0][1], str):
	chat_list = [chat_list]
	# remove None histories
	chat_list_not_none = [x for x in chat_list if x and len(x) > 0 and len(x[0]) == 2 and x[0][1] is not None]
	chat_list_none = [x for x in chat_list if x not in chat_list_not_none]
	if len(chat_list_none) > 0 and len(chat_list_not_none) == 0:
	raise ValueError("Invalid chat file")
	# dict with keys of short chat names, values of list of list of chatbot histories
	chat_state1 = args_list[-1]
	short_chats = list(chat_state1.keys())
	if len(chat_list_not_none) > 0:
	# make short_chat key from only first history, based upon question that is same anyways
	chat_first = chat_list_not_none[0]
	short_chat = get_short_chat(chat_first, short_chats)
	if short_chat:
	old_chat_lists = list(chat_state1.values())
	already_exists = any([is_chat_same(chat_list, x) for x in old_chat_lists])
	if not already_exists:
	chat_state1[short_chat] = chat_list.copy()

	# reverse so newest at top
	choices = list(chat_state1.keys()).copy()
	choices.reverse()

	return chat_state1, gr.update(choices=choices, value=None)

	def switch_chat(chat_key, chat_state1, num_model_lock=0):
	chosen_chat = chat_state1[chat_key]
	# deal with possible different size of chat list vs. current list
	ret_chat = [None] * (2 + num_model_lock)
	for chati in range(0, 2 + num_model_lock):
	ret_chat[chati % len(ret_chat)] = chosen_chat[chati % len(chosen_chat)]
	return tuple(ret_chat)

	def clear_texts(*args):
	return tuple([gr.Textbox.update(value='')] * len(args))

	def clear_scores():
	return gr.Textbox.update(value=res_value), \
	gr.Textbox.update(value='Response Score: NA'), \
	gr.Textbox.update(value='Response Score: NA')

	switch_chat_fun = functools.partial(switch_chat, num_model_lock=len(text_outputs))
	radio_chats.input(switch_chat_fun,
	inputs=[radio_chats, chat_state],
	outputs=[text_output, text_output2] + text_outputs) \
	.then(clear_scores, outputs=[score_text, score_text2, score_text_nochat])

	def remove_chat(chat_key, chat_state1):
	if isinstance(chat_key, str):
	chat_state1.pop(chat_key, None)
	return gr.update(choices=list(chat_state1.keys()), value=None), chat_state1

	remove_chat_event = remove_chat_btn.click(remove_chat,
	inputs=[radio_chats, chat_state], outputs=[radio_chats, chat_state],
	queue=False, api_name='remove_chat')

	def get_chats1(chat_state1):
	base = 'chats'
	makedirs(base, exist_ok=True)
	filename = os.path.join(base, 'chats_%s.json' % str(uuid.uuid4()))
	with open(filename, "wt") as f:
	f.write(json.dumps(chat_state1, indent=2))
	return filename

	export_chat_event = export_chats_btn.click(get_chats1, inputs=chat_state, outputs=chats_file, queue=False,
	api_name='export_chats' if allow_api else None)

	def add_chats_from_file(file, chat_state1, radio_chats1, chat_exception_text1):
	if not file:
	return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1
	if isinstance(file, str):
	files = [file]
	else:
	files = file
	if not files:
	return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1
	chat_exception_list = []
	for file1 in files:
	try:
	if hasattr(file1, 'name'):
	file1 = file1.name
	with open(file1, "rt") as f:
	new_chats = json.loads(f.read())
	for chat1_k, chat1_v in new_chats.items():
	# ignore chat1_k, regenerate and de-dup to avoid loss
	chat_state1, _ = save_chat(chat1_v, chat_state1, chat_is_list=True)
	except BaseException as e:
	t, v, tb = sys.exc_info()
	ex = ''.join(traceback.format_exception(t, v, tb))
	ex_str = "File %s exception: %s" % (file1, str(e))
	print(ex_str, flush=True)
	chat_exception_list.append(ex_str)
	chat_exception_text1 = '\n'.join(chat_exception_list)
	return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1

	# note for update_user_db_func output is ignored for db
	chatup_change_event = chatsup_output.change(add_chats_from_file,
	inputs=[chatsup_output, chat_state, radio_chats,
	chat_exception_text],
	outputs=[chatsup_output, chat_state, radio_chats,
	chat_exception_text],
	queue=False,
	api_name='add_to_chats' if allow_api else None)

	clear_chat_event = clear_chat_btn.click(fn=clear_texts,
	inputs=[text_output, text_output2] + text_outputs,
	outputs=[text_output, text_output2] + text_outputs,
	queue=False, api_name='clear' if allow_api else None) \
	.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False) \
	.then(clear_scores, outputs=[score_text, score_text2, score_text_nochat])

	clear_event = save_chat_btn.click(save_chat,
	inputs=[text_output, text_output2] + text_outputs + [chat_state],
	outputs=[chat_state, radio_chats],
	api_name='save_chat' if allow_api else None)
	if kwargs['score_model']:
	clear_event2 = clear_event.then(clear_scores, outputs=[score_text, score_text2, score_text_nochat])

	# NOTE: clear of instruction/iinput for nochat has to come after score,
	# because score for nochat consumes actual textbox, while chat consumes chat history filled by user()
	no_chat_args = dict(fn=fun,
	inputs=[model_state, my_db_state, selection_docs_state] + inputs_list,
	outputs=text_output_nochat,
	queue=queue,
	)
	submit_event_nochat = submit_nochat.click(**no_chat_args, api_name='submit_nochat' if allow_api else None) \
	.then(clear_torch_cache) \
	.then(**score_args_nochat, api_name='instruction_bot_score_nochat' if allow_api else None, queue=queue) \
	.then(clear_instruct, None, instruction_nochat) \
	.then(clear_instruct, None, iinput_nochat) \
	.then(clear_torch_cache)
	# copy of above with text box submission
	submit_event_nochat2 = instruction_nochat.submit(**no_chat_args) \
	.then(clear_torch_cache) \
	.then(**score_args_nochat, queue=queue) \
	.then(clear_instruct, None, instruction_nochat) \
	.then(clear_instruct, None, iinput_nochat) \
	.then(clear_torch_cache)

	submit_event_nochat_api = submit_nochat_api.click(fun_with_dict_str,
	inputs=[model_state, my_db_state, selection_docs_state,
	inputs_dict_str],
	outputs=text_output_nochat_api,
	queue=True, # required for generator
	api_name='submit_nochat_api' if allow_api else None) \
	.then(clear_torch_cache)

	def load_model(model_name, lora_weights, server_name, model_state_old, prompt_type_old, load_8bit,
	use_gpu_id, gpu_id):
	# ensure no API calls reach here
	if is_public:
	raise RuntimeError("Illegal access for %s" % model_name)
	# ensure old model removed from GPU memory
	if kwargs['debug']:
	print("Pre-switch pre-del GPU memory: %s" % get_torch_allocated(), flush=True)

	model0 = model_state0['model']
	if isinstance(model_state_old['model'], str) and model0 is not None:
	# best can do, move model loaded at first to CPU
	model0.cpu()

	if model_state_old['model'] is not None and not isinstance(model_state_old['model'], str):
	try:
	model_state_old['model'].cpu()
	except Exception as e:
	# sometimes hit NotImplementedError: Cannot copy out of meta tensor; no data!
	print("Unable to put model on CPU: %s" % str(e), flush=True)
	del model_state_old['model']
	model_state_old['model'] = None

	if model_state_old['tokenizer'] is not None and not isinstance(model_state_old['tokenizer'], str):
	del model_state_old['tokenizer']
	model_state_old['tokenizer'] = None

	clear_torch_cache()
	if kwargs['debug']:
	print("Pre-switch post-del GPU memory: %s" % get_torch_allocated(), flush=True)

	if model_name is None or model_name == no_model_str:
	# no-op if no model, just free memory
	# no detranscribe needed for model, never go into evaluate
	lora_weights = no_lora_str
	server_name = no_server_str
	return [None, None, None, model_name, server_name], \
	model_name, lora_weights, server_name, prompt_type_old, \
	gr.Slider.update(maximum=256), \
	gr.Slider.update(maximum=256)

	# don't deepcopy, can contain model itself
	all_kwargs1 = all_kwargs.copy()
	all_kwargs1['base_model'] = model_name.strip()
	all_kwargs1['load_8bit'] = load_8bit
	all_kwargs1['use_gpu_id'] = use_gpu_id
	all_kwargs1['gpu_id'] = int(gpu_id) # detranscribe
	model_lower = model_name.strip().lower()
	if model_lower in inv_prompt_type_to_model_lower:
	prompt_type1 = inv_prompt_type_to_model_lower[model_lower]
	else:
	prompt_type1 = prompt_type_old

	# detranscribe
	if lora_weights == no_lora_str:
	lora_weights = ''
	all_kwargs1['lora_weights'] = lora_weights.strip()
	if server_name == no_server_str:
	server_name = ''
	all_kwargs1['inference_server'] = server_name.strip()

	model1, tokenizer1, device1 = get_model(reward_type=False,
	**get_kwargs(get_model, exclude_names=['reward_type'],
	**all_kwargs1))
	clear_torch_cache()

	tokenizer_base_model = model_name
	prompt_dict1, error0 = get_prompt(prompt_type1, '',
	chat=False, context='', reduced=False, making_context=False,
	return_dict=True)
	model_state_new = dict(model=model1, tokenizer=tokenizer1, device=device1,
	base_model=model_name, tokenizer_base_model=tokenizer_base_model,
	lora_weights=lora_weights, inference_server=server_name,
	prompt_type=prompt_type1, prompt_dict=prompt_dict1,
	)

	max_max_new_tokens1 = get_max_max_new_tokens(model_state_new, **kwargs)

	if kwargs['debug']:
	print("Post-switch GPU memory: %s" % get_torch_allocated(), flush=True)
	return model_state_new, model_name, lora_weights, server_name, prompt_type1, \
	gr.Slider.update(maximum=max_max_new_tokens1), \
	gr.Slider.update(maximum=max_max_new_tokens1)

	def get_prompt_str(prompt_type1, prompt_dict1, which=0):
	if prompt_type1 in ['', None]:
	print("Got prompt_type %s: %s" % (which, prompt_type1), flush=True)
	return str({})
	prompt_dict1, prompt_dict_error = get_prompt(prompt_type1, prompt_dict1, chat=False, context='',
	reduced=False, making_context=False, return_dict=True)
	if prompt_dict_error:
	return str(prompt_dict_error)
	else:
	# return so user can manipulate if want and use as custom
	return str(prompt_dict1)

	get_prompt_str_func1 = functools.partial(get_prompt_str, which=1)
	get_prompt_str_func2 = functools.partial(get_prompt_str, which=2)
	prompt_type.change(fn=get_prompt_str_func1, inputs=[prompt_type, prompt_dict], outputs=prompt_dict, queue=False)
	prompt_type2.change(fn=get_prompt_str_func2, inputs=[prompt_type2, prompt_dict2], outputs=prompt_dict2,
	queue=False)

	def dropdown_prompt_type_list(x):
	return gr.Dropdown.update(value=x)

	def chatbot_list(x, model_used_in):
	return gr.Textbox.update(label=f'h2oGPT [Model: {model_used_in}]')

	load_model_args = dict(fn=load_model,
	inputs=[model_choice, lora_choice, server_choice, model_state, prompt_type,
	model_load8bit_checkbox, model_use_gpu_id_checkbox, model_gpu],
	outputs=[model_state, model_used, lora_used, server_used,
	# if prompt_type changes, prompt_dict will change via change rule
	prompt_type, max_new_tokens, min_new_tokens,
	])
	prompt_update_args = dict(fn=dropdown_prompt_type_list, inputs=prompt_type, outputs=prompt_type)
	chatbot_update_args = dict(fn=chatbot_list, inputs=[text_output, model_used], outputs=text_output)
	nochat_update_args = dict(fn=chatbot_list, inputs=[text_output_nochat, model_used], outputs=text_output_nochat)
	load_model_event = load_model_button.click(**load_model_args,
	api_name='load_model' if allow_api and is_public else None) \
	.then(**prompt_update_args) \
	.then(**chatbot_update_args) \
	.then(**nochat_update_args) \
	.then(clear_torch_cache)

	load_model_args2 = dict(fn=load_model,
	inputs=[model_choice2, lora_choice2, server_choice2, model_state2, prompt_type2,
	model_load8bit_checkbox2, model_use_gpu_id_checkbox2, model_gpu2],
	outputs=[model_state2, model_used2, lora_used2, server_used2,
	# if prompt_type2 changes, prompt_dict2 will change via change rule
	prompt_type2, max_new_tokens2, min_new_tokens2
	])
	prompt_update_args2 = dict(fn=dropdown_prompt_type_list, inputs=prompt_type2, outputs=prompt_type2)
	chatbot_update_args2 = dict(fn=chatbot_list, inputs=[text_output2, model_used2], outputs=text_output2)
	load_model_event2 = load_model_button2.click(**load_model_args2,
	api_name='load_model2' if allow_api and is_public else None) \
	.then(**prompt_update_args2) \
	.then(**chatbot_update_args2) \
	.then(clear_torch_cache)

	def dropdown_model_lora_server_list(model_list0, model_x,
	lora_list0, lora_x,
	server_list0, server_x,
	model_used1, lora_used1, server_used1,
	model_used2, lora_used2, server_used2,
	):
	model_new_state = [model_list0[0] + [model_x]]
	model_new_options = [*model_new_state[0]]
	x1 = model_x if model_used1 == no_model_str else model_used1
	x2 = model_x if model_used2 == no_model_str else model_used2
	ret1 = [gr.Dropdown.update(value=x1, choices=model_new_options),
	gr.Dropdown.update(value=x2, choices=model_new_options),
	'', model_new_state]

	lora_new_state = [lora_list0[0] + [lora_x]]
	lora_new_options = [*lora_new_state[0]]
	# don't switch drop-down to added lora if already have model loaded
	x1 = lora_x if model_used1 == no_model_str else lora_used1
	x2 = lora_x if model_used2 == no_model_str else lora_used2
	ret2 = [gr.Dropdown.update(value=x1, choices=lora_new_options),
	gr.Dropdown.update(value=x2, choices=lora_new_options),
	'', lora_new_state]

	server_new_state = [server_list0[0] + [server_x]]
	server_new_options = [*server_new_state[0]]
	# don't switch drop-down to added server if already have model loaded
	x1 = server_x if model_used1 == no_model_str else server_used1
	x2 = server_x if model_used2 == no_model_str else server_used2
	ret3 = [gr.Dropdown.update(value=x1, choices=server_new_options),
	gr.Dropdown.update(value=x2, choices=server_new_options),
	'', server_new_state]

	return tuple(ret1 + ret2 + ret3)

	add_model_lora_server_event = \
	add_model_lora_server_button.click(fn=dropdown_model_lora_server_list,
	inputs=[model_options_state, new_model] +
	[lora_options_state, new_lora] +
	[server_options_state, new_server] +
	[model_used, lora_used, server_used] +
	[model_used2, lora_used2, server_used2],
	outputs=[model_choice, model_choice2, new_model, model_options_state] +
	[lora_choice, lora_choice2, new_lora, lora_options_state] +
	[server_choice, server_choice2, new_server,
	server_options_state],
	queue=False)

	go_event = go_btn.click(lambda: gr.update(visible=False), None, go_btn, api_name="go" if allow_api else None,
	queue=False) \
	.then(lambda: gr.update(visible=True), None, normal_block, queue=False) \
	.then(load_model_args, queue=False).then(prompt_update_args, queue=False)

	def compare_textbox_fun(x):
	return gr.Textbox.update(visible=x)

	def compare_column_fun(x):
	return gr.Column.update(visible=x)

	def compare_prompt_fun(x):
	return gr.Dropdown.update(visible=x)

	def slider_fun(x):
	return gr.Slider.update(visible=x)

	compare_checkbox.select(compare_textbox_fun, compare_checkbox, text_output2,
	api_name="compare_checkbox" if allow_api else None) \
	.then(compare_column_fun, compare_checkbox, col_model2) \
	.then(compare_prompt_fun, compare_checkbox, prompt_type2) \
	.then(compare_textbox_fun, compare_checkbox, score_text2) \
	.then(slider_fun, compare_checkbox, max_new_tokens2) \
	.then(slider_fun, compare_checkbox, min_new_tokens2)
	# FIXME: add score_res2 in condition, but do better

	# callback for logging flagged input/output
	callback.setup(inputs_list + [text_output, text_output2] + text_outputs, "flagged_data_points")
	flag_btn.click(lambda *args: callback.flag(args), inputs_list + [text_output, text_output2] + text_outputs,
	None,
	preprocess=False,
	api_name='flag' if allow_api else None, queue=False)
	flag_btn_nochat.click(lambda *args: callback.flag(args), inputs_list + [text_output_nochat], None,
	preprocess=False,
	api_name='flag_nochat' if allow_api else None, queue=False)

	def get_system_info():
	if is_public:
	time.sleep(10) # delay to avoid spam since queue=False
	return gr.Textbox.update(value=system_info_print())

	system_event = system_btn.click(get_system_info, outputs=system_text,
	api_name='system_info' if allow_api else None, queue=False)

	def get_system_info_dict(system_input1, **kwargs1):
	if system_input1 != os.getenv("ADMIN_PASS", ""):
	return json.dumps({})
	exclude_list = ['admin_pass', 'examples']
	sys_dict = {k: v for k, v in kwargs1.items() if
	isinstance(v, (str, int, bool, float)) and k not in exclude_list}
	try:
	sys_dict.update(system_info())
	except Exception as e:
	# protection
	print("Exception: %s" % str(e), flush=True)
	return json.dumps(sys_dict)

	system_kwargs = all_kwargs.copy()
	system_kwargs.update(dict(command=str(' '.join(sys.argv))))
	get_system_info_dict_func = functools.partial(get_system_info_dict, **all_kwargs)

	system_dict_event = system_btn2.click(get_system_info_dict_func,
	inputs=system_input,
	outputs=system_text2,
	api_name='system_info_dict' if allow_api else None,
	queue=False, # queue to avoid spam
	)

	def get_hash():
	return kwargs['git_hash']

	system_event = system_btn3.click(get_hash,
	outputs=system_text3,
	api_name='system_hash' if allow_api else None,
	queue=False,
	)

	def count_chat_tokens(model_state1, chat1, prompt_type1, prompt_dict1,
	memory_restriction_level1=0,
	keep_sources_in_context1=False,
	):
	if model_state1 and not isinstance(model_state1['tokenizer'], str):
	tokenizer = model_state1['tokenizer']
	elif model_state0 and not isinstance(model_state0['tokenizer'], str):
	tokenizer = model_state0['tokenizer']
	else:
	tokenizer = None
	if tokenizer is not None:
	langchain_mode1 = 'LLM'
	add_chat_history_to_context1 = True
	# fake user message to mimic bot()
	chat1 = copy.deepcopy(chat1)
	chat1 = chat1 + [['user_message1', None]]
	model_max_length1 = tokenizer.model_max_length
	context1 = history_to_context(chat1, langchain_mode1,
	add_chat_history_to_context1,
	prompt_type1, prompt_dict1, chat1,
	model_max_length1,
	memory_restriction_level1, keep_sources_in_context1)
	return str(tokenizer(context1, return_tensors="pt")['input_ids'].shape[1])
	else:
	return "N/A"

	count_chat_tokens_func = functools.partial(count_chat_tokens,
	memory_restriction_level1=memory_restriction_level,
	keep_sources_in_context1=kwargs['keep_sources_in_context'])
	count_tokens_event = count_chat_tokens_btn.click(fn=count_chat_tokens,
	inputs=[model_state, text_output, prompt_type, prompt_dict],
	outputs=chat_token_count,
	api_name='count_tokens' if allow_api else None)

	# don't pass text_output, don't want to clear output, just stop it
	# cancel only stops outer generation, not inner generation or non-generation
	stop_btn.click(lambda: None, None, None,
	cancels=submits1 + submits2 + submits3 + submits4 +
	[submit_event_nochat, submit_event_nochat2] +
	[eventdb1, eventdb2, eventdb3] +
	[eventdb7, eventdb8, eventdb9, eventdb12] +
	db_events +
	[clear_event] +
	[submit_event_nochat_api, submit_event_nochat] +
	[load_model_event, load_model_event2] +
	[count_tokens_event]
	,
	queue=False, api_name='stop' if allow_api else None).then(clear_torch_cache, queue=False)

	demo.load(None, None, None, _js=get_dark_js() if kwargs['dark'] else None)

	demo.queue(concurrency_count=kwargs['concurrency_count'], api_open=kwargs['api_open'])
	favicon_path = "h2o-logo.svg"
	if not os.path.isfile(favicon_path):
	print("favicon_path=%s not found" % favicon_path, flush=True)
	favicon_path = None

	scheduler = BackgroundScheduler()
	scheduler.add_job(func=clear_torch_cache, trigger="interval", seconds=20)
	if is_public and \
	kwargs['base_model'] not in non_hf_types:
	# FIXME: disable for gptj, langchain or gpt4all modify print itself
	# FIXME: and any multi-threaded/async print will enter model output!
	scheduler.add_job(func=ping, trigger="interval", seconds=60)
	if is_public or os.getenv('PING_GPU'):
	scheduler.add_job(func=ping_gpu, trigger="interval", seconds=60 * 10)
	scheduler.start()

	# import control
	if kwargs['langchain_mode'] == 'Disabled' and \
	os.environ.get("TEST_LANGCHAIN_IMPORT") and \
	kwargs['base_model'] not in non_hf_types:
	assert 'gpt_langchain' not in sys.modules, "Dev bug, import of langchain when should not have"
	assert 'langchain' not in sys.modules, "Dev bug, import of langchain when should not have"

	demo.launch(share=kwargs['share'], server_name="0.0.0.0", show_error=True,
	favicon_path=favicon_path, prevent_thread_lock=True,
	auth=kwargs['auth'])
	if kwargs['verbose']:
	print("Started GUI", flush=True)
	if kwargs['block_gradio_exit']:
	demo.block_thread()


	def get_inputs_list(inputs_dict, model_lower, model_id=1):
	"""
	map gradio objects in locals() to inputs for evaluate().
	:param inputs_dict:
	:param model_lower:
	:param model_id: Which model (1 or 2) of 2
	:return:
	"""
	inputs_list_names = list(inspect.signature(evaluate).parameters)
	inputs_list = []
	inputs_dict_out = {}
	for k in inputs_list_names:
	if k == 'kwargs':
	continue
	if k in input_args_list + inputs_kwargs_list:
	# these are added at use time for args or partial for kwargs, not taken as input
	continue
	if 'mbart-' not in model_lower and k in ['src_lang', 'tgt_lang']:
	continue
	if model_id == 2:
	if k == 'prompt_type':
	k = 'prompt_type2'
	if k == 'prompt_used':
	k = 'prompt_used2'
	if k == 'max_new_tokens':
	k = 'max_new_tokens2'
	if k == 'min_new_tokens':
	k = 'min_new_tokens2'
	inputs_list.append(inputs_dict[k])
	inputs_dict_out[k] = inputs_dict[k]
	return inputs_list, inputs_dict_out


	def get_sources(db1s, langchain_mode, dbs=None, docs_state0=None):
	for k in db1s:
	set_userid(db1s[k])

	if langchain_mode in ['LLM']:
	source_files_added = "NA"
	source_list = []
	elif langchain_mode in ['wiki_full']:
	source_files_added = "Not showing wiki_full, takes about 20 seconds and makes 4MB file." \
	" Ask jon.mckinney@h2o.ai for file if required."
	source_list = []
	elif langchain_mode in db1s and len(db1s[langchain_mode]) == 2 and db1s[langchain_mode][0] is not None:
	db1 = db1s[langchain_mode]
	from gpt_langchain import get_metadatas
	metadatas = get_metadatas(db1[0])
	source_list = sorted(set([x['source'] for x in metadatas]))
	source_files_added = '\n'.join(source_list)
	elif langchain_mode in dbs and dbs[langchain_mode] is not None:
	from gpt_langchain import get_metadatas
	db1 = dbs[langchain_mode]
	metadatas = get_metadatas(db1)
	source_list = sorted(set([x['source'] for x in metadatas]))
	source_files_added = '\n'.join(source_list)
	else:
	source_list = []
	source_files_added = "None"
	sources_dir = "sources_dir"
	makedirs(sources_dir)
	sources_file = os.path.join(sources_dir, 'sources_%s_%s' % (langchain_mode, str(uuid.uuid4())))
	with open(sources_file, "wt") as f:
	f.write(source_files_added)
	source_list = docs_state0 + source_list
	return sources_file, source_list


	def set_userid(db1):
	# can only call this after function called so for specific userr, not in gr.State() that occurs during app init
	assert db1 is not None and len(db1) == 2
	if db1[1] is None:
	# uuid in db is used as user ID
	db1[1] = str(uuid.uuid4())


	def update_user_db(file, db1s, selection_docs_state1, chunk, chunk_size, langchain_mode, dbs=None, **kwargs):
	kwargs.update(selection_docs_state1)
	if file is None:
	raise RuntimeError("Don't use change, use input")

	try:
	return _update_user_db(file, db1s=db1s, chunk=chunk, chunk_size=chunk_size,
	langchain_mode=langchain_mode, dbs=dbs,
	**kwargs)
	except BaseException as e:
	print(traceback.format_exc(), flush=True)
	# gradio has issues if except, so fail semi-gracefully, else would hang forever in processing textbox
	ex_str = "Exception: %s" % str(e)
	source_files_added = """\
	<html>
	<body>
	<p>
	Sources: <br>
	</p>
	<div style="overflow-y: auto;height:400px">
	{0}
	</div>
	</body>
	</html>
	""".format(ex_str)
	doc_exception_text = str(e)
	return None, langchain_mode, source_files_added, doc_exception_text
	finally:
	clear_torch_cache()


	def get_lock_file(db1, langchain_mode):
	set_userid(db1)
	assert len(db1) == 2 and db1[1] is not None and isinstance(db1[1], str)
	user_id = db1[1]
	base_path = 'locks'
	makedirs(base_path)
	lock_file = os.path.join(base_path, "db_%s_%s.lock" % (langchain_mode.replace(' ', '_'), user_id))
	return lock_file


	def _update_user_db(file,
	db1s=None,
	chunk=None, chunk_size=None,
	dbs=None, db_type=None,
	langchain_mode='UserData',
	langchain_modes=None, # unused but required as part of selection_docs_state1
	langchain_mode_paths=None,
	visible_langchain_modes=None,
	use_openai_embedding=None,
	hf_embedding_model=None,
	caption_loader=None,
	enable_captions=None,
	captions_model=None,
	enable_ocr=None,
	enable_pdf_ocr=None,
	verbose=None,
	n_jobs=-1,
	is_url=None, is_txt=None,
	):
	assert db1s is not None
	assert chunk is not None
	assert chunk_size is not None
	assert use_openai_embedding is not None
	assert hf_embedding_model is not None
	assert caption_loader is not None
	assert enable_captions is not None
	assert captions_model is not None
	assert enable_ocr is not None
	assert enable_pdf_ocr is not None
	assert verbose is not None

	if dbs is None:
	dbs = {}
	assert isinstance(dbs, dict), "Wrong type for dbs: %s" % str(type(dbs))
	# assert db_type in ['faiss', 'chroma'], "db_type %s not supported" % db_type
	from gpt_langchain import add_to_db, get_db, path_to_docs
	# handle case of list of temp buffer
	if isinstance(file, list) and len(file) > 0 and hasattr(file[0], 'name'):
	file = [x.name for x in file]
	# handle single file of temp buffer
	if hasattr(file, 'name'):
	file = file.name
	if not isinstance(file, (list, tuple, typing.Generator)) and isinstance(file, str):
	file = [file]

	if langchain_mode == LangChainMode.DISABLED.value:
	return None, langchain_mode, get_source_files(), ""

	if langchain_mode in [LangChainMode.LLM.value]:
	# then switch to MyData, so langchain_mode also becomes way to select where upload goes
	# but default to mydata if nothing chosen, since safest
	if LangChainMode.MY_DATA.value in visible_langchain_modes:
	langchain_mode = LangChainMode.MY_DATA.value

	if langchain_mode_paths is None:
	langchain_mode_paths = {}
	user_path = langchain_mode_paths.get(langchain_mode)
	# UserData or custom, which has to be from user's disk
	if user_path is not None:
	# move temp files from gradio upload to stable location
	for fili, fil in enumerate(file):
	if isinstance(fil, str) and os.path.isfile(fil): # not url, text
	new_fil = os.path.normpath(os.path.join(user_path, os.path.basename(fil)))
	if os.path.normpath(os.path.abspath(fil)) != os.path.normpath(os.path.abspath(new_fil)):
	if os.path.isfile(new_fil):
	remove(new_fil)
	try:
	shutil.move(fil, new_fil)
	except FileExistsError:
	pass
	file[fili] = new_fil

	if verbose:
	print("Adding %s" % file, flush=True)
	sources = path_to_docs(file if not is_url and not is_txt else None,
	verbose=verbose,
	n_jobs=n_jobs,
	chunk=chunk, chunk_size=chunk_size,
	url=file if is_url else None,
	text=file if is_txt else None,
	enable_captions=enable_captions,
	captions_model=captions_model,
	enable_ocr=enable_ocr,
	enable_pdf_ocr=enable_pdf_ocr,
	caption_loader=caption_loader,
	)
	exceptions = [x for x in sources if x.metadata.get('exception')]
	exceptions_strs = [x.metadata['exception'] for x in exceptions]
	sources = [x for x in sources if 'exception' not in x.metadata]

	# below must at least come after langchain_mode is modified in case was LLM -> MyData,
	# so original langchain mode changed
	for k in db1s:
	set_userid(db1s[k])
	db1 = get_db1(db1s, langchain_mode)

	lock_file = get_lock_file(db1s[LangChainMode.MY_DATA.value], langchain_mode) # user-level lock, not db-level lock
	with filelock.FileLock(lock_file):
	if langchain_mode in db1s:
	if db1[0] is not None:
	# then add
	db, num_new_sources, new_sources_metadata = add_to_db(db1[0], sources, db_type=db_type,
	use_openai_embedding=use_openai_embedding,
	hf_embedding_model=hf_embedding_model)
	else:
	# in testing expect:
	# assert len(db1) == 2 and db1[1] is None, "Bad MyData db: %s" % db1
	# for production hit, when user gets clicky:
	assert len(db1) == 2, "Bad %s db: %s" % (langchain_mode, db1)
	assert db1[1] is not None, "db hash was None, not allowed"
	# then create
	# if added has to original state and didn't change, then would be shared db for all users
	persist_directory = os.path.join(scratch_base_dir, 'db_dir_%s_%s' % (langchain_mode, db1[1]))
	db = get_db(sources, use_openai_embedding=use_openai_embedding,
	db_type=db_type,
	persist_directory=persist_directory,
	langchain_mode=langchain_mode,
	hf_embedding_model=hf_embedding_model)
	if db is not None:
	db1[0] = db
	source_files_added = get_source_files(db=db1[0], exceptions=exceptions)
	return None, langchain_mode, source_files_added, '\n'.join(exceptions_strs)
	else:
	from gpt_langchain import get_persist_directory
	persist_directory = get_persist_directory(langchain_mode)
	if langchain_mode in dbs and dbs[langchain_mode] is not None:
	# then add
	db, num_new_sources, new_sources_metadata = add_to_db(dbs[langchain_mode], sources, db_type=db_type,
	use_openai_embedding=use_openai_embedding,
	hf_embedding_model=hf_embedding_model)
	else:
	# then create. Or might just be that dbs is unfilled, then it will fill, then add
	db = get_db(sources, use_openai_embedding=use_openai_embedding,
	db_type=db_type,
	persist_directory=persist_directory,
	langchain_mode=langchain_mode,
	hf_embedding_model=hf_embedding_model)
	dbs[langchain_mode] = db
	# NOTE we do not return db, because function call always same code path
	# return dbs[langchain_mode]
	# db in this code path is updated in place
	source_files_added = get_source_files(db=dbs[langchain_mode], exceptions=exceptions)
	return None, langchain_mode, source_files_added, '\n'.join(exceptions_strs)


	def get_db(db1s, langchain_mode, dbs=None):
	db1 = get_db1(db1s, langchain_mode)
	lock_file = get_lock_file(db1s[LangChainMode.MY_DATA.value], langchain_mode)

	with filelock.FileLock(lock_file):
	if langchain_mode in ['wiki_full']:
	# NOTE: avoid showing full wiki. Takes about 30 seconds over about 90k entries, but not useful for now
	db = None
	elif langchain_mode in db1s and len(db1) == 2 and db1[0] is not None:
	db = db1[0]
	elif dbs is not None and langchain_mode in dbs and dbs[langchain_mode] is not None:
	db = dbs[langchain_mode]
	else:
	db = None
	return db


	def get_source_files_given_langchain_mode(db1s, langchain_mode='UserData', dbs=None):
	db = get_db(db1s, langchain_mode, dbs=dbs)
	if langchain_mode in ['LLM'] or db is None:
	return "Sources: N/A"
	return get_source_files(db=db, exceptions=None)


	def get_source_files(db=None, exceptions=None, metadatas=None):
	if exceptions is None:
	exceptions = []

	# only should be one source, not confused
	# assert db is not None or metadatas is not None
	# clicky user
	if db is None and metadatas is None:
	return "No Sources at all"

	if metadatas is None:
	source_label = "Sources:"
	if db is not None:
	from gpt_langchain import get_metadatas
	metadatas = get_metadatas(db)
	else:
	metadatas = []
	adding_new = False
	else:
	source_label = "New Sources:"
	adding_new = True

	# below automatically de-dups
	from gpt_langchain import get_url
	small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('head')) for x in
	metadatas}
	# if small_dict is empty dict, that's ok
	df = pd.DataFrame(small_dict.items(), columns=['source', 'head'])
	df.index = df.index + 1
	df.index.name = 'index'
	source_files_added = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')

	if exceptions:
	exception_metadatas = [x.metadata for x in exceptions]
	small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('exception')) for x in
	exception_metadatas}
	# if small_dict is empty dict, that's ok
	df = pd.DataFrame(small_dict.items(), columns=['source', 'exception'])
	df.index = df.index + 1
	df.index.name = 'index'
	exceptions_html = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
	else:
	exceptions_html = ''

	if metadatas and exceptions:
	source_files_added = """\
	<html>
	<body>
	<p>
	{0} <br>
	</p>
	<div style="overflow-y: auto;height:400px">
	{1}
	{2}
	</div>
	</body>
	</html>
	""".format(source_label, source_files_added, exceptions_html)
	elif metadatas:
	source_files_added = """\
	<html>
	<body>
	<p>
	{0} <br>
	</p>
	<div style="overflow-y: auto;height:400px">
	{1}
	</div>
	</body>
	</html>
	""".format(source_label, source_files_added)
	elif exceptions_html:
	source_files_added = """\
	<html>
	<body>
	<p>
	Exceptions: <br>
	</p>
	<div style="overflow-y: auto;height:400px">
	{0}
	</div>
	</body>
	</html>
	""".format(exceptions_html)
	else:
	if adding_new:
	source_files_added = "No New Sources"
	else:
	source_files_added = "No Sources"

	return source_files_added


	def update_and_get_source_files_given_langchain_mode(db1s, langchain_mode, chunk, chunk_size,
	dbs=None, first_para=None,
	text_limit=None,
	langchain_mode_paths=None, db_type=None, load_db_if_exists=None,
	n_jobs=None, verbose=None):
	has_path = {k: v for k, v in langchain_mode_paths.items() if v}
	if langchain_mode in [LangChainMode.LLM.value, LangChainMode.MY_DATA.value]:
	# then assume user really meant UserData, to avoid extra clicks in UI,
	# since others can't be on disk, except custom user modes, which they should then select to query it
	if LangChainMode.USER_DATA.value in has_path:
	langchain_mode = LangChainMode.USER_DATA.value

	db = get_db(db1s, langchain_mode, dbs=dbs)

	from gpt_langchain import make_db
	db, num_new_sources, new_sources_metadata = make_db(use_openai_embedding=False,
	hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
	first_para=first_para, text_limit=text_limit,
	chunk=chunk,
	chunk_size=chunk_size,
	langchain_mode=langchain_mode,
	langchain_mode_paths=langchain_mode_paths,
	db_type=db_type,
	load_db_if_exists=load_db_if_exists,
	db=db,
	n_jobs=n_jobs,
	verbose=verbose)
	# during refreshing, might have "created" new db since not in dbs[] yet, so insert back just in case
	# so even if persisted, not kept up-to-date with dbs memory
	if langchain_mode in db1s:
	db1s[langchain_mode][0] = db
	else:
	dbs[langchain_mode] = db

	# return only new sources with text saying such
	return get_source_files(db=None, exceptions=None, metadatas=new_sources_metadata)


	def get_db1(db1s, langchain_mode1):
	if langchain_mode1 in db1s:
	db1 = db1s[langchain_mode1]
	else:
	# indicates to code that not scratch database
	db1 = [None, None]
	return db1