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 uuid
import filelock
import numpy as np
import pandas as pd
import requests
from iterators import TimeoutIterator
from gradio_utils.css import get_css
from gradio_utils.prompt_form import make_chatbots, get_chatbot_name
from src.db_utils import set_userid, get_username_direct
from src.tts_utils import combine_audios
# 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, LangChainTypes, langchain_modes_non_db, gr_to_lg, invalid_key_msg, \
LangChainAgent, docs_ordering_types, docs_token_handlings, docs_joiner_default
from gradio_themes import H2oTheme, SoftTheme, get_h2o_title, get_simple_title, \
get_dark_js, get_heap_js, wrap_js_to_lambda, \
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, model_names_curated, get_system_prompts, get_llava_prompts
from utils import flatten_list, zip_data, s3up, clear_torch_cache, get_torch_allocated, system_info_print, \
ping, makedirs, get_kwargs, system_info, ping_gpu, get_url, get_local_ip, \
save_generate_output, url_alive, remove, dict_to_html, text_to_html, lg_to_gr, str_to_dict, have_serpapi, \
have_librosa, have_gradio_pdf, have_pyrubberband, is_gradio_version4, have_fiftyone, n_gpus_global, \
_save_generate_tokens, get_accordion_named
from gen import get_model, languages_covered, evaluate, score_qa, inputs_kwargs_list, \
get_max_max_new_tokens, get_minmax_top_k_docs, history_to_context, langchain_actions, langchain_agents_list, \
evaluate_fake, merge_chat_conversation_history, switch_a_roo_llama, get_model_max_length_from_tokenizer, \
get_model_retry, remove_refs, get_on_disk_models, get_llama_lower_hf, model_name_to_prompt_type
from evaluate_params import eval_func_param_names, no_default_param_names, eval_func_param_names_defaults, \
input_args_list, key_overrides
from apscheduler.schedulers.background import BackgroundScheduler
def fix_text_for_gradio(text, fix_new_lines=False, fix_latex_dollars=True):
if isinstance(text, tuple):
# images, audio, etc.
return text
if not isinstance(text, str):
# e.g. list for extraction
text = str(text)
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('$', 'īšŠ')
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', '
')
text = '```'.join(ts)
return text
def is_from_ui(requests_state1):
return isinstance(requests_state1, dict) and 'username' in requests_state1 and requests_state1['username']
def is_valid_key(enforce_h2ogpt_api_key, enforce_h2ogpt_ui_key, h2ogpt_api_keys, h2ogpt_key1, requests_state1=None):
from_ui = is_from_ui(requests_state1)
if from_ui and not enforce_h2ogpt_ui_key:
# no token barrier
return 'not enforced'
elif not from_ui and not enforce_h2ogpt_api_key:
# no token barrier
return 'not enforced'
else:
valid_key = False
if isinstance(h2ogpt_api_keys, list) and h2ogpt_key1 in h2ogpt_api_keys:
# passed token barrier
valid_key = True
elif isinstance(h2ogpt_api_keys, str) and os.path.isfile(h2ogpt_api_keys):
with filelock.FileLock(h2ogpt_api_keys + '.lock'):
with open(h2ogpt_api_keys, 'rt') as f:
h2ogpt_api_keys = json.load(f)
if h2ogpt_key1 in h2ogpt_api_keys:
valid_key = True
return valid_key
def get_one_key(h2ogpt_api_keys, enforce_h2ogpt_api_key):
if not enforce_h2ogpt_api_key:
# return None so OpenAI server has no keyed access if not enforcing API key on h2oGPT regardless if keys passed
return None
if isinstance(h2ogpt_api_keys, list) and h2ogpt_api_keys:
return h2ogpt_api_keys[0]
elif isinstance(h2ogpt_api_keys, str) and os.path.isfile(h2ogpt_api_keys):
with filelock.FileLock(h2ogpt_api_keys + '.lock'):
with open(h2ogpt_api_keys, 'rt') as f:
h2ogpt_api_keys = json.load(f)
if h2ogpt_api_keys:
return h2ogpt_api_keys[0]
def get_prompt_type1(is_public, **kwargs):
prompt_types_strings_used = prompt_types_strings.copy()
if kwargs['model_lock']:
prompt_types_strings_used += [no_model_str]
default_prompt_type = kwargs['prompt_type'] or no_model_str
else:
default_prompt_type = kwargs['prompt_type'] or 'plain'
prompt_type = gr.Dropdown(prompt_types_strings_used,
value=default_prompt_type,
label="Choose/Select Prompt Type",
info="Auto-Detected if known (plain means failed to detect)",
visible=not kwargs['model_lock'],
interactive=not is_public,
)
return prompt_type
def get_prompt_type2(is_public, **kwargs):
prompt_types_strings_used = prompt_types_strings.copy()
if kwargs['model_lock']:
prompt_types_strings_used += [no_model_str]
default_prompt_type = kwargs['prompt_type'] or no_model_str
else:
default_prompt_type = kwargs['prompt_type'] or 'plain'
prompt_type2 = gr.Dropdown(prompt_types_strings_used,
value=default_prompt_type,
label="Choose/Select Prompt Type Model 2",
info="Auto-Detected if known (plain means failed to detect)",
visible=False and not kwargs['model_lock'],
interactive=not is_public)
return prompt_type2
def go_gradio(**kwargs):
page_title = kwargs['page_title']
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']
load_db_if_exists = kwargs['load_db_if_exists']
migrate_embedding_model = kwargs['migrate_embedding_model']
auto_migrate_db = kwargs['auto_migrate_db']
captions_model = kwargs['captions_model']
caption_loader = kwargs['caption_loader']
doctr_loader = kwargs['doctr_loader']
llava_model = kwargs['llava_model']
asr_model = kwargs['asr_model']
asr_loader = kwargs['asr_loader']
n_jobs = kwargs['n_jobs']
verbose = kwargs['verbose']
# 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']
visible_models_state0 = kwargs['visible_models_state0']
roles_state0 = kwargs['roles_state0']
# For Heap analytics
is_heap_analytics_enabled = kwargs['enable_heap_analytics']
heap_app_id = kwargs['heap_app_id']
# easy update of kwargs needed for evaluate() etc.
queue = True
allow_upload = allow_upload_to_user_data or allow_upload_to_my_data
allow_upload_api = allow_api and allow_upload
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'
if kwargs['visible_h2ogpt_links']:
description = """h2oGPT LLM Leaderboard LLM Studio
CodeLlama
đ¤ Models
h2oGPTe"""
else:
description = None
description_bottom = "If this host is busy, try
[Multi-Model](https://gpt.h2o.ai)
[CodeLlama](https://codellama.h2o.ai)
[Llama2 70B](https://llama.h2o.ai)
[Falcon 40B](https://falcon.h2o.ai)
[HF Spaces1](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot)
[HF Spaces2](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot2)
"
if is_hf:
description_bottom += '''
'''
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=page_title, analytics_enabled=False)
callback = gr.CSVLogger()
# modify, if model lock then don't show models, then need prompts in expert
kwargs['visible_models_tab'] = kwargs['visible_models_tab'] and not bool(kwargs['model_lock'])
# Initial model options
if kwargs['visible_all_prompter_models']:
model_options0 = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options']
else:
model_options0 = []
if kwargs['visible_curated_models']:
model_options0.extend(model_names_curated)
model_options0.extend(kwargs['extra_model_options'])
if kwargs['base_model'].strip() and kwargs['base_model'].strip() not in model_options0:
model_options0 = [kwargs['base_model'].strip()] + model_options0
if kwargs['add_disk_models_to_ui'] and kwargs['visible_models_tab'] and not kwargs['model_lock']:
model_options0.extend(get_on_disk_models(llamacpp_path=kwargs['llamacpp_path'],
use_auth_token=kwargs['use_auth_token'],
trust_remote_code=kwargs['trust_remote_code']))
model_options0 = sorted(set(model_options0))
# Initial LORA options
lora_options = kwargs['extra_lora_options']
if kwargs['lora_weights'].strip() and kwargs['lora_weights'].strip() not in lora_options:
lora_options = [kwargs['lora_weights'].strip()] + lora_options
# Initial server options
server_options = kwargs['extra_server_options']
if kwargs['inference_server'].strip() and 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] + sorted(model_options0)
lora_options = [no_lora_str] + sorted(lora_options)
server_options = [no_server_str] + sorted(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 !!! ]'
chat_name0 = get_chatbot_name(kwargs.get("base_model"), kwargs.get("llamacpp_dict", {}).get("model_path_llama"))
output_label0 = chat_name0 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, global_scope=False):
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 global_scope:
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 global_scope:
if not prompt_dict1 or which_model != 0:
prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1)
if not global_scope and not prompt_type1:
# if still not defined, use plain
prompt_type1 = 'plain'
return prompt_type1, prompt_dict1
def visible_models_to_model_choice(visible_models1, api=False):
if isinstance(visible_models1, list):
assert len(
visible_models1) >= 1, "Invalid visible_models1=%s, can only be single entry" % visible_models1
# just take first
model_active_choice1 = visible_models1[0]
elif isinstance(visible_models1, (str, int)):
model_active_choice1 = visible_models1
else:
assert isinstance(visible_models1, type(None)), "Invalid visible_models1=%s" % visible_models1
model_active_choice1 = visible_models1
if model_active_choice1 is not None:
if isinstance(model_active_choice1, str):
base_model_list = [
x['base_model'] if x['base_model'] != 'llama' or not x.get("llamacpp_dict", {}).get(
'model_path_llama', '') else x.get("llamacpp_dict", {})[
'model_path_llama'] for x in model_states]
if model_active_choice1 in base_model_list:
# if dups, will just be first one
model_active_choice1 = base_model_list.index(model_active_choice1)
else:
# NOTE: Could raise, but sometimes raising in certain places fails too hard and requires UI restart
if api:
raise ValueError(
"Invalid model %s, valid models are: %s" % (model_active_choice1, base_model_list))
model_active_choice1 = 0
else:
model_active_choice1 = 0
return model_active_choice1
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=visible_models_to_model_choice(kwargs['visible_models']),
global_scope=True, # don't assume state0 is the prompt for all models
)
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 update_auth_selection(auth_user, selection_docs_state1, save=False):
# in-place update of both
if 'selection_docs_state' not in auth_user:
auth_user['selection_docs_state'] = selection_docs_state0
for k, v in auth_user['selection_docs_state'].items():
if isinstance(selection_docs_state1[k], dict):
if save:
auth_user['selection_docs_state'][k].clear()
auth_user['selection_docs_state'][k].update(selection_docs_state1[k])
else:
selection_docs_state1[k].clear()
selection_docs_state1[k].update(auth_user['selection_docs_state'][k])
elif isinstance(selection_docs_state1[k], list):
if save:
auth_user['selection_docs_state'][k].clear()
auth_user['selection_docs_state'][k].extend(selection_docs_state1[k])
else:
selection_docs_state1[k].clear()
selection_docs_state1[k].extend(auth_user['selection_docs_state'][k])
else:
raise RuntimeError("Bad type: %s" % selection_docs_state1[k])
# BEGIN AUTH THINGS
def auth_func(username1, password1, auth_pairs=None, auth_filename=None,
auth_access=None,
auth_freeze=None,
guest_name=None,
selection_docs_state1=None,
selection_docs_state00=None,
**kwargs):
assert auth_freeze is not None
if selection_docs_state1 is None:
selection_docs_state1 = selection_docs_state00
assert selection_docs_state1 is not None
assert auth_filename and isinstance(auth_filename, str), "Auth file must be a non-empty string, got: %s" % str(
auth_filename)
if auth_access == 'open' and username1 == guest_name:
return True
if username1 == '':
# some issue with login
return False
with filelock.FileLock(auth_filename + '.lock'):
auth_dict = {}
if os.path.isfile(auth_filename):
try:
with open(auth_filename, 'rt') as f:
auth_dict = json.load(f)
except json.decoder.JSONDecodeError as e:
print("Auth exception: %s" % str(e), flush=True)
shutil.move(auth_filename, auth_filename + '.bak' + str(uuid.uuid4()))
auth_dict = {}
if username1 in auth_dict and username1 in auth_pairs:
if password1 == auth_dict[username1]['password'] and password1 == auth_pairs[username1]:
auth_user = auth_dict[username1]
update_auth_selection(auth_user, selection_docs_state1)
save_auth_dict(auth_dict, auth_filename)
return True
else:
return False
elif username1 in auth_dict:
if password1 == auth_dict[username1]['password']:
auth_user = auth_dict[username1]
update_auth_selection(auth_user, selection_docs_state1)
save_auth_dict(auth_dict, auth_filename)
return True
else:
return False
elif username1 in auth_pairs:
# copy over CLI auth to file so only one state to manage
auth_dict[username1] = dict(password=auth_pairs[username1], userid=str(uuid.uuid4()))
auth_user = auth_dict[username1]
update_auth_selection(auth_user, selection_docs_state1)
save_auth_dict(auth_dict, auth_filename)
return True
else:
if auth_access == 'closed':
return False
# open access
auth_dict[username1] = dict(password=password1, userid=str(uuid.uuid4()))
auth_user = auth_dict[username1]
update_auth_selection(auth_user, selection_docs_state1)
save_auth_dict(auth_dict, auth_filename)
if auth_access == 'open':
return True
else:
raise RuntimeError("Invalid auth_access: %s" % auth_access)
def auth_func_open(*args, **kwargs):
return True
def get_username(requests_state1):
username1 = None
if 'username' in requests_state1:
username1 = requests_state1['username']
return username1
def get_userid_auth_func(requests_state1, auth_filename=None, auth_access=None, guest_name=None, id0=None,
**kwargs):
if auth_filename and isinstance(auth_filename, str):
username1 = get_username(requests_state1)
if username1:
if username1 == guest_name:
return str(uuid.uuid4())
with filelock.FileLock(auth_filename + '.lock'):
if os.path.isfile(auth_filename):
with open(auth_filename, 'rt') as f:
auth_dict = json.load(f)
if username1 in auth_dict:
return auth_dict[username1]['userid']
# if here, then not persistently associated with username1,
# but should only be one-time asked if going to persist within a single session!
return id0 or str(uuid.uuid4())
get_userid_auth = functools.partial(get_userid_auth_func,
auth_filename=kwargs['auth_filename'],
auth_access=kwargs['auth_access'],
guest_name=kwargs['guest_name'],
)
if kwargs['auth_access'] == 'closed':
auth_message1 = "Closed access"
else:
auth_message1 = "WELCOME! Open access" \
" (%s/%s or any unique user/pass)" % (kwargs['guest_name'], kwargs['guest_name'])
if kwargs['auth_message'] is not None:
auth_message = kwargs['auth_message']
else:
auth_message = auth_message1
# always use same callable
auth_pairs0 = {}
if isinstance(kwargs['auth'], list):
for k, v in kwargs['auth']:
auth_pairs0[k] = v
authf = functools.partial(auth_func,
auth_pairs=auth_pairs0,
auth_filename=kwargs['auth_filename'],
auth_access=kwargs['auth_access'],
auth_freeze=kwargs['auth_freeze'],
guest_name=kwargs['guest_name'],
selection_docs_state00=copy.deepcopy(selection_docs_state0))
def get_request_state(requests_state1, request, db1s):
# if need to get state, do it now
if not requests_state1:
requests_state1 = requests_state0.copy()
if requests:
if not requests_state1.get('headers', '') and hasattr(request, 'headers'):
requests_state1.update(request.headers)
if not requests_state1.get('host', '') and hasattr(request, 'host'):
requests_state1.update(dict(host=request.host))
if not requests_state1.get('host2', '') and hasattr(request, 'client') and hasattr(request.client, 'host'):
requests_state1.update(dict(host2=request.client.host))
if not requests_state1.get('username', '') and hasattr(request, 'username'):
# use already-defined username instead of keep changing to new uuid
# should be same as in requests_state1
db_username = get_username_direct(db1s)
requests_state1.update(dict(username=request.username or db_username or str(uuid.uuid4())))
requests_state1 = {str(k): str(v) for k, v in requests_state1.items()}
return requests_state1
def user_state_setup(db1s, requests_state1, request: gr.Request, *args):
requests_state1 = get_request_state(requests_state1, request, db1s)
set_userid(db1s, requests_state1, get_userid_auth)
args_list = [db1s, requests_state1] + list(args)
return tuple(args_list)
# END AUTH THINGS
def allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
allow = False
allow |= langchain_action1 not in [LangChainAction.QUERY.value,
LangChainAction.IMAGE_QUERY.value,
LangChainAction.IMAGE_CHANGE.value,
LangChainAction.IMAGE_GENERATE.value,
LangChainAction.IMAGE_GENERATE_HIGH.value,
]
allow |= document_subset1 in [DocumentSubset.TopKSources.name]
if langchain_mode1 in [LangChainMode.LLM.value]:
allow = False
return allow
image_audio_loaders_options0, image_audio_loaders_options, \
pdf_loaders_options0, pdf_loaders_options, \
url_loaders_options0, url_loaders_options = lg_to_gr(**kwargs)
jq_schema0 = '.[]'
def click_js():
return """function audioRecord() {
var xPathRes = document.evaluate ('//*[contains(@class, "record")]', document, null, XPathResult.FIRST_ORDERED_NODE_TYPE, null);
xPathRes.singleNodeValue.click();}"""
def click_submit():
return """function check() {
document.getElementById("submit").click();
}"""
def click_stop():
return """function check() {
document.getElementById("stop").click();
}"""
if is_gradio_version4:
noqueue_kwargs = dict(concurrency_limit=None)
noqueue_kwargs2 = dict(concurrency_limit=None)
mic_kwargs = dict(js=click_js())
submit_kwargs = dict(js=click_submit())
stop_kwargs = dict(js=click_stop())
dark_kwargs = dict(js=wrap_js_to_lambda(0, get_dark_js()))
queue_kwargs = dict(default_concurrency_limit=kwargs['concurrency_count'])
mic_sources_kwargs = dict(sources=['microphone'],
waveform_options=dict(show_controls=False, show_recording_waveform=False))
else:
noqueue_kwargs = dict(queue=False)
noqueue_kwargs2 = dict()
mic_kwargs = dict(_js=click_js())
submit_kwargs = dict(_js=click_submit())
stop_kwargs = dict(_js=click_stop())
dark_kwargs = dict(_js=wrap_js_to_lambda(0, get_dark_js()))
queue_kwargs = dict(concurrency_count=kwargs['concurrency_count'])
mic_sources_kwargs = dict(source='microphone')
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'],
visible_models=visible_models_to_model_choice(kwargs['visible_models']),
h2ogpt_key=None, # only apply at runtime when doing API call with gradio inference server
)
)
def update_langchain_mode_paths(selection_docs_state1):
dup = selection_docs_state1['langchain_mode_paths'].copy()
for k, v in dup.items():
if k not in selection_docs_state1['langchain_modes']:
selection_docs_state1['langchain_mode_paths'].pop(k)
for k in selection_docs_state1['langchain_modes']:
if k not in selection_docs_state1['langchain_mode_types']:
# if didn't specify shared, then assume scratch if didn't login or personal if logged in
selection_docs_state1['langchain_mode_types'][k] = LangChainTypes.PERSONAL.value
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({})
if kwargs['enable_tts'] and kwargs['tts_model'].startswith('tts_models/'):
from src.tts_coqui import get_role_to_wave_map
roles_state = gr.State(roles_state0 if roles_state0 else get_role_to_wave_map())
else:
roles_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 = ['None']
viewable_docs_state = gr.State(viewable_docs_state0)
selection_docs_state0 = update_langchain_mode_paths(selection_docs_state0)
selection_docs_state = gr.State(selection_docs_state0)
requests_state0 = dict(headers='', host='', username='')
requests_state = gr.State(requests_state0)
if kwargs['visible_h2ogpt_logo']:
if description is None:
description = ''
gr.Markdown(f"""
{get_h2o_title(title, description, visible_h2ogpt_qrcode=kwargs['visible_h2ogpt_qrcode'])
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
user_can_do_sum = kwargs['langchain_mode'] != LangChainMode.DISABLED.value and \
(kwargs['visible_side_bar'] or kwargs['visible_system_tab'])
if user_can_do_sum:
extra_prompt_form = ". Just Click Submit for simple Summarize/Extract"
else:
extra_prompt_form = ""
if allow_upload:
extra_prompt_form += ". Clicking Ingest adds text as URL/ArXiv/YouTube/Text."
if kwargs['input_lines'] > 1:
instruction_label = "Shift-Enter to Submit, Enter adds lines%s" % extra_prompt_form
else:
instruction_label = "Enter to Submit, Shift-Enter adds lines%s" % extra_prompt_form
def get_langchain_choices(selection_docs_state1):
langchain_modes = selection_docs_state1['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 = 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, db1s, dbs1=None):
langchain_choices1 = get_langchain_choices(selection_docs_state1)
langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
langchain_mode_paths = {k: v for k, v in langchain_mode_paths.items() if k in langchain_choices1}
if langchain_mode_paths:
langchain_mode_paths = langchain_mode_paths.copy()
for langchain_mode1 in langchain_modes_non_db:
langchain_mode_paths.pop(langchain_mode1, None)
df1 = pd.DataFrame.from_dict(langchain_mode_paths.items(), orient='columns')
df1.columns = ['Collection', 'Path']
df1 = df1.set_index('Collection')
else:
df1 = pd.DataFrame(None)
langchain_mode_types = selection_docs_state1['langchain_mode_types']
langchain_mode_types = {k: v for k, v in langchain_mode_types.items() if k in langchain_choices1}
if langchain_mode_types:
langchain_mode_types = langchain_mode_types.copy()
for langchain_mode1 in langchain_modes_non_db:
langchain_mode_types.pop(langchain_mode1, None)
df2 = pd.DataFrame.from_dict(langchain_mode_types.items(), orient='columns')
df2.columns = ['Collection', 'Type']
df2 = df2.set_index('Collection')
from src.gpt_langchain import get_persist_directory, load_embed
persist_directory_dict = {}
embed_dict = {}
chroma_version_dict = {}
for langchain_mode3 in langchain_mode_types:
langchain_type3 = langchain_mode_types.get(langchain_mode3, LangChainTypes.EITHER.value)
persist_directory3, langchain_type3 = get_persist_directory(langchain_mode3,
langchain_type=langchain_type3,
db1s=db1s, dbs=dbs1)
got_embedding3, use_openai_embedding3, hf_embedding_model3 = load_embed(
persist_directory=persist_directory3)
persist_directory_dict[langchain_mode3] = persist_directory3
embed_dict[langchain_mode3] = 'OpenAI' if not hf_embedding_model3 else hf_embedding_model3
if os.path.isfile(os.path.join(persist_directory3, 'chroma.sqlite3')):
chroma_version_dict[langchain_mode3] = 'ChromaDB>=0.4'
elif os.path.isdir(os.path.join(persist_directory3, 'index')):
chroma_version_dict[langchain_mode3] = 'ChromaDB<0.4'
elif not os.listdir(persist_directory3):
if db_type == 'chroma':
chroma_version_dict[langchain_mode3] = 'ChromaDB>=0.4' # will be
elif db_type == 'chroma_old':
chroma_version_dict[langchain_mode3] = 'ChromaDB<0.4' # will be
else:
chroma_version_dict[langchain_mode3] = 'Weaviate' # will be
if isinstance(hf_embedding_model, dict):
hf_embedding_model3 = hf_embedding_model['name']
else:
hf_embedding_model3 = hf_embedding_model
assert isinstance(hf_embedding_model3, str)
embed_dict[langchain_mode3] = hf_embedding_model3 # will be
else:
chroma_version_dict[langchain_mode3] = 'Weaviate'
df3 = pd.DataFrame.from_dict(persist_directory_dict.items(), orient='columns')
df3.columns = ['Collection', 'Directory']
df3 = df3.set_index('Collection')
df4 = pd.DataFrame.from_dict(embed_dict.items(), orient='columns')
df4.columns = ['Collection', 'Embedding']
df4 = df4.set_index('Collection')
df5 = pd.DataFrame.from_dict(chroma_version_dict.items(), orient='columns')
df5.columns = ['Collection', 'DB']
df5 = df5.set_index('Collection')
else:
df2 = pd.DataFrame(None)
df3 = pd.DataFrame(None)
df4 = pd.DataFrame(None)
df5 = pd.DataFrame(None)
df_list = [df2, df1, df3, df4, df5]
df_list = [x for x in df_list if x.shape[1] > 0]
if len(df_list) > 1:
df = df_list[0].join(df_list[1:]).replace(np.nan, '').reset_index()
elif len(df_list) == 0:
df = df_list[0].replace(np.nan, '').reset_index()
else:
df = pd.DataFrame(None)
return df
normal_block = gr.Row(visible=not base_wanted, equal_height=False, elem_id="col_container")
with normal_block:
side_bar = gr.Column(elem_id="sidebar", scale=1, min_width=100, visible=kwargs['visible_side_bar'])
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')
visible_speak_me = kwargs['enable_tts'] and kwargs['predict_from_text_func'] is not None
speak_human_button = gr.Button("Speak Instruction", visible=visible_speak_me, size='sm')
speak_bot_button = gr.Button("Speak Response", visible=visible_speak_me, size='sm')
stop_speak_button = gr.Button("Stop/Clear Speak", visible=visible_speak_me, size='sm')
if kwargs['enable_tts'] and kwargs['tts_model'].startswith('tts_models/'):
from src.tts_coqui import get_roles
chatbot_role = get_roles(choices=list(roles_state.value.keys()), value=kwargs['chatbot_role'])
else:
chatbot_role = gr.Dropdown(choices=['None'], visible=False, value='None')
if kwargs['enable_tts'] and kwargs['tts_model'].startswith('microsoft'):
from src.tts import get_speakers_gr
speaker = get_speakers_gr(value=kwargs['speaker'])
else:
speaker = gr.Radio(visible=False)
min_tts_speed = 1.0 if not have_pyrubberband else 0.1
tts_speed = gr.Number(minimum=min_tts_speed, maximum=10.0, step=0.1,
value=kwargs['tts_speed'],
label='Speech Speed',
visible=kwargs['enable_tts'] and not is_public,
interactive=not is_public)
upload_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload
url_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_url_upload
if have_arxiv and have_librosa:
url_label = 'URLs/ArXiv/Youtube'
elif have_arxiv:
url_label = 'URLs/ArXiv'
elif have_librosa:
url_label = 'URLs/Youtube'
else:
url_label = 'URLs'
text_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_text_upload
fileup_output_text = gr.Textbox(visible=False)
with gr.Accordion("Upload", open=False, visible=upload_visible and kwargs['actions_in_sidebar']):
fileup_output = gr.File(show_label=False,
file_types=['.' + x for x in file_types],
# file_types=['*', '*.*'], # for iPhone etc. needs to be unconstrained else doesn't work with extension-based restrictions
file_count="multiple",
scale=1,
min_width=0,
elem_id="warning", elem_classes="feedback",
)
if kwargs['actions_in_sidebar']:
max_quality = gr.Checkbox(label="Max Ingest Quality", value=kwargs['max_quality'],
visible=not is_public)
gradio_upload_to_chatbot = gr.Checkbox(label="Add Doc to Chat",
value=kwargs['gradio_upload_to_chatbot'])
url_text = gr.Textbox(label=url_label,
# placeholder="Enter Submits",
max_lines=1,
interactive=True,
visible=kwargs['actions_in_sidebar'])
user_text_text = gr.Textbox(label='Paste Text',
# placeholder="Enter Submits",
interactive=True,
visible=text_visible and kwargs['actions_in_sidebar'])
database_visible = kwargs['langchain_mode'] != 'Disabled'
langchain_choices0 = get_langchain_choices(selection_docs_state0)
serp_visible = os.environ.get('SERPAPI_API_KEY') is not None and have_serpapi
allowed_actions = [x for x in langchain_actions if x in visible_langchain_actions]
default_action = allowed_actions[0] if len(allowed_actions) > 0 else None
if not kwargs['actions_in_sidebar']:
max_quality = gr.Checkbox(label="Max Ingest Quality",
value=kwargs['max_quality'],
visible=not is_public)
gradio_upload_to_chatbot = gr.Checkbox(label="Add Doc to Chat",
value=kwargs['gradio_upload_to_chatbot'])
if not kwargs['actions_in_sidebar']:
add_chat_history_to_context = gr.Checkbox(label="Include Chat History",
value=kwargs[
'add_chat_history_to_context'])
add_search_to_context = gr.Checkbox(label="Include Web Search",
value=kwargs['add_search_to_context'],
visible=serp_visible)
resources_acc_label = "Resources" if not is_public else "Collections"
langchain_mode_radio_kwargs = dict(
choices=langchain_choices0,
value=kwargs['langchain_mode'],
label="Collections",
show_label=True,
visible=kwargs['langchain_mode'] != 'Disabled',
min_width=100)
if is_public:
langchain_mode = gr.Radio(**langchain_mode_radio_kwargs)
with gr.Accordion(resources_acc_label, open=False, visible=database_visible and not is_public):
if not is_public:
langchain_mode = gr.Radio(**langchain_mode_radio_kwargs)
if kwargs['actions_in_sidebar']:
add_chat_history_to_context = gr.Checkbox(label="Chat History",
value=kwargs['add_chat_history_to_context'])
add_search_to_context = gr.Checkbox(label="Web Search",
value=kwargs['add_search_to_context'],
visible=serp_visible)
document_subset = gr.Radio([x.name for x in DocumentSubset],
label="Subset",
value=DocumentSubset.Relevant.name,
interactive=True,
visible=not is_public,
)
if kwargs['actions_in_sidebar']:
langchain_action = gr.Radio(
allowed_actions,
value=default_action,
label="Action",
visible=len(allowed_actions) > 1)
allowed_agents = [x for x in langchain_agents_list if x in visible_langchain_agents]
if os.getenv('OPENAI_API_KEY') is None and LangChainAgent.JSON.value in allowed_agents:
allowed_agents.remove(LangChainAgent.JSON.value)
if os.getenv('OPENAI_API_KEY') is None and LangChainAgent.PYTHON.value in allowed_agents:
allowed_agents.remove(LangChainAgent.PYTHON.value)
if LangChainAgent.PANDAS.value in allowed_agents:
allowed_agents.remove(LangChainAgent.PANDAS.value)
langchain_agents = gr.Dropdown(
allowed_agents,
value=None,
label="Agents",
multiselect=True,
interactive=True,
visible=not is_public and len(allowed_agents) > 0,
elem_id="langchain_agents",
filterable=False)
can_db_filter = kwargs['langchain_mode'] != 'Disabled' and kwargs['db_type'] in ['chroma',
'chroma_old']
document_choice_kwargs = dict(choices=docs_state0,
label="Document",
value=[DocumentChoice.ALL.value],
interactive=True,
multiselect=True,
visible=can_db_filter,
elem_id="multi-selection",
allow_custom_value=False,
)
if kwargs['document_choice_in_sidebar']:
document_choice = gr.Dropdown(**document_choice_kwargs)
visible_doc_track = upload_visible and kwargs['visible_doc_track'] and not kwargs[
'large_file_count_mode']
row_doc_track = gr.Row(visible=visible_doc_track)
with row_doc_track:
if kwargs['langchain_mode'] in langchain_modes_non_db:
doc_counts_str = "Pure LLM Mode"
else:
doc_counts_str = "Name: %s\nDocs: Unset\nChunks: Unset" % kwargs['langchain_mode']
text_doc_count = gr.Textbox(lines=3, label="Doc Counts", value=doc_counts_str,
visible=visible_doc_track)
text_file_last = gr.Textbox(lines=1, label="Newest Doc", value=None, visible=visible_doc_track)
new_files_last = gr.Textbox(label="New Docs full paths as dict of full file names and content",
value='{}',
visible=False)
text_viewable_doc_count = gr.Textbox(lines=2, label=None, visible=False)
col_tabs = gr.Column(elem_id="col-tabs", scale=10)
with col_tabs, gr.Tabs():
if kwargs['chat_tables']:
chat_tab = gr.Row(visible=True)
else:
chat_tab = gr.TabItem("Chat") \
if kwargs['visible_chat_tab'] else gr.Row(visible=False)
with chat_tab:
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'],
value=kwargs['iinput'],
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)
submit_nochat_api_plain = gr.Button("Submit nochat API Plain", 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)
visible_upload = (allow_upload_to_user_data or
allow_upload_to_my_data) and \
kwargs['langchain_mode'] != 'Disabled'
# CHAT
col_chat = gr.Column(visible=kwargs['chat'])
with col_chat:
with gr.Row():
with gr.Column(scale=50):
with gr.Row(elem_id="prompt-form-row"):
label_instruction = 'Ask anything or Ingest'
instruction = gr.Textbox(
lines=kwargs['input_lines'],
label=label_instruction,
info=instruction_label,
# info=None,
elem_id='prompt-form',
container=True,
)
mw0 = 20
mic_button = gr.Button(
elem_id="microphone-button" if kwargs['enable_stt'] else None,
value="đ´",
size="sm",
min_width=mw0,
visible=kwargs['enable_stt'])
attach_button = gr.UploadButton(
elem_id="attach-button" if visible_upload else None,
value=None,
label="Upload",
size="sm",
min_width=mw0,
file_types=['.' + x for x in file_types],
file_count="multiple",
visible=visible_upload)
add_button = gr.Button(
elem_id="add-button" if visible_upload and not kwargs[
'actions_in_sidebar'] else None,
value="Ingest",
size="sm",
min_width=mw0,
visible=visible_upload and not kwargs['actions_in_sidebar'])
# AUDIO
if kwargs['enable_stt']:
def action(btn, instruction1, audio_state1, stt_continue_mode=1):
# print("B0: %s %s" % (audio_state1[0], instruction1), flush=True)
"""Changes button text on click"""
if btn == 'đ´':
audio_state1[3] = 'on'
# print("A: %s %s" % (audio_state1[0], instruction1), flush=True)
if stt_continue_mode == 1:
audio_state1[0] = instruction1
audio_state1[1] = instruction1
audio_state1[2] = None
return 'â', instruction1, audio_state1
else:
audio_state1[3] = 'off'
if stt_continue_mode == 1:
audio_state1[0] = None # indicates done for race case
instruction1 = audio_state1[1]
audio_state1[2] = []
# print("B1: %s %s" % (audio_state1[0], instruction1), flush=True)
return 'đ´', instruction1, audio_state1
# while audio state used, entries are pre_text, instruction source, and audio chunks, condition
audio_state0 = [None, None, None, 'off']
audio_state = gr.State(value=audio_state0)
audio_output = gr.HTML(visible=False)
audio = gr.Audio(**mic_sources_kwargs, streaming=True, visible=False,
# max_length=30 if is_public else None,
elem_id='audio',
# waveform_options=dict(show_controls=True),
)
mic_button_kwargs = dict(fn=functools.partial(action,
stt_continue_mode=kwargs[
'stt_continue_mode']),
inputs=[mic_button, instruction,
audio_state],
outputs=[mic_button, instruction,
audio_state],
api_name='mic' if allow_api else None,
show_progress='hidden')
# JS first, then python, but all in one click instead of using .then() that will delay
mic_button.click(fn=lambda: None, **mic_kwargs, **noqueue_kwargs2) \
.then(**mic_button_kwargs)
audio.stream(fn=kwargs['transcriber_func'],
inputs=[audio_state, audio],
outputs=[audio_state, instruction],
show_progress='hidden')
submit_buttons = gr.Row(equal_height=False, visible=kwargs['visible_submit_buttons'])
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, elem_id="submit")
stop_btn = gr.Button(value="Stop", variant='secondary', size='sm',
min_width=mw1, elem_id='stop')
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)
if kwargs['enable_stt'] and (
kwargs['tts_action_phrases'] or kwargs['tts_stop_phrases']):
def detect_words(action_text1, stop_text1, text):
got_action_word = False
action_words = kwargs['tts_action_phrases']
if action_words:
for action_word in action_words:
if action_word.lower() in text.lower():
text = text[:text.lower().index(action_word.lower())]
print("Got action: %s %s" % (action_text1, text), flush=True)
got_action_word = True
if got_action_word:
action_text1 = action_text1 + '.'
got_stop_word = False
stop_words = kwargs['tts_stop_phrases']
if stop_words:
for stop_word in stop_words:
if stop_word.lower() in text.lower():
text = text[:text.lower().index(stop_word.lower())]
print("Got stop: %s %s" % (stop_text1, text), flush=True)
got_stop_word = True
if got_stop_word:
stop_text1 = stop_text1 + '.'
return action_text1, stop_text1, text
action_text = gr.Textbox(value='', visible=False)
stop_text = gr.Textbox(value='', visible=False)
# avoid if no action word, may take extra time
instruction.change(fn=detect_words,
inputs=[action_text, stop_text, instruction],
outputs=[action_text, stop_text, instruction])
def clear_audio_state():
return audio_state0
action_text.change(fn=clear_audio_state, outputs=audio_state) \
.then(fn=lambda: None, **submit_kwargs)
stop_text.change(fn=clear_audio_state, outputs=audio_state) \
.then(fn=lambda: None, **stop_kwargs)
visible_model_choice = bool(kwargs['model_lock']) and \
len(model_states) > 1 and \
kwargs['visible_visible_models']
with gr.Row(visible=not kwargs['actions_in_sidebar'] or visible_model_choice):
visible_models = gr.Dropdown(kwargs['all_possible_visible_models'],
label="Visible Models",
value=visible_models_state0,
interactive=True,
multiselect=True,
visible=visible_model_choice,
elem_id="multi-selection",
filterable=False,
max_choices=kwargs['max_visible_models'],
)
mw0 = 100
with gr.Column(min_width=mw0):
if not kwargs['actions_in_sidebar']:
langchain_action = gr.Radio(
allowed_actions,
value=default_action,
label='Action',
show_label=visible_model_choice,
visible=True,
min_width=mw0)
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'])
doc_selection_tab = gr.TabItem("Document Selection") \
if kwargs['visible_doc_selection_tab'] else gr.Row(visible=False)
with doc_selection_tab:
if kwargs['langchain_mode'] in langchain_modes_non_db:
if langchain_mode == LangChainMode.DISABLED.value:
inactive_collection = "#### Document Q/A Disabled -- Chat only mode"
else:
dlabel1 = 'Choose Resources->Collections and Pick Collection'
inactive_collection = "#### Not Chatting with Any Collection\n%s" % dlabel1
active_collection = gr.Markdown(value=inactive_collection)
else:
dlabel1 = 'Select Subset of Document(s) for Chat with Collection: %s' % kwargs['langchain_mode']
active_collection = gr.Markdown(
value="#### Chatting with Collection: %s" % kwargs['langchain_mode'])
if not kwargs['document_choice_in_sidebar']:
document_choice_kwargs.update(dict(label=dlabel1))
document_choice = gr.Dropdown(**document_choice_kwargs)
with gr.Row():
with gr.Column():
document_source_substrings = gr.Dropdown([], label='Source substrings (post-search filter)',
# info='Post-search filter',
interactive=True,
multiselect=True,
visible=can_db_filter,
allow_custom_value=True,
scale=0,
)
with gr.Column():
document_source_substrings_op = gr.Dropdown(['and', 'or'],
label='Source substrings operation',
interactive=True,
multiselect=False,
visible=can_db_filter,
allow_custom_value=False,
scale=0,
)
with gr.Column():
document_content_substrings = gr.Dropdown([],
label='Content substrings (search-time filter)',
# info="Search-time filter of list of words to pass to where_document={'$contains': word list}",
interactive=True,
multiselect=True,
visible=can_db_filter,
allow_custom_value=True,
scale=0,
)
with gr.Column():
document_content_substrings_op = gr.Dropdown(['and', 'or'],
label='Content substrings operation',
interactive=True,
multiselect=False,
visible=can_db_filter,
allow_custom_value=False,
scale=0,
)
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 and kwargs['large_file_count_mode'])
# handle API get sources
get_sources_api_btn = gr.Button(visible=False)
get_sources_api_text = gr.Textbox(visible=False)
get_document_api_btn = gr.Button(visible=False)
get_document_api_text = gr.Textbox(visible=False)
show_sources_btn = gr.Button(value="Show Sources from DB", scale=0, size='sm',
visible=sources_visible and kwargs['large_file_count_mode'])
delete_sources_btn = gr.Button(value="Delete Selected (not by substrings) 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
visible_add_remove_collection = visible_upload
with gr.Row():
with gr.Column(scale=1):
add_placeholder = "e.g. UserData2, shared, user_path2" \
if not is_public else "e.g. MyData2, personal (optional)"
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 from UI',
placeholder=remove_placeholder,
interactive=True)
purge_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection,
label='Purge Collection (UI, DB, & source files)',
placeholder=remove_placeholder,
interactive=True)
sync_sources_btn = gr.Button(
value="Synchronize DB and UI [only required if did not login and have shared docs]",
scale=0, size='sm',
visible=sources_visible and allow_upload_to_user_data and not kwargs[
'large_file_count_mode'])
load_langchain = gr.Button(
value="Load Collections State [only required if logged in another user ", scale=0,
size='sm',
visible=False and allow_upload_to_user_data and
kwargs['langchain_mode'] != 'Disabled')
with gr.Column(scale=5):
if kwargs['langchain_mode'] != 'Disabled' and visible_add_remove_collection:
df0 = get_df_langchain_mode_paths(selection_docs_state0, None, dbs1=dbs)
else:
df0 = pd.DataFrame(None)
langchain_mode_path_text = gr.Dataframe(value=df0,
visible=visible_add_remove_collection,
label='LangChain Mode-Path',
show_label=False,
interactive=False)
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')
doc_exception_text = gr.Textbox(value="", label='Document Exceptions',
interactive=False,
visible=kwargs['langchain_mode'] != 'Disabled')
if have_arxiv and have_librosa:
file_types_extra = ' URL YouTube ArXiv TEXT'
elif have_librosa:
file_types_extra = ' URL YouTube TEXT'
elif have_arxiv:
file_types_extra = ' URL ArXiv TEXT'
else:
file_types_extra = ' URL TEXT'
file_types_str = ' '.join(file_types) + file_types_extra
gr.Textbox(value=file_types_str, label='Document Types Supported',
lines=2,
interactive=False,
visible=kwargs['langchain_mode'] != 'Disabled')
doc_view_tab = gr.TabItem("Document Viewer") \
if kwargs['visible_doc_view_tab'] else gr.Row(visible=False)
with doc_view_tab:
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 and kwargs[
'large_file_count_mode'])
view_document_choice = gr.Dropdown(viewable_docs_state0,
label="Select Single Document to View",
value=None,
interactive=True,
multiselect=False,
visible=True,
elem_id="single-selection",
)
info_view_raw = "Raw text shown if render of original doc fails"
if is_public:
info_view_raw += " (Up to %s chunks in public portal)" % kwargs['max_raw_chunks']
view_raw_text_checkbox = gr.Checkbox(label="View Database Text", value=False,
info=info_view_raw,
visible=kwargs['db_type'] in ['chroma', 'chroma_old'])
with gr.Column(scale=4):
pass
doc_view = gr.HTML(visible=False)
doc_view2 = gr.Dataframe(visible=False)
doc_view3 = gr.JSON(visible=False)
doc_view4 = gr.Markdown(visible=False)
doc_view5 = gr.HTML(visible=False)
if have_gradio_pdf:
from gradio_pdf import PDF
doc_view6 = PDF(visible=False)
else:
doc_view6 = gr.HTML(visible=False)
doc_view7 = gr.Audio(visible=False)
doc_view8 = gr.Video(visible=False)
chat_tab = gr.TabItem("Chat History") \
if kwargs['visible_chat_history_tab'] else gr.Row(visible=False)
with chat_tab:
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.Row():
count_chat_tokens_btn = gr.Button(value="Count Chat Tokens",
visible=not is_public and not kwargs['model_lock'],
interactive=not is_public, size='sm')
chat_token_count = gr.Textbox(label="Chat Token Count Result", value=None,
visible=not is_public and not kwargs['model_lock'],
interactive=False)
expert_tab = gr.TabItem("Expert") \
if kwargs['visible_expert_tab'] else gr.Row(visible=False)
with expert_tab:
gr.Markdown("Prompt Control")
with gr.Row():
with gr.Column():
if not kwargs['visible_models_tab']:
# only show here if no models tab
prompt_type = get_prompt_type1(**kwargs)
prompt_type2 = get_prompt_type2(**kwargs)
system_prompt_type = gr.Dropdown(label="System Prompt Type",
info="Choose System Prompt Type",
value=kwargs['system_prompt'],
choices=get_system_prompts(),
filterable=True,
)
system_prompt = gr.Textbox(label='System Prompt',
info="Filled by choice above, or can enter your own custom system prompt. auto means automatic, which will auto-switch to DocQA prompt when using collections.",
value=kwargs['system_prompt'], lines=2)
def show_sys(x):
return x
system_prompt_type.change(fn=show_sys, inputs=system_prompt_type, outputs=system_prompt,
**noqueue_kwargs)
context = gr.Textbox(lines=2, label="System Pre-Context",
info="Directly pre-appended without prompt processing (before Pre-Conversation)",
value=kwargs['context'])
chat_conversation = gr.Textbox(lines=2, label="Pre-Conversation",
info="Pre-append conversation for instruct/chat models as List of tuple of (human, bot)",
value=kwargs['chat_conversation'])
text_context_list = gr.Textbox(lines=2, label="Text Doc Q/A",
info="List of strings, for document Q/A, for bypassing database (i.e. also works in LLM Mode)",
value=kwargs['chat_conversation'],
visible=not is_public, # primarily meant for API
)
iinput = gr.Textbox(lines=2, label="Input for Instruct prompt types",
info="If given for document query, added after query",
value=kwargs['iinput'],
placeholder=kwargs['placeholder_input'],
interactive=not is_public)
with gr.Column():
pre_prompt_query = gr.Textbox(label="Query Pre-Prompt",
info="In prompt template, added before document text chunks",
value=kwargs['pre_prompt_query'] or '')
prompt_query = gr.Textbox(label="Query Prompt",
info="Added after documents",
value=kwargs['prompt_query'] or '')
pre_prompt_summary = gr.Textbox(label="Summary Pre-Prompt",
info="In prompt template, added before documents",
value=kwargs['pre_prompt_summary'] or '')
prompt_summary = gr.Textbox(label="Summary Prompt",
info="In prompt template, added after documents text chunks (if query given, 'Focusing on {query}, ' is pre-appended)",
value=kwargs['prompt_summary'] or '')
hyde_llm_prompt = gr.Textbox(label="HYDE LLM Prompt",
info="When doing HYDE, this is first prompt, and in template the user query comes right after this.",
value=kwargs['hyde_llm_prompt'] or '')
llava_prompt_type = gr.Dropdown(label="LLaVa LLM Prompt Type",
info="Pick pre-defined LLaVa prompt",
value=kwargs['llava_prompt'],
choices=get_llava_prompts(),
filterable=True,
)
llava_prompt = gr.Textbox(label="LLaVa LLM Prompt",
info="LLaVa prompt",
value=kwargs['llava_prompt'],
lines=2)
def show_llava(x):
return x
llava_prompt_type.change(fn=show_llava, inputs=llava_prompt_type, outputs=llava_prompt,
**noqueue_kwargs)
gr.Markdown("Document Control")
with gr.Row(visible=not is_public):
image_audio_loaders = gr.CheckboxGroup(image_audio_loaders_options,
label="Force Image-Audio Reader",
value=image_audio_loaders_options0)
pdf_loaders = gr.CheckboxGroup(pdf_loaders_options,
label="Force PDF Reader",
value=pdf_loaders_options0)
url_loaders = gr.CheckboxGroup(url_loaders_options,
label="Force URL Reader",
info="Set env CRAWL_DEPTH to control depth for Scrape, default is 1 (given page + links on that page)",
value=url_loaders_options0)
jq_schema = gr.Textbox(label="JSON jq_schema", value=jq_schema0)
extract_frames = gr.Slider(value=kwargs['extract_frames'] if not is_public else 5,
step=1,
minimum=0,
maximum=5 if is_public else max(kwargs['extract_frames'], 1000),
label="Number of unique images to extract from videos",
info="If 0, just audio extracted if enabled",
visible=have_fiftyone)
min_top_k_docs, max_top_k_docs, label_top_k_docs = get_minmax_top_k_docs(is_public, True)
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 = gr.components.Checkbox(value=kwargs['chunk'],
label="Whether to chunk documents",
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)
docs_ordering_type = gr.Radio(
docs_ordering_types,
value=kwargs['docs_ordering_type'],
label="Document Sorting in LLM Context",
visible=True)
docs_token_handling = gr.Radio(
docs_token_handlings,
value=kwargs['docs_token_handling'],
label="Document Handling Mode for filling LLM Context",
visible=True)
docs_joiner = gr.Textbox(label="String to join lists and documents",
value=kwargs['docs_joiner'] or docs_joiner_default)
max_hyde_level = 0 if is_public else 5
hyde_level = gr.Slider(minimum=0, maximum=max_hyde_level, step=1,
value=kwargs['hyde_level'],
label='HYDE level',
info="Whether to use HYDE approach for LLM getting answer to embed (0=disabled, 1=non-doc LLM answer, 2=doc-based LLM answer)",
visible=kwargs['langchain_mode'] != 'Disabled',
interactive=not is_public)
hyde_template = gr.components.Textbox(value='auto',
label="HYDE Embedding Template",
info="HYDE approach for LLM getting answer to embed ('auto' means automatic, else enter template like '{query}'",
visible=True)
hyde_show_only_final = gr.components.Checkbox(value=kwargs['hyde_show_only_final'],
label="Only final HYDE shown",
info="Whether to only show final HYDE result",
visible=True)
doc_json_mode = gr.components.Checkbox(value=kwargs['doc_json_mode'],
label="JSON docs mode",
info="Whether to pass JSON to and get JSON back from LLM",
visible=True)
embed = gr.components.Checkbox(value=True,
label="Embed text",
info="For LangChain, whether to embed text",
visible=False)
gr.Markdown("LLM Control")
with gr.Row():
stream_output = gr.components.Checkbox(label="Stream output",
value=kwargs['stream_output'])
do_sample = gr.Checkbox(label="Sample",
info="Enable sampler (required for use of temperature, top_p, top_k). If temperature=0 is set, this is forced to False.",
value=kwargs['do_sample'])
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.")
temperature = gr.Slider(minimum=0, maximum=2,
value=kwargs['temperature'],
label="Temperature",
info="Lower is deterministic, higher more creative")
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'
)
penalty_alpha = gr.Slider(
minimum=0.0, maximum=2.0, step=0.01,
value=kwargs['penalty_alpha'], label="penalty_alpha",
info='penalty_alpha>0 and top_k>1 enables contrastive search'
)
# 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, visible=max_beams > 1)
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'],
)
min_max_new_tokens = gr.Slider(
minimum=1, maximum=max_max_new_tokens, step=1,
value=min(max_max_new_tokens, kwargs['min_max_new_tokens']),
label="Min. of Max output length",
visible=not is_public,
)
max_input_tokens = gr.Number(
minimum=-1 if not is_public else kwargs['max_input_tokens'],
maximum=128 * 1024 if not is_public else kwargs['max_input_tokens'],
step=1,
value=-1 if not is_public else kwargs['max_input_tokens'],
label="Max input length (treat as if model has more limited context, e.g. for context-filling when top_k_docs=-1)",
visible=not is_public,
)
max_total_input_tokens = gr.Number(
minimum=-1 if not is_public else kwargs['max_total_input_tokens'],
maximum=128 * 1024 if not is_public else kwargs['max_total_input_tokens'],
step=1,
value=-1 if not is_public else kwargs['max_total_input_tokens'],
label="Max input length across all LLM calls when doing summarization/extraction",
visible=not is_public,
)
early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search",
value=kwargs['early_stopping'], visible=max_beams > 1)
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, visible=max_beams > 1)
chat = gr.components.Checkbox(label="Chat mode", value=kwargs['chat'],
visible=False, # no longer support nochat in UI
interactive=not is_public,
)
clone_visible = visible = kwargs['enable_tts'] and kwargs['tts_model'].startswith('tts_models/')
if clone_visible:
markdown_label = "Speech Control and Voice Cloning"
else:
markdown_label = "Speech Control"
audio_visible = kwargs['enable_tts'] and kwargs['tts_model']
gr.Markdown(markdown_label, visible=audio_visible)
with gr.Row(visible=audio_visible):
if audio_visible:
speech_human = gr.Audio(value=None,
label="Generated Human Speech",
type="numpy",
streaming=True,
interactive=False,
show_label=True,
autoplay=True,
elem_id='human_audio',
visible=audio_visible)
speech_bot = gr.Audio(value=None,
label="Generated Bot Speech",
type="numpy",
streaming=True,
interactive=False,
show_label=True,
autoplay=True,
elem_id='bot_audio',
visible=audio_visible)
speech_bot2 = gr.Audio(value=None,
label="Generated Bot 2 Speech",
type="numpy",
streaming=True,
interactive=False,
show_label=True,
autoplay=False,
visible=False,
elem_id='bot2_audio')
else:
# Ensure not streaming media, just webconnect, if not doing TTS
speech_human = gr.Textbox(visible=False)
speech_bot = gr.Textbox(visible=False)
speech_bot2 = gr.Textbox(visible=False)
if kwargs['enable_tts'] and kwargs['tts_model'].startswith('tts_models/'):
from src.tts_coqui import get_languages_gr
tts_language = get_languages_gr(visible=True, value=kwargs['tts_language'])
else:
tts_language = gr.Dropdown(visible=False)
def process_audio(file1, t1=0, t2=30):
# use no more than 30 seconds
from pydub import AudioSegment
# in milliseconds
t1 = t1 * 1000
t2 = t2 * 1000
newAudio = AudioSegment.from_wav(file1)[t1:t2]
new_file = file1 + '.new.wav'
newAudio.export(new_file, format="wav")
return new_file
if audio_visible:
model_base = os.getenv('H2OGPT_MODEL_BASE', 'models/')
female_voice = os.path.join(model_base, "female.wav")
ref_voice_clone = gr.Audio(
label="File for Clone (x resets)",
type="filepath",
value=female_voice if os.path.isfile(female_voice) else None,
# max_length=30 if is_public else None,
visible=clone_visible,
)
ref_voice_clone.upload(process_audio, inputs=ref_voice_clone, outputs=ref_voice_clone)
else:
ref_voice_clone = gr.Textbox(visible=False)
if audio_visible:
mic_voice_clone = gr.Audio(
label="Mic for Clone (x resets)",
type="filepath",
**mic_sources_kwargs,
# max_length=30 if is_public else None,
visible=clone_visible,
)
mic_voice_clone.upload(process_audio, inputs=mic_voice_clone, outputs=mic_voice_clone)
else:
mic_voice_clone = gr.Textbox(visible=False)
choose_mic_voice_clone = gr.Checkbox(
label="Use Mic for Cloning",
value=False,
info="If unchecked, uses File",
visible=clone_visible,
)
role_name_to_add = gr.Textbox(value='', info="Name of Speaker to add", label="Speaker Style",
visible=clone_visible)
add_role = gr.Button(value="Clone Voice for new Speech Style", visible=clone_visible)
def add_role_func(name, file, mic, roles1, use_mic):
if use_mic and os.path.isfile(mic):
roles1[name] = mic
elif os.path.isfile(file):
roles1[name] = file
roles1[name] = process_audio(roles1[name])
return gr.Dropdown(choices=list(roles1.keys())), roles1
add_role_event = add_role.click(add_role_func,
inputs=[role_name_to_add, ref_voice_clone, mic_voice_clone,
roles_state,
choose_mic_voice_clone],
outputs=[chatbot_role, roles_state],
api_name='add_role' if allow_api else None,
**noqueue_kwargs2,
)
models_tab = gr.TabItem("Models") if kwargs['visible_models_tab'] else gr.Row(visible=False)
with models_tab:
load_msg = "Load (Download) Model" if not is_public \
else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO"
if kwargs['base_model'] not in ['', None, no_model_str]:
load_msg += ' [WARNING: Avoid --base_model on CLI for memory efficient Load-Unload]'
load_msg2 = load_msg + "2"
variant_load_msg = 'primary' if not is_public else 'secondary'
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=10, visible=not kwargs['model_lock']):
load_model_button = gr.Button(load_msg, variant=variant_load_msg, scale=0,
size='sm', interactive=not is_public)
unload_model_button = gr.Button("UnLoad Model", variant=variant_load_msg, scale=0,
size='sm', interactive=not is_public)
with gr.Row():
with gr.Column():
model_choice = gr.Dropdown(model_options_state.value[0],
label="Choose/Enter Base Model (HF name, TheBloke, file, URL)",
value=kwargs['base_model'] or
model_options_state.value[0],
allow_custom_value=not is_public)
lora_choice = gr.Dropdown(lora_options_state.value[0],
label="Choose/Enter LORA",
value=kwargs['lora_weights'] or
lora_options_state.value[0],
visible=kwargs['show_lora'],
allow_custom_value=not is_public)
server_choice = gr.Dropdown(server_options_state.value[0],
label="Choose/Enter Server",
value=kwargs['inference_server'] or
server_options_state.value[0],
visible=not is_public,
allow_custom_value=not is_public)
if kwargs['visible_models_tab']:
prompt_type = get_prompt_type1(**kwargs)
with gr.Column():
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)
with gr.Column(scale=1, visible=not kwargs['model_lock']):
with gr.Accordion("Precision", open=False, visible=True):
model_load8bit_checkbox = gr.components.Checkbox(
label="Load 8-bit [requires support]",
value=kwargs['load_8bit'], interactive=not is_public)
model_load4bit_checkbox = gr.components.Checkbox(
label="Load 4-bit [requires support]",
value=kwargs['load_4bit'], interactive=not is_public)
model_low_bit_mode = gr.Slider(value=kwargs['low_bit_mode'],
minimum=0, maximum=4, step=1,
label="low_bit_mode",
info="0: no quantization config 1: change compute 2: nf4 3: double quant 4: 2 and 3")
with gr.Accordion("GPU", open=False, visible=n_gpus != 0):
model_use_cpu_checkbox = gr.components.Checkbox(
label="Use CPU even if have GPUs",
value=False,
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)
llama_multi_gpu_info = "LLaMa.cpp does not support multi-GPU GPU selection, run h2oGPT with env CUDA_VISIBLE_DEVICES set to which GPU to use, else all are used."
model_gpu = gr.Dropdown(n_gpus_list,
label="GPU ID [-1 = all GPUs, if Choose is enabled]",
info=llama_multi_gpu_info,
value=kwargs['gpu_id'],
interactive=not is_public)
with gr.Accordion("Add-ons", open=False, visible=True):
model_attention_sinks = gr.components.Checkbox(
label="Enable Attention Sinks [requires support]",
value=kwargs['attention_sinks'], interactive=not is_public)
model_truncation_generation = gr.components.Checkbox(
label="Truncate generation (disable for attention sinks, enforced if required)",
value=kwargs['truncation_generation'], interactive=not is_public)
model_sink_dict = gr.Textbox(value=str(kwargs['sink_dict'] or {}),
label="sink_dict")
model_load_gptq = gr.Textbox(label="gptq",
info="For TheBloke, use: model",
value=kwargs['load_gptq'],
visible=kwargs['use_autogptq'],
interactive=not is_public)
model_gptq_dict = gr.Textbox(value=str(kwargs['gptq_dict'] or {}),
info="E.g. {'inject_fused_attention':False, 'disable_exllama': True}",
label="gptq_dict",
visible=kwargs['use_autogptq'])
model_load_awq = gr.Textbox(label="awq", value=kwargs['load_awq'],
info="For TheBloke, use: model",
interactive=not is_public)
model_load_exllama_checkbox = gr.components.Checkbox(
label="Load with exllama [requires support]",
value=kwargs['load_exllama'], interactive=not is_public)
model_exllama_dict = gr.Textbox(value=str(kwargs['exllama_dict'] or {}),
label="exllama_dict",
info="E.g. to split across 2 GPUs: {'set_auto_map':20,20}")
hf_label = "HuggingFace" if kwargs['use_autogptq'] else "HuggingFace (inc. GPTQ)"
with gr.Accordion(hf_label, open=False, visible=True):
model_safetensors_checkbox = gr.components.Checkbox(
label="Safetensors [required sometimes, e.g. GPTQ from TheBloke]",
value=kwargs['use_safetensors'], interactive=not is_public)
model_hf_model_dict = gr.Textbox(value=str(kwargs['hf_model_dict'] or {}),
label="hf_model_dict")
model_revision = gr.Textbox(label="revision",
value=kwargs['revision'],
info="Hash on HF to use",
interactive=not is_public)
with gr.Accordion("Current or Custom Model Prompt", open=False, visible=True):
prompt_dict = gr.Textbox(label="Current Prompt (or Custom)",
value=pprint.pformat(kwargs['prompt_dict'] or {},
indent=4),
interactive=not is_public, lines=6)
with gr.Accordion("Current or Custom Context Length", open=False, visible=True):
max_seq_len = gr.Number(value=kwargs['max_seq_len'] or -1,
minimum=-1,
maximum=2 ** 18,
precision=0,
info="If standard LLaMa-2, choose up to 4096 (-1 means choose max of model)",
label="max_seq_len")
max_seq_len_used = gr.Number(value=kwargs['max_seq_len'] or -1,
label="Current Max. Seq. Length",
interactive=False)
rope_scaling = gr.Textbox(value=str(kwargs['rope_scaling'] or {}),
label="rope_scaling",
info="Not required if in config.json. E.g. {'type':'linear', 'factor':4} for HF and {'alpha_value':4} for exllama")
acc_llama = gr.Accordion("LLaMa.cpp & GPT4All", open=False,
visible=kwargs['show_llama'])
with acc_llama:
# with row_llama:
model_path_llama = gr.Textbox(value=kwargs['llamacpp_dict']['model_path_llama'],
lines=4,
label="Choose LLaMa.cpp Model Path/URL (for Base Model: llama)",
visible=kwargs['show_llama'])
n_gpu_layers = gr.Number(value=kwargs['llamacpp_dict']['n_gpu_layers'],
minimum=0, maximum=100,
label="LLaMa.cpp Num. GPU Layers Offloaded",
visible=kwargs['show_llama'])
n_batch = gr.Number(value=kwargs['llamacpp_dict']['n_batch'],
minimum=0, maximum=2048,
label="LLaMa.cpp Batch Size",
visible=kwargs['show_llama'])
n_gqa = gr.Number(value=kwargs['llamacpp_dict']['n_gqa'],
minimum=0, maximum=32,
label="LLaMa.cpp Num. Group Query Attention (8 for 70B LLaMa2)",
visible=kwargs['show_llama'])
llamacpp_dict_more = gr.Textbox(value="{}",
lines=4,
label="Dict for other LLaMa.cpp/GPT4All options",
visible=kwargs['show_llama'])
model_name_gptj = gr.Textbox(value=kwargs['llamacpp_dict']['model_name_gptj'],
label="Choose GPT4All GPTJ Model Path/URL (for Base Model: gptj)",
visible=kwargs['show_gpt4all'])
model_name_gpt4all_llama = gr.Textbox(
value=kwargs['llamacpp_dict']['model_name_gpt4all_llama'],
label="Choose GPT4All LLaMa Model Path/URL (for Base Model: gpt4all_llama)",
visible=kwargs['show_gpt4all'])
col_model2 = gr.Column(visible=False)
with col_model2:
with gr.Row():
with gr.Column(scale=10, visible=not kwargs['model_lock']):
load_model_button2 = gr.Button(load_msg2, variant=variant_load_msg, scale=0,
size='sm', interactive=not is_public)
unload_model_button2 = gr.Button("UnLoad Model2", variant=variant_load_msg, scale=0,
size='sm', interactive=not is_public)
with gr.Row():
with gr.Column():
model_choice2 = gr.Dropdown(model_options_state.value[0],
label="Choose/Enter Model 2 (HF name, TheBloke, file, URL)",
value=no_model_str,
allow_custom_value=not is_public)
lora_choice2 = gr.Dropdown(lora_options_state.value[0],
label="Choose/Enter LORA 2",
value=no_lora_str,
visible=kwargs['show_lora'],
allow_custom_value=not is_public)
server_choice2 = gr.Dropdown(server_options_state.value[0],
label="Choose/Enter Server 2",
value=no_server_str,
visible=not is_public,
allow_custom_value=not is_public)
if kwargs['visible_models_tab']:
prompt_type2 = get_prompt_type2(**kwargs)
with gr.Column():
# 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 (Model 2)", value=no_lora_str,
visible=kwargs['show_lora'], interactive=False)
server_used2 = gr.Textbox(label="Current Server (Model 2)",
value=no_server_str,
interactive=False,
visible=not is_public)
with gr.Column(scale=1, visible=not kwargs['model_lock']):
with gr.Accordion("Precision", open=False, visible=True):
model_load8bit_checkbox2 = gr.components.Checkbox(
label="Load 8-bit (Model 2) [requires support]",
value=kwargs['load_8bit'], interactive=not is_public)
model_load4bit_checkbox2 = gr.components.Checkbox(
label="Load 4-bit (Model 2) [requires support]",
value=kwargs['load_4bit'], interactive=not is_public)
model_low_bit_mode2 = gr.Slider(value=kwargs['low_bit_mode'],
# ok that same as Model 1
minimum=0, maximum=4, step=1,
label="low_bit_mode (Model 2)")
with gr.Accordion("GPU", open=False, visible=n_gpus != 0):
model_use_cpu_checkbox2 = gr.components.Checkbox(
label="Use CPU even if have GPUs (Model 2)",
value=False,
interactive=not is_public)
model_use_gpu_id_checkbox2 = gr.components.Checkbox(
label="Choose Devices (Model 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 (Model 2) [-1 = all GPUs, if choose is enabled]",
info=llama_multi_gpu_info,
value=kwargs['gpu_id'],
interactive=not is_public)
with gr.Accordion("Add-ons", open=False, visible=True):
model_attention_sinks2 = gr.components.Checkbox(
label="Enable Attention Sinks [requires support] (Model 2)",
value=kwargs['attention_sinks'], interactive=not is_public)
model_truncation_generation2 = gr.components.Checkbox(
label="Truncate generation (disable for attention sinks) (Model 2)",
value=kwargs['truncation_generation'], interactive=not is_public)
model_sink_dict2 = gr.Textbox(value=str(kwargs['sink_dict'] or {}),
label="sink_dict (Model 2)")
model_load_gptq2 = gr.Textbox(label="gptq (Model 2)",
info="For TheBloke models, use: model",
value=kwargs['load_gptq'],
visible=kwargs['use_autogptq'],
interactive=not is_public)
model_gptq_dict2 = gr.Textbox(value=str(kwargs['gptq_dict'] or {}),
info="E.g. {'inject_fused_attention':False, 'disable_exllama': True}",
visible=kwargs['use_autogptq'],
label="gptq_dict (Model 2)")
model_load_awq2 = gr.Textbox(label="awq (Model 2)", value='',
interactive=not is_public)
model_load_exllama_checkbox2 = gr.components.Checkbox(
label="Load with exllama (Model 2) [requires support]",
value=False, interactive=not is_public)
model_exllama_dict2 = gr.Textbox(value=str(kwargs['exllama_dict'] or {}),
label="exllama_dict (Model 2)")
with gr.Accordion(hf_label, open=False, visible=True):
model_safetensors_checkbox2 = gr.components.Checkbox(
label="Safetensors (Model 2) [requires support]",
value=False, interactive=not is_public)
model_hf_model_dict2 = gr.Textbox(value=str(kwargs['hf_model_dict'] or {}),
label="hf_model_dict (Model 2)")
model_revision2 = gr.Textbox(label="revision (Model 2)", value='',
interactive=not is_public)
with gr.Accordion("Current or Custom Model Prompt", open=False, visible=True):
prompt_dict2 = gr.Textbox(label="Current Prompt (or Custom) (Model 2)",
value=pprint.pformat(kwargs['prompt_dict'] or {},
indent=4),
interactive=not is_public, lines=4)
with gr.Accordion("Current or Custom Context Length", open=False, visible=True):
max_seq_len2 = gr.Number(value=kwargs['max_seq_len'] or -1,
minimum=-1,
maximum=2 ** 18,
info="If standard LLaMa-2, choose up to 4096 (-1 means choose max of model)",
label="max_seq_len Model 2")
max_seq_len_used2 = gr.Number(value=-1,
label="mCurrent Max. Seq. Length (Model 2)",
interactive=False)
rope_scaling2 = gr.Textbox(value=str(kwargs['rope_scaling'] or {}),
label="rope_scaling Model 2")
acc_llama2 = gr.Accordion("LLaMa.cpp & GPT4All", open=False,
visible=kwargs['show_llama'])
with acc_llama2:
model_path_llama2 = gr.Textbox(
value=kwargs['llamacpp_dict']['model_path_llama'],
label="Choose LLaMa.cpp Model 2 Path/URL (for Base Model: llama)",
lines=4,
visible=kwargs['show_llama'])
n_gpu_layers2 = gr.Number(value=kwargs['llamacpp_dict']['n_gpu_layers'],
minimum=0, maximum=100,
label="LLaMa.cpp Num. GPU 2 Layers Offloaded",
visible=kwargs['show_llama'])
n_batch2 = gr.Number(value=kwargs['llamacpp_dict']['n_batch'],
minimum=0, maximum=2048,
label="LLaMa.cpp Model 2 Batch Size",
visible=kwargs['show_llama'])
n_gqa2 = gr.Number(value=kwargs['llamacpp_dict']['n_gqa'],
minimum=0, maximum=32,
label="LLaMa.cpp Model 2 Num. Group Query Attention (8 for 70B LLaMa2)",
visible=kwargs['show_llama'])
llamacpp_dict_more2 = gr.Textbox(value="{}",
lines=4,
label="Model 2 Dict for other LLaMa.cpp/GPT4All options",
visible=kwargs['show_llama'])
model_name_gptj2 = gr.Textbox(value=kwargs['llamacpp_dict']['model_name_gptj'],
label="Choose GPT4All GPTJ Model 2 Path/URL (for Base Model: gptj)",
visible=kwargs['show_gpt4all'])
model_name_gpt4all_llama2 = gr.Textbox(
value=kwargs['llamacpp_dict']['model_name_gpt4all_llama'],
label="Choose GPT4All LLaMa Model 2 Path/URL (for Base Model: gpt4all_llama)",
visible=kwargs['show_gpt4all'])
compare_checkbox = gr.components.Checkbox(label="Compare Two Models",
value=kwargs['model_lock'],
visible=not is_public and not kwargs['model_lock'])
with gr.Row(visible=not kwargs['model_lock'] and kwargs['enable_add_models_to_list_ui']):
with gr.Column(scale=50):
new_model = gr.Textbox(label="New Model name/path/URL", interactive=not is_public)
with gr.Column(scale=50):
new_lora = gr.Textbox(label="New LORA name/path/URL", 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,
variant=variant_load_msg,
size='sm', interactive=not is_public)
system_tab = gr.TabItem("System") \
if kwargs['visible_system_tab'] else gr.Row(visible=False)
with system_tab:
with gr.Row():
with gr.Column(scale=1):
side_bar_text = gr.Textbox('on' if kwargs['visible_side_bar'] else 'off',
visible=False, interactive=False)
doc_count_text = gr.Textbox('on' if kwargs['visible_doc_track'] else 'off',
visible=False, interactive=False)
submit_buttons_text = gr.Textbox('on' if kwargs['visible_submit_buttons'] else 'off',
visible=False, interactive=False)
visible_models_text = gr.Textbox('on' if kwargs['visible_visible_models'] else 'off',
visible=False, interactive=False)
side_bar_btn = gr.Button("Toggle SideBar", variant="secondary", size="sm")
doc_count_btn = gr.Button("Toggle SideBar Document Count/Show Newest", variant="secondary",
size="sm",
visible=langchain_mode != LangChainMode.DISABLED.value)
submit_buttons_btn = gr.Button("Toggle Submit Buttons", variant="secondary", size="sm")
visible_model_btn = gr.Button("Toggle Visible Models", 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')
pdf_height = gr.Slider(minimum=100, maximum=3000, value=kwargs['pdf_height'] or 800,
step=50, label='PDF Viewer Height',
visible=have_gradio_pdf and langchain_mode != LangChainMode.DISABLED.value)
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.Accordion("Admin", open=False, visible=True):
with gr.Column():
close_btn = gr.Button(value="Shutdown h2oGPT", size='sm',
visible=kwargs['close_button'] and kwargs[
'h2ogpt_pid'] is not None)
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)
system_btn4 = gr.Button(value='Get Model Names', visible=not is_public, size='sm')
system_text4 = gr.Textbox(label='Model Names', 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)
tos_tab = gr.TabItem("Terms of Service") \
if kwargs['visible_tos_tab'] else gr.Row(visible=False)
with tos_tab:
description = ""
description += """ DISCLAIMERS:
- The model was trained on The Pile and other data, which may contain objectionable content. Use at own risk.
"""
if kwargs['load_8bit']:
description += """- Model is loaded in 8-bit and has other restrictions on this host. UX can be worse than non-hosted version.
"""
description += """- Conversations may be used to improve h2oGPT. Do not share sensitive information.
"""
if 'h2ogpt-research' in kwargs['base_model']:
description += """- Research demonstration only, not used for commercial purposes.
"""
description += """- By using h2oGPT, you accept our Terms of Service
"""
gr.Markdown(value=description, show_label=False)
login_tab = gr.TabItem("Login") \
if kwargs['visible_login_tab'] else gr.Row(visible=False)
with login_tab:
extra_login = "\nDaily maintenance at midnight PST will not allow reconnection to state otherwise." if is_public else ""
gr.Markdown(
value="#### Login page to persist your state (database, documents, chat, chat history, model list)%s" % extra_login)
username_text = gr.Textbox(label="Username")
password_text = gr.Textbox(label="Password", type='password', visible=True)
login_msg = "Login (pick unique user/pass to persist your state)" if kwargs[
'auth_access'] == 'open' else "Login (closed access)"
login_btn = gr.Button(value=login_msg)
login_result_text = gr.Text(label="Login Result", interactive=False)
if kwargs['enforce_h2ogpt_api_key'] and kwargs['enforce_h2ogpt_ui_key']:
label_h2ogpt_key = "h2oGPT Token for API and UI access"
elif kwargs['enforce_h2ogpt_api_key']:
label_h2ogpt_key = "h2oGPT Token for API access"
elif kwargs['enforce_h2ogpt_ui_key']:
label_h2ogpt_key = "h2oGPT Token for UI access"
else:
label_h2ogpt_key = 'Unused'
h2ogpt_key = gr.Text(value='',
# do not use kwargs['h2ogpt_key'] here, that's only for gradio inference server
label=label_h2ogpt_key,
type='password',
visible=kwargs['enforce_h2ogpt_ui_key'], # only show if need for UI
)
hosts_tab = gr.TabItem("Hosts") \
if kwargs['visible_hosts_tab'] else gr.Row(visible=False)
with hosts_tab:
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],
**noqueue_kwargs,
api_name='zip_data' if allow_api else None)
s3up_event = s3up_btn.click(s3up, inputs=zip_text, outputs=s3up_text, **noqueue_kwargs,
api_name='s3up_data' if allow_api else None)
def clear_file_list():
return None
def set_loaders(max_quality1,
image_audio_loaders_options1=None,
pdf_loaders_options1=None,
url_loaders_options1=None,
image_audio_loaders_options01=None,
pdf_loaders_options01=None,
url_loaders_options01=None,
):
if not max_quality1:
return image_audio_loaders_options01, pdf_loaders_options01, url_loaders_options01
else:
return image_audio_loaders_options1, pdf_loaders_options1, url_loaders_options1
set_loaders_func = functools.partial(set_loaders,
image_audio_loaders_options1=image_audio_loaders_options,
pdf_loaders_options1=pdf_loaders_options,
url_loaders_options1=url_loaders_options,
image_audio_loaders_options01=image_audio_loaders_options0,
pdf_loaders_options01=pdf_loaders_options0,
url_loaders_options01=url_loaders_options0,
)
max_quality.change(fn=set_loaders_func,
inputs=max_quality,
outputs=[image_audio_loaders, pdf_loaders, url_loaders])
def get_model_lock_visible_list(visible_models1, all_possible_visible_models):
visible_list = []
for modeli, model in enumerate(all_possible_visible_models):
if visible_models1 is None or model in visible_models1 or modeli in visible_models1:
visible_list.append(True)
else:
visible_list.append(False)
return visible_list
def set_visible_models(visible_models1, num_model_lock=0, all_possible_visible_models=None):
if num_model_lock == 0:
num_model_lock = 3 # 2 + 1 (which is dup of first)
ret_list = [gr.Textbox(visible=True)] * num_model_lock
else:
assert isinstance(all_possible_visible_models, list)
assert num_model_lock == len(all_possible_visible_models)
visible_list = [False, False] + get_model_lock_visible_list(visible_models1,
all_possible_visible_models)
ret_list = [gr.Textbox(visible=x) for x in visible_list]
return tuple(ret_list)
visible_models_func = functools.partial(set_visible_models,
num_model_lock=len(text_outputs),
all_possible_visible_models=kwargs['all_possible_visible_models'])
visible_models.change(fn=visible_models_func,
inputs=visible_models,
outputs=[text_output, text_output2] + text_outputs,
)
# Add to UserData or custom user db
update_db_func = functools.partial(update_user_db_gr,
dbs=dbs,
db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
captions_model=captions_model,
caption_loader=caption_loader,
doctr_loader=doctr_loader,
llava_model=llava_model,
asr_model=asr_model,
asr_loader=asr_loader,
verbose=kwargs['verbose'],
n_jobs=kwargs['n_jobs'],
get_userid_auth=get_userid_auth,
image_audio_loaders_options0=image_audio_loaders_options0,
pdf_loaders_options0=pdf_loaders_options0,
url_loaders_options0=url_loaders_options0,
jq_schema0=jq_schema0,
enforce_h2ogpt_api_key=kwargs['enforce_h2ogpt_api_key'],
enforce_h2ogpt_ui_key=kwargs['enforce_h2ogpt_ui_key'],
h2ogpt_api_keys=kwargs['h2ogpt_api_keys'],
is_public=is_public,
use_pymupdf=kwargs['use_pymupdf'],
use_unstructured_pdf=kwargs['use_unstructured_pdf'],
use_pypdf=kwargs['use_pypdf'],
enable_pdf_ocr=kwargs['enable_pdf_ocr'],
enable_pdf_doctr=kwargs['enable_pdf_doctr'],
try_pdf_as_html=kwargs['try_pdf_as_html'],
gradio_upload_to_chatbot_num_max=kwargs['gradio_upload_to_chatbot_num_max'],
allow_upload_to_my_data=kwargs['allow_upload_to_my_data'],
allow_upload_to_user_data=kwargs['allow_upload_to_user_data'],
)
add_file_outputs = [fileup_output, langchain_mode]
add_file_kwargs = dict(fn=update_db_func,
inputs=[fileup_output, my_db_state, selection_docs_state, requests_state,
langchain_mode, chunk, chunk_size, embed,
image_audio_loaders,
pdf_loaders,
url_loaders,
jq_schema,
extract_frames,
llava_prompt,
h2ogpt_key,
],
outputs=add_file_outputs + [sources_text, doc_exception_text, text_file_last,
new_files_last],
queue=queue,
api_name='add_file' if allow_upload_api else None)
# then no need for add buttons, only single changeable db
user_state_kwargs = dict(fn=user_state_setup,
inputs=[my_db_state, requests_state, langchain_mode],
outputs=[my_db_state, requests_state, langchain_mode],
show_progress='minimal')
eventdb1a = fileup_output.upload(**user_state_kwargs)
eventdb1 = eventdb1a.then(**add_file_kwargs, show_progress='full')
event_attach1 = attach_button.upload(**user_state_kwargs)
attach_file_kwargs = add_file_kwargs.copy()
attach_file_kwargs['inputs'][0] = attach_button
attach_file_kwargs['outputs'][0] = attach_button
attach_file_kwargs['api_name'] = 'attach_file'
event_attach2 = event_attach1.then(**attach_file_kwargs, show_progress='full')
sync1 = sync_sources_btn.click(**user_state_kwargs)
# 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, requests_state,
langchain_mode, chunk, chunk_size, embed,
image_audio_loaders,
pdf_loaders,
url_loaders,
jq_schema,
extract_frames,
llava_prompt,
h2ogpt_key,
],
outputs=add_file_outputs + [sources_text, doc_exception_text, text_file_last,
new_files_last],
queue=queue,
api_name='add_file_api' if allow_upload_api 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(value='')
update_user_db_url_func = functools.partial(update_db_func, is_url=True,
is_txt=not kwargs['actions_in_sidebar'])
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, requests_state,
langchain_mode, chunk, chunk_size, embed,
image_audio_loaders,
pdf_loaders,
url_loaders,
jq_schema,
extract_frames,
llava_prompt,
h2ogpt_key,
],
outputs=add_url_outputs + [sources_text, doc_exception_text, text_file_last,
new_files_last],
queue=queue,
api_name='add_url' if allow_upload_api else None)
user_text_submit_kwargs = dict(fn=user_state_setup,
inputs=[my_db_state, requests_state, url_text, url_text],
outputs=[my_db_state, requests_state, url_text],
queue=queue,
show_progress='minimal')
eventdb2a = url_text.submit(**user_text_submit_kwargs)
# work around https://github.com/gradio-app/gradio/issues/4733
eventdb2 = eventdb2a.then(**add_url_kwargs, show_progress='full')
# small button version
add_url_kwargs_btn = add_url_kwargs.copy()
add_url_kwargs_btn.update(api_name='add_url_btn' if allow_upload_api else None)
def copy_text(instruction1):
return gr.Textbox(value=''), instruction1
eventdb2a_btn = add_button.click(copy_text, inputs=instruction, outputs=[instruction, url_text],
**noqueue_kwargs2)
eventdb2a_btn2 = eventdb2a_btn.then(**user_text_submit_kwargs)
eventdb2_btn = eventdb2a_btn2.then(**add_url_kwargs_btn, show_progress='full')
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, requests_state,
langchain_mode, chunk, chunk_size, embed,
image_audio_loaders,
pdf_loaders,
url_loaders,
jq_schema,
extract_frames,
llava_prompt,
h2ogpt_key,
],
outputs=add_text_outputs + [sources_text, doc_exception_text, text_file_last,
new_files_last],
queue=queue,
api_name='add_text' if allow_upload_api else None
)
eventdb3a = user_text_text.submit(fn=user_state_setup,
inputs=[my_db_state, requests_state, user_text_text, user_text_text],
outputs=[my_db_state, requests_state, user_text_text],
queue=queue,
show_progress='minimal')
eventdb3 = eventdb3a.then(**add_text_kwargs, show_progress='full')
db_events = [eventdb1a, eventdb1, eventdb1_api,
eventdb2a, eventdb2,
eventdb2a_btn, eventdb2_btn,
eventdb3a, eventdb3]
db_events.extend([event_attach1, event_attach2])
get_sources1 = functools.partial(get_sources_gr, dbs=dbs, docs_state0=docs_state0,
load_db_if_exists=load_db_if_exists,
db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
verbose=verbose,
get_userid_auth=get_userid_auth,
n_jobs=n_jobs,
)
# if change collection source, must clear doc selections from it to avoid inconsistency
def clear_doc_choice(langchain_mode1):
if langchain_mode1 in langchain_modes_non_db:
label1 = 'Choose Resources->Collections and Pick Collection' if not kwargs[
'document_choice_in_sidebar'] else "Document"
active_collection1 = "#### Not Chatting with Any Collection\n%s" % label1
else:
label1 = 'Select Subset of Document(s) for Chat with Collection: %s' % langchain_mode1 if not kwargs[
'document_choice_in_sidebar'] else "Document"
active_collection1 = "#### Chatting with Collection: %s" % langchain_mode1
return gr.Dropdown(choices=docs_state0, value=[DocumentChoice.ALL.value],
label=label1), gr.Markdown(value=active_collection1)
lg_change_event = langchain_mode.change(clear_doc_choice, inputs=langchain_mode,
outputs=[document_choice, active_collection],
queue=not kwargs['large_file_count_mode'])
def resize_col_tabs(x):
return gr.Dropdown(scale=x)
col_tabs_scale.change(fn=resize_col_tabs, inputs=col_tabs_scale, outputs=col_tabs, **noqueue_kwargs)
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, **noqueue_kwargs)
def resize_pdf_viewer_func(x):
return gr.update(height=x)
pdf_height.change(fn=resize_pdf_viewer_func, inputs=pdf_height, outputs=doc_view6, **noqueue_kwargs2)
def update_dropdown(x):
if DocumentChoice.ALL.value in x:
x.remove(DocumentChoice.ALL.value)
source_list = [DocumentChoice.ALL.value] + x
return gr.Dropdown(choices=source_list, value=[DocumentChoice.ALL.value])
get_sources_kwargs = dict(fn=get_sources1,
inputs=[my_db_state, selection_docs_state, requests_state, langchain_mode],
outputs=[file_source, docs_state, text_doc_count],
queue=queue)
eventdb7a = get_sources_btn.click(user_state_setup,
inputs=[my_db_state, requests_state, get_sources_btn, get_sources_btn],
outputs=[my_db_state, requests_state, get_sources_btn],
show_progress='minimal')
eventdb7 = eventdb7a.then(**get_sources_kwargs,
api_name='get_sources' if allow_api else None) \
.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
get_sources_api_args = dict(fn=functools.partial(get_sources1, api=True),
inputs=[my_db_state, selection_docs_state, requests_state, langchain_mode],
outputs=get_sources_api_text,
queue=queue)
get_sources_api_btn.click(**get_sources_api_args,
api_name='get_sources_api' if allow_api else None)
# 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_gr,
dbs=dbs,
load_db_if_exists=load_db_if_exists,
db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
verbose=verbose,
get_userid_auth=get_userid_auth,
n_jobs=n_jobs)
eventdb8a = show_sources_btn.click(user_state_setup,
inputs=[my_db_state, requests_state, show_sources_btn, show_sources_btn],
outputs=[my_db_state, requests_state, show_sources_btn],
show_progress='minimal')
show_sources_kwargs = dict(fn=show_sources1,
inputs=[my_db_state, selection_docs_state, requests_state, langchain_mode],
outputs=sources_text)
eventdb8 = eventdb8a.then(**show_sources_kwargs,
api_name='show_sources' if allow_api else None)
def update_viewable_dropdown(x):
return gr.Dropdown(choices=x,
value=viewable_docs_state0[0] if len(viewable_docs_state0) > 0 else None)
get_viewable_sources1 = functools.partial(get_sources_gr, dbs=dbs, docs_state0=viewable_docs_state0,
load_db_if_exists=load_db_if_exists,
db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
verbose=kwargs['verbose'],
get_userid_auth=get_userid_auth,
n_jobs=n_jobs)
get_viewable_sources_args = dict(fn=get_viewable_sources1,
inputs=[my_db_state, selection_docs_state, requests_state, langchain_mode],
outputs=[file_source, viewable_docs_state, text_viewable_doc_count],
queue=queue)
eventdb12a = get_viewable_sources_btn.click(user_state_setup,
inputs=[my_db_state, requests_state,
get_viewable_sources_btn, get_viewable_sources_btn],
outputs=[my_db_state, requests_state, get_viewable_sources_btn],
show_progress='minimal')
viewable_kwargs = dict(fn=update_viewable_dropdown, inputs=viewable_docs_state, outputs=view_document_choice)
eventdb12 = eventdb12a.then(**get_viewable_sources_args,
api_name='get_viewable_sources' if allow_api else None) \
.then(**viewable_kwargs)
view_doc_select_kwargs = dict(fn=user_state_setup,
inputs=[my_db_state, requests_state,
view_document_choice],
outputs=[my_db_state, requests_state],
show_progress='minimal')
eventdb_viewa = view_document_choice.select(**view_doc_select_kwargs)
show_doc_func = functools.partial(show_doc,
dbs1=dbs,
load_db_if_exists1=load_db_if_exists,
db_type1=db_type,
use_openai_embedding1=use_openai_embedding,
hf_embedding_model1=hf_embedding_model,
migrate_embedding_model_or_db1=migrate_embedding_model,
auto_migrate_db1=auto_migrate_db,
verbose1=verbose,
get_userid_auth1=get_userid_auth,
max_raw_chunks=kwargs['max_raw_chunks'],
api=False,
n_jobs=n_jobs,
)
# Note: Not really useful for API, so no api_name
show_doc_kwargs = dict(fn=show_doc_func,
inputs=[my_db_state, selection_docs_state, requests_state, langchain_mode,
view_document_choice, view_raw_text_checkbox,
text_context_list, pdf_height],
outputs=[doc_view, doc_view2, doc_view3, doc_view4,
doc_view5, doc_view6, doc_view7, doc_view8])
eventdb_viewa.then(**show_doc_kwargs)
view_raw_text_checkbox.change(**view_doc_select_kwargs) \
.then(**show_doc_kwargs)
show_doc_func_api = functools.partial(show_doc_func, api=True)
get_document_api_btn.click(fn=show_doc_func_api,
inputs=[my_db_state, selection_docs_state, requests_state, langchain_mode,
view_document_choice, view_raw_text_checkbox,
text_context_list, pdf_height],
outputs=get_document_api_text, api_name='get_document_api')
# 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_gr,
captions_model=captions_model,
caption_loader=caption_loader,
doctr_loader=doctr_loader,
llava_model=llava_model,
asr_model=asr_model,
asr_loader=asr_loader,
dbs=dbs,
first_para=kwargs['first_para'],
hf_embedding_model=hf_embedding_model,
use_openai_embedding=use_openai_embedding,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
text_limit=kwargs['text_limit'],
db_type=db_type,
load_db_if_exists=load_db_if_exists,
n_jobs=n_jobs, verbose=verbose,
get_userid_auth=get_userid_auth,
image_audio_loaders_options0=image_audio_loaders_options0,
pdf_loaders_options0=pdf_loaders_options0,
url_loaders_options0=url_loaders_options0,
jq_schema0=jq_schema0,
use_pymupdf=kwargs['use_pymupdf'],
use_unstructured_pdf=kwargs['use_unstructured_pdf'],
use_pypdf=kwargs['use_pypdf'],
enable_pdf_ocr=kwargs['enable_pdf_ocr'],
enable_pdf_doctr=kwargs['enable_pdf_doctr'],
try_pdf_as_html=kwargs['try_pdf_as_html'],
)
eventdb9a = refresh_sources_btn.click(user_state_setup,
inputs=[my_db_state, requests_state,
refresh_sources_btn, refresh_sources_btn],
outputs=[my_db_state, requests_state, refresh_sources_btn],
show_progress='minimal')
eventdb9 = eventdb9a.then(fn=refresh_sources1,
inputs=[my_db_state, selection_docs_state, requests_state,
langchain_mode, chunk, chunk_size,
image_audio_loaders,
pdf_loaders,
url_loaders,
jq_schema,
extract_frames,
llava_prompt,
],
outputs=sources_text,
api_name='refresh_sources' if allow_api else None)
delete_sources1 = functools.partial(del_source_files_given_langchain_mode_gr,
dbs=dbs,
load_db_if_exists=load_db_if_exists,
db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
verbose=verbose,
get_userid_auth=get_userid_auth,
n_jobs=n_jobs)
eventdb90a = delete_sources_btn.click(user_state_setup,
inputs=[my_db_state, requests_state,
delete_sources_btn, delete_sources_btn],
outputs=[my_db_state, requests_state, delete_sources_btn],
show_progress='minimal', **noqueue_kwargs2)
eventdb90 = eventdb90a.then(fn=delete_sources1,
inputs=[my_db_state, selection_docs_state, requests_state, document_choice,
langchain_mode],
outputs=sources_text,
api_name='delete_sources' if allow_api else None)
db_events.extend([eventdb90a, eventdb90])
def check_admin_pass(x):
return gr.update(visible=x == admin_pass)
def close_admin(x):
return gr.update(visible=not (x == admin_pass))
eventdb_logina = login_btn.click(user_state_setup,
inputs=[my_db_state, requests_state, login_btn, login_btn],
outputs=[my_db_state, requests_state, login_btn],
show_progress='minimal', **noqueue_kwargs2)
def login(db1s, selection_docs_state1, requests_state1, roles_state1,
model_options_state1, lora_options_state1, server_options_state1,
chat_state1,
langchain_mode1,
username1, password1,
text_output1, text_output21, *text_outputs1,
auth_filename=None, num_model_lock=0, pre_authorized=False):
# use full auth login to allow new users if open access etc.
if pre_authorized:
username1 = requests_state1['username']
password1 = None
authorized1 = True
else:
authorized1 = authf(username1, password1, selection_docs_state1=selection_docs_state1)
if authorized1:
if not isinstance(requests_state1, dict):
requests_state1 = {}
requests_state1['username'] = username1
set_userid_gr(db1s, requests_state1, get_userid_auth)
username2 = get_username(requests_state1)
text_outputs1 = list(text_outputs1)
success1, text_result, text_output1, text_output21, text_outputs1, langchain_mode1 = \
load_auth(db1s, requests_state1, auth_filename, selection_docs_state1=selection_docs_state1,
roles_state1=roles_state1,
model_options_state1=model_options_state1,
lora_options_state1=lora_options_state1,
server_options_state1=server_options_state1,
chat_state1=chat_state1, langchain_mode1=langchain_mode1,
text_output1=text_output1, text_output21=text_output21, text_outputs1=text_outputs1,
username_override=username1, password_to_check=password1)
else:
success1 = False
text_result = "Wrong password for user %s" % username1
df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1, db1s, dbs1=dbs)
if success1:
requests_state1['username'] = username1
label_instruction1 = 'Ask anything, %s' % requests_state1['username']
return db1s, selection_docs_state1, requests_state1, roles_state1, \
model_options_state1, lora_options_state1, server_options_state1, \
chat_state1, \
text_result, \
gr.update(label=label_instruction1), \
df_langchain_mode_paths1, \
gr.update(choices=list(roles_state1.keys())), \
gr.update(choices=list(chat_state1.keys()), value=None), \
gr.update(choices=get_langchain_choices(selection_docs_state1),
value=langchain_mode1), \
text_output1, text_output21, *tuple(text_outputs1)
login_func = functools.partial(login,
auth_filename=kwargs['auth_filename'],
num_model_lock=len(text_outputs),
pre_authorized=False,
)
load_login_func = functools.partial(login,
auth_filename=kwargs['auth_filename'],
num_model_lock=len(text_outputs),
pre_authorized=True,
)
login_inputs = [my_db_state, selection_docs_state, requests_state, roles_state,
model_options_state, lora_options_state, server_options_state,
chat_state,
langchain_mode,
username_text, password_text,
text_output, text_output2] + text_outputs
login_outputs = [my_db_state, selection_docs_state, requests_state, roles_state,
model_options_state, lora_options_state, server_options_state,
chat_state,
login_result_text,
instruction,
langchain_mode_path_text,
chatbot_role,
radio_chats,
langchain_mode,
text_output, text_output2] + text_outputs
eventdb_loginb = eventdb_logina.then(login_func,
inputs=login_inputs,
outputs=login_outputs,
queue=not kwargs['large_file_count_mode'])
admin_pass_textbox.submit(check_admin_pass, inputs=admin_pass_textbox, outputs=system_row,
**noqueue_kwargs) \
.then(close_admin, inputs=admin_pass_textbox, outputs=admin_row, **noqueue_kwargs)
def load_auth(db1s, requests_state1, auth_filename=None, selection_docs_state1=None,
roles_state1=None,
model_options_state1=None,
lora_options_state1=None,
server_options_state1=None,
chat_state1=None, langchain_mode1=None,
text_output1=None, text_output21=None, text_outputs1=None,
username_override=None, password_to_check=None):
# in-place assignment
if not auth_filename:
return False, "No auth file", text_output1, text_output21, text_outputs1
# if first time here, need to set userID
set_userid_gr(db1s, requests_state1, get_userid_auth)
if username_override:
username1 = username_override
else:
username1 = get_username(requests_state1)
success1 = False
with filelock.FileLock(auth_filename + '.lock'):
if os.path.isfile(auth_filename):
with open(auth_filename, 'rt') as f:
auth_dict = json.load(f)
if username1 in auth_dict:
auth_user = auth_dict[username1]
if password_to_check:
if auth_user['password'] != password_to_check:
return False, [], [], [], "Invalid password for user %s" % username1
if username_override:
# then use original user id
set_userid_direct_gr(db1s, auth_dict[username1]['userid'], username1)
if 'selection_docs_state' in auth_user:
update_auth_selection(auth_user, selection_docs_state1)
if 'roles_state' in auth_user:
roles_state1.update(auth_user['roles_state'])
if 'model_options_state' in auth_user and \
model_options_state1 and \
auth_user['model_options_state']:
model_options_state1[0].extend(auth_user['model_options_state'][0])
model_options_state1[0] = [x for x in model_options_state1[0] if
x != no_model_str and x]
model_options_state1[0] = [no_model_str] + sorted(set(model_options_state1[0]))
if 'lora_options_state' in auth_user and \
lora_options_state1 and \
auth_user['lora_options_state']:
lora_options_state1[0].extend(auth_user['lora_options_state'][0])
lora_options_state1[0] = [x for x in lora_options_state1[0] if x != no_lora_str and x]
lora_options_state1[0] = [no_lora_str] + sorted(set(lora_options_state1[0]))
if 'server_options_state' in auth_user and \
server_options_state1 and \
auth_user['server_options_state']:
server_options_state1[0].extend(auth_user['server_options_state'][0])
server_options_state1[0] = [x for x in server_options_state1[0] if
x != no_server_str and x]
server_options_state1[0] = [no_server_str] + sorted(set(server_options_state1[0]))
if 'chat_state' in auth_user:
chat_state1.update(auth_user['chat_state'])
if 'text_output' in auth_user:
text_output1 = auth_user['text_output']
if 'text_output2' in auth_user:
text_output21 = auth_user['text_output2']
if 'text_outputs' in auth_user:
text_outputs1 = auth_user['text_outputs']
if 'langchain_mode' in auth_user:
langchain_mode1 = auth_user['langchain_mode']
text_result = "Successful login for %s" % username1
success1 = True
else:
text_result = "No user %s" % username1
else:
text_result = "No auth file"
return success1, text_result, text_output1, text_output21, text_outputs1, langchain_mode1
def save_auth_dict(auth_dict, auth_filename):
backup_file = auth_filename + '.bak' + str(uuid.uuid4())
if os.path.isfile(auth_filename):
shutil.copy(auth_filename, backup_file)
try:
with open(auth_filename, 'wt') as f:
f.write(json.dumps(auth_dict, indent=2))
except BaseException as e:
print("Failure to save auth %s, restored backup: %s: %s" % (auth_filename, backup_file, str(e)),
flush=True)
shutil.copy(backup_file, auth_dict)
if os.getenv('HARD_ASSERTS'):
# unexpected in testing or normally
raise
def save_auth(selection_docs_state1, requests_state1, roles_state1,
model_options_state1, lora_options_state1, server_options_state1,
chat_state1, langchain_mode1,
text_output1, text_output21, text_outputs1,
auth_filename=None, auth_access=None, auth_freeze=None, guest_name=None,
):
if auth_freeze:
return
if not auth_filename:
return
# save to auth file
username1 = get_username(requests_state1)
with filelock.FileLock(auth_filename + '.lock'):
if os.path.isfile(auth_filename):
with open(auth_filename, 'rt') as f:
auth_dict = json.load(f)
if username1 in auth_dict:
auth_user = auth_dict[username1]
if selection_docs_state1:
update_auth_selection(auth_user, selection_docs_state1, save=True)
if roles_state1:
# overwrite
auth_user['roles_state'] = roles_state1
if model_options_state1:
# overwrite
auth_user['model_options_state'] = model_options_state1
if lora_options_state1:
# overwrite
auth_user['lora_options_state'] = lora_options_state1
if server_options_state1:
# overwrite
auth_user['server_options_state'] = server_options_state1
if chat_state1:
# overwrite
auth_user['chat_state'] = chat_state1
if text_output1:
auth_user['text_output'] = text_output1
if text_output21:
auth_user['text_output2'] = text_output21
if text_outputs1:
auth_user['text_outputs'] = text_outputs1
if langchain_mode1:
auth_user['langchain_mode'] = langchain_mode1
save_auth_dict(auth_dict, auth_filename)
def save_auth_wrap(*args, **kwargs):
save_auth(args[0], args[1], args[2],
args[3], args[4], args[5],
args[6], args[7], args[8], args[9],
args[10:], **kwargs
)
save_auth_func = functools.partial(save_auth_wrap,
auth_filename=kwargs['auth_filename'],
auth_access=kwargs['auth_access'],
auth_freeze=kwargs['auth_freeze'],
guest_name=kwargs['guest_name'],
)
save_auth_kwargs = dict(fn=save_auth_func,
inputs=[selection_docs_state, requests_state, roles_state,
model_options_state, lora_options_state, server_options_state,
chat_state, langchain_mode, text_output, text_output2] + text_outputs
)
lg_change_event_auth = lg_change_event.then(**save_auth_kwargs)
add_role_event_save_event = add_role_event.then(**save_auth_kwargs)
def add_langchain_mode(db1s, selection_docs_state1, requests_state1, langchain_mode1, y,
auth_filename=None, auth_freeze=None, guest_name=None):
assert auth_filename is not None
assert auth_freeze is not None
set_userid_gr(db1s, requests_state1, get_userid_auth)
username1 = get_username(requests_state1)
for k in db1s:
set_dbid_gr(db1s[k])
langchain_modes = selection_docs_state1['langchain_modes']
langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
langchain_mode_types = selection_docs_state1['langchain_mode_types']
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
# assume personal if don't have user_path
langchain_mode_type = y2[1] if len(y2) > 1 else LangChainTypes.PERSONAL.value
user_path = y2[2] if len(y2) > 2 else None # assume None if don't have user_path
if user_path in ['', "''"]:
# transcribe UI input
user_path = None
if langchain_mode_type not in [x.value for x in list(LangChainTypes)]:
textbox = "Invalid type %s" % langchain_mode_type
valid = False
langchain_mode2 = langchain_mode1
elif langchain_mode_type == LangChainTypes.SHARED.value and username1 == guest_name:
textbox = "Guests cannot add shared collections"
valid = False
langchain_mode2 = langchain_mode1
elif user_path is not None and langchain_mode_type == LangChainTypes.PERSONAL.value:
textbox = "Do not pass user_path for personal/scratch types"
valid = False
langchain_mode2 = langchain_mode1
elif user_path is not None and username1 == guest_name:
textbox = "Guests cannot add collections with path"
valid = False
langchain_mode2 = langchain_mode1
elif 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:
if user_path:
user_path = makedirs(user_path, exist_ok=True, use_base=True)
langchain_mode_paths.update({langchain_mode2: user_path})
langchain_mode_types.update({langchain_mode2: langchain_mode_type})
if langchain_mode2 not in langchain_modes:
langchain_modes.append(langchain_mode2)
textbox = ''
else:
valid = False
langchain_mode2 = langchain_mode1
textbox = "Invalid access. user allowed: %s " \
"personal/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(selection_docs_state1)
df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1, db1s, dbs1=dbs)
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()
from src.gpt_langchain import length_db1
db1s[langchain_mode2] = [None] * length_db1()
if valid:
chat_state1 = None
roles_state1 = None
model_options_state1 = None
lora_options_state1 = None
server_options_state1 = None
text_output1, text_output21, text_outputs1 = None, None, None
save_auth_func(selection_docs_state1, requests_state1, roles_state1,
model_options_state1, lora_options_state1, server_options_state1,
chat_state1, langchain_mode2,
text_output1, text_output21, text_outputs1,
)
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, requests_state1,
langchain_mode1, langchain_mode2, dbsu=None, auth_filename=None, auth_freeze=None,
guest_name=None,
purge=False):
assert auth_filename is not None
assert auth_freeze is not None
set_userid_gr(db1s, requests_state1, get_userid_auth)
for k in db1s:
set_dbid_gr(db1s[k])
assert dbsu is not None
langchain_modes = selection_docs_state1['langchain_modes']
langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
langchain_mode_types = selection_docs_state1['langchain_mode_types']
langchain_type2 = langchain_mode_types.get(langchain_mode2, LangChainTypes.EITHER.value)
changed_state = False
textbox = "Invalid access, cannot remove %s" % langchain_mode2
in_scratch_db = langchain_mode2 in db1s
in_user_db = dbsu is not None and langchain_mode2 in dbsu
if in_scratch_db and not allow_upload_to_my_data or \
in_user_db and not allow_upload_to_user_data or \
langchain_mode2 in langchain_modes_intrinsic:
can_remove = False
can_purge = False
if langchain_mode2 in langchain_modes_intrinsic:
can_purge = True
else:
can_remove = True
can_purge = True
# change global variables
if langchain_mode2 in langchain_modes or langchain_mode2 in langchain_mode_paths or langchain_mode2 in db1s:
if can_purge and purge:
# remove source files
from src.gpt_langchain import get_sources, del_from_db
sources_file, source_list, num_chunks, num_sources_str, db = \
get_sources(db1s, selection_docs_state1,
requests_state1, langchain_mode2, dbs=dbsu,
docs_state0=docs_state0,
load_db_if_exists=load_db_if_exists,
db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
verbose=verbose,
get_userid_auth=get_userid_auth,
n_jobs=n_jobs)
del_from_db(db, source_list, db_type=db_type)
for fil in source_list:
if os.path.isfile(fil):
print("Purged %s" % fil, flush=True)
remove(fil)
# remove db directory
from src.gpt_langchain import get_persist_directory
persist_directory, langchain_type2 = \
get_persist_directory(langchain_mode2, langchain_type=langchain_type2,
db1s=db1s, dbs=dbsu)
print("removed persist_directory %s" % persist_directory, flush=True)
remove(persist_directory)
textbox = "Purged, but did not remove %s" % langchain_mode2
if can_remove:
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 langchain_mode_types:
langchain_mode_types.pop(langchain_mode2)
if langchain_mode2 in db1s and langchain_mode2 != LangChainMode.MY_DATA.value:
# don't remove last MyData, used as user hash
db1s.pop(langchain_mode2)
textbox = ""
changed_state = True
else:
textbox = "%s is not visible" % langchain_mode2
# update
selection_docs_state1 = update_langchain_mode_paths(selection_docs_state1)
df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1, db1s, dbs1=dbs)
if changed_state:
chat_state1 = None
roles_state1 = None
model_options_state1 = None
lora_options_state1 = None
server_options_state1 = None
text_output1, text_output21, text_outputs1 = None, None, None
save_auth_func(selection_docs_state1, requests_state1, roles_state1,
model_options_state1, lora_options_state1, server_options_state1,
chat_state1, langchain_mode2,
text_output1, text_output21, text_outputs1,
)
return db1s, selection_docs_state1, \
gr.update(choices=get_langchain_choices(selection_docs_state1),
value=langchain_mode2), textbox, df_langchain_mode_paths1
eventdb20a = new_langchain_mode_text.submit(user_state_setup,
inputs=[my_db_state, requests_state,
new_langchain_mode_text, new_langchain_mode_text],
outputs=[my_db_state, requests_state, new_langchain_mode_text],
show_progress='minimal')
add_langchain_mode_func = functools.partial(add_langchain_mode,
auth_filename=kwargs['auth_filename'],
auth_freeze=kwargs['auth_freeze'],
guest_name=kwargs['guest_name'],
)
eventdb20b = eventdb20a.then(fn=add_langchain_mode_func,
inputs=[my_db_state, selection_docs_state, requests_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)
db_events.extend([eventdb20a, eventdb20b])
remove_langchain_mode_func = functools.partial(remove_langchain_mode,
dbsu=dbs,
auth_filename=kwargs['auth_filename'],
auth_freeze=kwargs['auth_freeze'],
guest_name=kwargs['guest_name'],
)
eventdb21a = remove_langchain_mode_text.submit(user_state_setup,
inputs=[my_db_state,
requests_state,
remove_langchain_mode_text, remove_langchain_mode_text],
outputs=[my_db_state,
requests_state, remove_langchain_mode_text],
show_progress='minimal')
remove_langchain_mode_kwargs = dict(fn=remove_langchain_mode_func,
inputs=[my_db_state, selection_docs_state, requests_state,
langchain_mode,
remove_langchain_mode_text],
outputs=[my_db_state, selection_docs_state, langchain_mode,
remove_langchain_mode_text,
langchain_mode_path_text])
eventdb21b = eventdb21a.then(**remove_langchain_mode_kwargs,
api_name='remove_langchain_mode_text' if allow_api and allow_upload_to_user_data else None)
db_events.extend([eventdb21a, eventdb21b])
eventdb22a = purge_langchain_mode_text.submit(user_state_setup,
inputs=[my_db_state,
requests_state,
purge_langchain_mode_text, purge_langchain_mode_text],
outputs=[my_db_state,
requests_state, purge_langchain_mode_text],
show_progress='minimal')
purge_langchain_mode_func = functools.partial(remove_langchain_mode_func, purge=True)
purge_langchain_mode_kwargs = dict(fn=purge_langchain_mode_func,
inputs=[my_db_state, selection_docs_state, requests_state,
langchain_mode,
purge_langchain_mode_text],
outputs=[my_db_state, selection_docs_state, langchain_mode,
purge_langchain_mode_text,
langchain_mode_path_text])
# purge_langchain_mode_kwargs = remove_langchain_mode_kwargs.copy()
# purge_langchain_mode_kwargs['fn'] = functools.partial(remove_langchain_mode_kwargs['fn'], purge=True)
eventdb22b = eventdb22a.then(**purge_langchain_mode_kwargs,
api_name='purge_langchain_mode_text' if allow_api and allow_upload_to_user_data else None)
eventdb22b_auth = eventdb22b.then(**save_auth_kwargs)
db_events.extend([eventdb22a, eventdb22b, eventdb22b_auth])
def load_langchain_gr(db1s, selection_docs_state1, requests_state1, langchain_mode1, auth_filename=None):
load_auth(db1s, requests_state1, auth_filename, selection_docs_state1=selection_docs_state1)
selection_docs_state1 = update_langchain_mode_paths(selection_docs_state1)
df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1, db1s, dbs1=dbs)
return selection_docs_state1, \
gr.update(choices=get_langchain_choices(selection_docs_state1),
value=langchain_mode1), df_langchain_mode_paths1
eventdbloadla = load_langchain.click(user_state_setup,
inputs=[my_db_state, requests_state, langchain_mode],
outputs=[my_db_state, requests_state, langchain_mode],
show_progress='minimal')
load_langchain_gr_func = functools.partial(load_langchain_gr,
auth_filename=kwargs['auth_filename'])
eventdbloadlb = eventdbloadla.then(fn=load_langchain_gr_func,
inputs=[my_db_state, selection_docs_state, requests_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)
if not kwargs['large_file_count_mode']:
# FIXME: Could add all these functions, inputs, outputs into single function for snappier GUI
# all update events when not doing large file count mode
# Note: Login touches langchain_mode, which triggers all these
lg_change_event2 = lg_change_event_auth.then(**get_sources_kwargs)
lg_change_event3 = lg_change_event2.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
lg_change_event4 = lg_change_event3.then(**show_sources_kwargs)
lg_change_event5 = lg_change_event4.then(**get_viewable_sources_args)
lg_change_event6 = lg_change_event5.then(**viewable_kwargs)
# add url text
eventdb2c = eventdb2.then(**get_sources_kwargs)
eventdb2d = eventdb2c.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
eventdb2e = eventdb2d.then(**show_sources_kwargs)
eventdb2f = eventdb2e.then(**get_viewable_sources_args)
eventdb2g = eventdb2f.then(**viewable_kwargs)
def docs_to_message(new_files_last1):
from src.gpt_langchain import image_types, audio_types
# already filtered by what can show in gradio
# https://github.com/gradio-app/gradio/issues/3728
added_history = []
for k, v in new_files_last1.items():
if any(k.endswith(x) for x in image_types):
user_message1 = (k,)
if v.startswith("The image"):
bot_message1 = "Thank you for uploading the Image. %s" % v
else:
bot_message1 = "Thank you for uploading the Image. Looks like: %s" % v
elif any(k.endswith(x) for x in audio_types):
user_message1 = (k,)
bot_message1 = "Thank you for uploading the Audio. Sounds like it says: %s" % v
else:
user_message1 = "Upload %s" % k
bot_message1 = "Thank you for uploading the File. Description:\n\n%s" % v
added_history.extend([[user_message1, bot_message1]])
return added_history
def update_chatbots(*args,
num_model_lock=0,
all_possible_visible_models=None,
for_errors=False,
gradio_errors_to_chatbot=False):
args_list = list(args)
gradio_upload_to_chatbot1 = args_list[0]
gradio_errors_to_chatbot1 = gradio_errors_to_chatbot and for_errors
do_show = gradio_upload_to_chatbot1 or gradio_errors_to_chatbot1
added_history = []
if not for_errors and str(args_list[1]).strip():
new_files_last1 = ast.literal_eval(args_list[1]) if isinstance(args_list[1], str) else {}
assert isinstance(new_files_last1, dict)
added_history = docs_to_message(new_files_last1)
elif str(args_list[1]).strip():
added_history = [(None, get_accordion_named(args_list[1], "Document Ingestion (maybe partial) Failure. Click Undo to remove this message.", font_size=2))]
compare_checkbox1 = args_list[2]
if num_model_lock > 0:
visible_models1 = args_list[3]
assert isinstance(visible_models1, list)
assert isinstance(all_possible_visible_models, list)
visible_list = get_model_lock_visible_list(visible_models1, all_possible_visible_models)
visible_list = [False, False] + visible_list
history_list = args_list[-num_model_lock - 2:]
assert len(all_possible_visible_models) + 2 == len(history_list)
else:
visible_list = [True, compare_checkbox1]
history_list = args_list[-num_model_lock - 2:]
assert len(history_list) > 0, "Bad history list: %s" % history_list
if do_show and added_history:
for hi, history in enumerate(history_list):
if not visible_list[hi]:
continue
# gradio_upload_to_chatbot_num_max
history_list[hi].extend(added_history)
if len(history_list) > 1:
return tuple(history_list)
else:
return history_list[0]
update_chatbots_func = functools.partial(update_chatbots,
num_model_lock=len(text_outputs),
all_possible_visible_models=kwargs['all_possible_visible_models']
)
update_chatbots_kwargs = dict(fn=update_chatbots_func,
inputs=[gradio_upload_to_chatbot,
new_files_last,
compare_checkbox,
visible_models,
text_output, text_output2] + text_outputs,
outputs=[text_output, text_output2] + text_outputs
)
update_chatbots_errors_func = functools.partial(update_chatbots,
num_model_lock=len(text_outputs),
all_possible_visible_models=kwargs[
'all_possible_visible_models'],
for_errors=True,
gradio_errors_to_chatbot=kwargs['gradio_errors_to_chatbot'],
)
update_chatbots_errors_kwargs = dict(fn=update_chatbots_errors_func,
inputs=[gradio_upload_to_chatbot,
doc_exception_text,
compare_checkbox,
visible_models,
text_output, text_output2] + text_outputs,
outputs=[text_output, text_output2] + text_outputs
)
# Ingest, add button
eventdb2c_btn = eventdb2_btn.then(**get_sources_kwargs)
eventdb2d_btn = eventdb2c_btn.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
eventdb2e_btn = eventdb2d_btn.then(**show_sources_kwargs)
eventdb2f_btn = eventdb2e_btn.then(**get_viewable_sources_args)
eventdb2g_btn = eventdb2f_btn.then(**viewable_kwargs)
eventdb2h_btn = eventdb2g_btn.then(**update_chatbots_kwargs)
if kwargs['gradio_errors_to_chatbot']:
eventdb2i_btn = eventdb2h_btn.then(**update_chatbots_errors_kwargs)
# file upload
eventdb1c = eventdb1.then(**get_sources_kwargs)
eventdb1d = eventdb1c.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
eventdb1e = eventdb1d.then(**show_sources_kwargs)
eventdb1f = eventdb1e.then(**get_viewable_sources_args)
eventdb1g = eventdb1f.then(**viewable_kwargs)
# add text by hitting enter
eventdb3c = eventdb3.then(**get_sources_kwargs)
eventdb3d = eventdb3c.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
eventdb3e = eventdb3d.then(**show_sources_kwargs)
eventdb3f = eventdb3e.then(**get_viewable_sources_args)
eventdb3g = eventdb3f.then(**viewable_kwargs)
# delete
eventdb90ua = eventdb90.then(**get_sources_kwargs)
eventdb90ub = eventdb90ua.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
eventdb90uc = eventdb90ub.then(**show_sources_kwargs)
eventdb90ud = eventdb90uc.then(**get_viewable_sources_args)
eventdb90ue = eventdb90ud.then(**viewable_kwargs)
# add langchain mode
eventdb20c = eventdb20b.then(**get_sources_kwargs)
eventdb20d = eventdb20c.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
eventdb20e = eventdb20d.then(**show_sources_kwargs)
eventdb20f = eventdb20e.then(**get_viewable_sources_args)
eventdb20g = eventdb20f.then(**viewable_kwargs)
# remove langchain mode
eventdb21c = eventdb21b.then(**get_sources_kwargs)
eventdb21d = eventdb21c.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
eventdb21e = eventdb21d.then(**show_sources_kwargs)
eventdb21f = eventdb21e.then(**get_viewable_sources_args)
eventdb21g = eventdb21f.then(**viewable_kwargs)
# purge collection
eventdb22c = eventdb22b_auth.then(**get_sources_kwargs)
eventdb22d = eventdb22c.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
eventdb22e = eventdb22d.then(**show_sources_kwargs)
eventdb22f = eventdb22e.then(**get_viewable_sources_args)
eventdb22g = eventdb22f.then(**viewable_kwargs)
# attach
event_attach3 = event_attach2.then(**get_sources_kwargs)
event_attach4 = event_attach3.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
event_attach5 = event_attach4.then(**show_sources_kwargs)
event_attach6 = event_attach5.then(**get_viewable_sources_args)
event_attach7 = event_attach6.then(**viewable_kwargs)
if kwargs['gradio_upload_to_chatbot']:
event_attach8 = event_attach7.then(**update_chatbots_kwargs)
sync2 = sync1.then(**get_sources_kwargs)
sync3 = sync2.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
sync4 = sync3.then(**show_sources_kwargs)
sync5 = sync4.then(**get_viewable_sources_args)
sync6 = sync5.then(**viewable_kwargs)
def update_model_dropdown(model_options_state1, lora_options_state1, server_options_state1,
model_choice1, lora_choice1, server_choice1,
model_choice12, lora_choice12, server_choice12):
return gr.Dropdown(choices=model_options_state1[0], value=model_choice1), \
gr.Dropdown(choices=lora_options_state1[0], value=lora_choice1), \
gr.Dropdown(choices=server_options_state1[0], value=server_choice1), \
gr.Dropdown(choices=model_options_state1[0], value=model_choice12), \
gr.Dropdown(choices=lora_options_state1[0], value=lora_choice12), \
gr.Dropdown(choices=server_options_state1[0], value=server_choice12)
eventdb_loginbb = eventdb_loginb.then(**get_sources_kwargs)
eventdb_loginc = eventdb_loginbb.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
# FIXME: Fix redundancy
eventdb_logind = eventdb_loginc.then(**show_sources_kwargs)
eventdb_logine = eventdb_logind.then(**get_viewable_sources_args)
eventdb_loginf = eventdb_logine.then(**viewable_kwargs)
eventdb_loginh = eventdb_loginf.then(fn=update_model_dropdown,
inputs=[model_options_state, lora_options_state, server_options_state,
model_choice, lora_choice, server_choice,
model_choice2, lora_choice2, server_choice2,
],
outputs=[model_choice, lora_choice, server_choice,
model_choice2, lora_choice2, server_choice2,
]
)
db_events.extend([lg_change_event_auth,
lg_change_event, lg_change_event2, lg_change_event3, lg_change_event4, lg_change_event5,
lg_change_event6] +
[eventdb2c, eventdb2d, eventdb2e, eventdb2f, eventdb2g] +
[eventdb1c, eventdb1d, eventdb1e, eventdb1f, eventdb1g] +
[eventdb3c, eventdb3d, eventdb3e, eventdb3f, eventdb3g] +
[eventdb90ua, eventdb90ub, eventdb90uc, eventdb90ud, eventdb90ue] +
[eventdb20c, eventdb20d, eventdb20e, eventdb20f, eventdb20g] +
[eventdb21c, eventdb21d, eventdb21e, eventdb21f, eventdb21g] +
[eventdb22b_auth, eventdb22c, eventdb22d, eventdb22e, eventdb22f, eventdb22g] +
[event_attach3, event_attach4, event_attach5, event_attach6, event_attach7] +
[sync1, sync2, sync3, sync4, sync5, sync6] +
[eventdb_logina, eventdb_loginb, eventdb_loginbb,
eventdb_loginc, eventdb_logind, eventdb_logine,
eventdb_loginf]
,
)
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}
kwargs_evaluate.update(dict(from_ui=True)) # default except for evaluate_nochat
# 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, plain_api=False, **kwargs1):
args_list = list(args1)
if str_api:
if plain_api:
# i.e. not fresh model, tells evaluate to use model_state0
args_list.insert(0, kwargs['model_state_none'].copy())
args_list.insert(1, my_db_state0.copy())
args_list.insert(2, selection_docs_state0.copy())
args_list.insert(3, requests_state0.copy())
args_list.insert(4, roles_state0.copy())
user_kwargs = args_list[len(input_args_list)]
assert isinstance(user_kwargs, str)
user_kwargs = ast.literal_eval(user_kwargs)
else:
assert not plain_api
user_kwargs = {k: v for k, v in zip(eval_func_param_names, args_list[len(input_args_list):])}
# control kwargs1 for evaluate
if 'answer_with_sources' not in user_kwargs:
kwargs1['answer_with_sources'] = -1 # just text chunk, not URL etc.
if 'show_accordions' not in user_kwargs:
kwargs1['show_accordions'] = False
if 'append_sources_to_chat' not in user_kwargs:
kwargs1['append_sources_to_chat'] = False
if 'append_sources_to_answer' not in user_kwargs:
kwargs1['append_sources_to_answer'] = False
if 'show_link_in_sources' not in user_kwargs:
kwargs1['show_link_in_sources'] = False
kwargs1['top_k_docs_max_show'] = 30
# only used for submit_nochat_api
user_kwargs['chat'] = False
if 'stream_output' not in user_kwargs:
user_kwargs['stream_output'] = False
if plain_api:
user_kwargs['stream_output'] = False
if 'langchain_mode' not in user_kwargs:
# if user doesn't specify, then assume disabled, not use default
if LangChainMode.LLM.value in kwargs['langchain_modes']:
user_kwargs['langchain_mode'] = LangChainMode.LLM.value
elif len(kwargs['langchain_modes']) >= 1:
user_kwargs['langchain_mode'] = kwargs['langchain_modes'][0]
else:
# disabled should always be allowed
user_kwargs['langchain_mode'] = LangChainMode.DISABLED.value
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'] = []
# be flexible
if 'instruction' in user_kwargs and 'instruction_nochat' not in user_kwargs:
user_kwargs['instruction_nochat'] = user_kwargs['instruction']
if 'iinput' in user_kwargs and 'iinput_nochat' not in user_kwargs:
user_kwargs['iinput_nochat'] = user_kwargs['iinput']
if 'visible_models' not in user_kwargs:
if kwargs['visible_models']:
if isinstance(kwargs['visible_models'], int):
user_kwargs['visible_models'] = [kwargs['visible_models']]
elif isinstance(kwargs['visible_models'], list):
# only take first one
user_kwargs['visible_models'] = [kwargs['visible_models'][0]]
else:
user_kwargs['visible_models'] = [0]
else:
# if no user version or default version, then just take first
user_kwargs['visible_models'] = [0]
if 'h2ogpt_key' not in user_kwargs:
user_kwargs['h2ogpt_key'] = None
if 'system_prompt' in user_kwargs and user_kwargs['system_prompt'] is None:
# avoid worrying about below default_kwargs -> args_list that checks if None
user_kwargs['system_prompt'] = 'None'
# by default don't do TTS unless specifically requested
if 'chatbot_role' not in user_kwargs:
user_kwargs['chatbot_role'] = 'None'
if 'speaker' not in user_kwargs:
user_kwargs['speaker'] = 'None'
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]
requests_state1 = args_list[3]
roles_state1 = args_list[4]
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)
stream_output1 = args_list[eval_func_param_names.index('stream_output')]
if len(model_states) >= 1:
visible_models1 = args_list[eval_func_param_names.index('visible_models')]
model_active_choice1 = visible_models_to_model_choice(visible_models1, api=True)
model_state1 = model_states[model_active_choice1 % len(model_states)]
for key in key_overrides:
if user_kwargs.get(key) is None and model_state1.get(key) is not None:
args_list[eval_func_param_names.index(key)] = model_state1[key]
if hasattr(model_state1['tokenizer'], 'model_max_length'):
# ensure listen to limit, with some buffer
# buffer = 50
buffer = 0
args_list[eval_func_param_names.index('max_new_tokens')] = min(
args_list[eval_func_param_names.index('max_new_tokens')],
model_state1['tokenizer'].model_max_length - buffer)
# override overall visible_models and h2ogpt_key if have model_specific one
# NOTE: only applicable if len(model_states) > 1 at moment
# else controlled by evaluate()
if 'visible_models' in model_state1 and model_state1['visible_models'] is not None:
assert isinstance(model_state1['visible_models'], (int, str, list, tuple))
which_model = visible_models_to_model_choice(model_state1['visible_models'])
args_list[eval_func_param_names.index('visible_models')] = which_model
if 'h2ogpt_key' in model_state1 and model_state1['h2ogpt_key'] is not None:
# remote server key if present
# i.e. may be '' and used to override overall local key
assert isinstance(model_state1['h2ogpt_key'], str)
args_list[eval_func_param_names.index('h2ogpt_key')] = model_state1['h2ogpt_key']
# final full bot() like input for prep_bot etc.
instruction_nochat1 = args_list[eval_func_param_names.index('instruction_nochat')] or \
args_list[eval_func_param_names.index('instruction')]
args_list[eval_func_param_names.index('instruction_nochat')] = \
args_list[eval_func_param_names.index('instruction')] = \
instruction_nochat1
history = [[instruction_nochat1, None]]
# NOTE: Set requests_state1 to None, so don't allow UI-like access, in case modify state via API
requests_state1_bot = None
args_list_bot = args_list + [model_state1, my_db_state1, selection_docs_state1, requests_state1_bot,
roles_state1] + [history]
# at this point like bot() as input
history, fun1, langchain_mode1, db1, requests_state1, \
valid_key, h2ogpt_key1, \
max_time1, stream_output1, \
chatbot_role1, speaker1, tts_language1, roles_state1, tts_speed1, langchain_action1 = \
prep_bot(*args_list_bot, kwargs_eval=kwargs1, plain_api=plain_api)
save_dict = dict()
ret = {}
ret_old = ''
history_str_old = ''
error_old = ''
audios = [] # in case not streaming, since audio is always streaming, need to accumulate for when yield
last_yield = None
res_dict = {}
try:
tgen0 = time.time()
for res in get_response(fun1, history, chatbot_role1, speaker1, tts_language1, roles_state1,
tts_speed1,
langchain_action1,
api=True):
history, error, sources, sources_str, prompt_raw, llm_answers, save_dict, audio1 = res
res_dict = {}
res_dict['response'] = history[-1][1]
res_dict['error'] = error
res_dict['sources'] = sources
res_dict['sources_str'] = sources_str
res_dict['prompt_raw'] = prompt_raw
res_dict['llm_answers'] = llm_answers
res_dict['save_dict'] = save_dict
res_dict['audio'] = audio1
error = res_dict.get('error', '')
sources = res_dict.get('sources', [])
save_dict = res_dict.get('save_dict', {})
# update save_dict
save_dict['error'] = error
save_dict['sources'] = sources
save_dict['valid_key'] = valid_key
save_dict['h2ogpt_key'] = h2ogpt_key1
if str_api and plain_api:
save_dict['which_api'] = 'str_plain_api'
elif str_api:
save_dict['which_api'] = 'str_api'
elif plain_api:
save_dict['which_api'] = 'plain_api'
else:
save_dict['which_api'] = 'nochat_api'
if 'extra_dict' not in save_dict:
save_dict['extra_dict'] = {}
if requests_state1:
save_dict['extra_dict'].update(requests_state1)
else:
save_dict['extra_dict'].update(dict(username='NO_REQUEST'))
if is_public:
# don't want to share actual endpoints
if 'save_dict' in res_dict and isinstance(res_dict['save_dict'], dict):
res_dict['save_dict'].pop('inference_server', None)
if 'extra_dict' in res_dict['save_dict'] and isinstance(res_dict['save_dict']['extra_dict'],
dict):
res_dict['save_dict']['extra_dict'].pop('inference_server', None)
# get response
if str_api:
# full return of dict, except constant items that can be read-off at end
res_dict_yield = res_dict.copy()
# do not stream: ['save_dict', 'prompt_raw', 'sources', 'sources_str', 'response_no_refs']
only_stream = ['response', 'llm_answers', 'audio']
for key in res_dict:
if key not in only_stream:
res_dict_yield.pop(key)
ret = res_dict_yield
elif kwargs['langchain_mode'] == 'Disabled':
ret = fix_text_for_gradio(res_dict['response'], fix_latex_dollars=False)
else:
ret = '
' + fix_text_for_gradio(res_dict['response'], fix_latex_dollars=False)
do_yield = False
could_yield = ret != ret_old
if kwargs['gradio_api_use_same_stream_limits']:
history_str = str(ret['response'] if isinstance(ret, dict) else str(ret))
delta_history = abs(len(history_str) - len(str(history_str_old)))
# even if enough data, don't yield if has been less than min_seconds
enough_data = delta_history > kwargs['gradio_ui_stream_chunk_size'] or (error != error_old)
beyond_min_time = last_yield is None or \
last_yield is not None and \
(time.time() - last_yield) > kwargs['gradio_ui_stream_chunk_min_seconds']
do_yield |= enough_data and beyond_min_time
# yield even if new data not enough if been long enough and have at least something to yield
enough_time = last_yield is None or \
last_yield is not None and \
(time.time() - last_yield) > kwargs['gradio_ui_stream_chunk_seconds']
do_yield |= enough_time and could_yield
# DEBUG: print("do_yield: %s : %s %s %s" % (do_yield, enough_data, beyond_min_time, enough_time), flush=True)
else:
do_yield = could_yield
if stream_output1 and do_yield:
last_yield = time.time()
# yield as it goes, else need to wait since predict only returns first yield
if isinstance(ret, dict):
ret_old = ret.copy() # copy normal one first
ret['audio'] = combine_audios(audios, audio=audio1, sr=24000 if chatbot_role1 else 16000,
expect_bytes=kwargs['return_as_byte'])
audios = [] # reset accumulation
yield ret
else:
ret_old = ret
yield ret
# just last response, not actually full history like bot() and all_bot() but that's all that changes
# we can ignore other dict entries as consequence of changes to main stream in 100% of current cases
# even if sources added last after full response done, final yield still yields left over
history_str_old = str(ret_old['response'] if isinstance(ret_old, dict) else str(ret_old))
else:
# collect unstreamed audios
audios.append(res_dict['audio'])
if time.time() - tgen0 > max_time1 + 10: # don't use actual, so inner has chance to complete
if str_api:
res_dict['save_dict']['extra_dict']['timeout'] = time.time() - tgen0
if verbose:
print("Took too long evaluate_nochat: %s" % (time.time() - tgen0), flush=True)
break
# yield if anything left over as can happen
# return back last ret
if str_api:
res_dict['save_dict']['extra_dict'] = _save_generate_tokens(res_dict.get('response', ''),
res_dict.get('save_dict', {}).get(
'extra_dict', {}))
ret = res_dict.copy()
if isinstance(ret, dict):
ret['audio'] = combine_audios(audios, audio=None,
expect_bytes=kwargs['return_as_byte'])
yield ret
finally:
clear_torch_cache(allow_skip=True)
clear_embeddings(user_kwargs['langchain_mode'], my_db_state1)
save_dict['save_dir'] = kwargs['save_dir']
save_generate_output(**save_dict)
kwargs_evaluate_nochat = kwargs_evaluate.copy()
# nominally never want sources appended for API calls, which is what nochat used for primarily
kwargs_evaluate_nochat.update(dict(append_sources_to_answer=False, from_ui=False, append_sources_to_chat=False))
fun = partial(evaluate_nochat,
default_kwargs1=default_kwargs,
str_api=False,
**kwargs_evaluate_nochat)
fun_with_dict_str = partial(evaluate_nochat,
default_kwargs1=default_kwargs,
str_api=True,
**kwargs_evaluate_nochat
)
fun_with_dict_str_plain = partial(evaluate_nochat,
default_kwargs1=default_kwargs,
str_api=True,
plain_api=True,
**kwargs_evaluate_nochat
)
dark_mode_btn.click(
None,
None,
None,
api_name="dark" if allow_api else None,
**dark_kwargs,
**noqueue_kwargs,
)
# Handle uploads from API
upload_api_btn = gr.UploadButton("Upload File Results", visible=False)
file_upload_api = gr.File(visible=False)
file_upload_text = gr.Textbox(visible=False)
def upload_file(files):
if isinstance(files, list):
file_paths = [file.name for file in files]
else:
file_paths = files.name
return file_paths, file_paths
upload_api_btn.upload(fn=upload_file,
inputs=upload_api_btn,
outputs=[file_upload_api, file_upload_text],
api_name='upload_api' if allow_upload_api else None)
def visible_toggle(x):
x = 'off' if x == 'on' else 'on'
return x, gr.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],
**noqueue_kwargs)
doc_count_btn.click(fn=visible_toggle,
inputs=doc_count_text,
outputs=[doc_count_text, row_doc_track],
**noqueue_kwargs)
submit_buttons_btn.click(fn=visible_toggle,
inputs=submit_buttons_text,
outputs=[submit_buttons_text, submit_buttons],
**noqueue_kwargs)
visible_model_btn.click(fn=visible_toggle,
inputs=visible_models_text,
outputs=[visible_models_text, visible_models],
**noqueue_kwargs)
# 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(allow_skip=True)
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]
history = get_llm_history(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(allow_skip=True)
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:
pass
# requirements.txt has comment that need to re-enable the below 2 lines
# 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)
if not user_message1 and langchain_action1 == LangChainAction.SUMMARIZE_MAP.value:
user_message1 = 'Summarize Collection: %s, Subset: %s, Documents: %s' % (langchain_mode1, document_subset1, document_choice1)
if not user_message1 and langchain_action1 == LangChainAction.EXTRACT.value:
user_message1 = 'Extract Collection: %s, Subset: %s, Documents: %s' % (langchain_mode1, document_subset1, document_choice1)
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,
all_possible_visible_models=None):
args_list = list(args)
visible_models1 = args_list[eval_func_param_names.index('visible_models')]
assert isinstance(all_possible_visible_models, list)
visible_list = get_model_lock_visible_list(visible_models1, all_possible_visible_models)
history_list = args_list[-num_model_lock:]
assert len(all_possible_visible_models) == len(history_list)
assert len(history_list) > 0, "Bad history list: %s" % history_list
for hi, history in enumerate(history_list):
if not visible_list[hi]:
continue
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 int(tokenizer.model_max_length)
else:
return 2000
def get_llm_history(history):
# avoid None users used for sources, errors, etc.
if history is None:
history = []
for ii in range(len(history) - 1, -1, -1):
if history[ii] and history[ii][0] is not None:
last_user_ii = ii
history = history[:last_user_ii + 1]
break
return history
def prep_bot(*args, retry=False, which_model=0, kwargs_eval=None, plain_api=False):
"""
: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
# NOTE: Update plain_api in evaluate_nochat too
args_list = list(args).copy()
model_state1 = args_list[-isize]
my_db_state1 = args_list[-isize + 1]
selection_docs_state1 = args_list[-isize + 2]
requests_state1 = args_list[-isize + 3]
roles_state1 = args_list[-isize + 4]
history = args_list[-1]
if not history:
history = []
# NOTE: For these, could check if None, then automatically use CLI values, but too complex behavior
prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
if prompt_type1 == no_model_str:
# deal with gradio dropdown
prompt_type1 = args_list[eval_func_param_names.index('prompt_type')] = None
prompt_dict1 = args_list[eval_func_param_names.index('prompt_dict')]
max_time1 = args_list[eval_func_param_names.index('max_time')]
stream_output1 = args_list[eval_func_param_names.index('stream_output')]
langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
langchain_action1 = args_list[eval_func_param_names.index('langchain_action')]
document_subset1 = args_list[eval_func_param_names.index('document_subset')]
h2ogpt_key1 = args_list[eval_func_param_names.index('h2ogpt_key')]
chat_conversation1 = args_list[eval_func_param_names.index('chat_conversation')]
valid_key = is_valid_key(kwargs['enforce_h2ogpt_api_key'],
kwargs['enforce_h2ogpt_ui_key'],
kwargs['h2ogpt_api_keys'], h2ogpt_key1,
requests_state1=requests_state1)
chatbot_role1 = args_list[eval_func_param_names.index('chatbot_role')]
speaker1 = args_list[eval_func_param_names.index('speaker')]
tts_language1 = args_list[eval_func_param_names.index('tts_language')]
tts_speed1 = args_list[eval_func_param_names.index('tts_speed')]
dummy_return = history, None, langchain_mode1, my_db_state1, requests_state1, \
valid_key, h2ogpt_key1, \
max_time1, stream_output1, chatbot_role1, speaker1, tts_language1, roles_state1, tts_speed1, \
langchain_action1
if not plain_api and (model_state1['model'] is None or model_state1['model'] == no_model_str):
# plain_api has no state, let evaluate() handle switch
return dummy_return
args_list = args_list[:-isize] # only keep rest needed for evaluate()
if not history:
if verbose:
print("No history", flush=True)
return dummy_return
instruction1 = history[-1][0]
if retry and history:
# if retry, pop history and move onto bot stuff
history = get_llm_history(history)
instruction1 = history[-1][0] if history and history[-1] and len(history[-1]) == 2 else None
if history and history[-1]:
history[-1][1] = None
if not instruction1:
return dummy_return
elif not instruction1:
if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1):
# if not retrying, then reject empty query
return dummy_return
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 dummy_return
evaluate_local = evaluate if valid_key else evaluate_fake
# 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
context1 = args_list[eval_func_param_names.index('context')]
chat_conversation1 = merge_chat_conversation_history(chat_conversation1, history)
args_list[eval_func_param_names.index('chat_conversation')] = chat_conversation1
if 'visible_models' in model_state1 and model_state1['visible_models'] is not None:
assert isinstance(model_state1['visible_models'], (int, str))
args_list[eval_func_param_names.index('visible_models')] = model_state1['visible_models']
if 'h2ogpt_key' in model_state1 and model_state1['h2ogpt_key'] is not None:
# i.e. may be '' and used to override overall local key
assert isinstance(model_state1['h2ogpt_key'], str)
args_list[eval_func_param_names.index('h2ogpt_key')] = model_state1['h2ogpt_key']
elif not args_list[eval_func_param_names.index('h2ogpt_key')]:
# now that checked if key was valid or not, now can inject default key in case gradio inference server
# only do if key not already set by user
args_list[eval_func_param_names.index('h2ogpt_key')] = kwargs['h2ogpt_key']
args_list[0] = instruction1 # override original instruction with history from user
args_list[2] = context1
eval_args = (model_state1, my_db_state1, selection_docs_state1, requests_state1, roles_state1)
assert len(eval_args) == len(input_args_list)
if kwargs_eval is None:
kwargs_eval = kwargs_evaluate
fun1 = partial(evaluate_local, *eval_args, *tuple(args_list), **kwargs_eval)
return history, fun1, langchain_mode1, my_db_state1, requests_state1, \
valid_key, h2ogpt_key1, \
max_time1, stream_output1, \
chatbot_role1, speaker1, tts_language1, roles_state1, tts_speed1, \
langchain_action1
def gen1_fake(fun1, history):
error = ''
sources = []
sources_str = ''
prompt_raw = ''
llm_answers = {}
save_dict = dict()
audio1 = None
yield history, error, sources, sources_str, prompt_raw, llm_answers, save_dict, audio1
return
def prepare_audio(chatbot_role1, speaker1, tts_language1, roles_state1, tts_speed1, langchain_action1):
from src.tts_sentence_parsing import init_sentence_state
sentence_state = init_sentence_state()
if langchain_action1 in [LangChainAction.EXTRACT.value]:
# don't do audio for extraction in any case
generate_speech_func_func = None
audio0 = None
audio1 = None
no_audio = None
elif kwargs['tts_model'].startswith('microsoft') and speaker1 not in [None, "None"]:
audio1 = None
from src.tts import get_speaker_embedding
speaker_embedding = get_speaker_embedding(speaker1, kwargs['model_tts'].device)
# audio0 = 16000, np.array([]).astype(np.int16)
from src.tts_utils import prepare_speech, get_no_audio
sr = 16000
audio0 = prepare_speech(sr=sr)
no_audio = get_no_audio(sr=sr)
generate_speech_func_func = functools.partial(kwargs['generate_speech_func'],
speaker=speaker1,
speaker_embedding=speaker_embedding,
sentence_state=sentence_state,
return_as_byte=kwargs['return_as_byte'],
sr=sr,
tts_speed=tts_speed1,
verbose=verbose)
elif kwargs['tts_model'].startswith('tts_models/') and chatbot_role1 not in [None, "None"]:
audio1 = None
from src.tts_utils import prepare_speech, get_no_audio
from src.tts_coqui import get_latent
sr = 24000
audio0 = prepare_speech(sr=sr)
no_audio = get_no_audio(sr=sr)
latent = get_latent(roles_state1[chatbot_role1], model=kwargs['model_xtt'])
generate_speech_func_func = functools.partial(kwargs['generate_speech_func'],
latent=latent,
language=tts_language1,
sentence_state=sentence_state,
return_as_byte=kwargs['return_as_byte'],
sr=sr,
tts_speed=tts_speed1,
verbose=verbose)
else:
generate_speech_func_func = None
audio0 = None
audio1 = None
no_audio = None
return audio0, audio1, no_audio, generate_speech_func_func
def get_response(fun1, history, chatbot_role1, speaker1, tts_language1, roles_state1, tts_speed1,
langchain_action1, api=False):
"""
bot that consumes history for user input
instruction (from input_list) itself is not consumed by bot
:return:
"""
error = ''
sources = []
save_dict = dict()
output_no_refs = ''
sources_str = ''
prompt_raw = ''
llm_answers = {}
audio0, audio1, no_audio, generate_speech_func_func = \
prepare_audio(chatbot_role1, speaker1, tts_language1, roles_state1, tts_speed1, langchain_action1)
if not fun1:
yield history, error, sources, sources_str, prompt_raw, llm_answers, save_dict, audio1
return
try:
for output_fun in fun1():
output = output_fun['response']
output_no_refs = output_fun['response_no_refs']
sources = output_fun['sources'] # FIXME: can show sources in separate text box etc.
sources_iter = [] # don't yield full prompt_raw every iteration, just at end
sources_str = output_fun['sources_str']
sources_str_iter = '' # don't yield full prompt_raw every iteration, just at end
prompt_raw = output_fun['prompt_raw']
prompt_raw_iter = '' # don't yield full prompt_raw every iteration, just at end
llm_answers = output_fun['llm_answers']
save_dict = output_fun.get('save_dict', {})
save_dict_iter = {}
# ensure good visually, else markdown ignores multiple \n
bot_message = fix_text_for_gradio(output, fix_latex_dollars=not api)
history[-1][1] = bot_message
if generate_speech_func_func is not None:
while True:
audio1, sentence, sentence_state = generate_speech_func_func(output_no_refs, is_final=False)
if audio0 is not None:
yield history, error, sources_iter, sources_str_iter, prompt_raw_iter, llm_answers, save_dict_iter, audio0
audio0 = None
yield history, error, sources_iter, sources_str_iter, prompt_raw_iter, llm_answers, save_dict_iter, audio1
if not sentence:
# while True to handle case when streaming is fast enough that see multiple sentences in single go
break
else:
yield history, error, sources_iter, sources_str_iter, prompt_raw_iter, llm_answers, save_dict_iter, audio0
if generate_speech_func_func:
# print("final %s %s" % (history[-1][1] is None, audio1 is None), flush=True)
audio1, sentence, sentence_state = generate_speech_func_func(output_no_refs, is_final=True)
if audio0 is not None:
yield history, error, sources, sources_str, prompt_raw, llm_answers, save_dict, audio0
else:
audio1 = None
# print("final2 %s %s" % (history[-1][1] is None, audio1 is None), flush=True)
yield history, error, sources, sources_str, prompt_raw, llm_answers, save_dict, audio1
except StopIteration:
# print("STOP ITERATION", flush=True)
yield history, error, sources, sources_str, prompt_raw, llm_answers, save_dict, no_audio
except RuntimeError as e:
if "generator raised StopIteration" in str(e):
# assume last entry was bad, undo
history.pop()
yield history, error, sources, sources_str, prompt_raw, llm_answers, save_dict, no_audio
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), sources, sources_str, prompt_raw, llm_answers, save_dict, no_audio
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, sources, sources_str, prompt_raw, llm_answers, save_dict, no_audio
raise
finally:
# clear_torch_cache()
# don't clear torch cache here, too early and stalls generation if used for all_bot()
pass
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 in ['chroma', 'chroma_old'] and langchain_mode1 not in ['LLM', 'Disabled', None, '']:
from gpt_langchain import clear_embedding, length_db1
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) == length_db1():
clear_embedding(db1[0])
nonelist = [None, '', 'None']
noneset = set(nonelist)
def choose_exc(x):
# don't expose ports etc. to exceptions window
if is_public:
return "Endpoint unavailable or failed"
else:
return x
def bot(*args, retry=False):
history, fun1, langchain_mode1, db1, requests_state1, \
valid_key, h2ogpt_key1, \
max_time1, stream_output1, \
chatbot_role1, speaker1, tts_language1, roles_state1, tts_speed1, \
langchain_action1 = prep_bot(*args, retry=retry)
save_dict = dict()
error = ''
error_with_str = ''
sources = []
history_str_old = ''
error_old = ''
sources_str = None
from src.tts_utils import get_no_audio
no_audio = get_no_audio()
audios = [] # in case not streaming, since audio is always streaming, need to accumulate for when yield
last_yield = None
try:
tgen0 = time.time()
for res in get_response(fun1, history, chatbot_role1, speaker1, tts_language1, roles_state1,
tts_speed1,
langchain_action1,
api=False):
do_yield = False
history, error, sources, sources_str, prompt_raw, llm_answers, save_dict, audio1 = res
error_with_str = get_accordion_named(choose_exc(error), "Generate Error",
font_size=2) if error not in ['', None, 'None'] else ''
# pass back to gradio only these, rest are consumed in this function
history_str = str(history)
could_yield = (
history_str != history_str_old or
error != error_old and
(error not in noneset or
error_old not in noneset))
if kwargs['gradio_ui_stream_chunk_size'] <= 0:
do_yield |= could_yield
else:
delta_history = abs(len(history_str) - len(history_str_old))
# even if enough data, don't yield if has been less than min_seconds
enough_data = delta_history > kwargs['gradio_ui_stream_chunk_size'] or (error != error_old)
beyond_min_time = last_yield is None or \
last_yield is not None and \
(time.time() - last_yield) > kwargs['gradio_ui_stream_chunk_min_seconds']
do_yield |= enough_data and beyond_min_time
# yield even if new data not enough if been long enough and have at least something to yield
enough_time = last_yield is None or \
last_yield is not None and \
(time.time() - last_yield) > kwargs['gradio_ui_stream_chunk_seconds']
do_yield |= enough_time and could_yield
# DEBUG: print("do_yield: %s : %s %s %s %s" % (do_yield, delta_history, enough_data, beyond_min_time, enough_time), flush=True)
if stream_output1 and do_yield:
audio1 = combine_audios(audios, audio=audio1, sr=24000 if chatbot_role1 else 16000,
expect_bytes=kwargs['return_as_byte'])
audios = [] # reset accumulation
yield history, error, audio1
history_str_old = history_str
error_old = error
last_yield = time.time()
else:
audios.append(audio1)
if time.time() - tgen0 > max_time1 + 10: # don't use actual, so inner has chance to complete
if verbose:
print("Took too long bot: %s" % (time.time() - tgen0), flush=True)
break
# yield if anything left over
final_audio = combine_audios(audios, audio=no_audio,
expect_bytes=kwargs['return_as_byte'])
if error_with_str:
if history and history[-1] and len(history[-1]) == 2 and error_with_str:
if not history[-1][1]:
history[-1][1] = error_with_str
else:
# separate bot if already text present
history.append((None, error_with_str))
if kwargs['append_sources_to_chat'] and sources_str:
history.append((None, sources_str))
yield history, error, final_audio
except BaseException as e:
print("evaluate_nochat exception: %s: %s" % (str(e), str(args)), flush=True)
raise
finally:
clear_torch_cache(allow_skip=True)
clear_embeddings(langchain_mode1, db1)
# save
if 'extra_dict' not in save_dict:
save_dict['extra_dict'] = {}
save_dict['valid_key'] = valid_key
save_dict['h2ogpt_key'] = h2ogpt_key1
if requests_state1:
save_dict['extra_dict'].update(requests_state1)
else:
save_dict['extra_dict'].update(dict(username='NO_REQUEST'))
save_dict['error'] = error
save_dict['sources'] = sources
save_dict['which_api'] = 'bot'
save_dict['save_dir'] = kwargs['save_dir']
save_generate_output(**save_dict)
def all_bot(*args, retry=False, model_states1=None, all_possible_visible_models=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')]
visible_models1 = args_list[eval_func_param_names.index('visible_models')]
assert isinstance(all_possible_visible_models, list)
assert len(all_possible_visible_models) == len(model_states1)
visible_list = get_model_lock_visible_list(visible_models1, all_possible_visible_models)
langchain_action1 = args_list[eval_func_param_names.index('langchain_action')]
isize = len(input_args_list) + 1 # states + chat history
db1s = None
requests_state1 = None
valid_key = False
h2ogpt_key1 = ''
sources_all = []
exceptions = []
save_dicts = []
audios = [] # in case not streaming, since audio is always streaming, need to accumulate for when yield
chatbot_role1 = None
try:
gen_list = []
num_visible_bots = sum(visible_list)
first_visible = True
for chatboti, (chatbot1, model_state1) in enumerate(zip(chatbots, model_states1)):
args_list1 = args_list0.copy()
# insert at -2 so is at -3, and after chatbot1 added, at -4
args_list1.insert(-isize + 2, model_state1)
# 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 chatbot1 is None:
chatbot1 = []
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 and requests_state1 should be same for every bot
history, fun1, langchain_mode1, db1s, requests_state1, \
valid_key, h2ogpt_key1, \
max_time1, stream_output1, \
chatbot_role1, speaker1, tts_language1, roles_state1, tts_speed1, \
langchain_action1 = \
prep_bot(*tuple(args_list1), retry=retry, which_model=chatboti)
if num_visible_bots == 1:
# no need to lag, will be faster this way
lag = 0
else:
lag = 1e-3
if visible_list[chatboti]:
gen1 = get_response(fun1, history,
chatbot_role1 if first_visible else 'None',
speaker1 if first_visible else 'None',
tts_language1 if first_visible else 'autodetect',
roles_state1 if first_visible else {},
tts_speed1 if first_visible else 1.0,
langchain_action1,
api=False,
)
# FIXME: only first visible chatbot is allowed to speak for now
first_visible = False
# always use stream or not, so do not block any iterator/generator
gen1 = TimeoutIterator(gen1, timeout=lag, sentinel=None, raise_on_exception=False,
whichi=chatboti)
# else timeout will truncate output for non-streaming case
else:
gen1 = gen1_fake(fun1, history)
gen_list.append(gen1)
finally:
pass
bots = bots_old = chatbots.copy()
bot_strs = bot_strs_old = str(chatbots)
exceptions = exceptions_old = [''] * len(bots_old)
exceptions_str = '\n'.join(
['Model %s: %s' % (iix, choose_exc(x)) for iix, x in enumerate(exceptions) if
x not in [None, '', 'None']])
exceptions_each_str = [''] * len(bots_old)
exceptions_old_str = exceptions_str
sources = sources_all_old = [[]] * len(bots_old)
sources_str = sources_str_all_old = [''] * len(bots_old)
sources_str_all = [None] * len(bots_old)
prompt_raw = prompt_raw_all_old = [''] * len(bots_old)
llm_answers = llm_answers_all_old = [{}] * len(bots_old)
save_dicts = save_dicts_old = [{}] * len(bots_old)
if kwargs['tts_model'].startswith('microsoft'):
from src.tts_utils import prepare_speech, get_no_audio
no_audio = get_no_audio(sr=16000)
elif kwargs['tts_model'].startswith('tts_models/'):
from src.tts_utils import prepare_speech, get_no_audio
no_audio = get_no_audio(sr=24000)
else:
no_audio = None
tgen0 = time.time()
last_yield = None
try:
for res1 in itertools.zip_longest(*gen_list):
do_yield = False
bots = [x[0] if x is not None and not isinstance(x, BaseException) else y
for x, y in zip(res1, bots_old)]
bot_strs = [str(x) for x in bots]
could_yield = any(x != y for x, y in zip(bot_strs, bot_strs_old))
if kwargs['gradio_ui_stream_chunk_size'] <= 0:
do_yield |= could_yield
else:
enough_data = any(abs(len(x) - len(y)) > kwargs['gradio_ui_stream_chunk_size']
for x, y in zip(bot_strs, bot_strs_old))
beyond_min_time = last_yield is None or \
last_yield is not None and \
(time.time() - last_yield) > kwargs['gradio_ui_stream_chunk_min_seconds']
do_yield |= enough_data and beyond_min_time
enough_time = last_yield is None or \
last_yield is not None and \
(time.time() - last_yield) > kwargs['gradio_ui_stream_chunk_seconds']
do_yield |= enough_time and could_yield
# DEBUG: print("do_yield: %s : %s %s %s" % (do_yield, enough_data, beyond_min_time, enough_time), flush=True)
if do_yield:
bot_strs_old = bot_strs.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_each_str = [
get_accordion_named(choose_exc(x), "Generate Error", font_size=2) if x not in ['', None,
'None'] else ''
for x in exceptions]
do_yield |= any(
x != y for x, y in zip(exceptions, exceptions_old) if (x not in noneset or y not in noneset))
exceptions_old = exceptions.copy()
sources_all = [x[2] if x is not None and not isinstance(x, BaseException) else y
for x, y in zip(res1, sources_all_old)]
sources_all_old = sources_all.copy()
sources_str_all = [x[3] if x is not None and not isinstance(x, BaseException) else y
for x, y in zip(res1, sources_str_all_old)]
sources_str_all_old = sources_str_all.copy()
prompt_raw_all = [x[4] if x is not None and not isinstance(x, BaseException) else y
for x, y in zip(res1, prompt_raw_all_old)]
prompt_raw_all_old = prompt_raw_all.copy()
llm_answers_all = [x[5] if x is not None and not isinstance(x, BaseException) else y
for x, y in zip(res1, llm_answers_all_old)]
llm_answers_all_old = llm_answers_all.copy()
save_dicts = [x[6] if x is not None and not isinstance(x, BaseException) else y
for x, y in zip(res1, save_dicts_old)]
save_dicts_old = save_dicts.copy()
exceptions_str = '\n'.join(
['Model %s: %s' % (iix, choose_exc(x)) for iix, x in enumerate(exceptions) if
x not in noneset])
audios_gen = [x[7] if x is not None and not isinstance(x, BaseException) else None for x in
res1]
audios_gen = [x for x in audios_gen if x is not None]
if os.getenv('HARD_ASSERTS'):
# FIXME: should only be 0 or 1 speaker in all_bot mode for now
assert len(audios_gen) in [0, 1], "Wrong len audios_gen: %s" % len(audios_gen)
audio1 = audios_gen[0] if len(audios_gen) == 1 else no_audio
do_yield |= audio1 != no_audio
# yield back to gradio only is bots + exceptions, rest are consumed locally
if stream_output1 and do_yield:
audio1 = combine_audios(audios, audio=audio1, sr=24000 if chatbot_role1 else 16000,
expect_bytes=kwargs['return_as_byte'])
audios = [] # reset accumulation
if len(bots) > 1:
yield tuple(bots + [exceptions_str, audio1])
else:
yield bots[0], exceptions_str, audio1
last_yield = time.time()
else:
audios.append(audio1)
if time.time() - tgen0 > max_time1 + 10: # don't use actual, so inner has chance to complete
if verbose:
print("Took too long all_bot: %s" % (time.time() - tgen0), flush=True)
break
if exceptions:
exceptions_reduced = [x for x in exceptions if x not in ['', None, 'None']]
if exceptions_reduced:
print("Generate exceptions: %s" % exceptions_reduced, flush=True)
# yield if anything left over as can happen (FIXME: Understand better)
final_audio = combine_audios(audios, audio=no_audio,
expect_bytes=kwargs['return_as_byte'])
# add error accordion
for boti, bot in enumerate(bots):
if bots[boti] and bots[boti][-1] and len(bots[boti][-1]) == 2 and exceptions_each_str[boti]:
if not bots[boti][-1][1]:
bots[boti][-1][1] = exceptions_each_str[boti]
else:
bots[boti].append((None, exceptions_each_str[boti]))
if kwargs['append_sources_to_chat'] and sources_str_all[boti]:
bots[boti].append((None, sources_str_all[boti]))
if len(bots) > 1:
yield tuple(bots + [exceptions_str, final_audio])
else:
yield bots[0], exceptions_str, final_audio
finally:
clear_torch_cache(allow_skip=True)
clear_embeddings(langchain_mode1, db1s)
# save
for sources, error, save_dict, model_name in zip(sources_all, exceptions, save_dicts,
all_possible_visible_models):
if 'extra_dict' not in save_dict:
save_dict['extra_dict'] = {}
if requests_state1:
save_dict['extra_dict'].update(requests_state1)
else:
save_dict['extra_dict'].update(dict(username='NO_REQUEST'))
save_dict['error'] = error
save_dict['sources'] = sources
save_dict['which_api'] = 'all_bot_%s' % model_name
save_dict['valid_key'] = valid_key
save_dict['h2ogpt_key'] = h2ogpt_key1
save_dict['save_dir'] = kwargs['save_dir']
save_generate_output(**save_dict)
# 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, requests_state,
roles_state] + [
text_output],
outputs=[text_output, chat_exception_text, speech_bot],
)
retry_bot_args = dict(fn=functools.partial(bot, retry=True),
inputs=inputs_list + [model_state, my_db_state, selection_docs_state, requests_state,
roles_state] + [
text_output],
outputs=[text_output, chat_exception_text, speech_bot],
)
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, requests_state,
roles_state] + [
text_output2],
outputs=[text_output2, chat_exception_text, speech_bot2],
)
retry_bot_args2 = dict(fn=functools.partial(bot, retry=True),
inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state,
requests_state, roles_state] + [
text_output2],
outputs=[text_output2, chat_exception_text, speech_bot2],
)
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),
all_possible_visible_models=kwargs['all_possible_visible_models']
),
inputs=inputs_list + text_outputs,
outputs=text_outputs,
)
all_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states,
all_possible_visible_models=kwargs['all_possible_visible_models']),
inputs=inputs_list + [my_db_state, selection_docs_state, requests_state, roles_state] +
text_outputs,
outputs=text_outputs + [chat_exception_text, speech_bot],
)
all_retry_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states,
all_possible_visible_models=kwargs[
'all_possible_visible_models'],
retry=True),
inputs=inputs_list + [my_db_state, selection_docs_state, requests_state,
roles_state] +
text_outputs,
outputs=text_outputs + [chat_exception_text, speech_bot],
)
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),
all_possible_visible_models=kwargs[
'all_possible_visible_models']
),
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),
all_possible_visible_models=kwargs['all_possible_visible_models']
),
inputs=inputs_list + text_outputs,
outputs=text_outputs,
)
def clear_instruct():
return gr.Textbox(value='')
def deselect_radio_chats():
return gr.update(value=None)
def clear_all():
return gr.Textbox(value=''), gr.Textbox(value=''), gr.update(value=None), \
gr.Textbox(value=''), gr.Textbox(value='')
if kwargs['model_states']:
submits1 = submits2 = submits3 = []
submits4 = []
triggers = [instruction, submit, retry_btn]
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, trigger1, in zip(user_args, bot_args, fun_name, fun_source, triggers):
submit_event11 = funs1(fn=user_state_setup,
inputs=[my_db_state, requests_state, trigger1, trigger1],
outputs=[my_db_state, requests_state, trigger1],
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=user_state_setup,
inputs=[my_db_state, requests_state, undo, undo],
outputs=[my_db_state, requests_state, undo],
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=user_state_setup,
inputs=[my_db_state, requests_state, instruction, instruction],
outputs=[my_db_state, requests_state, 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=user_state_setup,
inputs=[my_db_state, requests_state, submit, submit],
outputs=[my_db_state, requests_state, submit],
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=user_state_setup,
inputs=[my_db_state, requests_state, retry_btn, retry_btn],
outputs=[my_db_state, requests_state, retry_btn],
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=user_state_setup,
inputs=[my_db_state, requests_state, undo, undo],
outputs=[my_db_state, requests_state, undo],
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):
# 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 = str(stepxx[0]).replace('
', '').replace('
', '') if stepxx[0] is not None else None
answerx = str(stepxx[1]).replace('', '').replace('
', '') if stepxx[1] is not None else None
questiony = str(stepyy[0]).replace('', '').replace('
', '') if stepyy[0] is not None else None
answery = str(stepyy[1]).replace('', '').replace('
', '') 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, auth_filename=None, auth_freeze=None, raise_if_none=True):
args_list = list(args)
db1s = args_list[0]
requests_state1 = args_list[1]
args_list = args_list[2:]
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:
if raise_if_none:
raise ValueError("Invalid chat file")
else:
chat_state1 = args_list[-1]
choices = list(chat_state1.keys()).copy()
return chat_state1, gr.update(choices=choices, value=None)
# 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()
# save saved chats and chatbots to auth file
selection_docs_state1 = None
langchain_mode2 = None
roles_state1 = None
model_options_state1 = None
lora_options_state1 = None
server_options_state1 = None
text_output1 = chat_list[0]
text_output21 = chat_list[1]
text_outputs1 = chat_list[2:]
save_auth_func(selection_docs_state1, requests_state1, roles_state1,
model_options_state1, lora_options_state1, server_options_state1,
chat_state1, langchain_mode2,
text_output1, text_output21, text_outputs1,
)
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([[]] * len(args))
def clear_scores():
return gr.Textbox(value=res_value), \
gr.Textbox(value='Response Score: NA'), \
gr.Textbox(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],
**noqueue_kwargs, api_name='remove_chat')
def get_chats1(chat_state1):
base = 'chats'
base = makedirs(base, exist_ok=True, tmp_ok=True, use_base=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,
**noqueue_kwargs2,
api_name='export_chats' if allow_api else None)
def add_chats_from_file(db1s, requests_state1, file, chat_state1, radio_chats1, chat_exception_text1,
auth_filename=None, auth_freeze=None):
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(db1s, requests_state1, chat1_v, chat_state1, chat_is_list=True,
raise_if_none=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)
# save chat to auth file
selection_docs_state1 = None
langchain_mode2 = None
roles_state1 = None
model_options_state1 = None
lora_options_state1 = None
server_options_state1 = None
text_output1, text_output21, text_outputs1 = None, None, None
save_auth_func(selection_docs_state1, requests_state1, roles_state1,
model_options_state1, lora_options_state1, server_options_state1,
chat_state1, langchain_mode2,
text_output1, text_output21, text_outputs1,
)
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_eventa = chatsup_output.change(user_state_setup,
inputs=[my_db_state, requests_state, langchain_mode],
outputs=[my_db_state, requests_state, langchain_mode],
show_progress='minimal')
add_chats_from_file_func = functools.partial(add_chats_from_file,
auth_filename=kwargs['auth_filename'],
auth_freeze=kwargs['auth_freeze'],
)
chatup_change_event = chatup_change_eventa.then(add_chats_from_file_func,
inputs=[my_db_state, requests_state] +
[chatsup_output, chat_state, radio_chats,
chat_exception_text],
outputs=[chatsup_output, chat_state, radio_chats,
chat_exception_text],
**noqueue_kwargs,
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,
**noqueue_kwargs, api_name='clear' if allow_api else None) \
.then(deselect_radio_chats, inputs=None, outputs=radio_chats, **noqueue_kwargs) \
.then(clear_scores, outputs=[score_text, score_text2, score_text_nochat])
clear_eventa = save_chat_btn.click(user_state_setup,
inputs=[my_db_state, requests_state, langchain_mode],
outputs=[my_db_state, requests_state, langchain_mode],
show_progress='minimal', **noqueue_kwargs2)
save_chat_func = functools.partial(save_chat,
auth_filename=kwargs['auth_filename'],
auth_freeze=kwargs['auth_freeze'],
raise_if_none=False,
)
clear_event = clear_eventa.then(save_chat_func,
inputs=[my_db_state, requests_state] +
[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, requests_state,
roles_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(**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)
# copy of above with text box submission
submit_event_nochat2 = instruction_nochat.submit(**no_chat_args) \
.then(**score_args_nochat, queue=queue) \
.then(clear_instruct, None, instruction_nochat) \
.then(clear_instruct, None, iinput_nochat)
submit_event_nochat_api = submit_nochat_api.click(fun_with_dict_str,
inputs=[model_state, my_db_state, selection_docs_state,
requests_state, roles_state,
inputs_dict_str],
outputs=text_output_nochat_api,
queue=True, # required for generator
api_name='submit_nochat_api' if allow_api else None)
submit_event_nochat_api_plain = submit_nochat_api_plain.click(fun_with_dict_str_plain,
inputs=inputs_dict_str,
outputs=text_output_nochat_api,
**noqueue_kwargs,
api_name='submit_nochat_plain_api' if allow_api else None)
def load_model(model_name, lora_weights, server_name,
model_state_old,
prompt_type_old,
load_8bit, load_4bit, low_bit_mode,
load_gptq, load_awq, load_exllama, use_safetensors, revision,
use_cpu,
use_gpu_id, gpu_id,
max_seq_len1, rope_scaling1,
model_path_llama1, model_name_gptj1, model_name_gpt4all_llama1,
n_gpu_layers1, n_batch1, n_gqa1, llamacpp_dict_more1,
system_prompt1,
exllama_dict, gptq_dict, attention_sinks, sink_dict, truncation_generation, hf_model_dict,
model_options_state1, lora_options_state1, server_options_state1,
unload=False):
if unload:
model_name = no_model_str
lora_weights = no_lora_str
server_name = no_server_str
exllama_dict = str_to_dict(exllama_dict)
gptq_dict = str_to_dict(gptq_dict)
sink_dict = str_to_dict(sink_dict)
hf_model_dict = str_to_dict(hf_model_dict)
# switch-a-roo on base_model so can pass GGUF/GGML as base model
model_name0 = model_name
model_name, model_path_llama1, load_gptq, load_awq, n_gqa1 = \
switch_a_roo_llama(model_name, model_path_llama1, load_gptq, load_awq, n_gqa1,
kwargs['llamacpp_path'])
# after getting results, we always keep all items related to llama.cpp, gptj, gpt4all inside llamacpp_dict
llamacpp_dict = str_to_dict(llamacpp_dict_more1)
llamacpp_dict.update(dict(model_path_llama=model_path_llama1,
model_name_gptj=model_name_gptj1,
model_name_gpt4all_llama=model_name_gpt4all_llama1,
n_gpu_layers=n_gpu_layers1,
n_batch=n_batch1,
n_gqa=n_gqa1,
))
if model_name == 'llama' and not model_path_llama1:
raise ValueError("Must set model_path_llama if model_name==llama")
if model_name == 'gptj' and not model_name_gptj:
raise ValueError("Must set model_name_gptj if model_name==llama")
if model_name == 'gpt4all_llama' and not model_name_gpt4all_llama:
raise ValueError("Must set model_name_gpt4all_llama if model_name==llama")
# 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 and \
hasattr(model0, 'cpu'):
# 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):
if hasattr(model_state_old['model'], 'cpu'):
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(allow_skip=True)
if kwargs['debug']:
print("Pre-switch post-del GPU memory: %s" % get_torch_allocated(), flush=True)
if not model_name:
model_name = no_model_str
if 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
prompt_type_old = ''
model_path_llama1 = ''
model_name_gptj1 = ''
model_name_gpt4all_llama1 = ''
load_gptq = ''
load_awq = ''
return kwargs['model_state_none'].copy(), \
model_name, lora_weights, server_name, \
prompt_type_old, max_seq_len1, \
gr.Slider(maximum=256), \
gr.Slider(maximum=256), \
model_path_llama1, model_name_gptj1, model_name_gpt4all_llama1, \
load_gptq, load_awq, n_gqa1, \
n_batch1, n_gpu_layers1, llamacpp_dict_more1, \
model_options_state1, lora_options_state1, server_options_state1
# 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['load_4bit'] = load_4bit
all_kwargs1['low_bit_mode'] = low_bit_mode
all_kwargs1['load_gptq'] = load_gptq
all_kwargs1['load_awq'] = load_awq
all_kwargs1['load_exllama'] = load_exllama
all_kwargs1['use_safetensors'] = use_safetensors
all_kwargs1['revision'] = None if not revision else revision # transcribe, don't pass ''
all_kwargs1['use_gpu_id'] = use_gpu_id
all_kwargs1['gpu_id'] = int(gpu_id) if gpu_id not in [None, 'None'] else None # detranscribe
all_kwargs1['llamacpp_dict'] = llamacpp_dict
all_kwargs1['exllama_dict'] = exllama_dict
all_kwargs1['gptq_dict'] = gptq_dict
all_kwargs1['attention_sinks'] = attention_sinks
all_kwargs1['sink_dict'] = sink_dict
all_kwargs1['truncation_generation'] = truncation_generation
all_kwargs1['hf_model_dict'] = hf_model_dict
all_kwargs1['max_seq_len'] = int(max_seq_len1) if max_seq_len1 is not None and max_seq_len1 > 0 else None
try:
all_kwargs1['rope_scaling'] = str_to_dict(rope_scaling1) # transcribe
except:
print("Failed to use user input for rope_scaling dict", flush=True)
all_kwargs1['rope_scaling'] = {}
if use_cpu:
all_kwargs1['n_gpus'] = 0
elif use_gpu_id and all_kwargs1['gpu_id']:
all_kwargs1['n_gpus'] = 1
else:
all_kwargs1['n_gpus'] = n_gpus_global
prompt_type1 = model_name_to_prompt_type(model_name,
model_name0=model_name0,
llamacpp_dict=llamacpp_dict,
prompt_type_old=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()
gradio_model_kwargs = dict(reward_type=False,
**get_kwargs(get_model, exclude_names=['reward_type'],
**all_kwargs1))
model1, tokenizer1, device1 = get_model_retry(**gradio_model_kwargs)
clear_torch_cache()
tokenizer_base_model = model_name
prompt_dict1, error0 = get_prompt(prompt_type1, '',
context='', reduced=False, making_context=False,
return_dict=True, system_prompt=system_prompt1)
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,
# FIXME: not typically required, unless want to expose adding h2ogpt endpoint in UI
visible_models=None, h2ogpt_key=None,
)
max_seq_len1new = get_model_max_length_from_tokenizer(tokenizer1)
max_max_new_tokens1 = get_max_max_new_tokens(model_state_new, **kwargs)
# FIXME: Ensure stored in login state
if model_options_state1 and model_name0 not in model_options_state1[0]:
model_options_state1[0].extend([model_name0])
if lora_options_state1 and lora_weights not in lora_options_state1[0]:
lora_options_state1[0].extend([lora_weights])
if server_options_state1 and server_name not in server_options_state1[0]:
server_options_state1[0].extend([server_name])
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, max_seq_len1new, \
gr.Slider(maximum=max_max_new_tokens1), \
gr.Slider(maximum=max_max_new_tokens1), \
model_path_llama1, model_name_gptj1, model_name_gpt4all_llama1, \
load_gptq, load_awq, n_gqa1, \
n_batch1, n_gpu_layers1, llamacpp_dict_more1, \
model_options_state1, lora_options_state1, server_options_state1
def get_prompt_str(prompt_type1, prompt_dict1, system_prompt1, 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, context='',
reduced=False, making_context=False, return_dict=True,
system_prompt=system_prompt1)
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, system_prompt],
outputs=prompt_dict, **noqueue_kwargs)
prompt_type2.change(fn=get_prompt_str_func2, inputs=[prompt_type2, prompt_dict2, system_prompt],
outputs=prompt_dict2,
**noqueue_kwargs)
def dropdown_prompt_type_list(x):
return gr.Dropdown(value=x)
def chatbot_list(x, model_used_in, model_path_llama_in):
chat_name = get_chatbot_name(model_used_in, model_path_llama_in)
return gr.Textbox(label=chat_name)
load_model_inputs = [model_choice, lora_choice, server_choice, model_state, prompt_type,
model_load8bit_checkbox, model_load4bit_checkbox, model_low_bit_mode,
model_load_gptq, model_load_awq, model_load_exllama_checkbox,
model_safetensors_checkbox, model_revision,
model_use_cpu_checkbox,
model_use_gpu_id_checkbox, model_gpu,
max_seq_len, rope_scaling,
model_path_llama, model_name_gptj, model_name_gpt4all_llama,
n_gpu_layers, n_batch, n_gqa, llamacpp_dict_more,
system_prompt,
model_exllama_dict, model_gptq_dict,
model_attention_sinks, model_sink_dict,
model_truncation_generation,
model_hf_model_dict,
model_options_state, lora_options_state, server_options_state,
]
load_model_outputs = [model_state, model_used, lora_used, server_used,
# if prompt_type changes, prompt_dict will change via change rule
prompt_type, max_seq_len_used,
max_new_tokens, min_new_tokens,
model_path_llama, model_name_gptj, model_name_gpt4all_llama,
model_load_gptq, model_load_awq, n_gqa,
n_batch, n_gpu_layers, llamacpp_dict_more,
model_options_state, lora_options_state, server_options_state,
]
load_model_args = dict(fn=load_model,
inputs=load_model_inputs, outputs=load_model_outputs)
unload_model_args = dict(fn=functools.partial(load_model, unload=True),
inputs=load_model_inputs, outputs=load_model_outputs)
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, model_path_llama],
outputs=text_output)
nochat_update_args = dict(fn=chatbot_list, inputs=[text_output_nochat, model_used, model_path_llama],
outputs=text_output_nochat)
load_model_event = load_model_button.click(**load_model_args,
api_name='load_model' if allow_api and not is_public else None) \
.then(**prompt_update_args) \
.then(**chatbot_update_args) \
.then(**nochat_update_args) \
.then(clear_torch_cache) \
.then(**save_auth_kwargs)
unload_model_event = unload_model_button.click(**unload_model_args,
api_name='unload_model' if allow_api and not is_public else None) \
.then(**prompt_update_args) \
.then(**chatbot_update_args) \
.then(**nochat_update_args) \
.then(clear_torch_cache)
load_model_inputs2 = [model_choice2, lora_choice2, server_choice2, model_state2, prompt_type2,
model_load8bit_checkbox2, model_load4bit_checkbox2, model_low_bit_mode2,
model_load_gptq2, model_load_awq2, model_load_exllama_checkbox2,
model_safetensors_checkbox2, model_revision2,
model_use_cpu_checkbox2,
model_use_gpu_id_checkbox2, model_gpu2,
max_seq_len2, rope_scaling2,
model_path_llama2, model_name_gptj2, model_name_gpt4all_llama2,
n_gpu_layers2, n_batch2, n_gqa2, llamacpp_dict_more2,
system_prompt,
model_exllama_dict2, model_gptq_dict2,
model_attention_sinks2, model_sink_dict2,
model_truncation_generation2,
model_hf_model_dict2,
model_options_state, lora_options_state, server_options_state,
]
load_model_outputs2 = [model_state2, model_used2, lora_used2, server_used2,
# if prompt_type2 changes, prompt_dict2 will change via change rule
prompt_type2, max_seq_len_used2,
max_new_tokens2, min_new_tokens2,
model_path_llama2, model_name_gptj2, model_name_gpt4all_llama2,
model_load_gptq2, model_load_awq2, n_gqa2,
n_batch2, n_gpu_layers2, llamacpp_dict_more2,
model_options_state, lora_options_state, server_options_state,
]
load_model_args2 = dict(fn=load_model,
inputs=load_model_inputs2, outputs=load_model_outputs2)
unload_model_args2 = dict(fn=functools.partial(load_model, unload=True),
inputs=load_model_inputs2, outputs=load_model_outputs2)
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, model_path_llama2],
outputs=text_output2)
load_model_event2 = load_model_button2.click(**load_model_args2,
api_name='load_model2' if allow_api and not is_public else None) \
.then(**prompt_update_args2) \
.then(**chatbot_update_args2) \
.then(clear_torch_cache) \
.then(**save_auth_kwargs)
unload_model_event2 = unload_model_button2.click(**unload_model_args2,
api_name='unload_model2' if allow_api and not is_public else None) \
.then(**prompt_update_args) \
.then(**chatbot_update_args) \
.then(**nochat_update_args) \
.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]]
if no_model_str in model_new_options:
model_new_options.remove(no_model_str)
model_new_options = [no_model_str] + sorted(model_new_options)
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(value=x1, choices=model_new_options),
gr.Dropdown(value=x2, choices=model_new_options),
'', model_new_state]
lora_new_state = [lora_list0[0] + [lora_x]]
lora_new_options = [*lora_new_state[0]]
if no_lora_str in lora_new_options:
lora_new_options.remove(no_lora_str)
lora_new_options = [no_lora_str] + sorted(lora_new_options)
# 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(value=x1, choices=lora_new_options),
gr.Dropdown(value=x2, choices=lora_new_options),
'', lora_new_state]
server_new_state = [server_list0[0] + [server_x]]
server_new_options = [*server_new_state[0]]
if no_server_str in server_new_options:
server_new_options.remove(no_server_str)
server_new_options = [no_server_str] + sorted(server_new_options)
# 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(value=x1, choices=server_new_options),
gr.Dropdown(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],
**noqueue_kwargs)
go_event = go_btn.click(lambda: gr.update(visible=False), None, go_btn, api_name="go" if allow_api else None,
**noqueue_kwargs) \
.then(lambda: gr.update(visible=True), None, normal_block, **noqueue_kwargs) \
.then(**load_model_args, **noqueue_kwargs).then(**prompt_update_args, **noqueue_kwargs)
def compare_textbox_fun(x):
return gr.Textbox(visible=x)
def compare_column_fun(x):
return gr.Column(visible=x)
def compare_prompt_fun(x):
return gr.Dropdown(visible=x)
def slider_fun(x):
return gr.Slider(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, **noqueue_kwargs)
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, **noqueue_kwargs)
def get_system_info():
if is_public:
time.sleep(10) # delay to avoid spam since **noqueue_kwargs
return gr.Textbox(value=system_info_print())
system_event = system_btn.click(get_system_info, outputs=system_text,
api_name='system_info' if allow_api else None, **noqueue_kwargs)
def shutdown_func(h2ogpt_pid):
import psutil
parent = psutil.Process(h2ogpt_pid)
for child in parent.children(recursive=True):
child.kill()
parent.kill()
shutdown_event = close_btn.click(functools.partial(shutdown_func, h2ogpt_pid=kwargs['h2ogpt_pid']),
api_name='shutdown' if allow_api and not is_public and kwargs[
'h2ogpt_pid'] is not None else None,
**noqueue_kwargs)
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,
**noqueue_kwargs, # 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,
**noqueue_kwargs,
)
def get_model_names():
key_list = ['base_model', 'prompt_type', 'prompt_dict'] + list(kwargs['other_model_state_defaults'].keys())
# don't want to expose backend inference server IP etc.
# key_list += ['inference_server']
if len(model_states) >= 1:
local_model_states = model_states
elif model_state0 is not None:
local_model_states = [model_state0]
else:
local_model_states = []
return [{k: x[k] for k in key_list if k in x} for x in local_model_states]
models_list_event = system_btn4.click(get_model_names,
outputs=system_text4,
api_name='model_names' if allow_api else None,
**noqueue_kwargs,
)
def count_chat_tokens(model_state1, chat1, prompt_type1, prompt_dict1,
system_prompt1, chat_conversation1,
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_mode=langchain_mode1,
add_chat_history_to_context=add_chat_history_to_context1,
prompt_type=prompt_type1,
prompt_dict=prompt_dict1,
model_max_length=model_max_length1,
memory_restriction_level=memory_restriction_level1,
keep_sources_in_context=keep_sources_in_context1,
system_prompt=system_prompt1,
chat_conversation=chat_conversation1,
hyde_level=None,
gradio_errors_to_chatbot=kwargs['gradio_errors_to_chatbot'])
tokens = tokenizer(context1, return_tensors="pt")['input_ids']
if len(tokens.shape) == 1:
return str(tokens.shape[0])
elif len(tokens.shape) == 2:
return str(tokens.shape[1])
else:
return "N/A"
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_func,
inputs=[model_state, text_output, prompt_type, prompt_dict,
system_prompt, chat_conversation],
outputs=chat_token_count,
api_name='count_tokens' if allow_api else None)
speak_events = []
if kwargs['enable_tts'] and kwargs['predict_from_text_func'] is not None:
if kwargs['tts_model'].startswith('tts_models/'):
speak_human_event = speak_human_button.click(kwargs['predict_from_text_func'],
inputs=[instruction, chatbot_role, tts_language,
roles_state, tts_speed],
outputs=speech_human,
api_name='speak_human' if allow_api else None,
)
speak_events.extend([speak_human_event])
elif kwargs['tts_model'].startswith('microsoft'):
speak_human_event = speak_human_button.click(kwargs['predict_from_text_func'],
inputs=[instruction, speaker, tts_speed],
outputs=speech_human,
api_name='speak_human' if allow_api else None,
)
speak_events.extend([speak_human_event])
def wrap_pred_func(chatbot_role1, speaker1, tts_language1, roles_state1, tts_speed1,
visible_models1, text_output1, text_output21, *args,
all_models=[]):
# FIXME: Choose first visible
text_outputs1 = list(args)
text_outputss = [text_output1, text_output21] + text_outputs1
text_outputss = [x[-1][1] for x in text_outputss if len(x) >= 1 and len(x[-1]) == 2 and x[-1][1]]
response = text_outputss[0] if text_outputss else ''
keep_sources_in_context1 = False
langchain_mode1 = None # so always tries
hyde_level1 = None # so always tries
response = remove_refs(response, keep_sources_in_context1, langchain_mode1, hyde_level1,
kwargs['gradio_errors_to_chatbot'])
if kwargs['enable_tts'] and kwargs['predict_from_text_func'] is not None and response:
if kwargs['tts_model'].startswith('tts_models/') and chatbot_role1 not in [None, 'None']:
yield from kwargs['predict_from_text_func'](response, chatbot_role1, tts_language1, roles_state1,
tts_speed1)
elif kwargs['tts_model'].startswith('microsoft') and speaker1 not in [None, 'None']:
yield from kwargs['predict_from_text_func'](response, speaker1, tts_speed1)
speak_bot_event = speak_bot_button.click(wrap_pred_func,
inputs=[chatbot_role, speaker, tts_language, roles_state, tts_speed,
visible_models, text_output,
text_output2] + text_outputs,
outputs=speech_bot,
api_name='speak_bot' if allow_api else None,
)
speak_events.extend([speak_bot_event])
def stop_audio_func():
return None, None
if kwargs['enable_tts']:
stop_speak_button.click(stop_audio_func,
outputs=[speech_human, speech_bot],
cancels=speak_events, **noqueue_kwargs2)
# 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
clear_torch_cache_func_soft = functools.partial(clear_torch_cache, allow_skip=True)
stop_event = stop_btn.click(lambda: None, None, None,
cancels=submits1 + submits2 + submits3 + submits4 +
[submit_event_nochat, submit_event_nochat2] +
[eventdb1, eventdb2, eventdb3] +
[eventdb7a, eventdb7, eventdb8a, eventdb8, eventdb9a, eventdb9, eventdb12a,
eventdb12] +
db_events +
[eventdbloadla, eventdbloadlb] +
[clear_event] +
[submit_event_nochat_api, submit_event_nochat] +
[load_model_event, load_model_event2] +
[count_tokens_event] +
speak_events
,
**noqueue_kwargs, api_name='stop' if allow_api else None) \
.then(clear_torch_cache_func_soft, **noqueue_kwargs) \
.then(stop_audio_func, outputs=[speech_human, speech_bot])
if kwargs['auth'] is not None:
auth = authf
load_func = user_state_setup
load_inputs = [my_db_state, requests_state, login_btn, login_btn]
load_outputs = [my_db_state, requests_state, login_btn]
else:
auth = None
load_func, load_inputs, load_outputs = None, None, None
app_js = wrap_js_to_lambda(
len(load_inputs) if load_inputs else 0,
get_dark_js() if kwargs['dark'] else None,
get_heap_js(heap_app_id) if is_heap_analytics_enabled else None)
load_kwargs = dict(js=app_js) if is_gradio_version4 else dict(_js=app_js)
load_event = demo.load(fn=load_func, inputs=load_inputs, outputs=load_outputs, **load_kwargs)
if load_func:
load_event2 = load_event.then(load_login_func,
inputs=login_inputs,
outputs=login_outputs)
if not kwargs['large_file_count_mode']:
load_event3 = load_event2.then(**get_sources_kwargs)
load_event4 = load_event3.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
load_event5 = load_event4.then(**show_sources_kwargs)
load_event6 = load_event5.then(**get_viewable_sources_args)
load_event7 = load_event6.then(**viewable_kwargs)
demo.queue(**queue_kwargs, api_open=kwargs['api_open'])
favicon_file = "h2o-logo.svg"
favicon_path = kwargs['favicon_path'] or favicon_file
if not os.path.isfile(favicon_file):
print("favicon_path1=%s not found" % favicon_file, flush=True)
alt_path = os.path.dirname(os.path.abspath(__file__))
favicon_path = os.path.join(alt_path, favicon_file)
if not os.path.isfile(favicon_path):
print("favicon_path2: %s not found in %s" % (favicon_file, alt_path), flush=True)
alt_path = os.path.dirname(alt_path)
favicon_path = os.path.join(alt_path, favicon_file)
if not os.path.isfile(favicon_path):
print("favicon_path3: %s not found in %s" % (favicon_file, alt_path), flush=True)
favicon_path = None
if kwargs['prepare_offline_level'] > 0:
from src.prepare_offline import go_prepare_offline
go_prepare_offline(**locals())
return
scheduler = BackgroundScheduler()
if kwargs['clear_torch_cache_level'] in [0, 1]:
interval_time = 120
clear_torch_cache_func_periodic = clear_torch_cache_func_soft
else:
interval_time = 20
clear_torch_cache_func_periodic = clear_torch_cache
# don't require ever clear torch cache
scheduler.add_job(func=clear_torch_cache_func_periodic, trigger="interval", seconds=interval_time)
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 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"
# set port in case GRADIO_SERVER_PORT was already set in prior main() call,
# gradio does not listen if change after import
# Keep None if not set so can find an open port above used ports
server_port = os.getenv('GRADIO_SERVER_PORT')
if server_port is not None:
server_port = int(server_port)
demo.launch(share=kwargs['share'],
server_name=kwargs['server_name'],
show_error=True,
server_port=server_port,
favicon_path=favicon_path,
prevent_thread_lock=True,
auth=auth,
auth_message=auth_message,
root_path=kwargs['root_path'],
ssl_keyfile=kwargs['ssl_keyfile'],
ssl_verify=kwargs['ssl_verify'],
ssl_certfile=kwargs['ssl_certfile'],
ssl_keyfile_password=kwargs['ssl_keyfile_password'],
max_threads=max(128, 4 * kwargs['concurrency_count']) if isinstance(kwargs['concurrency_count'],
int) else 128,
)
showed_server_name = 'localhost' if kwargs['server_name'] == "0.0.0.0" else kwargs['server_name']
if kwargs['verbose'] or not (kwargs['base_model'] in ['gptj', 'gpt4all_llama']):
print("Started Gradio Server and/or GUI: server_name: %s port: %s" % (showed_server_name,
server_port),
flush=True)
if server_port is None:
server_port = '7860'
if kwargs['open_browser']:
# Open URL in a new tab, if a browser window is already open.
import webbrowser
webbrowser.open_new_tab(demo.local_url)
else:
print("Use local URL: %s" % demo.local_url, flush=True)
if kwargs['openai_server']:
from openai_server.server import run
url_split = demo.local_url.split(':')
if len(url_split) == 3:
gradio_prefix = ':'.join(url_split[0:1]).replace('//', '')
gradio_host = ':'.join(url_split[1:2]).replace('//', '')
gradio_port = ':'.join(url_split[2:]).split('/')[0]
else:
gradio_prefix = 'http'
gradio_host = ':'.join(url_split[0:1])
gradio_port = ':'.join(url_split[1:]).split('/')[0]
h2ogpt_key1 = get_one_key(kwargs['h2ogpt_api_keys'], kwargs['enforce_h2ogpt_api_key'])
# ensure can reach out
openai_host = gradio_host if gradio_host not in ['localhost', '127.0.0.1'] else '0.0.0.0'
run(wait=False,
host=openai_host,
port=kwargs['openai_port'],
gradio_prefix=gradio_prefix,
gradio_host=gradio_host,
gradio_port=gradio_port,
h2ogpt_key=h2ogpt_key1)
if kwargs['block_gradio_exit']:
demo.block_thread()
def show_doc(db1s, selection_docs_state1, requests_state1,
langchain_mode1,
single_document_choice1,
view_raw_text_checkbox1,
text_context_list1,
pdf_height,
dbs1=None,
load_db_if_exists1=None,
db_type1=None,
use_openai_embedding1=None,
hf_embedding_model1=None,
migrate_embedding_model_or_db1=None,
auto_migrate_db1=None,
verbose1=False,
get_userid_auth1=None,
max_raw_chunks=1000000,
api=False,
n_jobs=-1):
file = single_document_choice1
document_choice1 = [single_document_choice1]
content = None
db_documents = []
db_metadatas = []
if db_type1 in ['chroma', 'chroma_old']:
assert langchain_mode1 is not None
langchain_mode_paths = selection_docs_state1['langchain_mode_paths']
langchain_mode_types = selection_docs_state1['langchain_mode_types']
from src.gpt_langchain import set_userid, get_any_db, get_docs_and_meta
set_userid(db1s, requests_state1, get_userid_auth1)
top_k_docs = -1
db = get_any_db(db1s, langchain_mode1, langchain_mode_paths, langchain_mode_types,
dbs=dbs1,
load_db_if_exists=load_db_if_exists1,
db_type=db_type1,
use_openai_embedding=use_openai_embedding1,
hf_embedding_model=hf_embedding_model1,
migrate_embedding_model=migrate_embedding_model_or_db1,
auto_migrate_db=auto_migrate_db1,
for_sources_list=True,
verbose=verbose1,
n_jobs=n_jobs,
)
query_action = False # long chunks like would be used for summarize
# the below is as or filter, so will show doc or by chunk, unrestricted
from langchain.vectorstores import Chroma
if isinstance(db, Chroma):
# chroma >= 0.4
if view_raw_text_checkbox1:
one_filter = \
[{"source": {"$eq": x}, "chunk_id": {"$gte": 0}} if query_action else {"source": {"$eq": x},
"chunk_id": {
"$gte": -1}}
for x in document_choice1][0]
else:
one_filter = \
[{"source": {"$eq": x}, "chunk_id": {"$gte": 0}} if query_action else {"source": {"$eq": x},
"chunk_id": {
"$eq": -1}}
for x in document_choice1][0]
filter_kwargs = dict(filter={"$and": [dict(source=one_filter['source']),
dict(chunk_id=one_filter['chunk_id'])]})
else:
# migration for chroma < 0.4
one_filter = \
[{"source": {"$eq": x}, "chunk_id": {"$gte": 0}} if query_action else {"source": {"$eq": x},
"chunk_id": {
"$eq": -1}}
for x in document_choice1][0]
if view_raw_text_checkbox1:
# like or, full raw all chunk types
filter_kwargs = dict(filter=one_filter)
else:
filter_kwargs = dict(filter={"$and": [dict(source=one_filter['source']),
dict(chunk_id=one_filter['chunk_id'])]})
db_documents, db_metadatas = get_docs_and_meta(db, top_k_docs, filter_kwargs=filter_kwargs,
text_context_list=text_context_list1)
# order documents
from langchain.docstore.document import Document
docs_with_score = [(Document(page_content=result[0], metadata=result[1] or {}), 0)
for result in zip(db_documents, db_metadatas)]
doc_chunk_ids = [x.get('chunk_id', -1) for x in db_metadatas]
doc_page_ids = [x.get('page', 0) for x in db_metadatas]
doc_hashes = [x.get('doc_hash', 'None') for x in db_metadatas]
docs_with_score = [x for hx, px, cx, x in
sorted(zip(doc_hashes, doc_page_ids, doc_chunk_ids, docs_with_score),
key=lambda x: (x[0], x[1], x[2]))
# if cx == -1
]
db_metadatas = [x[0].metadata for x in docs_with_score][:max_raw_chunks]
db_documents = [x[0].page_content for x in docs_with_score][:max_raw_chunks]
# done reordering
if view_raw_text_checkbox1:
content = [dict_to_html(x) + '\n' + text_to_html(y) for x, y in zip(db_metadatas, db_documents)]
else:
content = [text_to_html(y) for x, y in zip(db_metadatas, db_documents)]
content = '\n'.join(content)
content = f"""
{file}
{content}
"""
if api:
if view_raw_text_checkbox1:
return dict(contents=db_documents, metadatas=db_metadatas)
else:
contents = [text_to_html(y, api=api) for y in db_documents]
metadatas = [dict_to_html(x, api=api) for x in db_metadatas]
return dict(contents=contents, metadatas=metadatas)
else:
assert not api, "API mode for get_document only supported for chroma"
dummy1 = gr.update(visible=False, value=None)
# backup is text dump of db version
if content:
dummy_ret = dummy1, dummy1, dummy1, dummy1, gr.update(visible=True, value=content), dummy1, dummy1, dummy1
if view_raw_text_checkbox1:
return dummy_ret
else:
dummy_ret = dummy1, dummy1, dummy1, dummy1, dummy1, dummy1, dummy1, dummy1
if not isinstance(file, str):
return dummy_ret
if file.lower().endswith('.html') or file.lower().endswith('.mhtml') or file.lower().endswith('.htm') or \
file.lower().endswith('.xml'):
try:
with open(file, 'rt') as f:
content = f.read()
return gr.update(visible=True, value=content), dummy1, dummy1, dummy1, dummy1, dummy1, dummy1, dummy1
except:
return dummy_ret
if file.lower().endswith('.md'):
try:
with open(file, 'rt') as f:
content = f.read()
return dummy1, dummy1, dummy1, gr.update(visible=True, value=content), dummy1, dummy1, dummy1, dummy1
except:
return dummy_ret
if file.lower().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), dummy1, dummy1, dummy1, dummy1
except:
return dummy_ret
if file.lower().endswith('.txt') or file.lower().endswith('.rst') or file.lower().endswith(
'.rtf') or file.lower().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), dummy1, dummy1, dummy1, dummy1
except:
return dummy_ret
func = None
if file.lower().endswith(".csv"):
func = pd.read_csv
elif file.lower().endswith(".pickle"):
func = pd.read_pickle
elif file.lower().endswith(".xls") or file.lower().endswith("xlsx"):
func = pd.read_excel
elif file.lower().endswith('.json'):
func = pd.read_json
# pandas doesn't show full thing, even if html view shows broken things still better
# elif file.lower().endswith('.xml'):
# func = pd.read_xml
if func is not None:
try:
df = func(file).head(100)
except:
# actual JSON required
with open(file, 'rt') as f:
json_blob = f.read()
return dummy1, dummy1, gr.update(visible=True, value=json_blob), dummy1, dummy1, dummy1, dummy1, dummy1
return dummy1, gr.update(visible=True, value=df), dummy1, dummy1, dummy1, dummy1, 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("""
"""), dummy1, dummy1, dummy1, dummy1, dummy1, dummy1, dummy1
elif have_gradio_pdf and os.path.isfile(file):
from gradio_pdf import PDF
return dummy1, dummy1, dummy1, dummy1, dummy1, PDF(file, visible=True, label=file, show_label=True,
height=pdf_height), dummy1, dummy1
else:
return dummy_ret
else:
return dummy_ret
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 update_user_db_gr(file, db1s, selection_docs_state1, requests_state1,
langchain_mode, chunk, chunk_size, embed,
image_audio_loaders,
pdf_loaders,
url_loaders,
jq_schema,
extract_frames,
llava_prompt,
h2ogpt_key,
captions_model=None,
caption_loader=None,
doctr_loader=None,
llava_model=None,
asr_model=None,
asr_loader=None,
dbs=None,
get_userid_auth=None,
**kwargs):
valid_key = is_valid_key(kwargs.pop('enforce_h2ogpt_api_key', None),
kwargs.pop('enforce_h2ogpt_ui_key', None),
kwargs.pop('h2ogpt_api_keys', []), h2ogpt_key,
requests_state1=requests_state1)
kwargs['from_ui'] = is_from_ui(requests_state1)
if not valid_key:
raise ValueError(invalid_key_msg)
loaders_dict, captions_model, asr_model = gr_to_lg(image_audio_loaders,
pdf_loaders,
url_loaders,
captions_model=captions_model,
asr_model=asr_model,
**kwargs,
)
if jq_schema is None:
jq_schema = kwargs['jq_schema0']
loaders_dict.update(dict(captions_model=captions_model,
caption_loader=caption_loader,
doctr_loader=doctr_loader,
llava_model=llava_model,
llava_prompt=llava_prompt,
asr_model=asr_model,
asr_loader=asr_loader,
jq_schema=jq_schema,
extract_frames=extract_frames,
))
kwargs.pop('image_audio_loaders_options0', None)
kwargs.pop('pdf_loaders_options0', None)
kwargs.pop('url_loaders_options0', None)
kwargs.pop('jq_schema0', None)
if not embed:
kwargs['use_openai_embedding'] = False
kwargs['hf_embedding_model'] = 'fake'
kwargs['migrate_embedding_model'] = False
# avoid dups after loaders_dict updated with new results
for k, v in loaders_dict.items():
if k in kwargs:
kwargs.pop(k, None)
from src.gpt_langchain import update_user_db
return update_user_db(file, db1s, selection_docs_state1, requests_state1,
langchain_mode=langchain_mode, chunk=chunk, chunk_size=chunk_size,
**loaders_dict,
dbs=dbs,
get_userid_auth=get_userid_auth,
**kwargs)
def get_sources_gr(db1s, selection_docs_state1, requests_state1, langchain_mode, dbs=None, docs_state0=None,
load_db_if_exists=None,
db_type=None,
use_openai_embedding=None,
hf_embedding_model=None,
migrate_embedding_model=None,
auto_migrate_db=None,
verbose=False,
get_userid_auth=None,
api=False,
n_jobs=-1):
from src.gpt_langchain import get_sources
sources_file, source_list, num_chunks, num_sources_str, db = \
get_sources(db1s, selection_docs_state1, requests_state1, langchain_mode,
dbs=dbs, docs_state0=docs_state0,
load_db_if_exists=load_db_if_exists,
db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
verbose=verbose,
get_userid_auth=get_userid_auth,
n_jobs=n_jobs,
)
if api:
return source_list
if langchain_mode in langchain_modes_non_db:
doc_counts_str = "LLM Mode\nNo Collection"
else:
doc_counts_str = "Collection: %s\nDocs: %s\nChunks: %d" % (langchain_mode, num_sources_str, num_chunks)
return sources_file, source_list, doc_counts_str
def get_source_files_given_langchain_mode_gr(db1s, selection_docs_state1, requests_state1,
langchain_mode,
dbs=None,
load_db_if_exists=None,
db_type=None,
use_openai_embedding=None,
hf_embedding_model=None,
migrate_embedding_model=None,
auto_migrate_db=None,
verbose=False,
get_userid_auth=None,
n_jobs=-1):
from src.gpt_langchain import get_source_files_given_langchain_mode
return get_source_files_given_langchain_mode(db1s, selection_docs_state1, requests_state1, None,
langchain_mode,
dbs=dbs,
load_db_if_exists=load_db_if_exists,
db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
verbose=verbose,
get_userid_auth=get_userid_auth,
delete_sources=False,
n_jobs=n_jobs)
def del_source_files_given_langchain_mode_gr(db1s, selection_docs_state1, requests_state1, document_choice1,
langchain_mode,
dbs=None,
load_db_if_exists=None,
db_type=None,
use_openai_embedding=None,
hf_embedding_model=None,
migrate_embedding_model=None,
auto_migrate_db=None,
verbose=False,
get_userid_auth=None,
n_jobs=-1):
from src.gpt_langchain import get_source_files_given_langchain_mode
return get_source_files_given_langchain_mode(db1s, selection_docs_state1, requests_state1, document_choice1,
langchain_mode,
dbs=dbs,
load_db_if_exists=load_db_if_exists,
db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
verbose=verbose,
get_userid_auth=get_userid_auth,
delete_sources=True,
n_jobs=n_jobs)
def update_and_get_source_files_given_langchain_mode_gr(db1s,
selection_docs_state,
requests_state,
langchain_mode, chunk, chunk_size,
image_audio_loaders,
pdf_loaders,
url_loaders,
jq_schema,
extract_frames,
llava_prompt,
captions_model=None,
caption_loader=None,
doctr_loader=None,
llava_model=None,
asr_model=None,
asr_loader=None,
dbs=None, first_para=None,
hf_embedding_model=None,
use_openai_embedding=None,
migrate_embedding_model=None,
auto_migrate_db=None,
text_limit=None,
db_type=None, load_db_if_exists=None,
n_jobs=None, verbose=None, get_userid_auth=None,
image_audio_loaders_options0=None,
pdf_loaders_options0=None,
url_loaders_options0=None,
jq_schema0=None,
use_pymupdf=None,
use_unstructured_pdf=None,
use_pypdf=None,
enable_pdf_ocr=None,
enable_pdf_doctr=None,
try_pdf_as_html=None,
):
from src.gpt_langchain import update_and_get_source_files_given_langchain_mode
loaders_dict, captions_model, asr_model = gr_to_lg(image_audio_loaders,
pdf_loaders,
url_loaders,
use_pymupdf=use_pymupdf,
use_unstructured_pdf=use_unstructured_pdf,
use_pypdf=use_pypdf,
enable_pdf_ocr=enable_pdf_ocr,
enable_pdf_doctr=enable_pdf_doctr,
try_pdf_as_html=try_pdf_as_html,
image_audio_loaders_options0=image_audio_loaders_options0,
pdf_loaders_options0=pdf_loaders_options0,
url_loaders_options0=url_loaders_options0,
captions_model=captions_model,
asr_model=asr_model,
)
if jq_schema is None:
jq_schema = jq_schema0
loaders_dict.update(dict(captions_model=captions_model,
caption_loader=caption_loader,
doctr_loader=doctr_loader,
llava_model=llava_model,
llava_prompt=llava_prompt,
asr_loader=asr_loader,
jq_schema=jq_schema,
extract_frames=extract_frames,
))
return update_and_get_source_files_given_langchain_mode(db1s,
selection_docs_state,
requests_state,
langchain_mode, chunk, chunk_size,
**loaders_dict,
dbs=dbs, first_para=first_para,
hf_embedding_model=hf_embedding_model,
use_openai_embedding=use_openai_embedding,
migrate_embedding_model=migrate_embedding_model,
auto_migrate_db=auto_migrate_db,
text_limit=text_limit,
db_type=db_type, load_db_if_exists=load_db_if_exists,
n_jobs=n_jobs, verbose=verbose,
get_userid_auth=get_userid_auth)
def set_userid_gr(db1s, requests_state1, get_userid_auth):
from src.gpt_langchain import set_userid
return set_userid(db1s, requests_state1, get_userid_auth)
def set_dbid_gr(db1):
from src.gpt_langchain import set_dbid
return set_dbid(db1)
def set_userid_direct_gr(db1s, userid, username):
from src.gpt_langchain import set_userid_direct
return set_userid_direct(db1s, userid, username)