import copy
import functools
import inspect
import json
import os
import pprint
import random
import sys
import traceback
import uuid
import filelock
import pandas as pd
import requests
import tabulate
from gradio_ui.css import get_css
from gradio_ui.prompt_form import make_prompt_form
# 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
from enums import DocumentChoices
from gradio_themes import H2oTheme, SoftTheme, get_h2o_title, get_simple_title, get_dark_js
from prompter import Prompter, \
prompt_type_to_model_name, prompt_types_strings, inv_prompt_type_to_model_lower, generate_prompt, non_hf_types, \
get_prompt
from utils import get_githash, flatten_list, zip_data, s3up, clear_torch_cache, get_torch_allocated, system_info_print, \
ping, get_short_name, get_url, makedirs, get_kwargs
from generate import get_model, languages_covered, evaluate, eval_func_param_names, score_qa, langchain_modes, \
inputs_kwargs_list, get_cutoffs, scratch_base_dir, evaluate_from_str, no_default_param_names, \
eval_func_param_names_defaults, get_max_max_new_tokens
from apscheduler.schedulers.background import BackgroundScheduler
def fix_newlines(text):
# ensure good visually, else markdown ignores multiple \n
# handle code blocks
ts = text.split('```')
for parti, part in enumerate(ts):
inside = parti % 2 == 1
if not inside:
ts[parti] = ts[parti].replace('\n', '<br>')
return '```'.join(ts)
def go_gradio(**kwargs):
allow_api = kwargs['allow_api']
is_public = kwargs['is_public']
is_hf = kwargs['is_hf']
memory_restriction_level = kwargs['memory_restriction_level']
n_gpus = kwargs['n_gpus']
admin_pass = kwargs['admin_pass']
model_state0 = kwargs['model_state0']
score_model_state0 = kwargs['score_model_state0']
dbs = kwargs['dbs']
db_type = kwargs['db_type']
visible_langchain_modes = kwargs['visible_langchain_modes']
allow_upload_to_user_data = kwargs['allow_upload_to_user_data']
allow_upload_to_my_data = kwargs['allow_upload_to_my_data']
enable_sources_list = kwargs['enable_sources_list']
enable_url_upload = kwargs['enable_url_upload']
enable_text_upload = kwargs['enable_text_upload']
use_openai_embedding = kwargs['use_openai_embedding']
hf_embedding_model = kwargs['hf_embedding_model']
enable_captions = kwargs['enable_captions']
captions_model = kwargs['captions_model']
enable_ocr = kwargs['enable_ocr']
caption_loader = kwargs['caption_loader']
# easy update of kwargs needed for evaluate() etc.
queue = True
allow_upload = allow_upload_to_user_data or allow_upload_to_my_data
kwargs.update(locals())
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 'h2ogpt-research' in kwargs['base_model']:
title += " [Research demonstration]"
more_info = """For more information, visit our GitHub pages: [h2oGPT](https://github.com/h2oai/h2ogpt) and [H2O-LLMStudio](https://github.com/h2oai/h2o-llmstudio)<br>"""
if is_public:
more_info += """<iframe src="https://ghbtns.com/github-btn.html?user=h2oai&repo=h2ogpt&type=star&count=true&size=small" frameborder="0" scrolling="0" width="150" height="20" title="GitHub"></iframe>"""
if kwargs['verbose']:
description = f"""Model {kwargs['base_model']} Instruct dataset.
For more information, visit our GitHub pages: [h2oGPT](https://github.com/h2oai/h2ogpt) and [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
Command: {str(' '.join(sys.argv))}
Hash: {get_githash()}
"""
else:
description = more_info
description += "If this host is busy, try [12B](https://gpt.h2o.ai), [Falcon 40B](http://falcon.h2o.ai), [HF Spaces1 12B](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot) or [HF Spaces2 12B](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot2)<br>"
description += """<p>By using h2oGPT, you accept our [Terms of Service](https://github.com/h2oai/h2ogpt/blob/main/docs/tos.md)</p>"""
if is_hf:
description += '''<a href="https://huggingface.co/spaces/h2oai/h2ogpt-chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" style="white-space: nowrap" alt="Duplicate Space"></a>'''
if kwargs['verbose']:
task_info_md = f"""
### Task: {kwargs['task_info']}"""
else:
task_info_md = ''
css_code = get_css(kwargs)
if kwargs['gradio_avoid_processing_markdown']:
from gradio_client import utils as client_utils
from gradio.components import Chatbot
# gradio has issue with taking too long to process input/output for markdown etc.
# Avoid for now, allow raw html to render, good enough for chatbot.
def _postprocess_chat_messages(self, chat_message: str):
if chat_message is None:
return None
elif isinstance(chat_message, (tuple, list)):
filepath = chat_message[0]
mime_type = client_utils.get_mimetype(filepath)
filepath = self.make_temp_copy_if_needed(filepath)
return {
"name": filepath,
"mime_type": mime_type,
"alt_text": chat_message[1] if len(chat_message) > 1 else None,
"data": None, # These last two fields are filled in by the frontend
"is_file": True,
}
elif isinstance(chat_message, str):
return chat_message
else:
raise ValueError(f"Invalid message for Chatbot component: {chat_message}")
Chatbot._postprocess_chat_messages = _postprocess_chat_messages
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()
theme = H2oTheme(**theme_kwargs) if kwargs['h2ocolors'] else SoftTheme(**theme_kwargs)
demo = gr.Blocks(theme=theme, css=css_code, title="h2oGPT", analytics_enabled=False)
callback = gr.CSVLogger()
model_options = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options']
if kwargs['base_model'].strip() not in model_options:
lora_options = [kwargs['base_model'].strip()] + model_options
lora_options = kwargs['extra_lora_options']
if kwargs['lora_weights'].strip() not in lora_options:
lora_options = [kwargs['lora_weights'].strip()] + lora_options
# always add in no lora case
# add fake space so doesn't go away in gradio dropdown
no_lora_str = no_model_str = '[None/Remove]'
lora_options = [no_lora_str] + kwargs['extra_lora_options'] # FIXME: why double?
# always add in no model case so can free memory
# add fake space so doesn't go away in gradio dropdown
model_options = [no_model_str] + model_options
# transcribe, will be detranscribed before use by evaluate()
if not kwargs['lora_weights'].strip():
kwargs['lora_weights'] = no_lora_str
if not kwargs['base_model'].strip():
kwargs['base_model'] = no_model_str
# transcribe for gradio
kwargs['gpu_id'] = str(kwargs['gpu_id'])
no_model_msg = 'h2oGPT [ !!! Please Load Model in Models Tab !!! ]'
output_label0 = f'h2oGPT [Model: {kwargs.get("base_model")}]' if kwargs.get(
'base_model') else no_model_msg
output_label0_model2 = no_model_msg
default_kwargs = {k: kwargs[k] for k in eval_func_param_names_defaults}
for k in no_default_param_names:
default_kwargs[k] = ''
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(['model', 'tokenizer', kwargs['device'], kwargs['base_model']])
model_state2 = gr.State([None, None, None, None])
model_options_state = gr.State([model_options])
lora_options_state = gr.State([lora_options])
my_db_state = gr.State([None, None])
chat_state = gr.State({})
# make user default first and default choice, dedup
docs_state00 = kwargs['document_choice'] + [x.name for x in list(DocumentChoices)]
docs_state0 = []
[docs_state0.append(x) for x in docs_state00 if x not in docs_state0]
docs_state = gr.State(docs_state0) # first is chosen as default
gr.Markdown(f"""
{get_h2o_title(title) if kwargs['h2ocolors'] else get_simple_title(title)}
{description}
{task_info_md}
""")
if is_hf:
gr.HTML(
)
# 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")
normal_block = gr.Row(visible=not base_wanted)
with normal_block:
with gr.Tabs():
with gr.Row():
col_nochat = gr.Column(visible=not kwargs['chat'])
with col_nochat: # FIXME: for model comparison, and check rest
if kwargs['langchain_mode'] == 'Disabled':
text_output_nochat = gr.Textbox(lines=5, label=output_label0).style(show_copy_button=True)
else:
# text looks a bit worse, but HTML links work
text_output_nochat = gr.HTML(label=output_label0)
instruction_nochat = gr.Textbox(
lines=kwargs['input_lines'],
label=instruction_label_nochat,
placeholder=kwargs['placeholder_instruction'],
)
iinput_nochat = gr.Textbox(lines=4, label="Input context for Instruction",
placeholder=kwargs['placeholder_input'])
submit_nochat = gr.Button("Submit")
flag_btn_nochat = gr.Button("Flag")
if not kwargs['auto_score']:
with gr.Column(visible=kwargs['score_model']):
score_btn_nochat = gr.Button("Score last prompt & response")
score_text_nochat = gr.Textbox("Response Score: NA", show_label=False)
else:
with gr.Column(visible=kwargs['score_model']):
score_text_nochat = gr.Textbox("Response Score: NA", show_label=False)
col_chat = gr.Column(visible=kwargs['chat'])
with col_chat:
with gr.Row():
text_output = gr.Chatbot(label=output_label0).style(height=kwargs['height'] or 400)
text_output2 = gr.Chatbot(label=output_label0_model2, visible=False).style(
height=kwargs['height'] or 400)
instruction, submit, stop_btn = make_prompt_form(kwargs)
with gr.Row():
clear = gr.Button("Save Chat / New Chat")
flag_btn = gr.Button("Flag")
if not kwargs['auto_score']: # FIXME: For checkbox model2
with gr.Column(visible=kwargs['score_model']):
with gr.Row():
score_btn = gr.Button("Score last prompt & response").style(
full_width=False, size='sm')
score_text = gr.Textbox("Response Score: NA", show_label=False)
score_res2 = gr.Row(visible=False)
with score_res2:
score_btn2 = gr.Button("Score last prompt & response 2").style(
full_width=False, size='sm')
score_text2 = gr.Textbox("Response Score2: NA", show_label=False)
else:
with gr.Column(visible=kwargs['score_model']):
score_text = gr.Textbox("Response Score: NA", show_label=False)
score_text2 = gr.Textbox("Response Score2: NA", show_label=False, visible=False)
retry = gr.Button("Regenerate")
undo = gr.Button("Undo")
submit_nochat_api = gr.Button("Submit nochat API", visible=False)
inputs_dict_str = gr.Textbox(label='API input for nochat', show_label=False, visible=False)
text_output_nochat_api = gr.Textbox(lines=5, label='API nochat output', visible=False).style(
show_copy_button=True)
with gr.TabItem("Chat"):
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")
radio_chats = gr.Radio(value=None, label="Saved Chats", visible=True, interactive=True,
type='value')
with gr.Row():
clear_chat_btn = gr.Button(value="Clear Chat", visible=True).style(size='sm')
export_chats_btn = gr.Button(value="Export Chats to Download").style(size='sm')
remove_chat_btn = gr.Button(value="Remove Selected Chat", visible=True).style(size='sm')
add_to_chats_btn = gr.Button("Import Chats from Upload").style(size='sm')
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.TabItem("Data Source"):
langchain_readme = get_url('https://github.com/h2oai/h2ogpt/blob/main/docs/README_LangChain.md',
from_str=True)
gr.HTML(value=f"""LangChain Support Disabled<p>
Run:<p>
<code>
python generate.py --langchain_mode=MyData
</code>
<p>
For more options see: {langchain_readme}""",
visible=kwargs['langchain_mode'] == 'Disabled', interactive=False)
data_row1 = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled')
with data_row1:
if is_hf:
# don't show 'wiki' since only usually useful for internal testing at moment
no_show_modes = ['Disabled', 'wiki']
else:
no_show_modes = ['Disabled']
allowed_modes = visible_langchain_modes.copy()
allowed_modes = [x for x in allowed_modes if x in dbs]
allowed_modes += ['ChatLLM', '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']
langchain_mode = gr.Radio(
[x for x in langchain_modes if x in allowed_modes and x not in no_show_modes],
value=kwargs['langchain_mode'],
label="Data Collection of Sources",
visible=kwargs['langchain_mode'] != 'Disabled')
data_row2 = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled')
with data_row2:
with gr.Column(scale=50):
document_choice = gr.Dropdown(docs_state.value,
label="Choose Subset of Doc(s) in Collection [click get sources to update]",
value=docs_state.value[0],
interactive=True,
multiselect=True,
)
with gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list):
get_sources_btn = gr.Button(value="Get Sources",
).style(full_width=False, size='sm')
show_sources_btn = gr.Button(value="Show Sources",
).style(full_width=False, size='sm')
refresh_sources_btn = gr.Button(value="Refresh Sources",
).style(full_width=False, size='sm')
# import control
if kwargs['langchain_mode'] != 'Disabled':
from gpt_langchain import file_types, have_arxiv
else:
have_arxiv = False
file_types = []
upload_row = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and allow_upload).style(
equal_height=False)
with upload_row:
with gr.Column():
file_types_str = '[' + ' '.join(file_types) + ']'
fileup_output = gr.File(label=f'Upload {file_types_str}',
file_types=file_types,
file_count="multiple",
elem_id="warning", elem_classes="feedback")
with gr.Row():
add_to_shared_db_btn = gr.Button("Add File(s) to UserData",
visible=allow_upload_to_user_data, elem_id='small_btn')
add_to_my_db_btn = gr.Button("Add File(s) to Scratch MyData",
visible=allow_upload_to_my_data,
elem_id='small_btn' if allow_upload_to_user_data else None,
).style(
size='sm' if not allow_upload_to_user_data else None)
with gr.Column(
visible=kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_url_upload):
url_label = 'URL (http/https) or ArXiv:' if have_arxiv else 'URL (http/https)'
url_text = gr.Textbox(label=url_label, interactive=True)
with gr.Row():
url_user_btn = gr.Button(value='Add URL content to Shared UserData',
visible=allow_upload_to_user_data, elem_id='small_btn')
url_my_btn = gr.Button(value='Add URL content to Scratch MyData',
visible=allow_upload_to_my_data,
elem_id='small_btn' if allow_upload_to_user_data else None,
).style(size='sm' if not allow_upload_to_user_data else None)
with gr.Column(
visible=kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_text_upload):
user_text_text = gr.Textbox(label='Paste Text [Shift-Enter more lines]', interactive=True)
with gr.Row():
user_text_user_btn = gr.Button(value='Add Text to Shared UserData',
visible=allow_upload_to_user_data,
elem_id='small_btn')
user_text_my_btn = gr.Button(value='Add Text to Scratch MyData',
visible=allow_upload_to_my_data,
elem_id='small_btn' if allow_upload_to_user_data else None,
).style(
size='sm' if not allow_upload_to_user_data else None)
with gr.Column(visible=False):
# WIP:
with gr.Row(visible=False).style(equal_height=False):
github_textbox = gr.Textbox(label="Github URL")
with gr.Row(visible=True):
github_shared_btn = gr.Button(value="Add Github to Shared UserData",
visible=allow_upload_to_user_data,
elem_id='small_btn')
github_my_btn = gr.Button(value="Add Github to Scratch MyData",
visible=allow_upload_to_my_data, elem_id='small_btn')
sources_row3 = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list).style(
equal_height=False)
with sources_row3:
with gr.Column(scale=1):
file_source = gr.File(interactive=False,
label="Download File w/Sources [click get sources to make file]")
with gr.Column(scale=2):
pass
sources_row = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list).style(
equal_height=False)
with sources_row:
sources_text = gr.HTML(label='Sources Added', interactive=False)
with gr.TabItem("Expert"):
with gr.Row():
with gr.Column():
stream_output = gr.components.Checkbox(label="Stream output",
value=kwargs['stream_output'])
prompt_type = gr.Dropdown(prompt_types_strings,
value=kwargs['prompt_type'], label="Prompt Type",
visible=not is_public)
prompt_type2 = gr.Dropdown(prompt_types_strings,
value=kwargs['prompt_type'], label="Prompt Type Model 2",
visible=not is_public and False)
do_sample = gr.Checkbox(label="Sample",
info="Enable sampler, required for use of temperature, top_p, top_k",
value=kwargs['do_sample'])
temperature = gr.Slider(minimum=0.01, maximum=3,
value=kwargs['temperature'],
label="Temperature",
info="Lower is deterministic (but may lead to repeats), Higher more creative (but may lead to hallucinations)")
top_p = gr.Slider(minimum=0, maximum=1,
value=kwargs['top_p'], label="Top p",
info="Cumulative probability of tokens to sample from")
top_k = gr.Slider(
minimum=0, maximum=100, step=1,
value=kwargs['top_k'], label="Top k",
info='Num. tokens to sample from'
)
# FIXME: https://github.com/h2oai/h2ogpt/issues/106
if os.getenv('TESTINGFAIL'):
max_beams = 8 if not (memory_restriction_level or is_public) else 1
else:
max_beams = 1
num_beams = gr.Slider(minimum=1, maximum=max_beams, step=1,
value=min(max_beams, kwargs['num_beams']), label="Beams",
info="Number of searches for optimal overall probability. "
"Uses more GPU memory/compute")
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,
)
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,
)
early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search",
value=kwargs['early_stopping'])
max_max_time = 60 * 20 if not is_public else 60 * 2
if is_hf:
max_max_time = min(max_max_time, 60 * 1)
max_time = gr.Slider(minimum=0, maximum=max_max_time, step=1,
value=min(max_max_time, kwargs['max_time']), label="Max. time",
info="Max. time to search optimal output.")
repetition_penalty = gr.Slider(minimum=0.01, maximum=3.0,
value=kwargs['repetition_penalty'],
label="Repetition Penalty")
num_return_sequences = gr.Slider(minimum=1, maximum=10, step=1,
value=kwargs['num_return_sequences'],
label="Number Returns", info="Must be <= num_beams",
visible=not is_public)
iinput = gr.Textbox(lines=4, label="Input",
placeholder=kwargs['placeholder_input'],
visible=not is_public)
context = gr.Textbox(lines=3, label="System Pre-Context",
info="Directly pre-appended without prompt processing",
visible=not is_public)
chat = gr.components.Checkbox(label="Chat mode", value=kwargs['chat'],
visible=not is_public)
count_chat_tokens_btn = gr.Button(value="Count Chat Tokens", visible=not is_public)
chat_token_count = gr.Textbox(label="Chat Token Count", value=None,
visible=not is_public, interactive=False)
chunk = gr.components.Checkbox(value=kwargs['chunk'],
label="Whether to chunk documents",
info="For LangChain",
visible=not is_public)
top_k_docs = gr.Slider(minimum=0, maximum=100, step=1,
value=kwargs['top_k_docs'],
label="Number of document chunks",
info="For LangChain",
visible=not is_public)
chunk_size = gr.Number(value=kwargs['chunk_size'],
label="Chunk size for document chunking",
info="For LangChain (ignored if chunk=False)",
visible=not is_public,
precision=0)
with gr.TabItem("Models"):
load_msg = "Load-Unload Model/LORA [unload works if did not use --base_model]" if not is_public \
else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO"
load_msg2 = "Load-Unload Model/LORA 2 [unload works if did not use --base_model]" if not is_public \
else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO 2"
compare_checkbox = gr.components.Checkbox(label="Compare Mode",
value=False, visible=not is_public)
with gr.Row():
n_gpus_list = [str(x) for x in list(range(-1, n_gpus))]
with gr.Column():
with gr.Row():
with gr.Column(scale=20):
model_choice = gr.Dropdown(model_options_state.value[0], label="Choose Model",
value=kwargs['base_model'])
lora_choice = gr.Dropdown(lora_options_state.value[0], label="Choose LORA",
value=kwargs['lora_weights'], visible=kwargs['show_lora'])
with gr.Column(scale=1):
load_model_button = gr.Button(load_msg).style(full_width=False, size='sm')
model_load8bit_checkbox = gr.components.Checkbox(
label="Load 8-bit [requires support]",
value=kwargs['load_8bit'])
model_infer_devices_checkbox = gr.components.Checkbox(
label="Choose Devices [If not Checked, use all GPUs]",
value=kwargs['infer_devices'])
model_gpu = gr.Dropdown(n_gpus_list,
label="GPU ID [-1 = all GPUs, if Choose is enabled]",
value=kwargs['gpu_id'])
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)
prompt_dict = gr.Textbox(label="Prompt (or Custom)",
value=pprint.pformat(kwargs['prompt_dict'], indent=4),
interactive=True, lines=4)
col_model2 = gr.Column(visible=False)
with col_model2:
with gr.Row():
with gr.Column(scale=20):
model_choice2 = gr.Dropdown(model_options_state.value[0], label="Choose Model 2",
value=no_model_str)
lora_choice2 = gr.Dropdown(lora_options_state.value[0], label="Choose LORA 2",
value=no_lora_str,
visible=kwargs['show_lora'])
with gr.Column(scale=1):
load_model_button2 = gr.Button(load_msg2).style(full_width=False, size='sm')
model_load8bit_checkbox2 = gr.components.Checkbox(
label="Load 8-bit 2 [requires support]",
value=kwargs['load_8bit'])
model_infer_devices_checkbox2 = gr.components.Checkbox(
label="Choose Devices 2 [If not Checked, use all GPUs]",
value=kwargs[
'infer_devices'])
model_gpu2 = gr.Dropdown(n_gpus_list,
label="GPU ID 2 [-1 = all GPUs, if choose is enabled]",
value=kwargs['gpu_id'])
# no model/lora loaded ever in model2 by default
model_used2 = gr.Textbox(label="Current Model 2", value=no_model_str)
lora_used2 = gr.Textbox(label="Current LORA 2", value=no_lora_str,
visible=kwargs['show_lora'])
prompt_dict2 = gr.Textbox(label="Prompt (or Custom) 2",
value=pprint.pformat(kwargs['prompt_dict'], indent=4),
interactive=True, lines=4)
with gr.Row():
with gr.Column(scale=50):
new_model = gr.Textbox(label="New Model HF name/path")
with gr.Row():
add_model_button = gr.Button("Add new model name").style(full_width=False, size='sm')
with gr.Column(scale=50):
new_lora = gr.Textbox(label="New LORA HF name/path", visible=kwargs['show_lora'])
with gr.Row():
add_lora_button = gr.Button("Add new LORA name", visible=kwargs['show_lora']).style(
full_width=False, size='sm')
with gr.TabItem("System"):
admin_row = gr.Row()
with admin_row:
admin_pass_textbox = gr.Textbox(label="Admin Password", type='password', visible=is_public)
admin_btn = gr.Button(value="Admin Access", visible=is_public)
system_row = gr.Row(visible=not is_public)
with system_row:
with gr.Column():
with gr.Row():
system_btn = gr.Button(value='Get System Info')
system_text = gr.Textbox(label='System Info', interactive=False).style(
show_copy_button=True)
with gr.Row():
zip_btn = gr.Button("Zip")
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")
s3up_text = gr.Textbox(label='S3UP result', interactive=False)
with gr.TabItem("Disclaimers"):
description = ""
description += """<p><b> DISCLAIMERS: </b><ul><i><li>The model was trained on The Pile and other data, which may contain objectionable content. Use at own risk.</i></li>"""
if kwargs['load_8bit']:
description += """<i><li> Model is loaded in 8-bit and has other restrictions on this host. UX can be worse than non-hosted version.</i></li>"""
description += """<i><li>Conversations may be used to improve h2oGPT. Do not share sensitive information.</i></li>"""
if 'h2ogpt-research' in kwargs['base_model']:
description += """<i><li>Research demonstration only, not used for commercial purposes.</i></li>"""
description += """<i><li>By using h2oGPT, you accept our <a href="https://github.com/h2oai/h2ogpt/blob/main/docs/tos.md">Terms of Service</a></i></li></ul></p>"""
gr.Markdown(value=description, show_label=False, interactive=False)
# Get flagged data
zip_data1 = functools.partial(zip_data, root_dirs=['flagged_data_points', kwargs['save_dir']])
zip_btn.click(zip_data1, inputs=None, outputs=[file_output, zip_text], queue=False,
api_name='zip_data' if allow_api else None)
s3up_btn.click(s3up, inputs=zip_text, outputs=s3up_text, queue=False,
api_name='s3up_data' if allow_api else None)
def make_add_visible(x):
return gr.update(visible=x is not None)
def clear_file_list():
return None
def make_invisible():
return gr.update(visible=False)
def make_visible():
return gr.update(visible=True)
def update_radio_to_user():
return gr.update(value='UserData')
# Add to UserData
update_user_db_func = functools.partial(update_user_db,
dbs=dbs, db_type=db_type, langchain_mode='UserData',
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
enable_captions=enable_captions,
captions_model=captions_model,
enable_ocr=enable_ocr,
caption_loader=caption_loader,
verbose=kwargs['verbose'],
)
# note for update_user_db_func output is ignored for db
add_to_shared_db_btn.click(update_user_db_func,
inputs=[fileup_output, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
chunk, chunk_size],
outputs=[add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
api_name='add_to_shared' if allow_api and allow_upload_to_user_data else None) \
.then(clear_file_list, outputs=fileup_output, queue=queue) \
.then(update_radio_to_user, inputs=None, outputs=langchain_mode, queue=False)
# .then(make_invisible, outputs=add_to_shared_db_btn, queue=queue)
# .then(make_visible, outputs=upload_button, queue=queue)
def clear_textbox():
return gr.Textbox.update(value='')
update_user_db_url_func = functools.partial(update_user_db_func, is_url=True)
url_user_btn.click(update_user_db_url_func,
inputs=[url_text, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
chunk, chunk_size],
outputs=[add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
api_name='add_url_to_shared' if allow_api and allow_upload_to_user_data else None) \
.then(clear_textbox, outputs=url_text, queue=queue) \
.then(update_radio_to_user, inputs=None, outputs=langchain_mode, queue=False)
update_user_db_txt_func = functools.partial(update_user_db_func, is_txt=True)
user_text_user_btn.click(update_user_db_txt_func,
inputs=[user_text_text, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
chunk, chunk_size],
outputs=[add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
api_name='add_text_to_shared' if allow_api and allow_upload_to_user_data else None) \
.then(clear_textbox, outputs=user_text_text, queue=queue) \
.then(update_radio_to_user, inputs=None, outputs=langchain_mode, queue=False)
# Add to MyData
def update_radio_to_my():
return gr.update(value='MyData')
update_my_db_func = functools.partial(update_user_db, dbs=dbs, db_type=db_type, langchain_mode='MyData',
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model,
enable_captions=enable_captions,
captions_model=captions_model,
enable_ocr=enable_ocr,
caption_loader=caption_loader,
verbose=kwargs['verbose'],
)
add_to_my_db_btn.click(update_my_db_func,
inputs=[fileup_output, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
chunk, chunk_size],
outputs=[my_db_state, add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
api_name='add_to_my' if allow_api and allow_upload_to_my_data else None) \
.then(clear_file_list, outputs=fileup_output, queue=queue) \
.then(update_radio_to_my, inputs=None, outputs=langchain_mode, queue=False)
# .then(make_invisible, outputs=add_to_shared_db_btn, queue=queue)
# .then(make_visible, outputs=upload_button, queue=queue)
update_my_db_url_func = functools.partial(update_my_db_func, is_url=True)
url_my_btn.click(update_my_db_url_func,
inputs=[url_text, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
chunk, chunk_size],
outputs=[my_db_state, add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
api_name='add_url_to_my' if allow_api and allow_upload_to_my_data else None) \
.then(clear_textbox, outputs=url_text, queue=queue) \
.then(update_radio_to_my, inputs=None, outputs=langchain_mode, queue=False)
update_my_db_txt_func = functools.partial(update_my_db_func, is_txt=True)
user_text_my_btn.click(update_my_db_txt_func,
inputs=[user_text_text, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
chunk, chunk_size],
outputs=[my_db_state, add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
api_name='add_txt_to_my' if allow_api and allow_upload_to_my_data else None) \
.then(clear_textbox, outputs=user_text_text, queue=queue) \
.then(update_radio_to_my, inputs=None, outputs=langchain_mode, queue=False)
get_sources1 = functools.partial(get_sources, dbs=dbs, docs_state0=docs_state0)
# if change collection source, must clear doc selections from it to avoid inconsistency
def clear_doc_choice():
return gr.Dropdown.update(choices=docs_state0, value=[docs_state0[0]])
langchain_mode.change(clear_doc_choice, inputs=None, outputs=document_choice)
def update_dropdown(x):
return gr.Dropdown.update(choices=x, value=[docs_state0[0]])
get_sources_btn.click(get_sources1, inputs=[my_db_state, langchain_mode], outputs=[file_source, docs_state],
queue=queue,
api_name='get_sources' if allow_api else None) \
.then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
# show button, else only show when add. Could add to above get_sources for download/dropdown, but bit much maybe
show_sources1 = functools.partial(get_source_files_given_langchain_mode, dbs=dbs)
show_sources_btn.click(fn=show_sources1, inputs=[my_db_state, langchain_mode], outputs=sources_text,
api_name='show_sources' if allow_api else None)
# Get inputs to evaluate() and make_db()
# don't deepcopy, can contain model itself
all_kwargs = kwargs.copy()
all_kwargs.update(locals())
refresh_sources1 = functools.partial(update_and_get_source_files_given_langchain_mode,
**get_kwargs(update_and_get_source_files_given_langchain_mode,
exclude_names=['db1', 'langchain_mode'],
**all_kwargs))
refresh_sources_btn.click(fn=refresh_sources1, inputs=[my_db_state, langchain_mode], outputs=sources_text,
api_name='refresh_sources' if allow_api else None)
def check_admin_pass(x):
return gr.update(visible=x == admin_pass)
def close_admin(x):
return gr.update(visible=not (x == admin_pass))
admin_btn.click(check_admin_pass, inputs=admin_pass_textbox, outputs=system_row, queue=False) \
.then(close_admin, inputs=admin_pass_textbox, outputs=admin_row, queue=False)
inputs_list, inputs_dict = get_inputs_list(all_kwargs, kwargs['model_lower'], model_id=1)
inputs_list2, inputs_dict2 = get_inputs_list(all_kwargs, kwargs['model_lower'], model_id=2)
from functools import partial
kwargs_evaluate = {k: v for k, v in all_kwargs.items() if k in inputs_kwargs_list}
# ensure present
for k in inputs_kwargs_list:
assert k in kwargs_evaluate, "Missing %s" % k
def evaluate_gradio(*args1, **kwargs1):
for res_dict in evaluate(*args1, **kwargs1):
yield '<br>' + fix_newlines(res_dict['response'])
fun = partial(evaluate_gradio,
**kwargs_evaluate)
fun2 = partial(evaluate_gradio,
**kwargs_evaluate)
fun_with_dict_str = partial(evaluate_from_str,
default_kwargs=default_kwargs,
**kwargs_evaluate
)
dark_mode_btn = gr.Button("Dark Mode", variant="primary").style(
size="sm",
)
# FIXME: Could add exceptions for non-chat but still streaming
exception_text = gr.Textbox(value="", visible=kwargs['chat'], label='Chat Exceptions', interactive=False)
dark_mode_btn.click(
None,
None,
None,
_js=get_dark_js(),
api_name="dark" if allow_api else None,
queue=False,
)
# Control chat and non-chat blocks, which can be independently used by chat checkbox swap
def col_nochat_fun(x):
return gr.Column.update(visible=not x)
def col_chat_fun(x):
return gr.Column.update(visible=x)
def context_fun(x):
return gr.Textbox.update(visible=not x)
chat.select(col_nochat_fun, chat, col_nochat, api_name="chat_checkbox" if allow_api else None) \
.then(col_chat_fun, chat, col_chat) \
.then(context_fun, chat, context) \
.then(col_chat_fun, chat, exception_text)
# 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, model2=False):
""" Similar to user() """
args_list = list(args)
if memory_restriction_level > 0:
max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256
else:
max_length_tokenize = 2048 - 256
cutoff_len = max_length_tokenize * 4 # restrict deberta related to max for LLM
smodel = score_model_state0[0]
stokenizer = score_model_state0[1]
sdevice = score_model_state0[2]
if not nochat:
history = args_list[-1]
if history is None:
if not model2:
# maybe only doing first model, no need to complain
print("Bad history in scoring last response, fix for now", flush=True)
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 'Response Score: NA'
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 'Response Score: Bad Question'
if answer is None:
return 'Response Score: Bad Answer'
try:
score = score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len)
finally:
clear_torch_cache()
if isinstance(score, str):
return 'Response Score: NA'
return 'Response Score: {:.1%}'.format(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, model2=True),
inputs=inputs_list2 + [text_output2],
outputs=[score_text2],
)
score_args_nochat = dict(fn=partial(score_fun, nochat=True),
inputs=inputs_list + [text_output_nochat],
outputs=[score_text_nochat],
)
if not kwargs['auto_score']:
score_event = score_btn.click(**score_args, queue=queue, api_name='score' if allow_api else None) \
.then(**score_args2, queue=queue, api_name='score2' if allow_api else None) \
.then(clear_torch_cache)
score_event_nochat = score_btn_nochat.click(**score_args_nochat, queue=queue,
api_name='score_nochat' if allow_api else None) \
.then(clear_torch_cache)
def user(*args, undo=False, sanitize_user_prompt=True, model2=False):
"""
User that fills history for bot
:param args:
:param undo:
:param sanitize_user_prompt:
:param model2:
: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
context1 = args_list[eval_func_param_names.index('context')]
prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
prompt_dict1 = args_list[eval_func_param_names.index('prompt_dict')]
chat1 = args_list[eval_func_param_names.index('chat')]
stream_output1 = args_list[eval_func_param_names.index('stream_output')]
if input1 and not user_message.endswith(':'):
user_message1 = user_message + ":" + input1
elif input1:
user_message1 = user_message + input1
else:
user_message1 = user_message
if sanitize_user_prompt:
from better_profanity import profanity
user_message1 = profanity.censor(user_message1)
# FIXME: WIP to use desired seperator when user enters nothing
prompter = Prompter(prompt_type1, prompt_dict1, debug=kwargs['debug'], chat=chat1,
stream_output=stream_output1)
if user_message1 in ['']:
# e.g. when user just hits enter in textbox,
# else will have <human>: <bot>: on single line, which seems to be "ok" for LLM but not usual
user_message1 = '\n'
user_message1 = fix_newlines(user_message1)
history = args_list[-1]
if undo and history:
history.pop()
args_list = args_list[:-1] # FYI, even if unused currently
if history is None:
if not model2:
# no need to complain so often unless model1
print("Bad history, fix for now", flush=True)
history = []
# ensure elements not mixed across models as output,
# even if input is currently same source
history = history.copy()
if undo:
return history
else:
# FIXME: compare, same history for now
return history + [[user_message1, None]]
def history_to_context(history, langchain_mode1, prompt_type1, prompt_dict1, chat1, model_max_length1):
# ensure output will be unique to models
_, _, _, max_prompt_length = get_cutoffs(memory_restriction_level,
for_context=True, model_max_length=model_max_length1)
history = copy.deepcopy(history)
context1 = ''
if max_prompt_length is not None and langchain_mode1 not in ['LLM']:
context1 = ''
# - 1 below because current instruction already in history from user()
for histi in range(0, len(history) - 1):
data_point = dict(instruction=history[histi][0], input='', output=history[histi][1])
prompt, pre_response, terminate_response, chat_sep = generate_prompt(data_point,
prompt_type1,
prompt_dict1,
chat1, reduced=True)
# md -> back to text, maybe not super important if model trained enough
if not kwargs['keep_sources_in_context']:
from gpt_langchain import source_prefix, source_postfix
import re
prompt = re.sub(f'{re.escape(source_prefix)}.*?{re.escape(source_postfix)}', '', prompt,
flags=re.DOTALL)
if prompt.endswith('\n<p>'):
prompt = prompt[:-4]
prompt = prompt.replace('<br>', chat_sep)
if not prompt.endswith(chat_sep):
prompt += chat_sep
# most recent first, add older if can
# only include desired chat history
if len(prompt + context1) > max_prompt_length:
break
context1 = prompt + context1
_, pre_response, terminate_response, chat_sep = generate_prompt({}, prompt_type1, prompt_dict1,
chat1, reduced=True)
if context1 and not context1.endswith(chat_sep):
context1 += chat_sep # ensure if terminates abruptly, then human continues on next line
return context1
def get_model_max_length(model_state1):
if model_state1 and not isinstance(model_state1[1], str):
tokenizer = model_state1[1]
elif model_state0 and not isinstance(model_state0[1], str):
tokenizer = model_state0[1]
else:
tokenizer = None
if tokenizer is not None:
return tokenizer.model_max_length
else:
return 2000
def bot(*args, retry=False):
"""
bot that consumes history for user input
instruction (from input_list) itself is not consumed by bot
:param args:
:param retry:
:return:
"""
# don't deepcopy, can contain model itself
args_list = list(args).copy()
model_state1 = args_list[-3]
my_db_state1 = args_list[-2]
history = args_list[-1]
if model_state1[0] is None or model_state1[0] == no_model_str:
history = []
yield history, ''
return
args_list = args_list[:-3] # only keep rest needed for evaluate()
langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
if retry and history:
history.pop()
if not args_list[eval_func_param_names.index('do_sample')]:
# if was not sampling, no point in retry unless change to sample
args_list[eval_func_param_names.index('do_sample')] = True
if not history:
print("No history", flush=True)
history = []
yield history, ''
return
instruction1 = history[-1][0]
if not instruction1:
# reject empty query, can sometimes go nuts
history = []
yield history, ''
return
prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
prompt_dict1 = args_list[eval_func_param_names.index('prompt_dict')]
chat1 = args_list[eval_func_param_names.index('chat')]
model_max_length1 = get_model_max_length(model_state1)
context1 = history_to_context(history, langchain_mode1, prompt_type1, prompt_dict1, chat1,
model_max_length1)
args_list[0] = instruction1 # override original instruction with history from user
args_list[2] = context1
fun1 = partial(evaluate,
model_state1,
my_db_state1,
**kwargs_evaluate)
try:
for output_fun in fun1(*tuple(args_list)):
output = output_fun['response']
extra = output_fun['sources'] # FIXME: can show sources in separate text box etc.
# ensure good visually, else markdown ignores multiple \n
bot_message = fix_newlines(output)
history[-1][1] = bot_message
yield history, ''
except StopIteration:
yield history, ''
except RuntimeError as e:
if "generator raised StopIteration" in str(e):
# assume last entry was bad, undo
history.pop()
yield history, ''
else:
if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None:
history[-1][1] = ''
yield history, str(e)
raise
except Exception as e:
# put error into user input
ex = "Exception: %s" % str(e)
if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None:
history[-1][1] = ''
yield history, ex
raise
finally:
clear_torch_cache()
return
# 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] + [text_output],
outputs=[text_output, exception_text],
)
retry_bot_args = dict(fn=functools.partial(bot, retry=True),
inputs=inputs_list + [model_state, my_db_state] + [text_output],
outputs=[text_output, exception_text],
)
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'], model2=True),
inputs=inputs_list2 + [text_output2],
outputs=text_output2,
)
bot_args2 = dict(fn=bot,
inputs=inputs_list2 + [model_state2, my_db_state] + [text_output2],
outputs=[text_output2, exception_text],
)
retry_bot_args2 = dict(fn=functools.partial(bot, retry=True),
inputs=inputs_list2 + [model_state2, my_db_state] + [text_output2],
outputs=[text_output2, exception_text],
)
undo_user_args2 = dict(fn=functools.partial(user, undo=True),
inputs=inputs_list2 + [text_output2],
outputs=text_output2,
)
def clear_instruct():
return gr.Textbox.update(value='')
if kwargs['auto_score']:
score_args_submit = score_args
score_args2_submit = score_args2
else:
score_args_submit = dict(fn=lambda: None, inputs=None, outputs=None)
score_args2_submit = dict(fn=lambda: None, inputs=None, outputs=None)
def deselect_radio_chats():
return gr.update(value=None)
# 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_event1a = instruction.submit(**user_args, queue=queue,
api_name='instruction' if allow_api else None)
submit_event1b = submit_event1a.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_event1d2 = submit_event1d.then(clear_torch_cache)
submit_event1e = submit_event1d2.then(**score_args_submit,
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_event1f2 = submit_event1f.then(clear_torch_cache)
submit_event1g = submit_event1f2.then(**score_args2_submit,
api_name='instruction_bot_score2' if allow_api else None, queue=queue)
submit_event1h = submit_event1g.then(clear_torch_cache)
# if hit enter on new instruction for submitting new query, no longer the saved chat
submit_event1i = submit_event1h.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False)
submit_event2a = submit.click(**user_args, api_name='submit' if allow_api else None)
submit_event2b = submit_event2a.then(**user_args2, api_name='submit2' if allow_api else None)
submit_event2c = submit_event2b.then(clear_instruct, None, instruction) \
.then(clear_instruct, None, iinput)
submit_event2d = submit_event2c.then(**bot_args, api_name='submit_bot' if allow_api else None, queue=queue)
submit_event2d2 = submit_event2d.then(clear_torch_cache)
submit_event2e = submit_event2d2.then(**score_args_submit, 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_event2f2 = submit_event2f.then(clear_torch_cache)
submit_event2g = submit_event2f2.then(**score_args2_submit, api_name='submit_bot_score2' if allow_api else None,
queue=queue)
submit_event2h = submit_event2g.then(clear_torch_cache)
# if submit new query, no longer the saved chat
submit_event2i = submit_event2h.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False)
submit_event3a = retry.click(**user_args, api_name='retry' if allow_api else None)
submit_event3b = submit_event3a.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_event3d2 = submit_event3d.then(clear_torch_cache)
submit_event3e = submit_event3d2.then(**score_args_submit, 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_event3f2 = submit_event3f.then(clear_torch_cache)
submit_event3g = submit_event3f2.then(**score_args2_submit, api_name='retry_bot_score2' if allow_api else None,
queue=queue)
submit_event3h = submit_event3g.then(clear_torch_cache)
# if retry, no longer the saved chat
submit_event3i = submit_event3h.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False)
# if undo, no longer the saved chat
submit_event4 = undo.click(**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_instruct, None, instruction) \
.then(clear_instruct, None, iinput) \
.then(**score_args_submit, api_name='undo_score' if allow_api else None) \
.then(**score_args2_submit, api_name='undo_score2' if allow_api else None) \
.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False) \
.then(clear_torch_cache)
# 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()
short_chat = dedup(short_chat, short_chats)
else:
short_chat = None
return short_chat
def is_chat_same(x, y):
# <p> etc. added in chat, try to remove some of that to help avoid dup entries when hit new conversation
is_same = True
# length of conversation has to be same
if len(x) != len(y):
return False
for stepx, stepy in zip(x, y):
if len(stepx) != len(stepy):
# something off with a conversation
return False
if len(stepx) != 2:
# something off
return False
if len(stepy) != 2:
# something off
return False
questionx = stepx[0].replace('<p>', '').replace('</p>', '') if stepx[0] is not None else None
answerx = stepx[1].replace('<p>', '').replace('</p>', '') if stepx[1] is not None else None
questiony = stepy[0].replace('<p>', '').replace('</p>', '') if stepy[0] is not None else None
answery = stepy[1].replace('<p>', '').replace('</p>', '') if stepy[1] is not None else None
if questionx != questiony or answerx != answery:
return False
return is_same
def save_chat(chat1, chat2, chat_state1):
short_chats = list(chat_state1.keys())
for chati in [chat1, chat2]:
if chati and len(chati) > 0 and len(chati[0]) == 2 and chati[0][1] is not None:
short_chat = get_short_chat(chati, short_chats)
if short_chat:
already_exists = any([is_chat_same(chati, x) for x in chat_state1.values()])
if not already_exists:
chat_state1[short_chat] = chati
return chat_state1
def update_radio_chats(chat_state1):
return gr.update(choices=list(chat_state1.keys()), value=None)
def switch_chat(chat_key, chat_state1):
chosen_chat = chat_state1[chat_key]
return chosen_chat, chosen_chat
radio_chats.input(switch_chat, inputs=[radio_chats, chat_state], outputs=[text_output, text_output2])
def remove_chat(chat_key, chat_state1):
chat_state1.pop(chat_key, None)
return chat_state1
remove_chat_btn.click(remove_chat, inputs=[radio_chats, chat_state], outputs=chat_state) \
.then(update_radio_chats, inputs=chat_state, outputs=radio_chats)
def get_chats1(chat_state1):
base = 'chats'
makedirs(base, exist_ok=True)
filename = os.path.join(base, 'chats_%s.json' % str(uuid.uuid4()))
with open(filename, "wt") as f:
f.write(json.dumps(chat_state1, indent=2))
return filename
export_chats_btn.click(get_chats1, inputs=chat_state, outputs=chats_file, queue=False,
api_name='export_chats' if allow_api else None)
def add_chats_from_file(file, chat_state1, add_btn):
if not file:
return chat_state1, add_btn
if isinstance(file, str):
files = [file]
else:
files = file
if not files:
return chat_state1, add_btn
for file1 in files:
try:
if hasattr(file1, 'name'):
file1 = file1.name
with open(file1, "rt") as f:
new_chats = json.loads(f.read())
for chat1_k, chat1_v in new_chats.items():
# ignore chat1_k, regenerate and de-dup to avoid loss
chat_state1 = save_chat(chat1_v, None, chat_state1)
except BaseException as e:
print("Add chats exception: %s" % str(e), flush=True)
return chat_state1, add_btn
# note for update_user_db_func output is ignored for db
add_to_chats_btn.click(add_chats_from_file,
inputs=[chatsup_output, chat_state, add_to_chats_btn],
outputs=[chat_state, add_to_my_db_btn], queue=False,
api_name='add_to_chats' if allow_api else None) \
.then(clear_file_list, outputs=chatsup_output, queue=False) \
.then(update_radio_chats, inputs=chat_state, outputs=radio_chats, queue=False)
clear_chat_btn.click(lambda: None, None, text_output, queue=False, api_name='clear' if allow_api else None) \
.then(lambda: None, None, text_output2, queue=False, api_name='clear2' if allow_api else None) \
.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False)
# does both models
clear.click(save_chat, inputs=[text_output, text_output2, chat_state], outputs=chat_state,
api_name='save_chat' if allow_api else None) \
.then(update_radio_chats, inputs=chat_state, outputs=radio_chats,
api_name='update_chats' if allow_api else None) \
.then(lambda: None, None, text_output, queue=False, api_name='clearB' if allow_api else None) \
.then(lambda: None, None, text_output2, queue=False, api_name='clearB2' if allow_api else None)
# 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] + inputs_list,
outputs=text_output_nochat,
queue=queue,
)
submit_event_nochat = submit_nochat.click(**no_chat_args, api_name='submit_nochat' if allow_api else None) \
.then(clear_torch_cache) \
.then(**score_args_nochat, api_name='instruction_bot_score_nochat' if allow_api else None, queue=queue) \
.then(clear_instruct, None, instruction_nochat) \
.then(clear_instruct, None, iinput_nochat) \
.then(clear_torch_cache)
# copy of above with text box submission
submit_event_nochat2 = instruction_nochat.submit(**no_chat_args) \
.then(clear_torch_cache) \
.then(**score_args_nochat, queue=queue) \
.then(clear_instruct, None, instruction_nochat) \
.then(clear_instruct, None, iinput_nochat) \
.then(clear_torch_cache)
submit_event_nochat_api = submit_nochat_api.click(fun_with_dict_str,
inputs=[model_state, my_db_state, inputs_dict_str],
outputs=text_output_nochat_api,
queue=True, # required for generator
api_name='submit_nochat_api' if allow_api else None) \
.then(clear_torch_cache)
def load_model(model_name, lora_weights, model_state_old, prompt_type_old, load_8bit, infer_devices, gpu_id):
# 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[0]
if isinstance(model_state_old[0], str) and model0 is not None:
# best can do, move model loaded at first to CPU
model0.cpu()
if model_state_old[0] is not None and not isinstance(model_state_old[0], str):
try:
model_state_old[0].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[0]
model_state_old[0] = None
if model_state_old[1] is not None and not isinstance(model_state_old[1], str):
del model_state_old[1]
model_state_old[1] = None
clear_torch_cache()
if kwargs['debug']:
print("Pre-switch post-del GPU memory: %s" % get_torch_allocated(), flush=True)
if model_name is None or model_name == no_model_str:
# no-op if no model, just free memory
# no detranscribe needed for model, never go into evaluate
lora_weights = no_lora_str
return [None, None, None, model_name], model_name, lora_weights, prompt_type_old
# 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['infer_devices'] = infer_devices
all_kwargs1['gpu_id'] = int(gpu_id) # detranscribe
model_lower = model_name.strip().lower()
if model_lower in inv_prompt_type_to_model_lower:
prompt_type1 = inv_prompt_type_to_model_lower[model_lower]
else:
prompt_type1 = prompt_type_old
# detranscribe
if lora_weights == no_lora_str:
lora_weights = ''
all_kwargs1['lora_weights'] = lora_weights.strip()
model1, tokenizer1, device1 = get_model(reward_type=False,
**get_kwargs(get_model, exclude_names=['reward_type'],
**all_kwargs1))
clear_torch_cache()
model_state_new = [model1, tokenizer1, device1, model_name]
max_max_new_tokens1 = get_max_max_new_tokens(model_state_new, **kwargs)
if kwargs['debug']:
print("Post-switch GPU memory: %s" % get_torch_allocated(), flush=True)
return model_state_new, model_name, lora_weights, prompt_type1, \
gr.Slider.update(maximum=max_max_new_tokens1), \
gr.Slider.update(maximum=max_max_new_tokens1)
def get_prompt_str(prompt_type1, prompt_dict1):
prompt_dict1, prompt_dict_error = get_prompt(prompt_type1, prompt_dict1, chat=False, context='',
reduced=False, return_dict=True)
if prompt_dict_error:
return str(prompt_dict_error)
else:
# return so user can manipulate if want and use as custom
return str(prompt_dict1)
prompt_type.change(fn=get_prompt_str, inputs=[prompt_type, prompt_dict], outputs=prompt_dict)
prompt_type2.change(fn=get_prompt_str, inputs=[prompt_type2, prompt_dict2], outputs=prompt_dict2)
def dropdown_prompt_type_list(x):
return gr.Dropdown.update(value=x)
def chatbot_list(x, model_used_in):
return gr.Textbox.update(label=f'h2oGPT [Model: {model_used_in}]')
load_model_args = dict(fn=load_model,
inputs=[model_choice, lora_choice, model_state, prompt_type,
model_load8bit_checkbox, model_infer_devices_checkbox, model_gpu],
outputs=[model_state, model_used, lora_used,
# if prompt_type changes, prompt_dict will change via change rule
prompt_type, max_new_tokens, min_new_tokens,
])
prompt_update_args = dict(fn=dropdown_prompt_type_list, inputs=prompt_type, outputs=prompt_type)
chatbot_update_args = dict(fn=chatbot_list, inputs=[text_output, model_used], outputs=text_output)
nochat_update_args = dict(fn=chatbot_list, inputs=[text_output_nochat, model_used], outputs=text_output_nochat)
if not is_public:
load_model_event = load_model_button.click(**load_model_args, api_name='load_model' if allow_api else None) \
.then(**prompt_update_args) \
.then(**chatbot_update_args) \
.then(**nochat_update_args) \
.then(clear_torch_cache)
load_model_args2 = dict(fn=load_model,
inputs=[model_choice2, lora_choice2, model_state2, prompt_type2,
model_load8bit_checkbox2, model_infer_devices_checkbox2, model_gpu2],
outputs=[model_state2, model_used2, lora_used2,
# if prompt_type2 changes, prompt_dict2 will change via change rule
prompt_type2, max_new_tokens2, min_new_tokens2
])
prompt_update_args2 = dict(fn=dropdown_prompt_type_list, inputs=prompt_type2, outputs=prompt_type2)
chatbot_update_args2 = dict(fn=chatbot_list, inputs=[text_output2, model_used2], outputs=text_output2)
if not is_public:
load_model_event2 = load_model_button2.click(**load_model_args2,
api_name='load_model2' if allow_api else None) \
.then(**prompt_update_args2) \
.then(**chatbot_update_args2) \
.then(clear_torch_cache)
def dropdown_model_list(list0, x):
new_state = [list0[0] + [x]]
new_options = [*new_state[0]]
return gr.Dropdown.update(value=x, choices=new_options), \
gr.Dropdown.update(value=x, choices=new_options), \
'', new_state
add_model_event = add_model_button.click(fn=dropdown_model_list,
inputs=[model_options_state, new_model],
outputs=[model_choice, model_choice2, new_model, model_options_state],
queue=False)
def dropdown_lora_list(list0, x, model_used1, lora_used1, model_used2, lora_used2):
new_state = [list0[0] + [x]]
new_options = [*new_state[0]]
# don't switch drop-down to added lora if already have model loaded
x1 = x if model_used1 == no_model_str else lora_used1
x2 = x if model_used2 == no_model_str else lora_used2
return gr.Dropdown.update(value=x1, choices=new_options), \
gr.Dropdown.update(value=x2, choices=new_options), \
'', new_state
add_lora_event = add_lora_button.click(fn=dropdown_lora_list,
inputs=[lora_options_state, new_lora, model_used, lora_used, model_used2,
lora_used2],
outputs=[lora_choice, lora_choice2, new_lora, lora_options_state],
queue=False)
go_btn.click(lambda: gr.update(visible=False), None, go_btn, api_name="go" if allow_api else None, queue=False) \
.then(lambda: gr.update(visible=True), None, normal_block, queue=False) \
.then(**load_model_args, queue=False).then(**prompt_update_args, queue=False)
def compare_textbox_fun(x):
return gr.Textbox.update(visible=x)
def compare_column_fun(x):
return gr.Column.update(visible=x)
def compare_prompt_fun(x):
return gr.Dropdown.update(visible=x)
def slider_fun(x):
return gr.Slider.update(visible=x)
compare_checkbox.select(compare_textbox_fun, compare_checkbox, text_output2,
api_name="compare_checkbox" if allow_api else None) \
.then(compare_column_fun, compare_checkbox, col_model2) \
.then(compare_prompt_fun, compare_checkbox, prompt_type2) \
.then(compare_textbox_fun, compare_checkbox, score_text2) \
.then(slider_fun, compare_checkbox, max_new_tokens2) \
.then(slider_fun, compare_checkbox, min_new_tokens2)
# FIXME: add score_res2 in condition, but do better
# callback for logging flagged input/output
callback.setup(inputs_list + [text_output, text_output2], "flagged_data_points")
flag_btn.click(lambda *args: callback.flag(args), inputs_list + [text_output, text_output2], None,
preprocess=False,
api_name='flag' if allow_api else None, queue=False)
flag_btn_nochat.click(lambda *args: callback.flag(args), inputs_list + [text_output_nochat], None,
preprocess=False,
api_name='flag_nochat' if allow_api else None, queue=False)
def get_system_info():
return gr.Textbox.update(value=system_info_print())
system_event = system_btn.click(get_system_info, outputs=system_text,
api_name='system_info' if allow_api else None, queue=False)
# don't pass text_output, don't want to clear output, just stop it
# cancel only stops outer generation, not inner generation or non-generation
stop_btn.click(lambda: None, None, None,
cancels=[submit_event1d, submit_event1f,
submit_event2d, submit_event2f,
submit_event3d, submit_event3f,
submit_event_nochat,
submit_event_nochat2,
],
queue=False, api_name='stop' if allow_api else None).then(clear_torch_cache, queue=False)
def count_chat_tokens(model_state1, chat1, prompt_type1, prompt_dict1):
if model_state1 and not isinstance(model_state1[1], str):
tokenizer = model_state1[1]
elif model_state0 and not isinstance(model_state0[1], str):
tokenizer = model_state0[1]
else:
tokenizer = None
if tokenizer is not None:
langchain_mode1 = 'ChatLLM'
# fake user message to mimic bot()
chat1 = copy.deepcopy(chat1)
chat1 = chat1 + [['user_message1', None]]
model_max_length1 = tokenizer.model_max_length
context1 = history_to_context(chat1, langchain_mode1, prompt_type1, prompt_dict1, chat1,
model_max_length1)
return str(tokenizer(context1, return_tensors="pt")['input_ids'].shape[1])
else:
return "N/A"
count_chat_tokens_btn.click(fn=count_chat_tokens, inputs=[model_state, text_output, prompt_type, prompt_dict],
outputs=chat_token_count, api_name='count_tokens' if allow_api else None)
demo.load(None, None, None, _js=get_dark_js() if kwargs['h2ocolors'] else None)
demo.queue(concurrency_count=kwargs['concurrency_count'], api_open=kwargs['api_open'])
favicon_path = "h2o-logo.svg"
scheduler = BackgroundScheduler()
scheduler.add_job(func=clear_torch_cache, trigger="interval", seconds=20)
if is_public and \
kwargs['base_model'] not in non_hf_types:
# FIXME: disable for gptj, langchain or gpt4all modify print itself
# FIXME: and any multi-threaded/async print will enter model output!
scheduler.add_job(func=ping, trigger="interval", seconds=60)
scheduler.start()
# import control
if kwargs['langchain_mode'] == 'Disabled' and \
os.environ.get("TEST_LANGCHAIN_IMPORT") and \
kwargs['base_model'] not in non_hf_types:
assert 'gpt_langchain' not in sys.modules, "Dev bug, import of langchain when should not have"
assert 'langchain' not in sys.modules, "Dev bug, import of langchain when should not have"
demo.launch(share=kwargs['share'], server_name="0.0.0.0", show_error=True,
favicon_path=favicon_path, prevent_thread_lock=True,
auth=kwargs['auth'])
if kwargs['verbose']:
print("Started GUI", flush=True)
if kwargs['block_gradio_exit']:
demo.block_thread()
input_args_list = ['model_state', 'my_db_state']
def get_inputs_list(inputs_dict, model_lower, model_id=1):
"""
map gradio objects in locals() to inputs for evaluate().
:param inputs_dict:
:param model_lower:
:param model_id: Which model (1 or 2) of 2
:return:
"""
inputs_list_names = list(inspect.signature(evaluate).parameters)
inputs_list = []
inputs_dict_out = {}
for k in inputs_list_names:
if k == 'kwargs':
continue
if k in input_args_list + inputs_kwargs_list:
# these are added at use time for args or partial for kwargs, not taken as input
continue
if 'mbart-' not in model_lower and k in ['src_lang', 'tgt_lang']:
continue
if model_id == 2:
if k == 'prompt_type':
k = 'prompt_type2'
if k == 'prompt_used':
k = 'prompt_used2'
if k == 'max_new_tokens':
k = 'max_new_tokens2'
if k == 'min_new_tokens':
k = 'min_new_tokens2'
inputs_list.append(inputs_dict[k])
inputs_dict_out[k] = inputs_dict[k]
return inputs_list, inputs_dict_out
def get_sources(db1, langchain_mode, dbs=None, docs_state0=None):
if langchain_mode in ['ChatLLM', 'LLM']:
source_files_added = "NA"
source_list = []
elif langchain_mode in ['wiki_full']:
source_files_added = "Not showing wiki_full, takes about 20 seconds and makes 4MB file." \
" Ask jon.mckinney@h2o.ai for file if required."
source_list = []
elif langchain_mode == 'MyData' and len(db1) > 0 and db1[0] is not None:
db_get = db1[0].get()
source_list = sorted(set([x['source'] for x in db_get['metadatas']]))
source_files_added = '\n'.join(source_list)
elif langchain_mode in dbs and dbs[langchain_mode] is not None:
db1 = dbs[langchain_mode]
db_get = db1.get()
source_list = sorted(set([x['source'] for x in db_get['metadatas']]))
source_files_added = '\n'.join(source_list)
else:
source_list = []
source_files_added = "None"
sources_file = 'sources_%s_%s' % (langchain_mode, str(uuid.uuid4()))
with open(sources_file, "wt") as f:
f.write(source_files_added)
source_list = docs_state0 + source_list
return sources_file, source_list
def update_user_db(file, db1, x, y, *args, dbs=None, langchain_mode='UserData', **kwargs):
try:
return _update_user_db(file, db1, x, y, *args, dbs=dbs, langchain_mode=langchain_mode, **kwargs)
except BaseException as e:
print(traceback.format_exc(), flush=True)
# gradio has issues if except, so fail semi-gracefully, else would hang forever in processing textbox
ex_str = "Exception: %s" % str(e)
source_files_added = """\
<html>
<body>
<p>
Sources: <br>
</p>
<div style="overflow-y: auto;height:400px">
{0}
</div>
</body>
</html>
""".format(ex_str)
if langchain_mode == 'MyData':
return db1, x, y, source_files_added
else:
return x, y, source_files_added
finally:
clear_torch_cache()
def _update_user_db(file, db1, x, y, chunk, chunk_size, dbs=None, db_type=None, langchain_mode='UserData',
use_openai_embedding=None,
hf_embedding_model=None,
caption_loader=None,
enable_captions=None,
captions_model=None,
enable_ocr=None,
verbose=None,
is_url=None, is_txt=None):
assert use_openai_embedding is not None
assert hf_embedding_model is not None
assert caption_loader is not None
assert enable_captions is not None
assert captions_model is not None
assert enable_ocr is not None
assert verbose is not None
assert isinstance(dbs, dict), "Wrong type for dbs: %s" % str(type(dbs))
assert db_type in ['faiss', 'chroma'], "db_type %s not supported" % db_type
from gpt_langchain import add_to_db, get_db, path_to_docs
# handle case of list of temp buffer
if isinstance(file, list) and len(file) > 0 and hasattr(file[0], 'name'):
file = [x.name for x in file]
# handle single file of temp buffer
if hasattr(file, 'name'):
file = file.name
if verbose:
print("Adding %s" % file, flush=True)
sources = path_to_docs(file if not is_url and not is_txt else None,
verbose=verbose,
chunk=chunk, chunk_size=chunk_size,
url=file if is_url else None,
text=file if is_txt else None,
enable_captions=enable_captions,
captions_model=captions_model,
enable_ocr=enable_ocr,
caption_loader=caption_loader,
)
exceptions = [x for x in sources if x.metadata.get('exception')]
sources = [x for x in sources if 'exception' not in x.metadata]
with filelock.FileLock("db_%s.lock" % langchain_mode.replace(' ', '_')):
if langchain_mode == 'MyData':
if db1[0] is not None:
# then add
db, num_new_sources, new_sources_metadata = add_to_db(db1[0], sources, db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model)
else:
assert len(db1) == 2 and db1[1] is None, "Bad MyData db: %s" % db1
# then create
# assign fresh hash for this user session, so not shared
# if added has to original state and didn't change, then would be shared db for all users
db1[1] = str(uuid.uuid4())
persist_directory = os.path.join(scratch_base_dir, 'db_dir_%s_%s' % (langchain_mode, db1[1]))
db = get_db(sources, use_openai_embedding=use_openai_embedding,
db_type=db_type,
persist_directory=persist_directory,
langchain_mode=langchain_mode,
hf_embedding_model=hf_embedding_model)
if db is None:
db1[1] = None
else:
db1[0] = db
source_files_added = get_source_files(db=db1[0], exceptions=exceptions)
return db1, x, y, source_files_added
else:
from gpt_langchain import get_persist_directory
persist_directory = get_persist_directory(langchain_mode)
if langchain_mode in dbs and dbs[langchain_mode] is not None:
# then add
db, num_new_sources, new_sources_metadata = add_to_db(dbs[langchain_mode], sources, db_type=db_type,
use_openai_embedding=use_openai_embedding,
hf_embedding_model=hf_embedding_model)
else:
# then create
db = get_db(sources, use_openai_embedding=use_openai_embedding,
db_type=db_type,
persist_directory=persist_directory,
langchain_mode=langchain_mode,
hf_embedding_model=hf_embedding_model)
dbs[langchain_mode] = db
# NOTE we do not return db, because function call always same code path
# return dbs[langchain_mode], x, y
# db in this code path is updated in place
source_files_added = get_source_files(db=dbs[langchain_mode], exceptions=exceptions)
return x, y, source_files_added
def get_db(db1, langchain_mode, dbs=None):
with filelock.FileLock("db_%s.lock" % langchain_mode.replace(' ', '_')):
if langchain_mode in ['wiki_full']:
# NOTE: avoid showing full wiki. Takes about 30 seconds over about 90k entries, but not useful for now
db = None
elif langchain_mode == 'MyData' and len(db1) > 0 and db1[0] is not None:
db = db1[0]
elif langchain_mode in dbs and dbs[langchain_mode] is not None:
db = dbs[langchain_mode]
else:
db = None
return db
def get_source_files_given_langchain_mode(db1, langchain_mode='UserData', dbs=None):
db = get_db(db1, langchain_mode, dbs=dbs)
if langchain_mode in ['ChatLLM', 'LLM'] or db is None:
return "Sources: N/A"
return get_source_files(db=db, exceptions=None)
def get_source_files(db=None, exceptions=None, metadatas=None):
if exceptions is None:
exceptions = []
# only should be one source, not confused
assert db is not None or metadatas is not None
if metadatas is None:
source_label = "Sources:"
if db is not None:
metadatas = db.get()['metadatas']
else:
metadatas = []
adding_new = False
else:
source_label = "New Sources:"
adding_new = True
# below automatically de-dups
from gpt_langchain import get_url
small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('head')) for x in
metadatas}
# if small_dict is empty dict, that's ok
df = pd.DataFrame(small_dict.items(), columns=['source', 'head'])
df.index = df.index + 1
df.index.name = 'index'
source_files_added = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
if exceptions:
exception_metadatas = [x.metadata for x in exceptions]
small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('exception')) for x in
exception_metadatas}
# if small_dict is empty dict, that's ok
df = pd.DataFrame(small_dict.items(), columns=['source', 'exception'])
df.index = df.index + 1
df.index.name = 'index'
exceptions_html = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
else:
exceptions_html = ''
if metadatas and exceptions:
source_files_added = """\
<html>
<body>
<p>
{0} <br>
</p>
<div style="overflow-y: auto;height:400px">
{1}
{2}
</div>
</body>
</html>
""".format(source_label, source_files_added, exceptions_html)
elif metadatas:
source_files_added = """\
<html>
<body>
<p>
{0} <br>
</p>
<div style="overflow-y: auto;height:400px">
{1}
</div>
</body>
</html>
""".format(source_label, source_files_added)
elif exceptions_html:
source_files_added = """\
<html>
<body>
<p>
Exceptions: <br>
</p>
<div style="overflow-y: auto;height:400px">
{0}
</div>
</body>
</html>
""".format(exceptions_html)
else:
if adding_new:
source_files_added = "No New Sources"
else:
source_files_added = "No Sources"
return source_files_added
def update_and_get_source_files_given_langchain_mode(db1, langchain_mode, dbs=None, first_para=None,
text_limit=None, chunk=None, chunk_size=None,
user_path=None, db_type=None, load_db_if_exists=None,
n_jobs=None, verbose=None):
db = get_db(db1, langchain_mode, dbs=dbs)
from gpt_langchain import make_db
db, num_new_sources, new_sources_metadata = make_db(use_openai_embedding=False,
hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
first_para=first_para, text_limit=text_limit,
chunk=chunk,
chunk_size=chunk_size,
langchain_mode=langchain_mode,
user_path=user_path,
db_type=db_type,
load_db_if_exists=load_db_if_exists,
db=db,
n_jobs=n_jobs,
verbose=verbose)
# return only new sources with text saying such
return get_source_files(db=None, exceptions=None, metadatas=new_sources_metadata)