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| import ast | |
| import copy | |
| import functools | |
| import inspect | |
| import itertools | |
| import json | |
| import os | |
| import pprint | |
| import random | |
| import shutil | |
| import sys | |
| import time | |
| import traceback | |
| import typing | |
| import uuid | |
| import filelock | |
| import pandas as pd | |
| import requests | |
| import tabulate | |
| from iterators import TimeoutIterator | |
| from gradio_utils.css import get_css | |
| from gradio_utils.prompt_form import make_chatbots | |
| # This is a hack to prevent Gradio from phoning home when it gets imported | |
| os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False' | |
| def my_get(url, **kwargs): | |
| print('Gradio HTTP request redirected to localhost :)', flush=True) | |
| kwargs.setdefault('allow_redirects', True) | |
| return requests.api.request('get', 'http://127.0.0.1/', **kwargs) | |
| original_get = requests.get | |
| requests.get = my_get | |
| import gradio as gr | |
| requests.get = original_get | |
| def fix_pydantic_duplicate_validators_error(): | |
| try: | |
| from pydantic import class_validators | |
| class_validators.in_ipython = lambda: True # type: ignore[attr-defined] | |
| except ImportError: | |
| pass | |
| fix_pydantic_duplicate_validators_error() | |
| from enums import DocumentSubset, no_model_str, no_lora_str, no_server_str, LangChainAction, LangChainMode, \ | |
| DocumentChoice, langchain_modes_intrinsic | |
| from gradio_themes import H2oTheme, SoftTheme, get_h2o_title, get_simple_title, get_dark_js, spacing_xsm, radius_xsm, \ | |
| text_xsm | |
| from prompter import prompt_type_to_model_name, prompt_types_strings, inv_prompt_type_to_model_lower, non_hf_types, \ | |
| get_prompt | |
| from utils import flatten_list, zip_data, s3up, clear_torch_cache, get_torch_allocated, system_info_print, \ | |
| ping, get_short_name, makedirs, get_kwargs, remove, system_info, ping_gpu, get_url, get_local_ip, \ | |
| save_collection_names | |
| from gen import get_model, languages_covered, evaluate, score_qa, inputs_kwargs_list, scratch_base_dir, \ | |
| get_max_max_new_tokens, get_minmax_top_k_docs, history_to_context, langchain_actions, langchain_agents_list, \ | |
| update_langchain | |
| from evaluate_params import eval_func_param_names, no_default_param_names, eval_func_param_names_defaults, \ | |
| input_args_list | |
| from apscheduler.schedulers.background import BackgroundScheduler | |
| def fix_text_for_gradio(text, fix_new_lines=False, fix_latex_dollars=True): | |
| if fix_latex_dollars: | |
| ts = text.split('```') | |
| for parti, part in enumerate(ts): | |
| inside = parti % 2 == 1 | |
| if not inside: | |
| ts[parti] = ts[parti].replace('$', '﹩') | |
| text = '```'.join(ts) | |
| if fix_new_lines: | |
| # let Gradio handle code, since got improved recently | |
| ## FIXME: below conflicts with Gradio, but need to see if can handle multiple \n\n\n etc. properly as is. | |
| # ensure good visually, else markdown ignores multiple \n | |
| # handle code blocks | |
| ts = text.split('```') | |
| for parti, part in enumerate(ts): | |
| inside = parti % 2 == 1 | |
| if not inside: | |
| ts[parti] = ts[parti].replace('\n', '<br>') | |
| text = '```'.join(ts) | |
| return text | |
| def go_gradio(**kwargs): | |
| allow_api = kwargs['allow_api'] | |
| is_public = kwargs['is_public'] | |
| is_hf = kwargs['is_hf'] | |
| memory_restriction_level = kwargs['memory_restriction_level'] | |
| n_gpus = kwargs['n_gpus'] | |
| admin_pass = kwargs['admin_pass'] | |
| model_states = kwargs['model_states'] | |
| dbs = kwargs['dbs'] | |
| db_type = kwargs['db_type'] | |
| visible_langchain_actions = kwargs['visible_langchain_actions'] | |
| visible_langchain_agents = kwargs['visible_langchain_agents'] | |
| allow_upload_to_user_data = kwargs['allow_upload_to_user_data'] | |
| allow_upload_to_my_data = kwargs['allow_upload_to_my_data'] | |
| enable_sources_list = kwargs['enable_sources_list'] | |
| enable_url_upload = kwargs['enable_url_upload'] | |
| enable_text_upload = kwargs['enable_text_upload'] | |
| use_openai_embedding = kwargs['use_openai_embedding'] | |
| hf_embedding_model = kwargs['hf_embedding_model'] | |
| enable_captions = kwargs['enable_captions'] | |
| captions_model = kwargs['captions_model'] | |
| enable_ocr = kwargs['enable_ocr'] | |
| enable_pdf_ocr = kwargs['enable_pdf_ocr'] | |
| caption_loader = kwargs['caption_loader'] | |
| # for dynamic state per user session in gradio | |
| model_state0 = kwargs['model_state0'] | |
| score_model_state0 = kwargs['score_model_state0'] | |
| my_db_state0 = kwargs['my_db_state0'] | |
| selection_docs_state0 = kwargs['selection_docs_state0'] | |
| # for evaluate defaults | |
| langchain_modes0 = kwargs['langchain_modes'] | |
| visible_langchain_modes0 = kwargs['visible_langchain_modes'] | |
| langchain_mode_paths0 = kwargs['langchain_mode_paths'] | |
| # easy update of kwargs needed for evaluate() etc. | |
| queue = True | |
| allow_upload = allow_upload_to_user_data or allow_upload_to_my_data | |
| kwargs.update(locals()) | |
| # import control | |
| if kwargs['langchain_mode'] != 'Disabled': | |
| from gpt_langchain import file_types, have_arxiv | |
| else: | |
| have_arxiv = False | |
| file_types = [] | |
| if 'mbart-' in kwargs['model_lower']: | |
| instruction_label_nochat = "Text to translate" | |
| else: | |
| instruction_label_nochat = "Instruction (Shift-Enter or push Submit to send message," \ | |
| " use Enter for multiple input lines)" | |
| title = 'h2oGPT' | |
| description = """<iframe src="https://ghbtns.com/github-btn.html?user=h2oai&repo=h2ogpt&type=star&count=true&size=small" frameborder="0" scrolling="0" width="250" height="20" title="GitHub"></iframe><small><a href="https://github.com/h2oai/h2ogpt">h2oGPT</a> <a href="https://github.com/h2oai/h2o-llmstudio">H2O LLM Studio</a><br><a href="https://huggingface.co./h2oai">🤗 Models</a>""" | |
| description_bottom = "If this host is busy, try<br>[Multi-Model](https://gpt.h2o.ai)<br>[Falcon 40B](https://falcon.h2o.ai)<br>[Vicuna 33B](https://wizardvicuna.h2o.ai)<br>[MPT 30B-Chat](https://mpt.h2o.ai)<br>[HF Spaces1](https://huggingface.co./spaces/h2oai/h2ogpt-chatbot)<br>[HF Spaces2](https://huggingface.co./spaces/h2oai/h2ogpt-chatbot2)<br>" | |
| if is_hf: | |
| description_bottom += '''<a href="https://huggingface.co./spaces/h2oai/h2ogpt-chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" style="white-space: nowrap" alt="Duplicate Space"></a>''' | |
| task_info_md = '' | |
| css_code = get_css(kwargs) | |
| if kwargs['gradio_offline_level'] >= 0: | |
| # avoid GoogleFont that pulls from internet | |
| if kwargs['gradio_offline_level'] == 1: | |
| # front end would still have to download fonts or have cached it at some point | |
| base_font = 'Source Sans Pro' | |
| else: | |
| base_font = 'Helvetica' | |
| theme_kwargs = dict(font=(base_font, 'ui-sans-serif', 'system-ui', 'sans-serif'), | |
| font_mono=('IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace')) | |
| else: | |
| theme_kwargs = dict() | |
| if kwargs['gradio_size'] == 'xsmall': | |
| theme_kwargs.update(dict(spacing_size=spacing_xsm, text_size=text_xsm, radius_size=radius_xsm)) | |
| elif kwargs['gradio_size'] in [None, 'small']: | |
| theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_sm, text_size=gr.themes.sizes.text_sm, | |
| radius_size=gr.themes.sizes.spacing_sm)) | |
| elif kwargs['gradio_size'] == 'large': | |
| theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_lg, text_size=gr.themes.sizes.text_lg), | |
| radius_size=gr.themes.sizes.spacing_lg) | |
| elif kwargs['gradio_size'] == 'medium': | |
| theme_kwargs.update(dict(spacing_size=gr.themes.sizes.spacing_md, text_size=gr.themes.sizes.text_md, | |
| radius_size=gr.themes.sizes.spacing_md)) | |
| theme = H2oTheme(**theme_kwargs) if kwargs['h2ocolors'] else SoftTheme(**theme_kwargs) | |
| demo = gr.Blocks(theme=theme, css=css_code, title="h2oGPT", analytics_enabled=False) | |
| callback = gr.CSVLogger() | |
| model_options0 = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options'] | |
| if kwargs['base_model'].strip() not in model_options0: | |
| model_options0 = [kwargs['base_model'].strip()] + model_options0 | |
| lora_options = kwargs['extra_lora_options'] | |
| if kwargs['lora_weights'].strip() not in lora_options: | |
| lora_options = [kwargs['lora_weights'].strip()] + lora_options | |
| server_options = kwargs['extra_server_options'] | |
| if kwargs['inference_server'].strip() not in server_options: | |
| server_options = [kwargs['inference_server'].strip()] + server_options | |
| if os.getenv('OPENAI_API_KEY'): | |
| if 'openai_chat' not in server_options: | |
| server_options += ['openai_chat'] | |
| if 'openai' not in server_options: | |
| server_options += ['openai'] | |
| # always add in no lora case | |
| # add fake space so doesn't go away in gradio dropdown | |
| model_options0 = [no_model_str] + model_options0 | |
| lora_options = [no_lora_str] + lora_options | |
| server_options = [no_server_str] + server_options | |
| # always add in no model case so can free memory | |
| # add fake space so doesn't go away in gradio dropdown | |
| # transcribe, will be detranscribed before use by evaluate() | |
| if not kwargs['base_model'].strip(): | |
| kwargs['base_model'] = no_model_str | |
| if not kwargs['lora_weights'].strip(): | |
| kwargs['lora_weights'] = no_lora_str | |
| if not kwargs['inference_server'].strip(): | |
| kwargs['inference_server'] = no_server_str | |
| # transcribe for gradio | |
| kwargs['gpu_id'] = str(kwargs['gpu_id']) | |
| no_model_msg = 'h2oGPT [ !!! Please Load Model in Models Tab !!! ]' | |
| output_label0 = f'h2oGPT [Model: {kwargs.get("base_model")}]' if kwargs.get( | |
| 'base_model') else no_model_msg | |
| output_label0_model2 = no_model_msg | |
| def update_prompt(prompt_type1, prompt_dict1, model_state1, which_model=0): | |
| if not prompt_type1 or which_model != 0: | |
| # keep prompt_type and prompt_dict in sync if possible | |
| prompt_type1 = kwargs.get('prompt_type', prompt_type1) | |
| prompt_dict1 = kwargs.get('prompt_dict', prompt_dict1) | |
| # prefer model specific prompt type instead of global one | |
| if not prompt_type1 or which_model != 0: | |
| prompt_type1 = model_state1.get('prompt_type', prompt_type1) | |
| prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1) | |
| if not prompt_dict1 or which_model != 0: | |
| # if still not defined, try to get | |
| prompt_dict1 = kwargs.get('prompt_dict', prompt_dict1) | |
| if not prompt_dict1 or which_model != 0: | |
| prompt_dict1 = model_state1.get('prompt_dict', prompt_dict1) | |
| return prompt_type1, prompt_dict1 | |
| default_kwargs = {k: kwargs[k] for k in eval_func_param_names_defaults} | |
| # ensure prompt_type consistent with prep_bot(), so nochat API works same way | |
| default_kwargs['prompt_type'], default_kwargs['prompt_dict'] = \ | |
| update_prompt(default_kwargs['prompt_type'], default_kwargs['prompt_dict'], | |
| model_state1=model_state0, which_model=0) | |
| for k in no_default_param_names: | |
| default_kwargs[k] = '' | |
| def dummy_fun(x): | |
| # need dummy function to block new input from being sent until output is done, | |
| # else gets input_list at time of submit that is old, and shows up as truncated in chatbot | |
| return x | |
| def allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1): | |
| allow = False | |
| allow |= langchain_action1 not in LangChainAction.QUERY.value | |
| allow |= document_subset1 in DocumentSubset.TopKSources.name | |
| if langchain_mode1 in [LangChainMode.LLM.value]: | |
| allow = False | |
| return allow | |
| with demo: | |
| # avoid actual model/tokenizer here or anything that would be bad to deepcopy | |
| # https://github.com/gradio-app/gradio/issues/3558 | |
| model_state = gr.State( | |
| dict(model='model', tokenizer='tokenizer', device=kwargs['device'], | |
| base_model=kwargs['base_model'], | |
| tokenizer_base_model=kwargs['tokenizer_base_model'], | |
| lora_weights=kwargs['lora_weights'], | |
| inference_server=kwargs['inference_server'], | |
| prompt_type=kwargs['prompt_type'], | |
| prompt_dict=kwargs['prompt_dict'], | |
| ) | |
| ) | |
| def update_langchain_mode_paths(db1s, selection_docs_state1): | |
| if allow_upload_to_my_data: | |
| selection_docs_state1['langchain_mode_paths'].update({k: None for k in db1s}) | |
| dup = selection_docs_state1['langchain_mode_paths'].copy() | |
| for k, v in dup.items(): | |
| if k not in selection_docs_state1['visible_langchain_modes']: | |
| selection_docs_state1['langchain_mode_paths'].pop(k) | |
| return selection_docs_state1 | |
| # Setup some gradio states for per-user dynamic state | |
| model_state2 = gr.State(kwargs['model_state_none'].copy()) | |
| model_options_state = gr.State([model_options0]) | |
| lora_options_state = gr.State([lora_options]) | |
| server_options_state = gr.State([server_options]) | |
| my_db_state = gr.State(my_db_state0) | |
| chat_state = gr.State({}) | |
| docs_state00 = kwargs['document_choice'] + [DocumentChoice.ALL.value] | |
| docs_state0 = [] | |
| [docs_state0.append(x) for x in docs_state00 if x not in docs_state0] | |
| docs_state = gr.State(docs_state0) | |
| viewable_docs_state0 = [] | |
| viewable_docs_state = gr.State(viewable_docs_state0) | |
| selection_docs_state0 = update_langchain_mode_paths(my_db_state0, selection_docs_state0) | |
| selection_docs_state = gr.State(selection_docs_state0) | |
| gr.Markdown(f""" | |
| {get_h2o_title(title, description) if kwargs['h2ocolors'] else get_simple_title(title, description)} | |
| """) | |
| # go button visible if | |
| base_wanted = kwargs['base_model'] != no_model_str and kwargs['login_mode_if_model0'] | |
| go_btn = gr.Button(value="ENTER", visible=base_wanted, variant="primary") | |
| nas = ' '.join(['NA'] * len(kwargs['model_states'])) | |
| res_value = "Response Score: NA" if not kwargs[ | |
| 'model_lock'] else "Response Scores: %s" % nas | |
| if kwargs['langchain_mode'] != LangChainMode.DISABLED.value: | |
| extra_prompt_form = ". For summarization, no query required, just click submit" | |
| else: | |
| extra_prompt_form = "" | |
| if kwargs['input_lines'] > 1: | |
| instruction_label = "Shift-Enter to Submit, Enter for more lines%s" % extra_prompt_form | |
| else: | |
| instruction_label = "Enter to Submit, Shift-Enter for more lines%s" % extra_prompt_form | |
| def get_langchain_choices(selection_docs_state1): | |
| langchain_modes = selection_docs_state1['langchain_modes'] | |
| visible_langchain_modes = selection_docs_state1['visible_langchain_modes'] | |
| if is_hf: | |
| # don't show 'wiki' since only usually useful for internal testing at moment | |
| no_show_modes = ['Disabled', 'wiki'] | |
| else: | |
| no_show_modes = ['Disabled'] | |
| allowed_modes = visible_langchain_modes.copy() | |
| # allowed_modes = [x for x in allowed_modes if x in dbs] | |
| allowed_modes += ['LLM'] | |
| if allow_upload_to_my_data and 'MyData' not in allowed_modes: | |
| allowed_modes += ['MyData'] | |
| if allow_upload_to_user_data and 'UserData' not in allowed_modes: | |
| allowed_modes += ['UserData'] | |
| choices = [x for x in langchain_modes if x in allowed_modes and x not in no_show_modes] | |
| return choices | |
| def get_df_langchain_mode_paths(selection_docs_state1): | |
| langchain_mode_paths = selection_docs_state1['langchain_mode_paths'] | |
| if langchain_mode_paths: | |
| df = pd.DataFrame.from_dict(langchain_mode_paths.items(), orient='columns') | |
| df.columns = ['Collection', 'Path'] | |
| else: | |
| df = pd.DataFrame(None) | |
| return df | |
| normal_block = gr.Row(visible=not base_wanted, equal_height=False) | |
| with normal_block: | |
| side_bar = gr.Column(elem_id="col_container", scale=1, min_width=100) | |
| with side_bar: | |
| with gr.Accordion("Chats", open=False, visible=True): | |
| radio_chats = gr.Radio(value=None, label="Saved Chats", show_label=False, | |
| visible=True, interactive=True, | |
| type='value') | |
| upload_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload | |
| with gr.Accordion("Upload", open=False, visible=upload_visible): | |
| with gr.Column(): | |
| with gr.Row(equal_height=False): | |
| file_types_str = '[' + ' '.join(file_types) + ' URL ArXiv TEXT' + ']' | |
| fileup_output = gr.File(label=f'Upload {file_types_str}', | |
| show_label=False, | |
| file_types=file_types, | |
| file_count="multiple", | |
| scale=1, | |
| min_width=0, | |
| elem_id="warning", elem_classes="feedback") | |
| fileup_output_text = gr.Textbox(visible=False) | |
| url_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_url_upload | |
| url_label = 'URL/ArXiv' if have_arxiv else 'URL' | |
| url_text = gr.Textbox(label=url_label, | |
| # placeholder="Enter Submits", | |
| max_lines=1, | |
| interactive=True) | |
| text_visible = kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_text_upload | |
| user_text_text = gr.Textbox(label='Paste Text', | |
| # placeholder="Enter Submits", | |
| interactive=True, | |
| visible=text_visible) | |
| github_textbox = gr.Textbox(label="Github URL", visible=False) # FIXME WIP | |
| database_visible = kwargs['langchain_mode'] != 'Disabled' | |
| with gr.Accordion("Resources", open=False, visible=database_visible): | |
| langchain_choices0 = get_langchain_choices(selection_docs_state0) | |
| langchain_mode = gr.Radio( | |
| langchain_choices0, | |
| value=kwargs['langchain_mode'], | |
| label="Collections", | |
| show_label=True, | |
| visible=kwargs['langchain_mode'] != 'Disabled', | |
| min_width=100) | |
| add_chat_history_to_context = gr.Checkbox(label="Chat History", | |
| value=kwargs['add_chat_history_to_context']) | |
| document_subset = gr.Radio([x.name for x in DocumentSubset], | |
| label="Subset", | |
| value=DocumentSubset.Relevant.name, | |
| interactive=True, | |
| ) | |
| allowed_actions = [x for x in langchain_actions if x in visible_langchain_actions] | |
| langchain_action = gr.Radio( | |
| allowed_actions, | |
| value=allowed_actions[0] if len(allowed_actions) > 0 else None, | |
| label="Action", | |
| visible=True) | |
| allowed_agents = [x for x in langchain_agents_list if x in visible_langchain_agents] | |
| langchain_agents = gr.Dropdown( | |
| langchain_agents_list, | |
| value=kwargs['langchain_agents'], | |
| label="Agents", | |
| multiselect=True, | |
| interactive=True, | |
| visible=False) # WIP | |
| col_tabs = gr.Column(elem_id="col_container", scale=10) | |
| with (col_tabs, gr.Tabs()): | |
| with gr.TabItem("Chat"): | |
| if kwargs['langchain_mode'] == 'Disabled': | |
| text_output_nochat = gr.Textbox(lines=5, label=output_label0, show_copy_button=True, | |
| visible=not kwargs['chat']) | |
| else: | |
| # text looks a bit worse, but HTML links work | |
| text_output_nochat = gr.HTML(label=output_label0, visible=not kwargs['chat']) | |
| with gr.Row(): | |
| # NOCHAT | |
| instruction_nochat = gr.Textbox( | |
| lines=kwargs['input_lines'], | |
| label=instruction_label_nochat, | |
| placeholder=kwargs['placeholder_instruction'], | |
| visible=not kwargs['chat'], | |
| ) | |
| iinput_nochat = gr.Textbox(lines=4, label="Input context for Instruction", | |
| placeholder=kwargs['placeholder_input'], | |
| visible=not kwargs['chat']) | |
| submit_nochat = gr.Button("Submit", size='sm', visible=not kwargs['chat']) | |
| flag_btn_nochat = gr.Button("Flag", size='sm', visible=not kwargs['chat']) | |
| score_text_nochat = gr.Textbox("Response Score: NA", show_label=False, | |
| visible=not kwargs['chat']) | |
| submit_nochat_api = gr.Button("Submit nochat API", visible=False) | |
| inputs_dict_str = gr.Textbox(label='API input for nochat', show_label=False, visible=False) | |
| text_output_nochat_api = gr.Textbox(lines=5, label='API nochat output', visible=False, | |
| show_copy_button=True) | |
| # CHAT | |
| col_chat = gr.Column(visible=kwargs['chat']) | |
| with col_chat: | |
| with gr.Row(): # elem_id='prompt-form-area'): | |
| with gr.Column(scale=50): | |
| instruction = gr.Textbox( | |
| lines=kwargs['input_lines'], | |
| label='Ask anything', | |
| placeholder=instruction_label, | |
| info=None, | |
| elem_id='prompt-form', | |
| container=True, | |
| ) | |
| submit_buttons = gr.Row(equal_height=False) | |
| with submit_buttons: | |
| mw1 = 50 | |
| mw2 = 50 | |
| with gr.Column(min_width=mw1): | |
| submit = gr.Button(value='Submit', variant='primary', size='sm', | |
| min_width=mw1) | |
| stop_btn = gr.Button(value="Stop", variant='secondary', size='sm', | |
| min_width=mw1) | |
| save_chat_btn = gr.Button("Save", size='sm', min_width=mw1) | |
| with gr.Column(min_width=mw2): | |
| retry_btn = gr.Button("Redo", size='sm', min_width=mw2) | |
| undo = gr.Button("Undo", size='sm', min_width=mw2) | |
| clear_chat_btn = gr.Button(value="Clear", size='sm', min_width=mw2) | |
| text_output, text_output2, text_outputs = make_chatbots(output_label0, output_label0_model2, | |
| **kwargs) | |
| with gr.Row(): | |
| with gr.Column(visible=kwargs['score_model']): | |
| score_text = gr.Textbox(res_value, | |
| show_label=False, | |
| visible=True) | |
| score_text2 = gr.Textbox("Response Score2: NA", show_label=False, | |
| visible=False and not kwargs['model_lock']) | |
| with gr.TabItem("Document Selection"): | |
| document_choice = gr.Dropdown(docs_state0, | |
| label="Select Subset of Document(s) %s" % file_types_str, | |
| value=[DocumentChoice.ALL.value], | |
| interactive=True, | |
| multiselect=True, | |
| visible=kwargs['langchain_mode'] != 'Disabled', | |
| ) | |
| sources_visible = kwargs['langchain_mode'] != 'Disabled' and enable_sources_list | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| get_sources_btn = gr.Button(value="Update UI with Document(s) from DB", scale=0, size='sm', | |
| visible=sources_visible) | |
| show_sources_btn = gr.Button(value="Show Sources from DB", scale=0, size='sm', | |
| visible=sources_visible) | |
| refresh_sources_btn = gr.Button(value="Update DB with new/changed files on disk", scale=0, | |
| size='sm', | |
| visible=sources_visible and allow_upload_to_user_data) | |
| with gr.Column(scale=4): | |
| pass | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| visible_add_remove_collection = (allow_upload_to_user_data or | |
| allow_upload_to_my_data) and \ | |
| kwargs['langchain_mode'] != 'Disabled' | |
| add_placeholder = "e.g. UserData2, user_path2 (optional)" \ | |
| if not is_public else "e.g. MyData2" | |
| remove_placeholder = "e.g. UserData2" if not is_public else "e.g. MyData2" | |
| new_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection, | |
| label='Add Collection', | |
| placeholder=add_placeholder, | |
| interactive=True) | |
| remove_langchain_mode_text = gr.Textbox(value="", visible=visible_add_remove_collection, | |
| label='Remove Collection', | |
| placeholder=remove_placeholder, | |
| interactive=True) | |
| load_langchain = gr.Button(value="Load LangChain State", scale=0, size='sm', | |
| visible=allow_upload_to_user_data and | |
| kwargs['langchain_mode'] != 'Disabled') | |
| with gr.Column(scale=1): | |
| df0 = get_df_langchain_mode_paths(selection_docs_state0) | |
| langchain_mode_path_text = gr.Dataframe(value=df0, | |
| visible=visible_add_remove_collection, | |
| label='LangChain Mode-Path', | |
| show_label=False, | |
| interactive=False) | |
| with gr.Column(scale=4): | |
| pass | |
| sources_row = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list, | |
| equal_height=False) | |
| with sources_row: | |
| with gr.Column(scale=1): | |
| file_source = gr.File(interactive=False, | |
| label="Download File w/Sources") | |
| with gr.Column(scale=2): | |
| sources_text = gr.HTML(label='Sources Added', interactive=False) | |
| doc_exception_text = gr.Textbox(value="", label='Document Exceptions', | |
| interactive=False, | |
| visible=kwargs['langchain_mode'] != 'Disabled') | |
| with gr.TabItem("Document Viewer"): | |
| with gr.Row(visible=kwargs['langchain_mode'] != 'Disabled'): | |
| with gr.Column(scale=2): | |
| get_viewable_sources_btn = gr.Button(value="Update UI with Document(s) from DB", scale=0, | |
| size='sm', | |
| visible=sources_visible) | |
| view_document_choice = gr.Dropdown(viewable_docs_state0, | |
| label="Select Single Document", | |
| value=None, | |
| interactive=True, | |
| multiselect=False, | |
| visible=True, | |
| ) | |
| with gr.Column(scale=4): | |
| pass | |
| document = 'http://infolab.stanford.edu/pub/papers/google.pdf' | |
| doc_view = gr.HTML(visible=False) | |
| doc_view2 = gr.Dataframe(visible=False) | |
| doc_view3 = gr.JSON(visible=False) | |
| doc_view4 = gr.Markdown(visible=False) | |
| with gr.TabItem("Chat History"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| remove_chat_btn = gr.Button(value="Remove Selected Saved Chats", visible=True, size='sm') | |
| flag_btn = gr.Button("Flag Current Chat", size='sm') | |
| export_chats_btn = gr.Button(value="Export Chats to Download", size='sm') | |
| with gr.Column(scale=4): | |
| pass | |
| with gr.Row(): | |
| chats_file = gr.File(interactive=False, label="Download Exported Chats") | |
| chatsup_output = gr.File(label="Upload Chat File(s)", | |
| file_types=['.json'], | |
| file_count='multiple', | |
| elem_id="warning", elem_classes="feedback") | |
| with gr.Row(): | |
| if 'mbart-' in kwargs['model_lower']: | |
| src_lang = gr.Dropdown(list(languages_covered().keys()), | |
| value=kwargs['src_lang'], | |
| label="Input Language") | |
| tgt_lang = gr.Dropdown(list(languages_covered().keys()), | |
| value=kwargs['tgt_lang'], | |
| label="Output Language") | |
| chat_exception_text = gr.Textbox(value="", visible=True, label='Chat Exceptions', | |
| interactive=False) | |
| with gr.TabItem("Expert"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| stream_output = gr.components.Checkbox(label="Stream output", | |
| value=kwargs['stream_output']) | |
| prompt_type = gr.Dropdown(prompt_types_strings, | |
| value=kwargs['prompt_type'], label="Prompt Type", | |
| visible=not kwargs['model_lock'], | |
| interactive=not is_public, | |
| ) | |
| prompt_type2 = gr.Dropdown(prompt_types_strings, | |
| value=kwargs['prompt_type'], label="Prompt Type Model 2", | |
| visible=False and not kwargs['model_lock'], | |
| interactive=not is_public) | |
| do_sample = gr.Checkbox(label="Sample", | |
| info="Enable sampler, required for use of temperature, top_p, top_k", | |
| value=kwargs['do_sample']) | |
| temperature = gr.Slider(minimum=0.01, maximum=2, | |
| value=kwargs['temperature'], | |
| label="Temperature", | |
| info="Lower is deterministic (but may lead to repeats), Higher more creative (but may lead to hallucinations)") | |
| top_p = gr.Slider(minimum=1e-3, maximum=1.0 - 1e-3, | |
| value=kwargs['top_p'], label="Top p", | |
| info="Cumulative probability of tokens to sample from") | |
| top_k = gr.Slider( | |
| minimum=1, maximum=100, step=1, | |
| value=kwargs['top_k'], label="Top k", | |
| info='Num. tokens to sample from' | |
| ) | |
| # FIXME: https://github.com/h2oai/h2ogpt/issues/106 | |
| if os.getenv('TESTINGFAIL'): | |
| max_beams = 8 if not (memory_restriction_level or is_public) else 1 | |
| else: | |
| max_beams = 1 | |
| num_beams = gr.Slider(minimum=1, maximum=max_beams, step=1, | |
| value=min(max_beams, kwargs['num_beams']), label="Beams", | |
| info="Number of searches for optimal overall probability. " | |
| "Uses more GPU memory/compute", | |
| interactive=False) | |
| max_max_new_tokens = get_max_max_new_tokens(model_state0, **kwargs) | |
| max_new_tokens = gr.Slider( | |
| minimum=1, maximum=max_max_new_tokens, step=1, | |
| value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length", | |
| ) | |
| min_new_tokens = gr.Slider( | |
| minimum=0, maximum=max_max_new_tokens, step=1, | |
| value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length", | |
| ) | |
| max_new_tokens2 = gr.Slider( | |
| minimum=1, maximum=max_max_new_tokens, step=1, | |
| value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length 2", | |
| visible=False and not kwargs['model_lock'], | |
| ) | |
| min_new_tokens2 = gr.Slider( | |
| minimum=0, maximum=max_max_new_tokens, step=1, | |
| value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length 2", | |
| visible=False and not kwargs['model_lock'], | |
| ) | |
| early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search", | |
| value=kwargs['early_stopping']) | |
| max_time = gr.Slider(minimum=0, maximum=kwargs['max_max_time'], step=1, | |
| value=min(kwargs['max_max_time'], | |
| kwargs['max_time']), label="Max. time", | |
| info="Max. time to search optimal output.") | |
| repetition_penalty = gr.Slider(minimum=0.01, maximum=3.0, | |
| value=kwargs['repetition_penalty'], | |
| label="Repetition Penalty") | |
| num_return_sequences = gr.Slider(minimum=1, maximum=10, step=1, | |
| value=kwargs['num_return_sequences'], | |
| label="Number Returns", info="Must be <= num_beams", | |
| interactive=not is_public) | |
| iinput = gr.Textbox(lines=4, label="Input", | |
| placeholder=kwargs['placeholder_input'], | |
| interactive=not is_public) | |
| context = gr.Textbox(lines=3, label="System Pre-Context", | |
| info="Directly pre-appended without prompt processing", | |
| interactive=not is_public) | |
| chat = gr.components.Checkbox(label="Chat mode", value=kwargs['chat'], | |
| visible=False, # no longer support nochat in UI | |
| interactive=not is_public, | |
| ) | |
| count_chat_tokens_btn = gr.Button(value="Count Chat Tokens", | |
| visible=not is_public and not kwargs['model_lock'], | |
| interactive=not is_public) | |
| chat_token_count = gr.Textbox(label="Chat Token Count", value=None, | |
| visible=not is_public and not kwargs['model_lock'], | |
| interactive=False) | |
| chunk = gr.components.Checkbox(value=kwargs['chunk'], | |
| label="Whether to chunk documents", | |
| info="For LangChain", | |
| visible=kwargs['langchain_mode'] != 'Disabled', | |
| interactive=not is_public) | |
| min_top_k_docs, max_top_k_docs, label_top_k_docs = get_minmax_top_k_docs(is_public) | |
| top_k_docs = gr.Slider(minimum=min_top_k_docs, maximum=max_top_k_docs, step=1, | |
| value=kwargs['top_k_docs'], | |
| label=label_top_k_docs, | |
| info="For LangChain", | |
| visible=kwargs['langchain_mode'] != 'Disabled', | |
| interactive=not is_public) | |
| chunk_size = gr.Number(value=kwargs['chunk_size'], | |
| label="Chunk size for document chunking", | |
| info="For LangChain (ignored if chunk=False)", | |
| minimum=128, | |
| maximum=2048, | |
| visible=kwargs['langchain_mode'] != 'Disabled', | |
| interactive=not is_public, | |
| precision=0) | |
| with gr.TabItem("Models"): | |
| model_lock_msg = gr.Textbox(lines=1, label="Model Lock Notice", | |
| placeholder="Started in model_lock mode, no model changes allowed.", | |
| visible=bool(kwargs['model_lock']), interactive=False) | |
| load_msg = "Load-Unload Model/LORA [unload works if did not use --base_model]" if not is_public \ | |
| else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO" | |
| load_msg2 = "Load-Unload Model/LORA 2 [unload works if did not use --base_model]" if not is_public \ | |
| else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO 2" | |
| variant_load_msg = 'primary' if not is_public else 'secondary' | |
| compare_checkbox = gr.components.Checkbox(label="Compare Mode", | |
| value=kwargs['model_lock'], | |
| visible=not is_public and not kwargs['model_lock']) | |
| with gr.Row(): | |
| n_gpus_list = [str(x) for x in list(range(-1, n_gpus))] | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(scale=20, visible=not kwargs['model_lock']): | |
| model_choice = gr.Dropdown(model_options_state.value[0], label="Choose Model", | |
| value=kwargs['base_model']) | |
| lora_choice = gr.Dropdown(lora_options_state.value[0], label="Choose LORA", | |
| value=kwargs['lora_weights'], visible=kwargs['show_lora']) | |
| server_choice = gr.Dropdown(server_options_state.value[0], label="Choose Server", | |
| value=kwargs['inference_server'], visible=not is_public) | |
| with gr.Column(scale=1, visible=not kwargs['model_lock']): | |
| load_model_button = gr.Button(load_msg, variant=variant_load_msg, scale=0, | |
| size='sm', interactive=not is_public) | |
| model_load8bit_checkbox = gr.components.Checkbox( | |
| label="Load 8-bit [requires support]", | |
| value=kwargs['load_8bit'], interactive=not is_public) | |
| model_use_gpu_id_checkbox = gr.components.Checkbox( | |
| label="Choose Devices [If not Checked, use all GPUs]", | |
| value=kwargs['use_gpu_id'], interactive=not is_public) | |
| model_gpu = gr.Dropdown(n_gpus_list, | |
| label="GPU ID [-1 = all GPUs, if Choose is enabled]", | |
| value=kwargs['gpu_id'], interactive=not is_public) | |
| model_used = gr.Textbox(label="Current Model", value=kwargs['base_model'], | |
| interactive=False) | |
| lora_used = gr.Textbox(label="Current LORA", value=kwargs['lora_weights'], | |
| visible=kwargs['show_lora'], interactive=False) | |
| server_used = gr.Textbox(label="Current Server", | |
| value=kwargs['inference_server'], | |
| visible=bool(kwargs['inference_server']) and not is_public, | |
| interactive=False) | |
| prompt_dict = gr.Textbox(label="Prompt (or Custom)", | |
| value=pprint.pformat(kwargs['prompt_dict'], indent=4), | |
| interactive=not is_public, lines=4) | |
| col_model2 = gr.Column(visible=False) | |
| with col_model2: | |
| with gr.Row(): | |
| with gr.Column(scale=20, visible=not kwargs['model_lock']): | |
| model_choice2 = gr.Dropdown(model_options_state.value[0], label="Choose Model 2", | |
| value=no_model_str) | |
| lora_choice2 = gr.Dropdown(lora_options_state.value[0], label="Choose LORA 2", | |
| value=no_lora_str, | |
| visible=kwargs['show_lora']) | |
| server_choice2 = gr.Dropdown(server_options_state.value[0], label="Choose Server 2", | |
| value=no_server_str, | |
| visible=not is_public) | |
| with gr.Column(scale=1, visible=not kwargs['model_lock']): | |
| load_model_button2 = gr.Button(load_msg2, variant=variant_load_msg, scale=0, | |
| size='sm', interactive=not is_public) | |
| model_load8bit_checkbox2 = gr.components.Checkbox( | |
| label="Load 8-bit 2 [requires support]", | |
| value=kwargs['load_8bit'], interactive=not is_public) | |
| model_use_gpu_id_checkbox2 = gr.components.Checkbox( | |
| label="Choose Devices 2 [If not Checked, use all GPUs]", | |
| value=kwargs[ | |
| 'use_gpu_id'], interactive=not is_public) | |
| model_gpu2 = gr.Dropdown(n_gpus_list, | |
| label="GPU ID 2 [-1 = all GPUs, if choose is enabled]", | |
| value=kwargs['gpu_id'], interactive=not is_public) | |
| # no model/lora loaded ever in model2 by default | |
| model_used2 = gr.Textbox(label="Current Model 2", value=no_model_str, | |
| interactive=False) | |
| lora_used2 = gr.Textbox(label="Current LORA 2", value=no_lora_str, | |
| visible=kwargs['show_lora'], interactive=False) | |
| server_used2 = gr.Textbox(label="Current Server 2", value=no_server_str, | |
| interactive=False, | |
| visible=not is_public) | |
| prompt_dict2 = gr.Textbox(label="Prompt (or Custom) 2", | |
| value=pprint.pformat(kwargs['prompt_dict'], indent=4), | |
| interactive=not is_public, lines=4) | |
| with gr.Row(visible=not kwargs['model_lock']): | |
| with gr.Column(scale=50): | |
| new_model = gr.Textbox(label="New Model name/path", interactive=not is_public) | |
| with gr.Column(scale=50): | |
| new_lora = gr.Textbox(label="New LORA name/path", visible=kwargs['show_lora'], | |
| interactive=not is_public) | |
| with gr.Column(scale=50): | |
| new_server = gr.Textbox(label="New Server url:port", interactive=not is_public) | |
| with gr.Row(): | |
| add_model_lora_server_button = gr.Button("Add new Model, Lora, Server url:port", scale=0, | |
| size='sm', interactive=not is_public) | |
| with gr.TabItem("System"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| side_bar_text = gr.Textbox('on', visible=False, interactive=False) | |
| submit_buttons_text = gr.Textbox('on', visible=False, interactive=False) | |
| side_bar_btn = gr.Button("Toggle SideBar", variant="secondary", size="sm") | |
| submit_buttons_btn = gr.Button("Toggle Submit Buttons", variant="secondary", size="sm") | |
| col_tabs_scale = gr.Slider(minimum=1, maximum=20, value=10, step=1, label='Window Size') | |
| text_outputs_height = gr.Slider(minimum=100, maximum=2000, value=kwargs['height'] or 400, | |
| step=50, label='Chat Height') | |
| dark_mode_btn = gr.Button("Dark Mode", variant="secondary", size="sm") | |
| with gr.Column(scale=4): | |
| pass | |
| system_visible0 = not is_public and not admin_pass | |
| admin_row = gr.Row() | |
| with admin_row: | |
| with gr.Column(scale=1): | |
| admin_pass_textbox = gr.Textbox(label="Admin Password", type='password', | |
| visible=not system_visible0) | |
| with gr.Column(scale=4): | |
| pass | |
| system_row = gr.Row(visible=system_visible0) | |
| with system_row: | |
| with gr.Column(): | |
| with gr.Row(): | |
| system_btn = gr.Button(value='Get System Info', size='sm') | |
| system_text = gr.Textbox(label='System Info', interactive=False, show_copy_button=True) | |
| with gr.Row(): | |
| system_input = gr.Textbox(label='System Info Dict Password', interactive=True, | |
| visible=not is_public) | |
| system_btn2 = gr.Button(value='Get System Info Dict', visible=not is_public, size='sm') | |
| system_text2 = gr.Textbox(label='System Info Dict', interactive=False, | |
| visible=not is_public, show_copy_button=True) | |
| with gr.Row(): | |
| system_btn3 = gr.Button(value='Get Hash', visible=not is_public, size='sm') | |
| system_text3 = gr.Textbox(label='Hash', interactive=False, | |
| visible=not is_public, show_copy_button=True) | |
| with gr.Row(): | |
| zip_btn = gr.Button("Zip", size='sm') | |
| zip_text = gr.Textbox(label="Zip file name", interactive=False) | |
| file_output = gr.File(interactive=False, label="Zip file to Download") | |
| with gr.Row(): | |
| s3up_btn = gr.Button("S3UP", size='sm') | |
| s3up_text = gr.Textbox(label='S3UP result', interactive=False) | |
| with gr.TabItem("Terms of Service"): | |
| description = "" | |
| description += """<p><b> DISCLAIMERS: </b><ul><i><li>The model was trained on The Pile and other data, which may contain objectionable content. Use at own risk.</i></li>""" | |
| if kwargs['load_8bit']: | |
| description += """<i><li> Model is loaded in 8-bit and has other restrictions on this host. UX can be worse than non-hosted version.</i></li>""" | |
| description += """<i><li>Conversations may be used to improve h2oGPT. Do not share sensitive information.</i></li>""" | |
| if 'h2ogpt-research' in kwargs['base_model']: | |
| description += """<i><li>Research demonstration only, not used for commercial purposes.</i></li>""" | |
| description += """<i><li>By using h2oGPT, you accept our <a href="https://github.com/h2oai/h2ogpt/blob/main/docs/tos.md">Terms of Service</a></i></li></ul></p>""" | |
| gr.Markdown(value=description, show_label=False, interactive=False) | |
| with gr.TabItem("Hosts"): | |
| gr.Markdown(f""" | |
| {description_bottom} | |
| {task_info_md} | |
| """) | |
| # Get flagged data | |
| zip_data1 = functools.partial(zip_data, root_dirs=['flagged_data_points', kwargs['save_dir']]) | |
| zip_event = zip_btn.click(zip_data1, inputs=None, outputs=[file_output, zip_text], queue=False, | |
| api_name='zip_data' if allow_api else None) | |
| s3up_event = s3up_btn.click(s3up, inputs=zip_text, outputs=s3up_text, queue=False, | |
| api_name='s3up_data' if allow_api else None) | |
| def clear_file_list(): | |
| return None | |
| def make_non_interactive(*args): | |
| if len(args) == 1: | |
| return gr.update(interactive=False) | |
| else: | |
| return tuple([gr.update(interactive=False)] * len(args)) | |
| def make_interactive(*args): | |
| if len(args) == 1: | |
| return gr.update(interactive=True) | |
| else: | |
| return tuple([gr.update(interactive=True)] * len(args)) | |
| # Add to UserData or custom user db | |
| update_db_func = functools.partial(update_user_db, | |
| dbs=dbs, | |
| db_type=db_type, | |
| use_openai_embedding=use_openai_embedding, | |
| hf_embedding_model=hf_embedding_model, | |
| captions_model=captions_model, | |
| enable_captions=enable_captions, | |
| caption_loader=caption_loader, | |
| enable_ocr=enable_ocr, | |
| enable_pdf_ocr=enable_pdf_ocr, | |
| verbose=kwargs['verbose'], | |
| n_jobs=kwargs['n_jobs'], | |
| ) | |
| add_file_outputs = [fileup_output, langchain_mode] | |
| add_file_kwargs = dict(fn=update_db_func, | |
| inputs=[fileup_output, my_db_state, selection_docs_state, chunk, chunk_size, | |
| langchain_mode], | |
| outputs=add_file_outputs + [sources_text, doc_exception_text], | |
| queue=queue, | |
| api_name='add_file' if allow_api and allow_upload_to_user_data else None) | |
| # then no need for add buttons, only single changeable db | |
| eventdb1a = fileup_output.upload(make_non_interactive, inputs=add_file_outputs, outputs=add_file_outputs, | |
| show_progress='minimal') | |
| eventdb1 = eventdb1a.then(**add_file_kwargs, show_progress='full') | |
| eventdb1b = eventdb1.then(make_interactive, inputs=add_file_outputs, outputs=add_file_outputs, | |
| show_progress='minimal') | |
| # deal with challenge to have fileup_output itself as input | |
| add_file_kwargs2 = dict(fn=update_db_func, | |
| inputs=[fileup_output_text, my_db_state, selection_docs_state, chunk, chunk_size, | |
| langchain_mode], | |
| outputs=add_file_outputs + [sources_text, doc_exception_text], | |
| queue=queue, | |
| api_name='add_file_api' if allow_api and allow_upload_to_user_data else None) | |
| eventdb1_api = fileup_output_text.submit(**add_file_kwargs2, show_progress='full') | |
| # note for update_user_db_func output is ignored for db | |
| def clear_textbox(): | |
| return gr.Textbox.update(value='') | |
| update_user_db_url_func = functools.partial(update_db_func, is_url=True) | |
| add_url_outputs = [url_text, langchain_mode] | |
| add_url_kwargs = dict(fn=update_user_db_url_func, | |
| inputs=[url_text, my_db_state, selection_docs_state, chunk, chunk_size, | |
| langchain_mode], | |
| outputs=add_url_outputs + [sources_text, doc_exception_text], | |
| queue=queue, | |
| api_name='add_url' if allow_api and allow_upload_to_user_data else None) | |
| eventdb2a = url_text.submit(fn=dummy_fun, inputs=url_text, outputs=url_text, queue=queue, | |
| show_progress='minimal') | |
| # work around https://github.com/gradio-app/gradio/issues/4733 | |
| eventdb2b = eventdb2a.then(make_non_interactive, inputs=add_url_outputs, outputs=add_url_outputs, | |
| show_progress='minimal') | |
| eventdb2 = eventdb2b.then(**add_url_kwargs, show_progress='full') | |
| eventdb2c = eventdb2.then(make_interactive, inputs=add_url_outputs, outputs=add_url_outputs, | |
| show_progress='minimal') | |
| update_user_db_txt_func = functools.partial(update_db_func, is_txt=True) | |
| add_text_outputs = [user_text_text, langchain_mode] | |
| add_text_kwargs = dict(fn=update_user_db_txt_func, | |
| inputs=[user_text_text, my_db_state, selection_docs_state, chunk, chunk_size, | |
| langchain_mode], | |
| outputs=add_text_outputs + [sources_text, doc_exception_text], | |
| queue=queue, | |
| api_name='add_text' if allow_api and allow_upload_to_user_data else None | |
| ) | |
| eventdb3a = user_text_text.submit(fn=dummy_fun, inputs=user_text_text, outputs=user_text_text, queue=queue, | |
| show_progress='minimal') | |
| eventdb3b = eventdb3a.then(make_non_interactive, inputs=add_text_outputs, outputs=add_text_outputs, | |
| show_progress='minimal') | |
| eventdb3 = eventdb3b.then(**add_text_kwargs, show_progress='full') | |
| eventdb3c = eventdb3.then(make_interactive, inputs=add_text_outputs, outputs=add_text_outputs, | |
| show_progress='minimal') | |
| db_events = [eventdb1a, eventdb1, eventdb1b, eventdb1_api, | |
| eventdb2a, eventdb2, eventdb2b, eventdb2c, | |
| eventdb3a, eventdb3b, eventdb3, eventdb3c] | |
| get_sources1 = functools.partial(get_sources, dbs=dbs, docs_state0=docs_state0) | |
| # if change collection source, must clear doc selections from it to avoid inconsistency | |
| def clear_doc_choice(): | |
| return gr.Dropdown.update(choices=docs_state0, value=DocumentChoice.ALL.value) | |
| langchain_mode.change(clear_doc_choice, inputs=None, outputs=document_choice, queue=False) | |
| def resize_col_tabs(x): | |
| return gr.Dropdown.update(scale=x) | |
| col_tabs_scale.change(fn=resize_col_tabs, inputs=col_tabs_scale, outputs=col_tabs, queue=False) | |
| def resize_chatbots(x, num_model_lock=0): | |
| if num_model_lock == 0: | |
| num_model_lock = 3 # 2 + 1 (which is dup of first) | |
| else: | |
| num_model_lock = 2 + num_model_lock | |
| return tuple([gr.update(height=x)] * num_model_lock) | |
| resize_chatbots_func = functools.partial(resize_chatbots, num_model_lock=len(text_outputs)) | |
| text_outputs_height.change(fn=resize_chatbots_func, inputs=text_outputs_height, | |
| outputs=[text_output, text_output2] + text_outputs, queue=False) | |
| def update_dropdown(x): | |
| return gr.Dropdown.update(choices=x, value=[docs_state0[0]]) | |
| get_sources_args = dict(fn=get_sources1, inputs=[my_db_state, langchain_mode], | |
| outputs=[file_source, docs_state], | |
| queue=queue, | |
| api_name='get_sources' if allow_api else None) | |
| eventdb7 = get_sources_btn.click(**get_sources_args) \ | |
| .then(fn=update_dropdown, inputs=docs_state, outputs=document_choice) | |
| # show button, else only show when add. Could add to above get_sources for download/dropdown, but bit much maybe | |
| show_sources1 = functools.partial(get_source_files_given_langchain_mode, dbs=dbs) | |
| eventdb8 = show_sources_btn.click(fn=show_sources1, inputs=[my_db_state, langchain_mode], outputs=sources_text, | |
| api_name='show_sources' if allow_api else None) | |
| def update_viewable_dropdown(x): | |
| return gr.Dropdown.update(choices=x, | |
| value=viewable_docs_state0[0] if len(viewable_docs_state0) > 0 else None) | |
| get_viewable_sources1 = functools.partial(get_sources, dbs=dbs, docs_state0=viewable_docs_state0) | |
| get_viewable_sources_args = dict(fn=get_viewable_sources1, inputs=[my_db_state, langchain_mode], | |
| outputs=[file_source, viewable_docs_state], | |
| queue=queue, | |
| api_name='get_viewable_sources' if allow_api else None) | |
| eventdb12 = get_viewable_sources_btn.click(**get_viewable_sources_args) \ | |
| .then(fn=update_viewable_dropdown, inputs=viewable_docs_state, | |
| outputs=view_document_choice) | |
| def show_doc(file): | |
| dummy1 = gr.update(visible=False, value=None) | |
| dummy_ret = dummy1, dummy1, dummy1, dummy1 | |
| if not isinstance(file, str): | |
| return dummy_ret | |
| if file.endswith('.md'): | |
| try: | |
| with open(file, 'rt') as f: | |
| content = f.read() | |
| return dummy1, dummy1, dummy1, gr.update(visible=True, value=content) | |
| except: | |
| return dummy_ret | |
| if file.endswith('.py'): | |
| try: | |
| with open(file, 'rt') as f: | |
| content = f.read() | |
| content = f"```python\n{content}\n```" | |
| return dummy1, dummy1, dummy1, gr.update(visible=True, value=content) | |
| except: | |
| return dummy_ret | |
| if file.endswith('.txt') or file.endswith('.rst') or file.endswith('.rtf') or file.endswith('.toml'): | |
| try: | |
| with open(file, 'rt') as f: | |
| content = f.read() | |
| content = f"```text\n{content}\n```" | |
| return dummy1, dummy1, dummy1, gr.update(visible=True, value=content) | |
| except: | |
| return dummy_ret | |
| func = None | |
| if file.endswith(".csv"): | |
| func = pd.read_csv | |
| elif file.endswith(".pickle"): | |
| func = pd.read_pickle | |
| elif file.endswith(".xls") or file.endswith("xlsx"): | |
| func = pd.read_excel | |
| elif file.endswith('.json'): | |
| func = pd.read_json | |
| elif file.endswith('.xml'): | |
| func = pd.read_xml | |
| if func is not None: | |
| try: | |
| df = func(file).head(100) | |
| except: | |
| return dummy_ret | |
| return dummy1, gr.update(visible=True, value=df), dummy1, dummy1 | |
| port = int(os.getenv('GRADIO_SERVER_PORT', '7860')) | |
| import pathlib | |
| absolute_path_string = os.path.abspath(file) | |
| url_path = pathlib.Path(absolute_path_string).as_uri() | |
| url = get_url(absolute_path_string, from_str=True) | |
| img_url = url.replace("""<a href=""", """<img src=""") | |
| if file.endswith('.png') or file.endswith('.jpg') or file.endswith('.jpeg'): | |
| return gr.update(visible=True, value=img_url), dummy1, dummy1, dummy1 | |
| elif file.endswith('.pdf') or 'arxiv.org/pdf' in file: | |
| if file.startswith('http') or file.startswith('https'): | |
| # if file is online, then might as well use google(?) | |
| document1 = file | |
| return gr.update(visible=True, | |
| value=f"""<iframe width="1000" height="800" src="https://docs.google.com/viewerng/viewer?url={document1}&embedded=true" frameborder="0" height="100%" width="100%"> | |
| </iframe> | |
| """), dummy1, dummy1, dummy1 | |
| else: | |
| ip = get_local_ip() | |
| document1 = url_path.replace('file://', f'http://{ip}:{port}/') | |
| # document1 = url | |
| return gr.update(visible=True, value=f"""<object data="{document1}" type="application/pdf"> | |
| <iframe src="https://docs.google.com/viewer?url={document1}&embedded=true"></iframe> | |
| </object>"""), dummy1, dummy1, dummy1 | |
| else: | |
| return dummy_ret | |
| view_document_choice.select(fn=show_doc, inputs=view_document_choice, | |
| outputs=[doc_view, doc_view2, doc_view3, doc_view4]) | |
| # Get inputs to evaluate() and make_db() | |
| # don't deepcopy, can contain model itself | |
| all_kwargs = kwargs.copy() | |
| all_kwargs.update(locals()) | |
| refresh_sources1 = functools.partial(update_and_get_source_files_given_langchain_mode, | |
| **get_kwargs(update_and_get_source_files_given_langchain_mode, | |
| exclude_names=['db1s', 'langchain_mode', 'chunk', | |
| 'chunk_size'], | |
| **all_kwargs)) | |
| eventdb9 = refresh_sources_btn.click(fn=refresh_sources1, | |
| inputs=[my_db_state, langchain_mode, chunk, chunk_size], | |
| outputs=sources_text, | |
| api_name='refresh_sources' if allow_api else None) | |
| def check_admin_pass(x): | |
| return gr.update(visible=x == admin_pass) | |
| def close_admin(x): | |
| return gr.update(visible=not (x == admin_pass)) | |
| admin_pass_textbox.submit(check_admin_pass, inputs=admin_pass_textbox, outputs=system_row, queue=False) \ | |
| .then(close_admin, inputs=admin_pass_textbox, outputs=admin_row, queue=False) | |
| def add_langchain_mode(db1s, selection_docs_state1, langchain_mode1, y): | |
| for k in db1s: | |
| set_userid(db1s[k]) | |
| langchain_modes = selection_docs_state1['langchain_modes'] | |
| langchain_mode_paths = selection_docs_state1['langchain_mode_paths'] | |
| visible_langchain_modes = selection_docs_state1['visible_langchain_modes'] | |
| user_path = None | |
| valid = True | |
| y2 = y.strip().replace(' ', '').split(',') | |
| if len(y2) >= 1: | |
| langchain_mode2 = y2[0] | |
| if len(langchain_mode2) >= 3 and langchain_mode2.isalnum(): | |
| # real restriction is: | |
| # ValueError: Expected collection name that (1) contains 3-63 characters, (2) starts and ends with an alphanumeric character, (3) otherwise contains only alphanumeric characters, underscores or hyphens (-), (4) contains no two consecutive periods (..) and (5) is not a valid IPv4 address, got me | |
| # but just make simpler | |
| user_path = y2[1] if len(y2) > 1 else None # assume scratch if don't have user_path | |
| if user_path in ['', "''"]: | |
| # for scratch spaces | |
| user_path = None | |
| if langchain_mode2 in langchain_modes_intrinsic: | |
| user_path = None | |
| textbox = "Invalid access to use internal name: %s" % langchain_mode2 | |
| valid = False | |
| langchain_mode2 = langchain_mode1 | |
| elif user_path and allow_upload_to_user_data or not user_path and allow_upload_to_my_data: | |
| langchain_mode_paths.update({langchain_mode2: user_path}) | |
| if langchain_mode2 not in visible_langchain_modes: | |
| visible_langchain_modes.append(langchain_mode2) | |
| if langchain_mode2 not in langchain_modes: | |
| langchain_modes.append(langchain_mode2) | |
| textbox = '' | |
| if user_path: | |
| makedirs(user_path, exist_ok=True) | |
| else: | |
| valid = False | |
| langchain_mode2 = langchain_mode1 | |
| textbox = "Invalid access. user allowed: %s " \ | |
| "scratch allowed: %s" % (allow_upload_to_user_data, allow_upload_to_my_data) | |
| else: | |
| valid = False | |
| langchain_mode2 = langchain_mode1 | |
| textbox = "Invalid, collection must be >=3 characters and alphanumeric" | |
| else: | |
| valid = False | |
| langchain_mode2 = langchain_mode1 | |
| textbox = "Invalid, must be like UserData2, user_path2" | |
| selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1) | |
| df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1) | |
| choices = get_langchain_choices(selection_docs_state1) | |
| if valid and not user_path: | |
| # needs to have key for it to make it known different from userdata case in _update_user_db() | |
| db1s[langchain_mode2] = [None, None] | |
| if valid: | |
| save_collection_names(langchain_modes, visible_langchain_modes, langchain_mode_paths, LangChainMode, | |
| db1s) | |
| return db1s, selection_docs_state1, gr.update(choices=choices, | |
| value=langchain_mode2), textbox, df_langchain_mode_paths1 | |
| def remove_langchain_mode(db1s, selection_docs_state1, langchain_mode1, langchain_mode2, dbsu=None): | |
| for k in db1s: | |
| set_userid(db1s[k]) | |
| assert dbsu is not None | |
| langchain_modes = selection_docs_state1['langchain_modes'] | |
| langchain_mode_paths = selection_docs_state1['langchain_mode_paths'] | |
| visible_langchain_modes = selection_docs_state1['visible_langchain_modes'] | |
| if langchain_mode2 in db1s and not allow_upload_to_my_data or \ | |
| dbsu is not None and langchain_mode2 in dbsu and not allow_upload_to_user_data or \ | |
| langchain_mode2 in langchain_modes_intrinsic: | |
| # NOTE: Doesn't fail if remove MyData, but didn't debug odd behavior seen with upload after gone | |
| textbox = "Invalid access, cannot remove %s" % langchain_mode2 | |
| df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1) | |
| else: | |
| # change global variables | |
| if langchain_mode2 in visible_langchain_modes: | |
| visible_langchain_modes.remove(langchain_mode2) | |
| textbox = "" | |
| else: | |
| textbox = "%s was not visible" % langchain_mode2 | |
| if langchain_mode2 in langchain_modes: | |
| langchain_modes.remove(langchain_mode2) | |
| if langchain_mode2 in langchain_mode_paths: | |
| langchain_mode_paths.pop(langchain_mode2) | |
| if langchain_mode2 in db1s: | |
| # remove db entirely, so not in list, else need to manage visible list in update_langchain_mode_paths() | |
| # FIXME: Remove location? | |
| if langchain_mode2 != LangChainMode.MY_DATA.value: | |
| # don't remove last MyData, used as user hash | |
| db1s.pop(langchain_mode2) | |
| # only show | |
| selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1) | |
| df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1) | |
| save_collection_names(langchain_modes, visible_langchain_modes, langchain_mode_paths, LangChainMode, | |
| db1s) | |
| return db1s, selection_docs_state1, \ | |
| gr.update(choices=get_langchain_choices(selection_docs_state1), | |
| value=langchain_mode2), textbox, df_langchain_mode_paths1 | |
| new_langchain_mode_text.submit(fn=add_langchain_mode, | |
| inputs=[my_db_state, selection_docs_state, langchain_mode, | |
| new_langchain_mode_text], | |
| outputs=[my_db_state, selection_docs_state, langchain_mode, | |
| new_langchain_mode_text, | |
| langchain_mode_path_text], | |
| api_name='new_langchain_mode_text' if allow_api and allow_upload_to_user_data else None) | |
| remove_langchain_mode_func = functools.partial(remove_langchain_mode, dbsu=dbs) | |
| remove_langchain_mode_text.submit(fn=remove_langchain_mode_func, | |
| inputs=[my_db_state, selection_docs_state, langchain_mode, | |
| remove_langchain_mode_text], | |
| outputs=[my_db_state, selection_docs_state, langchain_mode, | |
| remove_langchain_mode_text, | |
| langchain_mode_path_text], | |
| api_name='remove_langchain_mode_text' if allow_api and allow_upload_to_user_data else None) | |
| def update_langchain_gr(db1s, selection_docs_state1, langchain_mode1): | |
| for k in db1s: | |
| set_userid(db1s[k]) | |
| langchain_modes = selection_docs_state1['langchain_modes'] | |
| langchain_mode_paths = selection_docs_state1['langchain_mode_paths'] | |
| visible_langchain_modes = selection_docs_state1['visible_langchain_modes'] | |
| # in-place | |
| # update user collaborative collections | |
| update_langchain(langchain_modes, visible_langchain_modes, langchain_mode_paths, '') | |
| # update scratch single-user collections | |
| user_hash = db1s.get(LangChainMode.MY_DATA.value, '')[1] | |
| update_langchain(langchain_modes, visible_langchain_modes, langchain_mode_paths, user_hash) | |
| selection_docs_state1 = update_langchain_mode_paths(db1s, selection_docs_state1) | |
| df_langchain_mode_paths1 = get_df_langchain_mode_paths(selection_docs_state1) | |
| return selection_docs_state1, \ | |
| gr.update(choices=get_langchain_choices(selection_docs_state1), | |
| value=langchain_mode1), df_langchain_mode_paths1 | |
| load_langchain.click(fn=update_langchain_gr, | |
| inputs=[my_db_state, selection_docs_state, langchain_mode], | |
| outputs=[selection_docs_state, langchain_mode, langchain_mode_path_text], | |
| api_name='load_langchain' if allow_api and allow_upload_to_user_data else None) | |
| inputs_list, inputs_dict = get_inputs_list(all_kwargs, kwargs['model_lower'], model_id=1) | |
| inputs_list2, inputs_dict2 = get_inputs_list(all_kwargs, kwargs['model_lower'], model_id=2) | |
| from functools import partial | |
| kwargs_evaluate = {k: v for k, v in all_kwargs.items() if k in inputs_kwargs_list} | |
| # ensure present | |
| for k in inputs_kwargs_list: | |
| assert k in kwargs_evaluate, "Missing %s" % k | |
| def evaluate_nochat(*args1, default_kwargs1=None, str_api=False, **kwargs1): | |
| args_list = list(args1) | |
| if str_api: | |
| user_kwargs = args_list[len(input_args_list)] | |
| assert isinstance(user_kwargs, str) | |
| user_kwargs = ast.literal_eval(user_kwargs) | |
| else: | |
| user_kwargs = {k: v for k, v in zip(eval_func_param_names, args_list[len(input_args_list):])} | |
| # only used for submit_nochat_api | |
| user_kwargs['chat'] = False | |
| if 'stream_output' not in user_kwargs: | |
| user_kwargs['stream_output'] = False | |
| if 'langchain_mode' not in user_kwargs: | |
| # if user doesn't specify, then assume disabled, not use default | |
| user_kwargs['langchain_mode'] = 'Disabled' | |
| if 'langchain_action' not in user_kwargs: | |
| user_kwargs['langchain_action'] = LangChainAction.QUERY.value | |
| if 'langchain_agents' not in user_kwargs: | |
| user_kwargs['langchain_agents'] = [] | |
| set1 = set(list(default_kwargs1.keys())) | |
| set2 = set(eval_func_param_names) | |
| assert set1 == set2, "Set diff: %s %s: %s" % (set1, set2, set1.symmetric_difference(set2)) | |
| # correct ordering. Note some things may not be in default_kwargs, so can't be default of user_kwargs.get() | |
| model_state1 = args_list[0] | |
| my_db_state1 = args_list[1] | |
| selection_docs_state1 = args_list[2] | |
| args_list = [user_kwargs[k] if k in user_kwargs and user_kwargs[k] is not None else default_kwargs1[k] for k | |
| in eval_func_param_names] | |
| assert len(args_list) == len(eval_func_param_names) | |
| args_list = [model_state1, my_db_state1, selection_docs_state1] + args_list | |
| try: | |
| for res_dict in evaluate(*tuple(args_list), **kwargs1): | |
| if str_api: | |
| # full return of dict | |
| yield res_dict | |
| elif kwargs['langchain_mode'] == 'Disabled': | |
| yield fix_text_for_gradio(res_dict['response']) | |
| else: | |
| yield '<br>' + fix_text_for_gradio(res_dict['response']) | |
| finally: | |
| clear_torch_cache() | |
| clear_embeddings(user_kwargs['langchain_mode'], my_db_state1) | |
| fun = partial(evaluate_nochat, | |
| default_kwargs1=default_kwargs, | |
| str_api=False, | |
| **kwargs_evaluate) | |
| fun2 = partial(evaluate_nochat, | |
| default_kwargs1=default_kwargs, | |
| str_api=False, | |
| **kwargs_evaluate) | |
| fun_with_dict_str = partial(evaluate_nochat, | |
| default_kwargs1=default_kwargs, | |
| str_api=True, | |
| **kwargs_evaluate | |
| ) | |
| dark_mode_btn.click( | |
| None, | |
| None, | |
| None, | |
| _js=get_dark_js(), | |
| api_name="dark" if allow_api else None, | |
| queue=False, | |
| ) | |
| def visible_toggle(x): | |
| x = 'off' if x == 'on' else 'on' | |
| return x, gr.Column.update(visible=True if x == 'on' else False) | |
| side_bar_btn.click(fn=visible_toggle, | |
| inputs=side_bar_text, | |
| outputs=[side_bar_text, side_bar], | |
| queue=False) | |
| submit_buttons_btn.click(fn=visible_toggle, | |
| inputs=submit_buttons_text, | |
| outputs=[submit_buttons_text, submit_buttons], | |
| queue=False) | |
| # examples after submit or any other buttons for chat or no chat | |
| if kwargs['examples'] is not None and kwargs['show_examples']: | |
| gr.Examples(examples=kwargs['examples'], inputs=inputs_list) | |
| # Score | |
| def score_last_response(*args, nochat=False, num_model_lock=0): | |
| try: | |
| if num_model_lock > 0: | |
| # then lock way | |
| args_list = list(args).copy() | |
| outputs = args_list[-num_model_lock:] | |
| score_texts1 = [] | |
| for output in outputs: | |
| # same input, put into form good for _score_last_response() | |
| args_list[-1] = output | |
| score_texts1.append( | |
| _score_last_response(*tuple(args_list), nochat=nochat, | |
| num_model_lock=num_model_lock, prefix='')) | |
| if len(score_texts1) > 1: | |
| return "Response Scores: %s" % ' '.join(score_texts1) | |
| else: | |
| return "Response Scores: %s" % score_texts1[0] | |
| else: | |
| return _score_last_response(*args, nochat=nochat, num_model_lock=num_model_lock) | |
| finally: | |
| clear_torch_cache() | |
| def _score_last_response(*args, nochat=False, num_model_lock=0, prefix='Response Score: '): | |
| """ Similar to user() """ | |
| args_list = list(args) | |
| smodel = score_model_state0['model'] | |
| stokenizer = score_model_state0['tokenizer'] | |
| sdevice = score_model_state0['device'] | |
| if memory_restriction_level > 0: | |
| max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256 | |
| elif hasattr(stokenizer, 'model_max_length'): | |
| max_length_tokenize = stokenizer.model_max_length | |
| else: | |
| # limit to 1024, not worth OOMing on reward score | |
| max_length_tokenize = 2048 - 1024 | |
| cutoff_len = max_length_tokenize * 4 # restrict deberta related to max for LLM | |
| if not nochat: | |
| history = args_list[-1] | |
| if history is None: | |
| history = [] | |
| if smodel is not None and \ | |
| stokenizer is not None and \ | |
| sdevice is not None and \ | |
| history is not None and len(history) > 0 and \ | |
| history[-1] is not None and \ | |
| len(history[-1]) >= 2: | |
| os.environ['TOKENIZERS_PARALLELISM'] = 'false' | |
| question = history[-1][0] | |
| answer = history[-1][1] | |
| else: | |
| return '%sNA' % prefix | |
| else: | |
| answer = args_list[-1] | |
| instruction_nochat_arg_id = eval_func_param_names.index('instruction_nochat') | |
| question = args_list[instruction_nochat_arg_id] | |
| if question is None: | |
| return '%sBad Question' % prefix | |
| if answer is None: | |
| return '%sBad Answer' % prefix | |
| try: | |
| score = score_qa(smodel, stokenizer, max_length_tokenize, question, answer, cutoff_len) | |
| finally: | |
| clear_torch_cache() | |
| if isinstance(score, str): | |
| return '%sNA' % prefix | |
| return '{}{:.1%}'.format(prefix, score) | |
| def noop_score_last_response(*args, **kwargs): | |
| return "Response Score: Disabled" | |
| if kwargs['score_model']: | |
| score_fun = score_last_response | |
| else: | |
| score_fun = noop_score_last_response | |
| score_args = dict(fn=score_fun, | |
| inputs=inputs_list + [text_output], | |
| outputs=[score_text], | |
| ) | |
| score_args2 = dict(fn=partial(score_fun), | |
| inputs=inputs_list2 + [text_output2], | |
| outputs=[score_text2], | |
| ) | |
| score_fun_func = functools.partial(score_fun, num_model_lock=len(text_outputs)) | |
| all_score_args = dict(fn=score_fun_func, | |
| inputs=inputs_list + text_outputs, | |
| outputs=score_text, | |
| ) | |
| score_args_nochat = dict(fn=partial(score_fun, nochat=True), | |
| inputs=inputs_list + [text_output_nochat], | |
| outputs=[score_text_nochat], | |
| ) | |
| def update_history(*args, undo=False, retry=False, sanitize_user_prompt=False): | |
| """ | |
| User that fills history for bot | |
| :param args: | |
| :param undo: | |
| :param retry: | |
| :param sanitize_user_prompt: | |
| :return: | |
| """ | |
| args_list = list(args) | |
| user_message = args_list[eval_func_param_names.index('instruction')] # chat only | |
| input1 = args_list[eval_func_param_names.index('iinput')] # chat only | |
| prompt_type1 = args_list[eval_func_param_names.index('prompt_type')] | |
| langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')] | |
| langchain_action1 = args_list[eval_func_param_names.index('langchain_action')] | |
| langchain_agents1 = args_list[eval_func_param_names.index('langchain_agents')] | |
| document_subset1 = args_list[eval_func_param_names.index('document_subset')] | |
| document_choice1 = args_list[eval_func_param_names.index('document_choice')] | |
| if not prompt_type1: | |
| # shouldn't have to specify if CLI launched model | |
| prompt_type1 = kwargs['prompt_type'] | |
| # apply back | |
| args_list[eval_func_param_names.index('prompt_type')] = prompt_type1 | |
| if input1 and not user_message.endswith(':'): | |
| user_message1 = user_message + ":" + input1 | |
| elif input1: | |
| user_message1 = user_message + input1 | |
| else: | |
| user_message1 = user_message | |
| if sanitize_user_prompt: | |
| from better_profanity import profanity | |
| user_message1 = profanity.censor(user_message1) | |
| history = args_list[-1] | |
| if history is None: | |
| # bad history | |
| history = [] | |
| history = history.copy() | |
| if undo: | |
| if len(history) > 0: | |
| history.pop() | |
| return history | |
| if retry: | |
| if history: | |
| history[-1][1] = None | |
| return history | |
| if user_message1 in ['', None, '\n']: | |
| if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1): | |
| # reject non-retry submit/enter | |
| return history | |
| user_message1 = fix_text_for_gradio(user_message1) | |
| return history + [[user_message1, None]] | |
| def user(*args, undo=False, retry=False, sanitize_user_prompt=False): | |
| return update_history(*args, undo=undo, retry=retry, sanitize_user_prompt=sanitize_user_prompt) | |
| def all_user(*args, undo=False, retry=False, sanitize_user_prompt=False, num_model_lock=0): | |
| args_list = list(args) | |
| history_list = args_list[-num_model_lock:] | |
| assert len(history_list) > 0, "Bad history list: %s" % history_list | |
| for hi, history in enumerate(history_list): | |
| if num_model_lock > 0: | |
| hargs = args_list[:-num_model_lock].copy() | |
| else: | |
| hargs = args_list.copy() | |
| hargs += [history] | |
| history_list[hi] = update_history(*hargs, undo=undo, retry=retry, | |
| sanitize_user_prompt=sanitize_user_prompt) | |
| if len(history_list) > 1: | |
| return tuple(history_list) | |
| else: | |
| return history_list[0] | |
| def get_model_max_length(model_state1): | |
| if model_state1 and not isinstance(model_state1["tokenizer"], str): | |
| tokenizer = model_state1["tokenizer"] | |
| elif model_state0 and not isinstance(model_state0["tokenizer"], str): | |
| tokenizer = model_state0["tokenizer"] | |
| else: | |
| tokenizer = None | |
| if tokenizer is not None: | |
| return tokenizer.model_max_length | |
| else: | |
| return 2000 | |
| def prep_bot(*args, retry=False, which_model=0): | |
| """ | |
| :param args: | |
| :param retry: | |
| :param which_model: identifies which model if doing model_lock | |
| API only called for which_model=0, default for inputs_list, but rest should ignore inputs_list | |
| :return: last element is True if should run bot, False if should just yield history | |
| """ | |
| isize = len(input_args_list) + 1 # states + chat history | |
| # don't deepcopy, can contain model itself | |
| args_list = list(args).copy() | |
| model_state1 = args_list[-isize] | |
| my_db_state1 = args_list[-isize + 1] | |
| selection_docs_state1 = args_list[-isize + 2] | |
| history = args_list[-1] | |
| prompt_type1 = args_list[eval_func_param_names.index('prompt_type')] | |
| prompt_dict1 = args_list[eval_func_param_names.index('prompt_dict')] | |
| if model_state1['model'] is None or model_state1['model'] == no_model_str: | |
| return history, None, None, None | |
| args_list = args_list[:-isize] # only keep rest needed for evaluate() | |
| langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')] | |
| add_chat_history_to_context1 = args_list[eval_func_param_names.index('add_chat_history_to_context')] | |
| langchain_action1 = args_list[eval_func_param_names.index('langchain_action')] | |
| langchain_agents1 = args_list[eval_func_param_names.index('langchain_agents')] | |
| document_subset1 = args_list[eval_func_param_names.index('document_subset')] | |
| document_choice1 = args_list[eval_func_param_names.index('document_choice')] | |
| if not history: | |
| print("No history", flush=True) | |
| history = [] | |
| return history, None, None, None | |
| instruction1 = history[-1][0] | |
| if retry and history: | |
| # if retry, pop history and move onto bot stuff | |
| instruction1 = history[-1][0] | |
| history[-1][1] = None | |
| elif not instruction1: | |
| if not allow_empty_instruction(langchain_mode1, document_subset1, langchain_action1): | |
| # if not retrying, then reject empty query | |
| return history, None, None, None | |
| elif len(history) > 0 and history[-1][1] not in [None, '']: | |
| # reject submit button if already filled and not retrying | |
| # None when not filling with '' to keep client happy | |
| return history, None, None, None | |
| # shouldn't have to specify in API prompt_type if CLI launched model, so prefer global CLI one if have it | |
| prompt_type1, prompt_dict1 = update_prompt(prompt_type1, prompt_dict1, model_state1, | |
| which_model=which_model) | |
| # apply back to args_list for evaluate() | |
| args_list[eval_func_param_names.index('prompt_type')] = prompt_type1 | |
| args_list[eval_func_param_names.index('prompt_dict')] = prompt_dict1 | |
| chat1 = args_list[eval_func_param_names.index('chat')] | |
| model_max_length1 = get_model_max_length(model_state1) | |
| context1 = history_to_context(history, langchain_mode1, | |
| add_chat_history_to_context1, | |
| prompt_type1, prompt_dict1, chat1, | |
| model_max_length1, memory_restriction_level, | |
| kwargs['keep_sources_in_context']) | |
| args_list[0] = instruction1 # override original instruction with history from user | |
| args_list[2] = context1 | |
| fun1 = partial(evaluate, | |
| model_state1, | |
| my_db_state1, | |
| selection_docs_state1, | |
| *tuple(args_list), | |
| **kwargs_evaluate) | |
| return history, fun1, langchain_mode1, my_db_state1 | |
| def get_response(fun1, history): | |
| """ | |
| bot that consumes history for user input | |
| instruction (from input_list) itself is not consumed by bot | |
| :return: | |
| """ | |
| if not fun1: | |
| yield history, '' | |
| return | |
| try: | |
| for output_fun in fun1(): | |
| output = output_fun['response'] | |
| extra = output_fun['sources'] # FIXME: can show sources in separate text box etc. | |
| # ensure good visually, else markdown ignores multiple \n | |
| bot_message = fix_text_for_gradio(output) | |
| history[-1][1] = bot_message | |
| yield history, '' | |
| except StopIteration: | |
| yield history, '' | |
| except RuntimeError as e: | |
| if "generator raised StopIteration" in str(e): | |
| # assume last entry was bad, undo | |
| history.pop() | |
| yield history, '' | |
| else: | |
| if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None: | |
| history[-1][1] = '' | |
| yield history, str(e) | |
| raise | |
| except Exception as e: | |
| # put error into user input | |
| ex = "Exception: %s" % str(e) | |
| if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None: | |
| history[-1][1] = '' | |
| yield history, ex | |
| raise | |
| finally: | |
| clear_torch_cache() | |
| return | |
| def clear_embeddings(langchain_mode1, db1s): | |
| # clear any use of embedding that sits on GPU, else keeps accumulating GPU usage even if clear torch cache | |
| if db_type == 'chroma' and langchain_mode1 not in ['LLM', 'Disabled', None, '']: | |
| from gpt_langchain import clear_embedding | |
| db = dbs.get('langchain_mode1') | |
| if db is not None and not isinstance(db, str): | |
| clear_embedding(db) | |
| if db1s is not None and langchain_mode1 in db1s: | |
| db1 = db1s[langchain_mode1] | |
| if len(db1) == 2: | |
| clear_embedding(db1[0]) | |
| def bot(*args, retry=False): | |
| history, fun1, langchain_mode1, db1 = prep_bot(*args, retry=retry) | |
| try: | |
| for res in get_response(fun1, history): | |
| yield res | |
| finally: | |
| clear_torch_cache() | |
| clear_embeddings(langchain_mode1, db1) | |
| def all_bot(*args, retry=False, model_states1=None): | |
| args_list = list(args).copy() | |
| chatbots = args_list[-len(model_states1):] | |
| args_list0 = args_list[:-len(model_states1)] # same for all models | |
| exceptions = [] | |
| stream_output1 = args_list[eval_func_param_names.index('stream_output')] | |
| max_time1 = args_list[eval_func_param_names.index('max_time')] | |
| langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')] | |
| isize = len(input_args_list) + 1 # states + chat history | |
| db1s = None | |
| try: | |
| gen_list = [] | |
| for chatboti, (chatbot1, model_state1) in enumerate(zip(chatbots, model_states1)): | |
| args_list1 = args_list0.copy() | |
| args_list1.insert(-isize + 2, | |
| model_state1) # insert at -2 so is at -3, and after chatbot1 added, at -4 | |
| # if at start, have None in response still, replace with '' so client etc. acts like normal | |
| # assumes other parts of code treat '' and None as if no response yet from bot | |
| # can't do this later in bot code as racy with threaded generators | |
| if len(chatbot1) > 0 and len(chatbot1[-1]) == 2 and chatbot1[-1][1] is None: | |
| chatbot1[-1][1] = '' | |
| args_list1.append(chatbot1) | |
| # so consistent with prep_bot() | |
| # with model_state1 at -3, my_db_state1 at -2, and history(chatbot) at -1 | |
| # langchain_mode1 and my_db_state1 should be same for every bot | |
| history, fun1, langchain_mode1, db1s = prep_bot(*tuple(args_list1), retry=retry, | |
| which_model=chatboti) | |
| gen1 = get_response(fun1, history) | |
| if stream_output1: | |
| gen1 = TimeoutIterator(gen1, timeout=0.01, sentinel=None, raise_on_exception=False) | |
| # else timeout will truncate output for non-streaming case | |
| gen_list.append(gen1) | |
| bots_old = chatbots.copy() | |
| exceptions_old = [''] * len(bots_old) | |
| tgen0 = time.time() | |
| for res1 in itertools.zip_longest(*gen_list): | |
| if time.time() - tgen0 > max_time1: | |
| print("Took too long: %s" % max_time1, flush=True) | |
| break | |
| bots = [x[0] if x is not None and not isinstance(x, BaseException) else y for x, y in | |
| zip(res1, bots_old)] | |
| bots_old = bots.copy() | |
| def larger_str(x, y): | |
| return x if len(x) > len(y) else y | |
| exceptions = [x[1] if x is not None and not isinstance(x, BaseException) else larger_str(str(x), y) | |
| for x, y in zip(res1, exceptions_old)] | |
| exceptions_old = exceptions.copy() | |
| def choose_exc(x): | |
| # don't expose ports etc. to exceptions window | |
| if is_public: | |
| return "Endpoint unavailable or failed" | |
| else: | |
| return x | |
| exceptions_str = '\n'.join( | |
| ['Model %s: %s' % (iix, choose_exc(x)) for iix, x in enumerate(exceptions) if | |
| x not in [None, '', 'None']]) | |
| if len(bots) > 1: | |
| yield tuple(bots + [exceptions_str]) | |
| else: | |
| yield bots[0], exceptions_str | |
| if exceptions: | |
| exceptions = [x for x in exceptions if x not in ['', None, 'None']] | |
| if exceptions: | |
| print("Generate exceptions: %s" % exceptions, flush=True) | |
| finally: | |
| clear_torch_cache() | |
| clear_embeddings(langchain_mode1, db1s) | |
| # NORMAL MODEL | |
| user_args = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']), | |
| inputs=inputs_list + [text_output], | |
| outputs=text_output, | |
| ) | |
| bot_args = dict(fn=bot, | |
| inputs=inputs_list + [model_state, my_db_state, selection_docs_state] + [text_output], | |
| outputs=[text_output, chat_exception_text], | |
| ) | |
| retry_bot_args = dict(fn=functools.partial(bot, retry=True), | |
| inputs=inputs_list + [model_state, my_db_state, selection_docs_state] + [text_output], | |
| outputs=[text_output, chat_exception_text], | |
| ) | |
| retry_user_args = dict(fn=functools.partial(user, retry=True), | |
| inputs=inputs_list + [text_output], | |
| outputs=text_output, | |
| ) | |
| undo_user_args = dict(fn=functools.partial(user, undo=True), | |
| inputs=inputs_list + [text_output], | |
| outputs=text_output, | |
| ) | |
| # MODEL2 | |
| user_args2 = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt']), | |
| inputs=inputs_list2 + [text_output2], | |
| outputs=text_output2, | |
| ) | |
| bot_args2 = dict(fn=bot, | |
| inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state] + [text_output2], | |
| outputs=[text_output2, chat_exception_text], | |
| ) | |
| retry_bot_args2 = dict(fn=functools.partial(bot, retry=True), | |
| inputs=inputs_list2 + [model_state2, my_db_state, selection_docs_state] + [text_output2], | |
| outputs=[text_output2, chat_exception_text], | |
| ) | |
| retry_user_args2 = dict(fn=functools.partial(user, retry=True), | |
| inputs=inputs_list2 + [text_output2], | |
| outputs=text_output2, | |
| ) | |
| undo_user_args2 = dict(fn=functools.partial(user, undo=True), | |
| inputs=inputs_list2 + [text_output2], | |
| outputs=text_output2, | |
| ) | |
| # MODEL N | |
| all_user_args = dict(fn=functools.partial(all_user, | |
| sanitize_user_prompt=kwargs['sanitize_user_prompt'], | |
| num_model_lock=len(text_outputs), | |
| ), | |
| inputs=inputs_list + text_outputs, | |
| outputs=text_outputs, | |
| ) | |
| all_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states), | |
| inputs=inputs_list + [my_db_state, selection_docs_state] + text_outputs, | |
| outputs=text_outputs + [chat_exception_text], | |
| ) | |
| all_retry_bot_args = dict(fn=functools.partial(all_bot, model_states1=model_states, retry=True), | |
| inputs=inputs_list + [my_db_state, selection_docs_state] + text_outputs, | |
| outputs=text_outputs + [chat_exception_text], | |
| ) | |
| all_retry_user_args = dict(fn=functools.partial(all_user, retry=True, | |
| sanitize_user_prompt=kwargs['sanitize_user_prompt'], | |
| num_model_lock=len(text_outputs), | |
| ), | |
| inputs=inputs_list + text_outputs, | |
| outputs=text_outputs, | |
| ) | |
| all_undo_user_args = dict(fn=functools.partial(all_user, undo=True, | |
| sanitize_user_prompt=kwargs['sanitize_user_prompt'], | |
| num_model_lock=len(text_outputs), | |
| ), | |
| inputs=inputs_list + text_outputs, | |
| outputs=text_outputs, | |
| ) | |
| def clear_instruct(): | |
| return gr.Textbox.update(value='') | |
| def deselect_radio_chats(): | |
| return gr.update(value=None) | |
| def clear_all(): | |
| return gr.Textbox.update(value=''), gr.Textbox.update(value=''), gr.update(value=None), \ | |
| gr.Textbox.update(value=''), gr.Textbox.update(value='') | |
| if kwargs['model_states']: | |
| submits1 = submits2 = submits3 = [] | |
| submits4 = [] | |
| fun_source = [instruction.submit, submit.click, retry_btn.click] | |
| fun_name = ['instruction', 'submit', 'retry'] | |
| user_args = [all_user_args, all_user_args, all_retry_user_args] | |
| bot_args = [all_bot_args, all_bot_args, all_retry_bot_args] | |
| for userargs1, botarg1, funn1, funs1 in zip(user_args, bot_args, fun_name, fun_source): | |
| submit_event11 = funs1(fn=dummy_fun, | |
| inputs=instruction, outputs=instruction, queue=queue) | |
| submit_event1a = submit_event11.then(**userargs1, queue=queue, | |
| api_name='%s' % funn1 if allow_api else None) | |
| # if hit enter on new instruction for submitting new query, no longer the saved chat | |
| submit_event1b = submit_event1a.then(clear_all, inputs=None, | |
| outputs=[instruction, iinput, radio_chats, score_text, | |
| score_text2], | |
| queue=queue) | |
| submit_event1c = submit_event1b.then(**botarg1, | |
| api_name='%s_bot' % funn1 if allow_api else None, | |
| queue=queue) | |
| submit_event1d = submit_event1c.then(**all_score_args, | |
| api_name='%s_bot_score' % funn1 if allow_api else None, | |
| queue=queue) | |
| submits1.extend([submit_event1a, submit_event1b, submit_event1c, submit_event1d]) | |
| # if undo, no longer the saved chat | |
| submit_event4 = undo.click(fn=dummy_fun, | |
| inputs=instruction, outputs=instruction, queue=queue) \ | |
| .then(**all_undo_user_args, api_name='undo' if allow_api else None) \ | |
| .then(clear_all, inputs=None, outputs=[instruction, iinput, radio_chats, score_text, | |
| score_text2], queue=queue) \ | |
| .then(**all_score_args, api_name='undo_score' if allow_api else None) | |
| submits4 = [submit_event4] | |
| else: | |
| # in case 2nd model, consume instruction first, so can clear quickly | |
| # bot doesn't consume instruction itself, just history from user, so why works | |
| submit_event11 = instruction.submit(fn=dummy_fun, | |
| inputs=instruction, outputs=instruction, queue=queue) | |
| submit_event1a = submit_event11.then(**user_args, queue=queue, | |
| api_name='instruction' if allow_api else None) | |
| # if hit enter on new instruction for submitting new query, no longer the saved chat | |
| submit_event1a2 = submit_event1a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue) | |
| submit_event1b = submit_event1a2.then(**user_args2, api_name='instruction2' if allow_api else None) | |
| submit_event1c = submit_event1b.then(clear_instruct, None, instruction) \ | |
| .then(clear_instruct, None, iinput) | |
| submit_event1d = submit_event1c.then(**bot_args, api_name='instruction_bot' if allow_api else None, | |
| queue=queue) | |
| submit_event1e = submit_event1d.then(**score_args, | |
| api_name='instruction_bot_score' if allow_api else None, | |
| queue=queue) | |
| submit_event1f = submit_event1e.then(**bot_args2, api_name='instruction_bot2' if allow_api else None, | |
| queue=queue) | |
| submit_event1g = submit_event1f.then(**score_args2, | |
| api_name='instruction_bot_score2' if allow_api else None, queue=queue) | |
| submits1 = [submit_event1a, submit_event1a2, submit_event1b, submit_event1c, submit_event1d, | |
| submit_event1e, | |
| submit_event1f, submit_event1g] | |
| submit_event21 = submit.click(fn=dummy_fun, | |
| inputs=instruction, outputs=instruction, queue=queue) | |
| submit_event2a = submit_event21.then(**user_args, api_name='submit' if allow_api else None) | |
| # if submit new query, no longer the saved chat | |
| submit_event2a2 = submit_event2a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue) | |
| submit_event2b = submit_event2a2.then(**user_args2, api_name='submit2' if allow_api else None) | |
| submit_event2c = submit_event2b.then(clear_all, inputs=None, | |
| outputs=[instruction, iinput, radio_chats, score_text, score_text2], | |
| queue=queue) | |
| submit_event2d = submit_event2c.then(**bot_args, api_name='submit_bot' if allow_api else None, queue=queue) | |
| submit_event2e = submit_event2d.then(**score_args, | |
| api_name='submit_bot_score' if allow_api else None, | |
| queue=queue) | |
| submit_event2f = submit_event2e.then(**bot_args2, api_name='submit_bot2' if allow_api else None, | |
| queue=queue) | |
| submit_event2g = submit_event2f.then(**score_args2, | |
| api_name='submit_bot_score2' if allow_api else None, | |
| queue=queue) | |
| submits2 = [submit_event2a, submit_event2a2, submit_event2b, submit_event2c, submit_event2d, | |
| submit_event2e, | |
| submit_event2f, submit_event2g] | |
| submit_event31 = retry_btn.click(fn=dummy_fun, | |
| inputs=instruction, outputs=instruction, queue=queue) | |
| submit_event3a = submit_event31.then(**user_args, api_name='retry' if allow_api else None) | |
| # if retry, no longer the saved chat | |
| submit_event3a2 = submit_event3a.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=queue) | |
| submit_event3b = submit_event3a2.then(**user_args2, api_name='retry2' if allow_api else None) | |
| submit_event3c = submit_event3b.then(clear_instruct, None, instruction) \ | |
| .then(clear_instruct, None, iinput) | |
| submit_event3d = submit_event3c.then(**retry_bot_args, api_name='retry_bot' if allow_api else None, | |
| queue=queue) | |
| submit_event3e = submit_event3d.then(**score_args, | |
| api_name='retry_bot_score' if allow_api else None, | |
| queue=queue) | |
| submit_event3f = submit_event3e.then(**retry_bot_args2, api_name='retry_bot2' if allow_api else None, | |
| queue=queue) | |
| submit_event3g = submit_event3f.then(**score_args2, | |
| api_name='retry_bot_score2' if allow_api else None, | |
| queue=queue) | |
| submits3 = [submit_event3a, submit_event3a2, submit_event3b, submit_event3c, submit_event3d, | |
| submit_event3e, | |
| submit_event3f, submit_event3g] | |
| # if undo, no longer the saved chat | |
| submit_event4 = undo.click(fn=dummy_fun, | |
| inputs=instruction, outputs=instruction, queue=queue) \ | |
| .then(**undo_user_args, api_name='undo' if allow_api else None) \ | |
| .then(**undo_user_args2, api_name='undo2' if allow_api else None) \ | |
| .then(clear_all, inputs=None, outputs=[instruction, iinput, radio_chats, score_text, | |
| score_text2], queue=queue) \ | |
| .then(**score_args, api_name='undo_score' if allow_api else None) \ | |
| .then(**score_args2, api_name='undo_score2' if allow_api else None) | |
| submits4 = [submit_event4] | |
| # MANAGE CHATS | |
| def dedup(short_chat, short_chats): | |
| if short_chat not in short_chats: | |
| return short_chat | |
| for i in range(1, 1000): | |
| short_chat_try = short_chat + "_" + str(i) | |
| if short_chat_try not in short_chats: | |
| return short_chat_try | |
| # fallback and hope for best | |
| short_chat = short_chat + "_" + str(random.random()) | |
| return short_chat | |
| def get_short_chat(x, short_chats, short_len=20, words=4): | |
| if x and len(x[0]) == 2 and x[0][0] is not None: | |
| short_chat = ' '.join(x[0][0][:short_len].split(' ')[:words]).strip() | |
| if not short_chat: | |
| # e.g.summarization, try using answer | |
| short_chat = ' '.join(x[0][1][:short_len].split(' ')[:words]).strip() | |
| if not short_chat: | |
| short_chat = 'Unk' | |
| short_chat = dedup(short_chat, short_chats) | |
| else: | |
| short_chat = None | |
| return short_chat | |
| def is_chat_same(x, y): | |
| # <p> etc. added in chat, try to remove some of that to help avoid dup entries when hit new conversation | |
| is_same = True | |
| # length of conversation has to be same | |
| if len(x) != len(y): | |
| return False | |
| if len(x) != len(y): | |
| return False | |
| for stepx, stepy in zip(x, y): | |
| if len(stepx) != len(stepy): | |
| # something off with a conversation | |
| return False | |
| for stepxx, stepyy in zip(stepx, stepy): | |
| if len(stepxx) != len(stepyy): | |
| # something off with a conversation | |
| return False | |
| if len(stepxx) != 2: | |
| # something off | |
| return False | |
| if len(stepyy) != 2: | |
| # something off | |
| return False | |
| questionx = stepxx[0].replace('<p>', '').replace('</p>', '') if stepxx[0] is not None else None | |
| answerx = stepxx[1].replace('<p>', '').replace('</p>', '') if stepxx[1] is not None else None | |
| questiony = stepyy[0].replace('<p>', '').replace('</p>', '') if stepyy[0] is not None else None | |
| answery = stepyy[1].replace('<p>', '').replace('</p>', '') if stepyy[1] is not None else None | |
| if questionx != questiony or answerx != answery: | |
| return False | |
| return is_same | |
| def save_chat(*args, chat_is_list=False): | |
| args_list = list(args) | |
| if not chat_is_list: | |
| # list of chatbot histories, | |
| # can't pass in list with list of chatbot histories and state due to gradio limits | |
| chat_list = args_list[:-1] | |
| else: | |
| assert len(args_list) == 2 | |
| chat_list = args_list[0] | |
| # if old chat file with single chatbot, get into shape | |
| if isinstance(chat_list, list) and len(chat_list) > 0 and isinstance(chat_list[0], list) and len( | |
| chat_list[0]) == 2 and isinstance(chat_list[0][0], str) and isinstance(chat_list[0][1], str): | |
| chat_list = [chat_list] | |
| # remove None histories | |
| chat_list_not_none = [x for x in chat_list if x and len(x) > 0 and len(x[0]) == 2 and x[0][1] is not None] | |
| chat_list_none = [x for x in chat_list if x not in chat_list_not_none] | |
| if len(chat_list_none) > 0 and len(chat_list_not_none) == 0: | |
| raise ValueError("Invalid chat file") | |
| # dict with keys of short chat names, values of list of list of chatbot histories | |
| chat_state1 = args_list[-1] | |
| short_chats = list(chat_state1.keys()) | |
| if len(chat_list_not_none) > 0: | |
| # make short_chat key from only first history, based upon question that is same anyways | |
| chat_first = chat_list_not_none[0] | |
| short_chat = get_short_chat(chat_first, short_chats) | |
| if short_chat: | |
| old_chat_lists = list(chat_state1.values()) | |
| already_exists = any([is_chat_same(chat_list, x) for x in old_chat_lists]) | |
| if not already_exists: | |
| chat_state1[short_chat] = chat_list.copy() | |
| # reverse so newest at top | |
| choices = list(chat_state1.keys()).copy() | |
| choices.reverse() | |
| return chat_state1, gr.update(choices=choices, value=None) | |
| def switch_chat(chat_key, chat_state1, num_model_lock=0): | |
| chosen_chat = chat_state1[chat_key] | |
| # deal with possible different size of chat list vs. current list | |
| ret_chat = [None] * (2 + num_model_lock) | |
| for chati in range(0, 2 + num_model_lock): | |
| ret_chat[chati % len(ret_chat)] = chosen_chat[chati % len(chosen_chat)] | |
| return tuple(ret_chat) | |
| def clear_texts(*args): | |
| return tuple([gr.Textbox.update(value='')] * len(args)) | |
| def clear_scores(): | |
| return gr.Textbox.update(value=res_value), \ | |
| gr.Textbox.update(value='Response Score: NA'), \ | |
| gr.Textbox.update(value='Response Score: NA') | |
| switch_chat_fun = functools.partial(switch_chat, num_model_lock=len(text_outputs)) | |
| radio_chats.input(switch_chat_fun, | |
| inputs=[radio_chats, chat_state], | |
| outputs=[text_output, text_output2] + text_outputs) \ | |
| .then(clear_scores, outputs=[score_text, score_text2, score_text_nochat]) | |
| def remove_chat(chat_key, chat_state1): | |
| if isinstance(chat_key, str): | |
| chat_state1.pop(chat_key, None) | |
| return gr.update(choices=list(chat_state1.keys()), value=None), chat_state1 | |
| remove_chat_event = remove_chat_btn.click(remove_chat, | |
| inputs=[radio_chats, chat_state], outputs=[radio_chats, chat_state], | |
| queue=False, api_name='remove_chat') | |
| def get_chats1(chat_state1): | |
| base = 'chats' | |
| makedirs(base, exist_ok=True) | |
| filename = os.path.join(base, 'chats_%s.json' % str(uuid.uuid4())) | |
| with open(filename, "wt") as f: | |
| f.write(json.dumps(chat_state1, indent=2)) | |
| return filename | |
| export_chat_event = export_chats_btn.click(get_chats1, inputs=chat_state, outputs=chats_file, queue=False, | |
| api_name='export_chats' if allow_api else None) | |
| def add_chats_from_file(file, chat_state1, radio_chats1, chat_exception_text1): | |
| if not file: | |
| return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1 | |
| if isinstance(file, str): | |
| files = [file] | |
| else: | |
| files = file | |
| if not files: | |
| return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1 | |
| chat_exception_list = [] | |
| for file1 in files: | |
| try: | |
| if hasattr(file1, 'name'): | |
| file1 = file1.name | |
| with open(file1, "rt") as f: | |
| new_chats = json.loads(f.read()) | |
| for chat1_k, chat1_v in new_chats.items(): | |
| # ignore chat1_k, regenerate and de-dup to avoid loss | |
| chat_state1, _ = save_chat(chat1_v, chat_state1, chat_is_list=True) | |
| except BaseException as e: | |
| t, v, tb = sys.exc_info() | |
| ex = ''.join(traceback.format_exception(t, v, tb)) | |
| ex_str = "File %s exception: %s" % (file1, str(e)) | |
| print(ex_str, flush=True) | |
| chat_exception_list.append(ex_str) | |
| chat_exception_text1 = '\n'.join(chat_exception_list) | |
| return None, chat_state1, gr.update(choices=list(chat_state1.keys()), value=None), chat_exception_text1 | |
| # note for update_user_db_func output is ignored for db | |
| chatup_change_event = chatsup_output.change(add_chats_from_file, | |
| inputs=[chatsup_output, chat_state, radio_chats, | |
| chat_exception_text], | |
| outputs=[chatsup_output, chat_state, radio_chats, | |
| chat_exception_text], | |
| queue=False, | |
| api_name='add_to_chats' if allow_api else None) | |
| clear_chat_event = clear_chat_btn.click(fn=clear_texts, | |
| inputs=[text_output, text_output2] + text_outputs, | |
| outputs=[text_output, text_output2] + text_outputs, | |
| queue=False, api_name='clear' if allow_api else None) \ | |
| .then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False) \ | |
| .then(clear_scores, outputs=[score_text, score_text2, score_text_nochat]) | |
| clear_event = save_chat_btn.click(save_chat, | |
| inputs=[text_output, text_output2] + text_outputs + [chat_state], | |
| outputs=[chat_state, radio_chats], | |
| api_name='save_chat' if allow_api else None) | |
| if kwargs['score_model']: | |
| clear_event2 = clear_event.then(clear_scores, outputs=[score_text, score_text2, score_text_nochat]) | |
| # NOTE: clear of instruction/iinput for nochat has to come after score, | |
| # because score for nochat consumes actual textbox, while chat consumes chat history filled by user() | |
| no_chat_args = dict(fn=fun, | |
| inputs=[model_state, my_db_state, selection_docs_state] + inputs_list, | |
| outputs=text_output_nochat, | |
| queue=queue, | |
| ) | |
| submit_event_nochat = submit_nochat.click(**no_chat_args, api_name='submit_nochat' if allow_api else None) \ | |
| .then(clear_torch_cache) \ | |
| .then(**score_args_nochat, api_name='instruction_bot_score_nochat' if allow_api else None, queue=queue) \ | |
| .then(clear_instruct, None, instruction_nochat) \ | |
| .then(clear_instruct, None, iinput_nochat) \ | |
| .then(clear_torch_cache) | |
| # copy of above with text box submission | |
| submit_event_nochat2 = instruction_nochat.submit(**no_chat_args) \ | |
| .then(clear_torch_cache) \ | |
| .then(**score_args_nochat, queue=queue) \ | |
| .then(clear_instruct, None, instruction_nochat) \ | |
| .then(clear_instruct, None, iinput_nochat) \ | |
| .then(clear_torch_cache) | |
| submit_event_nochat_api = submit_nochat_api.click(fun_with_dict_str, | |
| inputs=[model_state, my_db_state, selection_docs_state, | |
| inputs_dict_str], | |
| outputs=text_output_nochat_api, | |
| queue=True, # required for generator | |
| api_name='submit_nochat_api' if allow_api else None) \ | |
| .then(clear_torch_cache) | |
| def load_model(model_name, lora_weights, server_name, model_state_old, prompt_type_old, load_8bit, | |
| use_gpu_id, gpu_id): | |
| # ensure no API calls reach here | |
| if is_public: | |
| raise RuntimeError("Illegal access for %s" % model_name) | |
| # ensure old model removed from GPU memory | |
| if kwargs['debug']: | |
| print("Pre-switch pre-del GPU memory: %s" % get_torch_allocated(), flush=True) | |
| model0 = model_state0['model'] | |
| if isinstance(model_state_old['model'], str) and model0 is not None: | |
| # best can do, move model loaded at first to CPU | |
| model0.cpu() | |
| if model_state_old['model'] is not None and not isinstance(model_state_old['model'], str): | |
| try: | |
| model_state_old['model'].cpu() | |
| except Exception as e: | |
| # sometimes hit NotImplementedError: Cannot copy out of meta tensor; no data! | |
| print("Unable to put model on CPU: %s" % str(e), flush=True) | |
| del model_state_old['model'] | |
| model_state_old['model'] = None | |
| if model_state_old['tokenizer'] is not None and not isinstance(model_state_old['tokenizer'], str): | |
| del model_state_old['tokenizer'] | |
| model_state_old['tokenizer'] = None | |
| clear_torch_cache() | |
| if kwargs['debug']: | |
| print("Pre-switch post-del GPU memory: %s" % get_torch_allocated(), flush=True) | |
| if model_name is None or model_name == no_model_str: | |
| # no-op if no model, just free memory | |
| # no detranscribe needed for model, never go into evaluate | |
| lora_weights = no_lora_str | |
| server_name = no_server_str | |
| return [None, None, None, model_name, server_name], \ | |
| model_name, lora_weights, server_name, prompt_type_old, \ | |
| gr.Slider.update(maximum=256), \ | |
| gr.Slider.update(maximum=256) | |
| # don't deepcopy, can contain model itself | |
| all_kwargs1 = all_kwargs.copy() | |
| all_kwargs1['base_model'] = model_name.strip() | |
| all_kwargs1['load_8bit'] = load_8bit | |
| all_kwargs1['use_gpu_id'] = use_gpu_id | |
| all_kwargs1['gpu_id'] = int(gpu_id) # detranscribe | |
| model_lower = model_name.strip().lower() | |
| if model_lower in inv_prompt_type_to_model_lower: | |
| prompt_type1 = inv_prompt_type_to_model_lower[model_lower] | |
| else: | |
| prompt_type1 = prompt_type_old | |
| # detranscribe | |
| if lora_weights == no_lora_str: | |
| lora_weights = '' | |
| all_kwargs1['lora_weights'] = lora_weights.strip() | |
| if server_name == no_server_str: | |
| server_name = '' | |
| all_kwargs1['inference_server'] = server_name.strip() | |
| model1, tokenizer1, device1 = get_model(reward_type=False, | |
| **get_kwargs(get_model, exclude_names=['reward_type'], | |
| **all_kwargs1)) | |
| clear_torch_cache() | |
| tokenizer_base_model = model_name | |
| prompt_dict1, error0 = get_prompt(prompt_type1, '', | |
| chat=False, context='', reduced=False, making_context=False, | |
| return_dict=True) | |
| model_state_new = dict(model=model1, tokenizer=tokenizer1, device=device1, | |
| base_model=model_name, tokenizer_base_model=tokenizer_base_model, | |
| lora_weights=lora_weights, inference_server=server_name, | |
| prompt_type=prompt_type1, prompt_dict=prompt_dict1, | |
| ) | |
| max_max_new_tokens1 = get_max_max_new_tokens(model_state_new, **kwargs) | |
| if kwargs['debug']: | |
| print("Post-switch GPU memory: %s" % get_torch_allocated(), flush=True) | |
| return model_state_new, model_name, lora_weights, server_name, prompt_type1, \ | |
| gr.Slider.update(maximum=max_max_new_tokens1), \ | |
| gr.Slider.update(maximum=max_max_new_tokens1) | |
| def get_prompt_str(prompt_type1, prompt_dict1, which=0): | |
| if prompt_type1 in ['', None]: | |
| print("Got prompt_type %s: %s" % (which, prompt_type1), flush=True) | |
| return str({}) | |
| prompt_dict1, prompt_dict_error = get_prompt(prompt_type1, prompt_dict1, chat=False, context='', | |
| reduced=False, making_context=False, return_dict=True) | |
| if prompt_dict_error: | |
| return str(prompt_dict_error) | |
| else: | |
| # return so user can manipulate if want and use as custom | |
| return str(prompt_dict1) | |
| get_prompt_str_func1 = functools.partial(get_prompt_str, which=1) | |
| get_prompt_str_func2 = functools.partial(get_prompt_str, which=2) | |
| prompt_type.change(fn=get_prompt_str_func1, inputs=[prompt_type, prompt_dict], outputs=prompt_dict, queue=False) | |
| prompt_type2.change(fn=get_prompt_str_func2, inputs=[prompt_type2, prompt_dict2], outputs=prompt_dict2, | |
| queue=False) | |
| def dropdown_prompt_type_list(x): | |
| return gr.Dropdown.update(value=x) | |
| def chatbot_list(x, model_used_in): | |
| return gr.Textbox.update(label=f'h2oGPT [Model: {model_used_in}]') | |
| load_model_args = dict(fn=load_model, | |
| inputs=[model_choice, lora_choice, server_choice, model_state, prompt_type, | |
| model_load8bit_checkbox, model_use_gpu_id_checkbox, model_gpu], | |
| outputs=[model_state, model_used, lora_used, server_used, | |
| # if prompt_type changes, prompt_dict will change via change rule | |
| prompt_type, max_new_tokens, min_new_tokens, | |
| ]) | |
| prompt_update_args = dict(fn=dropdown_prompt_type_list, inputs=prompt_type, outputs=prompt_type) | |
| chatbot_update_args = dict(fn=chatbot_list, inputs=[text_output, model_used], outputs=text_output) | |
| nochat_update_args = dict(fn=chatbot_list, inputs=[text_output_nochat, model_used], outputs=text_output_nochat) | |
| load_model_event = load_model_button.click(**load_model_args, | |
| api_name='load_model' if allow_api and is_public else None) \ | |
| .then(**prompt_update_args) \ | |
| .then(**chatbot_update_args) \ | |
| .then(**nochat_update_args) \ | |
| .then(clear_torch_cache) | |
| load_model_args2 = dict(fn=load_model, | |
| inputs=[model_choice2, lora_choice2, server_choice2, model_state2, prompt_type2, | |
| model_load8bit_checkbox2, model_use_gpu_id_checkbox2, model_gpu2], | |
| outputs=[model_state2, model_used2, lora_used2, server_used2, | |
| # if prompt_type2 changes, prompt_dict2 will change via change rule | |
| prompt_type2, max_new_tokens2, min_new_tokens2 | |
| ]) | |
| prompt_update_args2 = dict(fn=dropdown_prompt_type_list, inputs=prompt_type2, outputs=prompt_type2) | |
| chatbot_update_args2 = dict(fn=chatbot_list, inputs=[text_output2, model_used2], outputs=text_output2) | |
| load_model_event2 = load_model_button2.click(**load_model_args2, | |
| api_name='load_model2' if allow_api and is_public else None) \ | |
| .then(**prompt_update_args2) \ | |
| .then(**chatbot_update_args2) \ | |
| .then(clear_torch_cache) | |
| def dropdown_model_lora_server_list(model_list0, model_x, | |
| lora_list0, lora_x, | |
| server_list0, server_x, | |
| model_used1, lora_used1, server_used1, | |
| model_used2, lora_used2, server_used2, | |
| ): | |
| model_new_state = [model_list0[0] + [model_x]] | |
| model_new_options = [*model_new_state[0]] | |
| x1 = model_x if model_used1 == no_model_str else model_used1 | |
| x2 = model_x if model_used2 == no_model_str else model_used2 | |
| ret1 = [gr.Dropdown.update(value=x1, choices=model_new_options), | |
| gr.Dropdown.update(value=x2, choices=model_new_options), | |
| '', model_new_state] | |
| lora_new_state = [lora_list0[0] + [lora_x]] | |
| lora_new_options = [*lora_new_state[0]] | |
| # don't switch drop-down to added lora if already have model loaded | |
| x1 = lora_x if model_used1 == no_model_str else lora_used1 | |
| x2 = lora_x if model_used2 == no_model_str else lora_used2 | |
| ret2 = [gr.Dropdown.update(value=x1, choices=lora_new_options), | |
| gr.Dropdown.update(value=x2, choices=lora_new_options), | |
| '', lora_new_state] | |
| server_new_state = [server_list0[0] + [server_x]] | |
| server_new_options = [*server_new_state[0]] | |
| # don't switch drop-down to added server if already have model loaded | |
| x1 = server_x if model_used1 == no_model_str else server_used1 | |
| x2 = server_x if model_used2 == no_model_str else server_used2 | |
| ret3 = [gr.Dropdown.update(value=x1, choices=server_new_options), | |
| gr.Dropdown.update(value=x2, choices=server_new_options), | |
| '', server_new_state] | |
| return tuple(ret1 + ret2 + ret3) | |
| add_model_lora_server_event = \ | |
| add_model_lora_server_button.click(fn=dropdown_model_lora_server_list, | |
| inputs=[model_options_state, new_model] + | |
| [lora_options_state, new_lora] + | |
| [server_options_state, new_server] + | |
| [model_used, lora_used, server_used] + | |
| [model_used2, lora_used2, server_used2], | |
| outputs=[model_choice, model_choice2, new_model, model_options_state] + | |
| [lora_choice, lora_choice2, new_lora, lora_options_state] + | |
| [server_choice, server_choice2, new_server, | |
| server_options_state], | |
| queue=False) | |
| go_event = go_btn.click(lambda: gr.update(visible=False), None, go_btn, api_name="go" if allow_api else None, | |
| queue=False) \ | |
| .then(lambda: gr.update(visible=True), None, normal_block, queue=False) \ | |
| .then(**load_model_args, queue=False).then(**prompt_update_args, queue=False) | |
| def compare_textbox_fun(x): | |
| return gr.Textbox.update(visible=x) | |
| def compare_column_fun(x): | |
| return gr.Column.update(visible=x) | |
| def compare_prompt_fun(x): | |
| return gr.Dropdown.update(visible=x) | |
| def slider_fun(x): | |
| return gr.Slider.update(visible=x) | |
| compare_checkbox.select(compare_textbox_fun, compare_checkbox, text_output2, | |
| api_name="compare_checkbox" if allow_api else None) \ | |
| .then(compare_column_fun, compare_checkbox, col_model2) \ | |
| .then(compare_prompt_fun, compare_checkbox, prompt_type2) \ | |
| .then(compare_textbox_fun, compare_checkbox, score_text2) \ | |
| .then(slider_fun, compare_checkbox, max_new_tokens2) \ | |
| .then(slider_fun, compare_checkbox, min_new_tokens2) | |
| # FIXME: add score_res2 in condition, but do better | |
| # callback for logging flagged input/output | |
| callback.setup(inputs_list + [text_output, text_output2] + text_outputs, "flagged_data_points") | |
| flag_btn.click(lambda *args: callback.flag(args), inputs_list + [text_output, text_output2] + text_outputs, | |
| None, | |
| preprocess=False, | |
| api_name='flag' if allow_api else None, queue=False) | |
| flag_btn_nochat.click(lambda *args: callback.flag(args), inputs_list + [text_output_nochat], None, | |
| preprocess=False, | |
| api_name='flag_nochat' if allow_api else None, queue=False) | |
| def get_system_info(): | |
| if is_public: | |
| time.sleep(10) # delay to avoid spam since queue=False | |
| return gr.Textbox.update(value=system_info_print()) | |
| system_event = system_btn.click(get_system_info, outputs=system_text, | |
| api_name='system_info' if allow_api else None, queue=False) | |
| def get_system_info_dict(system_input1, **kwargs1): | |
| if system_input1 != os.getenv("ADMIN_PASS", ""): | |
| return json.dumps({}) | |
| exclude_list = ['admin_pass', 'examples'] | |
| sys_dict = {k: v for k, v in kwargs1.items() if | |
| isinstance(v, (str, int, bool, float)) and k not in exclude_list} | |
| try: | |
| sys_dict.update(system_info()) | |
| except Exception as e: | |
| # protection | |
| print("Exception: %s" % str(e), flush=True) | |
| return json.dumps(sys_dict) | |
| system_kwargs = all_kwargs.copy() | |
| system_kwargs.update(dict(command=str(' '.join(sys.argv)))) | |
| get_system_info_dict_func = functools.partial(get_system_info_dict, **all_kwargs) | |
| system_dict_event = system_btn2.click(get_system_info_dict_func, | |
| inputs=system_input, | |
| outputs=system_text2, | |
| api_name='system_info_dict' if allow_api else None, | |
| queue=False, # queue to avoid spam | |
| ) | |
| def get_hash(): | |
| return kwargs['git_hash'] | |
| system_event = system_btn3.click(get_hash, | |
| outputs=system_text3, | |
| api_name='system_hash' if allow_api else None, | |
| queue=False, | |
| ) | |
| def count_chat_tokens(model_state1, chat1, prompt_type1, prompt_dict1, | |
| memory_restriction_level1=0, | |
| keep_sources_in_context1=False, | |
| ): | |
| if model_state1 and not isinstance(model_state1['tokenizer'], str): | |
| tokenizer = model_state1['tokenizer'] | |
| elif model_state0 and not isinstance(model_state0['tokenizer'], str): | |
| tokenizer = model_state0['tokenizer'] | |
| else: | |
| tokenizer = None | |
| if tokenizer is not None: | |
| langchain_mode1 = 'LLM' | |
| add_chat_history_to_context1 = True | |
| # fake user message to mimic bot() | |
| chat1 = copy.deepcopy(chat1) | |
| chat1 = chat1 + [['user_message1', None]] | |
| model_max_length1 = tokenizer.model_max_length | |
| context1 = history_to_context(chat1, langchain_mode1, | |
| add_chat_history_to_context1, | |
| prompt_type1, prompt_dict1, chat1, | |
| model_max_length1, | |
| memory_restriction_level1, keep_sources_in_context1) | |
| return str(tokenizer(context1, return_tensors="pt")['input_ids'].shape[1]) | |
| else: | |
| return "N/A" | |
| count_chat_tokens_func = functools.partial(count_chat_tokens, | |
| memory_restriction_level1=memory_restriction_level, | |
| keep_sources_in_context1=kwargs['keep_sources_in_context']) | |
| count_tokens_event = count_chat_tokens_btn.click(fn=count_chat_tokens, | |
| inputs=[model_state, text_output, prompt_type, prompt_dict], | |
| outputs=chat_token_count, | |
| api_name='count_tokens' if allow_api else None) | |
| # don't pass text_output, don't want to clear output, just stop it | |
| # cancel only stops outer generation, not inner generation or non-generation | |
| stop_btn.click(lambda: None, None, None, | |
| cancels=submits1 + submits2 + submits3 + submits4 + | |
| [submit_event_nochat, submit_event_nochat2] + | |
| [eventdb1, eventdb2, eventdb3] + | |
| [eventdb7, eventdb8, eventdb9, eventdb12] + | |
| db_events + | |
| [clear_event] + | |
| [submit_event_nochat_api, submit_event_nochat] + | |
| [load_model_event, load_model_event2] + | |
| [count_tokens_event] | |
| , | |
| queue=False, api_name='stop' if allow_api else None).then(clear_torch_cache, queue=False) | |
| demo.load(None, None, None, _js=get_dark_js() if kwargs['dark'] else None) | |
| demo.queue(concurrency_count=kwargs['concurrency_count'], api_open=kwargs['api_open']) | |
| favicon_path = "h2o-logo.svg" | |
| if not os.path.isfile(favicon_path): | |
| print("favicon_path=%s not found" % favicon_path, flush=True) | |
| favicon_path = None | |
| scheduler = BackgroundScheduler() | |
| scheduler.add_job(func=clear_torch_cache, trigger="interval", seconds=20) | |
| if is_public and \ | |
| kwargs['base_model'] not in non_hf_types: | |
| # FIXME: disable for gptj, langchain or gpt4all modify print itself | |
| # FIXME: and any multi-threaded/async print will enter model output! | |
| scheduler.add_job(func=ping, trigger="interval", seconds=60) | |
| if is_public or os.getenv('PING_GPU'): | |
| scheduler.add_job(func=ping_gpu, trigger="interval", seconds=60 * 10) | |
| scheduler.start() | |
| # import control | |
| if kwargs['langchain_mode'] == 'Disabled' and \ | |
| os.environ.get("TEST_LANGCHAIN_IMPORT") and \ | |
| kwargs['base_model'] not in non_hf_types: | |
| assert 'gpt_langchain' not in sys.modules, "Dev bug, import of langchain when should not have" | |
| assert 'langchain' not in sys.modules, "Dev bug, import of langchain when should not have" | |
| demo.launch(share=kwargs['share'], server_name="0.0.0.0", show_error=True, | |
| favicon_path=favicon_path, prevent_thread_lock=True, | |
| auth=kwargs['auth']) | |
| if kwargs['verbose']: | |
| print("Started GUI", flush=True) | |
| if kwargs['block_gradio_exit']: | |
| demo.block_thread() | |
| def get_inputs_list(inputs_dict, model_lower, model_id=1): | |
| """ | |
| map gradio objects in locals() to inputs for evaluate(). | |
| :param inputs_dict: | |
| :param model_lower: | |
| :param model_id: Which model (1 or 2) of 2 | |
| :return: | |
| """ | |
| inputs_list_names = list(inspect.signature(evaluate).parameters) | |
| inputs_list = [] | |
| inputs_dict_out = {} | |
| for k in inputs_list_names: | |
| if k == 'kwargs': | |
| continue | |
| if k in input_args_list + inputs_kwargs_list: | |
| # these are added at use time for args or partial for kwargs, not taken as input | |
| continue | |
| if 'mbart-' not in model_lower and k in ['src_lang', 'tgt_lang']: | |
| continue | |
| if model_id == 2: | |
| if k == 'prompt_type': | |
| k = 'prompt_type2' | |
| if k == 'prompt_used': | |
| k = 'prompt_used2' | |
| if k == 'max_new_tokens': | |
| k = 'max_new_tokens2' | |
| if k == 'min_new_tokens': | |
| k = 'min_new_tokens2' | |
| inputs_list.append(inputs_dict[k]) | |
| inputs_dict_out[k] = inputs_dict[k] | |
| return inputs_list, inputs_dict_out | |
| def get_sources(db1s, langchain_mode, dbs=None, docs_state0=None): | |
| for k in db1s: | |
| set_userid(db1s[k]) | |
| if langchain_mode in ['LLM']: | |
| source_files_added = "NA" | |
| source_list = [] | |
| elif langchain_mode in ['wiki_full']: | |
| source_files_added = "Not showing wiki_full, takes about 20 seconds and makes 4MB file." \ | |
| " Ask [email protected] for file if required." | |
| source_list = [] | |
| elif langchain_mode in db1s and len(db1s[langchain_mode]) == 2 and db1s[langchain_mode][0] is not None: | |
| db1 = db1s[langchain_mode] | |
| from gpt_langchain import get_metadatas | |
| metadatas = get_metadatas(db1[0]) | |
| source_list = sorted(set([x['source'] for x in metadatas])) | |
| source_files_added = '\n'.join(source_list) | |
| elif langchain_mode in dbs and dbs[langchain_mode] is not None: | |
| from gpt_langchain import get_metadatas | |
| db1 = dbs[langchain_mode] | |
| metadatas = get_metadatas(db1) | |
| source_list = sorted(set([x['source'] for x in metadatas])) | |
| source_files_added = '\n'.join(source_list) | |
| else: | |
| source_list = [] | |
| source_files_added = "None" | |
| sources_dir = "sources_dir" | |
| makedirs(sources_dir) | |
| sources_file = os.path.join(sources_dir, 'sources_%s_%s' % (langchain_mode, str(uuid.uuid4()))) | |
| with open(sources_file, "wt") as f: | |
| f.write(source_files_added) | |
| source_list = docs_state0 + source_list | |
| return sources_file, source_list | |
| def set_userid(db1): | |
| # can only call this after function called so for specific userr, not in gr.State() that occurs during app init | |
| assert db1 is not None and len(db1) == 2 | |
| if db1[1] is None: | |
| # uuid in db is used as user ID | |
| db1[1] = str(uuid.uuid4()) | |
| def update_user_db(file, db1s, selection_docs_state1, chunk, chunk_size, langchain_mode, dbs=None, **kwargs): | |
| kwargs.update(selection_docs_state1) | |
| if file is None: | |
| raise RuntimeError("Don't use change, use input") | |
| try: | |
| return _update_user_db(file, db1s=db1s, chunk=chunk, chunk_size=chunk_size, | |
| langchain_mode=langchain_mode, dbs=dbs, | |
| **kwargs) | |
| except BaseException as e: | |
| print(traceback.format_exc(), flush=True) | |
| # gradio has issues if except, so fail semi-gracefully, else would hang forever in processing textbox | |
| ex_str = "Exception: %s" % str(e) | |
| source_files_added = """\ | |
| <html> | |
| <body> | |
| <p> | |
| Sources: <br> | |
| </p> | |
| <div style="overflow-y: auto;height:400px"> | |
| {0} | |
| </div> | |
| </body> | |
| </html> | |
| """.format(ex_str) | |
| doc_exception_text = str(e) | |
| return None, langchain_mode, source_files_added, doc_exception_text | |
| finally: | |
| clear_torch_cache() | |
| def get_lock_file(db1, langchain_mode): | |
| set_userid(db1) | |
| assert len(db1) == 2 and db1[1] is not None and isinstance(db1[1], str) | |
| user_id = db1[1] | |
| base_path = 'locks' | |
| makedirs(base_path) | |
| lock_file = os.path.join(base_path, "db_%s_%s.lock" % (langchain_mode.replace(' ', '_'), user_id)) | |
| return lock_file | |
| def _update_user_db(file, | |
| db1s=None, | |
| chunk=None, chunk_size=None, | |
| dbs=None, db_type=None, | |
| langchain_mode='UserData', | |
| langchain_modes=None, # unused but required as part of selection_docs_state1 | |
| langchain_mode_paths=None, | |
| visible_langchain_modes=None, | |
| use_openai_embedding=None, | |
| hf_embedding_model=None, | |
| caption_loader=None, | |
| enable_captions=None, | |
| captions_model=None, | |
| enable_ocr=None, | |
| enable_pdf_ocr=None, | |
| verbose=None, | |
| n_jobs=-1, | |
| is_url=None, is_txt=None, | |
| ): | |
| assert db1s is not None | |
| assert chunk is not None | |
| assert chunk_size is not None | |
| assert use_openai_embedding is not None | |
| assert hf_embedding_model is not None | |
| assert caption_loader is not None | |
| assert enable_captions is not None | |
| assert captions_model is not None | |
| assert enable_ocr is not None | |
| assert enable_pdf_ocr is not None | |
| assert verbose is not None | |
| if dbs is None: | |
| dbs = {} | |
| assert isinstance(dbs, dict), "Wrong type for dbs: %s" % str(type(dbs)) | |
| # assert db_type in ['faiss', 'chroma'], "db_type %s not supported" % db_type | |
| from gpt_langchain import add_to_db, get_db, path_to_docs | |
| # handle case of list of temp buffer | |
| if isinstance(file, list) and len(file) > 0 and hasattr(file[0], 'name'): | |
| file = [x.name for x in file] | |
| # handle single file of temp buffer | |
| if hasattr(file, 'name'): | |
| file = file.name | |
| if not isinstance(file, (list, tuple, typing.Generator)) and isinstance(file, str): | |
| file = [file] | |
| if langchain_mode == LangChainMode.DISABLED.value: | |
| return None, langchain_mode, get_source_files(), "" | |
| if langchain_mode in [LangChainMode.LLM.value]: | |
| # then switch to MyData, so langchain_mode also becomes way to select where upload goes | |
| # but default to mydata if nothing chosen, since safest | |
| if LangChainMode.MY_DATA.value in visible_langchain_modes: | |
| langchain_mode = LangChainMode.MY_DATA.value | |
| if langchain_mode_paths is None: | |
| langchain_mode_paths = {} | |
| user_path = langchain_mode_paths.get(langchain_mode) | |
| # UserData or custom, which has to be from user's disk | |
| if user_path is not None: | |
| # move temp files from gradio upload to stable location | |
| for fili, fil in enumerate(file): | |
| if isinstance(fil, str) and os.path.isfile(fil): # not url, text | |
| new_fil = os.path.normpath(os.path.join(user_path, os.path.basename(fil))) | |
| if os.path.normpath(os.path.abspath(fil)) != os.path.normpath(os.path.abspath(new_fil)): | |
| if os.path.isfile(new_fil): | |
| remove(new_fil) | |
| try: | |
| shutil.move(fil, new_fil) | |
| except FileExistsError: | |
| pass | |
| file[fili] = new_fil | |
| if verbose: | |
| print("Adding %s" % file, flush=True) | |
| sources = path_to_docs(file if not is_url and not is_txt else None, | |
| verbose=verbose, | |
| n_jobs=n_jobs, | |
| chunk=chunk, chunk_size=chunk_size, | |
| url=file if is_url else None, | |
| text=file if is_txt else None, | |
| enable_captions=enable_captions, | |
| captions_model=captions_model, | |
| enable_ocr=enable_ocr, | |
| enable_pdf_ocr=enable_pdf_ocr, | |
| caption_loader=caption_loader, | |
| ) | |
| exceptions = [x for x in sources if x.metadata.get('exception')] | |
| exceptions_strs = [x.metadata['exception'] for x in exceptions] | |
| sources = [x for x in sources if 'exception' not in x.metadata] | |
| # below must at least come after langchain_mode is modified in case was LLM -> MyData, | |
| # so original langchain mode changed | |
| for k in db1s: | |
| set_userid(db1s[k]) | |
| db1 = get_db1(db1s, langchain_mode) | |
| lock_file = get_lock_file(db1s[LangChainMode.MY_DATA.value], langchain_mode) # user-level lock, not db-level lock | |
| with filelock.FileLock(lock_file): | |
| if langchain_mode in db1s: | |
| if db1[0] is not None: | |
| # then add | |
| db, num_new_sources, new_sources_metadata = add_to_db(db1[0], sources, db_type=db_type, | |
| use_openai_embedding=use_openai_embedding, | |
| hf_embedding_model=hf_embedding_model) | |
| else: | |
| # in testing expect: | |
| # assert len(db1) == 2 and db1[1] is None, "Bad MyData db: %s" % db1 | |
| # for production hit, when user gets clicky: | |
| assert len(db1) == 2, "Bad %s db: %s" % (langchain_mode, db1) | |
| assert db1[1] is not None, "db hash was None, not allowed" | |
| # then create | |
| # if added has to original state and didn't change, then would be shared db for all users | |
| persist_directory = os.path.join(scratch_base_dir, 'db_dir_%s_%s' % (langchain_mode, db1[1])) | |
| db = get_db(sources, use_openai_embedding=use_openai_embedding, | |
| db_type=db_type, | |
| persist_directory=persist_directory, | |
| langchain_mode=langchain_mode, | |
| hf_embedding_model=hf_embedding_model) | |
| if db is not None: | |
| db1[0] = db | |
| source_files_added = get_source_files(db=db1[0], exceptions=exceptions) | |
| return None, langchain_mode, source_files_added, '\n'.join(exceptions_strs) | |
| else: | |
| from gpt_langchain import get_persist_directory | |
| persist_directory = get_persist_directory(langchain_mode) | |
| if langchain_mode in dbs and dbs[langchain_mode] is not None: | |
| # then add | |
| db, num_new_sources, new_sources_metadata = add_to_db(dbs[langchain_mode], sources, db_type=db_type, | |
| use_openai_embedding=use_openai_embedding, | |
| hf_embedding_model=hf_embedding_model) | |
| else: | |
| # then create. Or might just be that dbs is unfilled, then it will fill, then add | |
| db = get_db(sources, use_openai_embedding=use_openai_embedding, | |
| db_type=db_type, | |
| persist_directory=persist_directory, | |
| langchain_mode=langchain_mode, | |
| hf_embedding_model=hf_embedding_model) | |
| dbs[langchain_mode] = db | |
| # NOTE we do not return db, because function call always same code path | |
| # return dbs[langchain_mode] | |
| # db in this code path is updated in place | |
| source_files_added = get_source_files(db=dbs[langchain_mode], exceptions=exceptions) | |
| return None, langchain_mode, source_files_added, '\n'.join(exceptions_strs) | |
| def get_db(db1s, langchain_mode, dbs=None): | |
| db1 = get_db1(db1s, langchain_mode) | |
| lock_file = get_lock_file(db1s[LangChainMode.MY_DATA.value], langchain_mode) | |
| with filelock.FileLock(lock_file): | |
| if langchain_mode in ['wiki_full']: | |
| # NOTE: avoid showing full wiki. Takes about 30 seconds over about 90k entries, but not useful for now | |
| db = None | |
| elif langchain_mode in db1s and len(db1) == 2 and db1[0] is not None: | |
| db = db1[0] | |
| elif dbs is not None and langchain_mode in dbs and dbs[langchain_mode] is not None: | |
| db = dbs[langchain_mode] | |
| else: | |
| db = None | |
| return db | |
| def get_source_files_given_langchain_mode(db1s, langchain_mode='UserData', dbs=None): | |
| db = get_db(db1s, langchain_mode, dbs=dbs) | |
| if langchain_mode in ['LLM'] or db is None: | |
| return "Sources: N/A" | |
| return get_source_files(db=db, exceptions=None) | |
| def get_source_files(db=None, exceptions=None, metadatas=None): | |
| if exceptions is None: | |
| exceptions = [] | |
| # only should be one source, not confused | |
| # assert db is not None or metadatas is not None | |
| # clicky user | |
| if db is None and metadatas is None: | |
| return "No Sources at all" | |
| if metadatas is None: | |
| source_label = "Sources:" | |
| if db is not None: | |
| from gpt_langchain import get_metadatas | |
| metadatas = get_metadatas(db) | |
| else: | |
| metadatas = [] | |
| adding_new = False | |
| else: | |
| source_label = "New Sources:" | |
| adding_new = True | |
| # below automatically de-dups | |
| from gpt_langchain import get_url | |
| small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('head')) for x in | |
| metadatas} | |
| # if small_dict is empty dict, that's ok | |
| df = pd.DataFrame(small_dict.items(), columns=['source', 'head']) | |
| df.index = df.index + 1 | |
| df.index.name = 'index' | |
| source_files_added = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml') | |
| if exceptions: | |
| exception_metadatas = [x.metadata for x in exceptions] | |
| small_dict = {get_url(x['source'], from_str=True, short_name=True): get_short_name(x.get('exception')) for x in | |
| exception_metadatas} | |
| # if small_dict is empty dict, that's ok | |
| df = pd.DataFrame(small_dict.items(), columns=['source', 'exception']) | |
| df.index = df.index + 1 | |
| df.index.name = 'index' | |
| exceptions_html = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml') | |
| else: | |
| exceptions_html = '' | |
| if metadatas and exceptions: | |
| source_files_added = """\ | |
| <html> | |
| <body> | |
| <p> | |
| {0} <br> | |
| </p> | |
| <div style="overflow-y: auto;height:400px"> | |
| {1} | |
| {2} | |
| </div> | |
| </body> | |
| </html> | |
| """.format(source_label, source_files_added, exceptions_html) | |
| elif metadatas: | |
| source_files_added = """\ | |
| <html> | |
| <body> | |
| <p> | |
| {0} <br> | |
| </p> | |
| <div style="overflow-y: auto;height:400px"> | |
| {1} | |
| </div> | |
| </body> | |
| </html> | |
| """.format(source_label, source_files_added) | |
| elif exceptions_html: | |
| source_files_added = """\ | |
| <html> | |
| <body> | |
| <p> | |
| Exceptions: <br> | |
| </p> | |
| <div style="overflow-y: auto;height:400px"> | |
| {0} | |
| </div> | |
| </body> | |
| </html> | |
| """.format(exceptions_html) | |
| else: | |
| if adding_new: | |
| source_files_added = "No New Sources" | |
| else: | |
| source_files_added = "No Sources" | |
| return source_files_added | |
| def update_and_get_source_files_given_langchain_mode(db1s, langchain_mode, chunk, chunk_size, | |
| dbs=None, first_para=None, | |
| text_limit=None, | |
| langchain_mode_paths=None, db_type=None, load_db_if_exists=None, | |
| n_jobs=None, verbose=None): | |
| has_path = {k: v for k, v in langchain_mode_paths.items() if v} | |
| if langchain_mode in [LangChainMode.LLM.value, LangChainMode.MY_DATA.value]: | |
| # then assume user really meant UserData, to avoid extra clicks in UI, | |
| # since others can't be on disk, except custom user modes, which they should then select to query it | |
| if LangChainMode.USER_DATA.value in has_path: | |
| langchain_mode = LangChainMode.USER_DATA.value | |
| db = get_db(db1s, langchain_mode, dbs=dbs) | |
| from gpt_langchain import make_db | |
| db, num_new_sources, new_sources_metadata = make_db(use_openai_embedding=False, | |
| hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2", | |
| first_para=first_para, text_limit=text_limit, | |
| chunk=chunk, | |
| chunk_size=chunk_size, | |
| langchain_mode=langchain_mode, | |
| langchain_mode_paths=langchain_mode_paths, | |
| db_type=db_type, | |
| load_db_if_exists=load_db_if_exists, | |
| db=db, | |
| n_jobs=n_jobs, | |
| verbose=verbose) | |
| # during refreshing, might have "created" new db since not in dbs[] yet, so insert back just in case | |
| # so even if persisted, not kept up-to-date with dbs memory | |
| if langchain_mode in db1s: | |
| db1s[langchain_mode][0] = db | |
| else: | |
| dbs[langchain_mode] = db | |
| # return only new sources with text saying such | |
| return get_source_files(db=None, exceptions=None, metadatas=new_sources_metadata) | |
| def get_db1(db1s, langchain_mode1): | |
| if langchain_mode1 in db1s: | |
| db1 = db1s[langchain_mode1] | |
| else: | |
| # indicates to code that not scratch database | |
| db1 = [None, None] | |
| return db1 | |