import copy
import functools
import inspect
import json
import os
import pprint
import random
import sys
import traceback
import uuid
import filelock
import pandas as pd
import requests
import tabulate

from gradio_ui.css import get_css
from gradio_ui.prompt_form import make_prompt_form

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


def my_get(url, **kwargs):
    print('Gradio HTTP request redirected to localhost :)', flush=True)
    kwargs.setdefault('allow_redirects', True)
    return requests.api.request('get', 'http://127.0.0.1/', **kwargs)


original_get = requests.get
requests.get = my_get
import gradio as gr

requests.get = original_get

from enums import DocumentChoices
from gradio_themes import H2oTheme, SoftTheme, get_h2o_title, get_simple_title, get_dark_js
from prompter import Prompter, \
    prompt_type_to_model_name, prompt_types_strings, inv_prompt_type_to_model_lower, generate_prompt, non_hf_types, \
    get_prompt
from utils import get_githash, flatten_list, zip_data, s3up, clear_torch_cache, get_torch_allocated, system_info_print, \
    ping, get_short_name, get_url, makedirs, get_kwargs
from generate import get_model, languages_covered, evaluate, eval_func_param_names, score_qa, langchain_modes, \
    inputs_kwargs_list, get_cutoffs, scratch_base_dir, evaluate_from_str, no_default_param_names, \
    eval_func_param_names_defaults, get_max_max_new_tokens

from apscheduler.schedulers.background import BackgroundScheduler


def go_gradio(**kwargs):
    allow_api = kwargs['allow_api']
    is_public = kwargs['is_public']
    is_hf = kwargs['is_hf']
    memory_restriction_level = kwargs['memory_restriction_level']
    n_gpus = kwargs['n_gpus']
    admin_pass = kwargs['admin_pass']
    model_state0 = kwargs['model_state0']
    score_model_state0 = kwargs['score_model_state0']
    dbs = kwargs['dbs']
    db_type = kwargs['db_type']
    visible_langchain_modes = kwargs['visible_langchain_modes']
    allow_upload_to_user_data = kwargs['allow_upload_to_user_data']
    allow_upload_to_my_data = kwargs['allow_upload_to_my_data']
    enable_sources_list = kwargs['enable_sources_list']
    enable_url_upload = kwargs['enable_url_upload']
    enable_text_upload = kwargs['enable_text_upload']
    use_openai_embedding = kwargs['use_openai_embedding']
    hf_embedding_model = kwargs['hf_embedding_model']
    enable_captions = kwargs['enable_captions']
    captions_model = kwargs['captions_model']
    enable_ocr = kwargs['enable_ocr']
    caption_loader = kwargs['caption_loader']

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

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

    title = 'h2oGPT'
    if 'h2ogpt-research' in kwargs['base_model']:
        title += " [Research demonstration]"
    more_info = """For more information, visit our GitHub pages: [h2oGPT](https://github.com/h2oai/h2ogpt) and [H2O-LLMStudio](https://github.com/h2oai/h2o-llmstudio)<br>"""
    if is_public:
        more_info += """<iframe src="https://ghbtns.com/github-btn.html?user=h2oai&repo=h2ogpt&type=star&count=true&size=small" frameborder="0" scrolling="0" width="150" height="20" title="GitHub"></iframe>"""
    if kwargs['verbose']:
        description = f"""Model {kwargs['base_model']} Instruct dataset.
                      For more information, visit our GitHub pages: [h2oGPT](https://github.com/h2oai/h2ogpt) and [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
                      Command: {str(' '.join(sys.argv))}
                      Hash: {get_githash()}
                      """
    else:
        description = more_info
    description += "If this host is busy, try [12B](https://gpt.h2o.ai), [Falcon 40B](http://falcon.h2o.ai), [HF Spaces1 12B](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot) or [HF Spaces2 12B](https://huggingface.co/spaces/h2oai/h2ogpt-chatbot2)<br>"
    description += """<p>By using h2oGPT, you accept our [Terms of Service](https://github.com/h2oai/h2ogpt/blob/main/docs/tos.md)</p>"""
    if is_hf:
        description += '''<a href="https://huggingface.co/spaces/h2oai/h2ogpt-chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" style="white-space: nowrap" alt="Duplicate Space"></a>'''

    if kwargs['verbose']:
        task_info_md = f"""
        ### Task: {kwargs['task_info']}"""
    else:
        task_info_md = ''

    css_code = get_css(kwargs)

    if kwargs['gradio_avoid_processing_markdown']:
        from gradio_client import utils as client_utils
        from gradio.components import Chatbot

        # gradio has issue with taking too long to process input/output for markdown etc.
        # Avoid for now, allow raw html to render, good enough for chatbot.
        def _postprocess_chat_messages(self, chat_message: str):
            if chat_message is None:
                return None
            elif isinstance(chat_message, (tuple, list)):
                filepath = chat_message[0]
                mime_type = client_utils.get_mimetype(filepath)
                filepath = self.make_temp_copy_if_needed(filepath)
                return {
                    "name": filepath,
                    "mime_type": mime_type,
                    "alt_text": chat_message[1] if len(chat_message) > 1 else None,
                    "data": None,  # These last two fields are filled in by the frontend
                    "is_file": True,
                }
            elif isinstance(chat_message, str):
                return chat_message
            else:
                raise ValueError(f"Invalid message for Chatbot component: {chat_message}")

        Chatbot._postprocess_chat_messages = _postprocess_chat_messages

    if kwargs['gradio_offline_level'] >= 0:
        # avoid GoogleFont that pulls from internet
        if kwargs['gradio_offline_level'] == 1:
            # front end would still have to download fonts or have cached it at some point
            base_font = 'Source Sans Pro'
        else:
            base_font = 'Helvetica'
        theme_kwargs = dict(font=(base_font, 'ui-sans-serif', 'system-ui', 'sans-serif'),
                            font_mono=('IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'))
    else:
        theme_kwargs = dict()

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

    model_options = flatten_list(list(prompt_type_to_model_name.values())) + kwargs['extra_model_options']
    if kwargs['base_model'].strip() not in model_options:
        lora_options = [kwargs['base_model'].strip()] + model_options
    lora_options = kwargs['extra_lora_options']
    if kwargs['lora_weights'].strip() not in lora_options:
        lora_options = [kwargs['lora_weights'].strip()] + lora_options
    # always add in no lora case
    # add fake space so doesn't go away in gradio dropdown
    no_lora_str = no_model_str = '[None/Remove]'
    lora_options = [no_lora_str] + kwargs['extra_lora_options']  # FIXME: why double?
    # always add in no model case so can free memory
    # add fake space so doesn't go away in gradio dropdown
    model_options = [no_model_str] + model_options

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

    if not kwargs['base_model'].strip():
        kwargs['base_model'] = no_model_str

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

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

    default_kwargs = {k: kwargs[k] for k in eval_func_param_names_defaults}
    for k in no_default_param_names:
        default_kwargs[k] = ''

    with demo:
        # avoid actual model/tokenizer here or anything that would be bad to deepcopy
        # https://github.com/gradio-app/gradio/issues/3558
        model_state = gr.State(['model', 'tokenizer', kwargs['device'], kwargs['base_model']])
        model_state2 = gr.State([None, None, None, None])
        model_options_state = gr.State([model_options])
        lora_options_state = gr.State([lora_options])
        my_db_state = gr.State([None, None])
        chat_state = gr.State({})
        # make user default first and default choice, dedup
        docs_state00 = kwargs['document_choice'] + [x.name for x in list(DocumentChoices)]
        docs_state0 = []
        [docs_state0.append(x) for x in docs_state00 if x not in docs_state0]
        docs_state = gr.State(docs_state0)  # first is chosen as default
        gr.Markdown(f"""
            {get_h2o_title(title) if kwargs['h2ocolors'] else get_simple_title(title)}

            {description}
            {task_info_md}
            """)
        if is_hf:
            gr.HTML(
            )

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

        normal_block = gr.Row(visible=not base_wanted)
        with normal_block:
            with gr.Tabs():
                with gr.Row():
                    col_nochat = gr.Column(visible=not kwargs['chat'])
                    with col_nochat:  # FIXME: for model comparison, and check rest
                        if kwargs['langchain_mode'] == 'Disabled':
                            text_output_nochat = gr.Textbox(lines=5, label=output_label0).style(show_copy_button=True)
                        else:
                            # text looks a bit worse, but HTML links work
                            text_output_nochat = gr.HTML(label=output_label0)
                        instruction_nochat = gr.Textbox(
                            lines=kwargs['input_lines'],
                            label=instruction_label_nochat,
                            placeholder=kwargs['placeholder_instruction'],
                        )
                        iinput_nochat = gr.Textbox(lines=4, label="Input context for Instruction",
                                                   placeholder=kwargs['placeholder_input'])
                        submit_nochat = gr.Button("Submit")
                        flag_btn_nochat = gr.Button("Flag")
                        if not kwargs['auto_score']:
                            with gr.Column(visible=kwargs['score_model']):
                                score_btn_nochat = gr.Button("Score last prompt & response")
                                score_text_nochat = gr.Textbox("Response Score: NA", show_label=False)
                        else:
                            with gr.Column(visible=kwargs['score_model']):
                                score_text_nochat = gr.Textbox("Response Score: NA", show_label=False)

                    col_chat = gr.Column(visible=kwargs['chat'])
                    with col_chat:
                        with gr.Row():
                            text_output = gr.Chatbot(label=output_label0).style(height=kwargs['height'] or 400)
                            text_output2 = gr.Chatbot(label=output_label0_model2, visible=False).style(
                                height=kwargs['height'] or 400)

                        instruction, submit, stop_btn = make_prompt_form(kwargs)

                        with gr.Row():
                            clear = gr.Button("Save Chat / New Chat")
                            flag_btn = gr.Button("Flag")
                            if not kwargs['auto_score']:  # FIXME: For checkbox model2
                                with gr.Column(visible=kwargs['score_model']):
                                    with gr.Row():
                                        score_btn = gr.Button("Score last prompt & response").style(
                                            full_width=False, size='sm')
                                        score_text = gr.Textbox("Response Score: NA", show_label=False)
                                    score_res2 = gr.Row(visible=False)
                                    with score_res2:
                                        score_btn2 = gr.Button("Score last prompt & response 2").style(
                                            full_width=False, size='sm')
                                        score_text2 = gr.Textbox("Response Score2: NA", show_label=False)
                            else:
                                with gr.Column(visible=kwargs['score_model']):
                                    score_text = gr.Textbox("Response Score: NA", show_label=False)
                                    score_text2 = gr.Textbox("Response Score2: NA", show_label=False, visible=False)
                            retry = gr.Button("Regenerate")
                            undo = gr.Button("Undo")
                    submit_nochat_api = gr.Button("Submit nochat API", visible=False)
                    inputs_dict_str = gr.Textbox(label='API input for nochat', show_label=False, visible=False)
                    text_output_nochat_api = gr.Textbox(lines=5, label='API nochat output', visible=False).style(
                        show_copy_button=True)
                with gr.TabItem("Chat"):
                    with gr.Row():
                        if 'mbart-' in kwargs['model_lower']:
                            src_lang = gr.Dropdown(list(languages_covered().keys()),
                                                   value=kwargs['src_lang'],
                                                   label="Input Language")
                            tgt_lang = gr.Dropdown(list(languages_covered().keys()),
                                                   value=kwargs['tgt_lang'],
                                                   label="Output Language")
                    radio_chats = gr.Radio(value=None, label="Saved Chats", visible=True, interactive=True,
                                           type='value')
                    with gr.Row():
                        clear_chat_btn = gr.Button(value="Clear Chat", visible=True).style(size='sm')
                        export_chats_btn = gr.Button(value="Export Chats to Download").style(size='sm')
                        remove_chat_btn = gr.Button(value="Remove Selected Chat", visible=True).style(size='sm')
                        add_to_chats_btn = gr.Button("Import Chats from Upload").style(size='sm')
                    with gr.Row():
                        chats_file = gr.File(interactive=False, label="Download Exported Chats")
                        chatsup_output = gr.File(label="Upload Chat File(s)",
                                                 file_types=['.json'],
                                                 file_count='multiple',
                                                 elem_id="warning", elem_classes="feedback")
                with gr.TabItem("Data Source"):
                    langchain_readme = get_url('https://github.com/h2oai/h2ogpt/blob/main/docs/README_LangChain.md',
                                               from_str=True)
                    gr.HTML(value=f"""LangChain Support Disabled<p>
                            Run:<p>
                            <code>
                            python generate.py --langchain_mode=MyData
                            </code>
                            <p>
                            For more options see: {langchain_readme}""",
                            visible=kwargs['langchain_mode'] == 'Disabled', interactive=False)
                    data_row1 = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled')
                    with data_row1:
                        if is_hf:
                            # don't show 'wiki' since only usually useful for internal testing at moment
                            no_show_modes = ['Disabled', 'wiki']
                        else:
                            no_show_modes = ['Disabled']
                        allowed_modes = visible_langchain_modes.copy()
                        allowed_modes = [x for x in allowed_modes if x in dbs]
                        allowed_modes += ['ChatLLM', 'LLM']
                        if allow_upload_to_my_data and 'MyData' not in allowed_modes:
                            allowed_modes += ['MyData']
                        if allow_upload_to_user_data and 'UserData' not in allowed_modes:
                            allowed_modes += ['UserData']
                        langchain_mode = gr.Radio(
                            [x for x in langchain_modes if x in allowed_modes and x not in no_show_modes],
                            value=kwargs['langchain_mode'],
                            label="Data Collection of Sources",
                            visible=kwargs['langchain_mode'] != 'Disabled')
                    data_row2 = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled')
                    with data_row2:
                        with gr.Column(scale=50):
                            document_choice = gr.Dropdown(docs_state.value,
                                                          label="Choose Subset of Doc(s) in Collection [click get sources to update]",
                                                          value=docs_state.value[0],
                                                          interactive=True,
                                                          multiselect=True,
                                                          )
                        with gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list):
                            get_sources_btn = gr.Button(value="Get Sources",
                                                        ).style(full_width=False, size='sm')
                            show_sources_btn = gr.Button(value="Show Sources",
                                                         ).style(full_width=False, size='sm')
                            refresh_sources_btn = gr.Button(value="Refresh Sources",
                                                            ).style(full_width=False, size='sm')

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

                    upload_row = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and allow_upload).style(
                        equal_height=False)
                    with upload_row:
                        with gr.Column():
                            file_types_str = '[' + ' '.join(file_types) + ']'
                            fileup_output = gr.File(label=f'Upload {file_types_str}',
                                                    file_types=file_types,
                                                    file_count="multiple",
                                                    elem_id="warning", elem_classes="feedback")
                            with gr.Row():
                                add_to_shared_db_btn = gr.Button("Add File(s) to UserData",
                                                                 visible=allow_upload_to_user_data, elem_id='small_btn')
                                add_to_my_db_btn = gr.Button("Add File(s) to Scratch MyData",
                                                             visible=allow_upload_to_my_data,
                                                             elem_id='small_btn' if allow_upload_to_user_data else None,
                                                             ).style(
                                    size='sm' if not allow_upload_to_user_data else None)
                        with gr.Column(
                                visible=kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_url_upload):
                            url_label = 'URL (http/https) or ArXiv:' if have_arxiv else 'URL (http/https)'
                            url_text = gr.Textbox(label=url_label, interactive=True)
                            with gr.Row():
                                url_user_btn = gr.Button(value='Add URL content to Shared UserData',
                                                         visible=allow_upload_to_user_data, elem_id='small_btn')
                                url_my_btn = gr.Button(value='Add URL content to Scratch MyData',
                                                       visible=allow_upload_to_my_data,
                                                       elem_id='small_btn' if allow_upload_to_user_data else None,
                                                       ).style(size='sm' if not allow_upload_to_user_data else None)
                        with gr.Column(
                                visible=kwargs['langchain_mode'] != 'Disabled' and allow_upload and enable_text_upload):
                            user_text_text = gr.Textbox(label='Paste Text [Shift-Enter more lines]', interactive=True)
                            with gr.Row():
                                user_text_user_btn = gr.Button(value='Add Text to Shared UserData',
                                                               visible=allow_upload_to_user_data,
                                                               elem_id='small_btn')
                                user_text_my_btn = gr.Button(value='Add Text to Scratch MyData',
                                                             visible=allow_upload_to_my_data,
                                                             elem_id='small_btn' if allow_upload_to_user_data else None,
                                                             ).style(
                                    size='sm' if not allow_upload_to_user_data else None)
                        with gr.Column(visible=False):
                            # WIP:
                            with gr.Row(visible=False).style(equal_height=False):
                                github_textbox = gr.Textbox(label="Github URL")
                                with gr.Row(visible=True):
                                    github_shared_btn = gr.Button(value="Add Github to Shared UserData",
                                                                  visible=allow_upload_to_user_data,
                                                                  elem_id='small_btn')
                                    github_my_btn = gr.Button(value="Add Github to Scratch MyData",
                                                              visible=allow_upload_to_my_data, elem_id='small_btn')
                    sources_row3 = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list).style(
                        equal_height=False)
                    with sources_row3:
                        with gr.Column(scale=1):
                            file_source = gr.File(interactive=False,
                                                  label="Download File w/Sources [click get sources to make file]")
                        with gr.Column(scale=2):
                            pass
                    sources_row = gr.Row(visible=kwargs['langchain_mode'] != 'Disabled' and enable_sources_list).style(
                        equal_height=False)
                    with sources_row:
                        sources_text = gr.HTML(label='Sources Added', interactive=False)

                with gr.TabItem("Expert"):
                    with gr.Row():
                        with gr.Column():
                            stream_output = gr.components.Checkbox(label="Stream output",
                                                                   value=kwargs['stream_output'])
                            prompt_type = gr.Dropdown(prompt_types_strings,
                                                      value=kwargs['prompt_type'], label="Prompt Type",
                                                      visible=not is_public)
                            prompt_type2 = gr.Dropdown(prompt_types_strings,
                                                       value=kwargs['prompt_type'], label="Prompt Type Model 2",
                                                       visible=not is_public and False)
                            do_sample = gr.Checkbox(label="Sample",
                                                    info="Enable sampler, required for use of temperature, top_p, top_k",
                                                    value=kwargs['do_sample'])
                            temperature = gr.Slider(minimum=0.01, maximum=3,
                                                    value=kwargs['temperature'],
                                                    label="Temperature",
                                                    info="Lower is deterministic (but may lead to repeats), Higher more creative (but may lead to hallucinations)")
                            top_p = gr.Slider(minimum=0, maximum=1,
                                              value=kwargs['top_p'], label="Top p",
                                              info="Cumulative probability of tokens to sample from")
                            top_k = gr.Slider(
                                minimum=0, maximum=100, step=1,
                                value=kwargs['top_k'], label="Top k",
                                info='Num. tokens to sample from'
                            )
                            # FIXME: https://github.com/h2oai/h2ogpt/issues/106
                            if os.getenv('TESTINGFAIL'):
                                max_beams = 8 if not (memory_restriction_level or is_public) else 1
                            else:
                                max_beams = 1
                            num_beams = gr.Slider(minimum=1, maximum=max_beams, step=1,
                                                  value=min(max_beams, kwargs['num_beams']), label="Beams",
                                                  info="Number of searches for optimal overall probability.  "
                                                       "Uses more GPU memory/compute")
                            max_max_new_tokens = get_max_max_new_tokens(model_state0, **kwargs)
                            max_new_tokens = gr.Slider(
                                minimum=1, maximum=max_max_new_tokens, step=1,
                                value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length",
                            )
                            min_new_tokens = gr.Slider(
                                minimum=0, maximum=max_max_new_tokens, step=1,
                                value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length",
                            )
                            max_new_tokens2 = gr.Slider(
                                minimum=1, maximum=max_max_new_tokens, step=1,
                                value=min(max_max_new_tokens, kwargs['max_new_tokens']), label="Max output length 2",
                                visible=False,
                            )
                            min_new_tokens2 = gr.Slider(
                                minimum=0, maximum=max_max_new_tokens, step=1,
                                value=min(max_max_new_tokens, kwargs['min_new_tokens']), label="Min output length 2",
                                visible=False,
                            )
                            early_stopping = gr.Checkbox(label="EarlyStopping", info="Stop early in beam search",
                                                         value=kwargs['early_stopping'])
                            max_max_time = 60 * 20 if not is_public else 60 * 2
                            if is_hf:
                                max_max_time = min(max_max_time, 60 * 1)
                            max_time = gr.Slider(minimum=0, maximum=max_max_time, step=1,
                                                 value=min(max_max_time, kwargs['max_time']), label="Max. time",
                                                 info="Max. time to search optimal output.")
                            repetition_penalty = gr.Slider(minimum=0.01, maximum=3.0,
                                                           value=kwargs['repetition_penalty'],
                                                           label="Repetition Penalty")
                            num_return_sequences = gr.Slider(minimum=1, maximum=10, step=1,
                                                             value=kwargs['num_return_sequences'],
                                                             label="Number Returns", info="Must be <= num_beams",
                                                             visible=not is_public)
                            iinput = gr.Textbox(lines=4, label="Input",
                                                placeholder=kwargs['placeholder_input'],
                                                visible=not is_public)
                            context = gr.Textbox(lines=3, label="System Pre-Context",
                                                 info="Directly pre-appended without prompt processing",
                                                 visible=not is_public)
                            chat = gr.components.Checkbox(label="Chat mode", value=kwargs['chat'],
                                                          visible=not is_public)
                            count_chat_tokens_btn = gr.Button(value="Count Chat Tokens", visible=not is_public)
                            chat_token_count = gr.Textbox(label="Chat Token Count", value=None,
                                                          visible=not is_public, interactive=False)
                            chunk = gr.components.Checkbox(value=kwargs['chunk'],
                                                           label="Whether to chunk documents",
                                                           info="For LangChain",
                                                           visible=not is_public)
                            top_k_docs = gr.Slider(minimum=0, maximum=100, step=1,
                                                   value=kwargs['top_k_docs'],
                                                   label="Number of document chunks",
                                                   info="For LangChain",
                                                   visible=not is_public)
                            chunk_size = gr.Number(value=kwargs['chunk_size'],
                                                   label="Chunk size for document chunking",
                                                   info="For LangChain (ignored if chunk=False)",
                                                   visible=not is_public,
                                                   precision=0)

                with gr.TabItem("Models"):
                    load_msg = "Load-Unload Model/LORA [unload works if did not use --base_model]" if not is_public \
                        else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO"
                    load_msg2 = "Load-Unload Model/LORA 2 [unload works if did not use --base_model]" if not is_public \
                        else "LOAD-UNLOAD DISABLED FOR HOSTED DEMO 2"
                    compare_checkbox = gr.components.Checkbox(label="Compare Mode",
                                                              value=False, visible=not is_public)
                    with gr.Row():
                        n_gpus_list = [str(x) for x in list(range(-1, n_gpus))]
                        with gr.Column():
                            with gr.Row():
                                with gr.Column(scale=20):
                                    model_choice = gr.Dropdown(model_options_state.value[0], label="Choose Model",
                                                               value=kwargs['base_model'])
                                    lora_choice = gr.Dropdown(lora_options_state.value[0], label="Choose LORA",
                                                              value=kwargs['lora_weights'], visible=kwargs['show_lora'])
                                with gr.Column(scale=1):
                                    load_model_button = gr.Button(load_msg).style(full_width=False, size='sm')
                                    model_load8bit_checkbox = gr.components.Checkbox(
                                        label="Load 8-bit [requires support]",
                                        value=kwargs['load_8bit'])
                                    model_infer_devices_checkbox = gr.components.Checkbox(
                                        label="Choose Devices [If not Checked, use all GPUs]",
                                        value=kwargs['infer_devices'])
                                    model_gpu = gr.Dropdown(n_gpus_list,
                                                            label="GPU ID [-1 = all GPUs, if Choose is enabled]",
                                                            value=kwargs['gpu_id'])
                                    model_used = gr.Textbox(label="Current Model", value=kwargs['base_model'],
                                                            interactive=False)
                                    lora_used = gr.Textbox(label="Current LORA", value=kwargs['lora_weights'],
                                                           visible=kwargs['show_lora'], interactive=False)
                                    prompt_dict = gr.Textbox(label="Prompt (or Custom)",
                                                             value=pprint.pformat(kwargs['prompt_dict'], indent=4),
                                                             interactive=True, lines=4)
                        col_model2 = gr.Column(visible=False)
                        with col_model2:
                            with gr.Row():
                                with gr.Column(scale=20):
                                    model_choice2 = gr.Dropdown(model_options_state.value[0], label="Choose Model 2",
                                                                value=no_model_str)
                                    lora_choice2 = gr.Dropdown(lora_options_state.value[0], label="Choose LORA 2",
                                                               value=no_lora_str,
                                                               visible=kwargs['show_lora'])
                                with gr.Column(scale=1):
                                    load_model_button2 = gr.Button(load_msg2).style(full_width=False, size='sm')
                                    model_load8bit_checkbox2 = gr.components.Checkbox(
                                        label="Load 8-bit 2 [requires support]",
                                        value=kwargs['load_8bit'])
                                    model_infer_devices_checkbox2 = gr.components.Checkbox(
                                        label="Choose Devices 2 [If not Checked, use all GPUs]",
                                        value=kwargs[
                                            'infer_devices'])
                                    model_gpu2 = gr.Dropdown(n_gpus_list,
                                                             label="GPU ID 2 [-1 = all GPUs, if choose is enabled]",
                                                             value=kwargs['gpu_id'])
                                    # no model/lora loaded ever in model2 by default
                                    model_used2 = gr.Textbox(label="Current Model 2", value=no_model_str)
                                    lora_used2 = gr.Textbox(label="Current LORA 2", value=no_lora_str,
                                                            visible=kwargs['show_lora'])
                                    prompt_dict2 = gr.Textbox(label="Prompt (or Custom) 2",
                                                              value=pprint.pformat(kwargs['prompt_dict'], indent=4),
                                                              interactive=True, lines=4)
                    with gr.Row():
                        with gr.Column(scale=50):
                            new_model = gr.Textbox(label="New Model HF name/path")
                        with gr.Row():
                            add_model_button = gr.Button("Add new model name").style(full_width=False, size='sm')
                        with gr.Column(scale=50):
                            new_lora = gr.Textbox(label="New LORA HF name/path", visible=kwargs['show_lora'])
                        with gr.Row():
                            add_lora_button = gr.Button("Add new LORA name", visible=kwargs['show_lora']).style(
                                full_width=False, size='sm')
                with gr.TabItem("System"):
                    admin_row = gr.Row()
                    with admin_row:
                        admin_pass_textbox = gr.Textbox(label="Admin Password", type='password', visible=is_public)
                        admin_btn = gr.Button(value="Admin Access", visible=is_public)
                    system_row = gr.Row(visible=not is_public)
                    with system_row:
                        with gr.Column():
                            with gr.Row():
                                system_btn = gr.Button(value='Get System Info')
                                system_text = gr.Textbox(label='System Info', interactive=False).style(
                                    show_copy_button=True)

                            with gr.Row():
                                zip_btn = gr.Button("Zip")
                                zip_text = gr.Textbox(label="Zip file name", interactive=False)
                                file_output = gr.File(interactive=False, label="Zip file to Download")
                            with gr.Row():
                                s3up_btn = gr.Button("S3UP")
                                s3up_text = gr.Textbox(label='S3UP result', interactive=False)
                with gr.TabItem("Disclaimers"):
                    description = ""
                    description += """<p><b> DISCLAIMERS: </b><ul><i><li>The model was trained on The Pile and other data, which may contain objectionable content.  Use at own risk.</i></li>"""
                    if kwargs['load_8bit']:
                        description += """<i><li> Model is loaded in 8-bit and has other restrictions on this host. UX can be worse than non-hosted version.</i></li>"""
                    description += """<i><li>Conversations may be used to improve h2oGPT.  Do not share sensitive information.</i></li>"""
                    if 'h2ogpt-research' in kwargs['base_model']:
                        description += """<i><li>Research demonstration only, not used for commercial purposes.</i></li>"""
                    description += """<i><li>By using h2oGPT, you accept our <a href="https://github.com/h2oai/h2ogpt/blob/main/docs/tos.md">Terms of Service</a></i></li></ul></p>"""
                    gr.Markdown(value=description, show_label=False, interactive=False)

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

        def make_add_visible(x):
            return gr.update(visible=x is not None)

        def clear_file_list():
            return None

        def make_invisible():
            return gr.update(visible=False)

        def make_visible():
            return gr.update(visible=True)

        def update_radio_to_user():
            return gr.update(value='UserData')

        # Add to UserData
        update_user_db_func = functools.partial(update_user_db,
                                                dbs=dbs, db_type=db_type, langchain_mode='UserData',
                                                use_openai_embedding=use_openai_embedding,
                                                hf_embedding_model=hf_embedding_model,
                                                enable_captions=enable_captions,
                                                captions_model=captions_model,
                                                enable_ocr=enable_ocr,
                                                caption_loader=caption_loader,
                                                verbose=kwargs['verbose'],
                                                )

        # note for update_user_db_func output is ignored for db
        add_to_shared_db_btn.click(update_user_db_func,
                                   inputs=[fileup_output, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
                                           chunk, chunk_size],
                                   outputs=[add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
                                   api_name='add_to_shared' if allow_api else None) \
            .then(clear_file_list, outputs=fileup_output, queue=queue) \
            .then(update_radio_to_user, inputs=None, outputs=langchain_mode, queue=False)

        # .then(make_invisible, outputs=add_to_shared_db_btn, queue=queue)
        # .then(make_visible, outputs=upload_button, queue=queue)

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

        update_user_db_url_func = functools.partial(update_user_db_func, is_url=True)
        url_user_btn.click(update_user_db_url_func,
                           inputs=[url_text, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
                                   chunk, chunk_size],
                           outputs=[add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
                           api_name='add_url_to_shared' if allow_api else None) \
            .then(clear_textbox, outputs=url_text, queue=queue) \
            .then(update_radio_to_user, inputs=None, outputs=langchain_mode, queue=False)

        update_user_db_txt_func = functools.partial(update_user_db_func, is_txt=True)
        user_text_user_btn.click(update_user_db_txt_func,
                                 inputs=[user_text_text, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
                                         chunk, chunk_size],
                                 outputs=[add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
                                 api_name='add_text_to_shared' if allow_api else None) \
            .then(clear_textbox, outputs=user_text_text, queue=queue) \
            .then(update_radio_to_user, inputs=None, outputs=langchain_mode, queue=False)

        # Add to MyData
        def update_radio_to_my():
            return gr.update(value='MyData')

        update_my_db_func = functools.partial(update_user_db, dbs=dbs, db_type=db_type, langchain_mode='MyData',
                                              use_openai_embedding=use_openai_embedding,
                                              hf_embedding_model=hf_embedding_model,
                                              enable_captions=enable_captions,
                                              captions_model=captions_model,
                                              enable_ocr=enable_ocr,
                                              caption_loader=caption_loader,
                                              verbose=kwargs['verbose'],
                                              )

        add_to_my_db_btn.click(update_my_db_func,
                               inputs=[fileup_output, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
                                       chunk, chunk_size],
                               outputs=[my_db_state, add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
                               api_name='add_to_my' if allow_api else None) \
            .then(clear_file_list, outputs=fileup_output, queue=queue) \
            .then(update_radio_to_my, inputs=None, outputs=langchain_mode, queue=False)
        # .then(make_invisible, outputs=add_to_shared_db_btn, queue=queue)
        # .then(make_visible, outputs=upload_button, queue=queue)

        update_my_db_url_func = functools.partial(update_my_db_func, is_url=True)
        url_my_btn.click(update_my_db_url_func,
                         inputs=[url_text, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
                                 chunk, chunk_size],
                         outputs=[my_db_state, add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
                         api_name='add_url_to_my' if allow_api else None) \
            .then(clear_textbox, outputs=url_text, queue=queue) \
            .then(update_radio_to_my, inputs=None, outputs=langchain_mode, queue=False)

        update_my_db_txt_func = functools.partial(update_my_db_func, is_txt=True)
        user_text_my_btn.click(update_my_db_txt_func,
                               inputs=[user_text_text, my_db_state, add_to_shared_db_btn, add_to_my_db_btn,
                                       chunk, chunk_size],
                               outputs=[my_db_state, add_to_shared_db_btn, add_to_my_db_btn, sources_text], queue=queue,
                               api_name='add_txt_to_my' if allow_api else None) \
            .then(clear_textbox, outputs=user_text_text, queue=queue) \
            .then(update_radio_to_my, inputs=None, outputs=langchain_mode, queue=False)

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

        # if change collection source, must clear doc selections from it to avoid inconsistency
        def clear_doc_choice():
            return gr.Dropdown.update(choices=docs_state0, value=[docs_state0[0]])

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

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

        get_sources_btn.click(get_sources1, inputs=[my_db_state, langchain_mode], outputs=[file_source, docs_state],
                              queue=queue,
                              api_name='get_sources' if allow_api else None) \
            .then(fn=update_dropdown, inputs=docs_state, outputs=document_choice)
        # show button, else only show when add.  Could add to above get_sources for download/dropdown, but bit much maybe
        show_sources1 = functools.partial(get_source_files_given_langchain_mode, dbs=dbs)
        show_sources_btn.click(fn=show_sources1, inputs=[my_db_state, langchain_mode], outputs=sources_text,
                               api_name='show_sources' if allow_api else None)

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

        refresh_sources1 = functools.partial(update_and_get_source_files_given_langchain_mode,
                                             **get_kwargs(update_and_get_source_files_given_langchain_mode,
                                                          exclude_names=['db1', 'langchain_mode'],
                                                          **all_kwargs))
        refresh_sources_btn.click(fn=refresh_sources1, inputs=[my_db_state, langchain_mode], outputs=sources_text,
                                  api_name='refresh_sources' if allow_api else None)

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

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

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

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

        def evaluate_gradio(*args1, **kwargs1):
            for res_dict in evaluate(*args1, **kwargs1):
                yield '<br>' + res_dict['response'].replace("\n", "<br>")

        fun = partial(evaluate_gradio,
                      **kwargs_evaluate)
        fun2 = partial(evaluate_gradio,
                       **kwargs_evaluate)
        fun_with_dict_str = partial(evaluate_from_str,
                                    default_kwargs=default_kwargs,
                                    **kwargs_evaluate
                                    )

        dark_mode_btn = gr.Button("Dark Mode", variant="primary").style(
            size="sm",
        )
        # FIXME: Could add exceptions for non-chat but still streaming
        exception_text = gr.Textbox(value="", visible=kwargs['chat'], label='Chat Exceptions', interactive=False)
        dark_mode_btn.click(
            None,
            None,
            None,
            _js=get_dark_js(),
            api_name="dark" if allow_api else None,
            queue=False,
        )

        # Control chat and non-chat blocks, which can be independently used by chat checkbox swap
        def col_nochat_fun(x):
            return gr.Column.update(visible=not x)

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

        def context_fun(x):
            return gr.Textbox.update(visible=not x)

        chat.select(col_nochat_fun, chat, col_nochat, api_name="chat_checkbox" if allow_api else None) \
            .then(col_chat_fun, chat, col_chat) \
            .then(context_fun, chat, context) \
            .then(col_chat_fun, chat, exception_text)

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

        # Score
        def score_last_response(*args, nochat=False, model2=False):
            """ Similar to user() """
            args_list = list(args)

            if memory_restriction_level > 0:
                max_length_tokenize = 768 - 256 if memory_restriction_level <= 2 else 512 - 256
            else:
                max_length_tokenize = 2048 - 256
            cutoff_len = max_length_tokenize * 4  # restrict deberta related to max for LLM
            smodel = score_model_state0[0]
            stokenizer = score_model_state0[1]
            sdevice = score_model_state0[2]
            if not nochat:
                history = args_list[-1]
                if history is None:
                    if not model2:
                        # maybe only doing first model, no need to complain
                        print("Bad history in scoring last response, fix for now", flush=True)
                    history = []
                if smodel is not None and \
                        stokenizer is not None and \
                        sdevice is not None and \
                        history is not None and len(history) > 0 and \
                        history[-1] is not None and \
                        len(history[-1]) >= 2:
                    os.environ['TOKENIZERS_PARALLELISM'] = 'false'

                    question = history[-1][0]

                    answer = history[-1][1]
                else:
                    return 'Response Score: NA'
            else:
                answer = args_list[-1]
                instruction_nochat_arg_id = eval_func_param_names.index('instruction_nochat')
                question = args_list[instruction_nochat_arg_id]

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

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

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

        score_args = dict(fn=score_fun,
                          inputs=inputs_list + [text_output],
                          outputs=[score_text],
                          )
        score_args2 = dict(fn=partial(score_fun, model2=True),
                           inputs=inputs_list2 + [text_output2],
                           outputs=[score_text2],
                           )

        score_args_nochat = dict(fn=partial(score_fun, nochat=True),
                                 inputs=inputs_list + [text_output_nochat],
                                 outputs=[score_text_nochat],
                                 )
        if not kwargs['auto_score']:
            score_event = score_btn.click(**score_args, queue=queue, api_name='score' if allow_api else None) \
                .then(**score_args2, queue=queue, api_name='score2' if allow_api else None) \
                .then(clear_torch_cache)
            score_event_nochat = score_btn_nochat.click(**score_args_nochat, queue=queue,
                                                        api_name='score_nochat' if allow_api else None) \
                .then(clear_torch_cache)

        def user(*args, undo=False, sanitize_user_prompt=True, model2=False):
            """
            User that fills history for bot
            :param args:
            :param undo:
            :param sanitize_user_prompt:
            :param model2:
            :return:
            """
            args_list = list(args)
            user_message = args_list[eval_func_param_names.index('instruction')]  # chat only
            input1 = args_list[eval_func_param_names.index('iinput')]  # chat only
            context1 = args_list[eval_func_param_names.index('context')]
            prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
            prompt_dict1 = args_list[eval_func_param_names.index('prompt_dict')]
            chat1 = args_list[eval_func_param_names.index('chat')]
            stream_output1 = args_list[eval_func_param_names.index('stream_output')]
            if input1 and not user_message.endswith(':'):
                user_message1 = user_message + ":" + input1
            elif input1:
                user_message1 = user_message + input1
            else:
                user_message1 = user_message
            if sanitize_user_prompt:
                from better_profanity import profanity
                user_message1 = profanity.censor(user_message1)
            # FIXME: WIP to use desired seperator when user enters nothing
            prompter = Prompter(prompt_type1, prompt_dict1, debug=kwargs['debug'], chat=chat1,
                                stream_output=stream_output1)
            if user_message1 in ['']:
                # e.g. when user just hits enter in textbox,
                # else will have <human>: <bot>: on single line, which seems to be "ok" for LLM but not usual
                user_message1 = '\n'
            # ensure good visually, else markdown ignores multiple \n
            user_message1 = user_message1.replace('\n', '<br>')

            history = args_list[-1]
            if undo and history:
                history.pop()
            args_list = args_list[:-1]  # FYI, even if unused currently
            if history is None:
                if not model2:
                    # no need to complain so often unless model1
                    print("Bad history, fix for now", flush=True)
                history = []
            # ensure elements not mixed across models as output,
            # even if input is currently same source
            history = history.copy()
            if undo:
                return history
            else:
                # FIXME: compare, same history for now
                return history + [[user_message1, None]]

        def history_to_context(history, langchain_mode1, prompt_type1, prompt_dict1, chat1, model_max_length1):
            # ensure output will be unique to models
            _, _, _, max_prompt_length = get_cutoffs(memory_restriction_level,
                                                     for_context=True, model_max_length=model_max_length1)
            history = copy.deepcopy(history)

            context1 = ''
            if max_prompt_length is not None and langchain_mode1 not in ['LLM']:
                context1 = ''
                # - 1 below because current instruction already in history from user()
                for histi in range(0, len(history) - 1):
                    data_point = dict(instruction=history[histi][0], input='', output=history[histi][1])
                    prompt, pre_response, terminate_response, chat_sep = generate_prompt(data_point,
                                                                                         prompt_type1,
                                                                                         prompt_dict1,
                                                                                         chat1, reduced=True)
                    # md -> back to text, maybe not super important if model trained enough
                    if not kwargs['keep_sources_in_context']:
                        from gpt_langchain import source_prefix, source_postfix
                        import re
                        prompt = re.sub(f'{re.escape(source_prefix)}.*?{re.escape(source_postfix)}', '', prompt,
                                        flags=re.DOTALL)
                        if prompt.endswith('\n<p>'):
                            prompt = prompt[:-4]
                    prompt = prompt.replace('<br>', chat_sep)
                    if not prompt.endswith(chat_sep):
                        prompt += chat_sep
                    # most recent first, add older if can
                    # only include desired chat history
                    if len(prompt + context1) > max_prompt_length:
                        break
                    context1 = prompt + context1

                _, pre_response, terminate_response, chat_sep = generate_prompt({}, prompt_type1, prompt_dict1,
                                                                                chat1, reduced=True)
                if context1 and not context1.endswith(chat_sep):
                    context1 += chat_sep  # ensure if terminates abruptly, then human continues on next line
            return context1

        def get_model_max_length(model_state1):
            if model_state1 and not isinstance(model_state1[1], str):
                tokenizer = model_state1[1]
            elif model_state0 and not isinstance(model_state0[1], str):
                tokenizer = model_state0[1]
            else:
                tokenizer = None
            if tokenizer is not None:
                return tokenizer.model_max_length
            else:
                return 2000

        def bot(*args, retry=False):
            """
            bot that consumes history for user input
            instruction (from input_list) itself is not consumed by bot
            :param args:
            :param retry:
            :return:
            """
            # don't deepcopy, can contain model itself
            args_list = list(args).copy()
            model_state1 = args_list[-3]
            my_db_state1 = args_list[-2]
            history = args_list[-1]

            if model_state1[0] is None or model_state1[0] == no_model_str:
                history = []
                yield history, ''
                return

            args_list = args_list[:-3]  # only keep rest needed for evaluate()
            langchain_mode1 = args_list[eval_func_param_names.index('langchain_mode')]
            if retry and history:
                history.pop()
                if not args_list[eval_func_param_names.index('do_sample')]:
                    # if was not sampling, no point in retry unless change to sample
                    args_list[eval_func_param_names.index('do_sample')] = True
            if not history:
                print("No history", flush=True)
                history = []
                yield history, ''
                return
            instruction1 = history[-1][0]
            if not instruction1:
                # reject empty query, can sometimes go nuts
                history = []
                yield history, ''
                return
            prompt_type1 = args_list[eval_func_param_names.index('prompt_type')]
            prompt_dict1 = args_list[eval_func_param_names.index('prompt_dict')]
            chat1 = args_list[eval_func_param_names.index('chat')]
            model_max_length1 = get_model_max_length(model_state1)
            context1 = history_to_context(history, langchain_mode1, prompt_type1, prompt_dict1, chat1,
                                          model_max_length1)
            args_list[0] = instruction1  # override original instruction with history from user
            args_list[2] = context1
            fun1 = partial(evaluate,
                           model_state1,
                           my_db_state1,
                           **kwargs_evaluate)
            try:
                for output_fun in fun1(*tuple(args_list)):
                    output = output_fun['response']
                    extra = output_fun['sources']  # FIXME: can show sources in separate text box etc.
                    # ensure good visually, else markdown ignores multiple \n
                    bot_message = output.replace('\n', '<br>')
                    history[-1][1] = bot_message
                    yield history, ''
            except StopIteration:
                yield history, ''
            except RuntimeError as e:
                if "generator raised StopIteration" in str(e):
                    # assume last entry was bad, undo
                    history.pop()
                    yield history, ''
                else:
                    if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None:
                        history[-1][1] = ''
                    yield history, str(e)
                    raise
            except Exception as e:
                # put error into user input
                ex = "Exception: %s" % str(e)
                if history and len(history) > 0 and len(history[0]) > 1 and history[-1][1] is None:
                    history[-1][1] = ''
                yield history, ex
                raise
            finally:
                clear_torch_cache()
            return

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

        # MODEL2
        user_args2 = dict(fn=functools.partial(user, sanitize_user_prompt=kwargs['sanitize_user_prompt'], model2=True),
                          inputs=inputs_list2 + [text_output2],
                          outputs=text_output2,
                          )
        bot_args2 = dict(fn=bot,
                         inputs=inputs_list2 + [model_state2, my_db_state] + [text_output2],
                         outputs=[text_output2, exception_text],
                         )
        retry_bot_args2 = dict(fn=functools.partial(bot, retry=True),
                               inputs=inputs_list2 + [model_state2, my_db_state] + [text_output2],
                               outputs=[text_output2, exception_text],
                               )
        undo_user_args2 = dict(fn=functools.partial(user, undo=True),
                               inputs=inputs_list2 + [text_output2],
                               outputs=text_output2,
                               )

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

        if kwargs['auto_score']:
            score_args_submit = score_args
            score_args2_submit = score_args2
        else:
            score_args_submit = dict(fn=lambda: None, inputs=None, outputs=None)
            score_args2_submit = dict(fn=lambda: None, inputs=None, outputs=None)

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

        # in case 2nd model, consume instruction first, so can clear quickly
        # bot doesn't consume instruction itself, just history from user, so why works
        submit_event1a = instruction.submit(**user_args, queue=queue,
                                            api_name='instruction' if allow_api else None)
        submit_event1b = submit_event1a.then(**user_args2, api_name='instruction2' if allow_api else None)
        submit_event1c = submit_event1b.then(clear_instruct, None, instruction) \
            .then(clear_instruct, None, iinput)
        submit_event1d = submit_event1c.then(**bot_args, api_name='instruction_bot' if allow_api else None,
                                             queue=queue)
        submit_event1d2 = submit_event1d.then(clear_torch_cache)
        submit_event1e = submit_event1d2.then(**score_args_submit,
                                              api_name='instruction_bot_score' if allow_api else None,
                                              queue=queue)
        submit_event1f = submit_event1e.then(**bot_args2, api_name='instruction_bot2' if allow_api else None,
                                             queue=queue)
        submit_event1f2 = submit_event1f.then(clear_torch_cache)
        submit_event1g = submit_event1f2.then(**score_args2_submit,
                                              api_name='instruction_bot_score2' if allow_api else None, queue=queue)
        submit_event1h = submit_event1g.then(clear_torch_cache)
        # if hit enter on new instruction for submitting new query, no longer the saved chat
        submit_event1i = submit_event1h.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False)

        submit_event2a = submit.click(**user_args, api_name='submit' if allow_api else None)
        submit_event2b = submit_event2a.then(**user_args2, api_name='submit2' if allow_api else None)
        submit_event2c = submit_event2b.then(clear_instruct, None, instruction) \
            .then(clear_instruct, None, iinput)
        submit_event2d = submit_event2c.then(**bot_args, api_name='submit_bot' if allow_api else None, queue=queue)
        submit_event2d2 = submit_event2d.then(clear_torch_cache)
        submit_event2e = submit_event2d2.then(**score_args_submit, api_name='submit_bot_score' if allow_api else None,
                                              queue=queue)
        submit_event2f = submit_event2e.then(**bot_args2, api_name='submit_bot2' if allow_api else None, queue=queue)
        submit_event2f2 = submit_event2f.then(clear_torch_cache)
        submit_event2g = submit_event2f2.then(**score_args2_submit, api_name='submit_bot_score2' if allow_api else None,
                                              queue=queue)
        submit_event2h = submit_event2g.then(clear_torch_cache)
        # if submit new query, no longer the saved chat
        submit_event2i = submit_event2h.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False)

        submit_event3a = retry.click(**user_args, api_name='retry' if allow_api else None)
        submit_event3b = submit_event3a.then(**user_args2, api_name='retry2' if allow_api else None)
        submit_event3c = submit_event3b.then(clear_instruct, None, instruction) \
            .then(clear_instruct, None, iinput)
        submit_event3d = submit_event3c.then(**retry_bot_args, api_name='retry_bot' if allow_api else None,
                                             queue=queue)
        submit_event3d2 = submit_event3d.then(clear_torch_cache)
        submit_event3e = submit_event3d2.then(**score_args_submit, api_name='retry_bot_score' if allow_api else None,
                                              queue=queue)
        submit_event3f = submit_event3e.then(**retry_bot_args2, api_name='retry_bot2' if allow_api else None,
                                             queue=queue)
        submit_event3f2 = submit_event3f.then(clear_torch_cache)
        submit_event3g = submit_event3f2.then(**score_args2_submit, api_name='retry_bot_score2' if allow_api else None,
                                              queue=queue)
        submit_event3h = submit_event3g.then(clear_torch_cache)
        # if retry, no longer the saved chat
        submit_event3i = submit_event3h.then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False)

        # if undo, no longer the saved chat
        submit_event4 = undo.click(**undo_user_args, api_name='undo' if allow_api else None) \
            .then(**undo_user_args2, api_name='undo2' if allow_api else None) \
            .then(clear_instruct, None, instruction) \
            .then(clear_instruct, None, iinput) \
            .then(**score_args_submit, api_name='undo_score' if allow_api else None) \
            .then(**score_args2_submit, api_name='undo_score2' if allow_api else None) \
            .then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False) \
            .then(clear_torch_cache)

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

        def get_short_chat(x, short_chats, short_len=20, words=4):
            if x and len(x[0]) == 2 and x[0][0] is not None:
                short_chat = ' '.join(x[0][0][:short_len].split(' ')[:words]).strip()
                short_chat = dedup(short_chat, short_chats)
            else:
                short_chat = None
            return short_chat

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

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

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

        def save_chat(chat1, chat2, chat_state1):
            short_chats = list(chat_state1.keys())
            for chati in [chat1, chat2]:
                if chati and len(chati) > 0 and len(chati[0]) == 2 and chati[0][1] is not None:
                    short_chat = get_short_chat(chati, short_chats)
                    if short_chat:
                        already_exists = any([is_chat_same(chati, x) for x in chat_state1.values()])
                        if not already_exists:
                            chat_state1[short_chat] = chati
            return chat_state1

        def update_radio_chats(chat_state1):
            return gr.update(choices=list(chat_state1.keys()), value=None)

        def switch_chat(chat_key, chat_state1):
            chosen_chat = chat_state1[chat_key]
            return chosen_chat, chosen_chat

        radio_chats.input(switch_chat, inputs=[radio_chats, chat_state], outputs=[text_output, text_output2])

        def remove_chat(chat_key, chat_state1):
            chat_state1.pop(chat_key, None)
            return chat_state1

        remove_chat_btn.click(remove_chat, inputs=[radio_chats, chat_state], outputs=chat_state) \
            .then(update_radio_chats, inputs=chat_state, outputs=radio_chats)

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

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

        def add_chats_from_file(file, chat_state1, add_btn):
            if not file:
                return chat_state1, add_btn
            if isinstance(file, str):
                files = [file]
            else:
                files = file
            if not files:
                return chat_state1, add_btn
            for file1 in files:
                try:
                    if hasattr(file1, 'name'):
                        file1 = file1.name
                    with open(file1, "rt") as f:
                        new_chats = json.loads(f.read())
                        for chat1_k, chat1_v in new_chats.items():
                            # ignore chat1_k, regenerate and de-dup to avoid loss
                            chat_state1 = save_chat(chat1_v, None, chat_state1)
                except BaseException as e:
                    print("Add chats exception: %s" % str(e), flush=True)
            return chat_state1, add_btn

        # note for update_user_db_func output is ignored for db
        add_to_chats_btn.click(add_chats_from_file,
                               inputs=[chatsup_output, chat_state, add_to_chats_btn],
                               outputs=[chat_state, add_to_my_db_btn], queue=False,
                               api_name='add_to_chats' if allow_api else None) \
            .then(clear_file_list, outputs=chatsup_output, queue=False) \
            .then(update_radio_chats, inputs=chat_state, outputs=radio_chats, queue=False)

        clear_chat_btn.click(lambda: None, None, text_output, queue=False, api_name='clear' if allow_api else None) \
            .then(lambda: None, None, text_output2, queue=False, api_name='clear2' if allow_api else None) \
            .then(deselect_radio_chats, inputs=None, outputs=radio_chats, queue=False)

        # does both models
        clear.click(save_chat, inputs=[text_output, text_output2, chat_state], outputs=chat_state,
                    api_name='save_chat' if allow_api else None) \
            .then(update_radio_chats, inputs=chat_state, outputs=radio_chats,
                  api_name='update_chats' if allow_api else None) \
            .then(lambda: None, None, text_output, queue=False, api_name='clearB' if allow_api else None) \
            .then(lambda: None, None, text_output2, queue=False, api_name='clearB2' if allow_api else None)
        # NOTE: clear of instruction/iinput for nochat has to come after score,
        # because score for nochat consumes actual textbox, while chat consumes chat history filled by user()
        no_chat_args = dict(fn=fun,
                            inputs=[model_state, my_db_state] + inputs_list,
                            outputs=text_output_nochat,
                            queue=queue,
                            )
        submit_event_nochat = submit_nochat.click(**no_chat_args, api_name='submit_nochat' if allow_api else None) \
            .then(clear_torch_cache) \
            .then(**score_args_nochat, api_name='instruction_bot_score_nochat' if allow_api else None, queue=queue) \
            .then(clear_instruct, None, instruction_nochat) \
            .then(clear_instruct, None, iinput_nochat) \
            .then(clear_torch_cache)
        # copy of above with text box submission
        submit_event_nochat2 = instruction_nochat.submit(**no_chat_args) \
            .then(clear_torch_cache) \
            .then(**score_args_nochat, queue=queue) \
            .then(clear_instruct, None, instruction_nochat) \
            .then(clear_instruct, None, iinput_nochat) \
            .then(clear_torch_cache)

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

        def load_model(model_name, lora_weights, model_state_old, prompt_type_old, load_8bit, infer_devices, gpu_id):
            # ensure old model removed from GPU memory
            if kwargs['debug']:
                print("Pre-switch pre-del GPU memory: %s" % get_torch_allocated(), flush=True)

            model0 = model_state0[0]
            if isinstance(model_state_old[0], str) and model0 is not None:
                # best can do, move model loaded at first to CPU
                model0.cpu()

            if model_state_old[0] is not None and not isinstance(model_state_old[0], str):
                try:
                    model_state_old[0].cpu()
                except Exception as e:
                    # sometimes hit NotImplementedError: Cannot copy out of meta tensor; no data!
                    print("Unable to put model on CPU: %s" % str(e), flush=True)
                del model_state_old[0]
                model_state_old[0] = None

            if model_state_old[1] is not None and not isinstance(model_state_old[1], str):
                del model_state_old[1]
                model_state_old[1] = None

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

            if model_name is None or model_name == no_model_str:
                # no-op if no model, just free memory
                # no detranscribe needed for model, never go into evaluate
                lora_weights = no_lora_str
                return [None, None, None, model_name], model_name, lora_weights, prompt_type_old

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

            # detranscribe
            if lora_weights == no_lora_str:
                lora_weights = ''

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

            model_state_new = [model1, tokenizer1, device1, model_name]

            max_max_new_tokens1 = get_max_max_new_tokens(model_state_new, **kwargs)

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

        def get_prompt_str(prompt_type1, prompt_dict1):
            prompt_dict1, prompt_dict_error = get_prompt(prompt_type1, prompt_dict1, chat=False, context='',
                                                         reduced=False, return_dict=True)
            if prompt_dict_error:
                return str(prompt_dict_error)
            else:
                # return so user can manipulate if want and use as custom
                return str(prompt_dict1)

        prompt_type.change(fn=get_prompt_str, inputs=[prompt_type, prompt_dict], outputs=prompt_dict)
        prompt_type2.change(fn=get_prompt_str, inputs=[prompt_type2, prompt_dict2], outputs=prompt_dict2)

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

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

        load_model_args = dict(fn=load_model,
                               inputs=[model_choice, lora_choice, model_state, prompt_type,
                                       model_load8bit_checkbox, model_infer_devices_checkbox, model_gpu],
                               outputs=[model_state, model_used, lora_used,
                                        # if prompt_type changes, prompt_dict will change via change rule
                                        prompt_type, max_new_tokens, min_new_tokens,
                                        ])
        prompt_update_args = dict(fn=dropdown_prompt_type_list, inputs=prompt_type, outputs=prompt_type)
        chatbot_update_args = dict(fn=chatbot_list, inputs=[text_output, model_used], outputs=text_output)
        nochat_update_args = dict(fn=chatbot_list, inputs=[text_output_nochat, model_used], outputs=text_output_nochat)
        if not is_public:
            load_model_event = load_model_button.click(**load_model_args, api_name='load_model' if allow_api else None) \
                .then(**prompt_update_args) \
                .then(**chatbot_update_args) \
                .then(**nochat_update_args) \
                .then(clear_torch_cache)

        load_model_args2 = dict(fn=load_model,
                                inputs=[model_choice2, lora_choice2, model_state2, prompt_type2,
                                        model_load8bit_checkbox2, model_infer_devices_checkbox2, model_gpu2],
                                outputs=[model_state2, model_used2, lora_used2,
                                         # if prompt_type2 changes, prompt_dict2 will change via change rule
                                         prompt_type2, max_new_tokens2, min_new_tokens2
                                         ])
        prompt_update_args2 = dict(fn=dropdown_prompt_type_list, inputs=prompt_type2, outputs=prompt_type2)
        chatbot_update_args2 = dict(fn=chatbot_list, inputs=[text_output2, model_used2], outputs=text_output2)
        if not is_public:
            load_model_event2 = load_model_button2.click(**load_model_args2,
                                                         api_name='load_model2' if allow_api else None) \
                .then(**prompt_update_args2) \
                .then(**chatbot_update_args2) \
                .then(clear_torch_cache)

        def dropdown_model_list(list0, x):
            new_state = [list0[0] + [x]]
            new_options = [*new_state[0]]
            return gr.Dropdown.update(value=x, choices=new_options), \
                gr.Dropdown.update(value=x, choices=new_options), \
                '', new_state

        add_model_event = add_model_button.click(fn=dropdown_model_list,
                                                 inputs=[model_options_state, new_model],
                                                 outputs=[model_choice, model_choice2, new_model, model_options_state],
                                                 queue=False)

        def dropdown_lora_list(list0, x, model_used1, lora_used1, model_used2, lora_used2):
            new_state = [list0[0] + [x]]
            new_options = [*new_state[0]]
            # don't switch drop-down to added lora if already have model loaded
            x1 = x if model_used1 == no_model_str else lora_used1
            x2 = x if model_used2 == no_model_str else lora_used2
            return gr.Dropdown.update(value=x1, choices=new_options), \
                gr.Dropdown.update(value=x2, choices=new_options), \
                '', new_state

        add_lora_event = add_lora_button.click(fn=dropdown_lora_list,
                                               inputs=[lora_options_state, new_lora, model_used, lora_used, model_used2,
                                                       lora_used2],
                                               outputs=[lora_choice, lora_choice2, new_lora, lora_options_state],
                                               queue=False)

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

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

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

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

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

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

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

        def get_system_info():
            return gr.Textbox.update(value=system_info_print())

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

        # don't pass text_output, don't want to clear output, just stop it
        # cancel only stops outer generation, not inner generation or non-generation
        stop_btn.click(lambda: None, None, None,
                       cancels=[submit_event1d, submit_event1f,
                                submit_event2d, submit_event2f,
                                submit_event3d, submit_event3f,
                                submit_event_nochat,
                                submit_event_nochat2,
                                ],
                       queue=False, api_name='stop' if allow_api else None).then(clear_torch_cache, queue=False)

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

        count_chat_tokens_btn.click(fn=count_chat_tokens, inputs=[model_state, text_output, prompt_type, prompt_dict],
                                    outputs=chat_token_count, api_name='count_tokens' if allow_api else None)

        demo.load(None, None, None, _js=get_dark_js() if kwargs['h2ocolors'] else None)

    demo.queue(concurrency_count=kwargs['concurrency_count'], api_open=kwargs['api_open'])
    favicon_path = "h2o-logo.svg"

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

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

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


input_args_list = ['model_state', 'my_db_state']


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


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


def update_user_db(file, db1, x, y, *args, dbs=None, langchain_mode='UserData', **kwargs):
    try:
        return _update_user_db(file, db1, x, y, *args, dbs=dbs, langchain_mode=langchain_mode, **kwargs)
    except BaseException as e:
        print(traceback.format_exc(), flush=True)
        # gradio has issues if except, so fail semi-gracefully, else would hang forever in processing textbox
        ex_str = "Exception: %s" % str(e)
        source_files_added = """\
        <html>
          <body>
            <p>
               Sources: <br>
            </p>
               <div style="overflow-y: auto;height:400px">
               {0}
               </div>
          </body>
        </html>
        """.format(ex_str)
        if langchain_mode == 'MyData':
            return db1, x, y, source_files_added
        else:
            return x, y, source_files_added
    finally:
        clear_torch_cache()


def _update_user_db(file, db1, x, y, chunk, chunk_size, dbs=None, db_type=None, langchain_mode='UserData',
                    use_openai_embedding=None,
                    hf_embedding_model=None,
                    caption_loader=None,
                    enable_captions=None,
                    captions_model=None,
                    enable_ocr=None,
                    verbose=None,
                    is_url=None, is_txt=None):
    assert use_openai_embedding is not None
    assert hf_embedding_model is not None
    assert caption_loader is not None
    assert enable_captions is not None
    assert captions_model is not None
    assert enable_ocr is not None
    assert verbose is not None

    assert isinstance(dbs, dict), "Wrong type for dbs: %s" % str(type(dbs))
    assert db_type in ['faiss', 'chroma'], "db_type %s not supported" % db_type
    from gpt_langchain import add_to_db, get_db, path_to_docs
    # handle case of list of temp buffer
    if isinstance(file, list) and len(file) > 0 and hasattr(file[0], 'name'):
        file = [x.name for x in file]
    # handle single file of temp buffer
    if hasattr(file, 'name'):
        file = file.name
    if verbose:
        print("Adding %s" % file, flush=True)
    sources = path_to_docs(file if not is_url and not is_txt else None,
                           verbose=verbose,
                           chunk=chunk, chunk_size=chunk_size,
                           url=file if is_url else None,
                           text=file if is_txt else None,
                           enable_captions=enable_captions,
                           captions_model=captions_model,
                           enable_ocr=enable_ocr,
                           caption_loader=caption_loader,
                           )
    exceptions = [x for x in sources if x.metadata.get('exception')]
    sources = [x for x in sources if 'exception' not in x.metadata]

    with filelock.FileLock("db_%s.lock" % langchain_mode.replace(' ', '_')):
        if langchain_mode == 'MyData':
            if db1[0] is not None:
                # then add
                db, num_new_sources, new_sources_metadata = add_to_db(db1[0], sources, db_type=db_type,
                                                                      use_openai_embedding=use_openai_embedding,
                                                                      hf_embedding_model=hf_embedding_model)
            else:
                assert len(db1) == 2 and db1[1] is None, "Bad MyData db: %s" % db1
                # then create
                # assign fresh hash for this user session, so not shared
                # if added has to original state and didn't change, then would be shared db for all users
                db1[1] = str(uuid.uuid4())
                persist_directory = os.path.join(scratch_base_dir, 'db_dir_%s_%s' % (langchain_mode, db1[1]))
                db = get_db(sources, use_openai_embedding=use_openai_embedding,
                            db_type=db_type,
                            persist_directory=persist_directory,
                            langchain_mode=langchain_mode,
                            hf_embedding_model=hf_embedding_model)
            if db is None:
                db1[1] = None
            else:
                db1[0] = db
            source_files_added = get_source_files(db=db1[0], exceptions=exceptions)
            return db1, x, y, source_files_added
        else:
            from gpt_langchain import get_persist_directory
            persist_directory = get_persist_directory(langchain_mode)
            if langchain_mode in dbs and dbs[langchain_mode] is not None:
                # then add
                db, num_new_sources, new_sources_metadata = add_to_db(dbs[langchain_mode], sources, db_type=db_type,
                                                                      use_openai_embedding=use_openai_embedding,
                                                                      hf_embedding_model=hf_embedding_model)
            else:
                # then create
                db = get_db(sources, use_openai_embedding=use_openai_embedding,
                            db_type=db_type,
                            persist_directory=persist_directory,
                            langchain_mode=langchain_mode,
                            hf_embedding_model=hf_embedding_model)
            dbs[langchain_mode] = db
            # NOTE we do not return db, because function call always same code path
            # return dbs[langchain_mode], x, y
            # db in this code path is updated in place
            source_files_added = get_source_files(db=dbs[langchain_mode], exceptions=exceptions)
            return x, y, source_files_added


def get_db(db1, langchain_mode, dbs=None):
    with filelock.FileLock("db_%s.lock" % langchain_mode.replace(' ', '_')):
        if langchain_mode in ['wiki_full']:
            # NOTE: avoid showing full wiki.  Takes about 30 seconds over about 90k entries, but not useful for now
            db = None
        elif langchain_mode == 'MyData' and len(db1) > 0 and db1[0] is not None:
            db = db1[0]
        elif langchain_mode in dbs and dbs[langchain_mode] is not None:
            db = dbs[langchain_mode]
        else:
            db = None
    return db


def get_source_files_given_langchain_mode(db1, langchain_mode='UserData', dbs=None):
    db = get_db(db1, langchain_mode, dbs=dbs)
    return get_source_files(db=db, exceptions=None)


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

    # only should be one source, not confused
    assert db is not None or metadatas is not None

    if metadatas is None:
        source_label = "Sources:"
        if db is not None:
            metadatas = db.get()['metadatas']
        else:
            metadatas = []
        adding_new = False
    else:
        source_label = "New Sources:"
        adding_new = True

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

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

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

    return source_files_added


def update_and_get_source_files_given_langchain_mode(db1, langchain_mode, dbs=None, first_para=None,
                                                     text_limit=None, chunk=None, chunk_size=None,
                                                     user_path=None, db_type=None, load_db_if_exists=None,
                                                     n_jobs=None, verbose=None):
    db = get_db(db1, langchain_mode, dbs=dbs)

    from gpt_langchain import make_db
    db, num_new_sources, new_sources_metadata = make_db(use_openai_embedding=False,
                                                        hf_embedding_model="sentence-transformers/all-MiniLM-L6-v2",
                                                        first_para=first_para, text_limit=text_limit,
                                                        chunk=chunk,
                                                        chunk_size=chunk_size,
                                                        langchain_mode=langchain_mode,
                                                        user_path=user_path,
                                                        db_type=db_type,
                                                        load_db_if_exists=load_db_if_exists,
                                                        db=db,
                                                        n_jobs=n_jobs,
                                                        verbose=verbose)
    # return only new sources with text saying such
    return get_source_files(db=None, exceptions=None, metadatas=new_sources_metadata)