





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__lowercase											=      get_tests_dir('''fixtures''')
__lowercase											=      get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
__lowercase											=      get_tests_dir('''fixtures/dummy-config.json''')


class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        __UpperCamelCase  :str    						=	0







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[Any]    						=	AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h''')
        self.assertIsInstance(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Optional[int]    						=	AutoFeatureExtractor.from_pretrained(__lowercase)
        self.assertIsInstance(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			int:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase  :Tuple    						=	WavaVecaConfig()

            # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
            __UpperCamelCase  :List[Any]    						=	AutoFeatureExtractor.from_pretrained(__lowercase).to_dict()

            config_dict.pop('''feature_extractor_type''')
            __UpperCamelCase  :str    						=	WavaVecaFeatureExtractor(**__lowercase)

            # save in new folder
            model_config.save_pretrained(__lowercase)
            config.save_pretrained(__lowercase)

            __UpperCamelCase  :Any    						=	AutoFeatureExtractor.from_pretrained(__lowercase)

            # make sure private variable is not incorrectly saved
            __UpperCamelCase  :int    						=	json.loads(config.to_json_string())
            self.assertTrue('''_processor_class''' not in dict_as_saved)

        self.assertIsInstance(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Tuple    						=	AutoFeatureExtractor.from_pretrained(__lowercase)
        self.assertIsInstance(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[str]:
        with self.assertRaisesRegex(
            __lowercase				,				'''bert-base is not a local folder and is not a valid model identifier'''):
            __UpperCamelCase  :Union[str, Any]    						=	AutoFeatureExtractor.from_pretrained('''bert-base''')







    def     UpperCamelCase__     (			self) ->			Any:
        with self.assertRaisesRegex(
            __lowercase				,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
            __UpperCamelCase  :Any    						=	AutoFeatureExtractor.from_pretrained(__lowercase				,				revision='''aaaaaa''')







    def     UpperCamelCase__     (			self) ->			List[str]:
        with self.assertRaisesRegex(
            __lowercase				,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''				,				):
            __UpperCamelCase  :Union[str, Any]    						=	AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model''')







    def     UpperCamelCase__     (			self) ->			List[Any]:
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(__lowercase):
            __UpperCamelCase  :List[Any]    						=	AutoFeatureExtractor.from_pretrained(
                '''hf-internal-testing/test_dynamic_feature_extractor''')
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(__lowercase):
            __UpperCamelCase  :Dict    						=	AutoFeatureExtractor.from_pretrained(
                '''hf-internal-testing/test_dynamic_feature_extractor'''				,				trust_remote_code=__lowercase)

        __UpperCamelCase  :Tuple    						=	AutoFeatureExtractor.from_pretrained(
            '''hf-internal-testing/test_dynamic_feature_extractor'''				,				trust_remote_code=__lowercase)
        self.assertEqual(feature_extractor.__class__.__name__				,				'''NewFeatureExtractor''')

        # Test feature extractor can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
            feature_extractor.save_pretrained(__lowercase)
            __UpperCamelCase  :Union[str, Any]    						=	AutoFeatureExtractor.from_pretrained(__lowercase				,				trust_remote_code=__lowercase)
        self.assertEqual(reloaded_feature_extractor.__class__.__name__				,				'''NewFeatureExtractor''')







    def     UpperCamelCase__     (			self) ->			Any:
        try:
            AutoConfig.register('''custom'''				,				__lowercase)
            AutoFeatureExtractor.register(__lowercase				,				__lowercase)
            # Trying to register something existing in the Transformers library will raise an error
            with self.assertRaises(__lowercase):
                AutoFeatureExtractor.register(__lowercase				,				__lowercase)

            # Now that the config is registered, it can be used as any other config with the auto-API
            __UpperCamelCase  :Union[str, Any]    						=	CustomFeatureExtractor.from_pretrained(__lowercase)
            with tempfile.TemporaryDirectory() as tmp_dir:
                feature_extractor.save_pretrained(__lowercase)
                __UpperCamelCase  :Dict    						=	AutoFeatureExtractor.from_pretrained(__lowercase)
                self.assertIsInstance(__lowercase				,				__lowercase)

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]







    def     UpperCamelCase__     (			self) ->			str:


        class 			lowerCamelCase_  (     UpperCAmelCase_				):






            '''simple docstring'''






            a__			:		Any      			=   True

        try:
            AutoConfig.register('''custom'''				,				__lowercase)
            AutoFeatureExtractor.register(__lowercase				,				__lowercase)
            # If remote code is not set, the default is to use local
            __UpperCamelCase  :Union[str, Any]    						=	AutoFeatureExtractor.from_pretrained(
                '''hf-internal-testing/test_dynamic_feature_extractor''')
            self.assertEqual(feature_extractor.__class__.__name__				,				'''NewFeatureExtractor''')
            self.assertTrue(feature_extractor.is_local)

            # If remote code is disabled, we load the local one.
            __UpperCamelCase  :Tuple    						=	AutoFeatureExtractor.from_pretrained(
                '''hf-internal-testing/test_dynamic_feature_extractor'''				,				trust_remote_code=__lowercase)
            self.assertEqual(feature_extractor.__class__.__name__				,				'''NewFeatureExtractor''')
            self.assertTrue(feature_extractor.is_local)

            # If remote is enabled, we load from the Hub
            __UpperCamelCase  :Optional[int]    						=	AutoFeatureExtractor.from_pretrained(
                '''hf-internal-testing/test_dynamic_feature_extractor'''				,				trust_remote_code=__lowercase)
            self.assertEqual(feature_extractor.__class__.__name__				,				'''NewFeatureExtractor''')
            self.assertTrue(not hasattr(__lowercase				,				'''is_local'''))

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]






import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


__lowercase											=      '''bart'''
__lowercase											=      True



@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE			)
def lowerCamelCase						(     ):
    '''simple docstring'''







    if LOAD_DENSE_INDEX:
        __UpperCamelCase  :Union[str, Any]    						=	AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased'''			)
        __UpperCamelCase  :Union[str, Any]    						=	AutoModel.from_pretrained('''yjernite/retribert-base-uncased'''			).to('''cuda:0'''			)
        __UpperCamelCase  :Optional[int]    						=	qar_model.eval()
    else:
        __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	(None, None)
    if MODEL_TYPE == "bart":
        __UpperCamelCase  :str    						=	AutoTokenizer.from_pretrained('''yjernite/bart_eli5'''			)
        __UpperCamelCase  :List[Any]    						=	AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5'''			).to('''cuda:0'''			)
        __UpperCamelCase  :Any    						=	torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth'''			)
        sas_model.load_state_dict(save_dict['''model''']			)
        __UpperCamelCase  :List[str]    						=	sas_model.eval()
    else:
        __UpperCamelCase						, __UpperCamelCase  :Tuple    						=	make_qa_sas_model(
            model_name='''t5-small'''						,    from_file='''seq2seq_models/eli5_t5_model_1024_4.pth'''						,    device='''cuda:0'''			)
    return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)



@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE			)
def lowerCamelCase						(     ):
    '''simple docstring'''







    if LOAD_DENSE_INDEX:
        __UpperCamelCase  :int    						=	faiss.StandardGpuResources()
        __UpperCamelCase  :List[Any]    						=	datasets.load_dataset(path='''wiki_snippets'''						,    name='''wiki40b_en_100_0'''			)['''train''']
        __UpperCamelCase  :List[str]    						=	np.memmap(
            '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'''						,    dtype='''float32'''						,    mode='''r'''						,    shape=(wikiaab_passages.num_rows, 128)						,    )
        __UpperCamelCase  :Tuple    						=	faiss.IndexFlatIP(128			)
        __UpperCamelCase  :str    						=	faiss.index_cpu_to_gpu(SCREAMING_SNAKE_CASE						,    1						,    SCREAMING_SNAKE_CASE			)
        wikiaab_gpu_index_flat.add(SCREAMING_SNAKE_CASE			)  # TODO fix for larger GPU
    else:
        __UpperCamelCase						, __UpperCamelCase  :Any    						=	(None, None)
    __UpperCamelCase  :Optional[Any]    						=	Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]			)
    return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE			)
def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	datasets.load_dataset('''eli5'''						,    name='''LFQA_reddit'''			)
    __UpperCamelCase  :int    						=	elia['''train_eli5''']
    __UpperCamelCase  :List[str]    						=	np.memmap(
        '''eli5_questions_reps.dat'''						,    dtype='''float32'''						,    mode='''r'''						,    shape=(elia_train.num_rows, 128)			)
    __UpperCamelCase  :Optional[Any]    						=	faiss.IndexFlatIP(128			)
    eli5_train_q_index.add(SCREAMING_SNAKE_CASE			)
    return (elia_train, eli5_train_q_index)


__lowercase				, __lowercase				, __lowercase											=      load_indexes()
__lowercase				, __lowercase				, __lowercase				, __lowercase											=      load_models()
__lowercase				, __lowercase											=      load_train_data()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=10			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	embed_questions_for_retrieval([question]						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    __UpperCamelCase						, __UpperCamelCase  :Union[str, Any]    						=	eli5_train_q_index.search(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[Any]    						=	[elia_train[int(SCREAMING_SNAKE_CASE			)] for i in I[0]]
    return nn_examples



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE="wiki40b"						,    SCREAMING_SNAKE_CASE="dense"						,    SCREAMING_SNAKE_CASE=10			):
    '''simple docstring'''







    if source == "none":
        __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	(''' <P> '''.join(['''''' for _ in range(11			)]			).strip(), [])
    else:
        if method == "dense":
            __UpperCamelCase						, __UpperCamelCase  :List[str]    						=	query_qa_dense_index(
                SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
        else:
            __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	query_es_index(
                SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    index_name='''english_wiki40b_snippets_100w'''						,    n_results=SCREAMING_SNAKE_CASE						,    )
    __UpperCamelCase  :int    						=	[
        (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
    ]
    __UpperCamelCase  :int    						=	'''question: {} context: {}'''.format(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    return question_doc, support_list



@st.cache(
    hash_funcs={
        torch.Tensor: (lambda SCREAMING_SNAKE_CASE			: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda SCREAMING_SNAKE_CASE			: None),
    }			)
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=64						,    SCREAMING_SNAKE_CASE=256						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=2						,    SCREAMING_SNAKE_CASE=0.95						,    SCREAMING_SNAKE_CASE=0.8			):
    '''simple docstring'''







    with torch.no_grad():
        __UpperCamelCase  :Union[str, Any]    						=	qa_sas_generate(
            SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    num_answers=1						,    num_beams=SCREAMING_SNAKE_CASE						,    min_len=SCREAMING_SNAKE_CASE						,    max_len=SCREAMING_SNAKE_CASE						,    do_sample=SCREAMING_SNAKE_CASE						,    temp=SCREAMING_SNAKE_CASE						,    top_p=SCREAMING_SNAKE_CASE						,    top_k=SCREAMING_SNAKE_CASE						,    max_input_length=1_024						,    device='''cuda:0'''						,    )[0]
    return (answer, support_list)


st.title('''Long Form Question Answering with ELI5''')

# Start sidebar
__lowercase											=      '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
__lowercase											=      '''
<html>
  <head>
    <style>
      .img-container {
        padding-left: 90px;
        padding-right: 90px;
        padding-top: 50px;
        padding-bottom: 50px;
        background-color: #f0f3f9;
      }
    </style>
  </head>
  <body>
    <span class="img-container"> <!-- Inline parent element -->
      %s
    </span>
  </body>
</html>
''' % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
__lowercase											=      '''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)

__lowercase											=      [
    '''Answer the question''',
    '''View the retrieved document only''',
    '''View the most similar ELI5 question and answer''',
    '''Show me everything, please!''',
]
__lowercase											=      st.sidebar.checkbox('''Demo options''')
if demo_options:
    __lowercase											=      st.sidebar.selectbox(
        '''''',
        action_list,
        index=3,
    )
    __lowercase											=      action_list.index(action_st)
    __lowercase											=      st.sidebar.selectbox(
        '''''',
        ['''Show full text of passages''', '''Show passage section titles'''],
        index=0,
    )
    __lowercase											=      show_type == '''Show full text of passages'''
else:
    __lowercase											=      3
    __lowercase											=      True

__lowercase											=      st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
    __lowercase											=      '''
    ### Information retriever options

    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
    '''
    st.sidebar.markdown(retriever_info)
    __lowercase											=      st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
    __lowercase											=      st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
    __lowercase											=      '''wiki40b'''
    __lowercase											=      '''dense'''

__lowercase											=      '''beam'''
__lowercase											=      2
__lowercase											=      64
__lowercase											=      256
__lowercase											=      None
__lowercase											=      None
__lowercase											=      st.sidebar.checkbox('''Generation options''')
if generate_options:
    __lowercase											=      '''
    ### Answer generation options

    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
    **beam** search, or **sample** from the decoder\'s output probabilities.
    '''
    st.sidebar.markdown(generate_info)
    __lowercase											=      st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
    __lowercase											=      st.sidebar.slider(
        '''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
    )
    __lowercase											=      st.sidebar.slider(
        '''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
    )
    if sampled == "beam":
        __lowercase											=      st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
    else:
        __lowercase											=      st.sidebar.slider(
            '''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
        )
        __lowercase											=      st.sidebar.slider(
            '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
        )
        __lowercase											=      None

# start main text
__lowercase											=      [
    '''<MY QUESTION>''',
    '''How do people make chocolate?''',
    '''Why do we get a fever when we are sick?''',
    '''How can different animals perceive different colors?''',
    '''What is natural language processing?''',
    '''What\'s the best way to treat a sunburn?''',
    '''What exactly are vitamins ?''',
    '''How does nuclear energy provide electricity?''',
    '''What\'s the difference between viruses and bacteria?''',
    '''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
    '''Why do people like drinking coffee even though it tastes so bad?''',
    '''What happens when wine ages? How does it make the wine taste better?''',
    '''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
    '''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
    '''How does New Zealand have so many large bird predators?''',
]
__lowercase											=      st.selectbox(
    '''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
    __lowercase											=      st.text_input('''Enter your question here:''', '''''')
else:
    __lowercase											=      question_s

if st.button('''Show me!'''):
    if action in [0, 1, 3]:
        if index_type == "mixed":
            __lowercase				, __lowercase											=      make_support(question, source=wiki_source, method='''dense''', n_results=10)
            __lowercase				, __lowercase											=      make_support(question, source=wiki_source, method='''sparse''', n_results=10)
            __lowercase											=      []
            for res_d, res_s in zip(support_list_dense, support_list_sparse):
                if tuple(res_d) not in support_list:
                    support_list += [tuple(res_d)]
                if tuple(res_s) not in support_list:
                    support_list += [tuple(res_s)]
            __lowercase											=      support_list[:10]
            __lowercase											=      '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
        else:
            __lowercase				, __lowercase											=      make_support(question, source=wiki_source, method=index_type, n_results=10)
    if action in [0, 3]:
        __lowercase				, __lowercase											=      answer_question(
            question_doc,
            sas_model,
            sas_tokenizer,
            min_len=min_len,
            max_len=int(max_len),
            sampling=(sampled == '''sampled'''),
            n_beams=n_beams,
            top_p=top_p,
            temp=temp,
        )
        st.markdown('''### The model generated answer is:''')
        st.write(answer)
    if action in [0, 1, 3] and wiki_source != "none":
        st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
        for i, res in enumerate(support_list):
            __lowercase											=      '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
            __lowercase											=      res[1].strip()
            if sec_titles == "":
                __lowercase											=      '''[{}]({})'''.format(res[0], wiki_url)
            else:
                __lowercase											=      sec_titles.split(''' & ''')
                __lowercase											=      ''' & '''.join(
                    ['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
                )
            st.markdown(
                '''{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}'''.format(i + 1, res[0], sections),
                unsafe_allow_html=True,
            )
            if show_passages:
                st.write(
                    '''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
                )
    if action in [2, 3]:
        __lowercase											=      find_nearest_training(question)
        __lowercase											=      nn_train_list[0]
        st.markdown(
            '''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
        )
        __lowercase											=      [
            '''{}. {}'''.format(i + 1, '''  \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
            for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
            if i == 0 or sc > 2
        ]
        st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))


__lowercase											=      '''
---

**Disclaimer**

*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)





