




"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


A_						=				r'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''






@add_start_docstrings(__a )
class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"rag"
 lowercase__ 					=					True


 def __init__(				self:		Union[str, Any],      a_:		int=None,      a_:		Tuple=True,      a_:		Optional[int]=None,      a_:		List[str]=None,      a_:		int=None,      a_:		Optional[Any]=None,      a_:		List[str]=None,      a_:		Optional[Any]=" / ",      a_:		Tuple=" // ",      a_:		List[Any]=5,      a_:		Dict=300,      a_:		Tuple=768,      a_:		Optional[Any]=8,      a_:		int="wiki_dpr",      a_:		Any="train",      a_:		Optional[int]="compressed",      a_:		Optional[int]=None,      a_:		List[Any]=None,      a_:		Optional[Any]=False,      a_:		str=False,      a_:		Dict=0.0,      a_:		Union[str, Any]=True,      a_:		Union[str, Any]=False,      a_:		str=False,      a_:		List[str]=False,      a_:		Union[str, Any]=True,      a_:		Any=None,      **a_:		List[Any],      ):




   '''simple docstring'''



   super().__init__(
       bos_token_id=a_,      pad_token_id=a_,      eos_token_id=a_,      decoder_start_token_id=a_,      forced_eos_token_id=a_,      is_encoder_decoder=a_,      prefix=a_,      vocab_size=a_,      **a_,      )
   assert (
       "question_encoder" in kwargs and "generator" in kwargs
   ), "Config has to be initialized with question_encoder and generator config"
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""question_encoder"""		)
   _snake_case							:		List[str]								=  question_encoder_config.pop("""model_type"""		)
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""generator"""		)
   _snake_case							:		Any								=  decoder_config.pop("""model_type"""		)

   from ..auto.configuration_auto import AutoConfig

   _snake_case							:		Union[str, Any]								=  AutoConfig.for_model(a_,      **a_		)
   _snake_case							:		Optional[Any]								=  AutoConfig.for_model(a_,      **a_		)

   _snake_case							:		Any								=  reduce_loss
   _snake_case							:		Optional[int]								=  label_smoothing
   _snake_case							:		Dict								=  exclude_bos_score
   _snake_case							:		int								=  do_marginalize

   _snake_case							:		Optional[Any]								=  title_sep
   _snake_case							:		Any								=  doc_sep
   _snake_case							:		List[str]								=  n_docs
   _snake_case							:		Tuple								=  max_combined_length

   _snake_case							:		Optional[Any]								=  dataset
   _snake_case							:		Union[str, Any]								=  dataset_split
   _snake_case							:		Tuple								=  index_name

   _snake_case							:		Any								=  retrieval_vector_size
   _snake_case							:		Union[str, Any]								=  retrieval_batch_size
   _snake_case							:		str								=  passages_path
   _snake_case							:		Tuple								=  index_path
   _snake_case							:		List[Any]								=  use_dummy_dataset

   _snake_case							:		Optional[Any]								=  output_retrieved

   _snake_case							:		Tuple								=  do_deduplication

   _snake_case							:		Union[str, Any]								=  use_cache

   if self.forced_eos_token_id is None:
     _snake_case							:		Dict								=  getattr(self.generator,      """forced_eos_token_id""",      a_		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Any,      a_:		PretrainedConfig,      a_:		PretrainedConfig,      **a_:		Optional[Any]		):




   '''simple docstring'''



   return cls(question_encoder=question_encoder_config.to_dict(),      generator=generator_config.to_dict(),      **a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  copy.deepcopy(self.__dict__		)
   _snake_case							:		List[str]								=  self.question_encoder.to_dict()
   _snake_case							:		Tuple								=  self.generator.to_dict()
   _snake_case							:		Dict								=  self.__class__.model_type
   return output






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


SCREAMING_SNAKE_CASE       :      str												= "bart"
SCREAMING_SNAKE_CASE       :      Optional[int]												= True



@st.cache(allow_output_mutation=lowerCamelCase_ )
def 				UpperCamelCase_(      )    ->						int:
    if LOAD_DENSE_INDEX:
        _lowercase       :							str      		=		AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased' )
        _lowercase       :							Union[str, Any]      		=		AutoModel.from_pretrained('yjernite/retribert-base-uncased' ).to('cuda:0' )
        _lowercase       :							str      		=		qar_model.eval()
    else:
        _lowercase		,				_lowercase       :							Any      		=		(None, None)
    if MODEL_TYPE == "bart":
        _lowercase       :							Dict      		=		AutoTokenizer.from_pretrained('yjernite/bart_eli5' )
        _lowercase       :							int      		=		AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5' ).to('cuda:0' )
        _lowercase       :							Any      		=		torch.load('seq2seq_models/eli5_bart_model_blm_2.pth' )
        sas_model.load_state_dict(save_dict['model'] )
        _lowercase       :							List[Any]      		=		sas_model.eval()
    else:
        _lowercase		,				_lowercase       :							Union[str, Any]      		=		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=lowerCamelCase_ )
def 				UpperCamelCase_(      )    ->						str:
    if LOAD_DENSE_INDEX:
        _lowercase       :							Optional[Any]      		=		faiss.StandardGpuResources()
        _lowercase       :							Optional[int]      		=		datasets.load_dataset(path='wiki_snippets'							, name='wiki40b_en_100_0' )['train']
        _lowercase       :							Tuple      		=		np.memmap(
            'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'							, dtype='float32'							, mode='r'							, shape=(wikiaab_passages.num_rows, 128)							, )
        _lowercase       :							Any      		=		faiss.IndexFlatIP(128 )
        _lowercase       :							Union[str, Any]      		=		faiss.index_cpu_to_gpu(lowerCamelCase_							, 1							, lowerCamelCase_ )
        wikiaab_gpu_index_flat.add(lowerCamelCase_ )  # TODO fix for larger GPU
    else:
        _lowercase		,				_lowercase       :							Any      		=		(None, None)
    _lowercase       :							List[str]      		=		Elasticsearch([{'host': 'localhost', 'port': '9200'}] )
    return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=lowerCamelCase_ )
def 				UpperCamelCase_(      )    ->						Any:
    _lowercase       :							List[str]      		=		datasets.load_dataset('eli5'							, name='LFQA_reddit' )
    _lowercase       :							Optional[Any]      		=		elia['train_eli5']
    _lowercase       :							Tuple      		=		np.memmap(
        'eli5_questions_reps.dat'							, dtype='float32'							, mode='r'							, shape=(elia_train.num_rows, 128) )
    _lowercase       :							Union[str, Any]      		=		faiss.IndexFlatIP(128 )
    eli5_train_q_index.add(lowerCamelCase_ )
    return (elia_train, eli5_train_q_index)


SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      Any												= load_indexes()
SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      Optional[Any]												= load_models()
SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      List[Any]												= load_train_data()



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_=10 )    ->						List[str]:
    _lowercase       :							Any      		=		embed_questions_for_retrieval([question]							, lowerCamelCase_							, lowerCamelCase_ )
    _lowercase		,				_lowercase       :							List[str]      		=		eli5_train_q_index.search(lowerCamelCase_							, lowerCamelCase_ )
    _lowercase       :							List[str]      		=		[elia_train[int(lowerCamelCase_ )] for i in I[0]]
    return nn_examples



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_="wiki40b"							, lowerCamelCase_="dense"							, lowerCamelCase_=10 )    ->						Dict:
    if source == "none":
        _lowercase		,				_lowercase       :							Union[str, Any]      		=		(' <P> '.join(['' for _ in range(11 )] ).strip(), [])
    else:
        if method == "dense":
            _lowercase		,				_lowercase       :							Dict      		=		query_qa_dense_index(
                lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
        else:
            _lowercase		,				_lowercase       :							str      		=		query_es_index(
                lowerCamelCase_							, lowerCamelCase_							, index_name='english_wiki40b_snippets_100w'							, n_results=lowerCamelCase_							, )
    _lowercase       :							List[Any]      		=		[
        (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
    ]
    _lowercase       :							Union[str, Any]      		=		'question: {} context: {}'.format(lowerCamelCase_							, lowerCamelCase_ )
    return question_doc, support_list



@st.cache(
    hash_funcs={
        torch.Tensor: (lambda lowerCamelCase_ : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowerCamelCase_ : None),
    } )
def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_=64							, lowerCamelCase_=256							, lowerCamelCase_=False							, lowerCamelCase_=2							, lowerCamelCase_=0.95							, lowerCamelCase_=0.8 )    ->						Dict:
    with torch.no_grad():
        _lowercase       :							str      		=		qa_sas_generate(
            lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, num_answers=1							, num_beams=lowerCamelCase_							, min_len=lowerCamelCase_							, max_len=lowerCamelCase_							, do_sample=lowerCamelCase_							, temp=lowerCamelCase_							, top_p=lowerCamelCase_							, top_k=lowerCamelCase_							, max_input_length=1024							, device='cuda:0'							, )[0]
    return (answer, support_list)


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

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

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

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

SCREAMING_SNAKE_CASE       :      Union[str, Any]												= st.sidebar.checkbox("Retrieval options")
if retrieval_options:
 SCREAMING_SNAKE_CASE       :      Tuple												= "\n    ### Information retriever options\n\n    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n    "
 st.sidebar.markdown(retriever_info)
 SCREAMING_SNAKE_CASE       :      Dict												= st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
else:
 SCREAMING_SNAKE_CASE       :      int												= "wiki40b"
 SCREAMING_SNAKE_CASE       :      int												= "dense"

SCREAMING_SNAKE_CASE       :      str												= "beam"
SCREAMING_SNAKE_CASE       :      Optional[Any]												= 2
SCREAMING_SNAKE_CASE       :      List[str]												= 64
SCREAMING_SNAKE_CASE       :      Union[str, Any]												= 256
SCREAMING_SNAKE_CASE       :      Union[str, Any]												= None
SCREAMING_SNAKE_CASE       :      List[Any]												= None
SCREAMING_SNAKE_CASE       :      str												= st.sidebar.checkbox("Generation options")
if generate_options:
 SCREAMING_SNAKE_CASE       :      Any												= "\n    ### Answer generation options\n\n    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n    **beam** search, or **sample** from the decoder's output probabilities.\n    "
 st.sidebar.markdown(generate_info)
 SCREAMING_SNAKE_CASE       :      List[Any]												= st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
 SCREAMING_SNAKE_CASE       :      Tuple												= st.sidebar.slider(
     "Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
 )
 SCREAMING_SNAKE_CASE       :      int												= st.sidebar.slider(
     "Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
 )
 if sampled == "beam":
  SCREAMING_SNAKE_CASE       :      int												= st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  SCREAMING_SNAKE_CASE       :      Union[str, Any]												= st.sidebar.slider(
      "Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
  )
  SCREAMING_SNAKE_CASE       :      Any												= st.sidebar.slider(
      "Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
  )
  SCREAMING_SNAKE_CASE       :      str												= None

# start main text
SCREAMING_SNAKE_CASE       :      List[str]												= [
    "<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?",
]
SCREAMING_SNAKE_CASE       :      str												= 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>":
 SCREAMING_SNAKE_CASE       :      List[str]												= st.text_input("Enter your question here:", "")
else:
 SCREAMING_SNAKE_CASE       :      Optional[int]												= question_s

if st.button("Show me!"):
 if action in [0, 1, 3]:
  if index_type == "mixed":
   SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      Optional[int]												= make_support(question, source=wiki_source, method="dense", n_results=10)
   SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      Any												= make_support(question, source=wiki_source, method="sparse", n_results=10)
   SCREAMING_SNAKE_CASE       :      Tuple												= []
   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)]
   SCREAMING_SNAKE_CASE       :      Optional[Any]												= support_list[:10]
   SCREAMING_SNAKE_CASE       :      int												= "<P> " + " <P> ".join([res[-1] for res in support_list])
  else:
   SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      Any												= make_support(question, source=wiki_source, method=index_type, n_results=10)
 if action in [0, 3]:
  SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      Tuple												= 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):
   SCREAMING_SNAKE_CASE       :      Optional[Any]												= "https://en.wikipedia.org/wiki/{}".format(res[0].replace(" ", "_"))
   SCREAMING_SNAKE_CASE       :      List[Any]												= res[1].strip()
   if sec_titles == "":
    SCREAMING_SNAKE_CASE       :      Union[str, Any]												= "[{}]({})".format(res[0], wiki_url)
   else:
    SCREAMING_SNAKE_CASE       :      Any												= sec_titles.split(" & ")
    SCREAMING_SNAKE_CASE       :      List[Any]												= " & ".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]:
  SCREAMING_SNAKE_CASE       :      str												= find_nearest_training(question)
  SCREAMING_SNAKE_CASE       :      Any												= nn_train_list[0]
  st.markdown(
      "--- \n ### The most similar question in the ELI5 training set was: \n\n {}".format(train_exple["title"])
  )
  SCREAMING_SNAKE_CASE       :      str												= [
      "{}. {}".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)))


SCREAMING_SNAKE_CASE       :      Tuple												= "\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"

st.sidebar.markdown(disclaimer, unsafe_allow_html=True)


