import streamlit as st
from transformers import pipeline, AutoTokenizer, LEDForConditionalGeneration
import requests
from bs4 import BeautifulSoup
import nltk
import string
from streamlit.components.v1 import html
from sentence_transformers.cross_encoder import CrossEncoder as CE
import re
from typing import List, Tuple
import torch
SCITE_API_KEY = st.secrets["SCITE_API_KEY"]
# class CrossEncoder:
# def __init__(self, model_path: str, **kwargs):
# self.model = CE(model_path, **kwargs)
# def predict(self, sentences: List[Tuple[str,str]], batch_size: int = 32, show_progress_bar: bool = True) -> List[float]:
# return self.model.predict(
# sentences=sentences,
# batch_size=batch_size,
# show_progress_bar=show_progress_bar)
def remove_html(x):
soup = BeautifulSoup(x, 'html.parser')
text = soup.get_text()
return text.strip()
# 4 searches: strict y/n, supported y/n
# deduplicate
# search per query
# options are abstract search
# all search
def search(term, limit=10, clean=True, strict=True, all_mode=True, abstracts=True, abstract_only=False):
term = clean_query(term, clean=clean, strict=strict)
# heuristic, 2 searches strict and not? and then merge?
# https://api.scite.ai/search?mode=all&term=unit%20testing%20software&limit=10&date_from=2000&date_to=2022&offset=0&supporting_from=1&contrasting_from=0&contrasting_to=0&user_slug=domenic-rosati-keW5&compute_aggregations=true
contexts, docs = [], []
if not abstract_only:
mode = 'all'
if not all_mode:
mode = 'citations'
search = f"https://api.scite.ai/search?mode={mode}&term={term}&limit={limit}&offset=0&user_slug=domenic-rosati-keW5&compute_aggregations=false"
req = requests.get(
search,
headers={
'Authorization': f'Bearer {SCITE_API_KEY}'
}
)
try:
req.json()
except:
pass
contexts += [remove_html('\n'.join([cite['snippet'] for cite in doc['citations'] if cite['lang'] == 'en'])) for doc in req.json()['hits']]
docs += [(doc['doi'], doc['citations'], doc['title'], doc['abstract'] or '')
for doc in req.json()['hits']]
if abstracts or abstract_only:
search = f"https://api.scite.ai/search?mode=papers&abstract={term}&limit={limit}&offset=0&user_slug=domenic-rosati-keW5&compute_aggregations=false"
req = requests.get(
search,
headers={
'Authorization': f'Bearer {SCITE_API_KEY}'
}
)
try:
req.json()
contexts += [remove_html(doc['abstract'] or '') for doc in req.json()['hits']]
docs += [(doc['doi'], doc['citations'], doc['title'], doc['abstract'] or '')
for doc in req.json()['hits']]
except:
pass
return (
contexts,
docs
)
def find_source(text, docs, matched):
for doc in docs:
for snippet in doc[1]:
if text in remove_html(snippet.get('snippet', '')):
if matched and remove_html(snippet.get('snippet', '')).strip() != matched.strip():
continue
new_text = text
for sent in nltk.sent_tokenize(remove_html(snippet.get('snippet', ''))):
if text in sent:
new_text = sent
return {
'citation_statement': snippet['snippet'].replace('<strong class="highlight">', '').replace('</strong>', ''),
'text': new_text,
'from': snippet['source'],
'supporting': snippet['target'],
'source_title': remove_html(doc[2] or ''),
'source_link': f"https://scite.ai/reports/{doc[0]}"
}
if text in remove_html(doc[3]):
if matched and remove_html(doc[3]).strip() != matched.strip():
continue
new_text = text
sent_loc = None
sents = nltk.sent_tokenize(remove_html(doc[3]))
for i, sent in enumerate(sents):
if text in sent:
new_text = sent
sent_loc = i
context = remove_html(doc[3]).replace('<strong class="highlight">', '').replace('</strong>', '')
if sent_loc:
context_len = 3
sent_beg = sent_loc - context_len
if sent_beg <= 0: sent_beg = 0
sent_end = sent_loc + context_len
if sent_end >= len(sents):
sent_end = len(sents)
context = ''.join(sents[sent_beg:sent_end])
return {
'citation_statement': context,
'text': new_text,
'from': doc[0],
'supporting': doc[0],
'source_title': remove_html(doc[2] or ''),
'source_link': f"https://scite.ai/reports/{doc[0]}"
}
return None
# @st.experimental_singleton
# def init_models():
# nltk.download('stopwords')
# nltk.download('punkt')
# from nltk.corpus import stopwords
# stop = set(stopwords.words('english') + list(string.punctuation))
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# question_answerer = pipeline(
# "question-answering", model='nlpconnect/roberta-base-squad2-nq',
# device=0 if torch.cuda.is_available() else -1, handle_impossible_answer=False,
# )
# reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2', device=device)
# # queryexp_tokenizer = AutoTokenizer.from_pretrained("doc2query/all-with_prefix-t5-base-v1")
# # queryexp_model = AutoModelWithLMHead.from_pretrained("doc2query/all-with_prefix-t5-base-v1")
# return question_answerer, reranker, stop, device
# qa_model, reranker, stop, device = init_models() # queryexp_model, queryexp_tokenizer
def clean_query(query, strict=True, clean=True):
operator = ' '
if strict:
operator = ' AND '
query = operator.join(
[i for i in query.lower().split(' ') if clean and i not in stop])
if clean:
query = query.translate(str.maketrans('', '', string.punctuation))
return query
def card(title, context, score, link, supporting):
st.markdown(f"""
<div class="container-fluid">
<div class="row align-items-start">
<div class="col-md-12 col-sm-12">
<br>
<span>
{context}
[<b>Confidence: </b>{score}%]
</span>
<br>
<b>From <a href="{link}">{title}</a></b>
</div>
</div>
</div>
""", unsafe_allow_html=True)
html(f"""
<div
class="scite-badge"
data-doi="{supporting}"
data-layout="horizontal"
data-show-zero="false"
data-show-labels="false"
data-tally-show="true"
/>
<script
async
type="application/javascript"
src="https://cdn.scite.ai/badge/scite-badge-latest.min.js">
</script>
""", width=None, height=42, scrolling=False)
st.title("Scientific Question Answering with Citations")
st.write("""
Ask a scientific question and get an answer drawn from [scite.ai](https://scite.ai) corpus of over 1.1bn citation statements.
Answers are linked to source documents containing citations where users can explore further evidence from scientific literature for the answer.
For example try: Do tanning beds cause cancer?
""")
st.markdown("""
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4.0.0/dist/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
""", unsafe_allow_html=True)
# with st.expander("Settings (strictness, context limit, top hits)"):
# concat_passages = st.radio(
# "Concatenate passages as one long context?",
# ('yes', 'no'))
# present_impossible = st.radio(
# "Present impossible answers? (if the model thinks its impossible to answer should it still try?)",
# ('yes', 'no'))
# support_all = st.radio(
# "Use abstracts and titles as a ranking signal (if the words are matched in the abstract then the document is more relevant)?",
# ('no', 'yes'))
# support_abstracts = st.radio(
# "Use abstracts as a source document?",
# ('yes', 'no', 'abstract only'))
# strict_lenient_mix = st.radio(
# "Type of strict+lenient combination: Fallback or Mix? If fallback, strict is run first then if the results are less than context_lim we also search lenient. Mix will search them both and let reranking sort em out",
# ('mix', 'fallback'))
# confidence_threshold = st.slider('Confidence threshold for answering questions? This number represents how confident the model should be in the answers it gives. The number is out of 100%', 0, 100, 1)
# use_reranking = st.radio(
# "Use Reranking? Reranking will rerank the top hits using semantic similarity of document and query.",
# ('yes', 'no'))
# top_hits_limit = st.slider('Top hits? How many documents to use for reranking. Larger is slower but higher quality', 10, 300, 100)
# context_lim = st.slider('Context limit? How many documents to use for answering from. Larger is slower but higher quality', 10, 300, 25)
# def paraphrase(text, max_length=128):
# input_ids = queryexp_tokenizer.encode(text, return_tensors="pt", add_special_tokens=True)
# generated_ids = queryexp_model.generate(input_ids=input_ids, num_return_sequences=suggested_queries or 5, num_beams=suggested_queries or 5, max_length=max_length)
# queries = set([queryexp_tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids])
# preds = '\n * '.join(queries)
# return preds
def group_results_by_context(results):
result_groups = {}
for result in results:
if result['context'] not in result_groups:
result_groups[result['context']] = result
result_groups[result['context']]['texts'] = []
result_groups[result['context']]['texts'].append(
result['answer']
)
if result['score'] > result_groups[result['context']]['score']:
result_groups[result['context']]['score'] = result['score']
return list(result_groups.values())
def matched_context(start_i, end_i, contexts_string, seperator='---'):
# find seperators to identify start and end
doc_starts = [0]
for match in re.finditer(seperator, contexts_string):
doc_starts.append(match.end())
for i in range(len(doc_starts)):
if i == len(doc_starts) - 1:
if start_i >= doc_starts[i]:
return contexts_string[doc_starts[i]:len(contexts_string)].replace(seperator, '')
if start_i >= doc_starts[i] and end_i <= doc_starts[i+1]:
return contexts_string[doc_starts[i]:doc_starts[i+1]].replace(seperator, '')
return None
# def run_query_full(query, progress_bar):
# # if use_query_exp == 'yes':
# # query_exp = paraphrase(f"question2question: {query}")
# # st.markdown(f"""
# # If you are not getting good results try one of:
# # * {query_exp}
# # """)
# # could also try fallback if there are no good answers by score...
# limit = top_hits_limit or 100
# context_limit = context_lim or 10
# contexts_strict, orig_docs_strict = search(query, limit=limit, strict=True, all_mode=support_all == 'yes', abstracts= support_abstracts == 'yes', abstract_only=support_abstracts == 'abstract only')
# if strict_lenient_mix == 'fallback' and len(contexts_strict) < context_limit:
# contexts_lenient, orig_docs_lenient = search(query, limit=limit, strict=False, all_mode=support_all == 'yes', abstracts= support_abstracts == 'yes', abstract_only= support_abstracts == 'abstract only')
# contexts = list(
# set(contexts_strict + contexts_lenient)
# )
# orig_docs = orig_docs_strict + orig_docs_lenient
# elif strict_lenient_mix == 'mix':
# contexts_lenient, orig_docs_lenient = search(query, limit=limit, strict=False)
# contexts = list(
# set(contexts_strict + contexts_lenient)
# )
# orig_docs = orig_docs_strict + orig_docs_lenient
# else:
# contexts = list(
# set(contexts_strict)
# )
# orig_docs = orig_docs_strict
# progress_bar.progress(25)
# if len(contexts) == 0 or not ''.join(contexts).strip():
# return st.markdown("""
# <div class="container-fluid">
# <div class="row align-items-start">
# <div class="col-md-12 col-sm-12">
# Sorry... no results for that question! Try another...
# </div>
# </div>
# </div>
# """, unsafe_allow_html=True)
# if use_reranking == 'yes':
# sentence_pairs = [[query, context] for context in contexts]
# scores = reranker.predict(sentence_pairs, batch_size=len(sentence_pairs), show_progress_bar=False)
# hits = {contexts[idx]: scores[idx] for idx in range(len(scores))}
# sorted_contexts = [k for k,v in sorted(hits.items(), key=lambda x: x[0], reverse=True)]
# contexts = sorted_contexts[:context_limit]
# else:
# contexts = contexts[:context_limit]
# progress_bar.progress(50)
# if concat_passages == 'yes':
# context = '\n---'.join(contexts)
# model_results = qa_model(question=query, context=context, top_k=10, doc_stride=512 // 2, max_answer_len=128, max_seq_len=512, handle_impossible_answer=present_impossible=='yes')
# else:
# context = ['\n---\n'+ctx for ctx in contexts]
# model_results = qa_model(question=[query]*len(contexts), context=context, handle_impossible_answer=present_impossible=='yes')
# results = []
# progress_bar.progress(75)
# for i, result in enumerate(model_results):
# if concat_passages == 'yes':
# matched = matched_context(result['start'], result['end'], context)
# else:
# matched = matched_context(result['start'], result['end'], context[i])
# support = find_source(result['answer'], orig_docs, matched)
# if not support:
# continue
# results.append({
# "answer": support['text'],
# "title": support['source_title'],
# "link": support['source_link'],
# "context": support['citation_statement'],
# "score": result['score'],
# "doi": support["supporting"]
# })
# grouped_results = group_results_by_context(results)
# sorted_result = sorted(grouped_results, key=lambda x: x['score'], reverse=True)
# if confidence_threshold == 0:
# threshold = 0
# else:
# threshold = (confidence_threshold or 10) / 100
# sorted_result = list(filter(
# lambda x: x['score'] > threshold,
# sorted_result
# ))
# progress_bar.progress(100)
# for r in sorted_result:
# ctx = remove_html(r["context"])
# for answer in r['texts']:
# ctx = ctx.replace(answer.strip(), f"<mark>{answer.strip()}</mark>")
# # .replace( '<cite', '<a').replace('</cite', '</a').replace('data-doi="', 'href="https://scite.ai/reports/')
# title = r.get("title", '')
# score = round(round(r["score"], 4) * 100, 2)
# card(title, ctx, score, r['link'], r['doi'])
def run_query(query):
api_location = 'http://74.82.31.93'
resp_raw = requests.get(
f'{api_location}/question-answer?query={query}'
)
try:
resp = resp_raw.json()
except:
resp = {'results': []}
if len(resp.get('results', [])) == 0:
return st.markdown("""
<div class="container-fluid">
<div class="row align-items-start">
<div class="col-md-12 col-sm-12">
Sorry... no results for that question! Try another...
</div>
</div>
</div>
""", unsafe_allow_html=True)
for r in resp['results']:
ctx = remove_html(r["context"])
for answer in r['texts']:
ctx = ctx.replace(answer.strip(), f"<mark>{answer.strip()}</mark>")
# .replace( '<cite', '<a').replace('</cite', '</a').replace('data-doi="', 'href="https://scite.ai/reports/')
title = r.get("title", '')
score = round(round(r["score"], 4) * 100, 2)
card(title, ctx, score, r['link'], r['doi'])
query = st.text_input("Ask scientific literature a question", "")
if query != "":
with st.spinner('Loading...'):
run_query(query)