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import streamlit as st
from markdown import markdown
from annotated_text import annotation
import logging
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import TfidfRetriever
from haystack.pipelines import ExtractiveQAPipeline
from haystack.nodes import FARMReader
import time
import joblib
@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True)
def create_pipeline():
docs = joblib.load('docs.joblib')
document_store = InMemoryDocumentStore()
document_store.write_documents(docs)
retriever = TfidfRetriever(document_store)
reader = FARMReader(model_name_or_path="ixa-ehu/SciBERT-SQuAD-QuAC")
pipeline = ExtractiveQAPipeline(reader, retriever)
return pipeline
pipeline = create_pipeline()
def set_state_if_absent(key, value):
if key not in st.session_state:
st.session_state[key] = value
set_state_if_absent("question", 'Applications of AI and deep learning')
set_state_if_absent("results", None)
def reset_results(*args):
st.session_state.results = None
st.markdown('''# Welcome to **SRM RP explorer**!
This QA demo uses a [Haystack Extractive QA Pipeline](https://haystack.deepset.ai/components/ready-made-pipelines#extractiveqapipeline) with
an [InMemoryDocumentStore](https://haystack.deepset.ai/components/document-store) which contains abstracts of 17k+ research papers associated with SRM university''')
query = st.text_input('Enter a query to get started:', value=st.session_state.question, max_chars=100, on_change=reset_results)
def ask_question(query):
start = time.time()
prediction = pipeline.run(query=query, params={"Retriever": {"top_k": 6}, "Reader": {"top_k": 3}})
st.write('Time taken: %s s' % round(time.time()-start, 2))
results = []
for answer in prediction["answers"]:
answer = answer.to_dict()
if answer["answer"]:
results.append(
{
"title":answer["meta"]["name"],
"link":answer["meta"]["link"],
"context": "..." + answer["context"] + "...",
"answer": answer["answer"],
"score": round(answer["score"] * 100, 2),
"offset_start_in_doc": answer["offsets_in_document"][0]["start"],
}
)
else:
results.append(
{
"title":None,
"link":None,
"context": None,
"answer": None,
"score": round(answer["score"] * 100, 2),
}
)
return results
if query:
with st.spinner("👑 Performing semantic search on abstracts..."):
try:
msg = 'Asked ' + query
logging.info(msg)
st.session_state.results = ask_question(query)
except Exception as e:
logging.exception(e)
if st.session_state.results:
st.write('## Top Results')
for count, result in enumerate(st.session_state.results):
if result["answer"]:
answer, context = result["answer"], result["context"]
start_idx = context.find(answer)
end_idx = start_idx + len(answer)
st.markdown(f"**Title:** [{result['title']}]({result['link']})")
st.write(
markdown(context[:start_idx] + str(annotation(body=answer, label="RELEVANT", background="#67a17a", color='#ffffff')) + context[end_idx:]),
unsafe_allow_html=True,
)
st.markdown(f"**Relevance:** {result['score']}")
else:
st.info(
"🤔 Haystack is unsure whether any of the documents contain an answer to your question. Try to reformulate it!"
)
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