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import streamlit as st
from haystack.utils import fetch_archive_from_http, clean_wiki_text, convert_files_to_docs
from haystack.schema import Answer
from haystack.document_stores import InMemoryDocumentStore
from haystack.pipelines import ExtractiveQAPipeline
from haystack.nodes import FARMReader, TfidfRetriever
import logging
from markdown import markdown
from annotated_text import annotation
from PIL import Image
#Haystack Components
@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True)
def start_haystack():
document_store = InMemoryDocumentStore()
load_and_write_data(document_store)
retriever = TfidfRetriever(document_store=document_store)
reader = FARMReader(model_name_or_path="deepset/tinyroberta-squad2", use_gpu=True)
pipeline = ExtractiveQAPipeline(reader, retriever)
return pipeline
def load_and_write_data(document_store):
doc_dir = './article_txt_got'
docs = convert_files_to_docs(dir_path=doc_dir, clean_func=clean_wiki_text, split_paragraphs=True)
document_store.write_documents(docs)
pipeline = start_haystack()
def set_state_if_absent(key, value):
if key not in st.session_state:
st.session_state[key] = value
set_state_if_absent("question", "Who is Arya's father?")
set_state_if_absent("results", None)
def reset_results(*args):
st.session_state.results = None
#Streamlit App
st.title('Game of Thrones QA with Haystack')
image = Image.open('got-haystack.png')
st.image(image)
st.markdown( """
This QA demo uses a [Haystack Extractive QA Pipleine](https://haystack.deepset.ai/components/ready-made-pipelines#extractiveqapipeline) with
an [InMemoryDocumentStore](https://haystack.deepset.ai/components/document-store) which contains documents about Game of Thrones 👑
Go ahead and ask questions about the marvelous kingdom!
""", unsafe_allow_html=True)
question = st.text_input("", value=st.session_state.question, max_chars=100, on_change=reset_results)
def ask_question(question):
prediction = pipeline.run(query=question, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}})
results = []
for answer in prediction["answers"]:
answer = answer.to_dict()
if answer["answer"]:
results.append(
{
"context": "..." + answer["context"] + "...",
"answer": answer["answer"],
"relevance": round(answer["score"] * 100, 2),
"offset_start_in_doc": answer["offsets_in_document"][0]["start"],
}
)
else:
results.append(
{
"context": None,
"answer": None,
"relevance": round(answer["score"] * 100, 2),
}
)
return results
if question:
with st.spinner("👑 Performing neural search on royal scripts..."):
try:
msg = 'Asked ' + question
logging.info(msg)
st.session_state.results = ask_question(question)
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.write(
markdown(context[:start_idx] + str(annotation(answer, "ANSWER", "#964448")) + context[end_idx:]),
unsafe_allow_html=True,
)
st.markdown(f"**Relevance:** {result['relevance']}")
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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