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import streamlit as st
from transformers import pipeline
import fitz, io, os
from haystack.document_stores import ElasticsearchDocumentStore
from haystack.nodes import BM25Retriever, FARMReader
from haystack import Pipeline
from pprint import pprint
from haystack.utils import print_answers
st.title("Team 4 - Roberta Question Answering System")
st.write("Type in a question and some context and the system will try to answer it.")
method = st.radio(
"Choose a method to answer the question",
['Upload PDF', 'Upload TXT', 'Manual entry', 'JBNU'],
key='visibility',
horizontal=True
)
qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2", tokenizer="deepset/roberta-base-squad2")
host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
document_store = ElasticsearchDocumentStore(
host='121.186.58.11',
username="",
password="",
index="document"
)
retriever = BM25Retriever(document_store=document_store)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
querying_pipeline = Pipeline()
querying_pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
querying_pipeline.add_node(component=reader, name="Reader", inputs=["Retriever"])
if method == 'Manual entry':
question = st.text_input("Question:")
context = st.text_area("Context:")
if st.button("Answer"):
currenttime = 0
answer = qa_pipeline({"context": context, "question": question})
st.write(f"Answer: {answer['answer']}")
elif method == 'Upload PDF':
pdf_file = st.file_uploader("Upload PDF", type=["pdf"])
if pdf_file is not None:
pdf_file = fitz.open(stream=pdf_file.read(), filetype="pdf")
with pdf_file as doc:
text = ""
for page in doc:
text += page.get_text()
question = st.text_input("Question:")
if st.button("Answer"):
answer = qa_pipeline({"context": text, "question": question})
st.write(f"Answer: {answer['answer']}")
elif method == 'Upload TXT':
txt_file = st.file_uploader("Upload TXT", type=["txt"])
if txt_file is not None:
txt_file = io.StringIO(txt_file.read().decode('utf-8'))
contents = txt_file.read()
question = st.text_input("Question:")
if st.button("Answer"):
answer = qa_pipeline({"context": contents, "question": question})
st.write(f"Answer: {answer['answer']}")
elif method == 'JBNU':
question = st.text_input("Question:")
if st.button("Answer"):
prediction = querying_pipeline.run(
query=str(question),
params={
"Retriever": {"top_k": 10},
"Reader": {"top_k": 5}
}
)
for answer in prediction["answers"]:
st.write(f"Answer: {answer.answer}")
st.write(f"Context: {answer.context}")
st.write(f"Score: {answer.score}")
st.write("---") |