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# set path
import glob, os, sys; sys.path.append('../scripts')
#import helper
import scripts.process as pre
import scripts.clean as clean
#import needed libraries
import seaborn as sns
from pandas import DataFrame
import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
import pandas as pd
from sklearn.feature_extraction import _stop_words
import string
from tqdm.autonotebook import tqdm
import numpy as np
#Haystack Components
@st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True)
def start_haystack(temp.name, file):
document_store = InMemoryDocumentStore()
documents = pre.load_document(temp.name, file)
documents_processed = pre.preprocessing(documents)
document_store.write_documents(documents_processed)
retriever = TfidfRetriever(document_store=document_store)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2-distilled", use_gpu=True)
pipeline = ExtractiveQAPipeline(reader, retriever)
return pipeline
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
def app():
with st.container():
st.markdown("<h1 style='text-align: center; color: black;'> Keyword Search</h1>", unsafe_allow_html=True)
st.write(' ')
st.write(' ')
with st.expander("ℹ️ - About this app", expanded=True):
st.write(
"""
The *Keyword Search* app is an easy-to-use interface built in Streamlit for doing keyword search in policy document - developed by GIZ Data and the Sustainable Development Solution Network.
"""
)
st.markdown("")
st.markdown("")
st.markdown("## 📌 Step One: Upload document ")
with st.container():
file = st.file_uploader('Upload PDF File', type=['pdf', 'docx', 'txt'])
if file is not None:
with tempfile.NamedTemporaryFile(mode="wb") as temp:
bytes_data = file.getvalue()
temp.write(bytes_data)
st.write("Filename: ", file.name)
# load document
pipeline = start_haystack(temp.name, file)
#docs = pre.load_document(temp.name, file)
# preprocess document
#haystackDoc, dataframeDoc, textData, paraList = clean.preprocessing(docs)
question = st.text_input("Please enter your question here, we will look for the answer in the document.",
value="floods",)
if st.button("Find them."):
with st.spinner("👑 Performing semantic search on"):#+file.name+"..."):
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(body=answer, label="ANSWER", background="#964448", color='#ffffff')) + 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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