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# set path
import glob, os, sys; sys.path.append('../udfPreprocess')
#import helper
import udfPreprocess.docPreprocessing as pre
import udfPreprocess.cleaning as clean
#import needed libraries
import seaborn as sns
from pandas import DataFrame
from sentence_transformers import SentenceTransformer, CrossEncoder, util
# from keybert import KeyBERT
from transformers import pipeline
import matplotlib.pyplot as plt
import numpy as np
import streamlit as st
import pandas as pd
from rank_bm25 import BM25Okapi
from sklearn.feature_extraction import _stop_words
import string
from tqdm.autonotebook import tqdm
import numpy as np
import tempfile
import sqlite3
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():
def bm25_tokenizer(text):
tokenized_doc = []
for token in text.lower().split():
token = token.strip(string.punctuation)
if len(token) > 0 and token not in _stop_words.ENGLISH_STOP_WORDS:
tokenized_doc.append(token)
return tokenized_doc
def bm25TokenizeDoc(paraList):
tokenized_corpus = []
for passage in tqdm(paraList):
if len(passage.split()) >256:
temp = " ".join(passage.split()[:256])
tokenized_corpus.append(bm25_tokenizer(temp))
temp = " ".join(passage.split()[256:])
tokenized_corpus.append(bm25_tokenizer(temp))
else:
tokenized_corpus.append(bm25_tokenizer(passage))
return tokenized_corpus
def search(keyword):
##### BM25 search (lexical search) #####
bm25_scores = document_bm25.get_scores(bm25_tokenizer(keyword))
top_n = np.argpartition(bm25_scores, -10)[-10:]
bm25_hits = [{'corpus_id': idx, 'score': bm25_scores[idx]} for idx in top_n]
bm25_hits = sorted(bm25_hits, key=lambda x: x['score'], reverse=True)
##### Sematic Search #####
# Encode the query using the bi-encoder and find potentially relevant passages
#query = "Does document contain {} issues ?".format(keyword)
question_embedding = bi_encoder.encode(keyword, convert_to_tensor=True)
hits = util.semantic_search(question_embedding, document_embeddings, top_k=top_k)
hits = hits[0] # Get the hits for the first query
##### Re-Ranking #####
# Now, score all retrieved passages with the cross_encoder
#cross_inp = [[query, paraList[hit['corpus_id']]] for hit in hits]
#cross_scores = cross_encoder.predict(cross_inp)
# Sort results by the cross-encoder scores
#for idx in range(len(cross_scores)):
# hits[idx]['cross-score'] = cross_scores[idx]
return bm25_hits, hits
def show_results(keywordList):
for keyword in keywordList:
st.write("Results for Query: {}".format(keyword))
bm25_hits, hits = search(keyword)
st.markdown("""
We will provide with 2 kind of results. The 'lexical search' and the semantic search.
""")
# In the semantic search part we provide two kind of results one with only Retriever (Bi-Encoder) and other the ReRanker (Cross Encoder)
st.markdown("Top few lexical search (BM25) hits")
for hit in bm25_hits[0:5]:
if hit['score'] > 0.00:
st.write("\t Score: {:.3f}: \t{}".format(hit['score'], paraList[hit['corpus_id']].replace("\n", " ")))
# st.table(bm25_hits[0:3])
st.markdown("\n-------------------------\n")
st.markdown("Top few Bi-Encoder Retrieval hits")
hits = sorted(hits, key=lambda x: x['score'], reverse=True)
for hit in hits[0:5]:
# if hit['score'] > 0.45:
st.write("\t Score: {:.3f}: \t{}".format(hit['score'], paraList[hit['corpus_id']].replace("\n", " ")))
#st.table(hits[0:3]
@st.cache(allow_output_mutation=True)
def load_sentenceTransformer(name):
return SentenceTransformer(name)
docs = None
# asking user for either upload or select existing doc
choice = st.radio(label = 'Select the Document',
help = 'You can upload the document \
or else you can try a example document',
options = ('Upload Document', 'Try Example'),
horizontal = True)
if choice == 'Upload Document':
uploaded_file = st.file_uploader('Upload the File', type=['pdf', 'docx', 'txt'])
if uploaded_file is not None:
with tempfile.NamedTemporaryFile(mode="wb") as temp:
bytes_data = uploaded_file.getvalue()
temp.write(bytes_data)
st.write("Uploaded Filename: ", uploaded_file.name)
file_name = uploaded_file.name
file_path = temp.name
docs = pre.load_document(file_path, file_name)
haystackDoc, dataframeDoc, textData, paraList = clean.preprocessing(docs)
else:
# listing the options
option = st.selectbox('Select the example document',
('South Africa:Low Emission strategy',
'Ethiopia: 10 Year Development Plan'))
if option is 'South Africa:Low Emission strategy':
file_name = file_path = 'sample/South Africa_s Low Emission Development Strategy.txt'
st.write("Selected document:", file_name.split('/')[1])
# with open('sample/South Africa_s Low Emission Development Strategy.txt') as dfile:
# file = open('sample/South Africa_s Low Emission Development Strategy.txt', 'wb')
else:
# with open('sample/Ethiopia_s_2021_10 Year Development Plan.txt') as dfile:
file_name = file_path = 'sample/Ethiopia_s_2021_10 Year Development Plan.txt'
st.write("Selected document:", file_name.split('/')[1])
if option is not None:
docs = pre.load_document(file_path,file_name)
haystackDoc, dataframeDoc, textData, paraList = clean.preprocessing(docs)
if docs is not None:
bi_encoder = load_sentenceTransformer('msmarco-distilbert-cos-v5') # multi-qa-MiniLM-L6-cos-v1
bi_encoder.max_seq_length = 64 #Truncate long passages to 256 tokens
top_k = 32
document_embeddings = bi_encoder.encode(paraList, convert_to_tensor=True, show_progress_bar=False)
tokenized_corpus = bm25TokenizeDoc(paraList)
document_bm25 = BM25Okapi(tokenized_corpus)
keywordList = None
col1, col2 = st.columns(2)
with col1:
if st.button('Climate Change Keyword Search'):
keywordList = ['extreme weather', 'floods', 'droughts']
# show_results(keywordList)
with col2:
if st.button('Gender Keywords Search'):
keywordList = ['Gender', 'Women empowernment']
# show_results(keywordList)
keyword = st.text_input("Please enter here \
what you want to search, \
we will look for similar context \
in the document.",
value="",)
if st.button("Find them."):
keywordList = [keyword]
if keywordList is not None:
show_results(keywordList)
# @st.cache(allow_output_mutation=True)
# def load_sentenceTransformer(name):
# return SentenceTransformer(name)
# bi_encoder = load_sentenceTransformer('msmarco-distilbert-cos-v5') # multi-qa-MiniLM-L6-cos-v1
# bi_encoder.max_seq_length = 64 #Truncate long passages to 256 tokens
# top_k = 32
# #@st.cache(allow_output_mutation=True)
# #def load_crossEncoder(name):
# # return CrossEncoder(name)
# # cross_encoder = load_crossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
# document_embeddings = bi_encoder.encode(paraList, convert_to_tensor=True, show_progress_bar=False)
# def bm25_tokenizer(text):
# tokenized_doc = []
# for token in text.lower().split():
# token = token.strip(string.punctuation)
# if len(token) > 0 and token not in _stop_words.ENGLISH_STOP_WORDS:
# tokenized_doc.append(token)
# return tokenized_doc
# def bm25TokenizeDoc(paraList):
# tokenized_corpus = []
# for passage in tqdm(paraList):
# if len(passage.split()) >256:
# temp = " ".join(passage.split()[:256])
# tokenized_corpus.append(bm25_tokenizer(temp))
# temp = " ".join(passage.split()[256:])
# tokenized_corpus.append(bm25_tokenizer(temp))
# else:
# tokenized_corpus.append(bm25_tokenizer(passage))
# return tokenized_corpus
# tokenized_corpus = bm25TokenizeDoc(paraList)
# document_bm25 = BM25Okapi(tokenized_corpus)
# # def search(keyword):
# # ##### BM25 search (lexical search) #####
# # bm25_scores = document_bm25.get_scores(bm25_tokenizer(keyword))
# top_n = np.argpartition(bm25_scores, -10)[-10:]
# bm25_hits = [{'corpus_id': idx, 'score': bm25_scores[idx]} for idx in top_n]
# bm25_hits = sorted(bm25_hits, key=lambda x: x['score'], reverse=True)
# ##### Sematic Search #####
# # Encode the query using the bi-encoder and find potentially relevant passages
# #query = "Does document contain {} issues ?".format(keyword)
# question_embedding = bi_encoder.encode(keyword, convert_to_tensor=True)
# hits = util.semantic_search(question_embedding, document_embeddings, top_k=top_k)
# hits = hits[0] # Get the hits for the first query
# ##### Re-Ranking #####
# # Now, score all retrieved passages with the cross_encoder
# #cross_inp = [[query, paraList[hit['corpus_id']]] for hit in hits]
# #cross_scores = cross_encoder.predict(cross_inp)
# # Sort results by the cross-encoder scores
# #for idx in range(len(cross_scores)):
# # hits[idx]['cross-score'] = cross_scores[idx]
# return bm25_hits, hits
# def show_results(keywordList):
# for keyword in keywordList:
# bm25_hits, hits = search(keyword)
# st.markdown("""
# We will provide with 2 kind of results. The 'lexical search' and the semantic search.
# """)
# # In the semantic search part we provide two kind of results one with only Retriever (Bi-Encoder) and other the ReRanker (Cross Encoder)
# st.markdown("Top few lexical search (BM25) hits")
# for hit in bm25_hits[0:5]:
# if hit['score'] > 0.00:
# st.write("\t Score: {:.3f}: \t{}".format(hit['score'], paraList[hit['corpus_id']].replace("\n", " ")))
# # st.table(bm25_hits[0:3])
# st.markdown("\n-------------------------\n")
# st.markdown("Top few Bi-Encoder Retrieval hits")
# hits = sorted(hits, key=lambda x: x['score'], reverse=True)
# for hit in hits[0:5]:
# # if hit['score'] > 0.45:
# st.write("\t Score: {:.3f}: \t{}".format(hit['score'], paraList[hit['corpus_id']].replace("\n", " ")))
# #st.table(hits[0:3]
# # if docs is not None:
# # col1, col2 = st.columns(2)
# # with col1:
# # if st.button('Gender Keywords Search'):
# # keywordList = ['Gender Equality', 'Women empowernment']
# # show_results(keywordList)
# # with col2:
# # if st.button('Climate Change Keyword Search'):
# # keywordList = ['extreme weather', 'floods', 'droughts']
# # show_results(keywordList)
# # keyword = st.text_input("Please enter here \
# # what you want to search, \
# # we will look for similar context \
# # in the document.",
# # value="",)
# # if st.button("Find them."):
# # show_results([keyword])
# choice1 = st.radio(label = 'Keyword Search',
# help = 'Search \
# or else you can try a example document',
# options = ('Enter your own Query', 'Try Example'),
# horizontal = True)
# if choice1 == 'Enter your own Query':
# keyword = st.text_input("Please enter here \
# what you want to search, \
# we will look for similar context \
# in the document.",
# value="",)
# else:
# option1 = st.selectbox('Select the Predefined word cluster',
# ('Gender:[Gender Equality, Women empowernment]',
# 'Climate change:[extreme weather, floods, droughts]',
# ))
# if option1 == 'Gender:[Gender Equality, Women empowernment]':
# keywordList = ['Gender Equality', 'Women empowernment']
# else:
# keywordList = ['extreme weather', 'floods', 'droughts']
# option1 = st.selectbox('Select the Predefined word cluster',
# ('Gender:[Gender Equality, Women empowernment]',
# 'Climate change:[extreme weather, floods, droughts]',
# # 'Enter your Own Keyword Query'))
# if option1 == 'Enter your Own Keyword Query':
# keyword = st.text_input("Please enter here \
# what you want to search, \
# we will look for similar context \
# in the document.",
# value="",)
# elif option1 == 'Gender:[Gender Equality, Women empowernment]':
# keywordList = ['Gender Equality', 'Women empowernment']
# elif option1 == 'Climate change:[extreme weather, floods, droughts]':
# keywordList = ['extreme weather', 'floods', 'droughts']
# st.markdown("### 📌 Step Two: Search Keyword in Document ### ")
# @st.cache(allow_output_mutation=True)
# def load_sentenceTransformer(name):
# return SentenceTransformer(name)
# bi_encoder = load_sentenceTransformer('msmarco-distilbert-cos-v5') # multi-qa-MiniLM-L6-cos-v1
# bi_encoder.max_seq_length = 64 #Truncate long passages to 256 tokens
# top_k = 32
# #@st.cache(allow_output_mutation=True)
# #def load_crossEncoder(name):
# # return CrossEncoder(name)
# # cross_encoder = load_crossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
# document_embeddings = bi_encoder.encode(paraList, convert_to_tensor=True, show_progress_bar=False)
# def bm25_tokenizer(text):
# tokenized_doc = []
# for token in text.lower().split():
# token = token.strip(string.punctuation)
# if len(token) > 0 and token not in _stop_words.ENGLISH_STOP_WORDS:
# tokenized_doc.append(token)
# return tokenized_doc
# def bm25TokenizeDoc(paraList):
# tokenized_corpus = []
# for passage in tqdm(paraList):
# if len(passage.split()) >256:
# temp = " ".join(passage.split()[:256])
# tokenized_corpus.append(bm25_tokenizer(temp))
# temp = " ".join(passage.split()[256:])
# tokenized_corpus.append(bm25_tokenizer(temp))
# else:
# tokenized_corpus.append(bm25_tokenizer(passage))
# return tokenized_corpus
# tokenized_corpus = bm25TokenizeDoc(paraList)
# document_bm25 = BM25Okapi(tokenized_corpus)
# def search(keyword):
# ##### BM25 search (lexical search) #####
# bm25_scores = document_bm25.get_scores(bm25_tokenizer(keyword))
# top_n = np.argpartition(bm25_scores, -10)[-10:]
# bm25_hits = [{'corpus_id': idx, 'score': bm25_scores[idx]} for idx in top_n]
# bm25_hits = sorted(bm25_hits, key=lambda x: x['score'], reverse=True)
# ##### Sematic Search #####
# # Encode the query using the bi-encoder and find potentially relevant passages
# #query = "Does document contain {} issues ?".format(keyword)
# question_embedding = bi_encoder.encode(keyword, convert_to_tensor=True)
# hits = util.semantic_search(question_embedding, document_embeddings, top_k=top_k)
# hits = hits[0] # Get the hits for the first query
# ##### Re-Ranking #####
# # Now, score all retrieved passages with the cross_encoder
# #cross_inp = [[query, paraList[hit['corpus_id']]] for hit in hits]
# #cross_scores = cross_encoder.predict(cross_inp)
# # Sort results by the cross-encoder scores
# #for idx in range(len(cross_scores)):
# # hits[idx]['cross-score'] = cross_scores[idx]
# return bm25_hits, hits
# def show_results(keywordList):
# for keyword in keywordList:
# bm25_hits, hits = search(keyword)
# st.markdown("""
# We will provide with 2 kind of results. The 'lexical search' and the semantic search.
# """)
# # In the semantic search part we provide two kind of results one with only Retriever (Bi-Encoder) and other the ReRanker (Cross Encoder)
# st.markdown("Top few lexical search (BM25) hits")
# for hit in bm25_hits[0:5]:
# if hit['score'] > 0.00:
# st.write("\t Score: {:.3f}: \t{}".format(hit['score'], paraList[hit['corpus_id']].replace("\n", " ")))
# # st.table(bm25_hits[0:3])
# st.markdown("\n-------------------------\n")
# st.markdown("Top few Bi-Encoder Retrieval hits")
# hits = sorted(hits, key=lambda x: x['score'], reverse=True)
# for hit in hits[0:5]:
# # if hit['score'] > 0.45:
# st.write("\t Score: {:.3f}: \t{}".format(hit['score'], paraList[hit['corpus_id']].replace("\n", " ")))
# #st.table(hits[0:3]
# # if st.button("Find them."):
# # bm25_hits, hits = search(keyword)
# # st.markdown("""
# # We will provide with 2 kind of results. The 'lexical search' and the semantic search.
# # """)
# # # In the semantic search part we provide two kind of results one with only Retriever (Bi-Encoder) and other the ReRanker (Cross Encoder)
# # st.markdown("Top few lexical search (BM25) hits")
# # for hit in bm25_hits[0:5]:
# # if hit['score'] > 0.00:
# # st.write("\t Score: {:.3f}: \t{}".format(hit['score'], paraList[hit['corpus_id']].replace("\n", " ")))
# # # st.table(bm25_hits[0:3])
# # st.markdown("\n-------------------------\n")
# # st.markdown("Top few Bi-Encoder Retrieval hits")
# # hits = sorted(hits, key=lambda x: x['score'], reverse=True)
# # for hit in hits[0:5]:
# # # if hit['score'] > 0.45:
# # st.write("\t Score: {:.3f}: \t{}".format(hit['score'], paraList[hit['corpus_id']].replace("\n", " ")))
# # #st.table(hits[0:3]
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