#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Hamza Farooq
"""
from string import punctuation
from collections import Counter
from heapq import nlargest
import os
from sentence_transformers import SentenceTransformer, CrossEncoder, util
import datetime
import streamlit as st
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from matplotlib import pyplot as plt
import nltk
from rank_bm25 import BM25Okapi
from sklearn.feature_extraction import _stop_words
import string
from tqdm.autonotebook import tqdm
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
import scipy.spatial
import pickle
from sentence_transformers import SentenceTransformer, util
import torch
# import utils as utl
import time
import torch
import transformers
from transformers import BartTokenizer, BartForConditionalGeneration
from string import punctuation
# tr = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
import scipy.spatial
from sentence_transformers import SentenceTransformer, util
import torch
def main():
# Settings
st.set_page_config(layout="wide", page_title='Paris Hotel Finder', page_icon="đ" )
from string import punctuation
punctuation=punctuation+ '\n'
from sentence_transformers import SentenceTransformer, util
import torch
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
import scipy.spatial
from sentence_transformers import SentenceTransformer, util
import torch
#import os
@st.cache(allow_output_mutation=True)
def load_model():
return SentenceTransformer('all-MiniLM-L6-v2'),SentenceTransformer('multi-qa-MiniLM-L6-cos-v1'),CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
embedder,bi_encoder,cross_encoder = load_model()
#original_title = '<p style="font-family:IBM Mono; color:Blue; font-size: 20px;">Original image</p>'
st.title("travelle - Parisian Hotel Finder")
with st.expander("âšī¸ - About this app", expanded=True):
st.write(
"""
- travelle is a hotel search engine that allows users to enter free text query to make the search result personalized to user preference as opposed to other travel websites where a user has to spend hours going through hotel list.
- We use natural language processing and big data to return results customized for your preferences.
- A user can enter just about anything and we will narrow the results to what closely matches your requirements.
- For e.g. a user can enter a query like "Hotel near the Eiffel and cheaper than $300 per night with free breakfast" and we will find the closest results
"""
)
punctuation=punctuation+ '\n'
#import os
# embedder = SentenceTransformer('all-MiniLM-L6-v2')
def lower_case(input_str):
input_str = input_str.lower()
return input_str
df_all = pd.read_csv('paris_clean_newer.csv')
df_combined = df_all.sort_values(['Hotel']).groupby('Hotel', sort=False).text.apply(''.join).reset_index(name='all_review')
df_combined_paris_summary = pd.read_csv('df_combined_paris.csv')
df_combined_paris_summary = df_combined_paris_summary[['Hotel','summary']]
import re
# df_combined = pd.read_csv('df_combined.csv')
df_combined['all_review'] = df_combined['all_review'].apply(lambda x: re.sub('[^a-zA-z0-9\s]','',x))
df_combined['all_review']= df_combined['all_review'].apply(lambda x: lower_case(x))
df_basic = df_all[['Hotel','description','price_per_night']].drop_duplicates()
df_basic = df_basic.merge(df_combined_paris_summary,how='left')
df_combined_e = df_combined.merge(df_basic)
df_combined_e['all_review'] =df_combined_e['description']+ df_combined_e['all_review'] + df_combined_e['price_per_night']
df = df_combined_e.copy()
df_sentences = df_combined_e.set_index("all_review")
df_sentences = df_sentences["Hotel"].to_dict()
df_sentences_list = list(df_sentences.keys())
import pandas as pd
from tqdm import tqdm
from sentence_transformers import SentenceTransformer, util
df_sentences_list = [str(d) for d in tqdm(df_sentences_list)]
#
corpus = df_sentences_list
# corpus_embeddings = embedder.encode(corpus,show_progress_bar=True)
corpus_embeddings = np.load('embeddings.npy')
bi_encoder.max_seq_length = 512 #Truncate long passages to 256 tokens
top_k = 32 #Number of passages we want to retrieve with the bi-encoder
#The bi-encoder will retrieve 100 documents. We use a cross-encoder, to re-rank the results list to improve the quality
# corpus_embeddings_h = np.load('embeddings_h_r.npy')
with open('corpus_embeddings_bi_encoder.pickle', 'rb') as pkl:
doc_embedding = pickle.load(pkl)
with open('tokenized_corpus.pickle', 'rb') as pkl:
tokenized_corpus = pickle.load(pkl)
bm25 = BM25Okapi(tokenized_corpus)
passages = corpus
# We lower case our text and remove stop-words from indexing
def bm25_tokenizer(text):
tokenized_doc = []
for token in text.lower().split():
token = token.strip(string.punctuation)
if len(token) > 0:
tokenized_doc.append(token)
return tokenized_doc
def search(query):
# q = [str(userinput)]
# doc = nlp(str(userinput))
# ent_html = displacy.render(doc, style="ent", jupyter=False)
# Display the entity visualization in the browser:
st.markdown(query, unsafe_allow_html=True)
##### BM25 search (lexical search) #####
bm25_scores = bm25.get_scores(bm25_tokenizer(query))
top_n = np.argpartition(bm25_scores, -5)[-5:]
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)
bm25list = {}
st.title("Top-5 lexical search (BM25) hits")
for hit in bm25_hits[0:5]:
row_dict = df.loc[df['all_review']== corpus[hit['corpus_id']]]
st.subheader(row_dict['Hotel'].values[0])
de = df_basic.loc[df_basic.Hotel == row_dict['Hotel'].values[0]]
st.write(f'\tPrice Per night: {de.price_per_night.values[0]}')
st.write(f'Description: {de.description.values[0]}')
st.expander(de.description.values[0],expanded=False)
# try:
# st.write('Summary')
# st.expander(de.summary.values[0],expanded=False)
# except:
# None
# doc = corpus[hit['corpus_id']]
# kp.get_key_phrases(doc)
bm25list[row_dict['Hotel'].values[0]] = de.description.values[0][0:200]
#### Sematic Search #####
# Encode the query using the bi-encoder and find potentially relevant passages
question_embedding = bi_encoder.encode(query, convert_to_tensor=True)
# question_embedding = question_embedding.cuda()
hits = util.semantic_search(question_embedding, corpus_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, passages[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]
# Output of top-5 hits from bi-encoder
st.write("\n-------------------------\n")
st.title("Top-5 Bi-Encoder Retrieval hits")
hits = sorted(hits, key=lambda x: x['score'], reverse=True)
for hit in hits[0:5]:
# st.write("\t{:.3f}\t{}".format(hit['score'], passages[hit['corpus_id']].replace("\n", " ")))
row_dict = df.loc[df['all_review']== corpus[hit['corpus_id']]]
st.subheader(row_dict['Hotel'].values[0])
de = df_basic.loc[df_basic.Hotel == row_dict['Hotel'].values[0]]
st.write(f'\tPrice Per night: {de.price_per_night.values[0]}')
st.write(f'Description: {de.description.values[0]}')
st.expander(de.description.values[0])
# try:
# st.write('Summary')
# st.expander(de.summary.values[0],expanded=False)
# except:
# None
# Output of top-5 hits from re-ranker
st.write("\n-------------------------\n")
st.title("Top-5 Cross-Encoder Re-ranker hits")
hits = sorted(hits, key=lambda x: x['cross-score'], reverse=True)
for hit in hits[0:5]:
# st.write("\t{:.3f}\t{}".format(hit['cross-score'], passages[hit['corpus_id']].replace("\n", " ")))
row_dict = df.loc[df['all_review']== corpus[hit['corpus_id']]]
st.subheader(row_dict['Hotel'].values[0])
de = df_basic.loc[df_basic.Hotel == row_dict['Hotel'].values[0]]
st.write(f'\tPrice Per night: {de.price_per_night.values[0]}')
st.write(f'Description: {de.description.values[0]}')
st.expander(de.description.values[0])
# try:
# st.write('Summary')
# st.expander(de.summary.values[0],expanded=False)
# except:
# None
sampletext = 'e.g. Hotel near Eiffel Tower with big rooms'
userinput = st.text_input('Tell us what are you looking in your hotel?','e.g. Hotel near Eiffel Tower with big rooms',autocomplete="on")
da = st.date_input(
"Date Check-in",
datetime.date(2023, 6, 3))
dst = st.date_input(
"Date Check-out",
datetime.date(2023, 6, 8))
if not userinput or userinput == sampletext:
st.write("Please enter a query to get results")
else:
query = [str(userinput)]
doc = nlp(str(userinput))
search(str(userinput))
# We use cosine-similarity and torch.topk to find the highest 5 scores
if __name__ == '__main__':
main()