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corpus_embeddings_bi_encoder.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1854af45783940daefdea27ee8e42f026faefdc4ff4a41067c6ee4ca6eb74ade
+size 64918

embeddings.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3759225896afa4282dee721d96d1d1a8085cde7ccffe29e975568a5499a36548
+size 64640

paris-newer.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+
+
+@author: Hamza Farooq
+"""
+
+import spacy
+from spacy.lang.en.stop_words import STOP_WORDS
+from string import punctuation
+from collections import Counter
+from heapq import nlargest
+import os
+nlp = spacy.load("en_core_web_sm")
+from sentence_transformers import SentenceTransformer, CrossEncoder, util
+import datetime
+
+from spacy import displacy
+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="🎈"   )
+    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("ℹ️ - 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 and token not in _stop_words.ENGLISH_STOP_WORDS:
+                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(ent_html, 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()

tokenized_corpus.pickle

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e99b20be01f7889248d5b3f667df8947ae6ca676f3a525717305e5124c8b739e
+size 1261235