# -*- coding: utf-8 -*-
"""[Uma Namboothiripad]Assignment_2.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1_sofOjXRDnId49NOup4sdiVS1E_51T-b

Load the dataset below
"""

!pip install -U spacy
#first install the library that would help us use BERT in an easy to use interface
#https://github.com/UKPLab/sentence-transformers/tree/master/sentence_transformers
!pip install -U sentence-transformers

"""I was having issues connecting my csv file to the colab notebook, so I ended up connecting this to my drive"""

import spacy
from datasets import load_dataset
from spacy.lang.en.stop_words import STOP_WORDS
from string import punctuation
from collections import Counter
from heapq import nlargest

import pandas as pd
from tqdm import tqdm
from sentence_transformers import SentenceTransformer, util
print(pd.__version__)

! pip install -q kaggle

! pip install lightgbm

"""Setup Kaggle json credentials"""

from google.colab import files
files.upload()

!mkdir ~/.kaggle/
!cp kaggle.json ~/.kaggle/

!chmod 600 ~/.kaggle/kaggle.json

!kaggle datasets list

!kaggle datasets download -d hamzafarooq50/hotel-listings-and-reviews/HotelListInBarcelona__en2019100120191005.csv

!ls

!python -m spacy download en_core_web_sm

!kaggle datasets download --force -d hamzafarooq50/hotel-listings-and-reviews/hotelReviewsInBarcelona__en2019100120191005.csv

!ls

nlp = spacy.load("en_core_web_sm")

import re

import nltk
nltk.download('punkt')

from nltk.tokenize import word_tokenize

nltk.download('stopwords')

from nltk.corpus import stopwords

nltk.download('wordnet')
nltk.download('omw-1.4')
from nltk.stem import WordNetLemmatizer
from nltk.stem import WordNetLemmatizer

import os
import spacy
nlp = spacy.load("en_core_web_sm")
from spacy import displacy

text = """Example text"""
#text = "I really hope that France does not win the World Cup and Morocco makes it to the finals"
doc = nlp(text)
sentence_spans = list(doc.sents)
displacy.render(doc, jupyter = True, style="ent")

stopwords = list(STOP_WORDS)
from string import punctuation
punctuation = punctuation+ '\n'

import pandas as pd

import scipy.spatial
import pickle as pkl

!pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer
embedder = SentenceTransformer('all-MiniLM-L6-v2')
#embedder = SentenceTransformer('bert-base-nli-mean-tokens')

!pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer

embedder = SentenceTransformer('all-MiniLM-L6-v2')
embedder = SentenceTransformer('bert-base-nli-mean-tokens')


df = df = pd.read_csv('HotelListInBarcelona__en2019100120191005.csv',encoding = "ISO-8859-1")


!kaggle datasets download --force -d hamzafarooq50/hotel-listings-and-reviews

df.head()

df['hotel_name'].value_counts()

df['hotel_name'].drop_duplicates()

df_combined = df.sort_values(['hotel_name']).groupby('hotel_name', sort=False).hotel_features.apply(''.join).reset_index(name='hotel_features')

df_combined.head().T

import re

df_combined['hotel_features'] = df_combined['hotel_features'].apply(lambda x: re.sub('[^a-zA-z0-9\s]','',x))

def lower_case(input_str):
    input_str = input_str.lower()
    return input_str

df_combined['hotel_features']= df_combined['hotel_features'].apply(lambda x: lower_case(x))

df = df_combined

df_sentences = df_combined.set_index("hotel_features")
df_sentences = df_sentences["hotel_name"].to_dict()
df_sentences_list = list(df_sentences.keys())
len(df_sentences_list)

list(df_sentences.keys())[:5]

df_sentences_list = [str(d) for d in tqdm(df_sentences_list)]

# Corpus with example sentences
corpus = df_sentences_list
corpus_embeddings = embedder.encode(corpus,show_progress_bar=True)

corpus_embeddings[0]

queries = ['Hotel near tourist locations and with free WIFI',
           ]
query_embeddings = embedder.encode(queries,show_progress_bar=True)

import torch
# Query sentences:
queries = ['Hotel at least 10 minutes away from sagrada familia'
           ]


# Find the closest 3 sentences of the corpus for each query sentence based on cosine similarity
top_k = min(3, len(corpus))
for query in queries:
    query_embedding = embedder.encode(query, convert_to_tensor=True)

    # We use cosine-similarity and torch.topk to find the highest 5 scores
    cos_scores = util.pytorch_cos_sim(query_embedding, corpus_embeddings)[0]
    top_results = torch.topk(cos_scores, k=top_k)

    print("\n\n======================\n\n")
    print("Query:", query)
    print("\nTop 3 most similar sentences in corpus:")

    for score, idx in zip(top_results[0], top_results[1]):
        print("(Score: {:.4f})".format(score))
        print(corpus[idx], "(Score: {:.4f})".format(score))
        row_dict = df.loc[df['hotel_features']== corpus[idx]]
        print("paper_id:  " , row_dict['hotel_name'] , "\n")
    # for idx, distance in results[0:closest_n]:
    #     print("Score:   ", "(Score: %.4f)" % (1-distance) , "\n" )
    #     print("Paragraph:   ", corpus[idx].strip(), "\n" )
    #     row_dict = df.loc[df['all_review']== corpus[idx]]
    #     print("paper_id:  " , row_dict['Hotel'] , "\n")

model = SentenceTransformer('sentence-transformers/paraphrase-xlm-r-multilingual-v1')
embeddings = model.encode(corpus)
#print(embeddings)

query_embedding.shape

# Query sentences:
queries = ['Hotel at least 10 minutes away from good food',
           'quiet'
           ]


# Find the closest 5 sentences of the corpus for each query sentence based on cosine similarity
top_k = min(5, len(corpus))
for query in queries:
    
    query_embedding = model.encode(query, convert_to_tensor=True)

    # We use cosine-similarity and torch.topk to find the highest 5 scores
    cos_scores = util.pytorch_cos_sim(query_embedding, embeddings)[0]
    top_results = torch.topk(cos_scores, k=top_k)

    print("\n\n======================\n\n")
    print("Query:", query)
    print("\nTop 5 most similar sentences in corpus:")

    for score, idx in zip(top_results[0], top_results[1]):
        print("(Score: {:.4f})".format(score))
        print(corpus[idx], "(Score: {:.4f})".format(score))
        row_dict = df.loc[df['hotel_features']== corpus[idx]]
        print("paper_id:  " , row_dict['hotel_name'] , "\n")

df

hits = util.semantic_search(query_embedding, embeddings, top_k=5)
hits = hits[0]      #Get the hits for the first query
for hit in hits:
  print (hit)
  print("(Score: {:.4f})".format(hit['score']))
  print(corpus[hit['corpus_id']])
  row_dict = df.loc[df['hotel_features']== corpus[hit['corpus_id']]]
  print("paper_id:  " , row_dict['hotel_name'] , "\n")

!pip freeze > requirements.txt