sentance similarity.py

from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords,wordnet
from nltk.stem import WordNetLemmatizer
from itertools import product
import numpy
import nltk
#installing wordnet [nltk.download('wordnet')]
#installing punkt   [nltk.download('punkt')]
str1 =(input("enter your sentence 1:"))
str2 =(input("enter your sentence 2:"))
stop_words = set(stopwords.words("english"))

def sentance_similarity(str1,str2):



  filtered_sentence1 = []
  filtered_sentence2 = []
  lemm_sentence1 = []
  lemm_sentence2 = []
  sims = []
  temp1 = []
  temp2 = []
  simi = []
  final = []
  same_sent1 = []
  same_sent2 = []
  
  #Defining WordNet Lematizer for English Language
  lemmatizer  =  WordNetLemmatizer()

  #Tokenizing and removing the Stopwords in srt1
  for words1 in word_tokenize(str1):
    if words1 not in stop_words:
        if words1.isalnum():
            filtered_sentence1.append(words1)
   #Lemmatizing: Root Words in sentance1
  for i in filtered_sentence1:
    lemm_sentence1.append(lemmatizer.lemmatize(i))

   #Tokenizing and removing the Stopwords in str2   
  for words2 in word_tokenize(str2):
    if words2 not in stop_words:
        if words2.isalnum():
            filtered_sentence2.append(words2)
   #Lemmatizing: Root Words in sentance2
  for i in filtered_sentence2:
    lemm_sentence2.append(lemmatizer.lemmatize(i))
  
  #Similarity index calculation for each word
  for word1 in lemm_sentence1:
    simi =[]
    for word2 in lemm_sentence2:
        sims = []
        syns1 = wordnet.synsets(word1)
        syns2 = wordnet.synsets(word2)
        
        for sense1, sense2 in product(syns1, syns2):
            d = wordnet.wup_similarity(sense1, sense2)
            if d != None:
                sims.append(d)
    
        
        if sims != []:        
           max_sim = max(sims)
           
           simi.append(max_sim)
             
    if simi != []:
        max_final = max(simi)
        final.append(max_final)
  
  #Final Output  
  similarity_index = numpy.mean(final)
  similarity_index = round(similarity_index , 2)
  print("Sentence 1: ",str1)
  print("Sentence 2: ",str2)
  print("Similarity index value : ", similarity_index)

  if similarity_index>0.8:
    print("Similar")
  elif similarity_index>=0.6:
    print("Somewhat Similar")
  else:
    print("Not Similar")
sentance_similarity(str1,str2)