app.py

import os
import pandas as pd
import numpy as np
import streamlit as st
import re
import pickle
def preprocess(q):
    q=str(q).lower().strip()
    
    q=q.replace('%',' percent ')
    q=q.replace('@',' at ')
    q=q.replace('$',' dollar ')
    
    q=q.replace('[math]','')
    
    q=q.replace(',000,000,000 ','b ')
    q=q.replace(',000,000 ','m ')
    q=q.replace(',000 ','k ')
    
    import re
    q=re.sub(r'([0-9]+)000000000',r'\1b',q)
    q=re.sub(r'([0-9]+)000000',r'\1m',q)
    q=re.sub(r'([0-9]+)000',r'\1k',q)
    
    contractions = { 
    "ain't": "am not",
    "aren't": "are not",
    "can't": "can not",
    "can't've": "can not have",
    "'cause": "because",
    "could've": "could have",
    "couldn't": "could not",
    "couldn't've": "could not have",
    "didn't": "did not",
    "doesn't": "does not",
    "don't": "do not",
    "hadn't": "had not",
    "hadn't've": "had not have",
    "hasn't": "has not",
    "haven't": "have not",
    "he'd": "he would",
    "he'd've": "he would have",
    "he'll": "he will",
    "he'll've": "he will have",
    "he's": "he is",
    "how'd": "how did",
    "how'd'y": "how do you",
    "how'll": "how will",
    "how's": "how is",
    "i'd": "i would",
    "i'd've": "i would have",
    "i'll": "i will",
    "i'll've": "i will have",
    "i'm": "i am",
    "i've": "i have",
    "isn't": "is not",
    "it'd": "it would",
    "it'd've": "it would have",
    "it'll": "it will",
    "it'll've": "it will have",
    "it's": "it is",
    "let's": "let us",
    "ma'am": "madam",
    "mayn't": "may not",
    "might've": "might have",
    "mightn't": "might not",
    "mightn't've": "might not have",
    "must've": "must have",
    "mustn't": "must not",
    "mustn't've": "must not have",
    "needn't": "need not",
    "needn't've": "need not have",
    "o'clock": "of the clock",
    "oughtn't": "ought not",
    "oughtn't've": "ought not have",
    "shan't": "shall not",
    "sha'n't": "shall not",
    "shan't've": "shall not have",
    "she'd": "she would",
    "she'd've": "she would have",
    "she'll": "she will",
    "she'll've": "she will have",
    "she's": "she is",
    "should've": "should have",
    "shouldn't": "should not",
    "shouldn't've": "should not have",
    "so've": "so have",
    "so's": "so as",
    "that'd": "that would",
    "that'd've": "that would have",
    "that's": "that is",
    "there'd": "there would",
    "there'd've": "there would have",
    "there's": "there is",
    "they'd": "they would",
    "they'd've": "they would have",
    "they'll": "they will",
    "they'll've": "they will have",
    "they're": "they are",
    "they've": "they have",
    "to've": "to have",
    "wasn't": "was not",
    "we'd": "we would",
    "we'd've": "we would have",
    "we'll": "we will",
    "we'll've": "we will have",
    "we're": "we are",
    "we've": "we have",
    "weren't": "were not",
    "what'll": "what will",
    "what'll've": "what will have",
    "what're": "what are",
    "what's": "what is",
    "what've": "what have",
    "when's": "when is",
    "when've": "when have",
    "where'd": "where did",
    "where's": "where is",
    "where've": "where have",
    "who'll": "who will",
    "who'll've": "who will have",
    "who's": "who is",
    "who've": "who have",
    "why's": "why is",
    "why've": "why have",
    "will've": "will have",
    "won't": "will not",
    "won't've": "will not have",
    "would've": "would have",
    "wouldn't": "would not",
    "wouldn't've": "would not have",
    "y'all": "you all",
    "y'all'd": "you all would",
    "y'all'd've": "you all would have",
    "y'all're": "you all are",
    "y'all've": "you all have",
    "you'd": "you would",
    "you'd've": "you would have",
    "you'll": "you will",
    "you'll've": "you will have",
    "you're": "you are",
    "you've": "you have"
    }

    q_decontracted = []

    for word in q.split():
        if word in contractions:
            word = contractions[word]

        q_decontracted.append(word)

    q = ' '.join(q_decontracted)
    q = q.replace("'ve", " have")
    q = q.replace("n't", " not")
    q = q.replace("'re", " are")
    q = q.replace("'ll", " will")
    
    q=re.sub(re.compile('<.*?>'),'',q)
    
    import string
    q=q.translate(str.maketrans('', '', string.punctuation))
    
    return q
    
def common_words(row):
    w1=set(map(lambda word: word.lower().strip(),row['question1'].split(" ")))
    w2=set(map(lambda word: word.lower().strip(),row['question2'].split(" ")))
    return len(w1 & w2)

def total_words(row):
    w1=set(map(lambda word: word.lower().strip(),row['question1'].split(" ")))
    w2=set(map(lambda word: word.lower().strip(),row['question2'].split(" ")))
    return len(w1) + len(w2)

import nltk

nltk.download("stopwords")
from nltk.corpus import stopwords

def fetch_token_features(row):
    
    q1 = row['question1']
    q2 = row['question2']
    
    SAFE_DIV = 0.0001 

    STOP_WORDS = stopwords.words("english")
    
    token_features = [0.0]*8
    
    # Converting the Sentence into Tokens: 
    q1_tokens = q1.split()
    q2_tokens = q2.split()
    
    if len(q1_tokens) == 0 or len(q2_tokens) == 0:
        return token_features

    # Get the non-stopwords in Questions
    q1_words = set([word for word in q1_tokens if word not in STOP_WORDS])
    q2_words = set([word for word in q2_tokens if word not in STOP_WORDS])
    
    #Get the stopwords in Questions
    q1_stops = set([word for word in q1_tokens if word in STOP_WORDS])
    q2_stops = set([word for word in q2_tokens if word in STOP_WORDS])
    
    # Get the common non-stopwords from Question pair
    common_word_count = len(q1_words.intersection(q2_words))
    
    # Get the common stopwords from Question pair
    common_stop_count = len(q1_stops.intersection(q2_stops))
    
    # Get the common Tokens from Question pair
    common_token_count = len(set(q1_tokens).intersection(set(q2_tokens)))
    
    
    token_features[0] = common_word_count / (min(len(q1_words), len(q2_words)) + SAFE_DIV)
    token_features[1] = common_word_count / (max(len(q1_words), len(q2_words)) + SAFE_DIV)
    token_features[2] = common_stop_count / (min(len(q1_stops), len(q2_stops)) + SAFE_DIV)
    token_features[3] = common_stop_count / (max(len(q1_stops), len(q2_stops)) + SAFE_DIV)
    token_features[4] = common_token_count / (min(len(q1_tokens), len(q2_tokens)) + SAFE_DIV)
    token_features[5] = common_token_count / (max(len(q1_tokens), len(q2_tokens)) + SAFE_DIV)
    
    # Last word of both question is same or not
    token_features[6] = int(q1_tokens[-1] == q2_tokens[-1])
    
    # First word of both question is same or not
    token_features[7] = int(q1_tokens[0] == q2_tokens[0])
    
    return token_features

import distance

def fetch_length_features(row):
    
    q1 = row['question1']
    q2 = row['question2']
    
    length_features = [0.0]*3
    
    # Converting the Sentence into Tokens: 
    q1_tokens = q1.split()
    q2_tokens = q2.split()
    
    if len(q1_tokens) == 0 or len(q2_tokens) == 0:
        return length_features
    
    # Absolute length features
    length_features[0] = abs(len(q1_tokens) - len(q2_tokens))
    
    # Average Token Length of both Questions
    length_features[1] = (len(q1_tokens) + len(q2_tokens)) / 2
    
    # Find the longest common substring
    strs = list(distance.lcsubstrings(q1, q2))
    if strs:
        length_features[2] = len(strs[0]) / (min(len(q1), len(q2)) + 1)
    else:
        length_features[2] = 0.0
    
    return length_features

# Fuzzy Features
from fuzzywuzzy import fuzz

def fetch_fuzzy_features(row):
    
    q1 = row['question1']
    q2 = row['question2']
    
    fuzzy_features = [0.0]*4
    
    # fuzz_ratio
    fuzzy_features[0] = fuzz.QRatio(q1, q2)

    # fuzz_partial_ratio
    fuzzy_features[1] = fuzz.partial_ratio(q1, q2)

    # token_sort_ratio
    fuzzy_features[2] = fuzz.token_sort_ratio(q1, q2)

    # token_set_ratio
    fuzzy_features[3] = fuzz.token_set_ratio(q1, q2)

    return fuzzy_features

def all_prep(df):
    df['q1_len']=df['question1'].str.len()
    df['q2_len']=df['question2'].str.len()

    df['q1_num_words']=df['question1'].apply(lambda row: len(row.split(" ")))
    df['q2_num_words']=df['question2'].apply(lambda row: len(row.split(" ")))

    df['word_common']=df.apply(common_words,axis=1)
    df['word_total']=df.apply(total_words,axis=1)
    df['word_share']=round(df['word_common']/df['word_total'],2)

    token_features = df.apply(fetch_token_features, axis=1)
    df["cwc_min"]       = list(map(lambda x: x[0], token_features))
    df["cwc_max"]       = list(map(lambda x: x[1], token_features))
    df["csc_min"]       = list(map(lambda x: x[2], token_features))
    df["csc_max"]       = list(map(lambda x: x[3], token_features))
    df["ctc_min"]       = list(map(lambda x: x[4], token_features))
    df["ctc_max"]       = list(map(lambda x: x[5], token_features))
    df["last_word_eq"]  = list(map(lambda x: x[6], token_features))
    df["first_word_eq"] = list(map(lambda x: x[7], token_features))

    length_features = df.apply(fetch_length_features, axis=1)
    df['abs_len_diff'] = list(map(lambda x: x[0], length_features))
    df['mean_len'] = list(map(lambda x: x[1], length_features))
    df['longest_substr_ratio'] = list(map(lambda x: x[2], length_features))

    fuzzy_features = df.apply(fetch_fuzzy_features, axis=1)
    df['fuzz_ratio'] = list(map(lambda x: x[0], fuzzy_features))
    df['fuzz_partial_ratio'] = list(map(lambda x: x[1], fuzzy_features))
    df['token_sort_ratio'] = list(map(lambda x: x[2], fuzzy_features))
    df['token_set_ratio'] = list(map(lambda x: x[3], fuzzy_features))

    ndf2=df.drop(columns=['question1','question2'])
    import pickle
    import numpy as np
    with open("BOW.pkl", 'rb') as file:  
        cv = pickle.load(file)

    questions=list(df['question1'])+list(df['question2'])
    q1_arr,q2_arr=np.vsplit(cv.transform(questions).toarray(),2)
    temp_df=pd.concat([pd.DataFrame(q1_arr,index=ndf2.index),pd.DataFrame(q2_arr,index=ndf2.index)],axis=1)
    temp_df=pd.concat([ndf2,temp_df],axis=1)
    temp_df.columns = temp_df.columns.astype(str)

    return temp_df


def clear_text():
    st.session_state["text1"] = ""
    st.session_state["text2"] = ""


def main():
    st.title('Duplicate Question Detection (Approach 1)')
    st.write("Feature Engineering, BOW , Random Forest")

    q1 = st.text_input("Enter Question1", key="text1")
    q2 = st.text_input("Enter Question2", key="text2")

    data=[]
    df = pd.DataFrame(data)
    df['question1']=[preprocess(q1)]
    df['question2']=[preprocess(q2)]
    with open("RF.pkl", 'rb') as file:  
        rf = pickle.load(file)
    
    if st.button('Find'):
        z=rf.predict(all_prep(df))[0]
        if z==1:
            st.success("Duplicate")
        else:
            st.success("Not Duplicate")
        st.button("Clear", on_click=clear_text)


if __name__=='__main__':
    main()