helper.py

# importing libraries
import pandas as pd
import numpy as np
import re
import string
from nltk.stem import WordNetLemmatizer
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
from nltk.tokenize import RegexpTokenizer
from nltk import PorterStemmer, WordNetLemmatizer
import pickle
import nltk
nltk.download('wordnet')

# preprocessing functions

# converting tweet text to lower case
def text_lower(text):
    return text.str.lower()

# removing stopwoords from the tweet text
def clean_stopwords(text):
    # stopwords list that needs to be excluded from the data
    stopwordlist = ['a', 'about', 'above', 'after', 'again', 'ain', 'all', 'am', 'an',
             'and','any','are', 'as', 'at', 'be', 'because', 'been', 'before',
             'being', 'below', 'between','both', 'by', 'can', 'd', 'did', 'do',
             'does', 'doing', 'down', 'during', 'each','few', 'for', 'from',
             'further', 'had', 'has', 'have', 'having', 'he', 'her', 'here',
             'hers', 'herself', 'him', 'himself', 'his', 'how', 'i', 'if', 'in',
             'into','is', 'it', 'its', 'itself', 'just', 'll', 'm', 'ma',
             'me', 'more', 'most','my', 'myself', 'now', 'o', 'of', 'on', 'once',
             'only', 'or', 'other', 'our', 'ours','ourselves', 'out', 'own', 're',
             's', 'same', 'she', "shes", 'should', "shouldve",'so', 'some', 'such',
             't', 'than', 'that', "thatll", 'the', 'their', 'theirs', 'them',
             'themselves', 'then', 'there', 'these', 'they', 'this', 'those',
             'through', 'to', 'too','under', 'until', 'up', 've', 'very', 'was',
             'we', 'were', 'what', 'when', 'where','which','while', 'who', 'whom',
             'why', 'will', 'with', 'won', 'y', 'you', "youd","youll", "youre",
             "youve", 'your', 'yours', 'yourself', 'yourselves']
    STOPWORDS = set(stopwordlist)
    return " ".join([word for word in str(text).split() if word not in STOPWORDS])

# cleaning and removing punctuations
def clean_puctuations(text):
    english_puctuations = string.punctuation
    translator = str.maketrans('','', english_puctuations)
    return text.translate(translator)

# cleaning and removing repeating characters
def clean_repeating_characters(text):
    return re.sub(r'(.)1+', r'1', text)

# cleaning and removing URLs
def clean_URLs(text):
    return re.sub(r"((www.[^s]+)|(http\S+))","",text)

# cleaning and removing numeric data
def clean_numeric(text):
    return re.sub('[0-9]+', '', text)

# Tokenization of tweet text
def tokenize_tweet(text):
    tokenizer = RegexpTokenizer('\w+')
    text = text.apply(tokenizer.tokenize)
    return text

# stemming    
def text_stemming(text):
    st = PorterStemmer()
    text = [st.stem(word) for word in text]
    return text

# lemmatization
def text_lemmatization(text):
    lm = WordNetLemmatizer()
    text = [lm.lemmatize(word) for word in text]
    return text

# defining preprocess function

def preprocess(text):
    text = text_lower(text)
    text = text.apply(lambda text: clean_stopwords(text))
    text = text.apply(lambda x : clean_puctuations(x))
    text = text.apply(lambda x: clean_repeating_characters(x))
    text = text.apply(lambda x : clean_URLs(x))
    text = text.apply(lambda x: clean_numeric(x))
    text = tokenize_tweet(text)
    text = text.apply(lambda x: text_stemming(x))
    text = text.apply(lambda x: text_lemmatization(x))
    text = text.apply(lambda x : " ".join(x))
    return text

# Function for custom input prediction
def custom_input_prediction(text):
    import nltk
    nltk.download('omw-1.4')
    text = pd.Series(text)
    text = preprocess(text)
    text = [text[0],]
    # to use this function we will need to define vectoriser first 
    vectoriser = pickle.load(open("tdf_vectorizer", "rb"))
    text = vectoriser.transform(text)
    model = pickle.load(open("model.pkl", "rb"))
    prediction = model.predict(text)
    prediction = prediction[0]

    interpretations = {
        0 : "Age",
        1 : "Ethnicity",
        2 : "Gender",
        3 : "Not Cyberbullying",
        4 : "Other Cyberbullying",
        5 : "Religion"
    }

    for i in interpretations.keys():
        if i == prediction:
            return interpretations[i]