sms_text_verify.py

from nltk import text
from transformers import pipeline
import pandas as pd
import numpy as np
import re
import string
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
import tensorflow as tf


class  eng_news_zero_shot_model_pred():
    def __init__(self) -> None:
        self.classifier = pipeline("zero-shot-classification",model = "joeddav/xlm-roberta-large-xnli")
        super().__init__()
    # def wordopt(self, text):
    #     # text = text.lower()
    #     # text = re.sub('\[.*?\]', '', text)
    #     # text = re.sub("\\W"," ",text) 
    #     # # text = re.sub('https?://\S+|www\.\S+', '', text)
    #     # text = re.sub('<.*?>+', '', text)
    #     # text = re.sub('[%s]' % re.escape(string.punctuation), '', text)
    #     # text = re.sub('\n', '', text)
    #     # text = re.sub('\w*\d\w*', '', text)
    #     # stop_words = set(stopwords.words('english'))
    #     word_tokens = word_tokenize(text)
    #     filtered_sentence = ''

    #     for w in word_tokens:
    #         if w not in stop_words and len(filtered_sentence) == 0:
    #             filtered_sentence += w
    #         elif w not in stop_words and len(filtered_sentence) != 0:
    #             filtered_sentence += ' '+w
        
        
        # return filtered_sentence

    def predict(self, text):
        candidate_labels = ["phishing", "safe"]
        question = "Does this sms conatin  {} ? "
        # text = self.wordopt(text)
        return self.classifier(text,candidate_labels,hypothesis_template =question)

if __name__ == "__main__":

    test_obj = eng_news_zero_shot_model_pred()

    print(test_obj.predict(text= "Dear customer, your xxx bank account will be suspended! Please Re KYC Verification Update click here link http://446bdf227fc4.ngrok.io/xxxbank"))