Mvrick3027/Offensive-Classification-App

1import streamlit as st2import pickle3import string4from nltk.corpus import stopwords5import nltk6from nltk.stem.porter import PorterStemmer7import sklearn8 9ps = PorterStemmer()10 11def transform_text(text):12    text = text.lower()13    text = nltk.word_tokenize(text)14 15    y = []16    for i in text:17        if i.isalnum():18            y.append(i)19 20    text = y[:]21    y.clear()22 23    for i in text:24        if i not in stopwords.words('english') and i not in string.punctuation:25            y.append(i)26 27    text = y[:]28    y.clear()29 30    for i in text:31        y.append(ps.stem(i))32 33    return " ".join(y)34 35tfidf = pickle.load(open('vectorizer.pkl','rb'))36model = pickle.load(open('model.pkl','rb'))37 38st.title("offensive Text Classifier")39 40input_sms = st.text_input("Enter the message")41 42if st.button('predict'):43 44 45 transformed_sms = transform_text(input_sms)46 47 vector_input = tfidf.transform([transformed_sms])48 49 result = model.predict(vector_input)[0]50 51 if result == 1:52     st.header("offensive")53 else:54     st.header("non-offensive")