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from sklearn.feature_extraction.text import TfidfVectorizer
from TurkishStemmer import TurkishStemmer
import string
# import for loading python objects (scikit-learn models)
import pickle
import nltk
import streamlit as st
import sklearn
def custom_tokenizer_with_Turkish_stemmer(text):
# tokenize text
# tokens = text.split(" ")
tokens = nltk.word_tokenize(text)
print(tokens)
stems = [stemmerTR.stem(item.lower()) for item in tokens]
return stems
def predictSMSdata(test_text):
categories = ["legitimate", "spam"]
categories.sort()
# load model
filename1 = "LinearSVC_SMS_spam_TR.pickle"
file_handle1 = open(filename1, "rb")
classifier = pickle.load(file_handle1)
file_handle1.close()
# load tfidf_vectorizer for transforming test text data
filename2 = "tfidf_vectorizer_TR.pickle"
file_handle2 = open(filename2, "rb")
tfidf_vectorizer = pickle.load(file_handle2)
file_handle2.close()
test_list=[test_text]
tfidf_vectorizer_vectors_test = tfidf_vectorizer.transform(test_list)
predicted = classifier.predict(tfidf_vectorizer_vectors_test)
print(categories[predicted[0]])
return categories[predicted[0]]
stemmerTR = TurkishStemmer()
text = st.text_area("enter some text!")
if text:
out = predictSMSdata(text)
st.write("The category of SMS = " + out.upper())
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