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import streamlit as st
import pickle
import string
from nltk.corpus import stopwords
import nltk
from nltk.stem.porter import PorterStemmer
import sklearn
ps = PorterStemmer()
def Datapreprocessing(text):
text = text.lower()
text = nltk.word_tokenize(text)
y = []
for i in text:
if i.isalnum():
y.append(i)
text = y.copy()
y.clear()
for i in text:
if i not in string.punctuation and i not in stopwords.words('english'):
y.append(i)
text = y.copy()
y.clear()
for i in text:
y.append(ps.stem(i))
text = y[:]
y.clear()
text = " ".join(text)
return text
tfidf = pickle.load(open('vectorizer.pkl','rb'))
model = pickle.load(open('model.pkl','rb'))
st.title('Email/SMS Spam Classifier ')
input_sms = st.text_area('Enter the EmailSMS : ')
if st.button('Predict'):
# 1. preprocess
transformed_sms = Datapreprocessing(input_sms)
# 2. vectorize
vector_input = tfidf.transform([transformed_sms])
# 3. predict
result = model.predict(vector_input)[0]
# 4. Display
if result == 1:
st.header("Spam ! Be Careful AMIGO ;)")
else:
st.header("Not Spam ! Go ahead buddy :D ") |