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app.py
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app.py
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import streamlit as st
import string
import pickle
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
nltk.download('punkt')
nltk.download('stopwords')
ps = PorterStemmer()
def transform_text(text):
text = text.lower()
text = nltk.word_tokenize(text)
y = []
for i in text:
if i.isalnum():
y.append(i)
text = y[:]
y.clear()
for i in text:
if i not in stopwords.words('english') and i not in string.punctuation:
y.append(i)
text = y[:]
y.clear()
for i in text:
y.append(ps.stem(i))
return " ".join(y)
tfidf = pickle.load(open('vectorizer.pkl', 'rb'))
model = pickle.load(open('model.pkl', 'rb'))
st.title("Email Spam Classifier")
input_email = st.text_input("Enter the email body")
if st.button('Submit'):
transformed_email = transform_text(input_email)
vector_input = tfidf.transform([transformed_email])
result = model.predict(vector_input)[0]
if result == 1:
st.header("Spam")
else:
st.header("Not Spam")