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__init__.py
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import streamlit as st
import pickle
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
# Find NLTK data directory
nltk.data.path.append(nltk.data.find('corpora'))
# Download necessary NLTK data
nltk.download('punkt')
nltk.download('stopwords')
ps = PorterStemmer()
# Stemming
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)
# Load the vectorizer and model
tfidf = pickle.load(open('vectorizer.pkl', 'rb'))
model = pickle.load(open('model.pkl', 'rb'))
st.title("Spam Message Verifier System")
input_msg = st.text_area("Enter your message below:")
if st.button('Click here to check'):
# 1. Preprocess
transformed_msg = transform_text(input_msg)
# 2. Vectorize
vector_input = tfidf.transform([transformed_msg])
# 3. Predict
result = model.predict(vector_input)[0]
# 4. Display
if result == 1:
st.header("Spam")
else:
st.header("Not Spam")