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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
# 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/SMS SPAM Classifier")
# input_sms = st.text_area("Enter the message")
# if st.button('Predict'):
# # 1. preprocess
# transform_sms = transform_text(input_sms)
# # 2. vectorize
# vector_input = tfidf.transform([transform_sms])
# # 3. predict
# result = model.predict(vector_input)[0]
# # 4. Display
# if result == 1:
# st.header("Spam")
# else:
# st.header("Not Spam")
import streamlit as st
import pickle
import string
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
# Download NLTK resources if not already downloaded
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/SMS SPAM Classifier")
input_sms = st.text_area("Enter the message")
if st.button('Predict'):
# 1. preprocess
transform_sms = transform_text(input_sms)
# 2. vectorize
vector_input = tfidf.transform([transform_sms])
# 3. predict
result = model.predict(vector_input)[0]
# 4. Display
if result == 1:
st.header("Spam")
else:
st.header("Not Spam")
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