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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()
# Function to preprocess the input text
def transform_text(text):
text = text.lower() # Convert to lowercase
text = nltk.word_tokenize(text) # Tokenize the text
y = []
# Removing alphanumeric tokens
for i in text:
if i.isalnum():
y.append(i)
text = y[:]
y.clear()
# Removing stopwords and punctuation
for i in text:
if i not in stopwords.words('english') and i not in string.punctuation:
y.append(i)
text = y[:]
y.clear()
# Performing stemming
for i in text:
y.append(ps.stem(i))
return " ".join(y) # Join the list into a single string with spaces
# Load the saved models (TF-IDF Vectorizer and the classification model)
tfidf = pickle.load(open('vectorizer.pkl', 'rb'))
model = pickle.load(open('model.pkl', 'rb'))
# Setting up the main title and description
st.title("📧 Email/SMS Spam Classifier")
st.write("""
### Enter a message to determine whether it's Spam or Not Spam.
This classifier uses **natural language processing (NLP)** techniques to preprocess and predict based on your input.
""")
# Input text field for user to enter the message
st.write("#### Message Input:")
input_sms = st.text_area("Type or paste your message here", height=150)
# Add a button to trigger the classification
if st.button("🔍 Classify Message"):
if input_sms.strip(): # Ensure there's text in the input
## 1. Preprocess the input text
with st.spinner('Processing...'):
transformed_sms = transform_text(input_sms)
## 2. Vectorize the transformed text
vector_input = tfidf.transform([transformed_sms])
## 3. Predict the label (Spam or Not Spam)
result = model.predict(vector_input)[0]
## 4. Display the result with appropriate color and message
if result == 1:
st.success("🔴 This message is classified as **Spam**.")
else:
st.success("🟢 This message is classified as **Not Spam**.")
else:
st.warning("Please enter a valid message to classify.")
# Adding a footer with a reference to your classifier and author
st.markdown("""
---
Developed using **Streamlit** and **NLP techniques**.<br>
**Author**: **Aditya Yadav**
""", unsafe_allow_html=True) |