Upload app.py

app.py

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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)