Create app.py

app.py

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+import streamlit as st
+import pickle
+import nltk
+import string
+from nltk.corpus import stopwords
+from nltk.stem.porter import PorterStemmer
+
+# Initialize PorterStemmer
+ps = PorterStemmer()
+
+# Load the pre-trained model and TF-IDF vectorizer
+
+with open('model.pkl', 'rb') as fil:
+    model = pickle.load(fil)
+with open('vectorized.pkl', 'rb') as file:
+    tfidf = pickle.load(file)
+
+
+# Define the text preprocessing function
+def update_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)
+
+
+# Streamlit application title
+st.title("Email/SMS Spam Classifier")
+
+# Collecting the SMS text with a larger text area
+input_sms = st.text_area("Write the Message", height=150)
+
+# Add a button to trigger the prediction
+if st.button("Predict"):
+    # Preprocessing the text
+    transformed_sms = update_text(input_sms)
+
+    # Ensure the transformed SMS is not empty before vectorizing
+    if transformed_sms.strip():
+        # Vectorizing the SMS
+        vectorized_input = tfidf.transform([transformed_sms])
+
+        # Convert the sparse matrix to a dense format
+        vectorized_input_dense = vectorized_input.toarray()
+
+        # Predicting
+        try:
+            result = model.predict(vectorized_input_dense)[0]
+            if result == 1:
+                st.header("Spam")
+            else:
+                st.header("Not Spam")
+        except Exception as e:
+            st.error(f"Error during prediction: {e}")
+    else:
+        st.warning("Please enter a valid message.")