Create app.py

app.py

@@ -0,0 +1,42 @@
+import pickle
+import nltk
+import re
+from nltk.corpus import stopwords
+from nltk.stem import PorterStemmer
+from nltk.stem import WordNetLemmatizer
+import streamlit as st
+
+stemmer = PorterStemmer()
+lemmatizer = WordNetLemmatizer()
+
+cv = pickle.load(open('pickle_files/
+count_vectorizer.pkl', 'rb'))
+
+model = pickle.load(open('pickle_files/spam_model.pkl', 'rb'))
+
+
+def spam_or_ham(message):
+    message = re.sub('[^a-zA-Z]', ' ', message)
+    message = message.lower()
+    message = message.split()
+    message = [lemmatizer.lemmatize(word) for word in message if word not in set(stopwords.words('english'))]
+    message = ' '.join(message)
+    
+    X = cv.transform([message]).toarray()
+    prediction = model.predict(X)
+    if prediction:
+        return 'Not Spam'
+    else:
+        return 'Spam'
+
+
+st.title("Spam Classifier")
+
+message = st.text_input("Type a Message")
+
+if st.button("Check Spam or Ham"):
+    if message:
+        spam_check = spam_or_ham(message)
+        st.write(spam_check)
+    else:
+        st.write('Empty Message')