Create app.py

app.py

@@ -0,0 +1,29 @@
+tfidf=pickle.load(open('tfidf.pkl','rb'))
+model=pickle.load(open('model.pkl','rb'))
+
+def classify_msg(Message):
+    X=preprocess(Message)
+    X_vector=tfidf.transform([X])
+    prediction=model.predict(X_vector)[0]
+    return 'Spam' if prediction==1 else 'Not Spam'
+    
+    
+def preprocess(text):
+    text = text.lower()
+    tokens = nltk.word_tokenize(text)
+    text = []
+    for token in tokens:
+        if token not in stopwords.words('english') and token not in string.punctuation:
+            text.append(token)
+
+    return ' '.join(text)
+
+
+iface = gr.Interface(
+    fn=classify_msg,
+    inputs=gr.inputs.Textbox(),
+    outputs="text",
+)
+    
+if __name__ == "__main__":
+    iface.launch()