Update app.py

app.py

@@ -1,18 +1,24 @@
 import gradio as gr
 from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfVectorizer
 import joblib
 
 vectorizer = joblib.load('./vectorizer.pkl')
 nb_classifier = joblib.load('./nb_classifier.pkl')
-def classify(text):
+tfidf_vectorizer = joblib.load('./tfidf_vectorizer.pkl')
+random_forest = joblib.load('./random_forest.pkl')
+
+def classify(text,choice):
   corpus=[text]
-  features = vectorizer.transform(corpus)
-  features = features.toarray()
-  prediction = nb_classifier.predict(features)
+  features = vectorizer.transform(corpus).toarray()
+  if(choice == 1):
+      prediction = nb_classifier.predict(features)
+  elif(choice == 2):
+      prediction = random_forest.predict(features)
   if(prediction == 1):
      return "Fake News"
   else:
      return "Not Fake News"
 
-GUI = gr.Interface(inputs = ['text'], outputs = ['text'], fn = classify, title = "Fake News Detection System")
+GUI = gr.Interface(inputs = ['text',gr.radio(choices = [('Naive Bayes',1),'(Random Classifier',2)], label = "Model" )], outputs = ['text'], fn = classify, title = "Fake News Detection System")
 GUI.launch(debug = True)