app.py

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')
tfidf_vectorizer = joblib.load('./tfidf_vectorizer.pkl')
random_forest = joblib.load('./random_forest.pkl')

def classify(text,choice):
  corpus=[text]
  if(choice == 1):
      features = vectorizer.transform(corpus).toarray()
      prediction = nb_classifier.predict(features)
  elif(choice == 2):
      features = tfidf_vectorizer.transform(corpus).toarray()
      prediction = random_forest.predict(features)
  if(prediction == 1):
     return "Fake News"
  else:
     return "Not Fake News"
GUI = gr.Interface(
    inputs = ['text', gr.radio( options = [("Naive Bayes",1) , ("Random Forest",2) ] , value = 1 , label = "Model") ],
    outputs = ['text'],
    fn = classify,
    title = "Fake News Detection System"
)
GUI.launch(debug = True)