import streamlit as st
import pickle
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import PassiveAggressiveClassifier
model = PassiveAggressiveClassifier(max_iter=50)

with open('tfidf.pickle', 'rb') as f:
  tfidf = pickle.load(f)

PAGE_CONFIG = {"page_title":"My first ML app","page_icon":":smiley:","layout":"centered"}
st.set_page_config(**PAGE_CONFIG)
st.title("My first ML app")
st.subheader("Here is my (not so) awesome learning result")

menu = ["Home","About my startup"]
choice = st.sidebar.selectbox('Menu',menu)
if choice == 'Home':
	st.subheader("Let's get down to the details.")

title = st.text_input('News title', 'Queen Elizabeth buys an Unicorn')

with open('model.pkl', 'rb') as f:
	model = pickle.load(f)

def predict_news(news_text):
  prediction = model.predict(tfidf.transform([news_text]))
  if prediction[0] == 1:
    return("Possibly fake news")
  else:
    return("Possibly real news")

result = predict_news(title)
st.write('Fake classification: ', result)