Upload app.py

app.py

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+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 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)