Update app.py

app.py

@@ -8,6 +8,7 @@ nltk.download('punkt')
 download_if_non_existent('corpora/stopwords', 'stopwords')
 download_if_non_existent('taggers/averaged_perceptron_tagger', 'averaged_perceptron_tagger')
 download_if_non_existent('corpora/wordnet', 'wordnet')
+from torchtext.data.utils import get_tokenizer
 
 
 #################################################################### Streamlit interface
@@ -28,7 +29,8 @@ def load_cnn():
   model.eval()
 
   return model
-def predict_sentiment(text, model, vocab, tokenizer, torch_text = False):
+def predict_sentiment(text, model, vocab, torch_text = False):
+    tokenizer = get_tokenizer("basic_english")
     if torch_text == True:
         processor.transform(text)
         tokens = tokenizer(text)
@@ -75,7 +77,7 @@ with st.expander("Model 2: CNN Sentiment analysis"):
     user_input = st.text_area("Enter text here...", key='model2_input')
     if st.button('Analyze', key='model2_button'):
         # Displaying output
-        result = predict_sentiment(user_input, model_2, torch_text=True)
+        result = predict_sentiment(user_input, model_2, 16236, torch_text=True)
         if result >= 0.5:
             st.write('The sentiment is: Positive 😀', key='model2_poswrite')
         else: