Update pages/✨second.py

pages/✨second.py

@@ -47,10 +47,10 @@ user_review = st.text_input("Enter your comment:", "")
 user_review_clean = clean(user_review)
 user_features = vectorizer.transform([user_review_clean])
 prediction = model.predict(user_features)
-inputs = tokenizer(user_review_clean, max_length=512, padding=True, truncation=True, return_tensors='pt')
+inputs = tokenizer_bert(user_review_clean, max_length=512, padding=True, truncation=True, return_tensors='pt')
 outputs = model_bert(**inputs)
 prediction_bert = torch.nn.functional.softmax(outputs.logits, dim=1)
-prediction_bert = torch.argmax(predicted, dim=1).numpy()
+prediction_bert = torch.argmax(prediction_bert, dim=1).numpy()
 st.write("Comment by ML model:", user_review)
 
 if prediction == 0:
@@ -59,9 +59,9 @@ else:
     st.markdown("<p style='color: red;'>Toxic comment</p>", unsafe_allow_html=True)
 st.write("Comment by RuBERT:", user_review)
 
-if prediction == 0:
+if prediction_bert == 0:
     st.markdown("<p style='color: green;'>Controversial comment</p>", unsafe_allow_html=True)
-elif prediction == 1:
+elif prediction_bert == 1:
     st.markdown("<p style='color: red;'>Non-toxic comment</p>", unsafe_allow_html=True)
 else:
     st.markdown("<p style='color: red;'>Toxic comment</p>", unsafe_allow_html=True)