IvaElen commited on
Commit
1834f20
1 Parent(s): cecc930

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +3 -1
app.py CHANGED
@@ -153,13 +153,13 @@ def main():
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  #prediction = loaded_model.predict(input_vector)[0]
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  prediction_1 = round(proba_1[0])
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  end_time = time.time()
 
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  # Display the predicted sentiment
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  if prediction_1 == 0:
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  st.write('The sentiment of your review is negative.')
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  st.write('Predicted probability:', (1 - round(proba_1[0], 2))*100, '%')
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  else:
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  st.write('The sentiment of your review is positive.')
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- st.write('Predicted probability:', (round(proba_1[0], 2))*100, '%')
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  st.write('Processing time:', round(end_time - start_time, 4), 'seconds')
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  # Lena
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  if user_input is not None and submit:
@@ -169,6 +169,7 @@ def main():
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  input_tokens = preprocess_text(user_input, 500, tokenizer)
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  output = predict_sentiment(model2, input_tokens)
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  end_time = time.time()
 
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  st.write('The sentiment of your review is', output)
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  st.write('Processing time:', round(end_time - start_time, 4), 'seconds')
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  # Gala
@@ -178,6 +179,7 @@ def main():
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  start_time = time.time()
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  output = predict_sentence(user_input,model3)
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  end_time = time.time()
 
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  st.write('The sentiment of your review is', output)
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  st.write('Processing time:', round(end_time - start_time, 4), 'seconds')
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  #prediction = loaded_model.predict(input_vector)[0]
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  prediction_1 = round(proba_1[0])
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  end_time = time.time()
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+ st.header('Classic ML (LogReg on TF-IDF)')
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  # Display the predicted sentiment
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  if prediction_1 == 0:
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  st.write('The sentiment of your review is negative.')
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  st.write('Predicted probability:', (1 - round(proba_1[0], 2))*100, '%')
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  else:
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  st.write('The sentiment of your review is positive.')
 
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  st.write('Processing time:', round(end_time - start_time, 4), 'seconds')
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  # Lena
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  if user_input is not None and submit:
 
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  input_tokens = preprocess_text(user_input, 500, tokenizer)
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  output = predict_sentiment(model2, input_tokens)
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  end_time = time.time()
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+ st.header('ErnieModel')
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  st.write('The sentiment of your review is', output)
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  st.write('Processing time:', round(end_time - start_time, 4), 'seconds')
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  # Gala
 
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  start_time = time.time()
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  output = predict_sentence(user_input,model3)
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  end_time = time.time()
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+ st.header('bidirectional LSTM')
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  st.write('The sentiment of your review is', output)
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  st.write('Processing time:', round(end_time - start_time, 4), 'seconds')
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