Update app.py

app.py

@@ -118,29 +118,29 @@ def classify(df, new_column = True):
   print(formatted_sentences)
 
   MCTIinput_vector = []
-  # for sentence in formatted_sentences:
-  #   aux_vector = []
-  #   for word in sentence:
-  #     try:
-  #       aux_vector.append(reloaded_w2v_model.wv[word])
-  #     except:
-  #       aux_vector.append(np.zeros(item_shape))
-  #     MCTIinput_vector.append(aux_vector)
-  # del formatted_sentences
-
-  # MCTIinput_padded = pad_sequences(MCTIinput_vector, maxlen=2726, padding='pre')
-  # del MCTIinput_vector
-  # print("Sentenças com Padding")
-  # predictions = reconstructed_model_CNN.predict(MCTIinput_padded)
-  # del MCTIinput_padded
-  # print(predictions)
-
-  # cleaned_up_predictions = []
-  # for prediction in predictions:
-  #   cleaned_up_predictions.append(1 if prediction >= 0.5 else 0);
-  # del predictions
-
-  # df['classification'] = cleaned_up_predictions
+  for sentence in formatted_sentences:
+    aux_vector = []
+    for word in sentence:
+      try:
+        aux_vector.append(reloaded_w2v_model.wv[word])
+      except:
+        aux_vector.append(np.zeros(item_shape))
+      MCTIinput_vector.append(aux_vector)
+  del formatted_sentences
+
+  MCTIinput_padded = pad_sequences(MCTIinput_vector, maxlen=2726, padding='pre')
+  del MCTIinput_vector
+  print("Sentenças com Padding")
+  predictions = reconstructed_model_CNN.predict(MCTIinput_padded)
+  del MCTIinput_padded
+  print(predictions)
+
+  cleaned_up_predictions = []
+  for prediction in predictions:
+    cleaned_up_predictions.append(1 if prediction >= 0.5 else 0);
+  del predictions
+
+  df['classification'] = cleaned_up_predictions
   return df
 
 def gen_output(data):