Update app.py

app.py

@@ -115,39 +115,39 @@ def classify(df, new_column = True):
 
   print(sentencesMCTIList_xp8[0])
   print("##########################")
-  print(formatted_sentences[0])
-
-  # print("Transformado em W2V")
-  # words = list(reloaded_w2v_model.wv.vocab) 
-  # item_shape = np.shape(reloaded_w2v_model.wv[words[0]])
-  # # print(formatted_sentences)
-
-  # MCTIinput_vector = []
-  # for sentence in formatted_sentences:
-  #   aux_vector = []
-  #   for word in sentence:
-  #     if word in reloaded_w2v_model.wv.vocab:
-  #       aux_vector.append(reloaded_w2v_model.wv[word])
-  #     else:
-  #       aux_vector.append(np.zeros(item_shape))
-  #     MCTIinput_vector.append(aux_vector)
-  # del formatted_sentences
-  # print("Convertido W2V")
-  # MCTIinput_padded = pad_sequences(MCTIinput_vector, maxlen=2726, padding='pre')
-  # del MCTIinput_vector
-  # print("Sentenças com Padding")
-  # print(len(MCTIinput_padded))
-  # print(len(MCTIinput_padded[0]))
-  # 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
+  print(formatted_sentences[0][0])
+
+  print("Transformado em W2V")
+  words = list(reloaded_w2v_model.wv.vocab) 
+  item_shape = np.shape(reloaded_w2v_model.wv[words[0]])
+  # print(formatted_sentences)
+
+  MCTIinput_vector = []
+  for sentence in formatted_sentences:
+    aux_vector = []
+    for word in sentence:
+      if word in reloaded_w2v_model.wv.vocab:
+        aux_vector.append(reloaded_w2v_model.wv[word])
+      else:
+        aux_vector.append(np.zeros(item_shape))
+    MCTIinput_vector.append(aux_vector)
+  del formatted_sentences
+  print("Convertido W2V")
+  MCTIinput_padded = pad_sequences(MCTIinput_vector, maxlen=2726, padding='pre')
+  del MCTIinput_vector
+  print("Sentenças com Padding")
+  print(len(MCTIinput_padded))
+  print(len(MCTIinput_padded[0]))
+  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):