Update app.py

app.py

@@ -164,34 +164,34 @@ def main():
   # st.write('causality extraction finished')
   # st.write("--- %s seconds ---" % (time.time() - start_time))
 
-  # filename = 'Checkpoint-classification.sav'
-  # loaded_model = pickle.load(open(filename, 'rb'))
-  # loaded_vectorizer = pickle.load(open('vectorizefile_classification.pickle', 'rb'))
+  filename = 'Checkpoint-classification.sav'
+  loaded_model = pickle.load(open(filename, 'rb'))
+  loaded_vectorizer = pickle.load(open('vectorizefile_classification.pickle', 'rb'))
 
-  # pipeline_test_output = loaded_vectorizer.transform(class_list)
-  # predicted = loaded_model.predict(pipeline_test_output)
+  pipeline_test_output = loaded_vectorizer.transform(class_list)
+  predicted = loaded_model.predict(pipeline_test_output)
   
-  tokenizer = Tokenizer(num_words=100000)
-  tokenizer.fit_on_texts(class_list)
-  word_index = tokenizer.word_index
+  # tokenizer = Tokenizer(num_words=100000)
+  # tokenizer.fit_on_texts(class_list)
+  # word_index = tokenizer.word_index
   # text_embedding = np.zeros((len(word_index) + 1, 300))
   # for word, i in word_index.items():
   #     text_embedding[i] = nlp(word).vector
-  json_file = open('model.json', 'r')
-  loaded_model_json = json_file.read()
-  json_file.close()
-  loaded_model = model_from_json(loaded_model_json)
-  # load weights into new model
-  loaded_model.load_weights("model.h5")
+  # json_file = open('model.json', 'r')
+  # loaded_model_json = json_file.read()
+  # json_file.close()
+  # loaded_model = model_from_json(loaded_model_json)
+  # # load weights into new model
+  # loaded_model.load_weights("model.h5")
 
-  loss = tf.keras.losses.CategoricalCrossentropy() #from_logits=True
-  loaded_model.compile(loss=loss,optimizer=tf.keras.optimizers.Adam(1e-4))
+  # loss = tf.keras.losses.CategoricalCrossentropy() #from_logits=True
+  # loaded_model.compile(loss=loss,optimizer=tf.keras.optimizers.Adam(1e-4))
 
-  predictions = loaded_model.predict(pad_sequences(tokenizer.texts_to_sequences(class_list),maxlen=MAX_SEQUENCE_LENGTH))
-  predicted = np.argmax(predictions,axis=1)
+  # predictions = loaded_model.predict(pad_sequences(tokenizer.texts_to_sequences(class_list),maxlen=MAX_SEQUENCE_LENGTH))
+  # predicted = np.argmax(predictions,axis=1)
 
-  st.write(predictions)
-  st.write(predicted)
+  # st.write(predictions)
+  # st.write(predicted)
   # st.write('stakeholder taxonomy finished')
   # st.write("--- %s seconds ---" % (time.time() - start_time))
   pred1 = predicted