--- a
+++ b/allennlp/sentiment.py
@@ -0,0 +1,97 @@
+import sys
+import os
+import torch
+import re
+import loader
+# from allennlp.models.archival import *
+from allennlp.data import DatasetReader
+from allennlp.common.params import Params
+from allennlp.predictors.text_classifier import TextClassifierPredictor
+import time
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from ehrkit import ehrkit
+# from config import USERNAME, PASSWORD
+
+
+def load_glove():
+    # Loads GLOVE model
+    glove_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "glove_sentiment_predictor.txt")
+    if os.path.exists(glove_path):  # same dir for github
+        print('Loading Glove Sentiment Analysis Model')
+        predictor = torch.load(glove_path)
+    else:
+        print('Downloading Glove Sentiment Analysis Model')
+        predictor = loader.download_glove()
+    return predictor
+
+
+def load_roberta():
+    # Loads Roberta model
+    serialization_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'roberta', '')
+    config_file = os.path.join(serialization_dir, 'config.json')
+    if os.path.exists(config_file):
+        print('Loading Roberta Sentiment Analysis Model')
+        model_file = os.path.join(serialization_dir, 'whole_model.pt')
+        model = torch.load(model_file)
+        loaded_params = Params.from_file(config_file)
+        dataset_reader = DatasetReader.from_params(loaded_params.get('dataset_reader'))
+
+        # Gets predictor from model and dataset reader
+        predictor = TextClassifierPredictor(model, dataset_reader)
+
+        # weights_file = os.path.join(serialization_dir, 'weights.th')
+        # loaded_model = Model.load(loaded_params, serialization_dir, weights_file) # Takes forever
+        # archive = load_archive(os.path.join('roberta', 'model.tar.gz')) # takes forever
+    else:
+        print('Downloading Roberta Sentiment Analysis Model')
+        predictor = loader.download_roberta()
+    return predictor
+
+
+def get_doc():
+    doc_id = input("MIMIC Document ID [press Enter for random]: ")
+    if doc_id == '':
+        ehrdb.cur.execute("SELECT ROW_ID FROM mimic.NOTEEVENTS ORDER BY RAND() LIMIT 1")
+        doc_id = ehrdb.cur.fetchall()[0][0]
+        print('Document ID: %s' % doc_id)
+    try:
+        text = ehrdb.get_document(int(doc_id))
+        clean_text = re.sub('[^A-Za-z0-9\.\,\-\/]+', ' ', text).lower()
+        return doc_id, clean_text
+    except:
+        message = 'Error: There is no document with ID \'' + doc_id + '\' in mimic.NOTEEVENTS'
+        sys.exit(message)
+
+
+if __name__ == '__main__':
+    # ehrdb = ehrkit.start_session(USERNAME, PASSWORD)
+    ehrdb = ehrkit.start_session("jeremy.goldwasser@localhost", "mysql4710")
+    doc_id, clean_text = get_doc()
+    # print('LENGTH OF DOCUMENT: %d' % len(clean_text))
+
+    x = input('GloVe or RoBERTa predictor [g=GloVe, r=RoBERTa]? ')
+    if x == 'g':
+        glove_predictor = load_glove()
+        probs = glove_predictor.predict(clean_text)['probs']
+    elif x == 'r':
+        roberta_predictor = load_roberta()
+        try:
+            probs = roberta_predictor.predict(clean_text)['probs']
+        except:
+            print('Document too long for RoBERTa model. Using GLoVe instead.')
+            glove_predictor = load_glove()
+            probs = glove_predictor.predict(clean_text)['probs']
+    else:
+        sys.exit('Error: Must input \'g\' or  \'r\'')
+
+    classification = 'Positive' if probs[0] >= 0.5 else 'Negative'
+    print('Sentiment of document: %s' % classification)
+
+
+    # # jeremy.goldwasser@localhost
+    # # Save sentiment as json file
+    # sentiment = {'text': clean_text, 'sentiment': classification, 'prob': probs[0]}
+    # with open('predicted_sentiments/' + str(doc_id) + '.json', 'w', encoding='utf-8') as f:
+    #     json.dump(sentiment, f, ensure_ascii=False, indent=4)
+