--- a
+++ b/allennlp/loader.py
@@ -0,0 +1,139 @@
+from allennlp.predictors.predictor import Predictor
+from allennlp.predictors.text_classifier import TextClassifierPredictor
+from allennlp.predictors.sentence_tagger import SentenceTaggerPredictor
+
+from allennlp.models.archival import *
+from allennlp.data import DatasetReader
+from allennlp.common.params import Params
+from allennlp.models import Model
+import allennlp_models.classification
+import allennlp_models.tagging
+
+import torch
+import os
+import shutil
+
+
+def rm_tmp(tmp_start):
+    # Remove new directories in tmp
+    tmp_now = os.listdir('/tmp')
+    for i in tmp_now:
+        if i not in tmp_start:
+            print('removing directory /tmp/' + i)
+            shutil.rmtree('/tmp/' + i)
+
+
+def get_config(archive_path):
+    archive = load_archive(archive_path)
+    config = archive.config.duplicate()
+    return config
+
+
+def download_glove():
+    # Saves TextClassifierPredictor object
+    tmp_start = os.listdir('/tmp')
+    glove_predictor = Predictor.from_path("https://storage.googleapis.com/allennlp-public-models/basic_stanford_sentiment_treebank-2020.06.09.tar.gz")
+    glove_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'glove_sentiment_predictor.txt')
+    torch.save(glove_predictor, glove_path)
+    try:
+        rm_tmp(tmp_start)
+    except:
+        pass
+    return glove_predictor
+
+
+def download_roberta():
+    tmp_start = os.listdir('/tmp')
+    archive_path = "https://storage.googleapis.com/allennlp-public-models/sst-roberta-large-2020.06.08.tar.gz"
+    config = get_config(archive_path)
+    roberta_predictor = Predictor.from_path(archive_path)
+
+    serialization_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'roberta', '')
+    if os.path.exists(serialization_dir):
+        shutil.rmtree(serialization_dir)
+    os.makedirs(serialization_dir)
+
+    # Create config and model files
+    config.to_file(os.path.join(serialization_dir, 'config.json'))
+    with open(os.path.join(serialization_dir, 'whole_model.pt'), 'wb') as file:
+        torch.save(roberta_predictor._model, file)
+
+    try:
+        rm_tmp(tmp_start)
+    except:
+        pass
+    return roberta_predictor
+
+
+def download_ner():
+    tmp_start = os.listdir('/tmp')
+    archive_path = "https://storage.googleapis.com/allennlp-public-models/ner-model-2020.02.10.tar.gz"
+    config = get_config(archive_path)
+    ner_predictor = Predictor.from_path(archive_path)
+
+    serialization_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'elmo-ner', '')
+    if os.path.exists(serialization_dir):
+        shutil.rmtree(serialization_dir)
+    os.makedirs(serialization_dir)
+
+    config.to_file(os.path.join(serialization_dir, 'config.json'))
+    vocab = ner_predictor._model.vocab
+    vocab.save_to_files(os.path.join(serialization_dir, 'vocabulary'))
+    with open(os.path.join(serialization_dir, 'whole_model.pt'), 'wb') as file:
+        torch.save(ner_predictor._model.state_dict(), file)
+
+    try:
+        rm_tmp(tmp_start)
+    except:
+        pass
+    return ner_predictor
+
+
+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 = 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)
+    else:
+        print('Downloading Roberta Sentiment Analysis Model')
+        predictor = download_roberta()
+    return predictor
+
+
+def load_ner():
+    serialization_dir = "../allennlp/elmo-ner"
+
+    config_file = os.path.join(serialization_dir, 'config.json')
+    weights_file = os.path.join(serialization_dir, 'whole_model.pt')
+    loaded_params = Params.from_file(config_file)
+    loaded_model = Model.load(loaded_params, serialization_dir, weights_file)
+    dataset_reader = DatasetReader.from_params(loaded_params.get('dataset_reader'))
+
+    predictor = SentenceTaggerPredictor(loaded_model, dataset_reader)
+    return predictor
+
+if __name__ == "__main__":
+    download_glove()
+    download_roberta()
+    download_ner()
+