--- a
+++ b/wrapper_functions/EHRKit.py
@@ -0,0 +1,161 @@
+from scispacy_functions import get_abbreviations, get_hyponyms, get_linked_entities, get_named_entities
+from transformer_functions import get_translation, get_supported_translation_languages, get_single_summary, get_multi_summary_joint
+from utils import get_sents_stanza, get_multiple_sents_stanza, get_sents_pyrush, get_sents_scispacy
+from multi_doc_functions import get_clusters, get_similar_documents
+import numpy as np
+from stanza_functions import (
+    get_named_entities_stanza_biomed,
+    get_sents_stanza_biomed,
+    get_tokens_stanza_biomed,
+    get_part_of_speech_and_morphological_features,
+    get_lemmas_stanza_biomed,
+    get_dependency_stanza_biomed
+)
+
+class EHRKit:
+    """
+    EHRKit is the main class of this toolkit. An EHRKit object stores textual records and default models for various tasks.
+    Different tasks can be called from an EHRKit object to perform tasks on the stored textual records.
+
+    Args:
+        main_record (str): main textual record
+        support_records (list): list of auxiliary textual records (used in multi-document tasks)
+        scispacy_model (str): default model for scispacy tasks
+        bert_model (str): default model for pre-trained transformers
+        marian_model (str): default model for translation
+    """
+    def __init__(self,
+                 main_record="",
+                 supporting_records=[],
+                 scispacy_model="en_core_sci_sm",
+                 bert_model="microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract",
+                 marian_model="Helsinki-NLP/opus-mt-en-ROMANCE"):
+
+        self.main_record = main_record
+        self.supporting_records = supporting_records
+        self.scispacy_model = scispacy_model
+        self.bert_model = bert_model
+        self.marian_model = marian_model
+
+    ''' Functions for manipulating records and default models '''
+    def update_and_delete_main_record(self, main_record):
+        """
+        update current main_record, current main_record will be deleted
+        """
+        if main_record:
+            self.main_record = main_record
+        else:
+            raise TypeError('Invalid type for main_record')
+
+    def update_and_keep_main_record(self, main_record):
+        """
+        update current main_record, current main_record will be added to the end of supporting_records
+        """
+        if main_record:
+            self.supporting_records.append(self.main_record)
+            self.main_record = main_record
+        else:
+            raise TypeError('Invalid type for main_record')
+
+    def replace_supporting_records(self, supporting_records):
+        """
+        replace current supporting records
+        """
+        self.supporting_records = supporting_records
+
+    def add_supporting_records(self, supporting_records):
+        """
+        add additional supporting records to existing supporting records
+        """
+        self.supporting_records.extend(supporting_records)
+
+    def update_scispacy_model(self, scispacy_model):
+        self.scispacy_model = scispacy_model
+
+    def update_bert_model(self, bert_model):
+        self.bert_model = bert_model
+
+    def update_marian_model(self, marian_model):
+        self.marian_model = marian_model
+
+    ''' Functions for textual record processing '''
+    def get_abbreviations(self):
+        abbreviations = get_abbreviations(self.scispacy_model, self.main_record)
+        return abbreviations
+
+    def get_hyponyms(self):
+        hyponyms = get_hyponyms(self.scispacy_model, self.main_record)
+        return hyponyms
+
+    def get_linked_entities(self):
+        linked_entities = get_linked_entities(self.scispacy_model, self.main_record)
+        return linked_entities
+
+    def get_named_entities(self, tool='scispacy'):
+        if tool ==  'scispacy':
+            named_entities = get_named_entities(self.scispacy_model, self.main_record)
+        elif tool == 'stanza':
+            named_entities = get_named_entities_stanza_biomed(self.main_record)
+        return named_entities
+
+    def get_translation(self, target_language='Spanish'):
+        translation = get_translation(self.main_record, self.marian_model, target_language)
+        return translation
+
+    def get_supported_translation_languages(self):
+        return get_supported_translation_languages()
+
+    def get_sentences(self, tool='stanza'):
+        if tool == 'pyrush':
+            sents = get_sents_pyrush(self.main_record)
+            sents = [self.main_record[sent.begin:sent.end] for sent in sents]
+        elif tool == 'stanza':
+            sents = get_sents_stanza(self.main_record)
+        elif tool == 'scispacy':
+            sents = get_sents_scispacy(self.main_record)
+        elif tool == 'stanza-biomed':
+            sents = get_sents_stanza_biomed(self.main_record)
+        return sents
+
+    def get_tokens(self, tool='stanza-biomed'):
+        if tool == 'stanza-biomed':
+            tokens = get_tokens_stanza_biomed(self.main_record)
+        return tokens
+
+    def get_pos_tags(self, tool='stanza-biomed'):
+        if tool ==  'stanza-biomed':
+            tags = get_part_of_speech_and_morphological_features(self.main_record)
+        return tags
+
+    def get_lemmas(self, tool='stanza-biomed'):
+        if tool == 'stanza-biomed':
+            lemmas = get_lemmas_stanza_biomed(self.main_record)
+        return lemmas
+
+    def get_dependency(self, tool='stanza-biomed'):
+        if tool == 'stanza-biomed':
+            dependencies = get_dependency_stanza_biomed(self.main_record)
+        return dependencies
+
+    def get_clusters(self, k=2):
+        # combine main record and candidate records for clustering
+        docs = [self.main_record] + self.supporting_records
+        clusters = get_clusters(self.bert_model, docs, k)
+        return clusters
+
+    def get_similar_documents(self, k=2):
+        query_note = self.main_record
+        candidate_notes = self.supporting_records
+        # ids of candidates
+        candidates = np.array(range(len(candidate_notes)))
+        similar_docs = get_similar_documents(self.bert_model, query_note, candidate_notes, candidates, top_k=k)
+        return similar_docs
+
+    def get_single_record_summary(self):
+        summary = get_single_summary(self.main_record)
+        return summary
+
+    def get_multi_record_summary(self):
+        docs = [self.main_record] + self.supporting_records
+        summary = get_multi_summary_joint(docs)
+        return summary