Delete epi_pipeline.py

epi_pipeline.py

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-from typing import List, Dict, Union, Optional, Set, Tuple
-
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ALSO See NCATS LICENSE
-
-# Written by William Kariampuzha at NIH/NCATS. Adapted from code written by Jennifer John, et al. above
-
-# Each section has its own import statements to facilitate clean code reuse, except for typing which applies to all.
-# the `Any` type is used in place of the specific class variable, not necessarily to mean that any object type can go there...
-
-## Section: GATHER ABSTRACTS FROM APIs
-import requests
-import xml.etree.ElementTree as ET
-from nltk.corpus import stopwords
-STOPWORDS = set(stopwords.words('english'))
-from nltk import tokenize as nltk_tokenize
-
-#Retreives abstract and title (concatenated) from EBI API based on PubMed ID
-def PMID_getAb(PMID:Union[int,str]) -> str: 
-    url = 'https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:'+str(PMID)+'&resulttype=core'
-    r = requests.get(url)
-    root = ET.fromstring(r.content)
-    titles = [title.text for title in root.iter('title')]
-    abstracts = [abstract.text for abstract in root.iter('abstractText')]
-    if len(abstracts) > 0 and len(abstracts[0])>5:
-        return titles[0]+' '+abstracts[0]
-    else:
-        return ''
-    
-## This is the main, most comprehensive search_term function, it can take in a search term or a list of search terms and output a dictionary of {pmids:abstracts}
-## Gets results from searching through both PubMed and EBI search term APIs, also makes use of the EBI API for PMIDs. 
-## EBI API and PubMed API give different results
-# This makes n+2 API calls where n<=maxResults, which is slow 
-# There is a way to optimize by gathering abstracts from the EBI API when also getting pmids but did not pursue due to time constraints
-# Filtering can be 
-#   'strict' - must have some exact match to at leastone of search terms/phrases in text)
-#   'lenient' - part of the abstract must match at least one word in the search term phrases.
-#   'none'
-def search_getAbs(searchterm_list:Union[List[str],List[int],str], maxResults:int, filtering:str) -> Dict[str,str]:
-    #set of all pmids
-    pmids = set()
-    
-    #dictionary {pmid:abstract}
-    pmid_abs = {}
-    
-    #type validation, allows string or list input
-    if type(searchterm_list)!=list:
-        if type(searchterm_list)==str:
-            searchterm_list = [searchterm_list]
-        else:
-            searchterm_list = list(searchterm_list)
-    
-    #gathers pmids into a set first
-    for dz in searchterm_list:
-        term = ''
-        dz_words = dz.split()
-        for word in dz_words:
-            term += word + '%20'
-        query = term[:-3]
-
-        ## get pmid results from searching for disease name through PubMed API
-        url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term='+query#+"&retmax="+str(int(maxResults/len(searchterm_list)))
-        r = requests.get(url)
-        root = ET.fromstring(r.content)
-
-        # loop over resulting articles
-        for result in root.iter('IdList'):
-            if len(pmids) >= maxResults:
-                break
-            pmidlist = [pmid.text for pmid in result.iter('Id')]
-            pmids.update(pmidlist)
-
-        ## get results from searching for disease name through EBI API
-        url = 'https://www.ebi.ac.uk/europepmc/webservices/rest/search?query='+query+'&resulttype=core'
-        r = requests.get(url)
-        root = ET.fromstring(r.content)
-
-        # loop over resulting articles
-        for result in root.iter('result'):
-            if len(pmids) >= maxResults:
-                break
-            pmidlist = [pmid.text for pmid in result.iter('id')]
-            #can also gather abstract and title here but for some reason did not work as intended the first time. Optimize in future versions to reduce latency.
-            if len(pmidlist) > 0:
-                pmid = pmidlist[0]
-                if pmid[0].isdigit():
-                    pmids.add(pmid)
-    
-    #Construct sets for filtering (right before adding abstract to pmid_abs
-    # The purpose of this is to do a second check of the abstracts, filters out any abstracts unrelated to the search terms
-    #if filtering is 'lenient' or default
-    if filtering !='none' or filtering !='strict':
-        filter_terms = set(searchterm_list).union(set(str(re.sub(',','',' '.join(searchterm_list))).split()).difference(STOPWORDS))
-        '''
-        # The above is equivalent to this but uses less memory and may be faster:
-        #create a single string of the terms within the searchterm_list
-        joined = ' '.join(searchterm_list)
-        #remove commas
-        comma_gone = re.sub(',','',joined)
-        #split the string into list of words and convert list into a Pythonic set
-        split = set(comma_gone.split())
-        #remove the STOPWORDS from the set of key words
-        key_words = split.difference(STOPWORDS)
-        #create a new set of the list members in searchterm_list
-        search_set = set(searchterm_list)
-        #join the two sets
-        terms = search_set.union(key_words)
-        #if any word(s) in the abstract intersect with any of these terms then the abstract is good to go.
-        '''
-    
-    ## get abstracts from EBI PMID API and output a dictionary
-    for pmid in pmids:
-        abstract = PMID_getAb(pmid)
-        if len(abstract)>5:
-            #do filtering here
-            if filtering == 'strict':
-                uncased_ab = abstract.lower()
-                #Reversing the list hopefully cuts down on the number of if statements bc the search terms are ordered longest to shortest and shorter terms are more likely to be in the abstract
-                for term in reversed(searchterm_list):
-                    if term.lower() in uncased_ab:
-                        pmid_abs[pmid] = abstract
-                        break
-            elif filtering =='none':
-                pmid_abs[pmid] = abstract
-            
-            #Default filtering is 'lenient'.
-            else:
-                #Else and if are separated for readability and to better understand logical flow.
-                if set(filter_terms).intersection(set(nltk_tokenize.word_tokenize(abstract))):
-                    pmid_abs[pmid] = abstract
-                
-                    
-    print('Found',len(pmids),'PMIDs. Gathered',len(pmid_abs),'Relevant Abstracts.')
-    
-    return pmid_abs
-
-#This is a streamlit version of search_getAbs. Refer to search_getAbs for documentation
-import streamlit as st
-def streamlit_getAbs(searchterm_list:Union[List[str],List[int],str], maxResults:int, filtering:str) -> Dict[str,str]:
-    pmids = set()
-    
-    pmid_abs = {}
-    
-    if type(searchterm_list)!=list:
-        if type(searchterm_list)==str:
-            searchterm_list = [searchterm_list]
-        else:
-            searchterm_list = list(searchterm_list)
-    #maxResults is multiplied by a little bit because sometimes the results returned is more than maxResults
-    percent_by_step = 1/maxResults
-    with st.spinner("Gathering PubMed IDs..."):
-        PMIDs_bar = st.progress(0)
-        for dz in searchterm_list:
-            term = ''
-            dz_words = dz.split()
-            for word in dz_words:
-                term += word + '%20'
-            query = term[:-3]
-            #dividing by the len( of the search_ter
-            url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term='+query#+"&retmax="+str(int(maxResults/len(searchterm_list)))
-            r = requests.get(url)
-            root = ET.fromstring(r.content)
-    
-            for result in root.iter('IdList'):
-                for pmid in result.iter('Id'):
-                    if len(pmids) >= maxResults:
-                        break
-                    pmids.add(pmid.text)
-                    PMIDs_bar.progress(min(round(len(pmids)*percent_by_step,1),1.0))
-    
-            url = 'https://www.ebi.ac.uk/europepmc/webservices/rest/search?query='+query+'&resulttype=core'
-            r = requests.get(url)
-            root = ET.fromstring(r.content)
-    
-            for result in root.iter('result'):
-                if len(pmids) >= maxResults:
-                    break
-                pmidlist = [pmid.text for pmid in result.iter('id')]
-                if len(pmidlist) > 0:
-                    pmid = pmidlist[0]
-                    if pmid[0].isdigit():
-                        pmids.add(pmid)
-                        PMIDs_bar.progress(min(round(len(pmids)*percent_by_step,1),1.0))
-        PMIDs_bar.empty()
-    
-    with st.spinner("Found "+str(len(pmids))+" PMIDs. Gathering Abstracts and Filtering..."):
-        abstracts_bar = st.progress(0)
-        percent_by_step = 1/maxResults
-        if filtering !='none' or filtering !='strict':
-            filter_terms = set(searchterm_list).union(set(str(re.sub(',','',' '.join(searchterm_list))).split()).difference(STOPWORDS))
-    
-        for i, pmid in enumerate(pmids):
-            abstract = PMID_getAb(pmid)
-            if len(abstract)>5:
-                #do filtering here
-                if filtering == 'strict':
-                    uncased_ab = abstract.lower()
-                    #Reversing the list hopefully cuts down on the number of if statements bc the search terms are ordered longest to shortest and shorter terms are more likely to be in the abstract
-                    for term in reversed(searchterm_list):
-                        if term.lower() in uncased_ab:
-                            pmid_abs[pmid] = abstract                            
-                            break
-                elif filtering =='none':
-                    pmid_abs[pmid] = abstract
-                #Default filtering is 'lenient'.
-                else:
-                    #Else and if are separated for readability and to better understand logical flow.
-                    if set(filter_terms).intersection(set(nltk_tokenize.word_tokenize(abstract))):
-                        pmid_abs[pmid] = abstract
-            abstracts_bar.progress(min(round(i*percent_by_step,1),1.0))
-        abstracts_bar.empty()
-    found = len(pmids)
-    relevant = len(pmid_abs)
-    st.success('Found '+str(found)+' PMIDs. Gathered '+str(relevant)+' Relevant Abstracts. Classifying and extracting epidemiology information...')
-    
-    return pmid_abs, (found, relevant)
-
-## Section: LSTM RNN Epi Classification Model (EpiClassify4GARD)
-import os
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import tensorflow as tf
-import numpy as np
-import spacy
-
-class Classify_Pipeline:
-    def __init__(self,model:str='LSTM_RNN_Model'):
-        #Load spaCy models
-        self.nlp = spacy.load('en_core_web_lg')
-        self.nlpSci = spacy.load("en_ner_bc5cdr_md")
-        self.nlpSci2 = spacy.load('en_ner_bionlp13cg_md')
-        # load the tokenizer
-        with open('tokenizer.pickle', 'rb') as handle:
-            import pickle
-            self.classify_tokenizer = pickle.load(handle)
-        # Defaults to load my_model_orphanet_final, the most up-to-date version of the classification model,
-        # but can also be run on any other tf.keras model
-    
-        # load the model
-        self.classify_model = tf.keras.models.load_model(model)
-        # for preprocessing
-        from nltk.corpus import stopwords
-        self.STOPWORDS = set(stopwords.words('english'))
-        # Modes 
-        self.max_length = 300
-        self.trunc_type = 'post'
-        self.padding_type = 'post'
-
-    def __str__(self) -> str:
-        return "Instantiation: epi_classify = Classify_Pipeline({})".format(model) +"\n Calling: prob, isEpi = epi_classify(text) \n PubMed ID Predictions: abstracts, prob, isEpi = epi_classify.getPMIDPredictions(pmid)"
-    
-    def __call__(self, abstract:str) -> Tuple[float,bool]:
-        return self.getTextPredictions(abstract)
-    
-    def getTextPredictions(self, abstract:str) -> Tuple[float,bool]:
-        if len(abstract)>5:
-            # remove stopwords
-            for word in self.STOPWORDS:
-                token = ' ' + word + ' '
-                abstract = abstract.replace(token, ' ')
-                abstract = abstract.replace('  ', ' ')
-
-            # preprocess abstract
-            abstract_standard = [self.standardizeAbstract(self.standardizeSciTerms(abstract))]
-            sequence = self.classify_tokenizer.texts_to_sequences(abstract_standard)
-            padded = pad_sequences(sequence, maxlen=self.max_length, padding=self.padding_type, truncating=self.trunc_type)
-
-            y_pred1 = self.classify_model.predict(padded) # generate prediction
-            y_pred = np.argmax(y_pred1, axis=1) # get binary prediction
-
-            prob = y_pred1[0][1]
-            if y_pred == 1:
-                isEpi = True
-            else:
-                isEpi = False
-
-            return prob, isEpi
-        else:
-            return 0.0, False
-    
-    def getPMIDPredictions(self, pmid:Union[str,int]) -> Tuple[str,float,bool]:
-        abstract = PMID_getAb(pmid)
-        prob, isEpi = self.getTextPredictions(abstract)
-        return abstract, prob, isEpi
-    
-    # Standardize the abstract by replacing all named entities with their entity label.
-    # Eg. 3 patients reported at a clinic in England --> CARDINAL patients reported at a clinic in GPE
-    # expects the spaCy model en_core_web_lg as input
-    def standardizeAbstract(self, abstract:str) -> str:
-        doc = self.nlp(abstract)
-        newAbstract = abstract
-        for e in reversed(doc.ents):
-            if e.label_ in {'PERCENT','CARDINAL','GPE','LOC','DATE','TIME','QUANTITY','ORDINAL'}:
-                start = e.start_char
-                end = start + len(e.text)
-                newAbstract = newAbstract[:start] + e.label_ + newAbstract[end:]
-        return newAbstract
-
-    # Same as above but replaces biomedical named entities from scispaCy models
-    # Expects as input en_ner_bc5cdr_md and en_ner_bionlp13cg_md
-    def standardizeSciTerms(self, abstract:str) -> str:
-        doc = self.nlpSci(abstract)
-        newAbstract = abstract
-        for e in reversed(doc.ents):
-            start = e.start_char
-            end = start + len(e.text)
-            newAbstract = newAbstract[:start] + e.label_ + newAbstract[end:]
-
-        doc = self.nlpSci2(newAbstract)
-        for e in reversed(doc.ents):
-            start = e.start_char
-            end = start + len(e.text)
-            newAbstract = newAbstract[:start] + e.label_ + newAbstract[end:]
-        return newAbstract
-        
-## Section: GARD SEARCH
-# can identify rare diseases in text using the GARD dictionary from neo4j
-# and map a GARD ID, name, or synonym to all of the related synonyms for searching APIs
-from nltk import tokenize as nltk_tokenize
-class GARD_Search:
-    def __init__(self):
-        import json, codecs
-        #These are opened locally so that garbage collection removes them from memory
-        with codecs.open('gard-id-name-synonyms.json', 'r', 'utf-8-sig') as f:
-            diseases = json.load(f)
-        from nltk.corpus import stopwords
-        STOPWORDS = set(stopwords.words('english'))
-        
-        #keys are going to be disease names, values are going to be the GARD ID, set up this way bc dictionaries are faster lookup than lists
-        GARD_dict = {}
-        #Find out what the length of the longest disease name sequence is, of all names and synonyms. This is used by get_diseases
-        max_length = -1
-        for entry in diseases:
-            if entry['name'] not in GARD_dict.keys():
-                s = entry['name'].lower().strip()
-                if s not in STOPWORDS and len(s)>5:
-                    GARD_dict[s] = entry['gard_id']
-                    #compare length
-                    max_length = max(max_length,len(s.split()))
-
-            if entry['synonyms']:
-                for synonym in entry['synonyms']:
-                    if synonym not in GARD_dict.keys():
-                        s = synonym.lower().strip()
-                        if s not in STOPWORDS and len(s)>5:
-                            GARD_dict[s] = entry['gard_id']
-                            max_length = max(max_length,len(s.split()))
-                            
-        self.GARD_dict = GARD_dict
-        self.max_length = max_length
-    
-    def __str__(self) -> str:
-        return '''Instantiation: rd_identify = GARD_Search()
-                  Calling: diseases, ids = rd_identify(text) 
-                  Autosearch: search_terms = rd_identify.autosearch(searchterm)
-               '''
-    
-    def __call__(self, sentence:str) -> Tuple[List[str], List[str]]:
-        return self.get_diseases(sentence)
-    
-    #Works much faster if broken down into sentences.
-    #compares every phrase in a sentence to see if it matches anything in the GARD dictionary of diseases.
-    def get_diseases(self, sentence:str) -> Tuple[List[str], List[str]]:   
-        tokens = [s.lower().strip() for s in nltk_tokenize.word_tokenize(sentence)]
-        diseases = []
-        ids = []
-        i=0
-        #Iterates through every word, builds string that is max_length or less to compare.
-        while i <len(tokens):
-            #Find out the length of the comparison string, either max_length or less. This brings algorithm from O(n^2) to O(n) time
-            compare_length = min(len(tokens)-i, self.max_length)
-
-            #Compares longest sequences first and goes down until there is a match
-            #print('(start compare_length)',compare_length)
-            while compare_length>0:
-                s = ' '.join(tokens[i:i+compare_length])
-                if s.lower() in self.GARD_dict.keys():
-                    diseases.append(s)
-                    ids.append(self.GARD_dict[s.lower()])
-                    #Need to skip over the next few indexes
-                    i+=compare_length-1
-                    break
-                else:
-                    compare_length-=1
-            i+=1
-        return diseases,ids
-    
-    #Can search by 7-digit GARD_ID, 12-digit "GARD:{GARD_ID}", matched search term, or arbitrary search term
-    #Returns list of terms to search by
-    # search_term_list = autosearch(search_term, GARD_dict)
-    def autosearch(self, searchterm:Union[str,int], matching=2) -> List[str]:
-        #comparisons below only handly strings, allows int input
-        if type(searchterm) is not str:
-            searchterm = str(searchterm)
-
-        #for the disease names to match
-        searchterm = searchterm.lower()
-
-        while matching>=1:
-            #search in form of 'GARD:0000001'
-            if 'gard:' in searchterm and len(searchterm)==12:
-                searchterm = searchterm.replace('gard:','GARD:')
-                l = [k for k,v in self.GARD_dict.items() if v==searchterm]
-                l.sort(reverse=True, key=lambda x:len(x))
-                if len(l)>0:
-                    print("SEARCH TERM MATCHED TO GARD DICTIONARY. SEARCHING FOR: ",l)
-                    return l
-
-            #can take int or str of digits of variable input
-            #search in form of 777 or '777' or '00777' or '0000777'
-            elif searchterm[0].isdigit() and searchterm[-1].isdigit():
-                if len(searchterm)>7:
-                    raise ValueError('GARD ID IS NOT VALID. RE-ENTER SEARCH TERM')
-                searchterm = 'GARD:'+'0'*(7-len(str(searchterm)))+str(searchterm)
-                l = [k for k,v in self.GARD_dict.items() if v==searchterm]
-                l.sort(reverse=True, key=lambda x:len(x))
-                if len(l)>0:
-                    print("SEARCH TERM MATCHED TO GARD DICTIONARY. SEARCHING FOR: ",l)
-                    return l
-
-            #search in form of 'mackay shek carr syndrome' and returns all synonyms ('retinal degeneration with nanophthalmos, cystic macular degeneration, and angle closure glaucoma', 'retinal degeneration, nanophthalmos, glaucoma', 'mackay shek carr syndrome')
-            #considers the GARD ID as the lemma, and the search term as one form. maps the form to the lemma and then uses that lemma to find all related forms in the GARD dict. 
-            elif searchterm in self.GARD_dict.keys():
-                l = [k for k,v in self.GARD_dict.items() if v==self.GARD_dict[searchterm]]
-                l.sort(reverse=True, key=lambda x:len(x))
-                print("SEARCH TERM MATCHED TO GARD DICTIONARY. SEARCHING FOR: ",l)
-                return l
-
-            else:
-                #This can be replaced with some other common error in user input that is easily fixed
-                searchterm = searchterm.replace('-',' ')
-                searchterm = searchterm.replace("'s","")
-                return self.autosearch(searchterm, matching-1)
-        print("SEARCH TERM DID NOT MATCH TO GARD DICTIONARY. SEARCHING BY USER INPUT")
-        return [searchterm]
-
-## Section: BioBERT-based epidemiology NER Model (EpiExtract4GARD)
-from nltk import tokenize as nltk_tokenize
-from dataclasses import dataclass
-from torch.utils.data.dataset import Dataset
-from torch import nn
-import numpy as np
-from unidecode import unidecode
-import re
-from transformers import BertConfig, AutoModelForTokenClassification, BertTokenizer, Trainer
-from unidecode import unidecode
-from collections import OrderedDict
-from more_itertools import pairwise
-import json
-import pandas as pd
-
-# Subsection: Processing the abstracts into the correct data format
-@dataclass
-class NERInput:
-    """
-    A single training/test example for token classification.
-
-    Args:
-        guid: Unique id for the example.
-        words: list. The words of the sequence.
-        labels: (Optional) list. The labels for each word of the sequence. This should be
-        specified for train and dev examples, but not for test examples.
-    """
-    guid: str
-    words: List[str]
-    labels: Optional[List[str]]
-
-        
-@dataclass
-class InputFeatures:
-    """
-    A single set of features of data.
-    Property names are the same names as the corresponding inputs to a model.
-    """
-    input_ids: List[int]
-    attention_mask: List[int]
-    token_type_ids: Optional[List[int]] = None
-    label_ids: Optional[List[int]] = None
-        
-class NerDataset(Dataset):
-    features: List[InputFeatures]
-    pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index
-    # Use cross entropy ignore_index as padding label id so that only
-    # real label ids contribute to the loss later.
-
-    def __init__(
-        self,
-        abstract: str,
-        tokenizer: BertTokenizer,
-        config: BertConfig,
-    ):
-        # TODO clean up all this to leverage built-in features of tokenizers
-        ner_inputs = self.abstract2NERinputs(abstract)
-        
-        self.features = self.convert_NERinputs_to_features(
-            ner_inputs,
-            config,
-            tokenizer,
-            cls_token_at_end=bool(config.model_type in ["xlnet"]),
-            # xlnet has a cls token at the end
-            cls_token=tokenizer.cls_token,
-            cls_token_segment_id=2 if config.model_type in ["xlnet"] else 0,
-            sep_token=tokenizer.sep_token,
-            sep_token_extra=False,
-            # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
-            pad_on_left=bool(tokenizer.padding_side == "left"),
-            pad_token_segment_id=tokenizer.pad_token_type_id,
-            pad_token_label_id=self.pad_token_label_id,
-        )
-        self.ner_inputs = ner_inputs
-    
-    def __len__(self):
-        return len(self.features)
-
-    def __getitem__(self, i) -> InputFeatures:
-        return self.features[i]
-    
-    #Preprocessing function, turns abstracts into sentences
-    def str2sents(self, string:str) -> List[str]:
-        superscripts = re.findall('<sup>.</sup>', string)
-        for i in range(len(superscripts)):
-            string = re.sub('<sup>.</sup>', '^'+superscripts[i][5], string)
-        string = re.sub("<.{1,4}>|  *|  ", " ", string)
-        string = re.sub("^ |$|™|®|•|…", "" , string)
-        string = re.sub("♀", "female" , string)
-        string = re.sub("♂", "male" , string)
-        string = unidecode(string)
-        string = string.strip()
-        sentences = nltk_tokenize.sent_tokenize(string)
-        return sentences
-    
-    
-    def abstract2NERinputs(self, abstract:str) -> List[NERInput]:
-        guid_index = 0
-        sentences = self.str2sents(abstract)
-        ner_inputs = [NERInput(str(guid), nltk_tokenize.word_tokenize(sent), ["O" for i in range(len(nltk_tokenize.word_tokenize(sent)))]) for guid, sent in enumerate(sentences)]
-        return ner_inputs
-    
-    def convert_NERinputs_to_features(self, 
-        ner_inputs: List[NERInput],
-        model_config: BertConfig,
-        bert_tokenizer: BertTokenizer,
-        cls_token_at_end=False,
-        cls_token="[CLS]",
-        cls_token_segment_id=1,
-        sep_token="[SEP]",
-        sep_token_extra=False,
-        pad_on_left=False,
-        pad_token_segment_id=0,
-        pad_token_label_id=-100,
-        sequence_a_segment_id=0,
-        mask_padding_with_zero=True,
-    ) -> List[InputFeatures]:
-
-        label2id = model_config.label2id
-        pad_token = model_config.pad_token_id
-        max_seq_length = model_config.max_position_embeddings
-
-        features = []
-
-        for (input_index, ner_input) in enumerate(ner_inputs):
-            tokens = []
-            label_ids = []
-            for word, label in zip(ner_input.words, ner_input.labels):
-                word_tokens = bert_tokenizer.tokenize(word)
-
-                # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
-                if len(word_tokens) > 0:
-                    tokens.extend(word_tokens)
-                    # Use the real label id for the first token of the word, and padding ids for the remaining tokens
-                    label_ids.extend([label2id[label]] + [pad_token_label_id] * (len(word_tokens) - 1))
-
-            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
-            special_tokens_count = bert_tokenizer.num_special_tokens_to_add()
-            if len(tokens) > max_seq_length - special_tokens_count:
-                tokens = tokens[: (max_seq_length - special_tokens_count)]
-                label_ids = label_ids[: (max_seq_length - special_tokens_count)]
-
-            # The convention in BERT is:
-            # (a) For sequence pairs:
-            #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
-            #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
-            # (b) For single sequences:
-            #  tokens:   [CLS] the dog is hairy . [SEP]
-            #  type_ids:   0   0   0   0  0     0   0
-            #
-            # Where "type_ids" are used to indicate whether this is the first
-            # sequence or the second sequence. The embedding vectors for `type=0` and
-            # `type=1` were learned during pre-training and are added to the wordpiece
-            # embedding vector (and position vector). This is not *strictly* necessary
-            # since the [SEP] token unambiguously separates the sequences, but it makes
-            # it easier for the model to learn the concept of sequences.
-            #
-            # For classification tasks, the first vector (corresponding to [CLS]) is
-            # used as as the "sentence vector". Note that this only makes sense because
-            # the entire model is fine-tuned.
-            tokens += [sep_token]
-            label_ids += [pad_token_label_id]
-            if sep_token_extra:
-                # roberta uses an extra separator b/w pairs of sentences
-                tokens += [sep_token]
-                label_ids += [pad_token_label_id]
-            segment_ids = [sequence_a_segment_id] * len(tokens)
-
-            if cls_token_at_end:
-                tokens += [cls_token]
-                label_ids += [pad_token_label_id]
-                segment_ids += [cls_token_segment_id]
-            else:
-                tokens = [cls_token] + tokens
-                label_ids = [pad_token_label_id] + label_ids
-                segment_ids = [cls_token_segment_id] + segment_ids
-
-            input_ids = bert_tokenizer.convert_tokens_to_ids(tokens)
-
-            # The mask has 1 for real tokens and 0 for padding tokens. Only real
-            # tokens are attended to.
-            input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
-
-            # Zero-pad up to the sequence length.
-            padding_length = max_seq_length - len(input_ids)
-            if pad_on_left:
-                input_ids = ([pad_token] * padding_length) + input_ids
-                input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
-                segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
-                label_ids = ([pad_token_label_id] * padding_length) + label_ids
-            else:
-                input_ids += [pad_token] * padding_length
-                input_mask += [0 if mask_padding_with_zero else 1] * padding_length
-                segment_ids += [pad_token_segment_id] * padding_length
-                label_ids += [pad_token_label_id] * padding_length
-
-            assert len(input_ids) == max_seq_length
-            assert len(input_mask) == max_seq_length
-            assert len(segment_ids) == max_seq_length
-            assert len(label_ids) == max_seq_length
-
-            if "token_type_ids" not in bert_tokenizer.model_input_names:
-                segment_ids = None
-
-            features.append(
-                InputFeatures(
-                    input_ids=input_ids, attention_mask=input_mask, token_type_ids=segment_ids, label_ids=label_ids
-                )
-            )
-        return features
-
-# Subsection: Actual NER Pipeline
-class NER_Pipeline:
-    def __init__(self, name_or_path_to_model_folder:str = "ncats/EpiExtract4GARD-v2"):
-        self.bert_tokenizer = BertTokenizer.from_pretrained(name_or_path_to_model_folder)
-        #model = AutoModelForTokenClassification.from_pretrained(name_or_path_to_model_folder)
-        self.config = BertConfig.from_pretrained(name_or_path_to_model_folder)
-        self.labels = {re.sub(".-","",label) for label in self.config.label2id.keys() if label != "O"}
-        self.trainer = Trainer(model=AutoModelForTokenClassification.from_pretrained(name_or_path_to_model_folder))
-    
-    def __str__(self):
-        return "Instantiation: pipe = NER_Pipeline({})".format(name_or_path_to_model_folder) +"\n Calling: output_dict = pipe(text)"
-    
-    def __call__(self, text:str, rd_identify:Union[GARD_Search,None] = None):
-        output_dict = {label:[] for label in self.labels}
-        
-        dataset = NerDataset(text, self.bert_tokenizer, self.config)
-        predictions, label_ids, _ = self.trainer.predict(dataset)
-        preds_list, _ = self.align_predictions(predictions, label_ids)
-        #dataset.ner_inputs.labels = preds_list
-        for ner_input, sent_pred_list in zip(dataset.ner_inputs, preds_list):
-            ner_input.labels = sent_pred_list
-        
-        for sentence in dataset.ner_inputs:
-            entity = []
-            for idx, (current, nxt) in enumerate(pairwise(sentence.labels)):    
-                #Main concatenation algorithm
-                '''
-                Accounts for all variations of 
-                current = ['O','B-Tag`','I-Tag`']
-                nxt = ["O","B-Tag`","I-Tag`","B-Tag``","I-Tag``"]
-                and accounts for the final case
-                '''
-                if current != "O":
-                    current_ib, current_tag = self.get_tag(current)
-                    if nxt =="O":
-                        #add word at idx
-                        entity.append(sentence.words[idx])
-                        output_dict[current_tag].append(' '.join(entity))
-                        entity.clear()
-                    else:
-                        nxt_ib, nxt_tag = self.get_tag(nxt)
-                        if nxt_tag == current_tag:
-                            if nxt_ib =="B":
-                                entity.append(sentence.words[idx])
-                                output_dict[current_tag].append(' '.join(entity))
-                                entity.clear()
-                            #Continued "I"
-                            else:
-                                entity.append(sentence.words[idx])
-                        else:
-                            entity.append(sentence.words[idx])
-                            output_dict[current_tag].append(' '.join(entity))
-                            entity.clear()
-                            
-                #last case
-                if idx==len(sentence.labels)-2 and nxt!="O":
-                    _, nxt_tag = self.get_tag(nxt)
-                    entity.append(sentence.words[idx+1])
-                    output_dict[nxt_tag].append(' '.join(entity))
-                    entity.clear()
-        
-        if 'DIS' not in output_dict.keys() and rd_identify:
-            output_dict['DIS'] = []
-            output_dict['IDS'] = []
-            for sentence in dataset.ner_inputs:
-                diseases,ids = rd_identify(' '.join(sentence.words))
-                output_dict['DIS']+=diseases
-                output_dict['IDS']+=ids
-
-        #Clean up Output Dict
-        for entity, output in output_dict.items():
-            if not output:
-                output_dict[entity] = None
-            elif entity !='STAT':
-                #remove duplicates from list but keep ordering instead of using sets
-                output = list(OrderedDict.fromkeys(output)) 
-                output_dict[entity] = output
-
-        if output_dict['EPI'] and output_dict['STAT']:
-            return output_dict
-    
-    def align_predictions(self, predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:
-        preds = np.argmax(predictions, axis=2)
-        batch_size, seq_len = preds.shape
-        out_label_list = [[] for _ in range(batch_size)]
-        preds_list = [[] for _ in range(batch_size)]
-        for i in range(batch_size):
-            for j in range(seq_len):
-                if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
-                    out_label_list[i].append(self.config.id2label[label_ids[i][j]])
-                    preds_list[i].append(self.config.id2label[preds[i][j]])
-
-        return preds_list, out_label_list
-    
-    def get_tag(self, entity_name: str) -> Tuple[str, str]:
-        if entity_name.startswith("B-"):
-            bi = "B"
-            tag = entity_name[2:]
-        elif entity_name.startswith("I-"):
-            bi = "I"
-            tag = entity_name[2:]
-        else:
-            # It's not in B-, I- format
-            # Default to I- for continuation.
-            bi = "I"
-            tag = entity_name
-        return bi, tag
-
-    
-#This ensures that there is a standardized ordering of df columns while ensuring dynamics with multiple models. This is used by search_term_extraction.
-def order_labels(entity_classes:Union[Set[str],List[str]]) -> List[str]:
-    ordered_labels = []
-    label_order = ['DIS','ABRV','EPI','STAT','LOC','DATE','SEX','ETHN']
-    ordered_labels = [label for label in label_order if label in entity_classes]
-    #This adds any extra entities (from yet-to-be-created models) to the end of the ordered list of labels 
-    for entity in entity_classes:
-        if entity not in label_order:
-            ordered_labels.append(entity)
-    return ordered_labels
-
-# Given a search term and max results to return, this will acquire PubMed IDs and Title+Abstracts and Classify them as epidemiological.
-# It then extracts Epidemiologic Information[Disease GARD ID, Disease Name, Location, Epidemiologic Identifier, Epidemiologic Statistic] for each abstract
-# results = search_term_extraction(search_term, maxResults, filering, NER_pipeline, labels, extract_diseases, GARD_dict, max_length, classify_model_vars)
-#Returns a Pandas dataframe                                                                                                          
-def search_term_extraction(search_term:Union[int,str], maxResults:int, filtering:str, #for abstract search
-                           epi_ner:NER_Pipeline, #for biobert extraction 
-                           GARD_Search:GARD_Search, extract_diseases:bool, #for disease extraction
-                           epi_classify:Classify_Pipeline) -> pd.DataFrame: #for classification
-    
-    
-    #Format of Output
-    ordered_labels = order_labels(epi_ner.labels)
-    if extract_diseases:
-        columns = ['PMID', 'ABSTRACT','EPI_PROB','IsEpi','IDS','DIS']+ordered_labels
-    else:
-        columns = ['PMID', 'ABSTRACT','EPI_PROB','IsEpi']+ordered_labels
-    
-    results = pd.DataFrame(columns=columns)
-    
-    ##Check to see if search term maps to anything in the GARD dictionary, if so it pulls up all synonyms for the search
-    search_term_list = GARD_Search.autosearch(search_term)
-    
-    #Gather title+abstracts into a dictionary {pmid:abstract}
-    pmid_abs = search_getAbs(search_term_list, maxResults, filtering)
-    
-    for pmid, abstract in pmid_abs.items():
-        epi_prob, isEpi = epi_classify(abstract)
-        if isEpi:
-            if extract_diseases:
-                extraction = epi_ner(abstract, GARD_Search)
-            else:
-                extraction = epi_ner(abstract)
-                
-            if extraction:
-                extraction.update({'PMID':pmid, 'ABSTRACT':abstract, 'EPI_PROB':epi_prob, 'IsEpi':isEpi})
-                #Slow dataframe update
-                results = results.append(extraction, ignore_index=True)
-    
-    print(len(results),'abstracts classified as epidemiological.')
-    return results.sort_values('EPI_PROB', ascending=False)
-    
-#Returns a Pandas dataframe                                                                                                          
-def streamlit_extraction(search_term:Union[int,str], maxResults:int, filtering:str, #for abstract search
-                           epi_ner:NER_Pipeline, #for biobert extraction 
-                           GARD_Search:GARD_Search, extract_diseases:bool, #for disease extraction
-                           epi_classify:Classify_Pipeline) -> pd.DataFrame: #for classification
-   
-    #Format of Output
-    ordered_labels = order_labels(epi_ner.labels)
-    if extract_diseases:
-        columns = ['PMID', 'ABSTRACT','PROB_OF_EPI','IsEpi','IDS','DIS']+ordered_labels
-    else:
-        columns = ['PMID', 'ABSTRACT','PROB_OF_EPI','IsEpi']+ordered_labels
-    
-    results = pd.DataFrame(columns=columns)
-    
-    ##Check to see if search term maps to anything in the GARD dictionary, if so it pulls up all synonyms for the search
-    search_term_list = GARD_Search.autosearch(search_term)
-    if len(search_term_list)>1:
-        st.write("SEARCH TERM MATCHED TO GARD DICTIONARY. SEARCHING FOR: "+ str(search_term_list))
-    else:
-        st.write("SEARCHING FOR: "+ str(search_term_list))
-        
-    #Gather title+abstracts into a dictionary {pmid:abstract}
-    pmid_abs, sankey_initial = streamlit_getAbs(search_term_list, maxResults, filtering)
-    
-    if len(pmid_abs)==0:
-        st.error('No results were gathered. Enter a new search term.')
-        return None, None, None
-    else:
-        found, relevant = sankey_initial
-        epidemiologic = 0
-        i = 0
-        my_bar = st.progress(i)
-        percent_at_step = 100/len(pmid_abs)
-        for pmid, abstract in pmid_abs.items():
-            epi_prob, isEpi = epi_classify(abstract)
-            if isEpi:
-                if extract_diseases:
-                    extraction = epi_ner(abstract, GARD_Search)
-                else:
-                    extraction = epi_ner(abstract)
-                
-                if extraction:
-                    extraction.update({'PMID':pmid, 'ABSTRACT':abstract, 'PROB_OF_EPI':epi_prob, 'IsEpi':isEpi})
-                    #Slow dataframe update
-                    results = results.append(extraction, ignore_index=True)
-                    epidemiologic+=1
-            i+=1
-            my_bar.progress(min(round(i*percent_at_step/100,1),1.0))
-        
-        st.write(len(results),'abstracts classified as epidemiological.')
-        
-        sankey_data = (found, relevant, epidemiologic)
-        #Export the name and GARD ID to the ap for better integration on page.
-        name = search_term_list[-1].capitalize()
-        
-        if search_term_list[-1] in GARD_Search.GARD_dict.keys():
-            disease_gardID = (name, GARD_Search.GARD_dict[search_term_list[-1]])
-        else:
-            disease_gardID = (name, None)
-        
-        return results.sort_values('PROB_OF_EPI', ascending=False), sankey_data, disease_gardID
-
-#Identical to search_term_extraction, except it returns a JSON object instead of a df
-def API_extraction(search_term:Union[int,str], maxResults:int, filtering:str, #for abstract search
-                   epi_ner:NER_Pipeline, #for biobert extraction 
-                   GARD_Search:GARD_Search, extract_diseases:bool, #for disease extraction
-                   epi_classify:Classify_Pipeline) -> json: #for classification
-                                                                                                                                             
-    #Format of Output
-    ordered_labels = order_labels(epi_ner.labels)
-    if extract_diseases:
-        json_output = ['PMID', 'ABSTRACT','EPI_PROB','IsEpi','IDS','DIS']+ordered_labels
-    else:
-        json_output = ['PMID', 'ABSTRACT','EPI_PROB','IsEpi']+ordered_labels
-    
-    results = {'entries':[]} 
-    
-    ##Check to see if search term maps to anything in the GARD dictionary, if so it pulls up all synonyms for the search
-    search_term_list = GARD_Search.autosearch(search_term)
-    
-    #Gather title+abstracts into a dictionary {pmid:abstract}
-    pmid_abs = search_getAbs(search_term_list, maxResults, filtering)
-    
-    for pmid, abstract in pmid_abs.items():
-        epi_prob, isEpi = epi_classify(abstract)
-        if isEpi:
-            if extract_diseases:
-                extraction = epi_ner(abstract, GARD_Search)
-            else:
-                extraction = epi_ner(abstract)
-            if extraction:
-                extraction.update({'PMID':pmid, 'ABSTRACT':abstract, 'EPI_PROB':epi_prob})
-                extraction = OrderedDict([(term, extraction[term]) for term in json_output if term in extraction.keys()])
-                results['entries'].append(extraction)
-    
-    #sort 
-    results['entries'].sort(reverse=True, key=lambda x:x['EPI_PROB'])
-    
-    # float is not JSON serializable, so must convert all epi_probs to str
-    # This returns a map object, which is not JSON serializable
-    # results['entries'] = map(lambda entry:str(entry['EPI_PROB']),results['entries'])
-    
-    for entry in results['entries']:
-        entry['EPI_PROB'] = str(entry['EPI_PROB'])
-        
-    return json.dumps(results)
-    
-## Section: Deprecated Functions
-import requests
-import xml.etree.ElementTree as ET
-
-def search_Pubmed_API(searchterm_list:Union[List[str],str], maxResults:int) -> Dict[str,str]: #returns a dictionary of {pmids:abstracts} 
-    print('search_Pubmed_API is DEPRECATED. UTILIZE search_NCBI_API for NCBI ENTREZ API results. Utilize search_getAbs for most comprehensive results.')
-    return search_NCBI_API(searchterm_list, maxResults)
-
-def search_NCBI_API(searchterm_list:Union[List[str],str], maxResults:int) -> Dict[str,str]: #returns a dictionary of {pmids:abstracts} 
-    print('search_NCBI_API is DEPRECATED. Utilize search_getAbs for most comprehensive results.')
-    pmid_to_abs = {}
-    i = 0
-    
-    #type validation, allows string or list input
-    if type(searchterm_list)!=list:
-        if type(searchterm_list)==str:
-            searchterm_list = [searchterm_list]
-        else:
-            searchterm_list = list(searchterm_list)
-    
-    #gathers pmids into a set first
-    for dz in searchterm_list:
-        # get results from searching for disease name through PubMed API
-        term = ''
-        dz_words = dz.split()
-        for word in dz_words:
-            term += word + '%20'
-        query = term[:-3]
-        url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term='+query
-        r = requests.get(url)
-        root = ET.fromstring(r.content)    
-
-        # loop over resulting articles
-        for result in root.iter('IdList'):
-            pmids = [pmid.text for pmid in result.iter('Id')]
-            if i >= maxResults:
-                break
-            for pmid in pmids:
-                if pmid not in pmid_to_abs.keys():
-                    abstract = PMID_getAb(pmid)
-                    if len(abstract)>5:
-                        pmid_to_abs[pmid]=abstract
-                        i+=1
-                    
-    return pmid_to_abs
-
-def search_EBI_API(searchterm_list:Union[List[str],str], maxResults:int) -> Dict[str,str]: #returns a dictionary of {pmids:abstracts}    
-    print('DEPRECATED. Utilize search_getAbs for most comprehensive results.')
-    pmids_abs = {}
-    i = 0
-    
-    #type validation, allows string or list input
-    if type(searchterm_list)!=list:
-        if type(searchterm_list)==str:
-            searchterm_list = [searchterm_list]
-        else:
-            searchterm_list = list(searchterm_list)
-    
-    #gathers pmids into a set first
-    for dz in searchterm_list:
-        if i >= maxResults:
-            break
-        term = ''
-        dz_words = dz.split()
-        for word in dz_words:
-            term += word + '%20'
-        query = term[:-3]
-        url = 'https://www.ebi.ac.uk/europepmc/webservices/rest/search?query='+query+'&resulttype=core'
-        r = requests.get(url)
-        root = ET.fromstring(r.content)
-
-        # loop over resulting articles
-        for result in root.iter('result'):
-            if i >= maxResults:
-                break
-            pmids = [pmid.text for pmid in result.iter('id')]
-            if len(pmids) > 0:
-                pmid = pmids[0]
-                if pmid[0].isdigit():
-                    abstracts = [abstract.text for abstract in result.iter('abstractText')]
-                    titles = [title.text for title in result.iter('title')]
-                    if len(abstracts) > 0:# and len(abstracts[0])>5:
-                        pmids_abs[pmid] = titles[0]+' '+abstracts[0]
-                        i+=1
-    
-    return pmids_abs