app.py

import pandas as pd
#classify_abs is a dependency for extract_abs
import classify_abs
import extract_abs
#pd.set_option('display.max_colwidth', None)
import streamlit as st

#LSTM RNN Epi Classifier Model
classify_model_vars = classify_abs.init_classify_model()

#GARD Dictionary - For filtering and exact match disease/GARD ID identification
GARD_dict, max_length = extract_abs.load_GARD_diseases()

#BioBERT-based NER pipeline, open `entities` to see 
NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()

#max_results is Maximum number of PubMed ID's to retrieve BEFORE filtering

#filtering options are 'strict','lenient'(default), 'none'
if text:
  out = extract_abs.search_term_extraction(term, max_results, filtering,
                                              NER_pipeline, entity_classes, 
                                              extract_diseases,GARD_dict, max_length, 
                                              classify_model_vars)
  st.write(out)