EpiPipeline4RD / app.py
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Create app.py
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import pandas as pd
import classify_abs
#classify_abs is a dependency for extract_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.(out)