app.py

import nltk
nltk.download('stopwords')
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

########## Title for the Web App ##########
st.title("Text Classification for Service Feedback")

#st.header(body, anchor=None)
#st.subheader(body, anchor=None) 
#Anchor is for the URL, can be custom str

# https://docs.streamlit.io/library/api-reference/text/st.markdown

########## Create Input field ##########
disease_or_gard_id = st.text_input('Input a rare disease term or a GARD ID.', 'Fellman syndrome')

# st.code(body, language="python")

#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
max_results = st.sidebar.number_input(label, min_value=1, max_value=None, value=50)
# https://docs.streamlit.io/library/api-reference/widgets/st.number_input

# st.radio(label, options, index=0, format_func=special_internal_function, key=None, help=None, on_change=None, args=None, kwargs=None, *, disabled=False)
# https://docs.streamlit.io/library/api-reference/widgets/st.radio
filtering = st.sidebar.radio(
     "What type of filtering would you like?",
     ('Strict', 'Lenient', 'None'))

extract_diseases = st.sidebar.checkbox("Extract Rare Diseases", value=False)
# https://docs.streamlit.io/library/api-reference/widgets/st.checkbox

#filtering options are 'strict','lenient'(default), 'none'
if text:
  df = extract_abs.search_term_extraction(disease_or_gard_id, max_results, filtering,
                                           NER_pipeline, entity_classes, 
                                           extract_diseases,GARD_dict, max_length, 
                                           classify_model_vars)
  st.dataframe(df)
  #st.dataframe(data=None, width=None, height=None)