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("Epidemiology Extraction Pipeline for Rare Diseases by the National Center for Advancing Translational Sciences (NIH/NCATS)")

#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

col1, col2 = st.columns(2)

with col1:
    st.header("Rare ")
    disease_or_gard_id = st.text_input('Input a rare disease term or a GARD ID.', 'Fellman syndrome')
                                     
with col2:
    filtering = st.radio("What type of filtering would you like?",('Strict', 'Lenient', 'None'))
    extract_diseases = st.checkbox("Extract Rare Diseases", value=False)
    #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

with col1:
    with st.spinner('Loading Epidemiology Models and Dependencies...'):
        classify_model_vars = classify_abs.init_classify_model()
        st.success('Epidemiology Classification Model Loaded!')
        NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()
        st.success('Epidemiology Extraction Model Loaded!')
        GARD_dict, max_length = extract_abs.load_GARD_diseases()
    st.success('All Models and Dependencies Loaded!')

# 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

#LSTM RNN Epi Classifier Model
#with st.spinner('Loading Epidemiology Classification Model...'):
#    classify_model_vars = classify_abs.init_classify_model()
#st.success('Epidemiology Classification Model Loaded!')

#GARD Dictionary - For filtering and exact match disease/GARD ID identification
#with st.spinner('Loading GARD Rare Disease Dictionary...'):
#    GARD_dict, max_length = extract_abs.load_GARD_diseases()
#st.success('GARD Rare Disease Dictionary Loaded!')

#BioBERT-based NER pipeline, open `entities` to see 
#with st.spinner('Loading Epidemiology Extraction Model...'):
#    NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()
#st.success('Epidemiology Extraction Model Loaded!')


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