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")
st.subheader("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.number_input("Maximum number of articles to find in PubMed", 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!')
'''

#max_results is Maximum number of PubMed ID's to retrieve BEFORE filtering
    max_results = st.sidebar.number_input("Maximum number of articles to find in PubMed", 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

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!')

'''
#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.balloons()
  #st.dataframe(data=None, width=None, height=None)
  
# st.code(body, language="python")