Update app.py

app.py

@@ -9,46 +9,14 @@ 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!')
-'''
+st.subheader("National Center for Advancing Translational Sciences (NIH/NCATS)") 
 
 #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()
@@ -58,24 +26,8 @@ with st.spinner('Loading Epidemiology Models and Dependencies...'):
     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!')
-'''
+GARD_Disease_Id = st.text_input("Input a rare disease term or GARD ID.", value="Fellman syndrome")
 
-#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,