Update app.py

app.py

@@ -8,7 +8,7 @@ import extract_abs
 import streamlit as st
 
 ########## Title for the Web App ##########
-st.title("Epidemiology Extraction Pipeline for Rare Diseases")
+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) 
@@ -16,41 +16,50 @@ st.title("Epidemiology Extraction Pipeline for Rare Diseases")
 
 # 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")
+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'))
+#filtering = st.sidebar.radio("What type of filtering would you like?",('Strict', 'Lenient', 'None'))
 
-extract_diseases = st.sidebar.checkbox("Extract Rare Diseases", value=False)
+#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!')
+#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!')
-
+#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!')
+#with st.spinner('Loading Epidemiology Extraction Model...'):
+#    NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()
+#st.success('Epidemiology Extraction Model Loaded!')
 
-#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
 
 #filtering options are 'strict','lenient'(default), 'none'
 if text:
@@ -59,4 +68,6 @@ if text:
                                            extract_diseases,GARD_dict, max_length, 
                                            classify_model_vars)
   st.dataframe(df)
-  #st.dataframe(data=None, width=None, height=None)
+  #st.dataframe(data=None, width=None, height=None)
+  
+# st.code(body, language="python")