Update app.py

app.py

@@ -5,6 +5,26 @@ 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()
 
@@ -15,11 +35,23 @@ GARD_dict, max_length = extract_abs.load_GARD_diseases()
 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, key=None, help=None, on_change=None, args=None, kwargs=None, *, disabled=False)
+# https://docs.streamlit.io/library/api-reference/widgets/st.checkbox
 
 #filtering options are 'strict','lenient'(default), 'none'
 if text:
-  out = extract_abs.search_term_extraction(term, max_results, filtering,
-                                              NER_pipeline, entity_classes, 
-                                              extract_diseases,GARD_dict, max_length, 
-                                              classify_model_vars)
-  st.write(out)
+  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)