Update app.py

app.py

@@ -39,22 +39,21 @@ extract_diseases = st.sidebar.checkbox("Extract Rare Diseases", value=False)
 @st.cache(suppress_st_warning=True)
 def load_models():
     global classify_model_vars, NER_pipeline, entity_classes, GARD_dict, max_length
-    
-    with st.spinner('Loading Epidemiology Models and Dependencies...'):
-        classify_model_vars = classify_abs.init_classify_model()
-        NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()
-        GARD_dict, max_length = extract_abs.load_GARD_diseases()
-    st.success('All Models and Dependencies Loaded!')
+    classify_model_vars = classify_abs.init_classify_model()
+    NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()
+    GARD_dict, max_length = extract_abs.load_GARD_diseases()
+        
+with st.spinner('Loading Epidemiology Models and Dependencies...'):
+    load_models()
+st.success('All Models and Dependencies Loaded!')
 
-load_models()
-
-disease_or_gard_id = st.text_input("Input a rare disease term or GARD ID.", value="Fellman syndrome")
+disease_or_gard_id = st.text_input("Input a rare disease term or GARD ID.")
 
 if disease_or_gard_id:
   df = extract_abs.streamlit_extraction(disease_or_gard_id, max_results, filtering,
-                                           NER_pipeline, entity_classes, 
-                                           extract_diseases,GARD_dict, max_length, 
-                                           classify_model_vars)
+                                        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)