Update app.py

app.py

@@ -41,7 +41,7 @@ filtering = st.sidebar.radio("What type of filtering would you like?",('Strict',
 
 extract_diseases = st.sidebar.checkbox("Extract Rare Diseases", value=False)
 
-@st.experimental_singleton
+@st.experimental_singleton(show_spinner=False)
 def load_models_experimental():
     classify_model_vars = classify_abs.init_classify_model()
     NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()
@@ -62,10 +62,6 @@ def load_models():
     GARD_dict, max_length = extract_abs.load_GARD_diseases()
     return classify_tokenizer, classify_model, NER_pipeline, entity_classes, GARD_dict, max_length
 
-def convert_df(df):
-     # IMPORTANT: Cache the conversion to prevent computation on every rerun
-     return df.to_csv().encode('utf-8')
-
 with st.spinner('Loading Epidemiology Models and Dependencies...'):
     classify_model_vars, NER_pipeline, entity_classes, GARD_dict, max_length = load_models_experimental()
     #classify_tokenizer, classify_model, NER_pipeline, entity_classes, GARD_dict, max_length = load_models()
@@ -88,7 +84,7 @@ if disease_or_gard_id:
   csv = convert_df(df)
   st.download_button(
      label="Download epidemiology results for "+disease_or_gard_id+" as CSV",
-     data=csv,
+     data=df.to_csv().encode('utf-8'),
      file_name=disease_or_gard_id+'.csv',
      mime='text/csv',
  )