Update app.py

app.py

@@ -39,6 +39,13 @@ 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
+def load_models_experimental():
+    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()
+    return classify_model_vars, NER_pipeline, entity_classes, GARD_dict, max_length
+
 @st.cache(allow_output_mutation=True)
 def load_models():
     # load the tokenizer
@@ -54,12 +61,14 @@ def load_models():
     return classify_tokenizer, classify_model, NER_pipeline, entity_classes, GARD_dict, max_length
     
 with st.spinner('Loading Epidemiology Models and Dependencies...'):
-    classify_tokenizer, classify_model, NER_pipeline, entity_classes, GARD_dict, max_length = load_models()
+    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()
     #Load spaCy models which cannot be cached due to hash function error
-    nlp = spacy.load('en_core_web_lg')
-    nlpSci = spacy.load("en_ner_bc5cdr_md")
-    nlpSci2 = spacy.load('en_ner_bionlp13cg_md')
-    classify_model_vars = (nlp, nlpSci, nlpSci2, classify_model, classify_tokenizer)
+    #nlp = spacy.load('en_core_web_lg')
+    #nlpSci = spacy.load("en_ner_bc5cdr_md")
+    #nlpSci2 = spacy.load('en_ner_bionlp13cg_md')
+    #classify_model_vars = (nlp, nlpSci, nlpSci2, classify_model, classify_tokenizer)
 st.success('All Models and Dependencies Loaded!')
 
 disease_or_gard_id = st.text_input("Input a rare disease term or GARD ID.")