Update app.py

app.py

@@ -38,13 +38,27 @@ extract_diseases = st.sidebar.checkbox("Extract Rare Diseases", value=False)
 
 @st.cache
 def load_models():
-    classify_model_vars = classify_abs.init_classify_model()
+#Load spaCy models
+#    nlp = spacy.load('en_core_web_lg')
+    nlpSci = spacy.load("en_ner_bc5cdr_md")
+    nlpSci2 = spacy.load('en_ner_bionlp13cg_md')
+    
+    # load the tokenizer
+    with open('tokenizer.pickle', 'rb') as handle:
+        classify_tokenizer = pickle.load(handle)
+    
+    # load the model
+    classify_model = tf.keras.models.load_model(model) 
+    
+    #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
+    return nlpSci, nlpSci2, classify_tokenizer, classify_model, NER_pipeline, entity_classes, GARD_dict, max_length
     
 with st.spinner('Loading Epidemiology Models and Dependencies...'):
-    classify_model_vars, NER_pipeline, entity_classes, GARD_dict, max_length = load_models()
+    nlpSci, nlpSci2, classify_tokenizer, classify_model, NER_pipeline, entity_classes, GARD_dict, max_length = load_models()
+    nlp = spacy.load('en_core_web_lg')
+    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.")