add st.cache

app.py

@@ -7,27 +7,37 @@ import sys
 
 HISTORY_WEIGHT = 100 # set history weight (if found any keyword from history, it will priorities based on its weight)
 
-@st.cache(allow_output_mutation=True)
+@st.cache(allow_output_mutation=True, suppress_st_warning=True)
 def get_model(model):
 	return pipeline("fill-mask", model=model, top_k=10)#set the maximum of tokens to be retrieved after each inference to model
 
-@st.cache(allow_output_mutation=True)
+def hash_func(inp):
+    return True
+
+@st.cache(allow_output_mutation=True, suppress_st_warning=True)
 def loading_models(model='roberta-base'):
      return get_model(model), SentenceTransformer('all-MiniLM-L6-v2')
 
+@st.cache(allow_output_mutation=True, 
+          suppress_st_warning=True,
+          hash_funcs={'tokenizers.Tokenizer': hash_func, 'tokenizers.AddedToken': hash_func})
 def infer(text):
     global nlp
     return nlp(text+' '+nlp.tokenizer.mask_token)
 
+
+@st.cache(allow_output_mutation=True, 
+          suppress_st_warning=True,
+          hash_funcs={'tokenizers.Tokenizer': hash_func, 'tokenizers.AddedToken': hash_func})
 def sim(predicted_seq, sem_list):
     return semantic_model.encode(predicted_seq, convert_to_tensor=True), \
             semantic_model.encode(sem_list, convert_to_tensor=True)
     
-def hash_func(inp):
-    #bypass hash function
-    return True
+
     
-@st.cache(allow_output_mutation=True, hash_funcs={'tokenizers.Tokenizer': hash_func, 'tokenizers.AddedToken': hash_func})
+@st.cache(allow_output_mutation=True, 
+          suppress_st_warning=True,
+          hash_funcs={'tokenizers.Tokenizer': hash_func, 'tokenizers.AddedToken': hash_func})
 def main(text,semantic_text,history_keyword_text):
     global semantic_model, data_load_state
     data_load_state.text('Inference from model...')