Update app.py

app.py

@@ -4,7 +4,7 @@ import tokenizers
 import streamlit as st
 
 
-@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None, tokenizers.AddedToken: lambda _: None}, suppress_st_warning=True)
+@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None, tokenizers.AddedToken: lambda _: None, _regex.Pattern: lambda _: None}, suppress_st_warning=True)
 def get_model(model_name, model_path):
     tokenizer = transformers.GPT2Tokenizer.from_pretrained(model_name)
     model = transformers.GPT2LMHeadModel.from_pretrained(model_name)
@@ -13,7 +13,7 @@ def get_model(model_name, model_path):
     return model, tokenizer
 
 
-@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None, tokenizers.AddedToken: lambda _: None}, suppress_st_warning=True)
+@st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None, tokenizers.AddedToken: lambda _: None, _regex.Pattern: lambda _: None}, suppress_st_warning=True)
 def predict(text, model, tokenizer, n_beams=5, temperature=2.5, top_p=0.8, max_length=300):
     input_ids = tokenizer.encode(text, return_tensors="pt")
     with torch.no_grad():