Update app.py

app.py

@@ -2,10 +2,11 @@ import streamlit as st
 import numpy as np
 import pandas as pd
 
+import tokenizers  # for streamlit caching
 import transformers
 from transformers import TextClassificationPipeline, AutoTokenizer, AutoModelForSequenceClassification   
 
-@st.cache(suppress_st_warning=True, hash_funcs={transformers.AutoTokenizer: lambda _: None})
+@st.cache(suppress_st_warning=True, hash_funcs={tokenizers.Tokenizer: lambda _: None})
 def load_tok_and_model(): 
     tokenizer = AutoTokenizer.from_pretrained('distilbert-base-cased')
     model = AutoModelForSequenceClassification.from_pretrained(".")
@@ -16,7 +17,7 @@ CATEGORIES = ["Computer Science", "Economics", "Electrical Engineering", "Mathem
  "Q. Biology", "Q. Finances", "Statistics" , "Physics"]
     
     
-@st.cache(suppress_st_warning=True, hash_funcs={transformers.AutoTokenizer: lambda _: None})  
+@st.cache(suppress_st_warning=True, hash_funcs={tokenizers.Tokenizer: lambda _: None})  
 def forward_pass(title, abstract, tokenizer, model):    
     title_tensor = torch.tensor(tokenizer(title, padding="max_length", truncation=True, max_length=32)['input_ids'])
     abstract_tensor = torch.tensor(tokenizer(abstract, padding="max_length", truncation=True, max_length=480)['input_ids'])