Update app.py

app.py

@@ -1,6 +1,16 @@
 import streamlit as st
 import torch
 
+@st.cache
+def Model():
+  from transformers import BertTokenizer, BertForSequenceClassification
+  model_name = "google/bert_uncased_L-4_H-256_A-4"
+  tokenizer = BertTokenizer.from_pretrained(model_name)
+  model = BertForSequenceClassification.from_pretrained(model_name, num_labels=8)
+  bn_state_dict = torch.load('model_w.pt')
+  model.load_state_dict(bn_state_dict)
+  return model
+ 
 st.markdown("### Hello, world!")
 st.markdown("<img width=200px src='https://rozetked.me/images/uploads/dwoilp3BVjlE.jpg'>", unsafe_allow_html=True)
 # ^-- можно показывать пользователю текст, картинки, ограниченное подмножество html - всё как в jupyter
@@ -15,11 +25,6 @@ raw_predictions = pipe(text)
 
 st.markdown(f"{raw_predictions}")
 
-from transformers import BertTokenizer, BertForSequenceClassification
-model_name = "google/bert_uncased_L-4_H-256_A-4"
-tokenizer = BertTokenizer.from_pretrained(model_name)
-model = BertForSequenceClassification.from_pretrained(model_name, num_labels=8)
-bn_state_dict = torch.load('model_w.pt')
-model.load_state_dict(bn_state_dict)
+model = Model()
 st.markdown(f"{model}")
 # выводим результаты модели в текстовое поле, на потеху пользователю