update

app.py

@@ -29,40 +29,35 @@ def make_prediction(text):
   st.markdown("### Category prediction:")
   print_probs(pred_logits[0])
 
-
-@st.cache
-def model_init():
-  tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
-
-  model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=8)
-  model_name = "trained_model2"
-  model_path = model_name + '.zip'
-  model.load_state_dict(
-      torch.load(
-          model_path,
-          map_location=torch.device("cpu")
-      )
-  )
-  return tokenizer, model
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=8)
+model_name = "trained_model2"
+model_path = model_name + '.zip'
+model.load_state_dict(
+    torch.load(
+        model_path,
+        map_location=torch.device("cpu")
+    )
+)
 
 # MAIN
 from PIL import Image
 image = Image.open('logo.png')
 
 st.image(image)
-
-# st.markdown("<img src='https://centroderecursosmarista.org/wp-content/uploads/2013/05/arvix.jpg' class='center'>", unsafe_allow_html=True)
-# st.markdown("# Arxiv.org category classifier")
 st.markdown("# ")
-
 st.markdown("### Article Title")
+
 text1 = st.text_area("Введите название научной статьи для классификации", height=20)
 
 st.markdown("### Article Abstract")
 
 text2 = st.text_area("Введите описание статьи", height=200)
+
 common_text = text1 + text2
+
 if common_text != "":
-    tokenizer, model = model_init()
     make_prediction(common_text)
 
+