Update app.py

app.py

@@ -15,7 +15,7 @@ title = st.text_area("TITLE HERE")
 abstract = st.text_area("ABSTRACT HERE")
 # ^-- показать текстовое поле. В поле text лежит строка, которая находится там в данный момент
 
-@st.cache_data
+@st.cache
 def load_model_and_tokenizer():    
     tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
     model = BertForSequenceClassification.from_pretrained(
@@ -34,22 +34,22 @@ input_ids = tokens['input_ids']
 attention_mask = tokens['attention_mask']
 logits = model(input_ids, attention_mask)[0]
 
-tags_names = ['acc-phys',
+tags_names = ['Accelerator Physics',
  'adap-org',
  "adap-org",
- 'alg-geom',
- 'astro-ph',
- "astro-ph",
- 'chao-dyn',
- 'chem-ph',
+ 'Algebra-Geometry',
+ 'Astro-physics',
+ "Astro-physics",
+ 'Chao-dynamics',
+ 'Chemistry-physics',
  'cmp-lg',
  "cmp-lg",
  'comp-gas',
  'cond-mat',
  "cond-mat",
- 'cs',
+ 'Computer Science',
  'dg-ga',
- 'econ',
+ 'Economics',
  'eess',
  'funct-an',
  'gr-qc',
@@ -62,7 +62,7 @@ tags_names = ['acc-phys',
  "hep-ph",
  'hep-th',
  "hep-th",
- 'math',
+ 'Math',
  'math-ph',
  'mtrl-th',
  'nlin',
@@ -70,14 +70,14 @@ tags_names = ['acc-phys',
  'nucl-th',
  "nucl-th",
  'patt-sol',
- 'physics',
+ 'Physics',
  'q-alg',
- 'q-bio',
+ 'Quantitie-biology',
  'q-fin',
  'quant-ph',
  "quant-ph",
  'solv-int',
- 'stat']
+ 'Statistics']
 
 if abstract:
     abstract_tokens = tokenizer(abstract, padding=True, truncation=True, return_tensors="pt")