Update app.py

app.py

@@ -64,34 +64,36 @@ model, tokenizer = load_model()
 
 
 st.markdown("### Угадыватель")
-# 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
 
-title = st.text_area("Title HERE")
-abstract = st.text_area("Abstract HERE")
+
+title = st.text_area("Title здесь")
+abstract = st.text_area("Abstract здесь")
 ans = None
 
 
 if st.button('Предположить'):
-    text = title + " : " + abstract
-    inputs = tokenizer.encode_plus(
-                text,
-                None,
-                add_special_tokens=True,
-                max_length=256,
-                pad_to_max_length=True,
-                return_token_type_ids=True
-            )
-    
-    
-    ids = torch.Tensor(inputs['input_ids']).long()
-    mask = torch.Tensor(inputs['attention_mask']).long()
-    token_type_ids = torch.Tensor(inputs['token_type_ids']).long()
-    
-    ans = model(ids.unsqueeze(0), mask.unsqueeze(0), token_type_ids.unsqueeze(0))
-    idx = torch.nn.functional.softmax(ans[0], dim=0).argmax().item()
-    print('ANSLEN', ans.shape)
-    st.markdown(f'{idx_to_tag[idx]}')
+    if len(title) == 0 or len(abstract) == 0:
+        st.write("Вы ничего не ввели =(")
+    else:
+        text = title + " : " + abstract
+        inputs = tokenizer.encode_plus(
+                    text,
+                    None,
+                    add_special_tokens=True,
+                    max_length=256,
+                    pad_to_max_length=True,
+                    return_token_type_ids=True
+                )
+        
+        
+        ids = torch.Tensor(inputs['input_ids']).long()
+        mask = torch.Tensor(inputs['attention_mask']).long()
+        token_type_ids = torch.Tensor(inputs['token_type_ids']).long()
+        
+        ans = model(ids.unsqueeze(0), mask.unsqueeze(0), token_type_ids.unsqueeze(0))
+        idx = torch.nn.functional.softmax(ans[0], dim=0).argmax().item()
+        print('ANSLEN', ans.shape)
+        st.markdown(f'{idx_to_tag[idx]}')
 
 if st.button("Посмотреть топ"):
     if not ans:
@@ -112,26 +114,29 @@ if st.button("Посмотреть топ"):
         token_type_ids = torch.Tensor(inputs['token_type_ids']).long()
         
         ans = model(ids.unsqueeze(0), mask.unsqueeze(0), token_type_ids.unsqueeze(0))
-    elems = [el.item() for el in ans[0].argsort(descending=True)]
-    probs = ans[0].softmax(dim=0).detach().numpy()
-    str_ans = ''
-    current_prob = 0
-    current_elems = []
-    current_probs = []
-    idx = 0
-    
-    while current_prob < 0.95 and idx < len(elems):
-        current_elems.append(idx_to_tag[elems[idx]])
-        current_probs.append(probs[elems[idx]])
-        current_prob += probs[elems[idx]]
-        idx += 1
+    if len(title) == 0 or len(abstract) == 0:
+        st.write("Вы ничего не ввели =(")
+    else:
+        elems = [el.item() for el in ans[0].argsort(descending=True)]
+        probs = ans[0].softmax(dim=0).detach().numpy()
+        str_ans = ''
+        current_prob = 0
+        current_elems = []
+        current_probs = []
+        idx = 0
+        
+        while current_prob < 0.95 and idx < len(elems):
+            current_elems.append(idx_to_tag[elems[idx]])
+            current_probs.append(probs[elems[idx]])
+            current_prob += probs[elems[idx]]
+            idx += 1
+            
         
     
-
-    st.write(pd.DataFrame({
-    'Направление': current_elems,
-    'Вероятность': current_probs,
-    }))
+        st.write(pd.DataFrame({
+        'Направление': current_elems,
+        'Вероятность': current_probs,
+        }))