Update app.py

app.py

@@ -9,28 +9,31 @@ labels_articles = {1: 'Computer Science', 2: 'Economics', 3: "Electrical Enginee
                    4: "Mathematics", 5: "Physics", 6: "Quantitative Biology", 7: "Quantitative Finance", 
                    8: "Statistics"
                   }
-
-class Net(nn.Module):
-    def __init__(self):
-        super(Net,self).__init__()
-        self.layer = nn.Sequential(
-            nn.Linear(768, 256),
-            nn.ReLU(),
-            nn.Linear(256, 128),
-            nn.ReLU(),
-            nn.Linear(128, 8),
-          )
-
-    def forward(self,x):
-        return self.layer(x)
+def models():
+    class Net(nn.Module):
+        def __init__(self):
+            super(Net,self).__init__()
+            self.layer = nn.Sequential(
+                nn.Linear(768, 256),
+                nn.ReLU(),
+                nn.Linear(256, 128),
+                nn.ReLU(),
+                nn.Linear(128, 8),
+              )
+    
+        def forward(self,x):
+            return self.layer(x)
+        
+    model_second = Net()
+    model_second.load_state_dict(torch.load('model.txt'))
+    model_second.eval()
     
-model_second = Net()
-model_second.load_state_dict(torch.load('model.txt'))
-model_second.eval()
+    model_name = 'bert-base-uncased'
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model_first = AutoModel.from_pretrained(model_name)
+    return (model_first, model_second, tokenizer)
 
-model_name = 'bert-base-uncased'
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model_first = AutoModel.from_pretrained(model_name)
+model_first, model_second, tokenizer = models()
 
 title = st.text_area("Write the title of your article, please")
 abstract = st.text_area("Write the abstract")