Added a Model file

Model.py

@@ -0,0 +1,27 @@
+import torch
+from torch import nn
+
+# Neural Network
+class LeNet(nn.Module):
+    def __init__(self):
+        super(LeNet, self).__init__()
+        
+        self.convs = nn.Sequential(
+            nn.Conv2d(in_channels=1, out_channels=4, kernel_size=(5, 5)),   
+            nn.Tanh(),                                                      
+            nn.AvgPool2d(2, 2),                                            
+
+            nn.Conv2d(in_channels=4, out_channels=12, kernel_size=(5, 5)), 
+            nn.Tanh(),
+            nn.AvgPool2d(2, 2) 
+        )
+
+        self.linear = nn.Sequential(
+            nn.Linear(4*4*12,10)
+        )
+    
+    def forward(self, x):
+        x = self.convs(x)
+        x = torch.flatten(x, 1)
+
+        return self.linear(x)

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import torch
-from torch import nn
+from Model import LeNet
 
 labels = ['Zero','Um','Dois','Três','Quatro','Cinco','Seis','Sete','Oito', 'Nove']
 
@@ -13,30 +13,6 @@ else:
     print("CPU")
 
 
-# Neural Network
-class LeNet(nn.Module):
-    def __init__(self):
-        super(LeNet, self).__init__()
-        
-        self.convs = nn.Sequential(
-            nn.Conv2d(in_channels=1, out_channels=4, kernel_size=(5, 5)),   
-            nn.Tanh(),                                                      
-            nn.AvgPool2d(2, 2),                                            
-
-            nn.Conv2d(in_channels=4, out_channels=12, kernel_size=(5, 5)), 
-            nn.Tanh(),
-            nn.AvgPool2d(2, 2) 
-        )
-
-        self.linear = nn.Sequential(
-            nn.Linear(4*4*12,10)
-        )
-    
-    def forward(self, x):
-        x = self.convs(x)
-        x = torch.flatten(x, 1)
-
-        return self.linear(x)
 
 # Loading model
 model = LeNet().to(device)