Update app.py

app.py

@@ -54,7 +54,7 @@ class CNN(nn.Module):
         super(CNN, self).__init__()
         self.conv = nn.Conv2d(input_channels, 16, kernel_size=3, stride=1, padding=1)
         self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-        # Adjust the fully connected layer to accommodate the correct input size
+        # CNN
         self.fc = nn.Linear(16 * 4 * 8, output_dim)  # 16 * 4 * 8 = 512
 
     def forward(self, x):
@@ -80,7 +80,7 @@ nn_model = NN(128, 64, 32)
 cnn_model = CNN(1, 32)
 phi_model = PhiModel(128)
 
-dummy_input = torch.rand(1, 128)  # This is the input tensor for processing
+dummy_input = torch.rand(1, 128)  #input tensor for processing
 
 # Consciousness processing function
 def iit_consciousness_processing(dummy_input):
@@ -91,8 +91,8 @@ def iit_consciousness_processing(dummy_input):
     rnn_output = rnn_model(flat_input.unsqueeze(1))  # Reshape to match RNN input
     nn_output = nn_model(flat_input)
     
-    # Update CNN input shape to ensure it's correct
-    cnn_input = dummy_input.view(1, 1, 8, 16)  # Change this to match CNN input size
+    # Update CNN input shape
+    cnn_input = dummy_input.view(1, 1, 8, 16)  #to match CNN input size
     cnn_output = cnn_model(cnn_input)
     
     phi_output = phi_model(flat_input)