Update app.py

app.py

@@ -39,27 +39,27 @@ def update_graph(input_size, hidden_size, output_size, input_color, hidden_color
     # Connections from input layer to hidden layer
     for i in range(input_size):
         for j in range(hidden_size):
-            G.add_edge(f'I{i}', f'H{j}', weight=np.random.rand())
+            G.add_edge(f'I{i+1}', f'H{j+1}', weight=np.random.rand())
 
     # Connections from hidden layer to output layer
     for j in range(hidden_size):
         for k in range(output_size):
-            G.add_edge(f'H{j}', f'O{k}', weight=np.random.rand())
+            G.add_edge(f'H{j+1}', f'O{k+1}', weight=np.random.rand())
 
     # Calculate neuron positions
     pos = {}
     
     # Input layer positions
     for i in range(input_size):
-        pos[f'I{i}'] = (0, 1 - (i / (input_size - 1)))  # Vertically aligned
+        pos[f'I{i+1}'] = (0, 1 - (i / (input_size - 1)))  # Vertically aligned
 
     # Hidden layer positions
     for i in range(hidden_size):
-        pos[f'H{i}'] = (1, 1 - (i / (hidden_size - 1)))  # Vertically aligned
+        pos[f'H{i+1}'] = (1, 1 - (i / (hidden_size - 1)))  # Vertically aligned
 
     # Output layer positions
     for i in range(output_size):
-        pos[f'O{i}'] = (2, 1 - (i / (output_size - 1)))  # Vertically aligned
+        pos[f'O{i+1}'] = (2, 1 - (i / (output_size - 1)))  # Vertically aligned
 
     # Visualize edge weights
     edges = G.edges(data=True)