Update pages/Deeplearning for NLP.py

pages/Deeplearning for NLP.py

@@ -80,14 +80,14 @@ Attention mechanisms allow models to focus on key parts of an input sequence, im
 # Transformer Example
 st.subheader('🛠️ Transformer Example (Simplified):')
 if st.button('🖥️ Show Transformer Architecture'):
-    transformer_model = keras.Sequential([
-        layers.InputLayer(input_shape=(None, 512)),
-        layers.MultiHeadAttention(num_heads=8, key_dim=512),
-        layers.GlobalAveragePooling1D(),
-        layers.Dense(256, activation="relu"),
-        layers.Dense(1)
-    ])
-    st.write("**Transformer Architecture (Simplified):**")
+    input_layer = keras.Input(shape=(None, 512))
+    attention_output = layers.MultiHeadAttention(num_heads=8, key_dim=512)(input_layer, input_layer)
+    pooled_output = layers.GlobalAveragePooling1D()(attention_output)
+    dense1 = layers.Dense(256, activation="relu")(pooled_output)
+    output_layer = layers.Dense(1)(dense1)
+    
+    transformer_model = keras.Model(inputs=input_layer, outputs=output_layer)
+    st.write("**Transformer Architecture (Fixed Version):**")
     st.write(transformer_model)
 
 # Section 4: Key Attention Components