Update app.py

app.py

@@ -736,44 +736,42 @@ elif app_mode == "Model Training":
     model_name = st.selectbox("Select Model", model_options, help="Choose a model.")
 
     if model_name == "Gradient Boosting":
-                learning_rate = st.slider("Learning Rate", 0.01, 1.0, 0.1)
-                n_estimators = st.slider("Number of Estimators", 10, 200, 100)
-                max_depth = st.slider("Max Depth", 3, 20, 10)
-                hyperparams = {
-                    'learning_rate': learning_rate,
-                    'n_estimators': n_estimators,
-                    'max_depth': max_depth
-                }
-            elif model_name == "Neural Network":
-                hidden_layers = st.slider("Number of Hidden Layers", 1, 5, 2)
-                neurons_per_layer = st.slider("Neurons per Layer", 10, 200, 50)
-                activation = st.selectbox("Activation Function",
-                    ["relu", "tanh", "sigmoid", "selu", "swish"])
-                dropout_rate = st.slider("Dropout Rate", 0.0, 0.5, 0.2)
-                initializer = st.selectbox("Weight Initializer",
-                    ["glorot_uniform", "he_normal", "lecun_uniform"])
-                learning_rate = st.slider("Learning Rate", 0.0001, 0.1, 0.001, format="%.4f")
-                optimizer_choice = st.selectbox("Optimizer",
-                    ["Adam", "Nadam", "RMSprop", "SGD"])
-                batch_norm = st.checkbox("Batch Normalization", value=True)
-                regularization = st.checkbox("L2 Regularization")
-                epochs = st.slider("Epochs", 10, 200, 50)
-                batch_size = st.slider("Batch Size", 16, 128, 32)
-                hyperparams = {
-                    'hidden_layers': hidden_layers,
-                    'neurons_per_layer': neurons_per_layer,
-                    'activation': activation,
-                    'dropout_rate': dropout_rate,
-                    'initializer': initializer,
-                    'learning_rate': learning_rate,
-                    'optimizer_choice': optimizer_choice,
-                    'batch_norm': batch_norm,
-                    'regularization': regularization,
-                    'epochs': epochs,
-                    'batch_size': batch_size,
-                }
-            else:
-                hyperparams = {}
+    learning_rate = st.slider("Learning Rate", 0.01, 1.0, 0.1)
+    n_estimators = st.slider("Number of Estimators", 10, 200, 100)
+    max_depth = st.slider("Max Depth", 3, 20, 10)
+    hyperparams = {
+        'learning_rate': learning_rate,
+        'n_estimators': n_estimators,
+        'max_depth': max_depth
+    }
+        elif model_name == "Neural Network":
+            hidden_layers = st.slider("Number of Hidden Layers", 1, 5, 2)
+            neurons_per_layer = st.slider("Neurons per Layer", 10, 200, 50)
+            activation = st.selectbox("Activation Function", ["relu", "tanh", "sigmoid", "selu", "swish"])
+            dropout_rate = st.slider("Dropout Rate", 0.0, 0.5, 0.2)
+            initializer = st.selectbox("Weight Initializer", ["glorot_uniform", "he_normal", "lecun_uniform"])
+            learning_rate = st.slider("Learning Rate", 0.0001, 0.1, 0.001, format="%.4f")
+            optimizer_choice = st.selectbox("Optimizer", ["Adam", "Nadam", "RMSprop", "SGD"])
+            batch_norm = st.checkbox("Batch Normalization", value=True)
+            regularization = st.checkbox("L2 Regularization")
+            epochs = st.slider("Epochs", 10, 200, 50)
+            batch_size = st.slider("Batch Size", 16, 128, 32)
+            hyperparams = {
+                'hidden_layers': hidden_layers,
+                'neurons_per_layer': neurons_per_layer,
+                'activation': activation,
+                'dropout_rate': dropout_rate,
+                'initializer': initializer,
+                'learning_rate': learning_rate,
+                'optimizer_choice': optimizer_choice,
+                'batch_norm': batch_norm,
+                'regularization': regularization,
+                'epochs': epochs,
+                'batch_size': batch_size
+            }
+        
+                    else:
+                        hyperparams = {}
 
     # Train-Test Split
     st.subheader("✂️ Train-Test Split")