Update app.py

app.py

@@ -9,7 +9,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 from sklearn.utils import shuffle
 
-# Custom CSS
+# Custom CSS for sidebar
 st.markdown("""
     <style>
     .sidebar .css-12oz5g7 {
@@ -40,7 +40,7 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
-# Helper functions
+# Helper Functions
 def draw_neural_network(model):
     G = nx.DiGraph()
     pos = {}
@@ -127,23 +127,6 @@ def plot_learning_curves(history):
     plt.legend()
     st.pyplot(plt)
 
-st.title("Neural Network Playground")
-
-task_type = st.selectbox("Task Type", ["Classification", "Regression"])
-dataset = st.selectbox("Choose Dataset", ["Circles", "Exclusive OR", "Gaussian", "Spiral"])
-epochs = st.slider("Epochs", 1, 200, 50)
-
-col1, col2, col3 = st.columns(3)
-with col1:
-    learning_rate = st.slider("Learning Rate", 0.001, 1.0, 0.03, 0.001)
-with col2:
-    hidden_layers = st.slider("Hidden Layers", 1, 5, 3)
-    neuron_counts = [st.slider(f"Neurons in Layer {i+1}", 1, 20, 5) for i in range(hidden_layers)]
-with col3:
-    activation = st.selectbox("Activation", ["relu", "sigmoid", "tanh", "linear"])
-    regularization = st.selectbox("Regularization", ["None", "L1", "L2"])
-    reg_rate = st.slider("Reg. Rate", 0.0, 0.1, 0.01, 0.001) if regularization != "None" else 0.0
-
 def generate_dataset(name):
     if name == "Circles":
         return make_circles(n_samples=1000, noise=0.1, factor=0.5)
@@ -166,11 +149,32 @@ def generate_dataset(name):
             y[ix] = j
         return shuffle(X, y)
 
+# App Title
+st.title("🧠 Neural Network Playground")
+
+# Sidebar inputs
+task_type = st.selectbox("Task Type", ["Classification", "Regression"])
+dataset = st.selectbox("Choose Dataset", ["Circles", "Exclusive OR", "Gaussian", "Spiral"])
+epochs = st.slider("Epochs", 1, 200, 50)
+
+col1, col2, col3 = st.columns(3)
+with col1:
+    learning_rate = st.slider("Learning Rate", 0.001, 1.0, 0.03, 0.001)
+with col2:
+    hidden_layers = st.slider("Hidden Layers", 1, 5, 3)
+    neuron_counts = [st.slider(f"Neurons in Layer {i+1}", 1, 20, 5) for i in range(hidden_layers)]
+with col3:
+    activation = st.selectbox("Activation", ["relu", "sigmoid", "tanh", "linear"])
+    regularization = st.selectbox("Regularization", ["None", "L1", "L2"])
+    reg_rate = st.slider("Reg. Rate", 0.0, 0.1, 0.01, 0.001) if regularization != "None" else 0.0
+
+# Data preparation
 X, y = generate_dataset(dataset)
 scaler = StandardScaler()
 X = scaler.fit_transform(X)
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
 
+# Model building
 model = keras.Sequential()
 first = True
 for count in neuron_counts:
@@ -196,19 +200,26 @@ else:
 model.compile(optimizer=keras.optimizers.Adam(learning_rate), loss=loss, metrics=metrics)
 history = model.fit(X_train, y_train, epochs=epochs, batch_size=32, verbose=0, validation_split=0.2)
 
-st.subheader("Network Structure")
-draw_neural_network(model)
+# Visualization layout
+st.subheader("Visualizations")
+left_col, center_col, _ = st.columns([2, 2, 1])
+with left_col:
+    if task_type == "Classification":
+        st.markdown("### Decision Boundary")
+        plot_decision_boundary(X, y, model)
+    else:
+        st.markdown("### Regression Surface")
+        plot_regression_surface(X, y, model)
 
-if task_type == "Classification":
-    st.subheader("Decision Boundary")
-    plot_decision_boundary(X, y, model)
-else:
-    st.subheader("Regression Surface")
-    plot_regression_surface(X, y, model)
+with center_col:
+    st.markdown("### Neural Network Architecture")
+    draw_neural_network(model)
 
+# Learning Curves
 st.subheader("Learning Curves")
 plot_learning_curves(history)
 
+# Show Model Summary
 if st.checkbox("Show Model Summary"):
     st.subheader("Model Summary")
     model.summary(print_fn=lambda x: st.text(x))