Update app.py

app.py

@@ -8,9 +8,31 @@ import tensorflow as tf
 from tensorflow.keras import layers, models
 
 # -------------------------------
-# Helper Functions
+# Page Config
+# -------------------------------
+st.set_page_config(page_title="Neural Network Playground", layout="wide")
+
+# -------------------------------
+# Styling
 # -------------------------------
+st.markdown(
+    """
+    <style>
+    .stApp {
+        background-color: #f5f7fa;
+    }
+    h1, h2 {
+        color: #333333;
+        font-family: 'Segoe UI', sans-serif;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
 
+# -------------------------------
+# Helper Functions
+# -------------------------------
 def generate_data(dataset, test_size):
     if dataset == "moons":
         X, y = make_moons(n_samples=1000, noise=0.2, random_state=42)
@@ -18,9 +40,9 @@ def generate_data(dataset, test_size):
         X, y = make_circles(n_samples=1000, noise=0.2, factor=0.5, random_state=42)
     else:
         X, y = make_blobs(n_samples=1000, centers=2, cluster_std=1.5, random_state=42)
-    
+
     X = StandardScaler().fit_transform(X)
-    return train_test_split(X, y, test_size=1-test_size, random_state=42)
+    return train_test_split(X, y, test_size=1 - test_size, random_state=42)
 
 def build_model(activation, learning_rate):
     model = models.Sequential([
@@ -38,8 +60,7 @@ def plot_decision_boundary(model, X, y):
     xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200),
                          np.linspace(y_min, y_max, 200))
     grid = np.c_[xx.ravel(), yy.ravel()]
-    preds = model.predict(grid)
-    preds = preds.reshape(xx.shape)
+    preds = model.predict(grid, verbose=0).reshape(xx.shape)
 
     plt.contourf(xx, yy, preds, cmap="RdBu", alpha=0.6)
     plt.scatter(X[:, 0], X[:, 1], c=y, cmap="RdBu", edgecolors='white')
@@ -57,28 +78,50 @@ def plot_loss(history):
     st.pyplot(plt.gcf())
     plt.clf()
 
-def train_and_visualize(dataset, lr, act, split, batch):
-    X_train, X_test, y_train, y_test = generate_data(dataset, split)
-    model = build_model(act, lr)
-    history = model.fit(X_train, y_train, epochs=50, batch_size=batch,
-                        validation_data=(X_test, y_test), verbose=0)
-    X_combined = np.vstack((X_train, X_test))
-    y_combined = np.concatenate((y_train, y_test))
-    plot_decision_boundary(model, X_combined, y_combined)
-    plot_loss(history)
-
 # -------------------------------
-# Streamlit UI
+# Sidebar Inputs
 # -------------------------------
+with st.sidebar:
+    st.header("🔧 Hyperparameters")
+    dataset = st.selectbox("Select Dataset", ["moons", "circles", "blobs"])
+    learning_rate = st.number_input("Learning Rate", value=0.01, format="%.4f")
+    activation = st.selectbox("Activation Function", ["relu", "sigmoid", "tanh"])
+    split_ratio = st.slider("Train-Test Split", 0.5, 0.9, 0.7)
+    batch_size = st.number_input("Batch Size", value=32, step=16)
+    train_button = st.button("🚀 Train Model")
 
+# -------------------------------
+# Main App
+# -------------------------------
 st.title("🧠 Neural Network Playground")
+st.write("Interactively explore how neural networks learn decision boundaries with different hyperparameters and synthetic datasets.")
+
+if train_button:
+    with st.spinner("Training the neural network..."):
+        # Generate data
+        X_train, X_test, y_train, y_test = generate_data(dataset, split_ratio)
+
+        # Build and train model
+        model = build_model(activation, learning_rate)
+        history = model.fit(X_train, y_train, epochs=50, batch_size=batch_size,
+                            validation_data=(X_test, y_test), verbose=0)
+
+        # Evaluation
+        loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
+
+        # Display accuracy
+        st.metric("📊 Test Accuracy", f"{accuracy * 100:.2f}%")
+
+        # Tabs for output
+        tab1, tab2 = st.tabs(["🧭 Decision Boundary", "📉 Training vs Testing Loss"])
+        with tab1:
+            X_all = np.vstack((X_train, X_test))
+            y_all = np.concatenate((y_train, y_test))
+            plot_decision_boundary(model, X_all, y_all)
 
-dataset = st.selectbox("Choose Dataset", ["moons", "circles", "blobs"])
-learning_rate = st.number_input("Learning Rate", value=0.01, format="%.4f")
-activation = st.selectbox("Activation Function", ["relu", "sigmoid", "tanh"])
-split_ratio = st.slider("Train-Test Split Ratio", 0.5, 0.9, 0.7)
-batch_size = st.number_input("Batch Size", value=32, step=16)
+        with tab2:
+            plot_loss(history)
 
-if st.button("Train Model"):
-    with st.spinner("Training in progress..."):
-        train_and_visualize(dataset, learning_rate, activation, split_ratio, batch_size)
+        # Expandable section for model summary
+        with st.expander("📜 View Model Summary"):
+            model.summary(print_fn=lambda x: st.text(x))