Update app.py

app.py

@@ -11,155 +11,108 @@ from keras.layers import Dense
 from keras.optimizers import SGD
 from mlxtend.plotting import plot_decision_regions
 
-# -------------------------------
-# PAGE CONFIGURATION
-# -------------------------------
-st.set_page_config(page_title="🧪 Neural Network Playground", layout="centered")
-st.title("🧬 Neural Network Playground")
-
-# -------------------------------
-# SESSION INITIALIZATION
-# -------------------------------
-def init_session():
-    defaults = {
-        'X': None, 'y': None, 'model': None,
-        'X_train': None, 'y_train': None,
-        'history': None
-    }
-    for key, value in defaults.items():
-        if key not in st.session_state:
-            st.session_state[key] = value
-
-init_session()
-
-# -------------------------------
-# DATA GENERATION FUNCTION
-# -------------------------------
-def generate_data(dataset, samples, noise, factor):
-    if dataset == "make_circles":
-        return make_circles(n_samples=samples, noise=noise, factor=factor, random_state=42)
-    elif dataset == "make_moons":
-        return make_moons(n_samples=samples, noise=noise, random_state=42)
-    else:
-        return make_classification(n_samples=samples, n_features=2, n_informative=2,
-                                   n_redundant=0, n_clusters_per_class=1,
-                                   flip_y=noise, random_state=42)
-
-# -------------------------------
-# MODEL TRAINING FUNCTION
-# -------------------------------
-def train_model(X, y, test_size, learning_rate, batch_size, epochs):
-    X_train, _, y_train, _ = train_test_split(X, y, test_size=test_size, random_state=1)
-    scaler = StandardScaler()
-    X_train_scaled = scaler.fit_transform(X_train)
-
-    model = Sequential([
-        Dense(8, activation='relu', input_shape=(2,)),
-        Dense(4, activation='relu'),
-        Dense(1, activation='sigmoid')
-    ])
-    model.compile(optimizer=SGD(learning_rate=learning_rate),
-                  loss='binary_crossentropy',
-                  metrics=['accuracy'])
-    
-    history = model.fit(X_train_scaled, y_train,
-                        validation_split=0.2,
-                        epochs=epochs,
-                        batch_size=batch_size,
-                        verbose=0)
-
-    return model, X_train_scaled, y_train, history
-
-# -------------------------------
-# PLOT FUNCTIONS
-# -------------------------------
-def plot_dataset(X, y):
-    df = pd.DataFrame(X, columns=['x1', 'x2'])
-    df['label'] = y
-    fig, ax = plt.subplots()
-    sns.scatterplot(data=df, x='x1', y='x2', hue='label', palette='viridis', ax=ax)
-    st.pyplot(fig)
-
-def plot_decision_boundary(model, X, y):
-    fig, ax = plt.subplots()
-    plot_decision_regions(X, y, clf=model, legend=2, ax=ax)
-    st.pyplot(fig)
-
-def plot_loss_curve(history):
-    fig, ax = plt.subplots()
-    ax.plot(history.history['loss'], label='Training Loss')
-    ax.plot(history.history['val_loss'], label='Validation Loss')
-    ax.set_xlabel("Epochs")
-    ax.set_ylabel("Loss")
-    ax.set_title("Loss Curve")
-    ax.legend()
-    st.pyplot(fig)
-
-# -------------------------------
-# STREAMLIT TABS
-# -------------------------------
-tab1, tab2, tab3 = st.tabs([
-    "🔹 Step 1: Data Generator",
-    "🔹 Step 2: Train Neural Net",
-    "🔹 Step 3: Visualize Results"
-])
-
-# -------------------------------
-# TAB 1: DATA GENERATOR
-# -------------------------------
-with tab1:
-    st.header("🎲 Generate Dataset")
-    col1, col2 = st.columns(2)
-    dataset = col1.selectbox("Dataset Type", ["make_classification", "make_moons", "make_circles"])
-    samples = col2.slider("Samples", 100, 5000, 1000, 100)
-    noise = st.slider("Noise", 0.0, 1.0, 0.2)
-    factor = st.slider("Factor (only for Circles)", 0.1, 1.0, 0.5)
-
-    if st.button("Generate"):
-        X, y = generate_data(dataset, samples, noise, factor)
-        st.session_state.X = X
-        st.session_state.y = y
-        st.success("✅ Data generated!")
-        plot_dataset(X, y)
-
-# -------------------------------
-# TAB 2: TRAINING
-# -------------------------------
-with tab2:
-    st.header("🧠 Train Model")
-
+# --- Config ---
+st.set_page_config(page_title="Neural Net Lab", layout="wide")
+st.title("🔬 Interactive Neural Network Lab")
+
+# --- Session State Setup ---
+if 'X' not in st.session_state: st.session_state.X = None
+if 'y' not in st.session_state: st.session_state.y = None
+if 'model' not in st.session_state: st.session_state.model = None
+if 'history' not in st.session_state: st.session_state.history = None
+if 'X_train' not in st.session_state: st.session_state.X_train = None
+if 'y_train' not in st.session_state: st.session_state.y_train = None
+
+# --- Step 1: Dataset Generator ---
+with st.expander("📌 STEP 1: Generate Dataset", expanded=True):
+    st.markdown("Start by creating a synthetic 2D classification dataset.")
+
+    col1, col2, col3 = st.columns([2, 1, 1])
+    dataset_type = col1.selectbox("Choose a dataset type", ["make_classification", "make_moons", "make_circles"])
+    n_samples = col2.slider("Number of Samples", 100, 5000, 1000, step=100)
+    noise_level = col3.slider("Noise", 0.0, 1.0, 0.2)
+    circle_factor = st.slider("Factor (Circles only)", 0.1, 1.0, 0.5)
+
+    if st.button("🚀 Generate Dataset"):
+        if dataset_type == "make_moons":
+            X, y = make_moons(n_samples=n_samples, noise=noise_level, random_state=42)
+        elif dataset_type == "make_circles":
+            X, y = make_circles(n_samples=n_samples, noise=noise_level, factor=circle_factor, random_state=42)
+        else:
+            X, y = make_classification(n_samples=n_samples, n_features=2, n_informative=2,
+                                       n_redundant=0, n_clusters_per_class=1, flip_y=noise_level, random_state=42)
+        st.session_state.X, st.session_state.y = X, y
+        st.success("Dataset generated successfully! 🎉")
+
+    if st.session_state.X is not None:
+        df = pd.DataFrame(st.session_state.X, columns=["x1", "x2"])
+        df["label"] = st.session_state.y
+        st.markdown("### 🔍 Dataset Preview")
+        st.dataframe(df.head())
+
+        st.markdown("### 📊 Visualize")
+        fig, ax = plt.subplots()
+        sns.scatterplot(data=df, x="x1", y="x2", hue="label", palette="coolwarm", ax=ax)
+        st.pyplot(fig)
+
+# --- Step 2: Train Neural Network ---
+with st.expander("🤖 STEP 2: Train Neural Network"):
     if st.session_state.X is None:
-        st.warning("⚠️ Generate data first in Step 1.")
+        st.warning("⚠️ Please generate the dataset in Step 1.")
     else:
-        test_size = st.slider("Test Size (%)", 10, 90, 20) / 100
-        lr = st.selectbox("Learning Rate", [0.0001, 0.001, 0.01, 0.1])
-        batch_size = st.slider("Batch Size", 1, 512, 64)
+        st.subheader("🛠️ Model Configuration")
+        c1, c2, c3 = st.columns(3)
+        test_size = c1.slider("Test Split %", 10, 90, 20) / 100
+        learning_rate = c2.selectbox("Learning Rate", [0.0001, 0.001, 0.01, 0.1])
+        batch_size = c3.slider("Batch Size", 1, 512, 64)
         epochs = st.slider("Epochs", 10, 500, 100)
 
-        if st.button("Train Model"):
-            with st.spinner("Training in progress..."):
-                model, X_train, y_train, history = train_model(
-                    st.session_state.X, st.session_state.y,
-                    test_size, lr, batch_size, epochs
-                )
-                st.session_state.model = model
-                st.session_state.X_train = X_train
-                st.session_state.y_train = y_train
-                st.session_state.history = history
-                st.success("✅ Training Complete!")
-
-# -------------------------------
-# TAB 3: VISUALIZATION
-# -------------------------------
-with tab3:
-    st.header("📊 Model Visualization")
+        if st.button("🧠 Train the Model"):
+            st.info("📡 Preprocessing and Training in progress...")
+            X_train, _, y_train, _ = train_test_split(st.session_state.X, st.session_state.y, test_size=test_size, random_state=1)
+            scaler = StandardScaler()
+            X_scaled = scaler.fit_transform(X_train)
+
+            model = Sequential([
+                Dense(8, activation='relu', input_shape=(2,)),
+                Dense(4, activation='relu'),
+                Dense(1, activation='sigmoid')
+            ])
+            model.compile(optimizer=SGD(learning_rate=learning_rate), loss='binary_crossentropy', metrics=['accuracy'])
+
+            history = model.fit(X_scaled, y_train, validation_split=0.2,
+                                batch_size=batch_size, epochs=epochs, verbose=0)
+
+            st.session_state.model = model
+            st.session_state.history = history
+            st.session_state.X_train = X_scaled
+            st.session_state.y_train = y_train
+            st.success("✅ Model training complete!")
+
+            st.metric("Final Accuracy", f"{history.history['val_loss'][-1]:.4f}")
+            st.progress(100)
+
+# --- Step 3: Visualize Model Output ---
+with st.expander("📈 STEP 3: Visualize Model Output"):
     if st.session_state.model is None:
-        st.warning("⚠️ Train the model in Step 2 to visualize results.")
+        st.warning("⚠️ Train the model first in Step 2.")
     else:
-        st.subheader("🌐 Decision Boundary")
-        plot_decision_boundary(st.session_state.model,
-                               st.session_state.X_train,
-                               st.session_state.y_train)
-
-        st.subheader("📉 Training Loss Curve")
-        plot_loss_curve(st.session_state.history)
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.subheader("🌐 Decision Boundary")
+            fig1, ax1 = plt.subplots()
+            plot_decision_regions(st.session_state.X_train, st.session_state.y_train,
+                                  clf=st.session_state.model, ax=ax1, legend=2)
+            st.pyplot(fig1)
+
+        with col2:
+            st.subheader("📉 Training Loss Curve")
+            fig2, ax2 = plt.subplots()
+            ax2.plot(st.session_state.history.history['loss'], label='Train Loss')
+            ax2.plot(st.session_state.history.history['val_loss'], label='Val Loss')
+            ax2.set_title("Loss over Epochs")
+            ax2.legend()
+            st.pyplot(fig2)
+
+        st.success("🧪 Visualization ready!")