Update app.py

app.py

@@ -186,11 +186,11 @@ if problem_type == "Classification":
 # Main layout
 col_left, col_center, col_right = st.columns([1, 2, 1])
 
-# Left panel: Dataset with Seaborn
+# Left panel: Dataset with Seaborn (3x3 size)
 with col_left:
     st.markdown('<div class="panel">', unsafe_allow_html=True)
     st.subheader("Data")
-    fig, ax = plt.subplots(figsize=(3, 3))
+    fig, ax = plt.subplots(figsize=(3, 3))  # Fixed size for consistency
     if problem_type == "Classification":
         sns.scatterplot(x=fv[:, 0], y=fv[:, 1], hue=cv, palette="coolwarm", edgecolor="k", alpha=0.7, ax=ax, legend=False)
     else:
@@ -291,7 +291,7 @@ with col_center:
         st.button("Remove Layer", on_click=remove_layer)
     st.markdown('</div>', unsafe_allow_html=True)
 
-# Right panel: Output and Training
+# Right panel: Output and Training (decision region and loss plots stacked vertically, same size as dataset scatterplot)
 with col_right:
     st.markdown('<div class="panel">', unsafe_allow_html=True)
     st.subheader("Output")
@@ -321,7 +321,6 @@ with col_right:
             self.X, self.y = X, y
             self.losses = {"Epoch": [], "Train Loss": [], "Val Loss": []}
             self.placeholder = st.empty()
-            self.model = None
 
         def on_train_begin(self, logs=None):
             self.model = self.model  # Use the model passed implicitly by Keras
@@ -332,47 +331,49 @@ with col_right:
                 self.losses["Train Loss"].append(logs["loss"])
                 self.losses["Val Loss"].append(logs.get("val_loss", logs["loss"]))
                 with self.placeholder.container():
-                    col1, col2 = st.columns(2)
-                    with col1:
-                        plt.figure(figsize=(3, 3))
-                        if problem_type == "Classification":
-                            X_2d = self.X[:, :2]  # Use only first two features for 2D
-                            y_pred_proba = self.model.predict(X_2d, verbose=0) if self.model else np.zeros((len(X_2d), 1))
-                            y_pred = (y_pred_proba > 0.5).astype(int).ravel()
-                            try:
-                                plot_decision_regions(X_2d, self.y, clf=self.model, legend=2, colors='blue,red')
-                                plt.scatter(X_2d[:, 0], X_2d[:, 1], c=self.y, cmap='coolwarm', edgecolors='k', alpha=0.7)
-                                xx, yy = np.meshgrid(np.linspace(X_2d[:, 0].min(), X_2d[:, 0].max(), 100),
-                                                     np.linspace(X_2d[:, 1].min(), X_2d[:, 1].max(), 100))
-                                grid = np.c_[xx.ravel(), yy.ravel()]
-                                Z = self.model.predict(grid, verbose=0) if self.model else np.zeros((len(grid), 1))
-                                Z = (Z > 0.5).astype(int).reshape(xx.shape)
-                                plt.contour(xx, yy, Z, levels=[0.5], colors='black', linewidths=2)
-                            except Exception as e:
-                                st.warning(f"Decision region plot failed: {e}")
-                                xx, yy = np.meshgrid(np.linspace(X_2d[:, 0].min(), X_2d[:, 0].max(), 100),
-                                                     np.linspace(X_2d[:, 1].min(), X_2d[:, 1].max(), 100))
-                                grid = np.c_[xx.ravel(), yy.ravel()]
-                                Z = self.model.predict(grid, verbose=0) if self.model else np.zeros((len(grid), 1))
-                                Z = (Z > 0.5).astype(int).reshape(xx.shape)
-                                plt.contour(xx, yy, Z, levels=[0.5], colors='black', linewidths=2)
-                                plt.contourf(xx, yy, Z, alpha=0.3, cmap="coolwarm")
-                                plt.scatter(X_2d[:, 0], X_2d[:, 1], c=self.y, cmap="coolwarm", edgecolors="k", alpha=0.7)
-                        else:
-                            y_pred = self.model.predict(self.X, verbose=0) if self.model else np.zeros_like(self.X[:, 0])
-                            plt.scatter(self.X[:, 0], self.y, c="blue", alpha=0.5)
-                            plt.plot(self.X[:, 0], y_pred, "r-", linewidths=2)
-                        plt.gca().set_facecolor("#333")
-                        plt.xticks([])
-                        plt.yticks([])
-                        st.pyplot(plt)
-                    with col2:
-                        fig, ax = plt.subplots(figsize=(3, 3))
-                        ax.plot(self.losses["Epoch"], self.losses["Train Loss"], "b-", label="Train")
-                        ax.plot(self.losses["Epoch"], self.losses["Val Loss"], "r--", label="Val")
-                        ax.legend()
-                        ax.set_facecolor("#333")
-                        st.pyplot(fig)
+                    # Single column for vertical stacking
+                    st.subheader("Decision Region & Loss")
+                    # Decision region plot (3x3 size)
+                    fig1, ax1 = plt.subplots(figsize=(3, 3))  # Match dataset scatterplot size
+                    if problem_type == "Classification":
+                        X_2d = self.X[:, :2]  # Use only first two features for 2D
+                        y_pred_proba = self.model.predict(X_2d, verbose=0) if self.model else np.zeros((len(X_2d), 1))
+                        y_pred = (y_pred_proba > 0.5).astype(int).ravel()
+                        try:
+                            plot_decision_regions(X_2d, self.y, clf=self.model, legend=2, colors='blue,red')
+                            plt.scatter(X_2d[:, 0], X_2d[:, 1], c=self.y, cmap='coolwarm', edgecolors='k', alpha=0.7)
+                            xx, yy = np.meshgrid(np.linspace(X_2d[:, 0].min(), X_2d[:, 0].max(), 100),
+                                                 np.linspace(X_2d[:, 1].min(), X_2d[:, 1].max(), 100))
+                            grid = np.c_[xx.ravel(), yy.ravel()]
+                            Z = self.model.predict(grid, verbose=0) if self.model else np.zeros((len(grid), 1))
+                            Z = (Z > 0.5).astype(int).reshape(xx.shape)
+                            plt.contour(xx, yy, Z, levels=[0.5], colors='black', linewidths=2)
+                        except Exception as e:
+                            st.warning(f"Decision region plot failed: {e}")
+                            xx, yy = np.meshgrid(np.linspace(X_2d[:, 0].min(), X_2d[:, 0].max(), 100),
+                                                 np.linspace(X_2d[:, 1].min(), X_2d[:, 1].max(), 100))
+                            grid = np.c_[xx.ravel(), yy.ravel()]
+                            Z = self.model.predict(grid, verbose=0) if self.model else np.zeros((len(grid), 1))
+                            Z = (Z > 0.5).astype(int).reshape(xx.shape)
+                            plt.contour(xx, yy, Z, levels=[0.5], colors='black', linewidths=2)
+                            plt.contourf(xx, yy, Z, alpha=0.3, cmap="coolwarm")
+                            plt.scatter(X_2d[:, 0], X_2d[:, 1], c=self.y, cmap="coolwarm", edgecolors="k", alpha=0.7)
+                    else:
+                        y_pred = self.model.predict(self.X, verbose=0) if self.model else np.zeros_like(self.X[:, 0])
+                        plt.scatter(self.X[:, 0], self.y, c="blue", alpha=0.5)
+                        plt.plot(self.X[:, 0], y_pred, "r-", linewidths=2)
+                    ax1.set_facecolor("#333")
+                    ax1.set_xticks([])
+                    ax1.set_yticks([])
+                    st.pyplot(fig1)
+
+                    # Train-Val-Loss plot (3x3 size)
+                    fig2, ax2 = plt.subplots(figsize=(3, 3))  # Match dataset scatterplot size
+                    ax2.plot(self.losses["Epoch"], self.losses["Train Loss"], "b-", label="Train")
+                    ax2.plot(self.losses["Epoch"], self.losses["Val Loss"], "r--", label="Val")
+                    ax2.legend()
+                    ax2.set_facecolor("#333")
+                    st.pyplot(fig2)
             except Exception as e:
                 st.error(f"Error in epoch end: {e}")