fixed the problem of the decision boundary plot

app.py

@@ -7,6 +7,25 @@ from sklearn.model_selection import train_test_split
 from sklearn.svm import SVC
 from sklearn.metrics import confusion_matrix, classification_report
 
+# Function to visualize decision boundary
+def visualize_classifier(classifier, X, y, title=''):
+    min_x, max_x = X[:, 0].min() - 1.0, X[:, 0].max() + 1.0
+    min_y, max_y = X[:, 1].min() - 1.0, X[:, 1].max() + 1.0
+    mesh_step_size = 0.01
+    x_vals, y_vals = np.meshgrid(np.arange(min_x, max_x, mesh_step_size), 
+                                  np.arange(min_y, max_y, mesh_step_size))
+    output = classifier.predict(np.c_[x_vals.ravel(), y_vals.ravel()])
+    output = output.reshape(x_vals.shape)
+    fig, ax = plt.subplots()
+    ax.set_title(title)
+    ax.pcolormesh(x_vals, y_vals, output, cmap=plt.cm.gray, shading='auto')
+    ax.scatter(X[:, 0], X[:, 1], c=y, s=75, edgecolors='black', linewidth=1, cmap=plt.cm.Paired)
+    ax.set_xlim(x_vals.min(), x_vals.max())
+    ax.set_ylim(y_vals.min(), y_vals.max())
+    ax.set_xticks(np.arange(int(X[:, 0].min() - 1), int(X[:, 0].max() + 1), 1.0))
+    ax.set_yticks(np.arange(int(X[:, 1].min() - 1), int(X[:, 1].max() + 1), 1.0))
+    st.pyplot(fig)
+
 # Load the dataset
 st.title("SVM Kernel Performance Comparison")
 
@@ -39,7 +58,7 @@ if uploaded_file:
         y_pred = model.predict(X_test)
         cm = confusion_matrix(y_test, y_pred)
         cr = classification_report(y_test, y_pred, output_dict=True)
-        return cm, cr
+        return model, cm, cr
     
     # Streamlit tabs
     tab1, tab2, tab3 = st.tabs(["Linear Kernel", "Polynomial Kernel", "RBF Kernel"])
@@ -47,7 +66,7 @@ if uploaded_file:
     for tab, kernel in zip([tab1, tab2, tab3], ["linear", "poly", "rbf"]):
         with tab:
             st.write(f"## SVM with {kernel.capitalize()} Kernel")
-            cm, cr = evaluate_svm(kernel)
+            model, cm, cr = evaluate_svm(kernel)
             
             # Confusion matrix
             st.write("### Confusion Matrix")
@@ -62,6 +81,10 @@ if uploaded_file:
             st.write("### Classification Report")
             st.dataframe(pd.DataFrame(cr).transpose())
             
+            # Decision boundary
+            st.write("### Decision Boundary")
+            visualize_classifier(model, X.to_numpy(), y.to_numpy(), title=f"Decision Boundary - {kernel.capitalize()} Kernel")
+            
             # Explanation
             explanation = {
                 "linear": "The linear kernel performs well when the data is linearly separable.",