Update app.py

app.py

@@ -118,31 +118,29 @@ def explainability(_):
     explainer = shap.TreeExplainer(model)
     shap_values = explainer.shap_values(X_test)
 
-        try:
-    if isinstance(shap_values, list):
-        class_idx = 0
-        sv = shap_values[class_idx]
-    else:
-        sv = shap_values
-
-    # Use safe feature names if mismatch, fallback to dummy
-    num_features = sv.shape[1]
-    if num_features <= X_test.shape[1]:
-        feature_names = X_test.columns[:num_features]
-    else:
-        feature_names = [f"Feature_{i}" for i in range(num_features)]
-
-    X_shap_safe = pd.DataFrame(np.zeros_like(sv), columns=feature_names)
-
-    shap.summary_plot(sv, X_shap_safe, show=False)
-    shap_path = "./shap_plot.png"
-    plt.title("SHAP Summary")
-    plt.savefig(shap_path)
-    if wandb.run:
-        wandb.log({"shap_summary": wandb.Image(shap_path)})
-    plt.clf()
-
-
+    try:
+        if isinstance(shap_values, list):
+            class_idx = 0
+            sv = shap_values[class_idx]
+        else:
+            sv = shap_values
+
+        # Use safe feature names if mismatch, fallback to dummy
+        num_features = sv.shape[1]
+        if num_features <= X_test.shape[1]:
+            feature_names = X_test.columns[:num_features]
+        else:
+            feature_names = [f"Feature_{i}" for i in range(num_features)]
+
+        X_shap_safe = pd.DataFrame(np.zeros_like(sv), columns=feature_names)
+
+        shap.summary_plot(sv, X_shap_safe, show=False)
+        shap_path = "./shap_plot.png"
+        plt.title("SHAP Summary")
+        plt.savefig(shap_path)
+        if wandb.run:
+            wandb.log({"shap_summary": wandb.Image(shap_path)})
+        plt.clf()
 
     except Exception as e:
         shap_path = "./shap_error.png"