Update app.py

app.py

@@ -305,13 +305,13 @@ def evaluate_model(model, X_test, y_test, model_name):
     Evaluates the performance of a trained model on test data using various metrics.
     """
     # Make predictions
-y_pred = model.predict(X_test)
+    y_pred = model.predict(X_test)
 
     # Get classification report
-report = classification_report(y_test, y_pred, output_dict=True)
+    report = classification_report(y_test, y_pred, output_dict=True)
 
     # Extracting metrics
-metrics = {
+    metrics = {
         "precision_0": report["0"]["precision"],
         "precision_1": report["1"]["precision"],
         "recall_0": report["0"]["recall"],
@@ -322,7 +322,7 @@ metrics = {
         "macro_avg_recall": report["macro avg"]["recall"],
         "macro_avg_f1": report["macro avg"]["f1-score"],
         "accuracy": accuracy_score(y_test, y_pred)
-}
+    }
 
 # Convert dictionary to dataframe
 df = pd.DataFrame(metrics, index=[model_name]).round(2)