Update app.py

app.py

@@ -136,6 +136,7 @@ def train_and_stream(test_size, model_name, params, epochs, pause_s):
         classes = np.unique(y_train)
         for e in range(1, int(epochs) + 1):
             clf.partial_fit(X_train_s, y_train, classes=classes)
+
             y_pred = clf.predict(X_test_s)
             acc = accuracy_score(y_test, y_pred)
             f1 = f1_score(y_test, y_pred, average="weighted")
@@ -155,14 +156,17 @@ def train_and_stream(test_size, model_name, params, epochs, pause_s):
                 x=coords_test[:, 0], y=coords_test[:, 1], mode="markers",
                 name="Test set", marker=dict(size=10, symbol="circle-open", line=dict(width=2))
             ))
+
             metrics_md = (
                 f"### Metrieken (testset)\n"
                 f"**Accuracy:** {acc:.3f}  \n"
                 f"**F1 (gewogen):** {f1:.3f}  \n"
                 f"**ROC AUC:** {auc:.3f}\n"
             )
-            # stream: Plotly-dict + markdown-string
-            yield fig_epoch.to_dict(), metrics_md
+
+            # >>> Belangrijk: geef een **Figure**, geen dict
+            yield fig_epoch, metrics_md
+
             if pause_s and float(pause_s) > 0:
                 time.sleep(float(pause_s))
         return
@@ -186,16 +190,17 @@ def train_and_stream(test_size, model_name, params, epochs, pause_s):
             x=coords_test[:, 0], y=coords_test[:, 1], mode="markers",
             name="Test set", marker=dict(size=10, symbol="circle-open", line=dict(width=2)),
         ))
+
         metrics_md = (
             f"### Metrieken (testset)\n"
             f"**Accuracy:** {acc:.3f}  \n"
             f"**F1 (gewogen):** {f1:.3f}  \n"
             f"**ROC AUC:** {auc:.3f}\n"
         )
-        return fig.to_dict(), metrics_md
+        return fig, metrics_md
 
 def preview_dataset():
-    df, ycol = load_builtin_dataset()
+    df, _ = load_builtin_dataset()
     return df.head(10)
 
 def predict_row(model_name, params, row_index):
@@ -290,7 +295,6 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", neutral_hue="slate"))
             rf_depth=None if int(rf_depth_v) == 0 else int(rf_depth_v),
             svm_c=float(svm_c_v),
         )
-        # stream generator
         yield from train_and_stream(test_size_v, model_name_v, params, epochs_v, pause_v)
 
     demo.load(