Update app.py

app.py

@@ -241,8 +241,10 @@ def train_model(_):
                 "n_estimators": trial.suggest_int("n_estimators", 50, 200),
                 "max_depth": trial.suggest_int("max_depth", 3, 10),
             }
-            model = RandomForestClassifier()
+            model = RandomForestClassifier(**params)
             score = cross_val_score(model, X_train, y_train, cv=3).mean()
+        if wandb.run is None:
+            wandb.init(project="model_optimization", name=f"optuna_trial_{trial.number}", reinit=True)
             wandb.log({**params, "cv_score": score})
             return score