Update app.py

app.py

@@ -192,35 +192,6 @@ def train_model(_):
 
 
 
-def objective(trial):
-    params = {
-        "n_estimators": trial.suggest_int("n_estimators", 50, 200),
-        "max_depth": trial.suggest_int("max_depth", 3, 10),
-    }
-    model = RandomForestClassifier(**params)
-    score = cross_val_score(model, X_train, y_train, cv=3).mean()  # Now X_train and y_train are defined
-    wandb.log(params | {"cv_score": score})
-    return score
-
-study = optuna.create_study(direction="maximize")
-study.optimize(objective, n_trials=15)
-
-best_params = study.best_params
-model = RandomForestClassifier(**best_params)
-model.fit(X_train, y_train)
-y_pred = model.predict(X_test)
-
-metrics = {
-    "accuracy": accuracy_score(y_test, y_pred),
-    "precision": precision_score(y_test, y_pred, average="weighted", zero_division=0),
-    "recall": recall_score(y_test, y_pred, average="weighted", zero_division=0),
-    "f1_score": f1_score(y_test, y_pred, average="weighted", zero_division=0),
-}
-wandb.log(metrics)
-wandb_run.finish()
-
-
-
 def explainability(_):
     import warnings
     warnings.filterwarnings("ignore")