Update app.py

app.py

@@ -190,62 +190,6 @@ def train_model(_):
     wandb_run = wandb.init(project="huggingface-data-analysis", name=f"Optuna_Run_{run_counter}", reinit=True)
     run_counter += 1
 
-import optuna
-from sklearn.model_selection import train_test_split, cross_val_score
-from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
-from sklearn.linear_model import LogisticRegression
-import pandas as pd
-
-
-def prepare_data():
-    global df_global
-    if df_global is None:
-        raise ValueError("No dataset uploaded.")
-    
-    target = df_global.columns[-1]
-    X = df_global.iloc[:, :-1]
-    y = df_global[target]
-
-    return train_test_split(X, y, test_size=0.2, random_state=42)
-
-def make_objective(X_train, y_train):
-    def objective(trial):
-        model_type = trial.suggest_categorical("model_type", ["RandomForest", "GradientBoosting", "LogisticRegression"])
-
-        if model_type == "RandomForest":
-            model = RandomForestClassifier(
-                n_estimators=trial.suggest_int("n_estimators", 50, 300),
-                max_depth=trial.suggest_int("max_depth", 2, 32)
-            )
-        elif model_type == "GradientBoosting":
-            model = GradientBoostingClassifier(
-                n_estimators=trial.suggest_int("n_estimators", 50, 300),
-                learning_rate=trial.suggest_float("learning_rate", 0.01, 0.3),
-                max_depth=trial.suggest_int("max_depth", 2, 32)
-            )
-        else:
-            model = LogisticRegression(
-                C=trial.suggest_float("C", 1e-3, 1e2),
-                solver="liblinear"
-            )
-
-        score = cross_val_score(model, X_train, y_train, cv=3).mean()
-        return score
-
-    return objective
-
-# ✅ Call the functions in order
-X_train, X_test, y_train, y_test = prepare_data()
-objective = make_objective(X_train, y_train)  # 👈 wrap with your train data
-
-# ✅ Now run optimization
-study = optuna.create_study(direction="maximize")
-study.optimize(objective, n_trials=15)
-
-# ✅ Print the best params
-print("Best trial:")
-print(study.best_trial)
-
 
 
 def objective(trial):