Update app.py

app.py

@@ -38,15 +38,8 @@ from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping
 def GRUModel(trainX, trainy, testX=None, testy=None, epochs=1000, batch_size=64, learning_rate=0.0001, 
              l1_reg=0.001, l2_reg=0.001, dropout_rate=0.2, feature_selection=True, top_k=10):
     
-    # Apply feature selection using Random Forest Regressor
-    if feature_selection:
-        rf = RandomForestRegressor(n_estimators=100, random_state=42)
-        rf.fit(trainX, trainy)
-        feature_importances = rf.feature_importances_
-        important_indices = np.argsort(feature_importances)[-top_k:]  # Select top-k features
-        trainX = trainX[:, important_indices]
-        if testX is not None:
-            testX = testX[:, important_indices]
+    
+    
 
     # Scale the input data
     scaler = MinMaxScaler()