Update app.py

app.py

@@ -64,8 +64,15 @@ class AdvancedNN(nn.Module):
 
 # Train Advanced Neural Network
 X_train, X_test, y_train, y_test = train_test_split(contexts_encoded, emotions_target, test_size=0.2, random_state=42)
-X_train = X_train.toarray()  # Convert sparse matrix to dense
-y_train = y_train.to_numpy()  # Convert pandas Series to numpy array
+
+# Convert to dense array if it's a sparse matrix, otherwise leave as is
+X_train = X_train.toarray() if hasattr(X_train, 'toarray') else X_train
+X_test = X_test.toarray() if hasattr(X_test, 'toarray') else X_test
+
+# Ensure y_train and y_test are numpy arrays
+y_train = y_train.to_numpy() if hasattr(y_train, 'to_numpy') else np.array(y_train)
+y_test = y_test.to_numpy() if hasattr(y_test, 'to_numpy') else np.array(y_test)
+
 input_size = X_train.shape[1]
 hidden_size = 64
 num_classes = len(emotion_classes)
@@ -77,6 +84,13 @@ optimizer = optim.Adam(model.parameters(), lr=0.001)
 train_dataset = TensorDataset(torch.FloatTensor(X_train), torch.LongTensor(y_train))
 train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
 
+model = AdvancedNN(input_size, hidden_size, num_classes)
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+train_dataset = TensorDataset(torch.FloatTensor(X_train), torch.LongTensor(y_train))
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+
 # Ensemble with Random Forest and Gradient Boosting
 rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
 gb_model = GradientBoostingClassifier(n_estimators=100, random_state=42)