Update app.py

app.py

@@ -64,6 +64,8 @@ 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
 input_size = X_train.shape[1]
 hidden_size = 64
 num_classes = len(emotion_classes)
@@ -75,15 +77,6 @@ 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)
 
-num_epochs = 100
-for epoch in range(num_epochs):
-    for batch_X, batch_y in train_loader:
-        outputs = model(batch_X)
-        loss = criterion(outputs, batch_y)
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-
 # 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)