import torch
import torch.optim as optim
import torch.nn as nn

def train_model(model, trainloader, testloader, epochs=10, learning_rate=0.001):
    """
    Trains the PyTorch model.
    """
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    print(f"Training on device: {device}")
    model.to(device)

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)

    history = {'accuracy': [], 'loss': []}

    for epoch in range(epochs):
        running_loss = 0.0
        correct = 0
        total = 0
        
        model.train()
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data[0].to(device), data[1].to(device)

            optimizer.zero_grad()

            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        epoch_loss = running_loss / len(trainloader)
        epoch_acc = correct / total
        history['loss'].append(epoch_loss)
        history['accuracy'].append(epoch_acc)
        
        print(f'Epoch {epoch + 1}/{epochs} - Loss: {epoch_loss:.4f} - Accuracy: {epoch_acc:.4f}')

    print('Finished Training')
    return history