import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import v2
import datetime

time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
transform = v2.Compose([v2.ToImage() , v2.ToDtype(torch.float32, scale=True)])
# Download training data from open datasets.
training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=transform,
)

# Download test data from open datasets.
test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=transform,
)

train_loader = DataLoader(training_data, batch_size=64, shuffle=True)
test_loader = DataLoader(test_data, batch_size=64, shuffle=False)

class NeuralNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.network = nn.Sequential(
            nn.Linear(784, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 10),
        )
    def forward(self, x):
        x = self.flatten(x)
        return self.network(x)

model = NeuralNetwork().to(device)
print(model)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

epochs = 10
for epoch in range(epochs):
    model.train()
    for X, y in train_loader:
        X = X.to(device)
        y = y.to(device)
        pred = model(X)
        loss = loss_fn(pred, y)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
    print("Epoch:", epoch+1,"Loss:", loss.item())

model.eval()
correct = 0

with torch.no_grad():
        for X, y in test_loader:
            X = X.to(device)
            y = y.to(device)
            pred = model(X)
            correct += (pred.argmax(1) == y).float().sum().item()
print("Accuracy:", correct/len(test_loader))

torch.save(model.state_dict(), f"fashion_mnist_epochs{epochs}_{time}.pth")

print("Model saved")