import os
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
from tqdm import tqdm
import numpy as np

data_dir = "dataset"
batch_size = 4
num_epochs = 25
lr = 1e-4
img_size = 256
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

class NailDataset(Dataset):
    def __init__(self, img_dir, mask_dir, transform=None):
        self.img_dir = img_dir
        self.mask_dir = mask_dir
        self.transform = transform
        self.images = [f for f in os.listdir(img_dir) if f.endswith(".jpg") or f.endswith(".png")]

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        img_name = self.images[idx]
        img_path = os.path.join(self.img_dir, img_name)
        base_name = os.path.splitext(img_name)[0]
        mask_name = base_name + ".png"
        mask_path = os.path.join(self.mask_dir, mask_name)

        if not os.path.exists(mask_path):
            raise FileNotFoundError(f"Mask not found for {img_name} → {mask_path}")

        image = Image.open(img_path).convert("RGB")
        mask = Image.open(mask_path).convert("L")

        if self.transform:
            image = self.transform(image)
            mask = transforms.Resize((img_size, img_size))(mask)
            mask = transforms.ToTensor()(mask)
            mask = (mask > 0.5).float()

        return image, mask

transform = transforms.Compose([
    transforms.Resize((img_size, img_size)),
    transforms.ColorJitter(0.2, 0.2, 0.2),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
])

dataset = NailDataset(os.path.join(data_dir, "images"), os.path.join(data_dir, "masks"), transform)
train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()

        def CBR(in_channels, out_channels):
            return nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 3, padding=1),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True)
            )

        self.enc1 = CBR(3, 64)
        self.enc2 = CBR(64, 128)
        self.enc3 = CBR(128, 256)
        self.enc4 = CBR(256, 512)

        self.pool = nn.MaxPool2d(2)
        self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True)

        self.dec3 = CBR(512 + 256, 256)
        self.dec2 = CBR(256 + 128, 128)
        self.dec1 = CBR(128 + 64, 64)

        self.final = nn.Conv2d(64, 1, 1)

    def forward(self, x):
        e1 = self.enc1(x)
        e2 = self.enc2(self.pool(e1))
        e3 = self.enc3(self.pool(e2))
        e4 = self.enc4(self.pool(e3))

        d3 = self.up(e4)
        d3 = self.dec3(torch.cat([d3, e3], dim=1))
        d2 = self.up(d3)
        d2 = self.dec2(torch.cat([d2, e2], dim=1))
        d1 = self.up(d2)
        d1 = self.dec1(torch.cat([d1, e1], dim=1))
        out = torch.sigmoid(self.final(d1))
        return out

model = UNet().to(device)
criterion = nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)

for epoch in range(num_epochs):
    model.train()
    epoch_loss = 0
    loop = tqdm(train_loader, desc=f"Epoch [{epoch+1}/{num_epochs}]")
    for imgs, masks in loop:
        imgs, masks = imgs.to(device), masks.to(device)

        preds = model(imgs)
        loss = criterion(preds, masks)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        epoch_loss += loss.item()
        loop.set_postfix(loss=loss.item())

    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {epoch_loss/len(train_loader):.4f}")

os.makedirs("model", exist_ok=True)
torch.save(model.state_dict(), "model/nail_segmentation_unet.pt")
print("✅ Model saved as model/nail_segmentation_unet.pt")