import torch
import torchvision.models as models
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import natsort
import os
from PIL import Image
from torch.utils.data import DataLoader, Dataset
# 1. Load ResNet50 model and prepare data
resnet50 = models.resnet50(pretrained=True)
resnet50.eval()  # Set the model to evaluation mode

# Remove the last fully connected layer to use the model as a feature extractor
feature_extractor = torch.nn.Sequential(*list(resnet50.children())[:-1])

from torchvision import transforms

preprocess = transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
class CustomDataSet(Dataset):
    def __init__(self, main_dir, transform):
        self.main_dir = main_dir
        self.transform = transform
        all_imgs = os.listdir(main_dir)
        self.total_imgs = natsort.natsorted(all_imgs)

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

    def __getitem__(self, idx):
        img_loc = os.path.join(self.main_dir, self.total_imgs[idx])
        image = Image.open(img_loc).convert("RGB")
        tensor_image = self.transform(image)
        return tensor_image

# Load your dataset
from torch.utils.data import DataLoader, Dataset
my_dataset = CustomDataSet("diffusion_generated_image_256", transform=preprocess)
dataloader = DataLoader(my_dataset , batch_size=16, shuffle=False, 
                               num_workers=4)
# 2. Extract features
features = []
labels = []

with torch.no_grad():
    for inputs in dataloader:
        output = feature_extractor(inputs)
        output = output.view(output.size(0), -1)  # Flatten the output
        features.append(output)

features = torch.cat(features)

# 3. t-SNE Transformation
tsne = TSNE(n_components=2, random_state=0)
features_2d = tsne.fit_transform(features)

# 4. Plot the data
plt.figure(figsize=(10, 8))
plt.scatter(features_2d[:, 0], features_2d[:, 1], c=labels, cmap='jet', alpha=0.5)
plt.colorbar()
plt.title('t-SNE visualization of skin cancer mask generation')
plt.savefig('results/diffusion_generated_image_256.png')