import matplotlib.pyplot as plt
import torch
import torchvision.transforms as T
from PIL import Image
import gradio as gr
from featup.util import norm, unnorm, pca, remove_axes
from pytorch_lightning import seed_everything
import os
import requests
import os
import csv

def plot_feats(image, lr, hr):
    assert len(image.shape) == len(lr.shape) == len(hr.shape) == 3
    seed_everything(0)
    [lr_feats_pca, hr_feats_pca], _ = pca([lr.unsqueeze(0), hr.unsqueeze(0)], dim=9)
    fig, ax = plt.subplots(3, 3, figsize=(15, 15))
    ax[0, 0].imshow(image.permute(1, 2, 0).detach().cpu())
    ax[1, 0].imshow(image.permute(1, 2, 0).detach().cpu())
    ax[2, 0].imshow(image.permute(1, 2, 0).detach().cpu())

    ax[0, 0].set_title("Image", fontsize=22)
    ax[0, 1].set_title("Original", fontsize=22)
    ax[0, 2].set_title("Upsampled Features", fontsize=22)

    ax[0, 1].imshow(lr_feats_pca[0, :3].permute(1, 2, 0).detach().cpu())
    ax[0, 0].set_ylabel("PCA Components 1-3", fontsize=22)
    ax[0, 2].imshow(hr_feats_pca[0, :3].permute(1, 2, 0).detach().cpu())

    ax[1, 1].imshow(lr_feats_pca[0, 3:6].permute(1, 2, 0).detach().cpu())
    ax[1, 0].set_ylabel("PCA Components 4-6", fontsize=22)
    ax[1, 2].imshow(hr_feats_pca[0, 3:6].permute(1, 2, 0).detach().cpu())

    ax[2, 1].imshow(lr_feats_pca[0, 6:9].permute(1, 2, 0).detach().cpu())
    ax[2, 0].set_ylabel("PCA Components 7-9", fontsize=22)
    ax[2, 2].imshow(hr_feats_pca[0, 6:9].permute(1, 2, 0).detach().cpu())

    remove_axes(ax)
    plt.tight_layout()
    plt.close(fig)  # Close plt to avoid additional empty plots
    return fig


if __name__ == "__main__":

    def download_image(url, save_path):
        response = requests.get(url)
        with open(save_path, 'wb') as file:
            file.write(response.content)

    base_url = "https://marhamilresearch4.blob.core.windows.net/feature-upsampling-public/sample_images/"
    sample_images_urls = {
        "skate.jpg": base_url + "skate.jpg",
        "car.jpg": base_url + "car.jpg",
        "plant.png": base_url + "plant.png",
    }

    sample_images_dir = "/tmp/sample_images"

    # Ensure the directory for sample images exists
    os.makedirs(sample_images_dir, exist_ok=True)

    # Download each sample image
    for filename, url in sample_images_urls.items():
        save_path = os.path.join(sample_images_dir, filename)
        # Download the image if it doesn't already exist
        if not os.path.exists(save_path):
            print(f"Downloading {filename}...")
            download_image(url, save_path)
        else:
            print(f"{filename} already exists. Skipping download.")

    os.environ['TORCH_HOME'] = '/tmp/.cache'
    os.environ['GRADIO_EXAMPLES_CACHE'] = '/tmp/gradio_cache'
    csv.field_size_limit(100000000)
    options = ['dino16', 'vit', 'dinov2', 'clip', 'resnet50']

    image_input = gr.Image(label="Choose an image to featurize",
                           height=480,
                           type="pil",
                           image_mode='RGB',
                           sources=['upload', 'webcam', 'clipboard']
                           )
    model_option = gr.Radio(options, value="dino16", label='Choose a backbone to upsample')

    models = {o: torch.hub.load("mhamilton723/FeatUp", o) for o in options}


    def upsample_features(image, model_option):
        # Image preprocessing
        input_size = 224
        transform = T.Compose([
            T.Resize(input_size),
            T.CenterCrop((input_size, input_size)),
            T.ToTensor(),
            norm
        ])
        image_tensor = transform(image).unsqueeze(0).cuda()

        # Load the selected model
        upsampler = models[model_option].cuda()
        hr_feats = upsampler(image_tensor)
        lr_feats = upsampler.model(image_tensor)
        upsampler.cpu()

        return plot_feats(unnorm(image_tensor)[0], lr_feats[0], hr_feats[0])


    demo = gr.Interface(fn=upsample_features,
                        inputs=[image_input, model_option],
                        outputs="plot",
                        title="Feature Upsampling Demo",
                        description="This demo allows you to upsample features of an image using selected models.",
                        examples=[
                            ["/tmp/sample_images/skate.jpg", "dino16"],
                            ["/tmp/sample_images/car.jpg", "dinov2"],
                            ["/tmp/sample_images/plant.png", "dino16"],
                        ]

                        )

    demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)