app.py

# Author: Huzheng Yang
# %%
import copy
from datetime import datetime
import pickle
from functools import partial
from io import BytesIO
import json
import os
import uuid
import zipfile
import multiprocessing as mp

from einops import rearrange
from matplotlib import pyplot as plt
import matplotlib
USE_HUGGINGFACE_ZEROGPU = os.getenv("USE_HUGGINGFACE_ZEROGPU", "False").lower() in ["true", "1", "yes"]
DOWNLOAD_ALL_MODELS_DATASETS = os.getenv("DOWNLOAD_ALL_MODELS_DATASETS", "False").lower() in ["true", "1", "yes"]

if USE_HUGGINGFACE_ZEROGPU:  # huggingface ZeroGPU, dynamic GPU allocation 
    try:
        import spaces
    except:
        USE_HUGGINGFACE_ZEROGPU = False
        
if USE_HUGGINGFACE_ZEROGPU:
    BATCH_SIZE = 1
else:  # run on local machine
    BATCH_SIZE = 1

import gradio as gr

import torch
import torch.nn.functional as F
from PIL import Image
import numpy as np
import time
import threading

from ncut_pytorch.backbone import extract_features, load_model
from ncut_pytorch.backbone import MODEL_DICT, LAYER_DICT, RES_DICT
from ncut_pytorch import NCUT
from ncut_pytorch import eigenvector_to_rgb


DATASETS = {
    'Common': [
        ('mrm8488/ImageNet1K-val', 1000),
        ('UCSC-VLAA/Recap-COCO-30K', None),
        ('nateraw/pascal-voc-2012', None),
        ('johnowhitaker/imagenette2-320', 10),
        ('Multimodal-Fatima/CUB_train', 200),
        ('saragag/FlBirds', 7),
        ('microsoft/cats_vs_dogs', None),
        ('Robotkid2696/food_classification', 20),
        ('JapanDegitalMaterial/Places_in_Japan', None),
    ],
    'Ego': [
        ('EgoThink/EgoThink', None),   
    ],
    'Face': [
        ('nielsr/CelebA-faces', None),
        ('huggan/anime-faces', None),
    ],
    'Pose': [
        ('sayakpaul/poses-controlnet-dataset', None),
        ('razdab/sign_pose_M', None),
        ('Marqo/deepfashion-multimodal', None),
        ('Fiacre/small-animal-poses-controlnet-dataset', None),
        ('junjuice0/vtuber-tachi-e', None),
    ],
    'Hand': [
        ('trashsock/hands-images', 8),
        ('dduka/guitar-chords-v3', None),
    ],
    'Satellite': [
        ('arakesh/deepglobe-2448x2448', None),
        ('tanganke/eurosat', 10),
        ('wangyi111/EuroSAT-SAR', None),
        ('efoley/sar_tile_512', None),
    ],
    'Medical': [
        ('Mahadih534/Chest_CT-Scan_images-Dataset', None),
        ('TrainingDataPro/chest-x-rays', None),
        ('hongrui/mimic_chest_xray_v_1', None),
        ('sartajbhuvaji/Brain-Tumor-Classification', 4),
        ('Falah/Alzheimer_MRI', 4),
        ('Leonardo6/path-vqa', None),
        ('Itsunori/path-vqa_jap', None),
        ('ruby-jrl/isic-2024-2', None),
        ('VRJBro/lung_cancer_dataset', 5),
        ('keremberke/blood-cell-object-detection', None)
    ],
    'Miscs': [
        ('yashvoladoddi37/kanjienglish', None),
        ('Borismile/Anime-dataset', None),
        ('jainr3/diffusiondb-pixelart', None),
        ('jlbaker361/dcgan-eval-creative_gan_256_256', None),
        ('Francesco/csgo-videogame', None),
        ('Francesco/apex-videogame', None),
        ('huggan/pokemon', None),
        ('huggan/few-shot-universe', None),
        ('huggan/flowers-102-categories', None),
        ('huggan/inat_butterflies_top10k', None),
    ]
}
CENTER_CROP_DATASETS = ["razdab/sign_pose_M"]


from datasets import load_dataset

def download_all_datasets():
    for cat in DATASETS.keys():
        for tup in DATASETS[cat]:
            name = tup[0]
            print(f"Downloading {name}")
            try:
                load_dataset(name, trust_remote_code=True)
            except Exception as e:
                print(f"Error downloading {name}: {e}")

def compute_ncut(
    features,
    num_eig=100,
    num_sample_ncut=10000,
    affinity_focal_gamma=0.3,
    knn_ncut=10,
    knn_tsne=10,
    embedding_method="UMAP",
    embedding_metric='euclidean',
    num_sample_tsne=300,
    perplexity=150,
    n_neighbors=150,
    min_dist=0.1,
    sampling_method="QuickFPS",
    metric="cosine",
    indirect_connection=True,
    make_orthogonal=False,
    progess_start=0.4,
):        
    progress = gr.Progress()
    logging_str = ""
    
    num_nodes = np.prod(features.shape[:-1])
    if num_nodes / 2 < num_eig:
        # raise gr.Error("Number of eigenvectors should be less than half the number of nodes.")
        gr.Warning("Number of eigenvectors should be less than half the number of nodes.\n" f"Setting num_eig to {num_nodes // 2 - 1}.")
        num_eig = num_nodes // 2 - 1
        logging_str += f"Number of eigenvectors should be less than half the number of nodes.\n" f"Setting num_eig to {num_nodes // 2 - 1}.\n"
    
    start = time.time()
    progress(progess_start+0.0, desc="NCut")
    eigvecs, eigvals = NCUT(
        num_eig=num_eig,
        num_sample=num_sample_ncut,
        device="cuda" if torch.cuda.is_available() else "cpu",
        affinity_focal_gamma=affinity_focal_gamma,
        knn=knn_ncut,
        sample_method=sampling_method,
        distance=metric,
        normalize_features=False,
        indirect_connection=indirect_connection,
        make_orthogonal=make_orthogonal,
    ).fit_transform(features.reshape(-1, features.shape[-1]))
    # print(f"NCUT time: {time.time() - start:.2f}s")
    logging_str += f"NCUT time: {time.time() - start:.2f}s\n"
    
    start = time.time()
    progress(progess_start+0.01, desc="spectral-tSNE")
    _, rgb = eigenvector_to_rgb(
        eigvecs,
        method=embedding_method,
        metric=embedding_metric,
        num_sample=num_sample_tsne,
        perplexity=perplexity,
        n_neighbors=n_neighbors,
        min_distance=min_dist,
        knn=knn_tsne,
        device="cuda" if torch.cuda.is_available() else "cpu",
    )
    logging_str += f"{embedding_method} time: {time.time() - start:.2f}s\n"

    rgb = rgb.reshape(features.shape[:-1] + (3,))
    return rgb, logging_str, eigvecs


def compute_ncut_directed(
    features_1,
    features_2,
    num_eig=100,
    num_sample_ncut=10000,
    affinity_focal_gamma=0.3,
    knn_ncut=10,
    knn_tsne=10,
    embedding_method="UMAP",
    embedding_metric='euclidean',
    num_sample_tsne=300,
    perplexity=150,
    n_neighbors=150,
    min_dist=0.1,
    sampling_method="QuickFPS",
    metric="cosine",
    indirect_connection=False,
    make_orthogonal=False,
    make_symmetric=False,
    progess_start=0.4,
):        
    # print("Using directed_ncut")
    # print("features_1.shape", features_1.shape)
    # print("features_2.shape", features_2.shape)
    from directed_ncut import nystrom_ncut
    progress = gr.Progress()
    logging_str = ""
    
    num_nodes = np.prod(features_1.shape[:-2])
    if num_nodes / 2 < num_eig:
        # raise gr.Error("Number of eigenvectors should be less than half the number of nodes.")
        gr.Warning("Number of eigenvectors should be less than half the number of nodes.\n" f"Setting num_eig to {num_nodes // 2 - 1}.")
        num_eig = num_nodes // 2 - 1
        logging_str += f"Number of eigenvectors should be less than half the number of nodes.\n" f"Setting num_eig to {num_nodes // 2 - 1}.\n"
    
    start = time.time()
    progress(progess_start+0.0, desc="NCut")
    n_features = features_1.shape[-2]
    _features_1 = rearrange(features_1, "b h w d c -> (b h w) (d c)")
    _features_2 = rearrange(features_2, "b h w d c -> (b h w) (d c)")
    eigvecs, eigvals, _ = nystrom_ncut(
        _features_1,
        features_B=_features_2,
        num_eig=num_eig,
        num_sample=num_sample_ncut,
        device="cuda" if torch.cuda.is_available() else "cpu",
        affinity_focal_gamma=affinity_focal_gamma,
        knn=knn_ncut,
        sample_method=sampling_method,
        distance=metric,
        normalize_features=False,
        indirect_connection=indirect_connection,
        make_orthogonal=make_orthogonal,
        make_symmetric=make_symmetric,
        n_features=n_features,
    )
    # print(f"NCUT time: {time.time() - start:.2f}s")
    logging_str += f"NCUT time: {time.time() - start:.2f}s\n"
    
    start = time.time()
    progress(progess_start+0.01, desc="spectral-tSNE")
    _, rgb = eigenvector_to_rgb(
        eigvecs,
        method=embedding_method,
        metric=embedding_metric,
        num_sample=num_sample_tsne,
        perplexity=perplexity,
        n_neighbors=n_neighbors,
        min_distance=min_dist,
        knn=knn_tsne,
        device="cuda" if torch.cuda.is_available() else "cpu",
    )
    logging_str += f"{embedding_method} time: {time.time() - start:.2f}s\n"

    rgb = rgb.reshape(features_1.shape[:3] + (3,))
    return rgb, logging_str, eigvecs


def dont_use_too_much_green(image_rgb):
    # make sure the foval 40% of the image is red leading
    x1, x2 = int(image_rgb.shape[1] * 0.3), int(image_rgb.shape[1] * 0.7)
    y1, y2 = int(image_rgb.shape[2] * 0.3), int(image_rgb.shape[2] * 0.7)
    sum_values = image_rgb[:, x1:x2, y1:y2].mean((0, 1, 2))
    sorted_indices = sum_values.argsort(descending=True)
    image_rgb = image_rgb[:, :, :, sorted_indices]
    return image_rgb


def to_pil_images(images, target_size=512, resize=True):
    size = images[0].shape[1]
    multiplier = target_size // size
    res = int(size * multiplier)
    pil_images = [
            Image.fromarray((image * 255).cpu().numpy().astype(np.uint8))
            for image in images
        ]
    if resize:
        pil_images = [
            image.resize((res, res), Image.Resampling.NEAREST)
            for image in pil_images
        ]
    return pil_images
    


def pil_images_to_video(images, output_path, fps=5):
    # from pil images to numpy
    images = [np.array(image) for image in images]
    
    # print("Saving video to", output_path)
    import cv2
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    height, width, _ = images[0].shape
    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
    for image in images:
        out.write(cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
    out.release()
    return output_path

# save up to 100 videos in disk
class VideoCache:
    def __init__(self, max_videos=100):
        self.max_videos = max_videos
        self.videos = {}
    
    def add_video(self, video_path):
        if len(self.videos) >= self.max_videos:
            pop_path = self.videos.popitem()[0]
            try:
                os.remove(pop_path)
            except:
                pass
        self.videos[video_path] = video_path
    
    def get_video(self, video_path):
        return self.videos.get(video_path, None)

video_cache = VideoCache()
    
def get_random_path(length=10):
    import random
    import string
    name = ''.join(random.choices(string.ascii_lowercase + string.digits, k=length))
    path = f'/tmp/{name}.mp4'
    return path

default_images = ['./images/image_0.jpg', './images/image_1.jpg', './images/image_2.jpg', './images/image_3.jpg', './images/guitar_ego.jpg', './images/image_5.jpg']
default_outputs = ['./images/image-1.webp', './images/image-2.webp', './images/image-3.webp', './images/image-4.webp', './images/image-5.webp']
# default_outputs_independent = ['./images/image-6.webp', './images/image-7.webp', './images/image-8.webp', './images/image-9.webp', './images/image-10.webp']
default_outputs_independent = []

downscaled_images = ['./images/image_0_small.jpg', './images/image_1_small.jpg', './images/image_2_small.jpg', './images/image_3_small.jpg', './images/image_5_small.jpg']
downscaled_outputs = default_outputs

example_items = downscaled_images[:3] + downscaled_outputs[:3]


def run_alignedthreemodelattnnodes(images, model, batch_size=16):
    
    use_cuda = torch.cuda.is_available() 
    device = torch.device("cuda" if use_cuda else "cpu")
    
    if use_cuda:
        model = model.to(device)
        
    chunked_idxs = torch.split(torch.arange(images.shape[0]), batch_size)
    
    outputs = []
    for idxs in chunked_idxs:
        inp = images[idxs]
        if use_cuda:
            inp = inp.to(device)
        out = model(inp)  
        # normalize before save
        out = F.normalize(out, dim=-1)
        outputs.append(out.cpu().float())
    outputs = torch.cat(outputs, dim=0)

    return outputs


def _reds_colormap(image):
    # normed_data = image / image.max()  # Normalize to [0, 1]
    normed_data = image
    colormap = matplotlib.colormaps['inferno']  # Get the Reds colormap
    colored_image = colormap(normed_data)  # Apply colormap
    return (colored_image[..., :3] * 255).astype(np.uint8)  # Convert to RGB

# heatmap images
def apply_reds_colormap(images, size):
    # for i_image in range(images.shape[0]):
    #     images[i_image] -= images[i_image].min()
    #     images[i_image] /= images[i_image].max()
    # normed_data = [_reds_colormap(images[i]) for i in range(images.shape[0])]
    # normed_data = np.stack(normed_data)
    normed_data = _reds_colormap(images)
    normed_data = torch.tensor(normed_data).float()
    normed_data = rearrange(normed_data, "b h w c -> b c h w")
    normed_data = torch.nn.functional.interpolate(normed_data, size=size, mode="nearest")
    normed_data = rearrange(normed_data, "b c h w -> b h w c")
    normed_data = normed_data.cpu().numpy().astype(np.uint8)
    return normed_data

# Blend heatmap with the original image
def blend_image_with_heatmap(image, heatmap, opacity1=0.5, opacity2=0.5):
    blended = (1 - opacity1) * image + opacity2 * heatmap
    return blended.astype(np.uint8)


def segment_fg_bg(images):

    images = F.interpolate(images, (224, 224), mode="bilinear")

    # model = load_alignedthreemodel()
    model = load_model("CLIP(ViT-B-16/openai)")
    from ncut_pytorch.backbone import resample_position_embeddings
    pos_embed = model.model.visual.positional_embedding
    pos_embed = resample_position_embeddings(pos_embed, 14, 14)
    model.model.visual.positional_embedding = torch.nn.Parameter(pos_embed)
    
    batch_size = 4
    chunk_idxs = torch.split(torch.arange(images.shape[0]), batch_size)

    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    model.to(device)
    means = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
    stds = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
    
    fg_acts, bg_acts = [], []
    for chunk_idx in chunk_idxs:
        with torch.no_grad():
            input_images = images[chunk_idx].to(device)
            # transform the input images
            input_images = (input_images - means) / stds
            # output = model(input_images)[:, 5]
            output = model(input_images)['attn'][6]  # [B, H=14, W=14, C]
            fg_act = output[:, 6, 6].mean(0)
            bg_act = output[:, 0, 0].mean(0)
            fg_acts.append(fg_act)
            bg_acts.append(bg_act)
    fg_act = torch.stack(fg_acts, dim=0).mean(0)
    bg_act = torch.stack(bg_acts, dim=0).mean(0)
    fg_act = F.normalize(fg_act, dim=-1)
    bg_act = F.normalize(bg_act, dim=-1)
    
    # ref_image = default_images[0]
    # image = Image.open(ref_image).convert("RGB").resize((224, 224), Image.Resampling.BILINEAR)
    # image = torch.tensor(np.array(image)).permute(2, 0, 1).float().to(device)
    # image = (image / 255.0 - means) / stds
    # output = model(image)['attn'][6][0]
    # # print(output.shape)
    # # bg on the center
    # fg_act = output[5, 5]
    # # bg on the bottom left
    # bg_act = output[0, 0]
    # fg_act = F.normalize(fg_act, dim=-1)
    # bg_act = F.normalize(bg_act, dim=-1)
    
    # print(images.mean(), images.std())
    
    fg_act, bg_act = fg_act.to(device), bg_act.to(device)
    chunk_idxs = torch.split(torch.arange(images.shape[0]), batch_size)
    heatmap_fgs, heatmap_bgs = [], []
    for chunk_idx in chunk_idxs:
        with torch.no_grad():
            input_images = images[chunk_idx].to(device)
            # transform the input images
            input_images = (input_images - means) / stds
            # output = model(input_images)[:, 5]
            output = model(input_images)['attn'][6]
            output = F.normalize(output, dim=-1)
            heatmap_fg = output @ fg_act[:, None]  # [B, H, W, 1]
            heatmap_bg = output @ bg_act[:, None]  # [B, H, W, 1]
            heatmap_fgs.append(heatmap_fg.cpu())
            heatmap_bgs.append(heatmap_bg.cpu())
    heatmap_fg = torch.cat(heatmap_fgs, dim=0)
    heatmap_bg = torch.cat(heatmap_bgs, dim=0)
    return heatmap_fg, heatmap_bg


def make_cluster_plot(eigvecs, images, h=64, w=64, progess_start=0.6, advanced=False, clusters=50, eig_idx=None, title='cluster'):
    if clusters == 0:
        return [], []
    progress = gr.Progress()
    progress(progess_start, desc="Finding Clusters by FPS")
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    eigvecs = eigvecs.to(device)
    from ncut_pytorch.ncut_pytorch import farthest_point_sampling
    magnitude = torch.norm(eigvecs, dim=-1)
    
    # gr.Info("Finding Clusters by FPS, no magnitude filtering")
    top_p_idx = torch.arange(eigvecs.shape[0])
    if eig_idx is not None:
        top_p_idx = eig_idx
    # gr.Info("Finding Clusters by FPS, with magnitude filtering")
    # p = 0.8
    # top_p_idx = magnitude.argsort(descending=True)[:int(p * magnitude.shape[0])]
    
    
    ret_magnitude = magnitude.reshape(-1, h, w)
    
    
    num_samples = 300
    if num_samples > top_p_idx.shape[0]:
        num_samples = top_p_idx.shape[0]
    fps_idx = farthest_point_sampling(eigvecs[top_p_idx], num_samples)
    fps_idx = top_p_idx[fps_idx]
    
    # fps round 2 on the heatmap
    left = eigvecs[fps_idx, :].clone()
    right = eigvecs.clone()
    left = F.normalize(left, dim=-1)
    right = F.normalize(right, dim=-1)
    heatmap = left @ right.T
    heatmap = F.normalize(heatmap, dim=-1)  # [300, N_pixel]  PCA-> [300, 8]
    num_samples = clusters + 20   # 100/120
    if num_samples > fps_idx.shape[0]:
        num_samples = fps_idx.shape[0]
    r2_fps_idx = farthest_point_sampling(heatmap, num_samples)
    fps_idx = fps_idx[r2_fps_idx]
    
    # downsample to 256x256
    images = F.interpolate(images, (256, 256), mode="bilinear")
    images = images.cpu().numpy()
    images = images.transpose(0, 2, 3, 1)
    images = images * 255
    images = images.astype(np.uint8)
    
    
    # sort the fps_idx by the mean of the heatmap
    fps_heatmaps = {}
    sort_values = []
    top3_image_idx = {}
    top10_image_idx = {}
    for _, idx in enumerate(fps_idx):
        heatmap = F.cosine_similarity(eigvecs, eigvecs[idx][None], dim=-1)
        
        # def top_percentile(tensor, p=0.8, max_size=10000):
        #     tensor = tensor.clone().flatten()
        #     if tensor.shape[0] > max_size:
        #         tensor = tensor[torch.randperm(tensor.shape[0])[:max_size]]
        #     return tensor.quantile(p)
        # top_p = top_percentile(heatmap, p=0.5)
        top_p = 0.9
        
        heatmap = heatmap.reshape(-1, h, w)
        mask = (heatmap > top_p).float()
        # take top 3 masks only
        mask_sort_values = mask.mean((1, 2))
        _sort_value2 = (heatmap > 0.1).float().mean((1, 2)) * 0.1
        mask_sort_values += _sort_value2
        mask_sort_idx = torch.argsort(mask_sort_values, descending=True)
        mask = mask[mask_sort_idx[:3]] 
        sort_values.append(mask.mean().item())
        # fps_heatmaps[idx.item()] = heatmap.cpu()
        fps_heatmaps[idx.item()] = heatmap[mask_sort_idx[:6]].cpu()
        top3_image_idx[idx.item()] = mask_sort_idx[:3]
        top10_image_idx[idx.item()] = mask_sort_idx[:6]
    # do the sorting
    _sort_idx = torch.tensor(sort_values).argsort(descending=True)
    fps_idx = fps_idx[_sort_idx]
    # reverse the fps_idx
    # fps_idx = fps_idx.flip(0)
    # discard the big clusters
    
    # gr.Info("Discarding the biggest 10 clusters")
    # fps_idx = fps_idx[10:]
    # gr.Info("Not discarding the biggest 10 clusters")
    # gr.Info("Discarding the smallest 30 out of 80 sampled clusters")
    
    if not advanced:
        # shuffle the fps_idx
        fps_idx = fps_idx[torch.randperm(fps_idx.shape[0])]
    
        
    def plot_cluster_images(fps_idx_chunk, chunk_idx):
        fig, axs = plt.subplots(3, 5, figsize=(15, 9)) if not advanced else plt.subplots(6, 5, figsize=(15, 18))
        for ax in axs.flatten():
            ax.axis("off")
        for j, idx in enumerate(fps_idx_chunk):
            heatmap = fps_heatmaps[idx.item()]
            size = (images.shape[1], images.shape[2])
            heatmap = apply_reds_colormap(heatmap, size)
            image_idxs = top3_image_idx[idx.item()] if not advanced else top10_image_idx[idx.item()]
            for i, image_idx in enumerate(image_idxs):
                _heatmap = blend_image_with_heatmap(images[image_idx], heatmap[i])
                axs[i, j].imshow(_heatmap)
                if i == 0:
                    axs[i, j].set_title(f"{title} {chunk_idx * 5 + j + 1}", fontsize=24)
        plt.tight_layout(h_pad=0.5, w_pad=0.3)
        filename = f"{datetime.now():%Y%m%d%H%M%S%f}_{uuid.uuid4().hex}"
        tmp_path = f"/tmp/{filename}.png"
        plt.savefig(tmp_path, bbox_inches='tight', dpi=72)
        img = Image.open(tmp_path).convert("RGB")
        os.remove(tmp_path)
        plt.close()
        return img

    fig_images = []
    num_plots = clusters // 5
    plot_step_float = (1.0 - progess_start) / num_plots
    fps_idx_chunks = [fps_idx[i*5:(i+1)*5] for i in range(num_plots)]

    # with mp.Pool(processes=mp.cpu_count()) as pool:
    #     results = [pool.apply_async(plot_cluster_images, args=(chunk, i)) for i, chunk in enumerate(fps_idx_chunks)]
    #     for i, result in enumerate(results):
    #         progress(progess_start + i * plot_step_float, desc=f"Plotted {title}")
    #         fig_images.append(result.get())
    for i, chunk in enumerate(fps_idx_chunks):
        progress(progess_start + i * plot_step_float, desc=f"Plotted {title}")
        fig_images.append(plot_cluster_images(chunk, i))

    return fig_images, ret_magnitude

def make_cluster_plot_advanced(eigvecs, images, h=64, w=64):
    heatmap_fg, heatmap_bg = segment_fg_bg(images.clone())
    heatmap_bg = rearrange(heatmap_bg, 'b h w c -> b c h w')
    heatmap_fg = rearrange(heatmap_fg, 'b h w c -> b c h w')
    heatmap_fg = F.interpolate(heatmap_fg, (h, w), mode="bilinear")
    heatmap_bg = F.interpolate(heatmap_bg, (h, w), mode="bilinear")
    heatmap_fg = heatmap_fg.flatten()
    heatmap_bg = heatmap_bg.flatten()
    
    fg_minus_bg = heatmap_fg - heatmap_bg
    fg_mask = fg_minus_bg > fg_minus_bg.quantile(0.8)
    bg_mask = fg_minus_bg < fg_minus_bg.quantile(0.2)

    # fg_mask = heatmap_fg > heatmap_fg.quantile(0.8)
    # bg_mask = heatmap_bg > heatmap_bg.quantile(0.8)
    other_mask = ~(fg_mask | bg_mask)
    
    fg_idx = torch.arange(heatmap_fg.shape[0])[fg_mask]
    bg_idx = torch.arange(heatmap_bg.shape[0])[bg_mask]
    other_idx = torch.arange(heatmap_fg.shape[0])[other_mask]
    
    fg_images, _ = make_cluster_plot(eigvecs, images, h=h, w=w, advanced=True, clusters=100, eig_idx=fg_idx, title="fg")
    bg_images, _ = make_cluster_plot(eigvecs, images, h=h, w=w, advanced=True, clusters=20, eig_idx=bg_idx, title="bg")
    other_images, _ = make_cluster_plot(eigvecs, images, h=h, w=w, advanced=True, clusters=0, eig_idx=other_idx, title="other")
    
    cluster_images = fg_images + bg_images + other_images
    
    magitude = torch.norm(eigvecs, dim=-1)
    magitude = magitude.reshape(-1, h, w)
    
    # magitude = fg_minus_bg.reshape(-1, h, w)  #TODO
    
    return cluster_images, magitude
    
def ncut_run(
    model,
    images,
    model_name="DiNO(dino_vitb8_448)",
    layer=10,
    num_eig=100,
    node_type="block",
    affinity_focal_gamma=0.5,
    num_sample_ncut=10000,
    knn_ncut=10,
    embedding_method="tsne_3d",
    embedding_metric='euclidean',
    num_sample_tsne=1000,
    knn_tsne=10,
    perplexity=500,
    n_neighbors=500,
    min_dist=0.1,
    sampling_method="QuickFPS",
    ncut_metric="cosine",
    indirect_connection=True,
    make_orthogonal=False,
    old_school_ncut=False,
    recursion=False,
    recursion_l2_n_eigs=50,
    recursion_l3_n_eigs=20,
    recursion_metric="euclidean",
    recursion_l1_gamma=0.5,
    recursion_l2_gamma=0.5,
    recursion_l3_gamma=0.5,
    video_output=False,
    is_lisa=False,
    lisa_prompt1="",
    lisa_prompt2="",
    lisa_prompt3="",
    plot_clusters=False,
    alignedcut_eig_norm_plot=False,
    **kwargs,
):
    advanced = kwargs.get("advanced", False)
    directed = kwargs.get("directed", False)
    
    progress = gr.Progress()
    progress(0.2, desc="Feature Extraction")
    
    logging_str = ""
    if "AlignedThreeModelAttnNodes" == model_name:
        # dirty patch for the alignedcut paper
        resolution = (224, 224)
    else:
        resolution = RES_DICT[model_name]
    logging_str += f"Resolution: {resolution}\n"
    if perplexity >= num_sample_tsne or n_neighbors >= num_sample_tsne:
        # raise gr.Error("Perplexity must be less than the number of samples for t-SNE.")
        gr.Warning("Perplexity/n_neighbors must be less than the number of samples.\n" f"Setting Perplexity to {num_sample_tsne-1}.")
        logging_str += f"Perplexity/n_neighbors must be less than the number of samples.\n" f"Setting Perplexity to {num_sample_tsne-1}.\n"
        perplexity = num_sample_tsne - 1
        n_neighbors = num_sample_tsne - 1

    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        
    node_type = node_type.split(":")[0].strip()
        
    start = time.time()
    if "AlignedThreeModelAttnNodes" == model_name:
        # dirty patch for the alignedcut paper
        features = run_alignedthreemodelattnnodes(images, model, batch_size=BATCH_SIZE)
    elif is_lisa == True:
        # dirty patch for the LISA model
        features = []
        with torch.no_grad():
            model = model.cuda()
            images = images.cuda()
            lisa_prompts = [lisa_prompt1, lisa_prompt2, lisa_prompt3]
            for prompt in lisa_prompts:
                import bleach
                prompt = bleach.clean(prompt)
                prompt = prompt.strip()
                # print(prompt)
                # # copy the sting to a new string
                # copy_s = copy.copy(prompt)
                feature = model(images, input_str=prompt)[node_type][0]
                feature = F.normalize(feature, dim=-1)
                features.append(feature.cpu().float())
            features = torch.stack(features)
    else:
        features = extract_features(
            images, model, node_type=node_type, layer=layer-1, batch_size=BATCH_SIZE
        )
        if directed:
            node_type2 = kwargs.get("node_type2", None)
            features_B = extract_features(
                images, model, node_type=node_type2, layer=layer-1, batch_size=BATCH_SIZE
            )
    # print(f"Feature extraction time (gpu): {time.time() - start:.2f}s")
    logging_str += f"Backbone time: {time.time() - start:.2f}s\n"
    del model
    
    progress(0.4, desc="NCut")
    
    if recursion:
        rgbs = []
        all_eigvecs = []
        recursion_gammas = [recursion_l1_gamma, recursion_l2_gamma, recursion_l3_gamma]
        inp = features
        progress_start = 0.4
        for i, n_eigs in enumerate([num_eig, recursion_l2_n_eigs, recursion_l3_n_eigs]):
            logging_str += f"Recursion #{i+1}\n"
            progress_start += + 0.1 * i
            rgb, _logging_str, eigvecs = compute_ncut(
                inp,
                num_eig=n_eigs,
                num_sample_ncut=num_sample_ncut,
                affinity_focal_gamma=recursion_gammas[i],
                knn_ncut=knn_ncut,
                knn_tsne=knn_tsne,
                num_sample_tsne=num_sample_tsne,
                embedding_method=embedding_method,
                embedding_metric=embedding_metric,
                perplexity=perplexity,
                n_neighbors=n_neighbors,
                min_dist=min_dist,
                sampling_method=sampling_method,
                metric=ncut_metric if i == 0 else recursion_metric,
                indirect_connection=indirect_connection,
                make_orthogonal=make_orthogonal,
                progess_start=progress_start,
            )
            logging_str += _logging_str
            all_eigvecs.append(eigvecs.cpu().clone())
            
            
            if "AlignedThreeModelAttnNodes" == model_name:
                # dirty patch for the alignedcut paper
                start = time.time()
                progress(progress_start + 0.09, desc=f"Plotting Recursion {i+1}")
                pil_images = []
                for i_image in range(rgb.shape[0]):
                    _im = plot_one_image_36_grid(images[i_image], rgb[i_image])
                    pil_images.append(_im)
                rgbs.append(pil_images)
                logging_str += f"plot time: {time.time() - start:.2f}s\n"
            else:
                rgb = dont_use_too_much_green(rgb)
                rgbs.append(to_pil_images(rgb))
                
            inp = eigvecs.reshape(*features.shape[:-1], -1)
            if recursion_metric == "cosine":
                inp = F.normalize(inp, dim=-1)
                
        if not advanced:
            return rgbs[0], rgbs[1], rgbs[2], logging_str
        if "AlignedThreeModelAttnNodes" == model_name:
            return rgbs[0], rgbs[1], rgbs[2], logging_str
        
        if advanced:
            cluster_plots, norm_plots = [], []
            for i in range(3):
                eigvecs = all_eigvecs[i]
                # add norm plot, cluster plot
                start = time.time()
                progress_start = 0.6
                progress(progress_start, desc=f"Plotting Clusters Recursion #{i+1}")
                h, w = features.shape[1], features.shape[2]
                if torch.cuda.is_available():
                    images = images.cuda()
                _images = reverse_transform_image(images, stablediffusion="stable" in model_name.lower())
                cluster_images, eig_magnitude = make_cluster_plot_advanced(eigvecs, _images, h=h, w=w)
                logging_str += f"Recursion #{i+1} plot time: {time.time() - start:.2f}s\n"    
                
                norm_images = []
                vmin, vmax = eig_magnitude.min(), eig_magnitude.max()
                eig_magnitude = (eig_magnitude - vmin) / (vmax - vmin)
                eig_magnitude = eig_magnitude.cpu().numpy()
                colormap = matplotlib.colormaps['Reds']
                for i_image in range(eig_magnitude.shape[0]):
                    norm_image = colormap(eig_magnitude[i_image])
                    norm_images.append(torch.tensor(norm_image[..., :3]))
                norm_images = to_pil_images(norm_images)
                logging_str += f"Recursion #{i+1} Eigenvector Magnitude: [{vmin:.2f}, {vmax:.2f}]\n"
                gr.Info(f"Recursion #{i+1} Eigenvector Magnitude:</br> Min: {vmin:.2f}, Max: {vmax:.2f}", duration=10)
                
                cluster_plots.append(cluster_images)
                norm_plots.append(norm_images)
            
            return *rgbs, *norm_plots, *cluster_plots, logging_str
            
            
    if old_school_ncut:  # individual images
        logging_str += "Running NCut for each image independently\n"
        rgb = []
        progress_start = 0.4
        step_float = 0.6 / features.shape[0]
        for i_image in range(features.shape[0]):
            logging_str += f"Image #{i_image+1}\n"
            feature = features[i_image]
            _rgb, _logging_str, _ = compute_ncut(
                feature[None],
                num_eig=num_eig,
                num_sample_ncut=30000,
                affinity_focal_gamma=affinity_focal_gamma,
                knn_ncut=1,
                knn_tsne=10,
                num_sample_tsne=300,
                embedding_method=embedding_method,
                embedding_metric=embedding_metric,
                perplexity=perplexity,
                n_neighbors=n_neighbors,
                min_dist=min_dist,
                sampling_method=sampling_method,
                metric=ncut_metric,
                indirect_connection=indirect_connection,
                make_orthogonal=make_orthogonal,
                progess_start=progress_start+step_float*i_image,
            )
            logging_str += _logging_str
            rgb.append(_rgb[0])
        return to_pil_images(rgb), logging_str
    
    
    
    # ailgnedcut
    if not directed:
        rgb, _logging_str, eigvecs = compute_ncut(
            features,
            num_eig=num_eig,
            num_sample_ncut=num_sample_ncut,
            affinity_focal_gamma=affinity_focal_gamma,
            knn_ncut=knn_ncut,
            knn_tsne=knn_tsne,
            num_sample_tsne=num_sample_tsne,
            embedding_method=embedding_method,
            embedding_metric=embedding_metric,
            perplexity=perplexity,
            n_neighbors=n_neighbors,
            min_dist=min_dist,
            sampling_method=sampling_method,
            indirect_connection=indirect_connection,
            make_orthogonal=make_orthogonal,
            metric=ncut_metric,
        )
    if directed:
        head_index_text = kwargs.get("head_index_text", None)
        n_heads = features.shape[-2]   # (batch, h, w, n_heads, d)
        if head_index_text == 'all':
            head_idx = torch.arange(n_heads)
        else:
            _idxs = head_index_text.split(",")
            head_idx = torch.tensor([int(idx) for idx in _idxs])
        features_A = features[:, :, :, head_idx, :]
        features_B = features_B[:, :, :, head_idx, :]
        
        rgb, _logging_str, eigvecs = compute_ncut_directed(
            features_A,
            features_B,
            num_eig=num_eig,
            num_sample_ncut=num_sample_ncut,
            affinity_focal_gamma=affinity_focal_gamma,
            knn_ncut=knn_ncut,
            knn_tsne=knn_tsne,
            num_sample_tsne=num_sample_tsne,
            embedding_method=embedding_method,
            embedding_metric=embedding_metric,
            perplexity=perplexity,
            n_neighbors=n_neighbors,
            min_dist=min_dist,
            sampling_method=sampling_method,
            indirect_connection=False,
            make_orthogonal=make_orthogonal,
            metric=ncut_metric,
            make_symmetric=kwargs.get("make_symmetric", None),
        )
        
        
        
    logging_str += _logging_str
    
    if "AlignedThreeModelAttnNodes" == model_name:
        # dirty patch for the alignedcut paper
        start = time.time()
        progress(0.6, desc="Plotting")
        pil_images = []
        for i_image in range(rgb.shape[0]):
            _im = plot_one_image_36_grid(images[i_image], rgb[i_image])
            pil_images.append(_im)
        logging_str += f"plot time: {time.time() - start:.2f}s\n"
        return pil_images, logging_str
    
    
    if is_lisa == True:
        # dirty patch for the LISA model
        galleries = []
        for i_prompt in range(len(lisa_prompts)):
            _rgb = rgb[i_prompt]
            galleries.append(to_pil_images(_rgb))
        return *galleries, logging_str
    
    rgb = dont_use_too_much_green(rgb)
    
    if video_output:
        progress(0.8, desc="Saving Video")
        video_path = get_random_path()
        video_cache.add_video(video_path)
        pil_images_to_video(to_pil_images(rgb), video_path, fps=5)
        return video_path, logging_str
        
    cluster_images = None
    if plot_clusters and kwargs.get("n_ret", 1) > 1:
        start = time.time()
        progress_start = 0.6
        progress(progress_start, desc="Plotting Clusters")
        h, w = features.shape[1], features.shape[2]
        if torch.cuda.is_available():
            images = images.cuda()
        _images = reverse_transform_image(images, stablediffusion="stable" in model_name.lower())
        advanced = kwargs.get("advanced", False)
        if advanced:
            cluster_images, eig_magnitude = make_cluster_plot_advanced(eigvecs, _images, h=h, w=w)
        else:
            cluster_images, eig_magnitude = make_cluster_plot(eigvecs, _images, h=h, w=w, progess_start=progress_start, advanced=False)
        logging_str += f"plot time: {time.time() - start:.2f}s\n"
    
    norm_images = None
    if alignedcut_eig_norm_plot and kwargs.get("n_ret", 1) > 1:
        norm_images = []
        # eig_magnitude = torch.clamp(eig_magnitude, 0, 1)
        vmin, vmax = eig_magnitude.min(), eig_magnitude.max()
        eig_magnitude = (eig_magnitude - vmin) / (vmax - vmin)
        eig_magnitude = eig_magnitude.cpu().numpy()
        colormap = matplotlib.colormaps['Reds']
        for i_image in range(eig_magnitude.shape[0]):
            norm_image = colormap(eig_magnitude[i_image])
            # norm_image = (norm_image[..., :3] * 255).astype(np.uint8)
            # norm_images.append(Image.fromarray(norm_image))
            norm_images.append(torch.tensor(norm_image[..., :3]))
        norm_images = to_pil_images(norm_images)
        logging_str += "Eigenvector Magnitude\n"
        logging_str += f"Min: {vmin:.2f}, Max: {vmax:.2f}\n"
        gr.Info(f"Eigenvector Magnitude:</br> Min: {vmin:.2f}, Max: {vmax:.2f}", duration=10)
    
    return to_pil_images(rgb), cluster_images, norm_images, logging_str



def _ncut_run(*args, **kwargs):
    n_ret = kwargs.get("n_ret", 1)
    try:
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            
        ret = ncut_run(*args, **kwargs)
        
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        
        ret = list(ret)[:n_ret] + [ret[-1]]
        return ret
    except Exception as e:
        gr.Error(str(e))
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return *(None for _ in range(n_ret)), "Error: " + str(e)

    # ret = ncut_run(*args, **kwargs)
    # ret = list(ret)[:n_ret] + [ret[-1]]
    # return ret

if USE_HUGGINGFACE_ZEROGPU:
    @spaces.GPU(duration=30)
    def quick_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)

    @spaces.GPU(duration=45)
    def long_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)

    @spaces.GPU(duration=60)
    def longer_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)

    @spaces.GPU(duration=120)
    def super_duper_long_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)
    
    def cpu_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)

if not USE_HUGGINGFACE_ZEROGPU:
    def quick_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)

    def long_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)

    def longer_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)

    def super_duper_long_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)
    
    def cpu_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)

def extract_video_frames(video_path, max_frames=100):
    from decord import VideoReader
    vr = VideoReader(video_path)
    num_frames = len(vr)
    if num_frames > max_frames:
        gr.Warning(f"Video has {num_frames} frames. Only using {max_frames} frames. Evenly spaced.")
        frame_idx = np.linspace(0, num_frames - 1, max_frames, dtype=int).tolist()
    else:
        frame_idx = list(range(num_frames))
    frames = vr.get_batch(frame_idx).asnumpy()
    # return as list of PIL images
    return [(Image.fromarray(frames[i]), "") for i in range(frames.shape[0])]

def transform_image(image, resolution=(1024, 1024), stablediffusion=False):
    image = image.convert('RGB').resize(resolution, Image.LANCZOS)
    # Convert to torch tensor
    image = torch.tensor(np.array(image).transpose(2, 0, 1)).float()
    image = image / 255
    # Normalize
    if not stablediffusion:
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
        image = (image - torch.tensor(mean).view(3, 1, 1)) / torch.tensor(std).view(3, 1, 1)
    if stablediffusion:
        image = image * 2 - 1
    return image

def reverse_transform_image(image, stablediffusion=False):
    if stablediffusion:
        image = (image + 1) / 2
    else:
        mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1).to(image.device)
        std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1).to(image.device)
        image = image * std + mean
    image = torch.clamp(image, 0, 1)
    return image

def plot_one_image_36_grid(original_image, tsne_rgb_images):
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
    original_image = original_image * torch.tensor(std).view(3, 1, 1) + torch.tensor(mean).view(3, 1, 1)
    original_image = torch.clamp(original_image, 0, 1)
    
    fig = plt.figure(figsize=(20, 4))
    grid = plt.GridSpec(3, 14, hspace=0.1, wspace=0.1)

    ax1 = fig.add_subplot(grid[0:2, 0:2])
    img = original_image.cpu().float().numpy().transpose(1, 2, 0)

    def convert_and_pad_image(np_array, pad_size=20):
        """
        Converts a NumPy array of shape (height, width, 3) to a PNG image
        and pads the right and bottom sides with a transparent background.

        Args:
            np_array (numpy.ndarray): Input NumPy array of shape (height, width, 3)
            pad_size (int, optional): Number of pixels to pad on the right and bottom sides. Default is 20.

        Returns:
            PIL.Image: Padded PNG image with transparent background
        """
        # Convert NumPy array to PIL Image
        img = Image.fromarray(np_array)

        # Get the original size
        width, height = img.size

        # Create a new image with padding and transparent background
        new_width = width + pad_size
        new_height = height + pad_size
        padded_img = Image.new('RGBA', (new_width, new_height), color=(255, 255, 255, 0))

        # Paste the original image onto the padded image
        padded_img.paste(img, (0, 0))

        return padded_img
    
    img = convert_and_pad_image((img*255).astype(np.uint8))
    ax1.imshow(img)
    ax1.axis('off')

    model_names = ['CLIP', 'DINO', 'MAE']

    for i_model, model_name in enumerate(model_names):
        for i_layer in range(12):
            ax = fig.add_subplot(grid[i_model, i_layer+2])
            ax.imshow(tsne_rgb_images[i_layer+12*i_model].cpu().float().numpy())
            ax.axis('off')
            if i_model == 0:
                ax.set_title(f'Layer{i_layer}', fontsize=16)
            if i_layer == 0:
                ax.text(-0.1, 0.5, model_name, va="center", ha="center", fontsize=16, transform=ax.transAxes, rotation=90,)    
    plt.tight_layout()
    
    filename = uuid.uuid4()
    filename = f"/tmp/{filename}.png"
    plt.savefig(filename, bbox_inches='tight', pad_inches=0, dpi=100)
    
    img = Image.open(filename)
    img = img.convert("RGB")
    img = copy.deepcopy(img)
    
    os.remove(filename)
    
    plt.close()
    return img

def load_alignedthreemodel():
    import sys
    
    if "alignedthreeattn" not in sys.path:
        for _ in range(3):
            os.system("git clone https://huggingface.co/huzey/alignedthreeattn >> /dev/null 2>&1")
            os.system("git -C alignedthreeattn pull >> /dev/null 2>&1")
        # add to path
        sys.path.append("alignedthreeattn")
    
    
    from alignedthreeattn.alignedthreeattn_model import ThreeAttnNodes
    
    align_weights = torch.load("alignedthreeattn/align_weights.pth")
    model = ThreeAttnNodes(align_weights)
    
    return model

try:
    # pre-load the alignedthree model in case it fails to load
    load_alignedthreemodel()
except Exception as e:
    pass

promptable_diffusion_models = ["Diffusion(stabilityai/stable-diffusion-2)", "Diffusion(CompVis/stable-diffusion-v1-4)"]
promptable_segmentation_models = ["LISA(xinlai/LISA-7B-v1)"]


def run_fn(
    images,
    model_name="DiNO(dino_vitb8_448)",
    layer=10,
    num_eig=100,
    node_type="block",
    positive_prompt="",
    negative_prompt="",
    is_lisa=False,
    lisa_prompt1="",
    lisa_prompt2="",
    lisa_prompt3="",
    affinity_focal_gamma=0.5,
    num_sample_ncut=10000,
    knn_ncut=10,
    ncut_indirect_connection=True,
    ncut_make_orthogonal=False,
    embedding_method="tsne_3d",
    embedding_metric='euclidean',
    num_sample_tsne=300,
    knn_tsne=10,
    perplexity=150,
    n_neighbors=150,
    min_dist=0.1,
    sampling_method="QuickFPS",
    ncut_metric="cosine",
    old_school_ncut=False,
    max_frames=100,
    recursion=False,
    recursion_l2_n_eigs=50,
    recursion_l3_n_eigs=20,
    recursion_metric="euclidean",
    recursion_l1_gamma=0.5,
    recursion_l2_gamma=0.5,
    recursion_l3_gamma=0.5,
    node_type2="k",
    head_index_text='all',
    make_symmetric=False,
    n_ret=1,
    plot_clusters=False,
    alignedcut_eig_norm_plot=False,
    advanced=False,
    directed=False,
):
    # print(node_type2, head_index_text, make_symmetric)
    progress=gr.Progress()
    progress(0, desc="Starting")
    
    
    if images is None:
        gr.Warning("No images selected.")
        return *(None for _ in range(n_ret)), "No images selected."
    
    progress(0.05, desc="Processing Images")
    video_output = False
    if isinstance(images, str):
        images = extract_video_frames(images, max_frames=max_frames)
        video_output = True
    
    if sampling_method == "QuickFPS":
        sampling_method = "farthest"
        
    # resize the images before acquiring GPU
    if "AlignedThreeModelAttnNodes" == model_name:
        # dirty patch for the alignedcut paper
        resolution = (224, 224)
    else:
        resolution = RES_DICT[model_name]
    images = [tup[0] for tup in images]
    stablediffusion = True if "Diffusion" in model_name else False
    images = [transform_image(image, resolution=resolution, stablediffusion=stablediffusion) for image in images]
    images = torch.stack(images)
    
    progress(0.1, desc="Downloading Model")
    
    if is_lisa:
        import subprocess
        import sys
        import importlib
        gr.Warning("LISA model is not compatible with the current version of transformers. Please contact the LISA and Llava author for update.")
        gr.Warning("This is a dirty patch for the LISA model. switch to the old version of transformers.")
        gr.Warning("Not garanteed to work.")
        # LISA and Llava is not compatible with the current version of transformers
        # please contact the author for update
        # this is a dirty patch for the LISA model
        
        # pre-import the SD3 pipeline
        from diffusers import StableDiffusion3Pipeline
        
        # unloading the current transformers
        for module in list(sys.modules.keys()):
            if "transformers" in module:
                del sys.modules[module]
            

        def install_transformers_version(version, target_dir):
            """Install a specific version of transformers to a target directory."""
            if not os.path.exists(target_dir):
                os.makedirs(target_dir)
            
            # Use subprocess to run the pip command
            # subprocess.check_call([sys.executable, '-m', 'pip', 'install', f'transformers=={version}', '-t', target_dir])
            os.system(f"{sys.executable} -m pip install transformers=={version} -t {target_dir} >> /dev/null 2>&1")

        target_dir = '/tmp/lisa_transformers_v433'
        if not os.path.exists(target_dir):
            install_transformers_version('4.33.0', target_dir)

        # Add the new version path to sys.path
        sys.path.insert(0, target_dir)
        
        transformers = importlib.import_module("transformers")

    if not is_lisa:
        import subprocess
        import sys
        import importlib
        # remove the LISA model from the sys.path
            
        if "/tmp/lisa_transformers_v433" in sys.path:
            sys.path.remove("/tmp/lisa_transformers_v433")
        
        transformers = importlib.import_module("transformers")
        
    
    
    if "AlignedThreeModelAttnNodes" == model_name:
        # dirty patch for the alignedcut paper
        model = load_alignedthreemodel()
    else:
        model = load_model(model_name)
        
    if "stable" in model_name.lower() and "diffusion" in model_name.lower():
        model.timestep = layer
        layer = 1
        
    if model_name in promptable_diffusion_models:
        model.positive_prompt = positive_prompt
        model.negative_prompt = negative_prompt
        
    kwargs = {
        "model_name": model_name,
        "layer": layer,
        "num_eig": num_eig,
        "node_type": node_type,
        "affinity_focal_gamma": affinity_focal_gamma,
        "num_sample_ncut": num_sample_ncut,
        "knn_ncut": knn_ncut,
        "embedding_method": embedding_method,
        "embedding_metric": embedding_metric,
        "num_sample_tsne": num_sample_tsne,
        "knn_tsne": knn_tsne,
        "perplexity": perplexity,
        "n_neighbors": n_neighbors,
        "min_dist": min_dist,
        "sampling_method": sampling_method,
        "ncut_metric": ncut_metric,
        "indirect_connection": ncut_indirect_connection,
        "make_orthogonal": ncut_make_orthogonal,
        "old_school_ncut": old_school_ncut,
        "recursion": recursion,
        "recursion_l2_n_eigs": recursion_l2_n_eigs,
        "recursion_l3_n_eigs": recursion_l3_n_eigs,
        "recursion_metric": recursion_metric,
        "recursion_l1_gamma": recursion_l1_gamma,
        "recursion_l2_gamma": recursion_l2_gamma,
        "recursion_l3_gamma": recursion_l3_gamma,
        "video_output": video_output,
        "lisa_prompt1": lisa_prompt1,
        "lisa_prompt2": lisa_prompt2,
        "lisa_prompt3": lisa_prompt3,
        "is_lisa": is_lisa,
        "n_ret": n_ret,
        "plot_clusters": plot_clusters,
        "alignedcut_eig_norm_plot": alignedcut_eig_norm_plot,
        "advanced": advanced,
        "directed": directed,
        "node_type2": node_type2,
        "head_index_text": head_index_text,
        "make_symmetric": make_symmetric,
    }
    # print(kwargs)
    
    try:
        # try to aquiare GPU, can fail if the user is out of GPU quota
    
        if old_school_ncut:
            return super_duper_long_run(model, images, **kwargs)
        
        if is_lisa:
            return super_duper_long_run(model, images, **kwargs)
        
        num_images = len(images)
        if num_images >= 100:
            return super_duper_long_run(model, images, **kwargs)
        if 'diffusion' in model_name.lower():
            return super_duper_long_run(model, images, **kwargs)
        if recursion:
            return longer_run(model, images, **kwargs)
        if num_images >= 50:
            return longer_run(model, images, **kwargs)
        if old_school_ncut:
            return longer_run(model, images, **kwargs)
        if num_images >= 10:
            return long_run(model, images, **kwargs)
        if embedding_method == "UMAP":
            if perplexity >= 250 or num_sample_tsne >= 500:
                return longer_run(model, images, **kwargs)
            return long_run(model, images, **kwargs)
        if embedding_method == "t-SNE":
            if perplexity >= 250 or num_sample_tsne >= 500:
                return long_run(model, images, **kwargs)
            return quick_run(model, images, **kwargs)
        
        return quick_run(model, images, **kwargs)
    
    except gr.Error as e:
        # I assume this is a GPU quota error
        
        info1 = 'Running out of HuggingFace GPU Quota?</br> Please try <a style="white-space: nowrap;text-underline-offset: 2px;color: var(--body-text-color)" href="https://ncut-pytorch.readthedocs.io/en/latest/demo/">Demo hosted at UPenn</a></br>'
        info2 = 'Or try use the Python package that powers this app: <a style="white-space: nowrap;text-underline-offset: 2px;color: var(--body-text-color)" href="https://ncut-pytorch.readthedocs.io/en/latest/">ncut-pytorch</a>'
        info = info1 + info2
        
        message = "<b>HuggingFace: </b></br>" + e.message + "</br></br>---------</br>" + "<b>`ncut-pytorch` Developer: </b></br>" + info
        raise gr.Error(message, duration=0)


import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
import pytorch_lightning as pl

# Custom Dataset
class TwoTensorDataset(Dataset):
    def __init__(self, A, B):
        self.A = A
        self.B = B

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

    def __getitem__(self, idx):
        return self.A[idx], self.B[idx]

# MLP model
class MLP(pl.LightningModule):
    def __init__(self, num_layer=3, width=512, lr=3e-4, fitting_steps=10000, seg_loss_lambda=1.0):
        super().__init__()
        layers = [nn.Linear(3, width), nn.GELU()]
        for _ in range(num_layer - 1):
            layers.append(nn.Linear(width, width))
            layers.append(nn.GELU())
        layers.append(nn.Linear(width, 3))
        self.layers = nn.Sequential(*layers)
        self.mse_loss = nn.MSELoss()
        self.lr = lr
        self.fitting_steps = fitting_steps
        self.seg_loss_lambda = seg_loss_lambda
        self.progress = gr.Progress()

    def forward(self, x):
        return self.layers(x)

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self.forward(x)
        loss = self.mse_loss(y_hat, y)
        # loss = torch.nn.functional.mse_loss(torch.log(y_hat), torch.log(y))
        self.log("train_loss", loss)
        
        # add segmentation constraint
        bsz = x.shape[0]
        sample_size = 1000
        if bsz > sample_size:
            idx = torch.randperm(bsz)[:sample_size]
            x = x[idx]
            y_hat = y_hat[idx]
        
        old_dist = torch.pdist(x, p=2)
        new_dist = torch.pdist(y_hat, p=2)
        # seg_loss = torch.log((old_dist - new_dist)).pow(2).mean()
        seg_loss = self.mse_loss(old_dist, new_dist)
        self.log("seg_loss", seg_loss)
        loss += seg_loss * self.seg_loss_lambda
        
        step = self.global_step
        if step % 100 == 0:
            self.progress(step / self.fitting_steps, desc="Fitting MLP")
            
        return loss
    
    def predict_step(self, batch, batch_idx, dataloader_idx=None):
        x = batch[0]
        y_hat = self.forward(x)
        return y_hat

    def configure_optimizers(self):
        optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
        return optimizer


def fit_trans(rgb1, rgb2, num_layer=3, width=512, batch_size=256, lr=3e-4, fitting_steps=10000, fps_sample=4096, seg_loss_lambda=1.0):
    A = rgb1.clone()
    B = rgb2.clone()
    
    # FPS sample on the data
    from ncut_pytorch.ncut_pytorch import farthest_point_sampling
    A_idx = farthest_point_sampling(A, fps_sample)
    B_idx = farthest_point_sampling(B, fps_sample)
    A_B_idx = np.concatenate([A_idx, B_idx])
    A = A[A_B_idx]
    B = B[A_B_idx]

    from torch.utils.data import DataLoader, TensorDataset
    # Dataset and DataLoader
    dataset = TwoTensorDataset(A, B)
    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

    # Initialize model and trainer
    mlp = MLP(num_layer=num_layer, width=width, lr=lr, fitting_steps=fitting_steps, seg_loss_lambda=seg_loss_lambda)
    trainer = pl.Trainer(
        max_epochs=100000, 
        gpus=1, 
        max_steps=fitting_steps, 
        enable_checkpointing=False,
        enable_progress_bar=False,
        gradient_clip_val=1.0
    )

    # Create a DataLoader for tensor A
    batch_size = 256  # Define your batch size
    data_loader = DataLoader(TensorDataset(rgb1), batch_size=batch_size, shuffle=False)
    

    # Train the model
    trainer.fit(mlp, dataloader)

    mlp.progress(0.99, desc="Applying MLP")
    results = trainer.predict(mlp, data_loader)
    A_transformed = torch.cat(results, dim=0)
    
    return A_transformed

if USE_HUGGINGFACE_ZEROGPU:
    @spaces.GPU(duration=60)
    def _run_mlp_fit(*args, **kwargs):
        return fit_trans(*args, **kwargs)
else:
    def _run_mlp_fit(*args, **kwargs):
        return fit_trans(*args, **kwargs)


def run_mlp_fit(input_gallery, target_gallery, num_layer=3, width=512, batch_size=256, lr=3e-4, fitting_steps=10000, fps_sample=4096, seg_loss_lambda=1.0):
    # print("Fitting MLP")
    # print("Target Gallery Length:", len(target_gallery))
    # print("Input Gallery Length:", len(input_gallery))
    if target_gallery is None or len(target_gallery) == 0:
        raise gr.Error("No target images selected. Please use the Mark button to select the target images.")
    if input_gallery is None or len(input_gallery) == 0:
        raise gr.Error("No input images selected.")
    def gallery_to_rgb(gallery):
        images = [tup[0] for tup in gallery]
        rgb = []
        for image in images:
            if isinstance(image, str):
                image = Image.open(image)
            image = image.convert('RGB')
            image = torch.tensor(np.array(image)).float()
            image = image / 255
            rgb.append(image)
        rgb = torch.stack(rgb)
        shape = rgb.shape
        rgb = rgb.reshape(-1, 3)
        return rgb, shape
    
    target_rgb, target_shape = gallery_to_rgb(target_gallery)
    input_rgb, input_shape = gallery_to_rgb(input_gallery)
    
    input_transformed = _run_mlp_fit(input_rgb, target_rgb, num_layer=num_layer, width=width, batch_size=batch_size, lr=lr, 
                                    fitting_steps=fitting_steps, fps_sample=fps_sample, seg_loss_lambda=seg_loss_lambda)
    input_transformed = input_transformed.reshape(*input_shape)
    pil_images = to_pil_images(input_transformed, resize=False)
    return pil_images

    
def make_input_video_section():
    # gr.Markdown('### Input Video')
    input_gallery = gr.Video(value=None, label="Select video", elem_id="video-input", height="auto", show_share_button=False, interactive=True)
    gr.Markdown('_image backbone model is used to extract features from each frame, NCUT is computed on all frames_')
    max_frames_number = gr.Number(100, label="Max frames", elem_id="max_frames")
    # max_frames_number = gr.Slider(1, 200, step=1, label="Max frames", value=100, elem_id="max_frames")
    submit_button = gr.Button("🔴 RUN", elem_id="submit_button", variant='primary')
    clear_images_button = gr.Button("🗑️Clear", elem_id='clear_button', variant='stop')
    return input_gallery, submit_button, clear_images_button, max_frames_number


def load_dataset_images(is_advanced, dataset_name, num_images=10, 
                        is_filter=False, filter_by_class_text="0,1,2", 
                        is_random=False, seed=1):
    progress = gr.Progress()
    progress(0, desc="Loading Images")
    
    if dataset_name == "EgoExo":
        is_advanced = "Basic"
        
    if is_advanced == "Basic":
        gr.Info(f"Loaded images from EgoExo")
        return default_images
    try:
        progress(0.5, desc="Downloading Dataset")
        if 'EgoThink' in dataset_name:
            dataset = load_dataset(dataset_name, 'Activity', trust_remote_code=True)
        else:
            dataset = load_dataset(dataset_name, trust_remote_code=True)
        key = list(dataset.keys())[0]
        dataset = dataset[key]
    except Exception as e:
        raise gr.Error(f"Error loading dataset {dataset_name}: {e}")
    if num_images > len(dataset):
        num_images = len(dataset)
    
    if len(filter_by_class_text) == 0:
        is_filter = False
    
    if is_filter:
        progress(0.8, desc="Filtering Images")
        classes = [int(i) for i in filter_by_class_text.split(",")]
        labels = np.array(dataset['label'])
        unique_labels = np.unique(labels)
        valid_classes = [i for i in classes if i in unique_labels]
        invalid_classes = [i for i in classes if i not in unique_labels]
        if len(invalid_classes) > 0:
            gr.Warning(f"Classes {invalid_classes} not found in the dataset.")
        if len(valid_classes) == 0:
            raise gr.Error(f"Classes {classes} not found in the dataset.")
        # shuffle each class
        chunk_size = num_images // len(valid_classes)
        image_idx = []
        for i in valid_classes:
            idx = np.where(labels == i)[0]
            if is_random:
                idx = np.random.RandomState(seed).choice(idx, chunk_size, replace=False)
            else:
                idx = idx[:chunk_size]
            image_idx.extend(idx.tolist())
    if not is_filter:
        if is_random:
            image_idx = np.random.RandomState(seed).choice(len(dataset), num_images, replace=False).tolist()
        else:
            image_idx = list(range(num_images))
    key = 'image' if 'image' in dataset[0] else list(dataset[0].keys())[0]
    images = [dataset[i][key] for i in image_idx]
    gr.Info(f"Loaded {len(images)} images from {dataset_name}")
    del dataset
    
    if dataset_name in CENTER_CROP_DATASETS:
        def center_crop_image(img):
            # image: PIL image
            w, h = img.size
            min_hw = min(h, w)
            # center crop
            left = (w - min_hw) // 2
            top = (h - min_hw) // 2
            right = left + min_hw
            bottom = top + min_hw
            img = img.crop((left, top, right, bottom))
            return img
        images = [center_crop_image(image) for image in images]
    
    return images 

def load_and_append(existing_images, *args, **kwargs):
    new_images = load_dataset_images(*args, **kwargs)
    if new_images is None:
        return existing_images
    if len(new_images) == 0:
        return existing_images
    if existing_images is None:
        existing_images = []
    existing_images += new_images
    gr.Info(f"Total images: {len(existing_images)}")
    return existing_images

def make_input_images_section(rows=1, cols=3, height="auto", advanced=False, is_random=False, allow_download=False, markdown=True):
    if markdown:
        gr.Markdown('### Input Images')
    input_gallery = gr.Gallery(value=None, label="Input images", show_label=True, elem_id="input_images", columns=[cols], rows=[rows], object_fit="contain", height=height, type="pil", show_share_button=False,
                               format="webp")
    
    submit_button = gr.Button("🔴 RUN", elem_id="submit_button", variant='primary')
    with gr.Row():
        clear_images_button = gr.Button("🗑️ Clear", elem_id='clear_button', variant='stop')
        clear_images_button.click(fn=lambda: gr.update(value=None), outputs=[input_gallery])
        upload_button = gr.UploadButton(elem_id="upload_button", label="⬆️ Upload", variant='secondary', file_types=["image"], file_count="multiple")
        
        def convert_to_pil_and_append(images, new_images):
            if images is None:
                images = []
            if new_images is None:
                return images
            if isinstance(new_images, Image.Image):
                images.append(new_images)
            if isinstance(new_images, list):
                images += [Image.open(new_image) for new_image in new_images]
            if isinstance(new_images, str):
                images.append(Image.open(new_images))
            gr.Info(f"Total images: {len(images)}")
            return images
        upload_button.upload(convert_to_pil_and_append, inputs=[input_gallery, upload_button], outputs=[input_gallery])
    
    if allow_download:
        create_file_button, download_button = add_download_button(input_gallery, "input_images")
    
    gr.Markdown('### Load Datasets')
    advanced_radio = gr.Radio(["Basic", "Advanced"], label="Datasets Menu", value="Advanced" if advanced else "Basic", elem_id="advanced-radio", show_label=True)
    with gr.Column() as basic_block:
        # gr.Markdown('### Example Image Sets')
        def make_example(name, images, dataset_name):
            with gr.Row():
                button = gr.Button("Load\n"+name, elem_id=f"example-{name}", elem_classes="small-button", variant='secondary', size="sm", scale=1, min_width=60)
                gallery = gr.Gallery(value=images, label=name, show_label=True, columns=[3], rows=[1], interactive=False, height=80, scale=8, object_fit="cover", min_width=140, allow_preview=False)
            button.click(fn=lambda: gr.update(value=load_dataset_images(True, dataset_name, 100, is_random=True, seed=42)), outputs=[input_gallery])
            return gallery, button
        example_items = [
            ("EgoExo", ['./images/egoexo1.jpg', './images/egoexo3.jpg', './images/egoexo2.jpg'], "EgoExo"),
            ("Ego", ['./images/egothink1.jpg', './images/egothink2.jpg', './images/egothink3.jpg'], "EgoThink/EgoThink"),
            ("Face", ['./images/face1.jpg', './images/face2.jpg', './images/face3.jpg'], "nielsr/CelebA-faces"),
            ("Pose", ['./images/pose1.jpg', './images/pose2.jpg', './images/pose3.jpg'], "sayakpaul/poses-controlnet-dataset"),
            # ("CatDog", ['./images/catdog1.jpg', './images/catdog2.jpg', './images/catdog3.jpg'], "microsoft/cats_vs_dogs"),
            # ("Bird", ['./images/bird1.jpg', './images/bird2.jpg', './images/bird3.jpg'], "Multimodal-Fatima/CUB_train"),
            # ("ChestXray", ['./images/chestxray1.jpg', './images/chestxray2.jpg', './images/chestxray3.jpg'], "hongrui/mimic_chest_xray_v_1"),
            ("BrainMRI", ['./images/brain1.jpg', './images/brain2.jpg', './images/brain3.jpg'], "sartajbhuvaji/Brain-Tumor-Classification"),
            ("Kanji", ['./images/kanji1.jpg', './images/kanji2.jpg', './images/kanji3.jpg'], "yashvoladoddi37/kanjienglish"),
        ]
        for name, images, dataset_name in example_items:
            make_example(name, images, dataset_name)
    with gr.Column() as advanced_block:
        load_images_button = gr.Button("🔴 Load Images", elem_id="load-images-button", variant='primary')
        # dataset_names = DATASET_NAMES
        # dataset_classes = DATASET_CLASSES
        dataset_categories = list(DATASETS.keys())
        defualt_cat = dataset_categories[0]
        def get_choices(cat):
            return [tup[0] for tup in DATASETS[cat]]
        defualt_choices = get_choices(defualt_cat)
        with gr.Row():
            dataset_radio = gr.Radio(dataset_categories, label="Dataset Category", value=defualt_cat, elem_id="dataset-radio", show_label=True, min_width=600)
            # dataset_dropdown = gr.Dropdown(dataset_names, label="Dataset name", value="mrm8488/ImageNet1K-val", elem_id="dataset", min_width=300)
            dataset_dropdown = gr.Dropdown(defualt_choices, label="Dataset name", value=defualt_choices[0], elem_id="dataset", min_width=400)
            dataset_radio.change(fn=lambda x: gr.update(choices=get_choices(x), value=get_choices(x)[0]), inputs=dataset_radio, outputs=dataset_dropdown)
            # num_images_slider = gr.Number(10, label="Number of images", elem_id="num_images")
            num_images_slider = gr.Slider(1, 1000, step=1, label="Number of images", value=10, elem_id="num_images", min_width=200)
            if not is_random:
                filter_by_class_checkbox = gr.Checkbox(label="Filter by class", value=True, elem_id="filter_by_class_checkbox")
                filter_by_class_text = gr.Textbox(label="Class to select", value="0,33,99", elem_id="filter_by_class_text", info=f"e.g. `0,1,2`. (1000 classes)", visible=True)
                # is_random_checkbox = gr.Checkbox(label="Random shuffle", value=False, elem_id="random_seed_checkbox")
                # random_seed_slider = gr.Slider(0, 1000, step=1, label="Random seed", value=1, elem_id="random_seed", visible=False)
                is_random_checkbox = gr.Checkbox(label="Random shuffle", value=True, elem_id="random_seed_checkbox")
                random_seed_slider = gr.Slider(0, 1000, step=1, label="Random seed", value=1, elem_id="random_seed", visible=True)
            if is_random:
                filter_by_class_checkbox = gr.Checkbox(label="Filter by class", value=False, elem_id="filter_by_class_checkbox")
                filter_by_class_text = gr.Textbox(label="Class to select", value="0,33,99", elem_id="filter_by_class_text", info=f"e.g. `0,1,2`. (1000 classes)", visible=False)
                is_random_checkbox = gr.Checkbox(label="Random shuffle", value=True, elem_id="random_seed_checkbox")
                random_seed_slider = gr.Slider(0, 1000, step=1, label="Random seed", value=42, elem_id="random_seed", visible=True)
        
        # add functionality, save and load images to profile
        with gr.Accordion("Saved Image Profiles", open=False) as profile_accordion:
            with gr.Row():
                profile_text = gr.Textbox(label="Profile name", placeholder="Type here: Profile name to save/load/delete", elem_id="profile-name", scale=6, show_label=False)
                list_profiles_button = gr.Button("📋 List", elem_id="list-profile-button", variant='secondary', scale=3)
            with gr.Row():
                save_profile_button = gr.Button("💾 Save", elem_id="save-profile-button", variant='secondary')
                load_profile_button = gr.Button("📂 Load", elem_id="load-profile-button", variant='secondary')
                delete_profile_button = gr.Button("🗑️ Delete", elem_id="delete-profile-button", variant='secondary')
    
            class OnDiskProfiles:
                def __init__(self, profile_dir="demo_profiles"):
                    if not os.path.exists(profile_dir):
                        os.makedirs(profile_dir)
                    self.profile_dir = profile_dir
                
                def list_profiles(self):
                    profiles = os.listdir(self.profile_dir)
                    # remove hidden files
                    profiles = [p for p in profiles if not p.startswith(".")]
                    if len(profiles) == 0:
                        return "No profiles found."
                    profile_text = "</br>".join(profiles)
                    n_files = len(profiles)
                    profile_text = f"Number of profiles: {n_files}</br>---------</br>" + profile_text
                    return profile_text
                
                def save_profile(self, profile_name, images):
                    profile_path = os.path.join(self.profile_dir, profile_name)
                    if os.path.exists(profile_path):
                        raise gr.Error(f"Profile {profile_name} already exists.")
                    with open(profile_path, "wb") as f:
                        pickle.dump(images, f)
                    gr.Info(f"Profile {profile_name} saved.")
                    return profile_path
                
                def load_profile(self, profile_name, existing_images):
                    profile_path = os.path.join(self.profile_dir, profile_name)
                    if not os.path.exists(profile_path):
                        raise gr.Error(f"Profile {profile_name} not found.")
                    with open(profile_path, "rb") as f:
                        images = pickle.load(f)
                    gr.Info(f"Profile {profile_name} loaded.")
                    if existing_images is None:
                        existing_images = []
                    
                    return existing_images + images
                
                def delete_profile(self, profile_name):
                    profile_path = os.path.join(self.profile_dir, profile_name)
                    os.remove(profile_path)
                    gr.Info(f"Profile {profile_name} deleted.")
                    return profile_path
            
            home_dir = os.path.expanduser("~")
            defualt_dir = os.path.join(home_dir, ".cache")
            cache_dir = os.environ.get("DEMO_PROFILE_CACHE_DIR", defualt_dir)
            cache_dir = os.path.join(cache_dir, "demo_profiles")
            on_disk_profiles = OnDiskProfiles(cache_dir)
            save_profile_button.click(fn=lambda name, images: on_disk_profiles.save_profile(name, images), inputs=[profile_text, input_gallery])
            load_profile_button.click(fn=lambda name, existing_images: gr.update(value=on_disk_profiles.load_profile(name, existing_images)), inputs=[profile_text, input_gallery], outputs=[input_gallery])
            delete_profile_button.click(fn=lambda name: on_disk_profiles.delete_profile(name), inputs=profile_text)
            list_profiles_button.click(fn=lambda: gr.Info(on_disk_profiles.list_profiles(), duration=0))
    
    if advanced:
        advanced_block.visible = True
        basic_block.visible = False
    else:
        advanced_block.visible = False
        basic_block.visible = True
        
    # change visibility 
    advanced_radio.change(fn=lambda x: gr.update(visible=x=="Advanced"), inputs=advanced_radio, outputs=[advanced_block])
    advanced_radio.change(fn=lambda x: gr.update(visible=x=="Basic"), inputs=advanced_radio, outputs=[basic_block])
    
    def find_num_classes(dataset_name):
        num_classes = None
        for cat, datasets in DATASETS.items():
            datasets = [tup[0] for tup in datasets]
            if dataset_name in datasets:
                num_classes = DATASETS[cat][datasets.index(dataset_name)][1]
                break
        return num_classes
    
    def change_filter_options(dataset_name):
        num_classes = find_num_classes(dataset_name)
        if num_classes is None:
            return (gr.Checkbox(label="Filter by class", value=False, elem_id="filter_by_class_checkbox", visible=False),
                    gr.Textbox(label="Class to select", value="0,1,2", elem_id="filter_by_class_text", info="e.g. `0,1,2`. This dataset has no class label", visible=False))
        return (gr.Checkbox(label="Filter by class", value=True, elem_id="filter_by_class_checkbox", visible=True),
                gr.Textbox(label="Class to select", value="0,1,2", elem_id="filter_by_class_text", info=f"e.g. `0,1,2`. ({num_classes} classes)", visible=True))
    dataset_dropdown.change(fn=change_filter_options, inputs=dataset_dropdown, outputs=[filter_by_class_checkbox, filter_by_class_text])
    
    def change_filter_by_class(is_filter, dataset_name):
        num_classes = find_num_classes(dataset_name)
        return gr.Textbox(label="Class to select", value="0,1,2", elem_id="filter_by_class_text", info=f"e.g. `0,1,2`. ({num_classes} classes)", visible=is_filter)
    filter_by_class_checkbox.change(fn=change_filter_by_class, inputs=[filter_by_class_checkbox, dataset_dropdown], outputs=filter_by_class_text)
    
    def change_random_seed(is_random):
        return gr.Slider(0, 1000, step=1, label="Random seed", value=1, elem_id="random_seed", visible=is_random)
    is_random_checkbox.change(fn=change_random_seed, inputs=is_random_checkbox, outputs=random_seed_slider)
    
    
    
    load_images_button.click(load_and_append, 
                        inputs=[input_gallery, advanced_radio, dataset_dropdown, num_images_slider,
                                filter_by_class_checkbox, filter_by_class_text, 
                                is_random_checkbox, random_seed_slider],
                        outputs=[input_gallery])
    
    
    
    return input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button
    


# def random_rotate_rgb_gallery(images):
#     if images is None or len(images) == 0:
#         gr.Warning("No images selected.")
#         return []
#     # read webp images
#     images = [Image.open(image[0]).convert("RGB") for image in images]
#     images = [np.array(image).astype(np.float32) for image in images]
#     images = np.stack(images)
#     images = torch.tensor(images) / 255
#     position = np.random.choice([1, 2, 4, 5, 6])
#     images = rotate_rgb_cube(images, position)
#     images = to_pil_images(images, resize=False)
#     return images

def protect_original_image_in_plot(original_image, rotated_images):
    plot_h, plot_w = 332, 1542
    image_h, image_w = original_image.shape[1], original_image.shape[2]
    if not (plot_h == image_h and plot_w == image_w):
        return rotated_images
    protection_w = 190
    rotated_images[:, :, :protection_w] = original_image[:, :, :protection_w]
    return rotated_images

def sequence_rotate_rgb_gallery(images):
    if images is None or len(images) == 0:
        gr.Warning("No images selected.")
        return []
    # read webp images
    images = [Image.open(image[0]).convert("RGB") for image in images]
    images = [np.array(image).astype(np.float32) for image in images]
    images = np.stack(images)
    images = torch.tensor(images) / 255
    original_images = images.clone()
    rotation_matrix = torch.tensor([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).float()
    images = images @ rotation_matrix
    images = protect_original_image_in_plot(original_images, images)
    images = to_pil_images(images, resize=False)
    return images

def flip_rgb_gallery(images, axis=0):
    if images is None or len(images) == 0:
        gr.Warning("No images selected.")
        return []
    # read webp images
    images = [Image.open(image[0]).convert("RGB") for image in images]
    images = [np.array(image).astype(np.float32) for image in images]
    images = np.stack(images)
    images = torch.tensor(images) / 255
    original_images = images.clone()
    images = 1 - images
    images = protect_original_image_in_plot(original_images, images)
    images = to_pil_images(images, resize=False)
    return images

def add_rotate_flip_buttons(output_gallery):
    with gr.Row():
        rotate_button = gr.Button("🔄 Rotate", elem_id="rotate_button", variant='secondary')
        rotate_button.click(sequence_rotate_rgb_gallery, inputs=[output_gallery], outputs=[output_gallery])
        flip_button = gr.Button("🔃 Flip", elem_id="flip_button", variant='secondary')
        flip_button.click(flip_rgb_gallery, inputs=[output_gallery], outputs=[output_gallery])
    return rotate_button, flip_button

def add_download_button(gallery, filename_prefix="output"):
    
    def make_3x5_plot(images):
        plot_list = []
        
        # Split the list of images into chunks of 15
        chunks = [images[i:i + 15] for i in range(0, len(images), 15)]
        
        for chunk in chunks:
            fig, axs = plt.subplots(3, 4, figsize=(12, 9))
            for ax in axs.flatten():
                ax.axis("off")
            for ax, img in zip(axs.flatten(), chunk):
                img = img.convert("RGB")
                ax.imshow(img)
            
            plt.tight_layout(h_pad=0.5, w_pad=0.3)

            # Generate a unique filename
            filename = uuid.uuid4()
            tmp_path = f"/tmp/{filename}.png"
            
            # Save the plot to the temporary file
            plt.savefig(tmp_path, bbox_inches='tight', dpi=144)
            
            # Open the saved image
            img = Image.open(tmp_path)
            img = img.convert("RGB")
            img = copy.deepcopy(img)
            
            # Remove the temporary file
            os.remove(tmp_path)

            plot_list.append(img)
            plt.close()
        
        return plot_list
    
    def delete_file_after_delay(file_path, delay):
        def delete_file():
            if os.path.exists(file_path):
                os.remove(file_path)
        
        timer = threading.Timer(delay, delete_file)
        timer.start()
    
    def create_zip_file(images, filename_prefix=filename_prefix):
        if images is None or len(images) == 0:
            gr.Warning("No images selected.")
            return None
        gr.Info("Creating zip file for download...")
        images = [image[0] for image in images]
        if isinstance(images[0], str):
            images = [Image.open(image) for image in images]
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        
        zip_filename = f"/tmp/gallery_download/{filename_prefix}_{timestamp}.zip"
        os.makedirs(os.path.dirname(zip_filename), exist_ok=True)
        
        plots = make_3x5_plot(images)
        
        
        
        with zipfile.ZipFile(zip_filename, 'w') as zipf:
            # Create a temporary directory to store images and plots
            temp_dir = f"/tmp/gallery_download/images/{uuid.uuid4()}"
            os.makedirs(temp_dir)
            
            try:
                # Save images to the temporary directory
                for i, img in enumerate(images):
                    img = img.convert("RGB")
                    img_path = os.path.join(temp_dir, f"single_{i:04d}.jpg")
                    img.save(img_path)
                    zipf.write(img_path, f"single_{i:04d}.jpg")
                
                # Save plots to the temporary directory
                for i, plot in enumerate(plots):
                    plot = plot.convert("RGB")
                    plot_path = os.path.join(temp_dir, f"grid_{i:04d}.jpg")
                    plot.save(plot_path)
                    zipf.write(plot_path, f"grid_{i:04d}.jpg")
            finally:
                # Clean up the temporary directory
                for file in os.listdir(temp_dir):
                    os.remove(os.path.join(temp_dir, file))
                os.rmdir(temp_dir)
        
        # Schedule the deletion of the zip file after 24 hours (86400 seconds)
        delete_file_after_delay(zip_filename, 86400)
        gr.Info(f"File is ready for download: {os.path.basename(zip_filename)}")
        return gr.update(value=zip_filename, interactive=True)
    
    with gr.Row():
        create_file_button = gr.Button("📦 Pack", elem_id="create_file_button", variant='secondary')
        download_button = gr.DownloadButton(label="📥 Download", value=None, variant='secondary', elem_id="download_button", interactive=False)
        
        create_file_button.click(create_zip_file, inputs=[gallery], outputs=[download_button])
        def warn_on_click(filename):
            if filename is None:
                gr.Warning("No file to download, please `📦 Pack` first.")
            interactive = filename is not None
            return gr.update(interactive=interactive)
        download_button.click(warn_on_click, inputs=[download_button], outputs=[download_button])
    
    return create_file_button, download_button


def make_output_images_section(markdown=True, button=True):
    if markdown:
        gr.Markdown('### Output Images')
    output_gallery = gr.Gallery(format='png', value=[], label="NCUT Embedding", show_label=True, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto", show_share_button=True, interactive=False)
    if button:
        add_rotate_flip_buttons(output_gallery)
    return output_gallery

def make_parameters_section(is_lisa=False, model_ratio=True):
    gr.Markdown("### Parameters <a style='color: #0044CC;' href='https://ncut-pytorch.readthedocs.io/en/latest/how_to_get_better_segmentation/' target='_blank'>Help</a>")
    from ncut_pytorch.backbone import list_models, get_demo_model_names
    model_names = list_models()
    model_names = sorted(model_names)
    def get_filtered_model_names(name):
        return [m for m in model_names if name.lower() in m.lower()]
    def get_default_model_name(name):
        lst = get_filtered_model_names(name)
        if len(lst) > 1:
            return lst[1]
        return lst[0]
    
    if is_lisa:
        model_dropdown = gr.Dropdown(["LISA(xinlai/LISA-7B-v1)"], label="Backbone", value="LISA(xinlai/LISA-7B-v1)", elem_id="model_name")
        layer_slider = gr.Slider(1, 6, step=1, label="LISA decoder: Layer index", value=6, elem_id="layer", visible=False)        
        layer_names = ["dec_0_input", "dec_0_attn", "dec_0_block", "dec_1_input", "dec_1_attn", "dec_1_block"]
        positive_prompt = gr.Textbox(label="Prompt (Positive)", elem_id="prompt", placeholder="e.g. 'a photo of Gibson Les Pual guitar'", visible=False)
        negative_prompt = gr.Textbox(label="Prompt (Negative)", elem_id="prompt", placeholder="e.g. 'a photo from egocentric view'", visible=False)
        node_type_dropdown = gr.Dropdown(layer_names, label="LISA (SAM) decoder: Layer and Node", value="dec_1_block", elem_id="node_type")
    else:
        model_radio = gr.Radio(["CLIP", "DiNO", "Diffusion", "ImageNet", "MAE", "SAM"], label="Backbone", value="DiNO", elem_id="model_radio", show_label=True, visible=model_ratio)
        model_dropdown = gr.Dropdown(get_filtered_model_names("DiNO"), label="", value="DiNO(dino_vitb8_448)", elem_id="model_name", show_label=False)
        model_radio.change(fn=lambda x: gr.update(choices=get_filtered_model_names(x), value=get_default_model_name(x)), inputs=model_radio, outputs=[model_dropdown])
        layer_slider = gr.Slider(1, 12, step=1, label="Backbone: Layer index", value=10, elem_id="layer")
        positive_prompt = gr.Textbox(label="Prompt (Positive)", elem_id="prompt", placeholder="e.g. 'a photo of Gibson Les Pual guitar'")
        positive_prompt.visible = False
        negative_prompt = gr.Textbox(label="Prompt (Negative)", elem_id="prompt", placeholder="e.g. 'a photo from egocentric view'")
        negative_prompt.visible = False
        node_type_dropdown = gr.Dropdown(["attn: attention output", "mlp: mlp output", "block: sum of residual"], label="Backbone: Layer type", value="block: sum of residual", elem_id="node_type", info="which feature to take from each layer?")
    num_eig_slider = gr.Slider(1, 1000, step=1, label="NCUT: Number of eigenvectors", value=100, elem_id="num_eig", info='increase for smaller clusters')

    def change_layer_slider(model_name):
        # SD2, UNET
        if "stable" in model_name.lower() and "diffusion" in model_name.lower():
            from ncut_pytorch.backbone import SD_KEY_DICT
            default_layer = 'up_2_resnets_1_block' if 'diffusion-3' not in model_name else 'block_23'
            return (gr.Slider(1, 49, step=1, label="Diffusion: Timestep (Noise)", value=5, elem_id="layer", visible=True, info="Noise level, 50 is max noise"),
                    gr.Dropdown(SD_KEY_DICT[model_name], label="Diffusion: Layer and Node", value=default_layer, elem_id="node_type", info="U-Net (v1, v2) or DiT (v3)"))
        
        if model_name == "LISSL(xinlai/LISSL-7B-v1)":
            layer_names = ["dec_0_input", "dec_0_attn", "dec_0_block", "dec_1_input", "dec_1_attn", "dec_1_block"]
            default_layer = "dec_1_block"
            return (gr.Slider(1, 6, step=1, label="LISA decoder: Layer index", value=6, elem_id="layer", visible=False, info=""),
                    gr.Dropdown(layer_names, label="LISA decoder: Layer and Node", value=default_layer, elem_id="node_type"))

        layer_dict = LAYER_DICT
        if model_name in layer_dict:
            value = layer_dict[model_name]
            return (gr.Slider(1, value, step=1, label="Backbone: Layer index", value=value, elem_id="layer", visible=True, info=""),
                    gr.Dropdown(["attn: attention output", "mlp: mlp output", "block: sum of residual"], label="Backbone: Layer type", value="block: sum of residual", elem_id="node_type", info="which feature to take from each layer?"))
        else:
            value = 12
            return (gr.Slider(1, value, step=1, label="Backbone: Layer index", value=value, elem_id="layer", visible=True, info=""),
                    gr.Dropdown(["attn: attention output", "mlp: mlp output", "block: sum of residual"], label="Backbone: Layer type", value="block: sum of residual", elem_id="node_type", info="which feature to take from each layer?"))
    model_dropdown.change(fn=change_layer_slider, inputs=model_dropdown, outputs=[layer_slider, node_type_dropdown])
    
    def change_prompt_text(model_name):
        if model_name in promptable_diffusion_models:
            return (gr.Textbox(label="Prompt (Positive)", elem_id="prompt", placeholder="e.g. 'a photo of Gibson Les Pual guitar'", visible=True),
                    gr.Textbox(label="Prompt (Negative)", elem_id="prompt", placeholder="e.g. 'a photo from egocentric view'", visible=True))
        return (gr.Textbox(label="Prompt (Positive)", elem_id="prompt", placeholder="e.g. 'a photo of Gibson Les Pual guitar'", visible=False),
                gr.Textbox(label="Prompt (Negative)", elem_id="prompt", placeholder="e.g. 'a photo from egocentric view'", visible=False))
    model_dropdown.change(fn=change_prompt_text, inputs=model_dropdown, outputs=[positive_prompt, negative_prompt])
    
    with gr.Accordion("Advanced Parameters: NCUT", open=False):
        gr.Markdown("<a href='https://ncut-pytorch.readthedocs.io/en/latest/how_to_get_better_segmentation/' target='_blank'>Docs: How to Get Better Segmentation</a>")
        affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="NCUT: Affinity focal gamma", value=0.5, elem_id="affinity_focal_gamma", info="decrease for shaper segmentation")
        num_sample_ncut_slider = gr.Slider(100, 50000, step=100, label="NCUT: num_sample", value=10000, elem_id="num_sample_ncut", info="Nyström approximation")
        # sampling_method_dropdown = gr.Dropdown(["QuickFPS", "random"], label="NCUT: Sampling method", value="QuickFPS", elem_id="sampling_method", info="Nyström approximation")
        sampling_method_dropdown = gr.Radio(["QuickFPS", "random"], label="NCUT: Sampling method", value="QuickFPS", elem_id="sampling_method")
        # ncut_metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="NCUT: Distance metric", value="cosine", elem_id="ncut_metric")
        ncut_metric_dropdown = gr.Radio(["euclidean", "cosine"], label="NCUT: Distance metric", value="cosine", elem_id="ncut_metric")
        ncut_knn_slider = gr.Slider(1, 100, step=1, label="NCUT: KNN", value=10, elem_id="knn_ncut", info="Nyström approximation")
        ncut_indirect_connection = gr.Checkbox(label="indirect_connection", value=True, elem_id="ncut_indirect_connection", info="Add indirect connection to the sub-sampled graph")
        ncut_make_orthogonal = gr.Checkbox(label="make_orthogonal", value=False, elem_id="ncut_make_orthogonal", info="Apply post-hoc eigenvectors orthogonalization")
    with gr.Accordion("Advanced Parameters: Visualization", open=False):
        # embedding_method_dropdown = gr.Dropdown(["tsne_3d", "umap_3d", "umap_sphere", "tsne_2d", "umap_2d"], label="Coloring method", value="tsne_3d", elem_id="embedding_method")
        embedding_method_dropdown = gr.Radio(["tsne_3d", "umap_3d", "umap_sphere", "tsne_2d", "umap_2d"], label="Coloring method", value="tsne_3d", elem_id="embedding_method")
        # embedding_metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="t-SNE/UMAP metric", value="euclidean", elem_id="embedding_metric")
        embedding_metric_dropdown = gr.Radio(["euclidean", "cosine"], label="t-SNE/UMAP: metric", value="euclidean", elem_id="embedding_metric")
        num_sample_tsne_slider = gr.Slider(100, 10000, step=100, label="t-SNE/UMAP: num_sample", value=300, elem_id="num_sample_tsne", info="Nyström approximation")
        knn_tsne_slider = gr.Slider(1, 100, step=1, label="t-SNE/UMAP: KNN", value=10, elem_id="knn_tsne", info="Nyström approximation")
        perplexity_slider = gr.Slider(10, 1000, step=10, label="t-SNE: perplexity", value=150, elem_id="perplexity")
        n_neighbors_slider = gr.Slider(10, 1000, step=10, label="UMAP: n_neighbors", value=150, elem_id="n_neighbors")
        min_dist_slider = gr.Slider(0.1, 1, step=0.1, label="UMAP: min_dist", value=0.1, elem_id="min_dist")
    return [model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
            affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal,
            embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
            perplexity_slider, n_neighbors_slider, min_dist_slider, 
            sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt]

custom_css = """
#unlock_button {
    all: unset !important;
}
.form:has(#unlock_button) {
    all: unset !important;
}
"""
demo = gr.Blocks(
    theme=gr.themes.Base(spacing_size='md', text_size='lg', primary_hue='blue', neutral_hue='slate', secondary_hue='pink'),
    # fill_width=False,
    # title="ncut-pytorch",
    css=custom_css,
)
with demo:
    
    
    with gr.Tab('AlignedCut'):

        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section()
                num_images_slider.value = 30
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information", autofocus=False, autoscroll=False)
                
            with gr.Column(scale=5, min_width=200):
                output_gallery = make_output_images_section()
                cluster_gallery = gr.Gallery(value=[], label="Clusters", show_label=True, elem_id="clusters", columns=[2], rows=[2], object_fit="contain", height="auto", show_share_button=True, preview=False, interactive=False)
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section()

        false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
        no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
        
        submit_button.click(
            partial(run_fn, n_ret=2, plot_clusters=True),
            inputs=[
                input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                positive_prompt, negative_prompt,
                false_placeholder, no_prompt, no_prompt, no_prompt,
                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
            ],
            outputs=[output_gallery, cluster_gallery, logging_text],
            api_name="API_AlignedCut",
            scroll_to_output=True,
        )
        
    with gr.Tab('AlignedCut (Advanced)', visible=False) as tab_alignedcut_advanced:

        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section(allow_download=True)
                num_images_slider.value = 100
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information", autofocus=False, autoscroll=False, lines=20)
                
            with gr.Column(scale=5, min_width=200):
                output_gallery = make_output_images_section()
                add_download_button(output_gallery, "ncut_embed")
                norm_gallery = gr.Gallery(value=[], label="Eigenvector Magnitude", show_label=True, elem_id="eig_norm", columns=[3], rows=[1], object_fit="contain", height="auto", show_share_button=True, preview=False, interactive=False)
                add_download_button(norm_gallery, "eig_norm")
                cluster_gallery = gr.Gallery(value=[], label="Clusters", show_label=True, elem_id="clusters", columns=[2], rows=[4], object_fit="contain", height="auto", show_share_button=True, preview=False, interactive=False)
                add_download_button(cluster_gallery, "clusters")
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section()
                num_eig_slider.value = 100
        
        false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
        no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
        
        submit_button.click(
            partial(run_fn, n_ret=3, plot_clusters=True, alignedcut_eig_norm_plot=True, advanced=True),
            inputs=[
                input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                positive_prompt, negative_prompt,
                false_placeholder, no_prompt, no_prompt, no_prompt,
                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
            ],
            outputs=[output_gallery, cluster_gallery, norm_gallery, logging_text],
            scroll_to_output=True,
        )
        
    with gr.Tab('NCut'): 
        gr.Markdown('#### NCut (Legacy), not aligned, no Nyström approximation')
        gr.Markdown('Each image is solved independently, <em>color is <b>not</b> aligned across images</em>')
        
        gr.Markdown('---')
        gr.Markdown('<p style="text-align: center;"><b>NCut    vs.   AlignedCut</b></p>')
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('#### Pros')
                gr.Markdown('- Easy Solution. Use less eigenvectors.')
                gr.Markdown('- Exact solution. No Nyström approximation.')
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('#### Cons')
                gr.Markdown('- Not aligned. Distance is not preserved across images. No pseudo-labeling or correspondence.')
                gr.Markdown('- Poor complexity scaling. Unable to handle large number of pixels.')
        gr.Markdown('---')
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                gr.Markdown(' ')
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('<em>color is <b>not</b> aligned across images</em> 👇')


        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section()
                
            with gr.Column(scale=5, min_width=200):
                output_gallery = make_output_images_section()
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section()
                old_school_ncut_checkbox = gr.Checkbox(label="Old school NCut", value=True, elem_id="old_school_ncut")
                invisible_list = [old_school_ncut_checkbox, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal,
                                    num_sample_tsne_slider, knn_tsne_slider, sampling_method_dropdown, ncut_metric_dropdown]
                for item in invisible_list:
                    item.visible = False
                # logging text box
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
            
        false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
        no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
        
        submit_button.click(
            run_fn,
            inputs=[
                input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                positive_prompt, negative_prompt,
                false_placeholder, no_prompt, no_prompt, no_prompt,
                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown,
                old_school_ncut_checkbox
            ],
            outputs=[output_gallery, logging_text],
            api_name="API_NCut",
        )
        
    with gr.Tab('Recursive Cut'): 
        gr.Markdown('NCUT can be applied recursively, the eigenvectors from previous iteration is the input for the next iteration NCUT. ')
        gr.Markdown('__Recursive NCUT__ can amplify or weaken the connections, depending on the `affinity_focal_gamma` setting, please see [Documentation](https://ncut-pytorch.readthedocs.io/en/latest/how_to_get_better_segmentation/#recursive-ncut)')
                
        gr.Markdown('---')

        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section()
                num_images_slider.value = 100
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Recursion #1)')
                l1_gallery = gr.Gallery(format='png', value=[], label="Recursion #1", show_label=True, elem_id="ncut_l1", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                add_rotate_flip_buttons(l1_gallery)
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Recursion #2)')
                l2_gallery = gr.Gallery(format='png', value=[], label="Recursion #2", show_label=True, elem_id="ncut_l2", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                add_rotate_flip_buttons(l2_gallery)
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Recursion #3)')
                l3_gallery = gr.Gallery(format='png', value=[], label="Recursion #3", show_label=True, elem_id="ncut_l3", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                add_rotate_flip_buttons(l3_gallery)
        with gr.Row():
                
            with gr.Column(scale=5, min_width=200):
                with gr.Accordion("➡️ Recursion config", open=True):
                    l1_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #1: N eigenvectors", value=100, elem_id="l1_num_eig")
                    l2_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #2: N eigenvectors", value=50, elem_id="l2_num_eig")
                    l3_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #3: N eigenvectors", value=50, elem_id="l3_num_eig")
                    metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="Recursion distance metric", value="cosine", elem_id="recursion_metric")
                    l1_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #1: Affinity focal gamma", value=0.7, elem_id="recursion_l1_gamma")
                    l2_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #2: Affinity focal gamma", value=0.7, elem_id="recursion_l2_gamma")
                    l3_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #3: Affinity focal gamma", value=0.5, elem_id="recursion_l3_gamma")
            with gr.Column(scale=5, min_width=200):
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section()
                num_eig_slider.visible = False
                affinity_focal_gamma_slider.visible = False
        true_placeholder = gr.Checkbox(label="True placeholder", value=True, elem_id="true_placeholder")
        true_placeholder.visible = False
        false_placeholder = gr.Checkbox(label="False placeholder", value=False, elem_id="false_placeholder")
        false_placeholder.visible = False
        number_placeholder = gr.Number(0, label="Number placeholder", elem_id="number_placeholder")
        number_placeholder.visible = False
        no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
        
        submit_button.click(
            partial(run_fn, n_ret=3),
            inputs=[
                input_gallery, model_dropdown, layer_slider, l1_num_eig_slider, node_type_dropdown, 
                positive_prompt, negative_prompt,
                false_placeholder, no_prompt, no_prompt, no_prompt,
                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown,
                false_placeholder, number_placeholder, true_placeholder,
                l2_num_eig_slider, l3_num_eig_slider, metric_dropdown, 
                l1_affinity_focal_gamma_slider, l2_affinity_focal_gamma_slider, l3_affinity_focal_gamma_slider
            ],
            outputs=[l1_gallery, l2_gallery, l3_gallery, logging_text],
            api_name="API_RecursiveCut"
        )
        
    with gr.Tab('Recursive Cut (Advanced)', visible=False) as tab_recursivecut_advanced:

        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section(allow_download=True)
                num_images_slider.value = 100
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information", lines=20)
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Recursion #1)')
                l1_gallery = gr.Gallery(format='png', value=[], label="Recursion #1", show_label=True, elem_id="ncut_l1", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                add_rotate_flip_buttons(l1_gallery)
                add_download_button(l1_gallery, "ncut_embed_recur1")
                l1_norm_gallery = gr.Gallery(value=[], label="Recursion #1 Eigenvector Magnitude", show_label=True, elem_id="eig_norm", columns=[3], rows=[1], object_fit="contain", height="auto", show_share_button=True, preview=False, interactive=False)
                add_download_button(l1_norm_gallery, "eig_norm_recur1")
                l1_cluster_gallery = gr.Gallery(value=[], label="Recursion #1 Clusters", show_label=True, elem_id="clusters", columns=[2], rows=[4], object_fit="contain", height='auto', show_share_button=True, preview=False, interactive=False)
                add_download_button(l1_cluster_gallery, "clusters_recur1")
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Recursion #2)')
                l2_gallery = gr.Gallery(format='png', value=[], label="Recursion #2", show_label=True, elem_id="ncut_l2", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                add_rotate_flip_buttons(l2_gallery)
                add_download_button(l2_gallery, "ncut_embed_recur2")
                l2_norm_gallery = gr.Gallery(value=[], label="Recursion #2 Eigenvector Magnitude", show_label=True, elem_id="eig_norm", columns=[3], rows=[1], object_fit="contain", height="auto", show_share_button=True, preview=False, interactive=False)
                add_download_button(l2_norm_gallery, "eig_norm_recur2")
                l2_cluster_gallery = gr.Gallery(value=[], label="Recursion #2 Clusters", show_label=True, elem_id="clusters", columns=[2], rows=[4], object_fit="contain", height='auto', show_share_button=True, preview=False, interactive=False)
                add_download_button(l2_cluster_gallery, "clusters_recur2")
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Recursion #3)')
                l3_gallery = gr.Gallery(format='png', value=[], label="Recursion #3", show_label=True, elem_id="ncut_l3", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                add_rotate_flip_buttons(l3_gallery)
                add_download_button(l3_gallery, "ncut_embed_recur3")
                l3_norm_gallery = gr.Gallery(value=[], label="Recursion #3 Eigenvector Magnitude", show_label=True, elem_id="eig_norm", columns=[3], rows=[1], object_fit="contain", height="auto", show_share_button=True, preview=False, interactive=False)
                add_download_button(l3_norm_gallery, "eig_norm_recur3")
                l3_cluster_gallery = gr.Gallery(value=[], label="Recursion #3 Clusters", show_label=True, elem_id="clusters", columns=[2], rows=[4], object_fit="contain", height='auto', show_share_button=True, preview=False, interactive=False)
                add_download_button(l3_cluster_gallery, "clusters_recur3")
        
        with gr.Row():        
            with gr.Column(scale=5, min_width=200):
                with gr.Accordion("➡️ Recursion config", open=True):
                    l1_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #1: N eigenvectors", value=100, elem_id="l1_num_eig")
                    l2_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #2: N eigenvectors", value=50, elem_id="l2_num_eig")
                    l3_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #3: N eigenvectors", value=50, elem_id="l3_num_eig")
                    metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="Recursion distance metric", value="cosine", elem_id="recursion_metric")
                    l1_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #1: Affinity focal gamma", value=0.7, elem_id="recursion_l1_gamma")
                    l2_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #2: Affinity focal gamma", value=0.7, elem_id="recursion_l2_gamma")
                    l3_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #3: Affinity focal gamma", value=0.5, elem_id="recursion_l3_gamma")
            with gr.Column(scale=5, min_width=200):
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section()
                num_eig_slider.visible = False
                affinity_focal_gamma_slider.visible = False
        true_placeholder = gr.Checkbox(label="True placeholder", value=True, elem_id="true_placeholder")
        true_placeholder.visible = False
        false_placeholder = gr.Checkbox(label="False placeholder", value=False, elem_id="false_placeholder")
        false_placeholder.visible = False
        number_placeholder = gr.Number(0, label="Number placeholder", elem_id="number_placeholder")
        number_placeholder.visible = False
        no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
        
        submit_button.click(
            partial(run_fn, n_ret=9, advanced=True),
            inputs=[
                input_gallery, model_dropdown, layer_slider, l1_num_eig_slider, node_type_dropdown, 
                positive_prompt, negative_prompt,
                false_placeholder, no_prompt, no_prompt, no_prompt,
                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown,
                false_placeholder, number_placeholder, true_placeholder,
                l2_num_eig_slider, l3_num_eig_slider, metric_dropdown, 
                l1_affinity_focal_gamma_slider, l2_affinity_focal_gamma_slider, l3_affinity_focal_gamma_slider
            ],
            outputs=[l1_gallery, l2_gallery, l3_gallery, l1_norm_gallery, l2_norm_gallery, l3_norm_gallery, l1_cluster_gallery, l2_cluster_gallery, l3_cluster_gallery, logging_text],
        )

        
    with gr.Tab('Video'): 
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                video_input_gallery, submit_button, clear_video_button, max_frame_number = make_input_video_section()
            with gr.Column(scale=5, min_width=200):
                video_output_gallery = gr.Video(value=None, label="NCUT Embedding", elem_id="ncut", height="auto", show_share_button=False)
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section()
                num_sample_tsne_slider.value = 1000
                perplexity_slider.value = 500
                n_neighbors_slider.value = 500
                knn_tsne_slider.value = 20
                # logging text box
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
        clear_video_button.click(lambda x: (None, None), outputs=[video_input_gallery, video_output_gallery])
        place_holder_false = gr.Checkbox(label="Place holder", value=False, elem_id="place_holder_false")
        place_holder_false.visible = False
        false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
        no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
        
        submit_button.click(
            run_fn,
            inputs=[
                video_input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                positive_prompt, negative_prompt,
                false_placeholder, no_prompt, no_prompt, no_prompt,
                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown,
                place_holder_false, max_frame_number
            ],
            outputs=[video_output_gallery, logging_text],
            api_name="API_VideoCut",
        )    
    
    with gr.Tab('Text'): 
        try:
            from app_text import make_demo
        except ImportError:
            print("Debugging")
            from draft_gradio_app_text import make_demo
        make_demo()
        
    with gr.Tab('Vision-Language'):
        gr.Markdown('[LISA](https://arxiv.org/pdf/2308.00692) is a vision-language model. Input a text prompt and image, LISA generate segmentation masks.')
        gr.Markdown('In the mask decoder layers, LISA updates the image features w.r.t. the text prompt')
        gr.Markdown('This page aims to see how the text prompt affects the image features')
        gr.Markdown('---')
        gr.Markdown('<p style="text-align: center;">Color is <b>aligned</b> across 3 prompts. NCUT is computed on the concatenated features from 3 prompts.</p>')
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Prompt #1)')
                l1_gallery = gr.Gallery(format='png', value=[], label="Prompt #1", show_label=False, elem_id="ncut_p1", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                prompt1 = gr.Textbox(label="Input Prompt #1", elem_id="prompt1", value="where is the person, include the clothes, don't include the guitar and chair", lines=3)
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Prompt #2)')
                l2_gallery = gr.Gallery(format='png', value=[], label="Prompt #2", show_label=False, elem_id="ncut_p2", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                prompt2 = gr.Textbox(label="Input Prompt #2", elem_id="prompt2", value="where is the Gibson Les Pual guitar", lines=3)
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Prompt #3)')
                l3_gallery = gr.Gallery(format='png', value=[], label="Prompt #3", show_label=False, elem_id="ncut_p3", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                prompt3 = gr.Textbox(label="Input Prompt #3", elem_id="prompt3", value="where is the floor", lines=3)
                
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section()
                
            with gr.Column(scale=5, min_width=200):
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section(is_lisa=True)
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
        
        galleries = [l1_gallery, l2_gallery, l3_gallery]
        true_placeholder = gr.Checkbox(label="True placeholder", value=True, elem_id="true_placeholder", visible=False)
        submit_button.click(
            partial(run_fn, n_ret=len(galleries)),
            inputs=[
                input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                positive_prompt, negative_prompt,
                true_placeholder, prompt1, prompt2, prompt3,
                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
            ],
            outputs=galleries + [logging_text],
        )
                
    with gr.Tab('Model Aligned'): 
        gr.Markdown('This page reproduce the results from the paper [AlignedCut](https://arxiv.org/abs/2406.18344)')
        gr.Markdown('---')
        gr.Markdown('**Features are aligned across models and layers.** A linear alignment transform is trained for each model/layer, learning signal comes from 1) fMRI brain activation and 2) segmentation preserving eigen-constraints.')
        gr.Markdown('NCUT is computed on the concatenated graph of all models, layers, and images. Color is **aligned** across all models and layers.')
        gr.Markdown('')
        gr.Markdown("To see a good pattern, you will need to load 100~1000 images. 100 images need 10sec for RTX4090. Running out of HuggingFace GPU Quota? Try [Demo](https://ncut-pytorch.readthedocs.io/en/latest/demo/) hosted at UPenn")
        gr.Markdown('---')
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section()
                num_images_slider.value = 100
                
                
            with gr.Column(scale=5, min_width=200):
                output_gallery = make_output_images_section()
                gr.Markdown('### TIP1: use the `full-screen` button, and use `arrow keys` to navigate')
                gr.Markdown('---')
                gr.Markdown('Model: CLIP(ViT-B-16/openai), DiNOv2reg(dinov2_vitb14_reg), MAE(vit_base)')
                gr.Markdown('Layer type: attention output (attn), without sum of residual')
                gr.Markdown('### TIP2: for large image set, please increase the `num_sample` for t-SNE and NCUT')
                gr.Markdown('---')
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section(model_ratio=False)
                model_dropdown.value = "AlignedThreeModelAttnNodes"
                model_dropdown.visible = False
                layer_slider.visible = False
                node_type_dropdown.visible = False
                num_sample_ncut_slider.value = 10000
                num_sample_tsne_slider.value = 1000
                # logging text box
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
                        
        false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
        no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
        
        submit_button.click(
            run_fn,
            inputs=[
                input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                positive_prompt, negative_prompt,
                false_placeholder, no_prompt, no_prompt, no_prompt,
                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
            ],
            # outputs=galleries + [logging_text],
            outputs=[output_gallery, logging_text],
        )
        
    with gr.Tab('Model Aligned (Advanced)', visible=False) as tab_model_aligned_advanced: 
        gr.Markdown('This page reproduce the results from the paper [AlignedCut](https://arxiv.org/abs/2406.18344)')
        gr.Markdown('---')
        gr.Markdown('**Features are aligned across models and layers.** A linear alignment transform is trained for each model/layer, learning signal comes from 1) fMRI brain activation and 2) segmentation preserving eigen-constraints.')
        gr.Markdown('NCUT is computed on the concatenated graph of all models, layers, and images. Color is **aligned** across all models and layers.')
        gr.Markdown('')
        gr.Markdown("To see a good pattern, you will need to load 100~1000 images. 100 images need 10sec for RTX4090. Running out of HuggingFace GPU Quota? Try [Demo](https://ncut-pytorch.readthedocs.io/en/latest/demo/) hosted at UPenn")
        gr.Markdown('---')

        # with gr.Row():
        #     with gr.Column(scale=5, min_width=200):
        #         gr.Markdown('### Output (Recursion #1)')
        #         l1_gallery = gr.Gallery(format='png', value=[], label="Recursion #1", show_label=False, elem_id="ncut_l1", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
        #         add_output_images_buttons(l1_gallery)
        #     with gr.Column(scale=5, min_width=200):
        #         gr.Markdown('### Output (Recursion #2)')
        #         l2_gallery = gr.Gallery(format='png', value=[], label="Recursion #2", show_label=False, elem_id="ncut_l2", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
        #         add_output_images_buttons(l2_gallery)
        #     with gr.Column(scale=5, min_width=200):
        #         gr.Markdown('### Output (Recursion #3)')
        #         l3_gallery = gr.Gallery(format='png', value=[], label="Recursion #3", show_label=False, elem_id="ncut_l3", columns=[3], rows=[5], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
        #         add_output_images_buttons(l3_gallery)    
        gr.Markdown('### Output (Recursion #1)')
        l1_gallery = gr.Gallery(format='png', value=[], label="Recursion #1", show_label=True, elem_id="ncut_l1", columns=[100], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False, preview=True)
        add_rotate_flip_buttons(l1_gallery)
        add_download_button(l1_gallery, "modelaligned_recur1")
        gr.Markdown('### Output (Recursion #2)')
        l2_gallery = gr.Gallery(format='png', value=[], label="Recursion #2", show_label=True, elem_id="ncut_l2", columns=[100], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False, preview=True)
        add_rotate_flip_buttons(l2_gallery)
        add_download_button(l2_gallery, "modelaligned_recur2")
        gr.Markdown('### Output (Recursion #3)')
        l3_gallery = gr.Gallery(format='png', value=[], label="Recursion #3", show_label=True, elem_id="ncut_l3", columns=[100], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False, preview=True)
        add_rotate_flip_buttons(l3_gallery)  
        add_download_button(l3_gallery, "modelaligned_recur3")
    
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section(allow_download=True)
                num_images_slider.value = 100
                
                
            with gr.Column(scale=5, min_width=200):
                with gr.Accordion("➡️ Recursion config", open=True):
                    l1_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #1: N eigenvectors", value=100, elem_id="l1_num_eig")
                    l2_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #2: N eigenvectors", value=50, elem_id="l2_num_eig")
                    l3_num_eig_slider = gr.Slider(1, 1000, step=1, label="Recursion #3: N eigenvectors", value=50, elem_id="l3_num_eig")
                    metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="Recursion distance metric", value="cosine", elem_id="recursion_metric")
                    l1_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #1: Affinity focal gamma", value=0.5, elem_id="recursion_l1_gamma")
                    l2_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #2: Affinity focal gamma", value=0.5, elem_id="recursion_l2_gamma")
                    l3_affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="Recursion #3: Affinity focal gamma", value=0.5, elem_id="recursion_l3_gamma")
                gr.Markdown('---')
                gr.Markdown('Model: CLIP(ViT-B-16/openai), DiNOv2reg(dinov2_vitb14_reg), MAE(vit_base)')
                gr.Markdown('Layer type: attention output (attn), without sum of residual')
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section(model_ratio=False)
                num_eig_slider.visible = False
                affinity_focal_gamma_slider.visible = False
                model_dropdown.value = "AlignedThreeModelAttnNodes"
                model_dropdown.visible = False
                layer_slider.visible = False
                node_type_dropdown.visible = False
                num_sample_ncut_slider.value = 10000
                num_sample_tsne_slider.value = 1000
                # logging text box
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
                        
        true_placeholder = gr.Checkbox(label="True placeholder", value=True, elem_id="true_placeholder")
        true_placeholder.visible = False
        false_placeholder = gr.Checkbox(label="False placeholder", value=False, elem_id="false_placeholder")
        false_placeholder.visible = False
        number_placeholder = gr.Number(0, label="Number placeholder", elem_id="number_placeholder")
        number_placeholder.visible = False
        no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
        
        submit_button.click(
            partial(run_fn, n_ret=3, advanced=True),
            inputs=[
                input_gallery, model_dropdown, layer_slider, l1_num_eig_slider, node_type_dropdown, 
                positive_prompt, negative_prompt,
                false_placeholder, no_prompt, no_prompt, no_prompt,
                affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown,
                false_placeholder, number_placeholder, true_placeholder,
                l2_num_eig_slider, l3_num_eig_slider, metric_dropdown, 
                l1_affinity_focal_gamma_slider, l2_affinity_focal_gamma_slider, l3_affinity_focal_gamma_slider
            ],
            outputs=[l1_gallery, l2_gallery, l3_gallery, logging_text],
        )
    
    
    with gr.Tab('Compare Models'): 
        def add_one_model(i_model=1):
            with gr.Column(scale=5, min_width=200) as col:
                gr.Markdown(f'### Output Images')
                output_gallery = gr.Gallery(format='png', value=[], label="NCUT Embedding", show_label=False, elem_id=f"ncut{i_model}", columns=[3], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                submit_button = gr.Button("🔴 RUN", elem_id=f"submit_button{i_model}", variant='primary')
                add_rotate_flip_buttons(output_gallery)
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section()
                # logging text box
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
                false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
                no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
                
                submit_button.click(
                    run_fn,
                    inputs=[
                        input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                        positive_prompt, negative_prompt,
                        false_placeholder, no_prompt, no_prompt, no_prompt,
                        affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                        embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                        perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
                    ],
                    outputs=[output_gallery, logging_text]
                )
                
                return col

        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section()
                submit_button.visible = False

                
            for i in range(3):
                add_one_model()
                
        # Create rows and buttons in a loop
        rows = []
        buttons = []

        for i in range(4):
            row = gr.Row(visible=False)
            rows.append(row)
            
            with row:
                for j in range(4):
                    with gr.Column(scale=5, min_width=200):
                        add_one_model()

            button = gr.Button("➕ Add Compare", elem_id=f"add_button_{i}", visible=False if i > 0 else True, scale=3)
            buttons.append(button)
            
            if i > 0:
                # Reveal the current row and next button
                buttons[i - 1].click(fn=lambda x: gr.update(visible=True), outputs=row)
                buttons[i - 1].click(fn=lambda x: gr.update(visible=True), outputs=button)
                
                # Hide the current button
                buttons[i - 1].click(fn=lambda x: gr.update(visible=False), outputs=buttons[i - 1])

        # Last button only reveals the last row and hides itself
        buttons[-1].click(fn=lambda x: gr.update(visible=True), outputs=rows[-1])
        buttons[-1].click(fn=lambda x: gr.update(visible=False), outputs=buttons[-1])
    
    with gr.Tab('Compare Models (Advanced)', visible=False) as tab_compare_models_advanced: 
        
        target_images = gr.State([])
        input_images = gr.State([])
        def add_mlp_fitting_buttons(output_gallery, mlp_gallery, target_images=target_images, input_images=input_images):
            with gr.Row():
                # mark_as_target_button = gr.Button("mark target", elem_id=f"mark_as_target_button_{output_gallery.elem_id}", variant='secondary')
                # mark_as_input_button = gr.Button("mark input", elem_id=f"mark_as_input_button_{output_gallery.elem_id}", variant='secondary')
                mark_as_target_button = gr.Button("🎯 Mark Target", elem_id=f"mark_as_target_button_{output_gallery.elem_id}", variant='secondary')
            fit_to_target_button = gr.Button("🔴 [MLP] Fit", elem_id=f"fit_to_target_button_{output_gallery.elem_id}", variant='primary')
            def mark_fn(images, text="target"):
                if images is None:
                    raise gr.Error("No images selected")
                if len(images) == 0:
                    raise gr.Error("No images selected")
                num_images = len(images)
                gr.Info(f"Marked {num_images} images as {text}")
                images = [(Image.open(tup[0]), []) for tup in images]
                return images
            mark_as_target_button.click(partial(mark_fn, text="target"), inputs=[output_gallery], outputs=[target_images])
            # mark_as_input_button.click(partial(mark_fn, text="input"), inputs=[output_gallery], outputs=[input_images])
            
            with gr.Accordion("➡️ MLP Parameters", open=False):
                num_layers_slider = gr.Slider(2, 10, step=1, label="Number of Layers", value=3, elem_id=f"num_layers_slider_{output_gallery.elem_id}")
                width_slider = gr.Slider(128, 4096, step=128, label="Width", value=512, elem_id=f"width_slider_{output_gallery.elem_id}")
                batch_size_slider = gr.Slider(32, 4096, step=32, label="Batch Size", value=128, elem_id=f"batch_size_slider_{output_gallery.elem_id}")
                lr_slider = gr.Slider(1e-6, 1, step=1e-6, label="Learning Rate", value=3e-4, elem_id=f"lr_slider_{output_gallery.elem_id}")
                fitting_steps_slider = gr.Slider(1000, 100000, step=1000, label="Fitting Steps", value=30000, elem_id=f"fitting_steps_slider_{output_gallery.elem_id}")
                fps_sample_slider = gr.Slider(128, 50000, step=128, label="FPS Sample", value=10240, elem_id=f"fps_sample_slider_{output_gallery.elem_id}")
                segmentation_loss_lambda_slider = gr.Slider(0, 100, step=0.01, label="Segmentation Preserving Loss Lambda", value=1, elem_id=f"segmentation_loss_lambda_slider_{output_gallery.elem_id}")
                
            fit_to_target_button.click(
                run_mlp_fit,
                inputs=[output_gallery, target_images, num_layers_slider, width_slider, batch_size_slider, lr_slider, fitting_steps_slider, fps_sample_slider, segmentation_loss_lambda_slider],
                outputs=[mlp_gallery],
            )
                
        def add_one_model(i_model=1):
            with gr.Column(scale=5, min_width=200) as col:
                gr.Markdown(f'### Output Images')
                output_gallery = gr.Gallery(format='png', value=[], label="NCUT Embedding", show_label=True, elem_id=f"ncut{i_model}", columns=[3], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                submit_button = gr.Button("🔴 RUN", elem_id=f"submit_button{i_model}", variant='primary')
                add_rotate_flip_buttons(output_gallery)
                add_download_button(output_gallery, f"ncut_embed")
                mlp_gallery = gr.Gallery(format='png', value=[], label="MLP color align", show_label=True, elem_id=f"mlp{i_model}", columns=[3], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                add_mlp_fitting_buttons(output_gallery, mlp_gallery)
                add_download_button(mlp_gallery, f"mlp_color_align")
                norm_gallery = gr.Gallery(value=[], label="Eigenvector Magnitude", show_label=True, elem_id=f"eig_norm{i_model}", columns=[3], rows=[1], object_fit="contain", height="auto", show_share_button=True, preview=False, interactive=False)
                add_download_button(norm_gallery, f"eig_norm")
                cluster_gallery = gr.Gallery(value=[], label="Clusters", show_label=True, elem_id=f"clusters{i_model}", columns=[2], rows=[4], object_fit="contain", height="auto", show_share_button=True, preview=False, interactive=False)
                add_download_button(cluster_gallery, f"clusters")
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section()
                # logging text box
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
                false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
                no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
                
                submit_button.click(
                    partial(run_fn, n_ret=3, plot_clusters=True, alignedcut_eig_norm_plot=True, advanced=True),
                    inputs=[
                        input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                        positive_prompt, negative_prompt,
                        false_placeholder, no_prompt, no_prompt, no_prompt,
                        affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                        embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                        perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
                    ],
                    outputs=[output_gallery, cluster_gallery, norm_gallery, logging_text]
                )
                
                output_gallery.change(lambda x: gr.update(value=x), inputs=[output_gallery], outputs=[mlp_gallery])
                
                return output_gallery

        galleries = []
        
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section(allow_download=True)
                submit_button.visible = False

                
            for i in range(3):
                g = add_one_model()
                galleries.append(g)
                
        # Create rows and buttons in a loop
        rows = []
        buttons = []

        for i in range(4):
            row = gr.Row(visible=False)
            rows.append(row)
            
            with row:
                for j in range(4):
                    with gr.Column(scale=5, min_width=200):
                        g = add_one_model()
                        galleries.append(g)

            button = gr.Button("➕ Add Compare", elem_id=f"add_button_{i}", visible=False if i > 0 else True, scale=3)
            buttons.append(button)
            
            if i > 0:
                # Reveal the current row and next button
                buttons[i - 1].click(fn=lambda x: gr.update(visible=True), outputs=row)
                buttons[i - 1].click(fn=lambda x: gr.update(visible=True), outputs=button)
                
                # Hide the current button
                buttons[i - 1].click(fn=lambda x: gr.update(visible=False), outputs=buttons[i - 1])

        # Last button only reveals the last row and hides itself
        buttons[-1].click(fn=lambda x: gr.update(visible=True), outputs=rows[-1])
        buttons[-1].click(fn=lambda x: gr.update(visible=False), outputs=buttons[-1])


    with gr.Tab('Directed (experimental)', visible=False) as tab_directed_ncut: 
        
        target_images = gr.State([])
        input_images = gr.State([])
        def add_mlp_fitting_buttons(output_gallery, mlp_gallery, target_images=target_images, input_images=input_images):
            with gr.Row():
                # mark_as_target_button = gr.Button("mark target", elem_id=f"mark_as_target_button_{output_gallery.elem_id}", variant='secondary')
                # mark_as_input_button = gr.Button("mark input", elem_id=f"mark_as_input_button_{output_gallery.elem_id}", variant='secondary')
                mark_as_target_button = gr.Button("🎯 Mark Target", elem_id=f"mark_as_target_button_{output_gallery.elem_id}", variant='secondary')
            fit_to_target_button = gr.Button("🔴 [MLP] Fit", elem_id=f"fit_to_target_button_{output_gallery.elem_id}", variant='primary')
            def mark_fn(images, text="target"):
                if images is None:
                    raise gr.Error("No images selected")
                if len(images) == 0:
                    raise gr.Error("No images selected")
                num_images = len(images)
                gr.Info(f"Marked {num_images} images as {text}")
                images = [(Image.open(tup[0]), []) for tup in images]
                return images
            mark_as_target_button.click(partial(mark_fn, text="target"), inputs=[output_gallery], outputs=[target_images])
            # mark_as_input_button.click(partial(mark_fn, text="input"), inputs=[output_gallery], outputs=[input_images])
            
            with gr.Accordion("➡️ MLP Parameters", open=False):
                num_layers_slider = gr.Slider(2, 10, step=1, label="Number of Layers", value=3, elem_id=f"num_layers_slider_{output_gallery.elem_id}")
                width_slider = gr.Slider(128, 4096, step=128, label="Width", value=512, elem_id=f"width_slider_{output_gallery.elem_id}")
                batch_size_slider = gr.Slider(32, 4096, step=32, label="Batch Size", value=128, elem_id=f"batch_size_slider_{output_gallery.elem_id}")
                lr_slider = gr.Slider(1e-6, 1, step=1e-6, label="Learning Rate", value=3e-4, elem_id=f"lr_slider_{output_gallery.elem_id}")
                fitting_steps_slider = gr.Slider(1000, 100000, step=1000, label="Fitting Steps", value=30000, elem_id=f"fitting_steps_slider_{output_gallery.elem_id}")
                fps_sample_slider = gr.Slider(128, 50000, step=128, label="FPS Sample", value=10240, elem_id=f"fps_sample_slider_{output_gallery.elem_id}")
                segmentation_loss_lambda_slider = gr.Slider(0, 100, step=0.01, label="Segmentation Preserving Loss Lambda", value=1, elem_id=f"segmentation_loss_lambda_slider_{output_gallery.elem_id}")
                
            fit_to_target_button.click(
                run_mlp_fit,
                inputs=[output_gallery, target_images, num_layers_slider, width_slider, batch_size_slider, lr_slider, fitting_steps_slider, fps_sample_slider, segmentation_loss_lambda_slider],
                outputs=[mlp_gallery],
            )

        def make_parameters_section_2model(model_ratio=True):
            gr.Markdown("### Parameters <a style='color: #0044CC;' href='https://ncut-pytorch.readthedocs.io/en/latest/how_to_get_better_segmentation/' target='_blank'>Help</a>")
            from ncut_pytorch.backbone import list_models, get_demo_model_names
            model_names = list_models()
            model_names = sorted(model_names)
            # only CLIP DINO MAE is implemented for q k v
            ok_models = ["CLIP(ViT", "DiNO(", "MAE("]
            model_names = [m for m in model_names if any(ok in m for ok in ok_models)]
            
            def get_filtered_model_names(name):
                return [m for m in model_names if name.lower() in m.lower()]
            def get_default_model_name(name):
                lst = get_filtered_model_names(name)
                if len(lst) > 1:
                    return lst[1]
                return lst[0]
            

            model_radio = gr.Radio(["CLIP", "DiNO", "MAE"], label="Backbone", value="DiNO", elem_id="model_radio", show_label=True, visible=model_ratio)
            model_dropdown = gr.Dropdown(get_filtered_model_names("DiNO"), label="", value="DiNO(dino_vitb8_448)", elem_id="model_name", show_label=False)
            model_radio.change(fn=lambda x: gr.update(choices=get_filtered_model_names(x), value=get_default_model_name(x)), inputs=model_radio, outputs=[model_dropdown])
            layer_slider = gr.Slider(1, 12, step=1, label="Backbone: Layer index", value=10, elem_id="layer")
            positive_prompt = gr.Textbox(label="Prompt (Positive)", elem_id="prompt", placeholder="e.g. 'a photo of Gibson Les Pual guitar'")
            positive_prompt.visible = False
            negative_prompt = gr.Textbox(label="Prompt (Negative)", elem_id="prompt", placeholder="e.g. 'a photo from egocentric view'")
            negative_prompt.visible = False
            node_type_dropdown = gr.Dropdown(['q', 'k', 'v'], 
                                            label="Left-side Node Type", value="q", elem_id="node_type", info="In directed case, left-side SVD eigenvector is taken")
            node_type_dropdown2 = gr.Dropdown(['q', 'k', 'v'], 
                                            label="Right-side Node Type", value="k", elem_id="node_type2")
            head_index_text = gr.Textbox(value='all', label="Head Index", elem_id="head_index", type="text", info="which attention heads to use, comma separated, e.g. 0,1,2")
            make_symmetric = gr.Checkbox(label="Make Symmetric", value=False, elem_id="make_symmetric", info="make the graph symmetric by A = (A + A.T) / 2")
            
            num_eig_slider = gr.Slider(1, 1000, step=1, label="NCUT: Number of eigenvectors", value=100, elem_id="num_eig", info='increase for smaller clusters')

            def change_layer_slider(model_name):
                # SD2, UNET
                if "stable" in model_name.lower() and "diffusion" in model_name.lower():
                    from ncut_pytorch.backbone import SD_KEY_DICT
                    default_layer = 'up_2_resnets_1_block' if 'diffusion-3' not in model_name else 'block_23'
                    return (gr.Slider(1, 49, step=1, label="Diffusion: Timestep (Noise)", value=5, elem_id="layer", visible=True, info="Noise level, 50 is max noise"),
                            gr.Dropdown(SD_KEY_DICT[model_name], label="Diffusion: Layer and Node", value=default_layer, elem_id="node_type", info="U-Net (v1, v2) or DiT (v3)"))
                
                if model_name == "LISSL(xinlai/LISSL-7B-v1)":
                    layer_names = ["dec_0_input", "dec_0_attn", "dec_0_block", "dec_1_input", "dec_1_attn", "dec_1_block"]
                    default_layer = "dec_1_block"
                    return (gr.Slider(1, 6, step=1, label="LISA decoder: Layer index", value=6, elem_id="layer", visible=False, info=""),
                            gr.Dropdown(layer_names, label="LISA decoder: Layer and Node", value=default_layer, elem_id="node_type"))

                layer_dict = LAYER_DICT
                if model_name in layer_dict:
                    value = layer_dict[model_name]
                    return gr.Slider(1, value, step=1, label="Backbone: Layer index", value=value, elem_id="layer", visible=True, info="")
                else:
                    value = 12
                    return gr.Slider(1, value, step=1, label="Backbone: Layer index", value=value, elem_id="layer", visible=True, info="")            
            model_dropdown.change(fn=change_layer_slider, inputs=model_dropdown, outputs=layer_slider)
            
            def change_prompt_text(model_name):
                if model_name in promptable_diffusion_models:
                    return (gr.Textbox(label="Prompt (Positive)", elem_id="prompt", placeholder="e.g. 'a photo of Gibson Les Pual guitar'", visible=True),
                            gr.Textbox(label="Prompt (Negative)", elem_id="prompt", placeholder="e.g. 'a photo from egocentric view'", visible=True))
                return (gr.Textbox(label="Prompt (Positive)", elem_id="prompt", placeholder="e.g. 'a photo of Gibson Les Pual guitar'", visible=False),
                        gr.Textbox(label="Prompt (Negative)", elem_id="prompt", placeholder="e.g. 'a photo from egocentric view'", visible=False))
            model_dropdown.change(fn=change_prompt_text, inputs=model_dropdown, outputs=[positive_prompt, negative_prompt])
            
            with gr.Accordion("Advanced Parameters: NCUT", open=False):
                gr.Markdown("<a href='https://ncut-pytorch.readthedocs.io/en/latest/how_to_get_better_segmentation/' target='_blank'>Docs: How to Get Better Segmentation</a>")
                affinity_focal_gamma_slider = gr.Slider(0.01, 1, step=0.01, label="NCUT: Affinity focal gamma", value=0.5, elem_id="affinity_focal_gamma", info="decrease for shaper segmentation")
                num_sample_ncut_slider = gr.Slider(100, 50000, step=100, label="NCUT: num_sample", value=10000, elem_id="num_sample_ncut", info="Nyström approximation")
                # sampling_method_dropdown = gr.Dropdown(["QuickFPS", "random"], label="NCUT: Sampling method", value="QuickFPS", elem_id="sampling_method", info="Nyström approximation")
                sampling_method_dropdown = gr.Radio(["QuickFPS", "random"], label="NCUT: Sampling method", value="QuickFPS", elem_id="sampling_method")
                # ncut_metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="NCUT: Distance metric", value="cosine", elem_id="ncut_metric")
                ncut_metric_dropdown = gr.Radio(["euclidean", "cosine"], label="NCUT: Distance metric", value="cosine", elem_id="ncut_metric")
                ncut_knn_slider = gr.Slider(1, 100, step=1, label="NCUT: KNN", value=10, elem_id="knn_ncut", info="Nyström approximation")
                ncut_indirect_connection = gr.Checkbox(label="indirect_connection", value=False, elem_id="ncut_indirect_connection", info="TODO: Indirect connection is not implemented for directed NCUT", interactive=False)
                ncut_make_orthogonal = gr.Checkbox(label="make_orthogonal", value=False, elem_id="ncut_make_orthogonal", info="Apply post-hoc eigenvectors orthogonalization")
            with gr.Accordion("Advanced Parameters: Visualization", open=False):
                # embedding_method_dropdown = gr.Dropdown(["tsne_3d", "umap_3d", "umap_sphere", "tsne_2d", "umap_2d"], label="Coloring method", value="tsne_3d", elem_id="embedding_method")
                embedding_method_dropdown = gr.Radio(["tsne_3d", "umap_3d", "umap_sphere", "tsne_2d", "umap_2d"], label="Coloring method", value="tsne_3d", elem_id="embedding_method")
                # embedding_metric_dropdown = gr.Dropdown(["euclidean", "cosine"], label="t-SNE/UMAP metric", value="euclidean", elem_id="embedding_metric")
                embedding_metric_dropdown = gr.Radio(["euclidean", "cosine"], label="t-SNE/UMAP: metric", value="euclidean", elem_id="embedding_metric")
                num_sample_tsne_slider = gr.Slider(100, 10000, step=100, label="t-SNE/UMAP: num_sample", value=300, elem_id="num_sample_tsne", info="Nyström approximation")
                knn_tsne_slider = gr.Slider(1, 100, step=1, label="t-SNE/UMAP: KNN", value=10, elem_id="knn_tsne", info="Nyström approximation")
                perplexity_slider = gr.Slider(10, 1000, step=10, label="t-SNE: perplexity", value=150, elem_id="perplexity")
                n_neighbors_slider = gr.Slider(10, 1000, step=10, label="UMAP: n_neighbors", value=150, elem_id="n_neighbors")
                min_dist_slider = gr.Slider(0.1, 1, step=0.1, label="UMAP: min_dist", value=0.1, elem_id="min_dist")
            return [model_dropdown, layer_slider, node_type_dropdown, node_type_dropdown2, head_index_text, make_symmetric, num_eig_slider,
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal,
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt]
            
        def add_one_model(i_model=1):
            with gr.Column(scale=5, min_width=200) as col:
                gr.Markdown(f'### Output Images')
                output_gallery = gr.Gallery(format='png', value=[], label="NCUT Embedding", show_label=True, elem_id=f"ncut{i_model}", columns=[3], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                submit_button = gr.Button("🔴 RUN", elem_id=f"submit_button{i_model}", variant='primary')
                add_rotate_flip_buttons(output_gallery)
                add_download_button(output_gallery, f"ncut_embed")
                mlp_gallery = gr.Gallery(format='png', value=[], label="MLP color align", show_label=True, elem_id=f"mlp{i_model}", columns=[3], rows=[1], object_fit="contain", height="auto", show_fullscreen_button=True, interactive=False)
                add_mlp_fitting_buttons(output_gallery, mlp_gallery)
                add_download_button(mlp_gallery, f"mlp_color_align")
                norm_gallery = gr.Gallery(value=[], label="Eigenvector Magnitude", show_label=True, elem_id=f"eig_norm{i_model}", columns=[3], rows=[1], object_fit="contain", height="auto", show_share_button=True, preview=False, interactive=False)
                add_download_button(norm_gallery, f"eig_norm")
                cluster_gallery = gr.Gallery(value=[], label="Clusters", show_label=True, elem_id=f"clusters{i_model}", columns=[2], rows=[4], object_fit="contain", height="auto", show_share_button=True, preview=False, interactive=False)
                add_download_button(cluster_gallery, f"clusters")
                [
                    model_dropdown, layer_slider, node_type_dropdown, node_type_dropdown2, head_index_text, make_symmetric, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section_2model()
                # logging text box
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
                false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
                no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
                
                false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
                
                submit_button.click(
                    partial(run_fn, n_ret=3, plot_clusters=True, alignedcut_eig_norm_plot=True, advanced=True, directed=True),
                    inputs=[
                        input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                        positive_prompt, negative_prompt,
                        false_placeholder, no_prompt, no_prompt, no_prompt,
                        affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                        embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                        perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown,
                        *[false_placeholder for _ in range(9)],
                        node_type_dropdown2, head_index_text, make_symmetric
                    ],
                    outputs=[output_gallery, cluster_gallery, norm_gallery, logging_text]
                )
                
                output_gallery.change(lambda x: gr.update(value=x), inputs=[output_gallery], outputs=[mlp_gallery])
                
                return output_gallery

        galleries = []
        
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section(allow_download=True)
                submit_button.visible = False

                
            for i in range(3):
                g = add_one_model()
                galleries.append(g)
                
        # Create rows and buttons in a loop
        rows = []
        buttons = []

        for i in range(4):
            row = gr.Row(visible=False)
            rows.append(row)
            
            with row:
                for j in range(4):
                    with gr.Column(scale=5, min_width=200):
                        g = add_one_model()
                        galleries.append(g)

            button = gr.Button("➕ Add Compare", elem_id=f"add_button_{i}", visible=False if i > 0 else True, scale=3)
            buttons.append(button)
            
            if i > 0:
                # Reveal the current row and next button
                buttons[i - 1].click(fn=lambda x: gr.update(visible=True), outputs=row)
                buttons[i - 1].click(fn=lambda x: gr.update(visible=True), outputs=button)
                
                # Hide the current button
                buttons[i - 1].click(fn=lambda x: gr.update(visible=False), outputs=buttons[i - 1])

        # Last button only reveals the last row and hides itself
        buttons[-1].click(fn=lambda x: gr.update(visible=True), outputs=rows[-1])
        buttons[-1].click(fn=lambda x: gr.update(visible=False), outputs=buttons[-1])

    with gr.Tab('Application'):
        gr.Markdown("Draw some points on the image to find corrsponding segments in other images. E.g. click on one face to segment all the face. [Video Tutorial](https://ncut-pytorch.readthedocs.io/en/latest/gallery_application/)")
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                gr.Markdown("### Step 0: Load Images")
                input_gallery, submit_button, clear_images_button, dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_input_images_section(markdown=False)
                submit_button.visible = False
                num_images_slider.value = 30
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information", autofocus=False, autoscroll=False)
            with gr.Column(scale=5, min_width=200):
                gr.Markdown("### Step 1: NCUT Embedding")
                output_gallery = make_output_images_section(markdown=False, button=False)
                submit_button = gr.Button("🔴 RUN", elem_id="submit_button", variant='primary')
                add_rotate_flip_buttons(output_gallery)
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                    embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown, ncut_metric_dropdown, positive_prompt, negative_prompt
                ] = make_parameters_section()

                false_placeholder = gr.Checkbox(label="False", value=False, elem_id="false_placeholder", visible=False)
                no_prompt = gr.Textbox("", label="", elem_id="empty_placeholder", type="text", placeholder="", visible=False)
                
                submit_button.click(
                    partial(run_fn, n_ret=1),
                    inputs=[
                        input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                        positive_prompt, negative_prompt,
                        false_placeholder, no_prompt, no_prompt, no_prompt,
                        affinity_focal_gamma_slider, num_sample_ncut_slider, ncut_knn_slider, ncut_indirect_connection, ncut_make_orthogonal, 
                        embedding_method_dropdown, embedding_metric_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                        perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown, ncut_metric_dropdown
                    ],
                    outputs=[output_gallery, logging_text],
                )
                
            with gr.Column(scale=5, min_width=200):
                gr.Markdown("### Step 2a: Pick an Image")
                from gradio_image_prompter import ImagePrompter
                image_type_radio = gr.Radio(["Original", "NCUT"], label="Image Display Type", value="Original", elem_id="image_type_radio")
                with gr.Row():
                    image1_slider = gr.Slider(0, 100, step=1, label="Image#1 Index", value=0, elem_id="image1_slider", interactive=True)
                    image2_slider = gr.Slider(0, 100, step=1, label="Image#2 Index", value=1, elem_id="image2_slider", interactive=True)
                    image3_slider = gr.Slider(0, 100, step=1, label="Image#3 Index", value=2, elem_id="image3_slider", interactive=True)
                load_one_image_button = gr.Button("🔴 Load", elem_id="load_one_image_button", variant='primary')
                gr.Markdown("### Step 2b: Draw Points")
                gr.Markdown("""
                    <h5>
                    🖱️ Left Click: Foreground </br>
                    🖱️ Middle Click: Background </br></br>
                    Top Right Buttons: </br> 
                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" 
                    stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" 
                    style="vertical-align: middle; height: 1em; width: 1em; display: inline;">
                    <polyline points="1 4 1 10 7 10"></polyline>
                    <path d="M3.51 15a9 9 0 1 0 2.13-9.36L1 10"></path>
                    </svg> :
                    Remove Last Point
                    </br>
                    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" 
                    style="vertical-align: middle; height: 1em; width: 1em; display: inline;">
                    <g fill="none">
                    <path fill="currentColor" d="m5.505 11.41l.53.53l-.53-.53ZM3 14.952h-.75H3ZM9.048 21v.75V21ZM11.41 5.505l-.53-.53l.53.53Zm1.831 12.34a.75.75 0 0 0 1.06-1.061l-1.06 1.06ZM7.216 9.697a.75.75 0 1 0-1.06 1.061l1.06-1.06Zm10.749 2.362l-5.905 5.905l1.06 1.06l5.905-5.904l-1.06-1.06Zm-11.93-.12l5.905-5.905l-1.06-1.06l-5.905 5.904l1.06 1.06Zm0 6.025c-.85-.85-1.433-1.436-1.812-1.933c-.367-.481-.473-.79-.473-1.08h-1.5c0 .749.312 1.375.78 1.99c.455.596 1.125 1.263 1.945 2.083l1.06-1.06Zm-1.06-7.086c-.82.82-1.49 1.488-1.945 2.084c-.468.614-.78 1.24-.78 1.99h1.5c0-.29.106-.6.473-1.08c.38-.498.962-1.083 1.812-1.933l-1.06-1.06Zm7.085 7.086c-.85.85-1.435 1.433-1.933 1.813c-.48.366-.79.472-1.08.472v1.5c.75 0 1.376-.312 1.99-.78c.596-.455 1.264-1.125 2.084-1.945l-1.06-1.06Zm-7.085 1.06c.82.82 1.487 1.49 2.084 1.945c.614.468 1.24.78 1.989.78v-1.5c-.29 0-.599-.106-1.08-.473c-.497-.38-1.083-.962-1.933-1.812l-1.06 1.06Zm12.99-12.99c.85.85 1.433 1.436 1.813 1.933c.366.481.472.79.472 1.08h1.5c0-.749-.312-1.375-.78-1.99c-.455-.596-1.125-1.263-1.945-2.083l-1.06 1.06Zm1.06 7.086c.82-.82 1.49-1.488 1.945-2.084c.468-.614.78-1.24.78-1.99h-1.5c0 .29-.106.6-.473 1.08c-.38.498-.962 1.083-1.812 1.933l1.06 1.06Zm0-8.146c-.82-.82-1.487-1.49-2.084-1.945c-.614-.468-1.24-.78-1.989-.78v1.5c.29 0 .599.106 1.08.473c.497.38 1.083.962 1.933 1.812l1.06-1.06Zm-7.085 1.06c.85-.85 1.435-1.433 1.933-1.812c.48-.367.79-.473 1.08-.473v-1.5c-.75 0-1.376.312-1.99.78c-.596.455-1.264 1.125-2.084 1.945l1.06 1.06Zm2.362 10.749L7.216 9.698l-1.06 1.061l7.085 7.085l1.06-1.06Z"></path>
                    <path stroke="currentColor" stroke-linecap="round" stroke-width="1.5" d="M9 21h12"></path></g>
                    </svg> :
                    Clear All Points
                    </br>
                    (Known issue: please manually clear the points after loading new image)
                    </h5>
                """)
                prompt_image1 = ImagePrompter(show_label=False, elem_id="prompt_image1", interactive=False)
                prompt_image2 = ImagePrompter(show_label=False, elem_id="prompt_image2", interactive=False)
                prompt_image3 = ImagePrompter(show_label=False, elem_id="prompt_image3", interactive=False)
                # def update_number_of_images(images):
                #     if images is None:
                #         return gr.update(max=0, value=0)
                #     return gr.update(max=len(images)-1, value=1)
                # input_gallery.change(update_number_of_images, inputs=input_gallery, outputs=image1_slider)
                
                def update_prompt_image(original_images, ncut_images, image_type, index):
                    if image_type == "Original":
                        images = original_images
                    else:
                        images = ncut_images
                    if images is None:
                        return 
                    total_len = len(images)
                    if total_len == 0:
                        return 
                    if index >= total_len:
                        index = total_len - 1
                                            
                    return ImagePrompter(value={'image': images[index][0], 'points': []}, interactive=True)
                    # return gr.Image(value=images[index][0], elem_id=f"prompt_image{randint}", interactive=True)
                load_one_image_button.click(update_prompt_image, inputs=[input_gallery, output_gallery, image_type_radio, image1_slider], outputs=[prompt_image1])
                load_one_image_button.click(update_prompt_image, inputs=[input_gallery, output_gallery, image_type_radio, image2_slider], outputs=[prompt_image2])
                load_one_image_button.click(update_prompt_image, inputs=[input_gallery, output_gallery, image_type_radio, image3_slider], outputs=[prompt_image3])
                
                image3_slider.visible = False
                prompt_image3.visible = False
                
                
                
            with gr.Column(scale=5, min_width=200):
                gr.Markdown("### Step 3: Segment and Crop")
                mask_gallery = gr.Gallery(value=[], label="Segmentation Masks", show_label=True, elem_id="mask_gallery", columns=[3], rows=[1], object_fit="contain", height="auto", show_share_button=True, interactive=False)
                run_crop_button = gr.Button("🔴 RUN", elem_id="run_crop_button", variant='primary')
                add_download_button(mask_gallery, "mask")
                distance_threshold_slider = gr.Slider(0, 1, step=0.01, label="Mask Threshold (Foreground)", value=0.5, elem_id="distance_threshold", info="increase for smaller mask")
                negative_distance_threshold_slider = gr.Slider(0, 1, step=0.01, label="Mask Threshold (Background)", value=0.5, elem_id="distance_threshold", info="increase for smaller mask")
                overlay_image_checkbox = gr.Checkbox(label="Overlay Original Image", value=True, elem_id="overlay_image_checkbox")
                # filter_small_area_checkbox = gr.Checkbox(label="Noise Reduction", value=True, elem_id="filter_small_area_checkbox")
                distance_power_slider = gr.Slider(-3, 3, step=0.01, label="Distance Power", value=0.5, elem_id="distance_power", info="d = d^p", visible=False)
                crop_gallery = gr.Gallery(value=[], label="Cropped Images", show_label=True, elem_id="crop_gallery", columns=[3], rows=[1], object_fit="contain", height="auto", show_share_button=True, interactive=False)
                add_download_button(crop_gallery, "cropped")
                crop_expand_slider = gr.Slider(1.0, 2.0, step=0.1, label="Crop bbox Expand Factor", value=1.0, elem_id="crop_expand", info="increase for larger crop", visible=True)
                area_threshold_slider = gr.Slider(0, 100, step=0.1, label="Area Threshold (%)", value=3, elem_id="area_threshold", info="for noise filtering (area of connected components)", visible=False)
                
                # logging_image = gr.Image(value=None, label="Logging Image", elem_id="logging_image", interactive=False)
                
                # prompt_image.change(lambda x: gr.update(value=x.get('image', None)), inputs=prompt_image, outputs=[logging_image])
                
                def relative_xy(prompts):
                    image = prompts['image']
                    points = np.asarray(prompts['points'])
                    if points.shape[0] == 0:
                        return [], []
                    is_point = points[:, 5] == 4.0
                    points = points[is_point]
                    is_positive = points[:, 2] == 1.0
                    is_negative = points[:, 2] == 0.0
                    xy = points[:, :2].tolist()
                    if isinstance(image, str):
                        image = Image.open(image)
                        image = np.array(image)
                    h, w = image.shape[:2]
                    new_xy = [(x/w, y/h) for x, y in xy]
                    # print(new_xy)
                    return new_xy, is_positive
                
                def xy_rgb(prompts, image_idx, ncut_images):
                    image = ncut_images[image_idx]
                    xy, is_positive = relative_xy(prompts)
                    rgbs = []
                    for i, (x, y) in enumerate(xy):
                        rgb = image.getpixel((int(x*image.width), int(y*image.height)))
                        rgbs.append((rgb, is_positive[i]))
                    return rgbs
                
                def run_crop(original_images, ncut_images, prompts1, prompts2, prompts3, image_idx1, image_idx2, image_idx3,
                            crop_expand, distance_threshold, distance_power, area_threshold, overlay_image, negative_distance_threshold):
                    ncut_images = [image[0] for image in ncut_images]
                    if len(ncut_images) == 0:
                        return []
                    if isinstance(ncut_images[0], str):
                        ncut_images = [Image.open(image) for image in ncut_images]
                    
                    rgbs = xy_rgb(prompts1, image_idx1, ncut_images) + \
                            xy_rgb(prompts2, image_idx2, ncut_images) + \
                            xy_rgb(prompts3, image_idx3, ncut_images)
                    # print(rgbs)
                    
                    
                    ncut_images = [np.array(image).astype(np.float32) for image in ncut_images]
                    ncut_pixels = [image.reshape(-1, 3) for image in ncut_images]
                    h, w = ncut_images[0].shape[:2]
                    ncut_pixels = torch.tensor(np.array(ncut_pixels).reshape(-1, 3)) / 255
                    # normalized_ncut_pixels = F.normalize(ncut_pixels, p=2, dim=-1)
                    
                    def to_mask(heatmap, threshold):
                        heatmap = 1 / (heatmap + 1e-6)
                        if heatmap.shape[0] > 10000:
                            random_idx = np.random.choice(heatmap.shape[0], 10000, replace=False)
                            vmin, vmax = heatmap[random_idx].quantile(0.01), heatmap[random_idx].quantile(0.99)
                        else:
                            vmin, vmax = heatmap.quantile(0.01), heatmap.quantile(0.99)
                        heatmap = (heatmap - vmin) / (vmax - vmin)
                        heatmap = heatmap.reshape(len(ncut_images), h, w)
                        mask = heatmap > threshold
                        return mask
                    
                    positive_masks, negative_masks = [], []
                    for rgb, is_positive in rgbs:
                        rgb = torch.tensor(rgb).float() / 255
                        distance = (ncut_pixels - rgb[None]).norm(dim=-1)
                        distance = distance.squeeze(-1)
                        if is_positive:
                            positive_masks.append(to_mask(distance, distance_threshold))
                        else:
                            negative_masks.append(to_mask(distance, negative_distance_threshold))
                    if len(positive_masks) == 0:
                        raise gr.Error("No prompt points. Please draw some points on the image.")
                    positive_masks = torch.stack(positive_masks)
                    positive_mask = positive_masks.any(dim=0)
                    if len(negative_masks) > 0:
                        negative_masks = torch.stack(negative_masks)
                        negative_mask = negative_masks.any(dim=0)
                        positive_mask = positive_mask & ~negative_mask
                    
                    
                    # convert to PIL
                    mask = positive_mask.cpu().numpy()
                    mask = mask.astype(np.uint8) * 255
                    mask = [Image.fromarray(mask[i]) for i in range(len(mask))]
                    
                    import cv2
                    def get_bboxes_and_clean_mask(pil_mask, min_area=500):
                        """
                        Args:
                        - pil_mask: A Pillow image of a binary mask with 255 for the object and 0 for the background.
                        - min_area: Minimum area for a connected component to be considered valid (default 500).
                        
                        Returns:
                        - bounding_boxes: List of bounding boxes for valid objects (x, y, width, height).
                        - cleaned_pil_mask: A Pillow image of the cleaned mask, with small components removed.
                        """
                        # Convert the Pillow image to a NumPy array
                        mask = np.array(pil_mask)

                        # Ensure the mask is binary (0 or 255)
                        mask = np.where(mask > 127, 255, 0).astype(np.uint8)

                        # Remove small noise using morphological operations (denoising)
                        kernel = np.ones((5, 5), np.uint8)
                        cleaned_mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)

                        # Find connected components in the cleaned mask
                        num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(cleaned_mask, connectivity=8)

                        # Initialize an empty mask to store the final cleaned mask
                        final_cleaned_mask = np.zeros_like(cleaned_mask)

                        # Collect bounding boxes for components that are larger than the threshold and update the cleaned mask
                        bounding_boxes = []
                        for i in range(1, num_labels):  # Skip label 0 (background)
                            x, y, w, h, area = stats[i]
                            if area >= min_area:
                                # Add the bounding box of the valid component
                                bounding_boxes.append((x, y, w, h))
                                # Keep the valid components in the final cleaned mask
                                final_cleaned_mask[labels == i] = 255

                        # Convert the final cleaned mask back to a Pillow image
                        cleaned_pil_mask = Image.fromarray(final_cleaned_mask)

                        return bounding_boxes, cleaned_pil_mask  
                    
                    bboxs, filtered_masks = zip(*[get_bboxes_and_clean_mask(_mask) for _mask in mask])

                    original_images = [image[0] for image in original_images]
                    if isinstance(original_images[0], str):
                        original_images = [Image.open(image) for image in original_images]
                    

                    # combine the masks, also draw the bounding boxes
                    combined_masks = []
                    for i_image in range(len(mask)):
                        noisy_mask = np.array(mask[i_image].convert("RGB"))
                        bbox = bboxs[i_image]
                        clean_mask = np.array(filtered_masks[i_image].convert("RGB"))
                        combined_mask = noisy_mask * 0.4 + clean_mask
                        combined_mask = np.clip(combined_mask, 0, 255).astype(np.uint8)
                        if overlay_image:
                            combined_mask[:, :, 0] = 0  # remove red channel
                            combined_mask[:, :, 1] = 0  # remove green channel
                            _image = original_images[i_image].convert("RGB").resize((combined_mask.shape[1], combined_mask.shape[0]))
                            _image = np.array(_image)
                            combined_mask = 0.5 * combined_mask + 0.5 * _image
                            combined_mask = np.clip(combined_mask, 0, 255).astype(np.uint8)
                        for x, y, w, h in bbox:
                            cv2.rectangle(combined_mask, (x-1, y-1), (x + w+2, y + h+2), (255, 0, 0), 2)
                        combined_mask = Image.fromarray(combined_mask)
                        combined_masks.append(combined_mask)
                    
                    def extend_the_mask(xywh, factor=1.5):
                        x, y, w, h = xywh
                        x -= w * (factor - 1) / 2
                        y -= h * (factor - 1) / 2
                        w *= factor
                        h *= factor
                        return x, y, w, h

                    def resize_the_mask(xywh, original_size, target_size):
                        x, y, w, h = xywh
                        x *= target_size[0] / original_size[0]
                        y *= target_size[1] / original_size[1]
                        w *= target_size[0] / original_size[0]
                        h *= target_size[1] / original_size[1]
                        x, y, w, h = int(x), int(y), int(w), int(h)
                        return x, y, w, h
                    
                    def crop_image(image, xywh, mask_h, mask_w, factor=1.0):
                        x, y, w, h = xywh
                        x, y, w, h = resize_the_mask((x, y, w, h), (mask_h, mask_w), image.size)
                        _x, _y, _w, _h = extend_the_mask((x, y, w, h), factor=factor)
                        crop = image.crop((_x, _y, _x + _w, _y + _h))
                        return crop
                    
                    mask_h, mask_w = filtered_masks[0].size
                    cropped_images = []
                    for _image, _bboxs in zip(original_images, bboxs):
                        for _bbox in _bboxs:
                            cropped_images.append(crop_image(_image, _bbox, mask_h, mask_w, factor=crop_expand))
                    
                    return combined_masks, cropped_images
                    
                run_crop_button.click(run_crop,
                    inputs=[input_gallery, output_gallery, prompt_image1, prompt_image2, prompt_image3, image1_slider, image2_slider, image3_slider,
                            crop_expand_slider, distance_threshold_slider, distance_power_slider, 
                            area_threshold_slider, overlay_image_checkbox, negative_distance_threshold_slider],
                    outputs=[mask_gallery, crop_gallery])
                
    
    with gr.Tab('📄About'):
        with gr.Column():
            gr.Markdown("**This demo is for Python package `ncut-pytorch`, please visit the [Documentation](https://ncut-pytorch.readthedocs.io/)**")
            gr.Markdown("**All the models and functions used for this demo are in the Python package `ncut-pytorch`**")
            gr.Markdown("---")
            gr.Markdown("---")
            gr.Markdown("**Normalized Cuts**, aka. spectral clustering, is a graphical method to analyze data grouping in the affinity eigenvector space. It has been widely used for unsupervised segmentation in the 2000s.")
            gr.Markdown("*Normalized Cuts and Image Segmentation, Jianbo Shi and Jitendra Malik, 2000*")
            gr.Markdown("---")        
            gr.Markdown("**We have improved NCut, with some advanced features:**")
            gr.Markdown("- **Nyström** Normalized Cut, is a new approximation algorithm developed for large-scale graph cuts, a large-graph of million nodes can be processed in under 10s (cpu) or 2s (gpu).")
            gr.Markdown("- **spectral-tSNE** visualization, a new method to visualize the high-dimensional eigenvector space with 3D RGB cube. Color is aligned across images, color infers distance in representation.")
            gr.Markdown("*paper in prep, Yang 2024*")
            gr.Markdown("*AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature Space, Huzheng Yang, James Gee\*, and Jianbo Shi\*, 2024*")
            gr.Markdown("---")        
            gr.Markdown("---")        
            gr.Markdown('<p style="text-align: center;">We thank HuggingFace for hosting this demo.</p>')        
        
        # unlock the hidden tab
        with gr.Row():
            with gr.Column(scale=5):
                gr.Markdown("")
            with gr.Column(scale=5):
                hidden_button = gr.Checkbox(label="🤗", value=False, elem_id="unlock_button", visible=True, interactive=True)
            with gr.Column(scale=5):
                gr.Markdown("")
            
        n_smiles = gr.State(0)
        unlock_value = 6

        def update_smile(n_smiles):
            n_smiles = n_smiles + 1
            n_smiles = unlock_value if n_smiles > unlock_value else n_smiles
            if n_smiles == unlock_value - 2:
                gr.Info("click one more time to unlock", 2)
            if n_smiles == unlock_value:
                label = "🔓 unlocked"
                return n_smiles, gr.update(label=label, value=True, interactive=False)
            label = ["😊"] * n_smiles
            label = "".join(label)
            return n_smiles, gr.update(label=label, value=False)
        
        def unlock_tabs_with_info(n_smiles):
            if n_smiles == unlock_value:
                gr.Info("🔓 unlocked tabs", 2)
                return gr.update(visible=True)
            return gr.update()

        def unlock_tabs(n_smiles):
            if n_smiles == unlock_value:
                return gr.update(visible=True)
            return gr.update()
        
        hidden_button.change(update_smile, [n_smiles], [n_smiles, hidden_button])
        hidden_button.change(unlock_tabs_with_info, n_smiles, tab_alignedcut_advanced)
        hidden_button.change(unlock_tabs, n_smiles, tab_model_aligned_advanced)
        hidden_button.change(unlock_tabs, n_smiles, tab_recursivecut_advanced)
        hidden_button.change(unlock_tabs, n_smiles, tab_compare_models_advanced)
        hidden_button.change(unlock_tabs, n_smiles, tab_directed_ncut)
                
    # with gr.Row():
        # with gr.Column():
            # gr.Markdown("##### This demo is for `ncut-pytorch`, [Documentation](https://ncut-pytorch.readthedocs.io/) ")
        # with gr.Column():
            # gr.Markdown("###### Running out of GPU Quota? Try [Demo](https://ncut-pytorch.readthedocs.io/en/latest/demo/) hosted at UPenn")
    with gr.Row():
        gr.Markdown("**This demo is for Python package `ncut-pytorch`, [Documentation](https://ncut-pytorch.readthedocs.io/)**")

# for local development
if os.path.exists("/hf_token.txt"):
    os.environ["HF_ACCESS_TOKEN"] = open("/hf_token.txt").read().strip()

    
if DOWNLOAD_ALL_MODELS_DATASETS:
    from ncut_pytorch.backbone import download_all_models
    # t1 = threading.Thread(target=download_all_models).start()
    # t1.join()
    # t3 = threading.Thread(target=download_all_datasets).start()
    # t3.join()
    download_all_models()
    download_all_datasets()
    
    from ncut_pytorch.backbone_text import download_all_models
    # t2 = threading.Thread(target=download_all_models).start()
    # t2.join()
    download_all_models()

demo.launch(share=True)



# # %%
# # debug
# # change working directory to "/"
# os.chdir("/")
# images = [(Image.open(image), None) for image in default_images]
# ret = run_fn(images, num_eig=30)
# # %%

# %%