app.py

# Author: Huzheng Yang
# %%
import os
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 = 4
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, eigenvector_to_rgb

DATASET_TUPS = [
    # (name, num_classes)
    ('UCSC-VLAA/Recap-COCO-30K', None),
    ('nateraw/pascal-voc-2012', None),
    ('johnowhitaker/imagenette2-320', 10),
    ('jainr3/diffusiondb-pixelart', None),
    ('nielsr/CelebA-faces', None),
    ('JapanDegitalMaterial/Places_in_Japan', None),
    ('Borismile/Anime-dataset', None),
    ('Multimodal-Fatima/CUB_train', 200),
    ('mrm8488/ImageNet1K-val', 1000),
    ("trashsock/hands-images", 8),
]
DATASET_NAMES = [tup[0] for tup in DATASET_TUPS]
DATASET_CLASSES = [tup[1] for tup in DATASET_TUPS]

from datasets import load_dataset

def download_all_datasets():
    for name in DATASET_NAMES:
        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",
    num_sample_tsne=300,
    perplexity=150,
    n_neighbors=150,
    min_dist=0.1,
    sampling_method="fps",
    metric="cosine",
):        
    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()
    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,
    ).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()
    _, rgb = eigenvector_to_rgb(
        eigvecs,
        method=embedding_method,
        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 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):
    size = images[0].shape[1]
    target = 256
    multiplier = target // size
    res = int(size * multiplier)
    return [
        Image.fromarray((image * 255).cpu().numpy().astype(np.uint8)).resize((res, res), Image.Resampling.NEAREST)
        for image in 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 ncut_run(
    model,
    images,
    model_name="SAM(sam_vit_b)",
    layer=-1,
    num_eig=100,
    node_type="block",
    affinity_focal_gamma=0.3,
    num_sample_ncut=10000,
    knn_ncut=10,
    embedding_method="UMAP",
    num_sample_tsne=1000,
    knn_tsne=10,
    perplexity=500,
    n_neighbors=500,
    min_dist=0.1,
    sampling_method="fps",
    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,
):
    logging_str = ""
    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()
    features = extract_features(
        images, model, node_type=node_type, 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"
    
    if recursion:
        rgbs = []
        inp = features
        for i, n_eigs in enumerate([num_eig, recursion_l2_n_eigs, recursion_l3_n_eigs]):
            logging_str += f"Recursion #{i+1}\n"
            rgb, _logging_str, eigvecs = compute_ncut(
                inp,
                num_eig=n_eigs,
                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,
                perplexity=perplexity,
                n_neighbors=n_neighbors,
                min_dist=min_dist,
                sampling_method=sampling_method,
                metric="cosine" if i == 0 else recursion_metric,
            )
            logging_str += _logging_str
            rgb = dont_use_too_much_green(rgb)
            rgbs.append(to_pil_images(rgb))
            inp = eigvecs.reshape(*features.shape[:3], -1)
            if recursion_metric == "cosine":
                inp = F.normalize(inp, dim=-1)
        return rgbs[0], rgbs[1], rgbs[2], logging_str
    
    if old_school_ncut:  # individual images
        logging_str += "Running NCut for each image independently\n"
        rgb = []
        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=500,
                embedding_method=embedding_method,
                perplexity=perplexity,
                n_neighbors=n_neighbors,
                min_dist=min_dist,
                sampling_method=sampling_method,
            )
            logging_str += _logging_str
            rgb.append(_rgb[0])
    
    if not old_school_ncut:  # joint across all images
        rgb, _logging_str, _ = 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,
            perplexity=perplexity,
            n_neighbors=n_neighbors,
            min_dist=min_dist,
            sampling_method=sampling_method,
        )
        logging_str += _logging_str
        
        rgb = dont_use_too_much_green(rgb)
        
    
    if video_output:
        video_path = get_random_path()
        video_cache.add_video(video_path)
        pil_images_to_video(to_pil_images(rgb), video_path)
        return video_path, logging_str
    else:
        return to_pil_images(rgb), logging_str

def _ncut_run(*args, **kwargs):
    try:
        ret = ncut_run(*args, **kwargs)
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return ret
    except Exception as e:
        gr.Error(str(e))
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        return [], "Error: " + str(e)

if USE_HUGGINGFACE_ZEROGPU:
    @spaces.GPU(duration=20)
    def quick_run(*args, **kwargs):
        return _ncut_run(*args, **kwargs)

    @spaces.GPU(duration=30)
    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)

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 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)):
    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
    image = (image - 0.5) / 0.5
    return image

def run_fn(
    images,
    model_name="SAM(sam_vit_b)",
    layer=-1,
    num_eig=100,
    node_type="block",
    affinity_focal_gamma=0.3,
    num_sample_ncut=10000,
    knn_ncut=10,
    embedding_method="UMAP",
    num_sample_tsne=1000,
    knn_tsne=10,
    perplexity=500,
    n_neighbors=500,
    min_dist=0.1,
    sampling_method="fps",
    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,
):
    
    if images is None:
        gr.Warning("No images selected.")
        return [], "No images selected."
    
    video_output = False
    if isinstance(images, str):
        images = extract_video_frames(images, max_frames=max_frames)
        video_output = True
    
    if sampling_method == "fps":
        sampling_method = "farthest"
        
    # resize the images before acquiring GPU
    resolution = RES_DICT[model_name]
    images = [tup[0] for tup in images]
    images = [transform_image(image, resolution=resolution) for image in images]
    images = torch.stack(images)
    
    model = load_model(model_name)
    if "stable" in model_name.lower() and "diffusion" in model_name.lower():
        model.timestep = layer
        layer = 1
    
    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,
        "num_sample_tsne": num_sample_tsne,
        "knn_tsne": knn_tsne,
        "perplexity": perplexity,
        "n_neighbors": n_neighbors,
        "min_dist": min_dist,
        "sampling_method": sampling_method,
        "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,
    }
    # print(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)



def make_input_images_section():
    gr.Markdown('### Input Images')
    input_gallery = gr.Gallery(value=None, label="Select images", show_label=False, elem_id="images", columns=[3], rows=[1], object_fit="contain", height="auto", type="pil", show_share_button=False)
    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

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)
    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 make_dataset_images_section(advanced=False):

    gr.Markdown('### Load Datasets')
    load_images_button = gr.Button("Load", elem_id="load-images-button", variant='secondary')
    advanced_radio = gr.Radio(["Basic", "Advanced"], label="Datasets", value="Advanced" if advanced else "Basic", elem_id="advanced-radio")
    with gr.Column() as basic_block:
        example_gallery = gr.Gallery(value=example_items, label="Example Set A", show_label=False, columns=[3], rows=[2], object_fit="scale-down", height="200px", show_share_button=False, elem_id="example-gallery")
    with gr.Column() as advanced_block:
        dataset_names = DATASET_NAMES
        dataset_classes = DATASET_CLASSES
        with gr.Row():
            dataset_dropdown = gr.Dropdown(dataset_names, label="Dataset name", value="mrm8488/ImageNet1K-val", elem_id="dataset", min_width=300)
            num_images_slider = gr.Number(10, label="Number of images", elem_id="num_images")
            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)
    
    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 change_filter_options(dataset_name):
        idx = dataset_names.index(dataset_name)
        num_classes = dataset_classes[idx]
        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):
        idx = dataset_names.index(dataset_name)
        num_classes = dataset_classes[idx]
        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)
    
        
    def load_dataset_images(is_advanced, dataset_name, num_images=10, 
                            is_filter=True, filter_by_class_text="0,1,2", 
                            is_random=False, seed=1):
        if is_advanced == "Basic":
            gr.Info("Loaded images from Ego-Exo4D")
            return default_images
        try:
            dataset = load_dataset(dataset_name, trust_remote_code=True)
            key = list(dataset.keys())[0]
            dataset = dataset[key]
        except Exception as e:
            gr.Error(f"Error loading dataset {dataset_name}: {e}")
            return None
        if num_images > len(dataset):
            num_images = len(dataset)
        
        if is_filter:
            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:
                gr.Error(f"Classes {classes} not found in the dataset.")
                return None
            # 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))
        images = [dataset[i]['image'] for i in image_idx]
        gr.Info(f"Loaded {len(images)} images from {dataset_name}")
        return images   
    
    load_images_button.click(load_dataset_images, 
                        inputs=[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 dataset_dropdown, num_images_slider, random_seed_slider, load_images_button
    
def make_output_images_section():
    gr.Markdown('### Output Images')
    output_gallery = gr.Gallery(value=[], label="NCUT Embedding", show_label=False, elem_id="ncut", columns=[3], rows=[1], object_fit="contain", height="auto")
    return output_gallery

def make_parameters_section():
    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)
    model_dropdown = gr.Dropdown(model_names, label="Backbone", value="DiNO(dino_vitb8_448)", elem_id="model_name")
    layer_slider = gr.Slider(1, 12, step=1, label="Backbone: Layer index", value=10, elem_id="layer")
    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 more 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="From the SD U-Net"))
        
        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),
                    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.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?"),
                    gr.Slider(1, value, step=1, label="Backbone: Layer index", value=value, elem_id="layer", visible=True))
    model_dropdown.change(fn=change_layer_slider, inputs=model_dropdown, outputs=[layer_slider, node_type_dropdown])
    
    with gr.Accordion("➡️ Click to expand: more parameters", 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(["fps", "random"], label="NCUT: Sampling method", value="fps", elem_id="sampling_method", info="Nyström approximation")
        knn_ncut_slider = gr.Slider(1, 100, step=1, label="NCUT: KNN", value=10, elem_id="knn_ncut", info="Nyström approximation")
        embedding_method_dropdown = gr.Dropdown(["tsne_3d", "umap_3d", "umap_shpere", "tsne_2d", "umap_2d"], label="Coloring method", value="tsne_3d", elem_id="embedding_method")
        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, knn_ncut_slider, 
            embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
            perplexity_slider, n_neighbors_slider, min_dist_slider, 
            sampling_method_dropdown]

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",
)
with demo:
    with gr.Tab('AlignedCut'):

        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button = make_input_images_section()
                dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_dataset_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, knn_ncut_slider, 
                    embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown
                ] = make_parameters_section()
                # logging text box
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")

        clear_images_button.click(lambda x: ([], []), outputs=[input_gallery, output_gallery])
        
        
        submit_button.click(
            run_fn,
            inputs=[
                input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
                embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_dropdown
            ],
            outputs=[output_gallery, logging_text],
            api_name="API_AlignedCut"
        )
        
    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 = make_input_images_section()
                dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_dataset_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, knn_ncut_slider, 
                    embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown
                ] = 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, knn_ncut_slider,
                                    num_sample_tsne_slider, knn_tsne_slider, sampling_method_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")
            
        clear_images_button.click(lambda x: ([], []), outputs=[input_gallery, output_gallery])
        submit_button.click(
            run_fn,
            inputs=[
                input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
                embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_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__ amplifies small object parts, 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):
                gr.Markdown('### Output (Recursion #1)')
                l1_gallery = gr.Gallery(value=[], label="Recursion #1", show_label=False, elem_id="ncut_l1", columns=[3], rows=[5], object_fit="contain", height="auto")
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Recursion #2)')
                l2_gallery = gr.Gallery(value=[], label="Recursion #2", show_label=False, elem_id="ncut_l2", columns=[3], rows=[5], object_fit="contain", height="auto")
            with gr.Column(scale=5, min_width=200):
                gr.Markdown('### Output (Recursion #3)')
                l3_gallery = gr.Gallery(value=[], label="Recursion #3", show_label=False, elem_id="ncut_l3", columns=[3], rows=[5], object_fit="contain", height="auto")
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                input_gallery, submit_button, clear_images_button = make_input_images_section()
                dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_dataset_images_section(advanced=True)
                num_images_slider.value = 100
                clear_images_button.visible = False
                
            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")
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
                    embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown
                ] = make_parameters_section()
                num_eig_slider.visible = False
                affinity_focal_gamma_slider.visible = False
                # logging text box
        with gr.Row():
            with gr.Column(scale=5, min_width=200):
                gr.Markdown(' ')
            with gr.Column(scale=5, min_width=200):
                gr.Markdown(' ')
            with gr.Column(scale=5, min_width=200):
                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
        clear_images_button.click(lambda x: ([],), outputs=[input_gallery])
        submit_button.click(
            run_fn,
            inputs=[
                input_gallery, model_dropdown, layer_slider, l1_num_eig_slider, node_type_dropdown, 
                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
                embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_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('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, knn_ncut_slider, 
                    embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown
                ] = 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
        submit_button.click(
            run_fn,
            inputs=[
                video_input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
                embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_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('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(value=[], label="NCUT Embedding", show_label=False, elem_id=f"ncut{i_model}", columns=[3], rows=[1], object_fit="contain", height="auto")
                submit_button = gr.Button("🔴 RUN", elem_id=f"submit_button{i_model}", variant='primary')
                [
                    model_dropdown, layer_slider, node_type_dropdown, num_eig_slider, 
                    affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
                    embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                    perplexity_slider, n_neighbors_slider, min_dist_slider, 
                    sampling_method_dropdown
                ] = make_parameters_section()
                # logging text box
                logging_text = gr.Textbox("Logging information", label="Logging", elem_id="logging", type="text", placeholder="Logging information")
                submit_button.click(
                    run_fn,
                    inputs=[
                        input_gallery, model_dropdown, layer_slider, num_eig_slider, node_type_dropdown, 
                        affinity_focal_gamma_slider, num_sample_ncut_slider, knn_ncut_slider, 
                        embedding_method_dropdown, num_sample_tsne_slider, knn_tsne_slider, 
                        perplexity_slider, n_neighbors_slider, min_dist_slider, sampling_method_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 = make_input_images_section()
                clear_images_button.click(lambda x: ([],), outputs=[input_gallery])
                submit_button.visible = False
                dataset_dropdown, num_images_slider, random_seed_slider, load_images_button = make_dataset_images_section(advanced=True)

                
            for i in range(2):
                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(3):
                    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])
        


if DOWNLOAD_ALL_MODELS_DATASETS:
    from ncut_pytorch.backbone import download_all_models
    threading.Thread(target=download_all_models).start()
    from ncut_pytorch.backbone_text import download_all_models
    threading.Thread(target=download_all_models).start()
    
    threading.Thread(target=download_all_datasets).start()
    
demo.launch(share=True)


# %%