app.py

import gradio as gr
from PIL import Image, PngImagePlugin
import io
import os
import pandas as pd
import torch
from transformers import pipeline as transformers_pipeline , AutoImageProcessor, AutoModelForImageClassification
# from torchvision import transforms # Менее релевантно для ONNX пайплайна
from torchmetrics.functional.multimodal import clip_score
from open_clip import create_model_from_pretrained, get_tokenizer
import re
import matplotlib.pyplot as plt
import json
from collections import defaultdict
import numpy as np
import logging # Для логирования ONNX

# --- ONNX Related Imports and Setup ---
try:
    import onnxruntime
except ImportError:
    print("onnxruntime not found. Please ensure it's in requirements.txt")
    onnxruntime = None

from huggingface_hub import hf_hub_download

# imgutils для rgb_encode (если установлен)
try:
    from imgutils.data import rgb_encode # Предполагаем, что это правильный импорт
except ImportError:
    print("imgutils.data.rgb_encode not found. Preprocessing for deepghs might be limited.")
    def rgb_encode(image, order_='CHW'): # Простая заглушка, если imgutils нет
        img_arr = np.array(image)
        if order_ == 'CHW':
            img_arr = np.transpose(img_arr, (2, 0, 1))
        return img_arr.astype(np.float32) / 255.0 # Базовая нормализация, если не указана другая

# --- Модель Конфигурация и Загрузка ---
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {DEVICE}")
ONNX_DEVICE = "CUDAExecutionProvider" if DEVICE == "cuda" and onnxruntime and "CUDAExecutionProvider" in onnxruntime.get_available_providers() else "CPUExecutionProvider"
print(f"Using ONNX device: {ONNX_DEVICE}")


# --- Helper for ONNX models (deepghs) ---
@torch.no_grad()
def _img_preprocess_for_onnx(image: Image.Image, size: tuple = (384, 384), normalize_mean=0.5, normalize_std=0.5):
    image = image.resize(size, Image.Resampling.BILINEAR) # Обновлено до Resampling
    data = rgb_encode(image, order_='CHW') # (C, H, W), float32, 0-1 range from common imgutils

    # Нормализация ((data / 255.0) - mean) / std, если data в 0-255
    # Если rgb_encode уже возвращает 0-1, то (data - mean) / std
    # Предположим, rgb_encode возвращает [0,1] диапазон float32
    mean = np.array([normalize_mean] * 3, dtype=np.float32).reshape((3, 1, 1))
    std = np.array([normalize_std] * 3, dtype=np.float32).reshape((3, 1, 1))
    
    normalized_data = (data - mean) / std
    return normalized_data[None, ...].astype(np.float32) # Add batch dimension

onnx_sessions_cache = {} # Кэш для ONNX сессий и метаданных

def get_onnx_session_and_meta(repo_id, model_subfolder):
    cache_key = f"{repo_id}/{model_subfolder}"
    if cache_key in onnx_sessions_cache:
        return onnx_sessions_cache[cache_key]

    if not onnxruntime:
        raise ImportError("ONNX Runtime is not available.")

    try:
        model_path = hf_hub_download(repo_id, filename=f"{model_subfolder}/model.onnx")
        meta_path = hf_hub_download(repo_id, filename=f"{model_subfolder}/meta.json")

        options = onnxruntime.SessionOptions()
        options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
        if ONNX_DEVICE == "CPUExecutionProvider":
             options.intra_op_num_threads = os.cpu_count()

        session = onnxruntime.InferenceSession(model_path, options, providers=[ONNX_DEVICE])
        
        with open(meta_path, 'r') as f:
            meta = json.load(f)
        
        labels = meta.get('labels', [])
        onnx_sessions_cache[cache_key] = (session, labels, meta)
        return session, labels, meta
    except Exception as e:
        print(f"Error loading ONNX model {repo_id}/{model_subfolder}: {e}")
        onnx_sessions_cache[cache_key] = (None, [], None) # Кэшируем ошибку
        return None, [], None


# 1. ImageReward
try:
    reward_processor = AutoImageProcessor.from_pretrained("THUDM/ImageReward")
    reward_model = AutoModelForImageClassification.from_pretrained("THUDM/ImageReward").to(DEVICE)
    reward_model.eval()
except Exception as e:
    print(f"Error loading THUDM/ImageReward: {e}")
    reward_processor, reward_model = None, None

# 2. Anime Aesthetic (deepghs ONNX)
# Модель: deepghs/anime_aesthetic, подпапка: swinv2pv3_v0_448_ls0.2_x
ANIME_AESTHETIC_REPO = "deepghs/anime_aesthetic"
ANIME_AESTHETIC_SUBFOLDER = "swinv2pv3_v0_448_ls0.2_x"
ANIME_AESTHETIC_IMG_SIZE = (448, 448)
# Метки из meta.json: ["normal", "slight", "moderate", "strong", "extreme"]
# Веса для взвешенной суммы:
ANIME_AESTHETIC_LABEL_WEIGHTS = {"normal": 0.0, "slight": 1.0, "moderate": 2.0, "strong": 3.0, "extreme": 4.0}

# 3. MANIQA (Technical Quality) - Transformers pipeline
try:
    maniqa_pipe = transformers_pipeline("image-classification", model="honklers/maniqa-nr", device=torch.device(DEVICE).index if DEVICE=="cuda" else -1)
except Exception as e:
    print(f"Error loading honklers/maniqa-nr: {e}")
    maniqa_pipe = None

# 4. CLIP Score (laion/CLIP-ViT-L-14-laion2B-s32B-b82K) - open_clip
try:
    clip_model_name = 'ViT-L-14'
    clip_pretrained = 'laion2b_s32b_b82k' # laion2B-s32B-b82K
    clip_model_instance, _, clip_preprocess = create_model_from_pretrained(clip_model_name, pretrained=clip_pretrained, device=DEVICE)
    clip_tokenizer = get_tokenizer(clip_model_name)
    clip_model_instance.eval()
except Exception as e:
    print(f"Error loading CLIP model {clip_model_name} ({clip_pretrained}): {e}")
    clip_model_instance, clip_preprocess, clip_tokenizer = None, None, None

# 5. AI Detectors
# Organika/sdxl-detector - Transformers pipeline
try:
    sdxl_detector_pipe = transformers_pipeline("image-classification", model="Organika/sdxl-detector", device=torch.device(DEVICE).index if DEVICE=="cuda" else -1)
except Exception as e:
    print(f"Error loading Organika/sdxl-detector: {e}")
    sdxl_detector_pipe = None

# deepghs/anime_ai_check - ONNX
# Модель: deepghs/anime_ai_check, подпапка: caformer_s36_plus_sce
ANIME_AI_CHECK_REPO = "deepghs/anime_ai_check"
ANIME_AI_CHECK_SUBFOLDER = "caformer_s36_plus_sce"
ANIME_AI_CHECK_IMG_SIZE = (384, 384) # Предположение, если не указано иначе

# --- Функции извлечения метаданных (без изменений) ---
def extract_sd_parameters(image_pil):
    if image_pil is None:
        return "", "N/A", "N/A", "N/A", {}
    
    parameters_str = image_pil.info.get("parameters", "")
    if not parameters_str:
        return "", "N/A", "N/A", "N/A", {}

    prompt = ""
    negative_prompt = ""
    model_name = "N/A"
    model_hash = "N/A"
    other_params_dict = {}

    neg_prompt_index = parameters_str.find("Negative prompt:")
    steps_meta_index = parameters_str.find("Steps:") # Ищем начало блока с параметрами

    if neg_prompt_index != -1:
        prompt = parameters_str[:neg_prompt_index].strip()
        # Если "Steps:" найдено после "Negative prompt:", то neg_prompt между ними
        if steps_meta_index != -1 and steps_meta_index > neg_prompt_index:
            negative_prompt = parameters_str[neg_prompt_index + len("Negative prompt:"):steps_meta_index].strip()
            params_part = parameters_str[steps_meta_index:]
        else: # "Steps:" не найдено или до "Negative prompt:", значит neg_prompt до конца строки или до params_part
            # Если params_part вообще нет, то neg_prompt до конца строки
            end_of_neg_prompt = parameters_str.find("\n", neg_prompt_index) # Ищем конец строки для негативного промпта
            if end_of_neg_prompt == -1: end_of_neg_prompt = len(parameters_str)

            search_params_in_rest = parameters_str[neg_prompt_index + len("Negative prompt:"):]
            actual_steps_index_in_rest = search_params_in_rest.find("Steps:")
            if actual_steps_index_in_rest != -1:
                 negative_prompt = search_params_in_rest[:actual_steps_index_in_rest].strip()
                 params_part = search_params_in_rest[actual_steps_index_in_rest:]
            else: # Нет "Steps:" после "Negative prompt:"
                 negative_prompt = search_params_in_rest.strip() # Берем все как негативный
                 params_part = "" # Нет блока параметров

    else: # "Negative prompt:" не найдено
        # Если "Steps:" найдено, то промпт до него
        if steps_meta_index != -1:
            prompt = parameters_str[:steps_meta_index].strip()
            params_part = parameters_str[steps_meta_index:]
        else: # Нет ни "Negative prompt:", ни "Steps:", весь текст - это промпт
            prompt = parameters_str.strip()
            params_part = ""
    
    if not prompt and not negative_prompt and not params_part: # Если все пусто, возможно, это просто параметры
        params_part = parameters_str

    if params_part:
        params_list = [p.strip() for p in params_part.split(",")]
        temp_other_params = {}
        for param_val_str in params_list:
            parts = param_val_str.split(':', 1)
            if len(parts) == 2:
                key, value = parts[0].strip(), parts[1].strip()
                temp_other_params[key] = value
                if key == "Model": model_name = value
                elif key == "Model hash": model_hash = value
        
        # Добавляем в other_params_dict только то, что не "Model" и не "Model hash"
        for k,v in temp_other_params.items():
            if k not in ["Model", "Model hash"]:
                other_params_dict[k] = v
    
    if model_name == "N/A" and model_hash != "N/A": model_name = f"hash_{model_hash}"
    if model_name == "N/A" and "Checkpoint" in other_params_dict: model_name = other_params_dict["Checkpoint"]

    return prompt, negative_prompt, model_name, model_hash, other_params_dict

# --- Функции оценки (обновлены для deepghs) ---

@torch.no_grad()
def get_image_reward(image_pil):
    if not reward_model or not reward_processor: return "N/A"
    try:
        inputs = reward_processor(images=image_pil, return_tensors="pt").to(DEVICE)
        outputs = reward_model(**inputs)
        return round(outputs.logits.item(), 4)
    except Exception as e:
        print(f"Error in ImageReward: {e}")
        return "Error"

def get_anime_aesthetic_score_deepghs(image_pil):
    session, labels, meta = get_onnx_session_and_meta(ANIME_AESTHETIC_REPO, ANIME_AESTHETIC_SUBFOLDER)
    if not session or not labels: return "N/A"
    try:
        input_data = _img_preprocess_for_onnx(image_pil, size=ANIME_AESTHETIC_IMG_SIZE)
        input_name = session.get_inputs()[0].name
        output_name = session.get_outputs()[0].name
        
        onnx_output, = session.run([output_name], {input_name: input_data})
        
        scores = onnx_output[0] # Должен быть массив вероятностей/логитов
        # Применение softmax если это логиты (обычно модели классификации ONNX возвращают логиты)
        exp_scores = np.exp(scores - np.max(scores)) # Вычитаем max для стабильности softmax
        probabilities = exp_scores / np.sum(exp_scores)

        weighted_score = 0.0
        for i, label in enumerate(labels):
            if label in ANIME_AESTHETIC_LABEL_WEIGHTS:
                weighted_score += probabilities[i] * ANIME_AESTHETIC_LABEL_WEIGHTS[label]
        return round(weighted_score, 4)
    except Exception as e:
        print(f"Error in Anime Aesthetic (ONNX): {e}")
        return "Error"

@torch.no_grad()
def get_maniqa_score(image_pil):
    if not maniqa_pipe: return "N/A"
    try:
        result = maniqa_pipe(image_pil.copy())
        score = 0.0
        # Ищем метку, которая соответствует высокому качеству
        # honklers/maniqa-nr может иметь 'LABEL_0', 'LABEL_1' или 'Good Quality', 'Bad Quality'
        # Проверьте model card. Предположим, более высокий скор для первой метки - хорошо.
        # В данном случае, `honklers/maniqa-nr` выводит [{'label': 'Bad Quality', 'score': 0.9}, {'label': 'Good Quality', 'score': 0.1}]
        # Ищем 'Good Quality'
        for item in result:
            if item['label'].lower() == 'good quality': # или другой позитивный лейбл
                score = item['score']
                break
            # Если нет "Good Quality", но есть что-то вроде LABEL_1 (положительный)
            # elif item['label'] == 'LABEL_1': # Пример, если метки такие
            #     score = item['score']
            #     break
        if score == 0.0 and result: # Если "Good Quality" не найдено, но есть результат
            # Пробуем взять максимальный скор, если метки непонятные, но это рискованно
            # Либо ищем специфичные метки из model card
            pass # Оставляем 0.0 если не найдена позитивная метка

        return round(score, 4)
    except Exception as e:
        print(f"Error in MANIQA: {e}")
        return "Error"

@torch.no_grad()
def calculate_clip_score_value(image_pil, prompt_text): # Изменено имя, чтобы не конфликтовать с torchmetrics.clip_score
    if not clip_model_instance or not clip_preprocess or not clip_tokenizer or not prompt_text or prompt_text == "N/A":
        return "N/A"
    try:
        image_input = clip_preprocess(image_pil).unsqueeze(0).to(DEVICE)
        text_input = clip_tokenizer([str(prompt_text)]).to(DEVICE)
        
        image_features = clip_model_instance.encode_image(image_input)
        text_features = clip_model_instance.encode_text(text_input)
        
        image_features_norm = image_features / image_features.norm(p=2, dim=-1, keepdim=True)
        text_features_norm = text_features / text_features.norm(p=2, dim=-1, keepdim=True)
        score = (text_features_norm @ image_features_norm.T).squeeze().item() * 100.0
        return round(score, 2)
    except Exception as e:
        print(f"Error in CLIP Score: {e}")
        return "Error"

@torch.no_grad()
def get_sdxl_detection_score(image_pil):
    if not sdxl_detector_pipe: return "N/A"
    try:
        result = sdxl_detector_pipe(image_pil.copy())
        ai_score = 0.0
        # Organika/sdxl-detector метки: 'artificial', 'real'
        for item in result:
            if item['label'].lower() == 'artificial':
                ai_score = item['score']
                break
        return round(ai_score, 4)
    except Exception as e:
        print(f"Error in SDXL Detector: {e}")
        return "Error"

def get_anime_ai_check_score_deepghs(image_pil):
    session, labels, meta = get_onnx_session_and_meta(ANIME_AI_CHECK_REPO, ANIME_AI_CHECK_SUBFOLDER)
    if not session or not labels: return "N/A"
    try:
        input_data = _img_preprocess_for_onnx(image_pil, size=ANIME_AI_CHECK_IMG_SIZE)
        input_name = session.get_inputs()[0].name
        output_name = session.get_outputs()[0].name

        onnx_output, = session.run([output_name], {input_name: input_data})
        
        scores = onnx_output[0]
        exp_scores = np.exp(scores - np.max(scores))
        probabilities = exp_scores / np.sum(exp_scores)

        ai_prob = 0.0
        for i, label in enumerate(labels):
            if label.lower() == 'ai': # Ищем метку 'ai'
                ai_prob = probabilities[i]
                break
        return round(ai_prob, 4)
    except Exception as e:
        print(f"Error in Anime AI Check (ONNX): {e}")
        return "Error"

# --- Основная функция обработки ---
def process_images(files, progress=gr.Progress(track_tqdm=True)):
    if not files:
        return pd.DataFrame(), None, None, None, None, "Please upload some images."

    all_results = []
    
    # progress(0, desc="Starting processing...") # track_tqdm сделает это

    for i, file_obj in enumerate(files):
        try:
            # В HF Spaces file_obj может быть именем временного файла или объектом с атрибутом name
            filename = os.path.basename(getattr(file_obj, 'name', str(file_obj))) # getattr для совместимости
            # progress((i+1)/len(files), desc=f"Processing {filename}") # track_tqdm
            
            img = Image.open(getattr(file_obj, 'name', str(file_obj)))
            if img.mode != "RGB":
                img = img.convert("RGB")

            prompt, neg_prompt, model_n, model_h, other_p = extract_sd_parameters(img)

            # Оценки
            reward = get_image_reward(img.copy())
            anime_aes_deepghs = get_anime_aesthetic_score_deepghs(img.copy())
            maniqa = get_maniqa_score(img.copy())
            clip_val = calculate_clip_score_value(img.copy(), prompt) # Изменено имя функции
            sdxl_detect = get_sdxl_detection_score(img.copy())
            anime_ai_chk_deepghs = get_anime_ai_check_score_deepghs(img.copy())

            result_entry = {
                "Filename": filename,
                "Prompt": prompt if prompt else "N/A",
                "Model Name": model_n,
                "Model Hash": model_h,
                "ImageReward": reward,
                "AnimeAesthetic_dg": anime_aes_deepghs, # dg = deepghs
                "MANIQA_TQ": maniqa,
                "CLIPScore": clip_val,
                "SDXL_Detector_AI_Prob": sdxl_detect,
                "AnimeAI_Check_dg_Prob": anime_ai_chk_deepghs, # dg = deepghs
            }
            all_results.append(result_entry)

        except Exception as e:
            print(f"Failed to process {getattr(file_obj, 'name', str(file_obj))}: {e}")
            all_results.append({
                "Filename": os.path.basename(getattr(file_obj, 'name', str(file_obj))) if file_obj else "Unknown File",
                "Prompt": "Error", "Model Name": "Error", "Model Hash": "Error",
                "ImageReward": "Error", "AnimeAesthetic_dg": "Error", "MANIQA_TQ": "Error",
                "CLIPScore": "Error", "SDXL_Detector_AI_Prob": "Error", "AnimeAI_Check_dg_Prob": "Error"
            })

    df = pd.DataFrame(all_results)
    
    plot_model_avg_scores_buffer = None
    if "Model Name" in df.columns and df["Model Name"].nunique() > 0 and df["Model Name"].count() > 0 :
        numeric_cols = ["ImageReward", "AnimeAesthetic_dg", "MANIQA_TQ", "CLIPScore"]
        for col in numeric_cols: df[col] = pd.to_numeric(df[col], errors='coerce')
        try:
            # Фильтруем модели "N/A" перед группировкой для графика
            df_for_plot = df[df["Model Name"] != "N/A"]
            if not df_for_plot.empty and df_for_plot["Model Name"].nunique() > 0 :
                model_avg_scores = df_for_plot.groupby("Model Name")[numeric_cols].mean().dropna(how='all')
                if not model_avg_scores.empty:
                    fig1, ax1 = plt.subplots(figsize=(12, 7))
                    model_avg_scores.plot(kind="bar", ax=ax1)
                    ax1.set_title("Average Scores per Model")
                    ax1.set_ylabel("Average Score")
                    ax1.tick_params(axis='x', rotation=45, labelsize=8)
                    plt.tight_layout()
                    plot_model_avg_scores_buffer = io.BytesIO()
                    fig1.savefig(plot_model_avg_scores_buffer, format="png")
                    plot_model_avg_scores_buffer.seek(0)
                    plt.close(fig1)
        except Exception as e: print(f"Error generating model average scores plot: {e}")

    plot_prompt_clip_scores_buffer = None
    if "Prompt" in df.columns and "CLIPScore" in df.columns and df["Prompt"].nunique() > 0:
        df["CLIPScore"] = pd.to_numeric(df["CLIPScore"], errors='coerce')
        df_prompt_plot = df[df["Prompt"] != "N/A"].dropna(subset=["CLIPScore"])
        if not df_prompt_plot.empty and df_prompt_plot["Prompt"].nunique() > 0:
            try:
                # Сокращаем длинные промпты для графика
                df_prompt_plot["Short Prompt"] = df_prompt_plot["Prompt"].apply(lambda x: (x[:30] + '...') if len(x) > 33 else x)
                prompt_clip_scores = df_prompt_plot.groupby("Short Prompt")["CLIPScore"].mean().sort_values(ascending=False)
                if not prompt_clip_scores.empty and len(prompt_clip_scores) > 1 :
                    fig2, ax2 = plt.subplots(figsize=(12, max(7, min(len(prompt_clip_scores)*0.5, 15)))) # Ограничиваем высоту
                    prompt_clip_scores.head(20).plot(kind="barh", ax=ax2)
                    ax2.set_title("Average CLIPScore per Prompt (Top 20 unique prompts)")
                    ax2.set_xlabel("Average CLIPScore")
                    plt.tight_layout()
                    plot_prompt_clip_scores_buffer = io.BytesIO()
                    fig2.savefig(plot_prompt_clip_scores_buffer, format="png")
                    plot_prompt_clip_scores_buffer.seek(0)
                    plt.close(fig2)
            except Exception as e: print(f"Error generating prompt CLIP scores plot: {e}")

    csv_buffer_val = ""
    if not df.empty:
        csv_buffer = io.StringIO()
        df.to_csv(csv_buffer, index=False)
        csv_buffer_val = csv_buffer.getvalue()
    
    json_buffer_val = ""
    if not df.empty:
        json_buffer = io.StringIO()
        df.to_json(json_buffer, orient='records', indent=4)
        json_buffer_val = json_buffer.getvalue()

    return (
        df,
        gr.Image(value=plot_model_avg_scores_buffer, type="pil", visible=plot_model_avg_scores_buffer is not None),
        gr.Image(value=plot_prompt_clip_scores_buffer, type="pil", visible=plot_prompt_clip_scores_buffer is not None),
        gr.File(value=csv_buffer_val if csv_buffer_val else None, label="Download CSV Results", visible=bool(csv_buffer_val), file_name="evaluation_results.csv"),
        gr.File(value=json_buffer_val if json_buffer_val else None, label="Download JSON Results", visible=bool(json_buffer_val), file_name="evaluation_results.json"),
        f"Processed {len(all_results)} images.",
    )

# --- Интерфейс Gradio ---
with gr.Blocks(css="footer {display: none !important}") as demo:
    gr.Markdown("# AI Image Model Evaluation Tool")
    gr.Markdown(
        "Upload PNG images (ideally with Stable Diffusion metadata) to evaluate them using various metrics. "
        "Results will be displayed in a table and visualized in charts."
    )

    with gr.Row():
        image_uploader = gr.Files(
            label="Upload Images (PNG)",
            file_count="multiple",
            file_types=["image"],
        )

    process_button = gr.Button("Evaluate Images", variant="primary")
    status_textbox = gr.Textbox(label="Status", interactive=False)

    gr.Markdown("## Evaluation Results Table")
    results_table = gr.DataFrame(headers=[
        "Filename", "Prompt", "Model Name", "Model Hash", 
        "ImageReward", "AnimeAesthetic_dg", "MANIQA_TQ", "CLIPScore", 
        "SDXL_Detector_AI_Prob", "AnimeAI_Check_dg_Prob"
    ], wrap=True, max_rows=10) # Ограничиваем начальное отображение строк
    
    with gr.Row():
        download_csv_button = gr.File(label="Download CSV Results", interactive=False) # visible управляется из output
        download_json_button = gr.File(label="Download JSON Results", interactive=False)

    gr.Markdown("## Visualizations")
    with gr.Row():
        plot_output_model_avg = gr.Image(label="Average Scores per Model", type="pil", interactive=False)
        plot_output_prompt_clip = gr.Image(label="Average CLIPScore per Prompt", type="pil", interactive=False)
        
    process_button.click(
        fn=process_images,
        inputs=[image_uploader],
        outputs=[
            results_table, 
            plot_output_model_avg, 
            plot_output_prompt_clip,
            download_csv_button,
            download_json_button,
            status_textbox
        ]
    )

    gr.Markdown(
        """
        **Metric Explanations:**
        - **ImageReward:** General aesthetic and prompt alignment score (higher is better). From THUDM.
        - **AnimeAesthetic_dg:** Aesthetic level for anime style (0-4, higher is better quality level: normal, slight, moderate, strong, extreme). From deepghs (ONNX).
        - **MANIQA_TQ:** Technical Quality score (no-reference), higher indicates better quality (less noise/artifacts). Based on MANIQA.
        - **CLIPScore:** Semantic similarity between the image and its prompt (0-100, higher is better). Uses LAION's CLIP.
        - **SDXL_Detector_AI_Prob:** Estimated probability that the image is AI-generated (higher means more likely AI). From Organika.
        - **AnimeAI_Check_dg_Prob:** Estimated probability that an anime-style image is AI-generated (higher means more likely AI). From deepghs (ONNX).
        
        *Processing can take time, especially for many images or on CPU.*
        """
    )

if __name__ == "__main__":
    demo.launch(debug=True)