Create app.py

app.py

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+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)