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openfree commited on Jan 12

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119ab57

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1 Parent(s): f527774

Update app-backup-datasets.py

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app-backup-datasets.py +291 -293

app-backup-datasets.py CHANGED Viewed

@@ -12,6 +12,8 @@ target_models = {
     "seawolf2357/hanbok": "https://huggingface.co/seawolf2357/hanbok",
     "seawolf2357/ntower": "https://huggingface.co/seawolf2357/ntower",
     "LGAI-EXAONE/EXAONE-3.5-32B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-32B-Instruct",
     "LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct",
     "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
@@ -587,6 +589,68 @@ def get_models_data(progress=gr.Progress()):
 # 관심 스페이스 URL 리스트와 정보
 target_spaces = {
     "fantos/x-mas": "https://huggingface.co/spaces/fantos/x-mas",
     "openfree/Korean-Leaderboard": "https://huggingface.co/spaces/openfree/Korean-Leaderboard",
     "ginipick/FLUXllama": "https://huggingface.co/spaces/ginipick/FLUXllama",
@@ -617,24 +681,32 @@ target_spaces = {
     "fantos/miro-game": "https://huggingface.co/spaces/fantos/miro-game",
     "kolaslab/shooting": "https://huggingface.co/spaces/kolaslab/shooting",
     "VIDraft/Mouse-Hackathon": "https://huggingface.co/spaces/VIDraft/Mouse-Hackathon",
-    "upstage/open-ko-llm-leaderboard": "https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard",
-    "LGAI-EXAONE/EXAONE-3.5-Instruct-Demo": "https://huggingface.co/spaces/LGAI-EXAONE/EXAONE-3.5-Instruct-Demo",
-    "r3gm/DiffuseCraft": "https://huggingface.co/spaces/r3gm/DiffuseCraft",
-    "LeeSangHoon/HierSpeech_TTS": "https://huggingface.co/spaces/LeeSangHoon/HierSpeech_TTS",
-    "etri-vilab/KOALA": "https://huggingface.co/spaces/etri-vilab/KOALA",
-    "etri-vilab/Ko-LLaVA": "https://huggingface.co/spaces/etri-vilab/Ko-LLaVA",
     "cutechicken/TankWar3D": "https://huggingface.co/spaces/cutechicken/TankWar3D",
     "kolaslab/RC4-EnDecoder": "https://huggingface.co/spaces/kolaslab/RC4-EnDecoder",
     "kolaslab/simulator": "https://huggingface.co/spaces/kolaslab/simulator",
     "kolaslab/calculator": "https://huggingface.co/spaces/kolaslab/calculator",
     "etri-vilab/Ko-LLaVA": "https://huggingface.co/spaces/etri-vilab/Ko-LLaVA",
     "etri-vilab/KOALA": "https://huggingface.co/spaces/etri-vilab/KOALA",
     "naver-clova-ix/donut-base-finetuned-cord-v2": "https://huggingface.co/spaces/naver-clova-ix/donut-base-finetuned-cord-v2",
-    "NCSOFT/VARCO_Arena": "https://huggingface.co/spaces/NCSOFT/VARCO_Arena"
 }
 def get_spaces_data(sort_type="trending", progress=gr.Progress()):
@@ -991,330 +1063,256 @@ def refresh_data():
-target_datasets = {
-    "aiqtech/kolaw": "https://huggingface.co/datasets/aiqtech/kolaw"
-    # 필요한 데이터셋 추가
-}
-def get_korea_datasets():
-    """Korea 관련 데이터셋 검색"""
-    params = {
-        "search": "korea",
-        "full": "True",
-        "limit": 10000
-    }
     try:
-        response = requests.get(
-            "https://huggingface.co/api/datasets",
-            headers={'Accept': 'application/json'},  # Authorization 대신 Accept 헤더 사용
-            params=params
-        )
-        if response.status_code == 200:
-            korea_datasets = response.json()
-            print(f"Found {len(korea_datasets)} Korea-related datasets")
-            return korea_datasets
         else:
-            print(f"Failed to fetch Korea datasets: {response.status_code}")
-            return []
-    except Exception as e:
-        print(f"Error fetching Korea datasets: {str(e)}")
-        return []
-def get_all_datasets(limit=10000):
-    """모든 데이터셋과 Korea 관련 데이터셋 가져오기"""
-    all_datasets = []
-    page_size = 1000
-    for offset in range(0, limit, page_size):
-        params = {
-            'limit': min(page_size, limit - offset),
-            'full': 'True',
-            'offset': offset
-        }
-        response = requests.get(
-            "https://huggingface.co/api/datasets",
-            headers={'Accept': 'application/json'},  # Authorization 대신 Accept 헤더 사용
-            params=params
-        )
-        if response.status_code == 200:
-            all_datasets.extend(response.json())
-            print(f"Fetched datasets {offset+1} to {offset+len(response.json())}")
-        else:
-            print(f"Failed to fetch datasets at offset {offset}: {response.status_code}")
-            break
-    # Korea 검색 결과도 동일하게 확장
-    korea_params = {
-        "search": "korea",
-        "full": "True",
-        "limit": limit
-    }
-    korea_response = requests.get(
-        "https://huggingface.co/api/datasets",
-        headers={'Accept': 'application/json'},  # Authorization 대신 Accept 헤더 사용
-        params=korea_params
-    )
-    if korea_response.status_code == 200:
-        korea_datasets = korea_response.json()
-        print(f"Fetched {len(korea_datasets)} Korea-related datasets")
-        # 중복 제거하면서 Korea 데이터셋 추가
-        existing_ids = {dataset.get('id', '') for dataset in all_datasets}
-        for dataset in korea_datasets:
-            if dataset.get('id', '') not in existing_ids:
-                all_datasets.append(dataset)
-                existing_ids.add(dataset.get('id', ''))
-    print(f"Total unique datasets: {len(all_datasets)}")
-    return all_datasets[:limit]
-def get_datasets_data(progress=gr.Progress()):
-    def calculate_rank(dataset_id, all_global_datasets, korea_datasets):
-        # 글로벌 순위 확인
-        global_rank = next((idx for idx, d in enumerate(all_global_datasets, 1)
-                          if d.get('id', '').strip() == dataset_id.strip()), None)
-        # Korea 데이터셋인 경우
-        is_korea = any(d.get('id', '').strip() == dataset_id.strip() for d in korea_datasets)
-        if is_korea:
-            # Korea 데이터셋 중에서의 순위 확인
-            korea_rank = next((idx for idx, d in enumerate(korea_datasets, 1)
-                             if d.get('id', '').strip() == dataset_id.strip()), None)
-            if korea_rank:
-                return min(global_rank or 10001, korea_rank + 1000), True
-        return global_rank if global_rank else 'Not in top 10000', is_korea
     try:
-        progress(0, desc="Fetching datasets...")
-        if not HF_TOKEN:
-            fig = create_error_plot()
-            error_html = """
-            <div style='padding: 20px; background: #fee; border-radius: 10px; margin: 10px 0;'>
-                <h3 style='color: #c00;'>⚠️ API 인증이 필요합니다</h3>
-                <p>HuggingFace API 토큰이 설정되지 않았습니다.</p>
-            </div>
-            """
-            empty_df = pd.DataFrame(columns=['Global Rank', 'Dataset ID', 'Title', 'Downloads', 'Likes', 'Korea Search', 'URL'])
-            return fig, error_html, empty_df
-        # 일반 데이터셋과 Korea 관련 데이터셋 모두 가져오기
-        all_global_datasets = get_all_datasets(limit=10000)
-        korea_datasets = get_korea_datasets()
-        print(f"Total global datasets fetched: {len(all_global_datasets)}")
-        print(f"Total Korea datasets fetched: {len(korea_datasets)}")
-        filtered_datasets = []
-        for dataset_id in target_datasets.keys():
-            try:
-                normalized_id = dataset_id.strip('/')
-                dataset_url_api = f"https://huggingface.co/api/datasets/{normalized_id}"
-                response = requests.get(
-                    dataset_url_api,
-                    headers={'Authorization': f'Bearer {HF_TOKEN}'}
-                )
-                if response.status_code == 200:
-                    dataset_data = response.json()
-                    rank, is_korea = calculate_rank(dataset_id, all_global_datasets, korea_datasets)
-                    filtered_datasets.append({
-                        'id': dataset_id,
-                        'global_rank': rank,
-                        'downloads': dataset_data.get('downloads', 0),
-                        'likes': dataset_data.get('likes', 0),
-                        'title': dataset_data.get('title', 'No Title'),
-                        'is_korea': is_korea
-                    })
-                else:
-                    filtered_datasets.append({
-                        'id': dataset_id,
-                        'global_rank': 'Not in top 10000',
-                        'downloads': 0,
-                        'likes': 0,
-                        'title': 'No Title',
-                        'is_korea': False
-                    })
-            except Exception as e:
-                print(f"Error processing {dataset_id}: {str(e)}")
-                continue
-        filtered_datasets.sort(key=lambda x: float('inf') if isinstance(x['global_rank'], str) else x['global_rank'])
-        # 시각화 데이터 준비
-        valid_datasets = [d for d in filtered_datasets if isinstance(d['global_rank'], (int, float))]
-        fig = go.Figure()
-        if valid_datasets:
-            ids = [d['id'] for d in valid_datasets]
-            ranks = [d['global_rank'] for d in valid_datasets]
-            fig.add_trace(go.Bar(
-                x=ids,
-                y=[3001 - r for r in ranks],
-                text=[f"Rank: #{r}<br>{'🇰🇷 Korea Dataset<br>' if d['is_korea'] else ''}"
-                      f"Downloads: {format(d['downloads'], ',')}<br>"
-                      f"Likes: {format(d['likes'], ',')}"
-                      for r, d in zip(ranks, valid_datasets)],
-                textposition='auto',
-                marker_color=['rgba(255,0,0,0.6)' if d['is_korea'] else 'rgba(0,0,255,0.6)'
-                            for d in valid_datasets],
-                opacity=0.8
-            ))
-            fig.update_layout(
-                title="HuggingFace Datasets Global Rankings (Up to #3000)",
-                xaxis_title="Dataset ID",
-                yaxis_title="Global Rank",
-                yaxis=dict(
-                    ticktext=[f"#{i}" for i in range(1, 10001, 100)],
-                    tickvals=[10001 - i for i in range(1, 10001, 100)],
-                    range=[0, 10000]
-                ),
-                height=800,
-                showlegend=False,
-                template='plotly_white',
-                xaxis_tickangle=-45
-            )
-        # HTML 카드 생성
-        html_content = """
-        <div style='padding: 20px; background: #f5f5f5;'>
-            <h2 style='color: #2c3e50;'>Datasets Rankings (Up to #3000)</h2>
-            <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 20px;'>
-        """
-        for dataset in filtered_datasets:
-            rank_display = f"Global Rank #{dataset['global_rank']}" if isinstance(dataset['global_rank'], (int, float)) else "Not in top 3000"
-            korea_badge = "🇰🇷 Korea Search Result" if dataset['is_korea'] else ""
-            html_content += f"""
-            <div style='
-                background: white;
-                padding: 20px;
-                border-radius: 10px;
-                box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-                transition: transform 0.2s;
-                {f"border: 2px solid #e74c3c;" if dataset['is_korea'] else ""}
-            '>
-                <h3 style='color: #34495e;'>{rank_display}</h3>
-                <h4 style='color: #2c3e50;'>{dataset['id']}</h4>
-                <p style='color: #e74c3c; font-weight: bold;'>{korea_badge}</p>
-                <p style='color: #7f8c8d;'>⬇️ Downloads: {format(dataset['downloads'], ',')}</p>
-                <p style='color: #7f8c8d;'>👍 Likes: {format(dataset['likes'], ',')}</p>
-                <a href='{target_datasets[dataset['id']]}'
-                   target='_blank'
-                   style='
-                    display: inline-block;
-                    padding: 8px 16px;
-                    background: #3498db;
-                    color: white;
-                    text-decoration: none;
-                    border-radius: 5px;
-                    transition: background 0.3s;
-                   '>
-                   Visit Dataset 🔗
-                </a>
-            </div>
-            """
-        html_content += "</div></div>"
-        # 데이터프레임 생성
-        df = pd.DataFrame([{
-            'Global Rank': f"#{d['global_rank']}" if isinstance(d['global_rank'], (int, float)) else d['global_rank'],
-            'Dataset ID': d['id'],
-            'Title': d['title'],
-            'Downloads': format(d['downloads'], ','),
-            'Likes': format(d['likes'], ','),
-            'Korea Search': '🇰🇷' if d['is_korea'] else '',
-            'URL': target_datasets[d['id']]
-        } for d in filtered_datasets])
-        progress(1.0, desc="Complete!")
-        return fig, html_content, df
-    except Exception as e:
-        print(f"Error in get_datasets_data: {str(e)}")
-        error_fig = create_error_plot()
-        error_html = f"""
-        <div style='padding: 20px; background: #fee; border-radius: 10px; margin: 10px 0;'>
-            <h3 style='color: #c00;'>⚠️ 오류가 발생했습니다</h3>
-            <p>{str(e)}</p>
-        </div>
-        """
-        empty_df = pd.DataFrame(columns=['Global Rank', 'Dataset ID', 'Title', 'Downloads', 'Likes', 'Korea Search', 'URL'])
-        return error_fig, error_html, empty_df
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
     # 🤗 허깅페이스 '한국(언어) 리더보드'
-    HuggingFace가 제공하는 Spaces, Models, Datasets의 실시간 인기 순위를 반영하여 '한국안(기업/언어)'의 리스트(공개,검색,리더보드 등)만 분석. (c)'한국인공지능진흥협회' / 요청: [email protected]
     """)
     refresh_btn = gr.Button("🔄 새로 고침", variant="primary")
     with gr.Tab("Spaces Trending"):
         trending_plot = gr.Plot()
         trending_info = gr.HTML()
-        trending_df = gr.DataFrame()
     with gr.Tab("Models Trending"):
         models_plot = gr.Plot()
         models_info = gr.HTML()
-        models_df = gr.DataFrame()
-    with gr.Tab("Datasets Trending"):
-        datasets_plot = gr.Plot()
-        datasets_info = gr.HTML()
-        datasets_df = gr.DataFrame()
     def refresh_all_data():
-        spaces_results = get_spaces_data("trending")
-        models_results = get_models_data()
-        datasets_results = get_datasets_data()
-        return [
-            *spaces_results,
-            *models_results,
-            *datasets_results
-        ]
     refresh_btn.click(
-        refresh_all_data,
         outputs=[
             trending_plot, trending_info, trending_df,
             models_plot, models_info, models_df,
-            datasets_plot, datasets_info, datasets_df
         ]
     )
     # 초기 데이터 로드
-    spaces_results = get_spaces_data("trending")
-    models_results = get_models_data()
-    datasets_results = get_datasets_data()
-    trending_plot.value, trending_info.value, trending_df.value = spaces_results
-    models_plot.value, models_info.value, models_df.value = models_results
-    datasets_plot.value, datasets_info.value, datasets_df.value = datasets_results
 # Gradio 앱 실행
 demo.launch(
     server_name="0.0.0.0",
     server_port=7860,
-    share=False
 )

     "seawolf2357/hanbok": "https://huggingface.co/seawolf2357/hanbok",
     "seawolf2357/ntower": "https://huggingface.co/seawolf2357/ntower",
+    "openfree/claude-monet": "https://huggingface.co/openfree/claude-monet",
     "LGAI-EXAONE/EXAONE-3.5-32B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-32B-Instruct",
     "LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct",
     "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
 # 관심 스페이스 URL 리스트와 정보
 target_spaces = {
+    "kolaslab/Quantum"": "https://huggingface.co/spaces/kolaslab/Quantum",
+    "openfree/webtoon": "https://huggingface.co/spaces/openfree/webtoon",
+    "immunobiotech/ChicagoGallery": "https://huggingface.co/spaces/immunobiotech/ChicagoGallery",
+    "immunobiotech/MetropolitanMuseum": "https://huggingface.co/spaces/immunobiotech/MetropolitanMuseum",
+    "immunobiotech/opensky": "https://huggingface.co/spaces/immunobiotech/opensky",
+    "kolaslab/Audio-Visualizer": "https://huggingface.co/spaces/kolaslab/Audio-Visualizer",
+    "kolaslab/Radio-Learning": "https://huggingface.co/spaces/kolaslab/Radio-Learning",
+    "kolaslab/Future-Gallaxy": "https://huggingface.co/spaces/kolaslab/Future-Gallaxy",
+    "openfree/ProteinGenesis": "https://huggingface.co/spaces/openfree/ProteinGenesis",
+    "openfree/2025saju": "https://huggingface.co/spaces/openfree/2025saju",
+    "ginigen/Dokdo-membership": "https://huggingface.co/spaces/ginigen/Dokdo-membership",
+    "VIDraft/eum": "https://huggingface.co/spaces/VIDraft/eum",
+    "kolaslab/VisionART": "https://huggingface.co/spaces/kolaslab/VisionART",
+    "aiqtech/FLUX-military": "https://huggingface.co/spaces/aiqtech/FLUX-military",
+    "fantaxy/Rolls-Royce": "https://huggingface.co/spaces/fantaxy/Rolls-Royce",
+    "seawolf2357/flux-korea-hanbok-lora": "https://huggingface.co/spaces/seawolf2357/flux-korea-hanbok-lora",
+    "seawolf2357/flux-korea-palace-lora": "https://huggingface.co/spaces/seawolf2357/flux-korea-palace-lora",
+    "aiqcamp/flux-cat-lora": "https://huggingface.co/spaces/aiqcamp/flux-cat-lora",
+    "gunship999/SexyImages": "https://huggingface.co/spaces/gunship999/SexyImages",
+    "aiqtech/flux-claude-monet-lora": "https://huggingface.co/spaces/aiqtech/flux-claude-monet-lora",
+    "ginigen/CANVAS-o3": "https://huggingface.co/spaces/ginigen/CANVAS-o3",
+    "kolaslab/world-sdr": "https://huggingface.co/spaces/kolaslab/world-sdr",
+    "seawolf2357/3D-Avatar-Generator": "https://huggingface.co/spaces/seawolf2357/3D-Avatar-Generator",
+    "fantaxy/playground25": "https://huggingface.co/spaces/fantaxy/playground25",
+    "openfree/ultpixgen": "https://huggingface.co/spaces/openfree/ultpixgen",
+    "kolaslab/VISION-NIGHT": "https://huggingface.co/spaces/kolaslab/VISION-NIGHT",
+    "kolaslab/FLUX-WEB": "https://huggingface.co/spaces/kolaslab/FLUX-WEB",
+    "seawolf2357/REALVISXL-V5": "https://huggingface.co/spaces/seawolf2357/REALVISXL-V5",
+    "ginipick/Dokdo-multimodal": "https://huggingface.co/spaces/ginipick/Dokdo-multimodal",
+    "ginigen/theater": "https://huggingface.co/spaces/ginigen/theater",
+    "VIDraft/stock": "https://huggingface.co/spaces/VIDraft/stock",
+    "fantos/flxcontrol": "https://huggingface.co/spaces/fantos/flxcontrol",
+    "fantos/textcutobject": "https://huggingface.co/spaces/fantos/textcutobject",
+    "ginipick/FLUX-Prompt-Generator": "https://huggingface.co/spaces/ginipick/FLUX-Prompt-Generator",
+    "fantaxy/flxloraexp": "https://huggingface.co/spaces/fantaxy/flxloraexp",
+    "fantos/flxloraexp": "https://huggingface.co/spaces/fantos/flxloraexp",
+    "seawolf2357/flxloraexp": "https://huggingface.co/spaces/seawolf2357/flxloraexp",
+    "ginipick/flxloraexp": "https://huggingface.co/spaces/ginipick/flxloraexp",
+    "ginipick/FLUX-Prompt-Generator": "https://huggingface.co/spaces/ginipick/FLUX-Prompt-Generator",
+    "ginigen/Dokdo": "https://huggingface.co/spaces/ginigen/Dokdo",
+    "aiqcamp/imagemagic": "https://huggingface.co/spaces/aiqcamp/imagemagic",
+    "openfree/ColorRevive": "https://huggingface.co/spaces/openfree/ColorRevive",
+    "VIDraft/RAGOndevice": "https://huggingface.co/spaces/VIDraft/RAGOndevice",
+    "gunship999/Radar-Bluetooth": "https://huggingface.co/spaces/gunship999/Radar-Bluetooth",
+    "gunship999/WiFi-VISION": "https://huggingface.co/spaces/gunship999/WiFi-VISION",
+    "gunship999/SONAR-Radar": "https://huggingface.co/spaces/gunship999/SONAR-Radar",
+    "aiqcamp/AudioLlama": "https://huggingface.co/spaces/aiqcamp/AudioLlama",
+    "ginigen/FLUXllama-Multilingual": "https://huggingface.co/spaces/ginigen/FLUXllama-Multilingual",
+    "ginipick/ginimedi": "https://huggingface.co/spaces/ginipick/ginimedi",
+    "ginipick/ginilaw": "https://huggingface.co/spaces/ginipick/ginilaw",
+    "ginipick/ginipharm": "https://huggingface.co/spaces/ginipick/ginipharm",
+    "ginipick/FitGen": "https://huggingface.co/spaces/ginipick/FitGen",
+    "fantaxy/FLUX-Animations": "https://huggingface.co/spaces/fantaxy/FLUX-Animations",
+    "fantaxy/Remove-Video-Background": "https://huggingface.co/spaces/fantaxy/Remove-Video-Background",
+    "fantaxy/ofai-flx-logo": "https://huggingface.co/spaces/fantaxy/ofai-flx-logo",
+    "fantaxy/flx-pulid": "https://huggingface.co/spaces/fantaxy/flx-pulid",
+    "fantaxy/flx-upscale": "https://huggingface.co/spaces/fantaxy/flx-upscale",
+    "aiqcamp/Fashion-FLUX": "https://huggingface.co/spaces/aiqcamp/Fashion-FLUX",
+    "ginipick/StyleGen": "https://huggingface.co/spaces/ginipick/StyleGen",
+    "openfree/StoryStar": "https://huggingface.co/spaces/openfree/StoryStar",
     "fantos/x-mas": "https://huggingface.co/spaces/fantos/x-mas",
     "openfree/Korean-Leaderboard": "https://huggingface.co/spaces/openfree/Korean-Leaderboard",
     "ginipick/FLUXllama": "https://huggingface.co/spaces/ginipick/FLUXllama",
     "fantos/miro-game": "https://huggingface.co/spaces/fantos/miro-game",
     "kolaslab/shooting": "https://huggingface.co/spaces/kolaslab/shooting",
     "VIDraft/Mouse-Hackathon": "https://huggingface.co/spaces/VIDraft/Mouse-Hackathon",
+    "aiqmaster/stocksimulation": "https://huggingface.co/spaces/aiqmaster/stocksimulation",
+    "aiqmaster/assetai": "https://huggingface.co/spaces/aiqmaster/assetai",
+    "aiqmaster/stockai": "https://huggingface.co/spaces/aiqmaster/stockai",
     "cutechicken/TankWar3D": "https://huggingface.co/spaces/cutechicken/TankWar3D",
     "kolaslab/RC4-EnDecoder": "https://huggingface.co/spaces/kolaslab/RC4-EnDecoder",
     "kolaslab/simulator": "https://huggingface.co/spaces/kolaslab/simulator",
     "kolaslab/calculator": "https://huggingface.co/spaces/kolaslab/calculator",
+    "aiqtech/kofaceid": "https://huggingface.co/spaces/aiqtech/kofaceid",
+    "fantaxy/fastvideogena": "https://huggingface.co/spaces/fantaxy/fastvideogen",
+    "fantos/cogvidx": "https://huggingface.co/spaces/fantos/cogvidx",
+    "fantos/flxfashmodel": "https://huggingface.co/spaces/fantos/flxfashmodel",
+    "fantos/kolcontrl": "https://huggingface.co/spaces/fantos/kolcontrl",
+    "fantos/EveryText": "https://huggingface.co/spaces/fantos/EveryText",
+    "aiqtech/cinevid": "https://huggingface.co/spaces/aiqtech/cinevid",
+    "aiqtech/FLUX-Ghibli-Studio-LoRA": "https://huggingface.co/spaces/aiqtech/FLUX-Ghibli-Studio-LoRA",
+    "aiqtech/flxgif": "https://huggingface.co/spaces/aiqtech/flxgif",
+    "aiqtech/imaginpaint": "https://huggingface.co/spaces/aiqtech/imaginpaint",
+    "upstage/open-ko-llm-leaderboard": "https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard",
+    "LGAI-EXAONE/EXAONE-3.5-Instruct-Demo": "https://huggingface.co/spaces/LGAI-EXAONE/EXAONE-3.5-Instruct-Demo",
+    "LeeSangHoon/HierSpeech_TTS": "https://huggingface.co/spaces/LeeSangHoon/HierSpeech_TTS",
     "etri-vilab/Ko-LLaVA": "https://huggingface.co/spaces/etri-vilab/Ko-LLaVA",
     "etri-vilab/KOALA": "https://huggingface.co/spaces/etri-vilab/KOALA",
     "naver-clova-ix/donut-base-finetuned-cord-v2": "https://huggingface.co/spaces/naver-clova-ix/donut-base-finetuned-cord-v2",
+    "NCSOFT/VARCO_Arena": "https://huggingface.co/spaces/NCSOFT/VARCO_Arena"
 }
 def get_spaces_data(sort_type="trending", progress=gr.Progress()):
+def create_registration_bar_chart(data, type_name="Spaces"):
     try:
+        # TOP 기준 설정
+        top_limit = 500 if type_name == "Spaces" else 3000
+        # DataFrame인 경우 처리
+        if isinstance(data, pd.DataFrame):
+            if type_name == "Models":
+                # 3000위 이내의 모델만 필터링
+                data = data[data['Global Rank'].apply(lambda x: isinstance(x, (int, float)) or (isinstance(x, str) and x.startswith('#')))]
+                data = data[data['Global Rank'].apply(lambda x: int(str(x).replace('#', '')) if isinstance(x, str) else x) <= top_limit]
+            elif type_name == "Spaces":
+                # 500위 이내의 스페이스만 필터링
+                data = data[data['Rank'].apply(lambda x: isinstance(x, (int, float))) & (data['Rank'] <= top_limit)]
+            # ID 컬럼 선택
+            id_column = 'Space ID' if type_name == "Spaces" else 'Model ID'
+            registrations = data[id_column].apply(lambda x: x.split('/')[0]).value_counts()
         else:
+            # 리스트나 다른 형태의 데이터인 경우 처리
+            registrations = {}
+            for item in data:
+                if isinstance(item, dict):
+                    rank = item.get('global_rank' if type_name == "Models" else 'rank')
+                    if isinstance(rank, str) or rank > top_limit:
+                        continue
+                    creator = item.get('id', '').split('/')[0]
+                    registrations[creator] = registrations.get(creator, 0) + 1
+            registrations = pd.Series(registrations)
+        # 정렬된 데이터 준비
+        registrations = registrations.sort_values(ascending=False)
+        fig = go.Figure(data=[go.Bar(
+            x=registrations.index,
+            y=registrations.values,
+            text=registrations.values,
+            textposition='auto',
+            marker_color='#FF6B6B'
+        )])
+        fig.update_layout(
+            title=f"Korean {type_name} Registrations by Creator (Top {top_limit})",
+            xaxis_title="Creator ID",
+            yaxis_title="Number of Registrations",
+            showlegend=False,
+            height=400,
+            width=700
+        )
+        return fig
+    except Exception as e:
+        print(f"Error in create_registration_bar_chart: {str(e)}")
+        return go.Figure()
+def create_pie_chart(data, total_count, type_name="Spaces"):
     try:
+        # TOP 기준 설정
+        top_limit = 500 if type_name == "Spaces" else 3000
+        # DataFrame인 경우 처리
+        if isinstance(data, pd.DataFrame):
+            if type_name == "Models":
+                # 3000위 이내의 모델만 필터링
+                data = data[data['Global Rank'].apply(lambda x: isinstance(x, (int, float)) or (isinstance(x, str) and x.startswith('#')))]
+                data = data[data['Global Rank'].apply(lambda x: int(str(x).replace('#', '')) if isinstance(x, str) else x) <= top_limit]
+            elif type_name == "Spaces":
+                # 500위 이내의 스페이스만 필터링
+                data = data[data['Rank'].apply(lambda x: isinstance(x, (int, float))) & (data['Rank'] <= top_limit)]
+            korean_count = len(data)
+        else:
+            # 리스트나 다른 형태의 데이터인 경우 처리
+            if type_name == "Models":
+                korean_count = sum(1 for item in data if isinstance(item.get('global_rank'), (int, float)) and item.get('global_rank') <= top_limit)
+            else:
+                korean_count = sum(1 for item in data if isinstance(item.get('rank'), (int, float)) and item.get('rank') <= top_limit)
+        other_count = total_count - korean_count
+        fig = go.Figure(data=[go.Pie(
+            labels=[f'Korean {type_name} in Top {top_limit}', f'Other {type_name} in Top {top_limit}'],
+            values=[korean_count, other_count],
+            hole=.3,
+            marker_colors=['#FF6B6B', '#4ECDC4'],
+            textinfo='percent+value',
+            hovertemplate="<b>%{label}</b><br>" +
+                         "Count: %{value}<br>" +
+                         "Percentage: %{percent}<br>"
+        )])
+        fig.update_layout(
+            title=f"Korean vs Other {type_name} Distribution (Top {top_limit})",
+            showlegend=True,
+            height=400,
+            width=500
+        )
+        return fig
+    except Exception as e:
+        print(f"Error in create_pie_chart: {str(e)}")
+        return go.Figure()
+def refresh_all_data():
+    spaces_results = get_spaces_data("trending")
+    models_results = get_models_data()
+    # Spaces 차트 생성
+    spaces_pie = create_pie_chart(spaces_results[2], 500, "Spaces")
+    spaces_bar = create_registration_bar_chart(spaces_results[2], "Spaces")
+    # Models 차트 생성
+    models_pie = create_pie_chart(models_results[2], 3000, "Models")
+    models_bar = create_registration_bar_chart(models_results[2], "Models")
+    return [
+        spaces_results[0], spaces_results[1], spaces_results[2],
+        spaces_pie, spaces_bar,
+        models_results[0], models_results[1], models_results[2],
+        models_pie, models_bar
+    ]
+with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css="""
+    #spaces_pie, #models_pie {
+        min-height: 400px;
+        border-radius: 10px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    #spaces_bar, #models_bar {
+        min-height: 400px;
+        border-radius: 10px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+""") as demo:
     gr.Markdown("""
     # 🤗 허깅페이스 '한국(언어) 리더보드'
+    HuggingFace가 제공하는 Spaces와 Models 실시간 인기 순위 반영하여 '한국인(기업/언어)'의 리스트(공개,검색,리더보드 등)만 분석. (c)'한국인공지능진흥협회' / 요청: [email protected]
+    """)
+    # 이미지와 설명 추가
+    gr.Markdown("""
+    ### [Hot NEWS] 허깅페이스 선정 12월 'TOP 12'에 한국 'ginipick'의 'FLUXllama'와 'Text3D' 2종이 선정됨
     """)
+    gr.Image("HF-TOP12.png", show_label=False)
+    # 새로 고침 버튼 (기존 코드)
     refresh_btn = gr.Button("🔄 새로 고침", variant="primary")
     with gr.Tab("Spaces Trending"):
         trending_plot = gr.Plot()
+        with gr.Row():
+            # 원형 그래프와 막대 그래프를 위한 컨테이너 추가
+            with gr.Column(scale=1):
+                spaces_pie_chart = gr.Plot(
+                    label="Korean Spaces Distribution",
+                    elem_id="spaces_pie"
+                )
+            with gr.Column(scale=2):
+                spaces_bar_chart = gr.Plot(
+                    label="Registrations by Creator",
+                    elem_id="spaces_bar"
+                )
         trending_info = gr.HTML()
+        trending_df = gr.DataFrame(
+            headers=["Rank", "Space ID", "Title", "Likes", "URL"],
+            datatype=["number", "str", "str", "number", "str"],
+            row_count=(10, "dynamic")
+        )
     with gr.Tab("Models Trending"):
         models_plot = gr.Plot()
+        with gr.Row():
+            # 원형 그래프와 막대 그래프를 위한 컨테이너 추가
+            with gr.Column(scale=1):
+                models_pie_chart = gr.Plot(
+                    label="Korean Models Distribution",
+                    elem_id="models_pie"
+                )
+            with gr.Column(scale=2):
+                models_bar_chart = gr.Plot(
+                    label="Registrations by Creator",
+                    elem_id="models_bar"
+                )
         models_info = gr.HTML()
+        models_df = gr.DataFrame(
+            headers=["Global Rank", "Model ID", "Title", "Downloads", "Likes", "Korea Search", "URL"],
+            datatype=["str", "str", "str", "str", "str", "str", "str"],
+            row_count=(10, "dynamic")
+        )
     def refresh_all_data():
+        try:
+            spaces_results = get_spaces_data("trending")
+            models_results = get_models_data()
+            # Spaces 차트 생성
+            spaces_pie = create_pie_chart(spaces_results[2], 500, "Spaces")
+            spaces_bar = create_registration_bar_chart(spaces_results[2], "Spaces")
+            # Models 차트 생성
+            models_pie = create_pie_chart(models_results[2], 3000, "Models")
+            models_bar = create_registration_bar_chart(models_results[2], "Models")
+            return [
+                spaces_results[0], spaces_results[1], spaces_results[2],
+                spaces_pie, spaces_bar,
+                models_results[0], models_results[1], models_results[2],
+                models_pie, models_bar
+            ]
+        except Exception as e:
+            print(f"Error in refresh_all_data: {str(e)}")
+            # 에러 발생 시 기본값 반환
+            return [None] * 10
+    # 새로고침 버튼 클릭 이벤트 핸들러
     refresh_btn.click(
+        fn=refresh_all_data,
         outputs=[
             trending_plot, trending_info, trending_df,
+            spaces_pie_chart, spaces_bar_chart,
             models_plot, models_info, models_df,
+            models_pie_chart, models_bar_chart
         ]
     )
     # 초기 데이터 로드
+    try:
+        initial_data = refresh_all_data()
+        # 초기값 설정
+        trending_plot.value = initial_data[0]
+        trending_info.value = initial_data[1]
+        trending_df.value = initial_data[2]
+        spaces_pie_chart.value = initial_data[3]
+        spaces_bar_chart.value = initial_data[4]
+        models_plot.value = initial_data[5]
+        models_info.value = initial_data[6]
+        models_df.value = initial_data[7]
+        models_pie_chart.value = initial_data[8]
+        models_bar_chart.value = initial_data[9]
+    except Exception as e:
+        print(f"Error loading initial data: {str(e)}")
+        gr.Warning("초기 데이터 로드 중 오류가 발생했습니다.")
 # Gradio 앱 실행
 demo.launch(
     server_name="0.0.0.0",
     server_port=7860,
+    share=False,
+    show_error=True
 )