Update app.py

app.py

@@ -8,6 +8,7 @@ import os
 import time
 from huggingface_hub import HfApi, list_spaces, list_models
 from functools import lru_cache
+from concurrent.futures import ThreadPoolExecutor, as_completed
 
 # ═══════════════════════════════════════════════════════════
 # 🔧 ZeroGPU 초기화
@@ -32,398 +33,17 @@ app_initialized = initialize_app()
 HF_TOKEN = os.getenv("HF_TOKEN")
 hf_api = HfApi(token=HF_TOKEN) if HF_TOKEN else HfApi()
 
-# ═══════════════════════════════════════════════════════════
-# 📦 추가 스페이스 목록 (Featured Spaces)
-# ═══════════════════════════════════════════════════════════
-
-FEATURED_SPACES = {
-    "ginigen/Fashion-Fit360": "https://huggingface.co/spaces/ginigen/Fashion-Fit360",
-    "aiqtech/Nano-Banana-API": "https://huggingface.co/spaces/aiqtech/Nano-Banana-API",
-    "ginigen/Nano-Banana-Video": "https://huggingface.co/spaces/ginigen/Nano-Banana-Video",
-    "openfree/News-AI": "https://huggingface.co/spaces/openfree/News-AI",
-    "openfree/Creative-Arena-Leaderboard": "https://huggingface.co/spaces/openfree/Creative-Arena-Leaderboard",
-    "Heartsync/Nano-Banana": "https://huggingface.co/spaces/Heartsync/Nano-Banana",
-    "ginigen/webtoon-studio": "https://huggingface.co/spaces/ginigen/webtoon-studio",
-    "VIDraft/gpt-oss-RAG": "https://huggingface.co/spaces/VIDraft/gpt-oss-RAG",
-    "openfree/OpenAI-gpt-oss": "https://huggingface.co/spaces/openfree/OpenAI-gpt-oss",
-    "openfree/AGI-NOVEL": "https://huggingface.co/spaces/openfree/AGI-NOVEL",
-    "cutechicken/3D-Airforce-Simulator": "https://huggingface.co/spaces/cutechicken/3D-Airforce-Simulator",
-    "ginipick/Private-AI": "https://huggingface.co/spaces/ginipick/Private-AI",
-    "openfree/Best-AI": "https://huggingface.co/spaces/openfree/Best-AI",
-    "aiqtech/Heatmap-Leaderboard": "https://huggingface.co/spaces/aiqtech/Heatmap-Leaderboard",
-    "ginigen/Seedance-Free": "https://huggingface.co/spaces/ginigen/Seedance-Free",
-    "openfree/K-AI": "https://huggingface.co/spaces/openfree/K-AI",
-    "VIDraft/SOMA-AGI": "https://huggingface.co/spaces/VIDraft/SOMA-AGI",
-    "openfree/AI": "https://huggingface.co/spaces/openfree/AI",
-    "openfree/Chart-GPT": "https://huggingface.co/spaces/openfree/Chart-GPT",
-    "fantaxy/fantasy-novel": "https://huggingface.co/spaces/fantaxy/fantasy-novel",
-    "VIDraft/Gemma-3-R1984-1B": "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-1B",
-    "ginigen/FLUX-Ghibli-LoRA2": "https://huggingface.co/spaces/ginigen/FLUX-Ghibli-LoRA2",
-    "ginigen/Flux-VIDEO": "https://huggingface.co/spaces/ginigen/Flux-VIDEO",
-    "aiqcamp/deepseek-r1-0528": "https://huggingface.co/spaces/aiqcamp/deepseek-r1-0528",
-    "VIDraft/BAGEL-Websearch": "https://huggingface.co/spaces/VIDraft/BAGEL-Websearch",
-    "ginigen/3D-LLAMA-V1": "https://huggingface.co/spaces/ginigen/3D-LLAMA-V1",
-    "ginipick/Change-Hair": "https://huggingface.co/spaces/ginipick/Change-Hair",
-    "VIDraft/Voice-Clone-Podcast": "https://huggingface.co/spaces/VIDraft/Voice-Clone-Podcast",
-    "ginipick/PDF-EXAM": "https://huggingface.co/spaces/ginipick/PDF-EXAM",
-    "openfree/Naming": "https://huggingface.co/spaces/openfree/Naming",
-    "VIDraft/ACE-Singer": "https://huggingface.co/spaces/VIDraft/ACE-Singer",
-    "openfree/Live-Podcast": "https://huggingface.co/spaces/openfree/Live-Podcast",
-    "openfree/AI-Podcast": "https://huggingface.co/spaces/openfree/AI-Podcast",
-    "ginipick/AI-BOOK": "https://huggingface.co/spaces/ginipick/AI-BOOK",
-    "openfree/Web-Manager": "https://huggingface.co/spaces/openfree/Web-Manager",
-    "openfree/Game-Gallery": "https://huggingface.co/spaces/openfree/Game-Gallery",
-    "openfree/RPG-GAME": "https://huggingface.co/spaces/openfree/RPG-GAME",
-    "openfree/Evolution-Simulation": "https://huggingface.co/spaces/openfree/Evolution-Simulation",
-    "ginigen/3D-VIDEO": "https://huggingface.co/spaces/ginigen/3D-VIDEO",
-    "ginipick/Pharmacy": "https://huggingface.co/spaces/ginipick/Pharmacy",
-    "openfree/Battle-Royale-GAME": "https://huggingface.co/spaces/openfree/Battle-Royale-GAME",
-    "openfree/repo-graph": "https://huggingface.co/spaces/openfree/repo-graph",
-    "openfree/Chain-of-Recursive-Thoughts": "https://huggingface.co/spaces/openfree/Chain-of-Recursive-Thoughts",
-    "ginigen/Mistral-Perflexity": "https://huggingface.co/spaces/ginigen/Mistral-Perflexity",
-    "ginigen/Every-Text": "https://huggingface.co/spaces/ginigen/Every-Text",
-    "openfree/svg": "https://huggingface.co/spaces/openfree/svg",
-    "VIDraft/phi-4-reasoning-plus": "https://huggingface.co/spaces/VIDraft/phi-4-reasoning-plus",
-    "ginigen/VisualCloze": "https://huggingface.co/spaces/ginigen/VisualCloze",
-    "ginigen/framepack-i2v": "https://huggingface.co/spaces/ginigen/framepack-i2v",
-    "fantos/Physics-Playground": "https://huggingface.co/spaces/fantos/Physics-Playground",
-    "fantos/EveryText": "https://huggingface.co/spaces/fantos/EveryText",
-    "ginipick/Qwen3-32B": "https://huggingface.co/spaces/ginipick/Qwen3-32B",
-    "ginipick/Qwen3-235B-A22B": "https://huggingface.co/spaces/ginipick/Qwen3-235B-A22B",
-    "keeperballon/vibe-coding-tetris": "https://huggingface.co/spaces/keeperballon/vibe-coding-tetris",
-    "keeperballon/multi-llm": "https://huggingface.co/spaces/keeperballon/multi-llm",
-    "VIDraft/Qwen3-8B": "https://huggingface.co/spaces/VIDraft/Qwen3-8B",
-    "ginigen/interior-design": "https://huggingface.co/spaces/ginigen/interior-design",
-    "ginigen/perflexity-clone": "https://huggingface.co/spaces/ginigen/perflexity-clone",
-    "ginigen/blogger": "https://huggingface.co/spaces/ginigen/blogger",
-    "openfree/Badge": "https://huggingface.co/spaces/openfree/Badge",
-    "openfree/ZeroGPU-Leader": "https://huggingface.co/spaces/openfree/ZeroGPU-Leader",
-    "cutechicken/World-of-Tank-GAME": "https://huggingface.co/spaces/cutechicken/World-of-Tank-GAME",
-    "openfree/Llama-4-Maverick-17B-Research": "https://huggingface.co/spaces/openfree/Llama-4-Maverick-17B-Research",
-    "fantaxy/Model-Leaderboard": "https://huggingface.co/spaces/fantaxy/Model-Leaderboard",
-    "ginigen/FLUX-Open-Ghibli-Studio": "https://huggingface.co/spaces/ginigen/FLUX-Open-Ghibli-Studio",
-    "openfree/Gemma-3-R1984-12B": "https://huggingface.co/spaces/openfree/Gemma-3-R1984-12B",
-    "openfree/Stock-Trading-Analysis": "https://huggingface.co/spaces/openfree/Stock-Trading-Analysis",
-    "aiqtech/Contributors-Leaderboard": "https://huggingface.co/spaces/aiqtech/Contributors-Leaderboard",
-    "VIDraft/Gemma-3-R1984-27B": "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-27B",
-    "openfree/Deepseek-v3-0324-Research": "https://huggingface.co/spaces/openfree/Deepseek-v3-0324-Research",
-    "openfree/Korean-Exam-Leaderboard": "https://huggingface.co/spaces/openfree/Korean-Exam-Leaderboard",
-    "openfree/Idea-Transformer": "https://huggingface.co/spaces/openfree/Idea-Transformer",
-    "VIDraft/Unique3D": "https://huggingface.co/spaces/VIDraft/Unique3D",
-    "openfree/Compare-RAG-CHAT": "https://huggingface.co/spaces/openfree/Compare-RAG-CHAT",
-    "openfree/AgentX-Travel": "https://huggingface.co/spaces/openfree/AgentX-Travel",
-    "openfree/SonicOrbit": "https://huggingface.co/spaces/openfree/SonicOrbit",
-    "VIDraft/Space-Leaderboard": "https://huggingface.co/spaces/VIDraft/Space-Leaderboard",
-    "ginipick/3D-GAME": "https://huggingface.co/spaces/ginipick/3D-GAME",
-    "ginigen/Graph-Mind": "https://huggingface.co/spaces/ginigen/Graph-Mind",
-    "openfree/deepseek_r1_API": "https://huggingface.co/spaces/openfree/deepseek_r1_API",
-    "openfree/Mouse2-HF": "https://huggingface.co/spaces/openfree/Mouse2-HF",
-    "ginipick/open_Deep-Research": "https://huggingface.co/spaces/ginipick/open_Deep-Research",
-    "ginigen/LLaDA": "https://huggingface.co/spaces/ginigen/LLaDA",
-    "VIDraft/PHI4-Multimodal": "https://huggingface.co/spaces/VIDraft/PHI4-Multimodal",
-    "openfree/Datasets-Convertor": "https://huggingface.co/spaces/openfree/Datasets-Convertor",
-    "ginigen/Ovis2-8B": "https://huggingface.co/spaces/ginigen/Ovis2-8B",
-    "ginigen/FLUX-Eternity": "https://huggingface.co/spaces/ginigen/FLUX-Eternity",
-    "ginigen/OmniParser-v2-pro": "https://huggingface.co/spaces/ginigen/OmniParser-v2-pro",
-    "ginigen/panorama-images": "https://huggingface.co/spaces/ginigen/panorama-images",
-    "ginigen/Celebrity": "https://huggingface.co/spaces/ginigen/Celebrity",
-    "ginigen/Fashion-Pose-Control": "https://huggingface.co/spaces/ginigen/Fashion-Pose-Control",
-    "ginigen/VoiceClone-TTS": "https://huggingface.co/spaces/ginigen/VoiceClone-TTS",
-    "openfree/Article-Generator": "https://huggingface.co/spaces/openfree/Article-Generator",
-    "openfree/chess": "https://huggingface.co/spaces/openfree/chess",
-    "fantos/Panorama": "https://huggingface.co/spaces/fantos/Panorama",
-    "ginigen/Multi-LoRAgen": "https://huggingface.co/spaces/ginigen/Multi-LoRAgen",
-    "ginigen/canvas-studio": "https://huggingface.co/spaces/ginigen/canvas-studio",
-    "fantos/Magic-Drawings": "https://huggingface.co/spaces/fantos/Magic-Drawings",
-    "ginigen/Lumina-Image-PLUS": "https://huggingface.co/spaces/ginigen/Lumina-Image-PLUS",
-    "VIDraft/mouse-webgen": "https://huggingface.co/spaces/VIDraft/mouse-webgen",
-    "immunobiotech/MUSIC-Jukebox": "https://huggingface.co/spaces/immunobiotech/MUSIC-Jukebox",
-    "kolaslab/Binaural-Beat-Generator": "https://huggingface.co/spaces/kolaslab/Binaural-Beat-Generator",
-    "ginigen/Flux-LayerDiffuse": "https://huggingface.co/spaces/ginigen/Flux-LayerDiffuse",
-    "VIDraft/Portrait-Animation": "https://huggingface.co/spaces/VIDraft/Portrait-Animation",
-    "ginigen/text3d-r1": "https://huggingface.co/spaces/ginigen/text3d-r1",
-    "immunobiotech/drug-discovery": "https://huggingface.co/spaces/immunobiotech/drug-discovery",
-    "openfree/VisionOCR-Chat": "https://huggingface.co/spaces/openfree/VisionOCR-Chat",
-    "immunobiotech/MICHELIN": "https://huggingface.co/spaces/immunobiotech/MICHELIN",
-    "ginipick/PharmAI-Korea": "https://huggingface.co/spaces/ginipick/PharmAI-Korea",
-    "ginigen/MagicFace-V3": "https://huggingface.co/spaces/ginigen/MagicFace-V3",
-    "aiqcamp/MindMap": "https://huggingface.co/spaces/aiqcamp/MindMap",
-    "ginigen/3D-LLAMA": "https://huggingface.co/spaces/ginigen/3D-LLAMA",
-    "openfree/VectorFlow": "https://huggingface.co/spaces/openfree/VectorFlow",
-    "openfree/webtoon-gen": "https://huggingface.co/spaces/openfree/webtoon-gen",
-    "VIDraft/topic-prediction": "https://huggingface.co/spaces/VIDraft/topic-prediction",
-    "openfree/MoneyRadar2": "https://huggingface.co/spaces/openfree/MoneyRadar2",
-    "openfree/trending-board-2025": "https://huggingface.co/spaces/openfree/trending-board-2025",
-    "VIDraft/PapersImpact": "https://huggingface.co/spaces/VIDraft/PapersImpact",
-    "VIDraft/EveryRAG": "https://huggingface.co/spaces/VIDraft/EveryRAG",
-    "ginipick/Any3D": "https://huggingface.co/spaces/ginipick/Any3D",
-    "ginigen/Canvas-pro": "https://huggingface.co/spaces/ginigen/Canvas-pro",
-    "openfree/image-to-vector": "https://huggingface.co/spaces/openfree/image-to-vector",
-    "ginipick/QR-Canvas-plus": "https://huggingface.co/spaces/ginipick/QR-Canvas-plus",
-    "openfree/Korean-Leaderboard-2025": "https://huggingface.co/spaces/openfree/Korean-Leaderboard-2025",
-    "ginipick/DeepSeekR1-LIVE": "https://huggingface.co/spaces/ginipick/DeepSeekR1-LIVE",
-    "ginigen/ColPali-multi": "https://huggingface.co/spaces/ginigen/ColPali-multi",
-    "ginigen/Janus-Pro-7B": "https://huggingface.co/spaces/ginigen/Janus-Pro-7B",
-    "ginigen/Animagine": "https://huggingface.co/spaces/ginigen/Animagine",
-    "ginipick/OpenSUNO": "https://huggingface.co/spaces/ginipick/OpenSUNO",
-    "openfree/PDF-RAG": "https://huggingface.co/spaces/openfree/PDF-RAG",
-    "fantos/Ranking-Tracker": "https://huggingface.co/spaces/fantos/Ranking-Tracker",
-    "aiqcamp/Multilingual-Images": "https://huggingface.co/spaces/aiqcamp/Multilingual-Images",
-    "aiqcamp/Gemini2-Flash-Thinking": "https://huggingface.co/spaces/aiqcamp/Gemini2-Flash-Thinking",
-    "kolaslab/8bit-gamemusic": "https://huggingface.co/spaces/kolaslab/8bit-gamemusic",
-    "openfree/pepe": "https://huggingface.co/spaces/openfree/pepe",
-    "openfree/MoneyRadar": "https://huggingface.co/spaces/openfree/MoneyRadar",
-    "ginipick/QR-Canvas": "https://huggingface.co/spaces/ginipick/QR-Canvas",
-    "openfree/MagicFace": "https://huggingface.co/spaces/openfree/MagicFace",
-    "openfree/pick-spaces": "https://huggingface.co/spaces/openfree/pick-spaces",
-    "aiqcamp/diagram": "https://huggingface.co/spaces/aiqcamp/diagram",
-    "openfree/korea-president-yoon": "https://huggingface.co/spaces/openfree/korea-president-yoon",
-    "VIDraft/PaperImpact": "https://huggingface.co/spaces/VIDraft/PaperImpact",
-    "openfree/CryptoVision": "https://huggingface.co/spaces/openfree/CryptoVision",
-    "gunship999/Gunship-3D-FPS": "https://huggingface.co/spaces/gunship999/Gunship-3D-FPS",
-    "fantos/VoiceClone": "https://huggingface.co/spaces/fantos/VoiceClone",
-    "VIDraft/ChemGenesis": "https://huggingface.co/spaces/VIDraft/ChemGenesis",
-    "openfree/webtoon": "https://huggingface.co/spaces/openfree/webtoon",
-    "kolaslab/Quantum": "https://huggingface.co/spaces/kolaslab/Quantum",
-    "openfree/ProteinGenesis": "https://huggingface.co/spaces/openfree/ProteinGenesis",
-    "openfree/2025saju": "https://huggingface.co/spaces/openfree/2025saju",
-    "kolaslab/VisionART": "https://huggingface.co/spaces/kolaslab/VisionART",
-    "ginigen/CANVAS-o3": "https://huggingface.co/spaces/ginigen/CANVAS-o3",
-    "kolaslab/world-sdr": "https://huggingface.co/spaces/kolaslab/world-sdr",
-    "openfree/ultpixgen": "https://huggingface.co/spaces/openfree/ultpixgen",
-    "ginigen/theater": "https://huggingface.co/spaces/ginigen/theater",
-    "VIDraft/stock": "https://huggingface.co/spaces/VIDraft/stock",
-    "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",
-    "ginigen/FLUXllama-Multilingual": "https://huggingface.co/spaces/ginigen/FLUXllama-Multilingual",
-    "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",
-    "openfree/Korean-Leaderboard": "https://huggingface.co/spaces/openfree/Korean-Leaderboard",
-    "ginipick/FLUXllama": "https://huggingface.co/spaces/ginipick/FLUXllama",
-    "ginipick/SORA-3D": "https://huggingface.co/spaces/ginipick/SORA-3D",
-    "fantaxy/Sound-AI-SFX": "https://huggingface.co/spaces/fantaxy/Sound-AI-SFX",
-    "ginigen/Canvas": "https://huggingface.co/spaces/ginigen/Canvas",
-    "ginipick/time-machine": "https://huggingface.co/spaces/ginipick/time-machine",
-    "openfree/Tetris-Game": "https://huggingface.co/spaces/openfree/Tetris-Game",
-    "openfree/everychat": "https://huggingface.co/spaces/openfree/everychat",
-    "VIDraft/mouse1": "https://huggingface.co/spaces/VIDraft/mouse1",
-    "kolaslab/alpha-go": "https://huggingface.co/spaces/kolaslab/alpha-go",
-    "ginipick/text3d": "https://huggingface.co/spaces/ginipick/text3d",
-    "openfree/trending-board": "https://huggingface.co/spaces/openfree/trending-board",
-    "cutechicken/tankwar": "https://huggingface.co/spaces/cutechicken/tankwar",
-    "openfree/game-jewel": "https://huggingface.co/spaces/openfree/game-jewel",
-    "VIDraft/mouse-chat": "https://huggingface.co/spaces/VIDraft/mouse-chat",
-    "ginipick/AccDiffusion": "https://huggingface.co/spaces/ginipick/AccDiffusion",
-    "openfree/GiniGEN": "https://huggingface.co/spaces/openfree/GiniGEN",
-    "openfree/trending-news-24": "https://huggingface.co/spaces/openfree/trending-news-24",
-    "ginipick/Realtime-FLUX": "https://huggingface.co/spaces/ginipick/Realtime-FLUX",
-    "VIDraft/prime-number": "https://huggingface.co/spaces/VIDraft/prime-number",
-    "kolaslab/zombie-game": "https://huggingface.co/spaces/kolaslab/zombie-game",
-    "kolaslab/shooting": "https://huggingface.co/spaces/kolaslab/shooting",
-    "VIDraft/Mouse-Hackathon": "https://huggingface.co/spaces/VIDraft/Mouse-Hackathon",
-    "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",
-    
-    # ═══════════════════════════════════════════════════════════
-    # seawolf2357 Collection - NEW Launch
-    # ═══════════════════════════════════════════════════════════
-    "VIDraft/NH-Prediction": "https://huggingface.co/spaces/VIDraft/NH-Prediction",
-    "ginipick/NH-Korea": "https://huggingface.co/spaces/ginipick/NH-Korea",
-    "fantaxy/playground25": "https://huggingface.co/spaces/fantaxy/playground25",
-    "openfree/Cycle-Navigator": "https://huggingface.co/spaces/openfree/Cycle-Navigator",
-    "openfree/Vibe-Game": "https://huggingface.co/spaces/openfree/Vibe-Game",
-    "ginipick/IDEA-DESIGN": "https://huggingface.co/spaces/ginipick/IDEA-DESIGN",
-    "openfree/DreamO-video": "https://huggingface.co/spaces/openfree/DreamO-video",
-    "openfree/Pulsar-Display": "https://huggingface.co/spaces/openfree/Pulsar-Display",
-    "immunobiotech/Gemini-MICHELIN-kr": "https://huggingface.co/spaces/immunobiotech/Gemini-MICHELIN-kr",
-    "immunobiotech/Gemini-MICHELIN": "https://huggingface.co/spaces/immunobiotech/Gemini-MICHELIN",
-    "aiqcamp/SEO-checker": "https://huggingface.co/spaces/aiqcamp/SEO-checker",
-    "openfree/qwen3-235b-a22b-research": "https://huggingface.co/spaces/openfree/qwen3-235b-a22b-research",
-    "aiqcamp/ENGLISH-Speaking-Scoring": "https://huggingface.co/spaces/aiqcamp/ENGLISH-Speaking-Scoring",
-    "openfree/qwen3-30b-a3b-research": "https://huggingface.co/spaces/openfree/qwen3-30b-a3b-research",
-    "aiqcamp/gemini-2.5-flash-preview": "https://huggingface.co/spaces/aiqcamp/gemini-2.5-flash-preview",
-    "aiqcamp/MCP-kokoro": "https://huggingface.co/spaces/aiqcamp/MCP-kokoro",
-    "VIDraft/Local-RAG-llama-3-8b": "https://huggingface.co/spaces/VIDraft/Local-RAG-llama-3-8b",
-    "VIDraft/FramePack_rotate_landscape": "https://huggingface.co/spaces/VIDraft/FramePack_rotate_landscape",
-    "yokoha/CSV": "https://huggingface.co/spaces/yokoha/CSV",
-    "yokoha/CSV-Parquet-Convertors": "https://huggingface.co/spaces/yokoha/CSV-Parquet-Convertors",
-    "openfree/Llama-4-Scout-17B-Research": "https://huggingface.co/spaces/openfree/Llama-4-Scout-17B-Research",
-    "aiqtech/LLM-Token-Visual": "https://huggingface.co/spaces/aiqtech/LLM-Token-Visual",
-    "VIDraft/ThinkFlow-llama": "https://huggingface.co/spaces/VIDraft/ThinkFlow-llama",
-    "VIDraft/Flex-preview": "https://huggingface.co/spaces/VIDraft/Flex-preview",
-    "VIDraft/Open-Meme-Studio": "https://huggingface.co/spaces/VIDraft/Open-Meme-Studio",
-    "ginigen/Ghibli-Meme-Studio": "https://huggingface.co/spaces/ginigen/Ghibli-Meme-Studio",
-    "ginigen/FLUX-Text-Tree-Image": "https://huggingface.co/spaces/ginigen/FLUX-Text-Tree-Image",
-    "ginigen/SAJU": "https://huggingface.co/spaces/ginigen/SAJU",
-    "ginipick/AgentX-Papers": "https://huggingface.co/spaces/ginipick/AgentX-Papers",
-    
-    # ═══════��═══════════════════════════════════════════════════
-    # Heartsync Spaces (humangen.ai)
-    # ═══════════════════════════════════════════════════════════
-    "Heartsync/Nano-Banana": "https://huggingface.co/spaces/Heartsync/Nano-Banana",
-    "Heartsync/humangen": "https://huggingface.co/spaces/Heartsync/humangen",
-    "Heartsync/EXAM-GEN": "https://huggingface.co/spaces/Heartsync/EXAM-GEN",
-    "Heartsync/VEO3-RealTime": "https://huggingface.co/spaces/Heartsync/VEO3-RealTime",
-    "Heartsync/Qwen-Image-2512-Fast": "https://huggingface.co/spaces/Heartsync/Qwen-Image-2512-Fast",
-    "Heartsync/NSFW-Uncensored-video2": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video2",
-    "Heartsync/Adult": "https://huggingface.co/spaces/Heartsync/Adult",
-    "Heartsync/NSFW-Uncensored-photo": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-photo",
-    "Heartsync/NSFW-image": "https://huggingface.co/spaces/Heartsync/NSFW-image",
-    "Heartsync/FREE-NSFW-HUB": "https://huggingface.co/spaces/Heartsync/FREE-NSFW-HUB",
-    "Heartsync/NSFW-Uncensored-image": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-image",
-    "Heartsync/PornHUB": "https://huggingface.co/spaces/Heartsync/PornHUB",
-    "Heartsync/NSFW-Uncensored-image2": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-image2",
-    "Heartsync/Hentai-Adult": "https://huggingface.co/spaces/Heartsync/Hentai-Adult",
-    "Heartsync/Dream-wan2-2-faster-Pro": "https://huggingface.co/spaces/Heartsync/Dream-wan2-2-faster-Pro",
-    "Heartsync/nsfwnovel": "https://huggingface.co/spaces/Heartsync/nsfwnovel",
-    "Heartsync/nsfwt2i": "https://huggingface.co/spaces/Heartsync/nsfwt2i",
-    "Heartsync/WAN2-1-fast-T2V-FusioniX": "https://huggingface.co/spaces/Heartsync/WAN2-1-fast-T2V-FusioniX",
-    "Heartsync/nsfwadult": "https://huggingface.co/spaces/Heartsync/nsfwadult",
-    "Heartsync/zlora": "https://huggingface.co/spaces/Heartsync/zlora",
-    "Heartsync/multimodal-chat-mbti-INFJ": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-INFJ",
-    "Heartsync/wan2-1-fast-security": "https://huggingface.co/spaces/Heartsync/wan2-1-fast-security",
-    "Heartsync/NSFW-detection": "https://huggingface.co/spaces/Heartsync/NSFW-detection",
-    "Heartsync/wan2_2-I2V-14B-FAST": "https://huggingface.co/spaces/Heartsync/wan2_2-I2V-14B-FAST",
-    "Heartsync/FLUX-Vision": "https://huggingface.co/spaces/Heartsync/FLUX-Vision",
-    "Heartsync/Qwen-Image-LORA": "https://huggingface.co/spaces/Heartsync/Qwen-Image-LORA",
-    "Heartsync/Character": "https://huggingface.co/spaces/Heartsync/Character",
-    "Heartsync/FLite-Texture-ULTRA": "https://huggingface.co/spaces/Heartsync/FLite-Texture-ULTRA",
-    "Heartsync/Wan-2.1-T2V-1.3B-LoRA": "https://huggingface.co/spaces/Heartsync/Wan-2.1-T2V-1.3B-LoRA",
-    "Heartsync/NSFW-novels": "https://huggingface.co/spaces/Heartsync/NSFW-novels",
-    "Heartsync/pose-generator-space": "https://huggingface.co/spaces/Heartsync/pose-generator-space",
-    "Heartsync/convert": "https://huggingface.co/spaces/Heartsync/convert",
-    "Heartsync/NSFW-Uncensored-new": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-new",
-    "Heartsync/image-ip-composer": "https://huggingface.co/spaces/Heartsync/image-ip-composer",
-    "Heartsync/Papers-Leaderboard": "https://huggingface.co/spaces/Heartsync/Papers-Leaderboard",
-    "Heartsync/voteaide": "https://huggingface.co/spaces/Heartsync/voteaide",
-    "Heartsync/modeld": "https://huggingface.co/spaces/Heartsync/modeld",
-    
-    # ═══════════════════════════════════════════════════════════
-    # seawolf2357 Spaces
-    # ═══════════════════════════════════════════════════════════
-    "seawolf2357/REALVISXL-V5": "https://huggingface.co/spaces/seawolf2357/REALVISXL-V5",
-    "seawolf2357/ocrlatex": "https://huggingface.co/spaces/seawolf2357/ocrlatex",
-    "seawolf2357/sd-prompt-gen": "https://huggingface.co/spaces/seawolf2357/sd-prompt-gen",
-    "seawolf2357/voicechat": "https://huggingface.co/spaces/seawolf2357/voicechat",
-    "seawolf2357/Sparc3D": "https://huggingface.co/spaces/seawolf2357/Sparc3D",
-    "seawolf2357/apsf": "https://huggingface.co/spaces/seawolf2357/apsf",
-    "seawolf2357/Flowise-AI": "https://huggingface.co/spaces/seawolf2357/Flowise-AI",
-    "seawolf2357/3D-Avatar-Generator": "https://huggingface.co/spaces/seawolf2357/3D-Avatar-Generator",
-    "seawolf2357/Ghibli-Multilingual-Text-rendering": "https://huggingface.co/spaces/seawolf2357/Ghibli-Multilingual-Text-rendering",
-    
-    # ══════════════════════════════════���════════════════════════
-    # shuja666 (관련 커뮤니티)
-    # ═══════════════════════════════════════════════════════════
-    "shuja666/seawolf2357-hanbok": "https://huggingface.co/spaces/shuja666/seawolf2357-hanbok",
-    
-    # ═══════════════════════════════════════════════════════════
-    # openfree Collection - [New] AI Technologies & Services
-    # (중복 제외 신규 스페이스만)
-    # ═══════════════════════════════════════════════════════════
-    "Heartsync/hwp": "https://huggingface.co/spaces/Heartsync/hwp",
-    "ginipick/spaces-research-korea": "https://huggingface.co/spaces/ginipick/spaces-research-korea",
-    "fantaxy/Space-Leaderboard": "https://huggingface.co/spaces/fantaxy/Space-Leaderboard",
-    "ginigen/Workflow-Canvas": "https://huggingface.co/spaces/ginigen/Workflow-Canvas",
-    "ginigen/deepseek-r1-0528-API": "https://huggingface.co/spaces/ginigen/deepseek-r1-0528-API",
-    "VIDraft/money-radar-korea": "https://huggingface.co/spaces/VIDraft/money-radar-korea",
-    "VIDraft/Agentic-AI-CHAT": "https://huggingface.co/spaces/VIDraft/Agentic-AI-CHAT",
-    "VIDraft/Gemma-3-R1984-4B": "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-4B",
-    "VIDraft/Gemma-3-R1984-12B": "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-12B",
-    "ginigen/SFX-Sound-magic": "https://huggingface.co/spaces/ginigen/SFX-Sound-magic",
-    "openfree/Multilingual-TTS": "https://huggingface.co/spaces/openfree/Multilingual-TTS",
-    "ginipick/10m-marketing": "https://huggingface.co/spaces/ginipick/10m-marketing",
-    "VIDraft/Mistral-RAG-BitSix": "https://huggingface.co/spaces/VIDraft/Mistral-RAG-BitSix",
-    "aiqcamp/REMOVAL-TEXT-IMAGE": "https://huggingface.co/spaces/aiqcamp/REMOVAL-TEXT-IMAGE",
-    "aiqcamp/Mistral-Devstral-API": "https://huggingface.co/spaces/aiqcamp/Mistral-Devstral-API",
-    "aiqcamp/deepseek-r1-0528-qwen3-8b": "https://huggingface.co/spaces/aiqcamp/deepseek-r1-0528-qwen3-8b",
-    "Heartsync/WAN-VIDEO-AUDIO": "https://huggingface.co/spaces/Heartsync/WAN-VIDEO-AUDIO",
-    "VIDraft/Gemma-3-R1984-1B-0613": "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-1B-0613",
-    "Heartsync/NSFW-Uncensored-REAL": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-REAL",
-    "VIDraft/Robo-Beam": "https://huggingface.co/spaces/VIDraft/Robo-Beam",
-    "VIDraft/voice-trans": "https://huggingface.co/spaces/VIDraft/voice-trans",
-    "Heartsync/WAN2-1-fast-T2V-FusioniX2": "https://huggingface.co/spaces/Heartsync/WAN2-1-fast-T2V-FusioniX2",
-    "ginigen/VEO3-Directors": "https://huggingface.co/spaces/ginigen/VEO3-Directors",
-    "ginigen/VEO3-Free": "https://huggingface.co/spaces/ginigen/VEO3-Free",
-    "ginigen/VEO3-Free-mirror": "https://huggingface.co/spaces/ginigen/VEO3-Free-mirror",
-    "openfree/Open-GAMMA": "https://huggingface.co/spaces/openfree/Open-GAMMA",
-    "fantaxy/YTB-TEST": "https://huggingface.co/spaces/fantaxy/YTB-TEST",
-    "VIDraft/MOUSE-Workflow": "https://huggingface.co/spaces/VIDraft/MOUSE-Workflow",
-    "aiqtech/SOMA-Oriental": "https://huggingface.co/spaces/aiqtech/SOMA-Oriental",
-    "VIDraft/AI": "https://huggingface.co/spaces/VIDraft/AI",
-    "openfree/Model-Atlas": "https://huggingface.co/spaces/openfree/Model-Atlas",
-    "aiqtech/fastvlm-webgpu": "https://huggingface.co/spaces/aiqtech/fastvlm-webgpu",
-    "aiqtech/Wan_FusionX_with_Loras": "https://huggingface.co/spaces/aiqtech/Wan_FusionX_with_Loras",
-    "VIDraft/DNA-CASINO": "https://huggingface.co/spaces/VIDraft/DNA-CASINO",
-    "ginigen/Flux-Kontext-Style": "https://huggingface.co/spaces/ginigen/Flux-Kontext-Style",
-    "ginigen/higgs_audio-Enhanced": "https://huggingface.co/spaces/ginigen/higgs_audio-Enhanced",
-    "Heartsync/addit": "https://huggingface.co/spaces/Heartsync/addit",
-    "ginigen/Flux-Kontext-FaceLORA": "https://huggingface.co/spaces/ginigen/Flux-Kontext-FaceLORA",
-    "openfree/Korea-Leaderboard": "https://huggingface.co/spaces/openfree/Korea-Leaderboard",
-    "openfree/AI-Calcurator": "https://huggingface.co/spaces/openfree/AI-Calcurator",
-    "ginigen/AGI-Screenplay": "https://huggingface.co/spaces/ginigen/AGI-Screenplay",
-    "openfree/AGI-Screenplay-Pro": "https://huggingface.co/spaces/openfree/AGI-Screenplay-Pro",
-    "fantaxy/AGI-LEADERBOARD": "https://huggingface.co/spaces/fantaxy/AGI-LEADERBOARD",
-    "openfree/AGI-WebNovel": "https://huggingface.co/spaces/openfree/AGI-WebNovel",
-    "openfree/AGI-WebNovel-Gallery": "https://huggingface.co/spaces/openfree/AGI-WebNovel-Gallery",
-    "ginigen/Wan-2.2-Enhanced": "https://huggingface.co/spaces/ginigen/Wan-2.2-Enhanced",
-    "Heartsync/Wan-2.2-ADULT": "https://huggingface.co/spaces/Heartsync/Wan-2.2-ADULT",
-    "aiqtech/NSFW-Real": "https://huggingface.co/spaces/aiqtech/NSFW-Real",
-    "VIDraft/skin-disease-identification": "https://huggingface.co/spaces/VIDraft/skin-disease-identification",
-    "ginigen/AGI-WebToon-KOREA": "https://huggingface.co/spaces/ginigen/AGI-WebToon-KOREA",
-    "ginigen/Hair-Pick": "https://huggingface.co/spaces/ginigen/Hair-Pick",
-    "VIDraft/DNA-Diffusion": "https://huggingface.co/spaces/VIDraft/DNA-Diffusion",
-    "openfree/WAR-Game-Simul": "https://huggingface.co/spaces/openfree/WAR-Game-Simul",
-    "VIDraft/News-Stream": "https://huggingface.co/spaces/VIDraft/News-Stream",
-    "openfree/Face-blurring": "https://huggingface.co/spaces/openfree/Face-blurring",
-    "openfree/Nano-Banana-Upscale": "https://huggingface.co/spaces/openfree/Nano-Banana-Upscale",
-    "openfree/Free-Nano-Banana": "https://huggingface.co/spaces/openfree/Free-Nano-Banana",
-    "ginigen/Nano-Banana-PRO": "https://huggingface.co/spaces/ginigen/Nano-Banana-PRO",
-    "VIDraft/company": "https://huggingface.co/spaces/VIDraft/company",
-    "VIDraft/Creative-Design": "https://huggingface.co/spaces/VIDraft/Creative-Design",
-    "VIDraft/Github-HF": "https://huggingface.co/spaces/VIDraft/Github-HF",
-    "VIDraft/ROBO-R1984": "https://huggingface.co/spaces/VIDraft/ROBO-R1984",
-    "VIDraft/XRAY": "https://huggingface.co/spaces/VIDraft/XRAY",
-    "openfree/Dots-OCR": "https://huggingface.co/spaces/openfree/Dots-OCR",
-    "VIDraft/Darwin-Qwen3-4B": "https://huggingface.co/spaces/VIDraft/Darwin-Qwen3-4B",
-    "ginigen/wan2_2-I2V-14B-FAST": "https://huggingface.co/spaces/ginigen/wan2_2-I2V-14B-FAST",
-    "ginigen/Today": "https://huggingface.co/spaces/ginigen/Today",
-    "ginigen/AI": "https://huggingface.co/spaces/ginigen/AI",
-    "ginigen/SAJU-FAST": "https://huggingface.co/spaces/ginigen/SAJU-FAST",
-    "ginipick/SAJU-Couple": "https://huggingface.co/spaces/ginipick/SAJU-Couple",
-    "ginigen/Family": "https://huggingface.co/spaces/ginigen/Family",
-    "ginigen/Markets": "https://huggingface.co/spaces/ginigen/Markets",
-    "fantos/Heatmap-Leaderboard-KOREA": "https://huggingface.co/spaces/fantos/Heatmap-Leaderboard-KOREA",
-    "VIDraft/AGI-Personals": "https://huggingface.co/spaces/VIDraft/AGI-Personals",
-    "VIDraft/Yolo26": "https://huggingface.co/spaces/VIDraft/Yolo26",
-    "VIDraft/video-background-remove": "https://huggingface.co/spaces/VIDraft/video-background-remove",
-    "openfree/Polymarket": "https://huggingface.co/spaces/openfree/Polymarket",
-    "VIDraft/clone-git-repo-to-space": "https://huggingface.co/spaces/VIDraft/clone-git-repo-to-space",
-    "VIDraft/THEME-STYLE": "https://huggingface.co/spaces/VIDraft/THEME-STYLE",
-    "openfree/leaderboard-korea": "https://huggingface.co/spaces/openfree/leaderboard-korea",
-}
-
 # ═══════════════════════════════════════════════════════════
 # 🖼️ 프로필 사진 캐시 및 조회
 # ═══════════════════════════════════════════════════════════
 
-# 프로필 사진 캐시 (메모리)
 avatar_cache = {}
 
 def get_avatar_url(author: str) -> str:
-    """
-    HuggingFace 사용자/조직의 아바타 URL 조회
-    캐시를 사용하여 중복 요청 방지
-    """
+    """HuggingFace 사용자/조직의 아바타 URL 조회"""
     if not author:
         return get_default_avatar(author)
     
-    # 캐시 확인
     if author in avatar_cache:
         return avatar_cache[author]
     
@@ -434,7 +54,7 @@ def get_avatar_url(author: str) -> str:
         if HF_TOKEN:
             headers["Authorization"] = f"Bearer {HF_TOKEN}"
         
-        # 방법 1: 조직 정보 조회
+        # 조직 정보 조회
         try:
             org_url = f"https://huggingface.co/api/organizations/{author}"
             response = requests.get(org_url, headers=headers, timeout=10)
@@ -450,7 +70,7 @@ def get_avatar_url(author: str) -> str:
         except:
             pass
         
-        # 방법 2: 사용자 정보 조회
+        # 사용자 정보 조회
         try:
             user_url = f"https://huggingface.co/api/users/{author}/overview"
             response = requests.get(user_url, headers=headers, timeout=10)
@@ -467,26 +87,9 @@ def get_avatar_url(author: str) -> str:
         except:
             pass
         
-        # 방법 3: 직접 사용자 API
-        try:
-            user_url = f"https://huggingface.co/api/users/{author}"
-            response = requests.get(user_url, headers=headers, timeout=10)
-            
-            if response.status_code == 200:
-                data = response.json()
-                avatar_url = data.get('avatarUrl') or data.get('avatar_url')
-                if avatar_url:
-                    if avatar_url.startswith('/'):
-                        avatar_url = f"https://huggingface.co{avatar_url}"
-                    avatar_cache[author] = avatar_url
-                    return avatar_url
-        except:
-            pass
-        
     except Exception as e:
         print(f"[Avatar] Error fetching avatar for {author}: {e}")
     
-    # 모든 방법 실패 시 기본 아바타
     default_avatar = get_default_avatar(author)
     avatar_cache[author] = default_avatar
     return default_avatar
@@ -506,22 +109,16 @@ def get_default_avatar(author: str = None) -> str:
     return "https://huggingface.co/front/assets/huggingface_logo-noborder.svg"
 
 def preload_avatars(authors: list, progress_callback=None):
-    """여러 저자의 아바타를 미리 로드 (배치 처리)"""
+    """여러 저자의 아바타를 미리 로드"""
     unique_authors = list(set(author for author in authors if author))
     loaded = 0
-    total = len(unique_authors)
-    
-    print(f"[Avatar] Starting to preload {total} unique authors...")
     
     for idx, author in enumerate(unique_authors):
         if author not in avatar_cache:
             get_avatar_url(author)
             loaded += 1
-            if loaded % 5 == 0:
-                time.sleep(0.2)
-        
-        if progress_callback and idx % 10 == 0:
-            progress_callback(idx / total)
+            if loaded % 10 == 0:
+                time.sleep(0.1)
     
     print(f"[Avatar] Preloaded {loaded} new avatars, total cached: {len(avatar_cache)}")
 
@@ -567,7 +164,7 @@ def generate_unique_colors(count: int) -> list:
 # ═══════════════════════════════════════════════════════════
 
 ORGANIZATIONS = [
-    # 대기업 - 브랜드 색상 사용
+    # 대기업
     {"color": "#03C75A", "authors": ["naver-hyperclovax", "naver-ellm", "naver", "navervision", "naver-clova-ix", "naver-clova-ocr", "naver-ai"]},
     {"color": "#FFD200", "authors": ["kakaocorp", "kakaobrain", "kakaobank"]},
     {"color": "#1428A0", "authors": ["SamsungResearch", "SamsungSDS-Research", "SamsungSAILMontreal"]},
@@ -625,7 +222,7 @@ def get_all_authors():
     return authors
 
 def get_author_color(author: str) -> str:
-    """저자의 색상 반환 (대소문자 구분 없음)"""
+    """저자의 색상 반환"""
     author_lower = author.lower()
     for org in ORGANIZATIONS:
         for org_author in org["authors"]:
@@ -634,220 +231,226 @@ def get_author_color(author: str) -> str:
     return "#6d7b8d"
 
 # ═══════════════════════════════════════════════════════════
-# 🌐 HuggingFace Hub API를 통한 정확한 데이터 조회
+# 🌐 HuggingFace API - 전역 순위 조회 (likes 기준)
 # ═══════════════════════════════════════════════════════════
 
-def get_all_spaces_with_ranks(limit=2400, progress=None):
-    """전역 스페이스 순위 목록 조회 (likes 기준 정렬)"""
-    print(f"[Spaces] Fetching top {limit} spaces by likes...")
-    all_spaces = {}
-    
-    try:
-        count = 0
-        for idx, space in enumerate(list_spaces(
-            sort="likes", 
-            direction=-1, 
-            limit=limit, 
-            full=True,
-            token=HF_TOKEN
-        )):
-            space_id = space.id
-            all_spaces[space_id] = {
-                "rank": idx + 1,
-                "id": space_id,
-                "likes": space.likes if hasattr(space, 'likes') else 0,
-                "title": space.cardData.get('title', 'No Title') if hasattr(space, 'cardData') and space.cardData else 'No Title',
-                "author": space.author if hasattr(space, 'author') else space_id.split('/')[0],
-            }
-            count += 1
-            
-            if progress and count % 200 == 0:
-                progress(count / limit * 0.5, desc=f"Loading spaces: {count}/{limit}")
-            
-            if count >= limit:
-                break
-        
-        print(f"[Spaces] Loaded {len(all_spaces)} spaces")
-        return all_spaces
-        
-    except Exception as e:
-        print(f"[Spaces] Error with huggingface_hub, falling back to API: {e}")
-        return get_all_spaces_with_ranks_api(limit, progress)
-
-def get_all_spaces_with_ranks_api(limit=2400, progress=None):
-    """API 폴백"""
-    all_spaces = {}
+def get_global_space_ranks(limit=10000, progress=None):
+    """
+    전역 스페이스 순위 조회 - likes 기준
+    space_id -> global_rank 매핑 반환
+    """
+    print(f"[Global Ranks] Fetching top {limit} spaces by likes...")
+    global_ranks = {}
     
     try:
-        url = "https://huggingface.co/api/spaces"
         headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
+        url = "https://huggingface.co/api/spaces"
         
         params = {
             "sort": "likes",
             "direction": -1,
-            "limit": min(limit, 10000),
-            "full": "true"
+            "limit": limit,
+            "full": "false"  # 빠른 조회를 위해 기본 정보만
         }
         
-        response = requests.get(url, params=params, headers=headers, timeout=120)
+        response = requests.get(url, params=params, headers=headers, timeout=180)
+        
         if response.status_code == 200:
             spaces_list = response.json()
-            print(f"[Spaces API] Got {len(spaces_list)} spaces")
+            print(f"[Global Ranks] Got {len(spaces_list)} spaces from API")
             
-            for idx, space in enumerate(spaces_list[:limit]):
+            for idx, space in enumerate(spaces_list):
                 space_id = space.get('id', '')
                 if space_id:
-                    all_spaces[space_id] = {
-                        "rank": idx + 1,
-                        "id": space_id,
-                        "likes": space.get('likes', 0),
-                        "title": space.get('cardData', {}).get('title') if space.get('cardData') else space.get('title', 'No Title'),
-                        "author": space_id.split('/')[0],
-                    }
-        
-        return all_spaces
-        
-    except Exception as e:
-        print(f"[Spaces API] Error: {e}")
-        return {}
-
-def get_all_models_with_ranks(limit=3000, progress=None):
-    """전역 모델 순위 목록 조회 (downloads 기준 정렬)"""
-    print(f"[Models] Fetching top {limit} models by downloads...")
-    all_models = {}
-    
-    try:
-        count = 0
-        for idx, model in enumerate(list_models(
-            sort="downloads", 
-            direction=-1, 
-            limit=limit, 
-            full=True,
-            token=HF_TOKEN
-        )):
-            model_id = model.id
-            all_models[model_id] = {
-                "rank": idx + 1,
-                "id": model_id,
-                "downloads": model.downloads if hasattr(model, 'downloads') else 0,
-                "likes": model.likes if hasattr(model, 'likes') else 0,
-                "title": model.id.split('/')[-1] if '/' in model.id else model.id,
-                "author": model.author if hasattr(model, 'author') else model_id.split('/')[0],
-            }
-            count += 1
+                    global_ranks[space_id] = idx + 1
             
-            if progress and count % 300 == 0:
-                progress(count / limit * 0.5, desc=f"Loading models: {count}/{limit}")
+            if progress:
+                progress(0.3, desc=f"Loaded {len(global_ranks)} global rankings")
+        else:
+            print(f"[Global Ranks] API error: {response.status_code}")
             
-            if count >= limit:
-                break
-        
-        print(f"[Models] Loaded {len(all_models)} models")
-        return all_models
-        
     except Exception as e:
-        print(f"[Models] Error with huggingface_hub, falling back to API: {e}")
-        return get_all_models_with_ranks_api(limit, progress)
+        print(f"[Global Ranks] Error: {e}")
+    
+    return global_ranks
 
-def get_all_models_with_ranks_api(limit=3000, progress=None):
-    """API 폴백"""
-    all_models = {}
+def get_global_model_ranks(limit=10000, progress=None):
+    """
+    전역 모델 순위 조회 - downloads 기준
+    model_id -> global_rank 매핑 반환
+    """
+    print(f"[Global Model Ranks] Fetching top {limit} models by downloads...")
+    global_ranks = {}
     
     try:
-        url = "https://huggingface.co/api/models"
         headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
+        url = "https://huggingface.co/api/models"
         
         params = {
             "sort": "downloads",
             "direction": -1,
-            "limit": min(limit, 10000),
-            "full": "true"
+            "limit": limit,
+            "full": "false"
         }
         
-        response = requests.get(url, params=params, headers=headers, timeout=120)
+        response = requests.get(url, params=params, headers=headers, timeout=180)
+        
         if response.status_code == 200:
             models_list = response.json()
-            print(f"[Models API] Got {len(models_list)} models")
+            print(f"[Global Model Ranks] Got {len(models_list)} models from API")
             
-            for idx, model in enumerate(models_list[:limit]):
+            for idx, model in enumerate(models_list):
                 model_id = model.get('id', '')
                 if model_id:
-                    all_models[model_id] = {
-                        "rank": idx + 1,
-                        "id": model_id,
-                        "downloads": model.get('downloads', 0),
-                        "likes": model.get('likes', 0),
-                        "title": model.get('title', model_id.split('/')[-1]),
-                        "author": model_id.split('/')[0],
-                    }
+                    global_ranks[model_id] = idx + 1
+            
+            if progress:
+                progress(0.3, desc=f"Loaded {len(global_ranks)} global model rankings")
+        else:
+            print(f"[Global Model Ranks] API error: {response.status_code}")
+            
+    except Exception as e:
+        print(f"[Global Model Ranks] Error: {e}")
+    
+    return global_ranks
+
+# ═══════════════════════════════════════════════════════════
+# 🔍 한국 저자별 스페이스/모델 직접 조회
+# ═══════════════════════════════════════════════════════════
+
+def get_spaces_by_author(author: str) -> list:
+    """특정 저자의 모든 스페이스 조회"""
+    spaces = []
+    try:
+        headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
+        url = f"https://huggingface.co/api/spaces"
+        
+        params = {
+            "author": author,
+            "full": "true",
+            "limit": 500
+        }
         
-        return all_models
+        response = requests.get(url, params=params, headers=headers, timeout=30)
         
+        if response.status_code == 200:
+            spaces_data = response.json()
+            for space in spaces_data:
+                space_id = space.get('id', '')
+                if space_id:
+                    spaces.append({
+                        "id": space_id,
+                        "likes": space.get('likes', 0),
+                        "title": space.get('cardData', {}).get('title', 'No Title') if space.get('cardData') else space.get('title', 'No Title'),
+                        "author": author,
+                        "url": f"https://huggingface.co/spaces/{space_id}",
+                        "createdAt": space.get('createdAt', ''),
+                    })
     except Exception as e:
-        print(f"[Models API] Error: {e}")
-        return {}
+        print(f"[Spaces by Author] Error for {author}: {e}")
+    
+    return spaces
 
-def get_space_info(space_id: str) -> dict:
-    """개별 스페이스 정보 조회"""
+def get_models_by_author(author: str) -> list:
+    """특정 저자의 모든 모델 조회"""
+    models = []
     try:
         headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
-        url = f"https://huggingface.co/api/spaces/{space_id}"
-        response = requests.get(url, headers=headers, timeout=10)
+        url = f"https://huggingface.co/api/models"
+        
+        params = {
+            "author": author,
+            "full": "true",
+            "limit": 500
+        }
+        
+        response = requests.get(url, params=params, headers=headers, timeout=30)
         
         if response.status_code == 200:
-            data = response.json()
-            return {
-                "id": space_id,
-                "likes": data.get('likes', 0),
-                "title": data.get('cardData', {}).get('title', 'No Title') if data.get('cardData') else 'No Title',
-                "author": space_id.split('/')[0],
-            }
+            models_data = response.json()
+            for model in models_data:
+                model_id = model.get('id', '')
+                if model_id:
+                    models.append({
+                        "id": model_id,
+                        "downloads": model.get('downloads', 0),
+                        "likes": model.get('likes', 0),
+                        "title": model_id.split('/')[-1] if '/' in model_id else model_id,
+                        "author": author,
+                        "url": f"https://huggingface.co/{model_id}",
+                    })
     except Exception as e:
-        print(f"[Space Info] Error fetching {space_id}: {e}")
-    
-    return None
-
-def filter_korean_spaces(all_spaces_ranked: dict) -> list:
-    """전역 순위 데이터에서 한국 관련 스페이스만 필터링 + Featured Spaces 추가"""
-    korean_authors = set(author.lower() for author in get_all_authors())
-    korean_spaces = []
-    existing_ids = set()
-    
-    # 1. API에서 가져온 한국 스페이스 추가
-    for space_id, space_data in all_spaces_ranked.items():
-        author = space_data.get('author', '').lower()
-        if author in korean_authors:
-            space_data['url'] = f"https://huggingface.co/spaces/{space_id}"
-            space_data['source'] = 'api'
-            korean_spaces.append(space_data)
-            existing_ids.add(space_id)
-    
-    # 2. Featured Spaces 중 아직 추가되지 않은 것들 추가
-    print(f"[Filter] Checking {len(FEATURED_SPACES)} featured spaces...")
-    featured_added = 0
-    
-    for space_id in FEATURED_SPACES.keys():
-        if space_id not in existing_ids:
-            # API에서 스페이스 정보 조회
-            space_info = get_space_info(space_id)
-            if space_info:
-                space_info['rank'] = 99999  # Featured는 순위 밖
-                space_info['url'] = f"https://huggingface.co/spaces/{space_id}"
-                space_info['source'] = 'featured'
-                korean_spaces.append(space_info)
-                existing_ids.add(space_id)
-                featured_added += 1
-            
-            # Rate limiting
-            if featured_added % 10 == 0:
-                time.sleep(0.3)
+        print(f"[Models by Author] Error for {author}: {e}")
+    
+    return models
+
+def get_all_korean_spaces(progress=None) -> list:
+    """모든 한국 저자의 스페이스 조회"""
+    all_spaces = []
+    authors = get_all_authors()
+    total = len(authors)
+    
+    print(f"[Korean Spaces] Fetching spaces for {total} Korean authors...")
     
-    print(f"[Filter] Added {featured_added} featured spaces")
+    for idx, author in enumerate(authors):
+        spaces = get_spaces_by_author(author)
+        all_spaces.extend(spaces)
+        
+        if progress and idx % 5 == 0:
+            progress(0.3 + (idx / total) * 0.4, desc=f"Fetching {author}... ({len(all_spaces)} spaces)")
+        
+        # Rate limiting
+        if idx % 10 == 0:
+            time.sleep(0.2)
+    
+    print(f"[Korean Spaces] Total: {len(all_spaces)} spaces from {total} authors")
+    return all_spaces
+
+def get_all_korean_models(progress=None) -> list:
+    """모든 한국 저자의 모델 조회"""
+    all_models = []
+    authors = get_all_authors()
+    total = len(authors)
+    
+    print(f"[Korean Models] Fetching models for {total} Korean authors...")
     
-    # 3. 순위 기준 정렬 (Featured는 뒤로)
-    korean_spaces.sort(key=lambda x: (x['rank'] == 99999, x['rank'], -x.get('likes', 0)))
+    for idx, author in enumerate(authors):
+        models = get_models_by_author(author)
+        all_models.extend(models)
+        
+        if progress and idx % 5 == 0:
+            progress(0.3 + (idx / total) * 0.4, desc=f"Fetching {author}... ({len(all_models)} models)")
+        
+        if idx % 10 == 0:
+            time.sleep(0.2)
     
-    # 4. 계정별 순위 계산
+    print(f"[Korean Models] Total: {len(all_models)} models from {total} authors")
+    return all_models
+
+# ═══════════════════════════════════════════════════════════
+# 📊 데이터 처리 및 순위 계산
+# ═══════════════════════════════════════════════════════════
+
+def process_spaces_data(korean_spaces: list, global_ranks: dict, progress=None) -> list:
+    """
+    한국 스페이스 데이터 처리
+    - 전역 순위 매핑
+    - likes 기준 정렬
+    - 계정별 순위 계산
+    """
+    # 전역 순위 매핑
+    for space in korean_spaces:
+        space_id = space['id']
+        space['global_rank'] = global_ranks.get(space_id, None)
+    
+    # likes 기준 정렬 (내림차순)
+    korean_spaces.sort(key=lambda x: (-x['likes'], x['id']))
+    
+    # 한국 내 순위 계산
+    for idx, space in enumerate(korean_spaces):
+        space['korean_rank'] = idx + 1
+    
+    # 계정별 순위 계산
     author_counts = {}
     for space in korean_spaces:
         author = space['author']
@@ -857,25 +460,32 @@ def filter_korean_spaces(all_spaces_ranked: dict) -> list:
         space['author_rank'] = author_counts[author]
     
     # 프로필 사진 미리 로드
-    authors = [s['author'] for s in korean_spaces]
+    authors = list(set(s['author'] for s in korean_spaces))
     preload_avatars(authors)
     
-    print(f"[Filter] Found {len(korean_spaces)} Korean spaces (API: {len(korean_spaces) - featured_added}, Featured: {featured_added})")
+    if progress:
+        progress(0.9, desc="Processing complete")
+    
     return korean_spaces
 
-def filter_korean_models(all_models_ranked: dict) -> list:
-    """전역 순위 데이터에서 한국 관련 모델만 필터링"""
-    korean_authors = set(author.lower() for author in get_all_authors())
-    korean_models = []
+def process_models_data(korean_models: list, global_ranks: dict, progress=None) -> list:
+    """
+    한국 모델 데이터 처리
+    - 전역 순위 매핑
+    - downloads 기준 정렬
+    - 계정별 순위 계산
+    """
+    # 전역 순위 매핑
+    for model in korean_models:
+        model_id = model['id']
+        model['global_rank'] = global_ranks.get(model_id, None)
     
-    for model_id, model_data in all_models_ranked.items():
-        author = model_data.get('author', '').lower()
-        if author in korean_authors:
-            model_data['url'] = f"https://huggingface.co/{model_id}"
-            model_data['source'] = 'api'
-            korean_models.append(model_data)
+    # downloads 기준 정렬 (내림차순)
+    korean_models.sort(key=lambda x: (-x['downloads'], x['id']))
     
-    korean_models.sort(key=lambda x: x['rank'])
+    # 한국 내 순위 계산
+    for idx, model in enumerate(korean_models):
+        model['korean_rank'] = idx + 1
     
     # 계정별 순위 계산
     author_counts = {}
@@ -887,10 +497,12 @@ def filter_korean_models(all_models_ranked: dict) -> list:
         model['author_rank'] = author_counts[author]
     
     # 프로필 사진 미리 로드
-    authors = [m['author'] for m in korean_models]
+    authors = list(set(m['author'] for m in korean_models))
     preload_avatars(authors)
     
-    print(f"[Filter] Found {len(korean_models)} Korean models in top rankings")
+    if progress:
+        progress(0.9, desc="Processing complete")
+    
     return korean_models
 
 # ═══════════════════════════════════════════════════════════
@@ -918,70 +530,30 @@ def create_error_plot():
 def get_spaces_data(progress=gr.Progress()):
     """스페이스 데이터 조회 및 시각화"""
     try:
-        progress(0, desc="Fetching global space rankings (Top 2400)...")
+        progress(0, desc="Fetching global space rankings...")
         
-        all_spaces_ranked = get_all_spaces_with_ranks(limit=2400, progress=progress)
+        # 1. 전역 순위 조회 (최대 10000개)
+        global_ranks = get_global_space_ranks(limit=10000, progress=progress)
         
-        progress(0.6, desc="Filtering Korean spaces and adding featured...")
+        progress(0.2, desc="Fetching Korean spaces...")
         
-        korean_spaces = filter_korean_spaces(all_spaces_ranked)
+        # 2. 한국 저자 스페이스 직접 조회
+        korean_spaces = get_all_korean_spaces(progress=progress)
         
-        progress(0.8, desc="Creating visualization...")
+        progress(0.7, desc="Processing data...")
         
-        fig = go.Figure()
+        # 3. 데이터 처리
+        processed_spaces = process_spaces_data(korean_spaces, global_ranks, progress)
         
-        if korean_spaces:
-            # API 소스인 것만 차트에 표시 (순위가 있는 것)
-            api_spaces = [s for s in korean_spaces if s.get('source') == 'api']
-            colors = [get_author_color(s['author']) for s in api_spaces]
-            
-            fig.add_trace(go.Bar(
-                x=[s['id'] for s in api_spaces],
-                y=[2401 - s['rank'] for s in api_spaces],
-                text=[f"Global: #{s['rank']}<br>Author: #{s.get('author_rank', '-')}<br>Likes: {s['likes']}" 
-                      for s in api_spaces],
-                textposition='auto',
-                marker_color=colors,
-                opacity=0.85,
-                hovertemplate="<b>%{x}</b><br>%{text}<extra></extra>"
-            ))
-            
-            fig.update_layout(
-                title={
-                    'text': f'🏆 Korean Spaces Global Rankings (Top 2400) - Found: {len(korean_spaces)} (API: {len(api_spaces)})',
-                    'y': 0.95, 'x': 0.5,
-                    'xanchor': 'center', 'yanchor': 'top',
-                    'font': {'size': 20, 'color': '#5a6677'}
-                },
-                xaxis_title='Space ID',
-                yaxis_title='Global Rank',
-                yaxis=dict(
-                    ticktext=[f"#{i}" for i in range(1, 2401, 100)],
-                    tickvals=[2401 - i for i in range(1, 2401, 100)],
-                    range=[0, 2400]
-                ),
-                height=500,
-                showlegend=False,
-                paper_bgcolor='#e0e5ec',
-                plot_bgcolor='#e0e5ec',
-                xaxis_tickangle=-45,
-                font=dict(color='#6d7b8d'),
-                margin=dict(l=60, r=30, t=60, b=100)
-            )
-        
-        html_content = generate_space_cards_html(korean_spaces)
-        
-        df = pd.DataFrame([{
-            'Global Rank': f"#{s['rank']}" if s['rank'] != 99999 else "Featured",
-            'Author Rank': f"#{s.get('author_rank', '-')}",
-            'Space ID': s['id'],
-            'Title': s.get('title', 'N/A'),
-            'Likes': s['likes'],
-            'Source': s.get('source', 'api').upper(),
-            'URL': s['url']
-        } for s in korean_spaces])
+        progress(0.85, desc="Creating visualization...")
+        
+        # 4. 시각화
+        fig = create_spaces_chart(processed_spaces)
+        html_content = generate_space_cards_html(processed_spaces)
+        df = create_spaces_dataframe(processed_spaces)
         
         progress(1.0, desc="Complete!")
+        
         return fig, html_content, df
         
     except Exception as e:
@@ -993,68 +565,30 @@ def get_spaces_data(progress=gr.Progress()):
 def get_models_data(progress=gr.Progress()):
     """모델 데이터 조회 및 시각화"""
     try:
-        progress(0, desc="Fetching global model rankings (Top 3000)...")
+        progress(0, desc="Fetching global model rankings...")
         
-        all_models_ranked = get_all_models_with_ranks(limit=3000, progress=progress)
+        # 1. 전역 순위 조회
+        global_ranks = get_global_model_ranks(limit=10000, progress=progress)
         
-        progress(0.6, desc="Filtering Korean models...")
+        progress(0.2, desc="Fetching Korean models...")
         
-        korean_models = filter_korean_models(all_models_ranked)
+        # 2. 한국 저자 모델 직접 조회
+        korean_models = get_all_korean_models(progress=progress)
         
-        progress(0.8, desc="Creating visualization...")
+        progress(0.7, desc="Processing data...")
         
-        fig = go.Figure()
+        # 3. 데이터 처리
+        processed_models = process_models_data(korean_models, global_ranks, progress)
         
-        if korean_models:
-            colors = [get_author_color(m['author']) for m in korean_models]
-            
-            fig.add_trace(go.Bar(
-                x=[m['id'] for m in korean_models],
-                y=[3001 - m['rank'] for m in korean_models],
-                text=[f"Global: #{m['rank']}<br>Author: #{m.get('author_rank', '-')}<br>Downloads: {m['downloads']:,}" 
-                      for m in korean_models],
-                textposition='auto',
-                marker_color=colors,
-                opacity=0.85,
-                hovertemplate="<b>%{x}</b><br>%{text}<extra></extra>"
-            ))
-            
-            fig.update_layout(
-                title={
-                    'text': f'🏆 Korean Models Global Rankings (Top 3000) - Found: {len(korean_models)}',
-                    'y': 0.95, 'x': 0.5,
-                    'xanchor': 'center', 'yanchor': 'top',
-                    'font': {'size': 20, 'color': '#5a6677'}
-                },
-                xaxis_title='Model ID',
-                yaxis_title='Global Rank',
-                yaxis=dict(
-                    ticktext=[f"#{i}" for i in range(1, 3001, 200)],
-                    tickvals=[3001 - i for i in range(1, 3001, 200)],
-                    range=[0, 3000]
-                ),
-                height=500,
-                showlegend=False,
-                paper_bgcolor='#e0e5ec',
-                plot_bgcolor='#e0e5ec',
-                xaxis_tickangle=-45,
-                font=dict(color='#6d7b8d'),
-                margin=dict(l=60, r=30, t=60, b=100)
-            )
-        
-        html_content = generate_model_cards_html(korean_models)
-        
-        df = pd.DataFrame([{
-            'Global Rank': f"#{m['rank']}",
-            'Author Rank': f"#{m.get('author_rank', '-')}",
-            'Model ID': m['id'],
-            'Title': m.get('title', 'N/A'),
-            'Downloads': f"{m['downloads']:,}",
-            'Likes': m['likes'],
-            'URL': m['url']
-        } for m in korean_models])
+        progress(0.85, desc="Creating visualization...")
+        
+        # 4. 시각화
+        fig = create_models_chart(processed_models)
+        html_content = generate_model_cards_html(processed_models)
+        df = create_models_dataframe(processed_models)
         
         progress(1.0, desc="Complete!")
+        
         return fig, html_content, df
         
     except Exception as e:
@@ -1063,58 +597,155 @@ def get_models_data(progress=gr.Progress()):
         traceback.print_exc()
         return create_error_plot(), f"<div style='color: #c00;'>Error: {e}</div>", pd.DataFrame()
 
+def create_spaces_chart(spaces: list):
+    """스페이스 차트 생성"""
+    fig = go.Figure()
+    
+    if not spaces:
+        return create_error_plot()
+    
+    # 상위 50개만 차트에 표시
+    top_spaces = spaces[:50]
+    colors = [get_author_color(s['author']) for s in top_spaces]
+    
+    # 전역 순위가 있는 것과 없는 것 구분
+    global_ranked = [s for s in top_spaces if s.get('global_rank')]
+    
+    fig.add_trace(go.Bar(
+        x=[s['id'] for s in top_spaces],
+        y=[s['likes'] for s in top_spaces],
+        text=[f"🇰🇷 #{s['korean_rank']}<br>🌍 #{s['global_rank'] if s.get('global_rank') else 'N/A'}<br>❤️ {s['likes']:,}" 
+              for s in top_spaces],
+        textposition='auto',
+        marker_color=colors,
+        opacity=0.85,
+        hovertemplate="<b>%{x}</b><br>%{text}<extra></extra>"
+    ))
+    
+    fig.update_layout(
+        title={
+            'text': f'🏆 Korean Spaces by Likes (Total: {len(spaces)} | Top 50 shown)',
+            'y': 0.95, 'x': 0.5,
+            'xanchor': 'center', 'yanchor': 'top',
+            'font': {'size': 20, 'color': '#5a6677'}
+        },
+        xaxis_title='Space ID',
+        yaxis_title='Likes',
+        height=500,
+        showlegend=False,
+        paper_bgcolor='#e0e5ec',
+        plot_bgcolor='#e0e5ec',
+        xaxis_tickangle=-45,
+        font=dict(color='#6d7b8d'),
+        margin=dict(l=60, r=30, t=60, b=100)
+    )
+    
+    return fig
+
+def create_models_chart(models: list):
+    """모델 차트 생성"""
+    fig = go.Figure()
+    
+    if not models:
+        return create_error_plot()
+    
+    # 상위 50개만 차트에 표시
+    top_models = models[:50]
+    colors = [get_author_color(m['author']) for m in top_models]
+    
+    fig.add_trace(go.Bar(
+        x=[m['id'] for m in top_models],
+        y=[m['downloads'] for m in top_models],
+        text=[f"🇰🇷 #{m['korean_rank']}<br>🌍 #{m['global_rank'] if m.get('global_rank') else 'N/A'}<br>���️ {m['downloads']:,}" 
+              for m in top_models],
+        textposition='auto',
+        marker_color=colors,
+        opacity=0.85,
+        hovertemplate="<b>%{x}</b><br>%{text}<extra></extra>"
+    ))
+    
+    fig.update_layout(
+        title={
+            'text': f'🏆 Korean Models by Downloads (Total: {len(models)} | Top 50 shown)',
+            'y': 0.95, 'x': 0.5,
+            'xanchor': 'center', 'yanchor': 'top',
+            'font': {'size': 20, 'color': '#5a6677'}
+        },
+        xaxis_title='Model ID',
+        yaxis_title='Downloads',
+        height=500,
+        showlegend=False,
+        paper_bgcolor='#e0e5ec',
+        plot_bgcolor='#e0e5ec',
+        xaxis_tickangle=-45,
+        font=dict(color='#6d7b8d'),
+        margin=dict(l=60, r=30, t=60, b=100)
+    )
+    
+    return fig
+
+def create_spaces_dataframe(spaces: list) -> pd.DataFrame:
+    """스페이스 데이터프레임 생성"""
+    return pd.DataFrame([{
+        'Korean Rank': f"#{s['korean_rank']}",
+        'Global Rank': f"#{s['global_rank']}" if s.get('global_rank') else "N/A",
+        'Author Rank': f"#{s.get('author_rank', '-')}",
+        'Space ID': s['id'],
+        'Title': s.get('title', 'N/A'),
+        'Likes': s['likes'],
+        'URL': s['url']
+    } for s in spaces])
+
+def create_models_dataframe(models: list) -> pd.DataFrame:
+    """모델 데이터프레임 생성"""
+    return pd.DataFrame([{
+        'Korean Rank': f"#{m['korean_rank']}",
+        'Global Rank': f"#{m['global_rank']}" if m.get('global_rank') else "N/A",
+        'Author Rank': f"#{m.get('author_rank', '-')}",
+        'Model ID': m['id'],
+        'Title': m.get('title', 'N/A'),
+        'Downloads': f"{m['downloads']:,}",
+        'Likes': m['likes'],
+        'URL': m['url']
+    } for m in models])
+
 # ═══════════════════════════════════════════════════════════
-# 🎴 HTML 카드 생성 (프로필 사진 + 계정별 순위 포함)
+# 🎴 HTML 카드 생성
 # ═══════════════════════════════════════════════════════════
 
 def generate_space_cards_html(spaces: list) -> str:
-    """스페이스 카드 HTML 생성 - 프로필 사진 + 계정별 순위 포함"""
+    """스페이스 카드 HTML 생성"""
     
-    # API 소스와 Featured 소스 분리
-    api_spaces = [s for s in spaces if s.get('source') == 'api']
-    featured_spaces = [s for s in spaces if s.get('source') == 'featured']
+    # 전역 순위가 있는 것과 없는 것 분리
+    global_ranked = [s for s in spaces if s.get('global_rank')]
+    not_ranked = [s for s in spaces if not s.get('global_rank')]
     
     html = f"""
     <div style='padding: 20px; background: #e0e5ec; border-radius: 20px;
                 box-shadow: inset 4px 4px 8px #b8bec5, inset -4px -4px 8px #ffffff;'>
         <h2 style='color: #5a6677; text-shadow: 2px 2px 4px #ffffff, -1px -1px 3px rgba(0,0,0,0.1); 
                    margin-bottom: 20px; font-size: 24px;'>
-            🚀 Korean Spaces in Global Top 2400 (Total: {len(spaces)} | API: {len(api_spaces)} | Featured: {len(featured_spaces)})
+            🚀 Korean Spaces Leaderboard (Total: {len(spaces)} | Global Ranked: {len(global_ranked)})
         </h2>
     """
     
-    # API Spaces 섹션
-    if api_spaces:
-        html += f"""
-        <h3 style='color: #5a6677; margin: 20px 0 15px 0; font-size: 18px;'>
-            📊 Global Ranked Spaces ({len(api_spaces)})
-        </h3>
-        <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(320px, 1fr)); gap: 20px;'>
-        """
-        
-        for space in api_spaces:
-            html += generate_space_card_html(space, show_global_rank=True)
-        
-        html += "</div>"
+    # Top Spaces (상위 100개만 카드로 표시)
+    top_spaces = spaces[:100]
     
-    # Featured Spaces 섹션
-    if featured_spaces:
-        html += f"""
-        <h3 style='color: #5a6677; margin: 30px 0 15px 0; font-size: 18px;'>
-            ⭐ Featured Spaces ({len(featured_spaces)})
-        </h3>
-        <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(320px, 1fr)); gap: 20px;'>
-        """
-        
-        for space in featured_spaces:
-            html += generate_space_card_html(space, show_global_rank=False)
-        
-        html += "</div>"
+    html += f"""
+    <h3 style='color: #5a6677; margin: 20px 0 15px 0; font-size: 18px;'>
+        📊 Top 100 Korean Spaces by Likes
+    </h3>
+    <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(320px, 1fr)); gap: 20px;'>
+    """
     
-    html += "</div>"
+    for space in top_spaces:
+        html += generate_space_card_html(space)
+    
+    html += "</div></div>"
     return html
 
-def generate_space_card_html(space: dict, show_global_rank: bool = True) -> str:
+def generate_space_card_html(space: dict) -> str:
     """개별 스페이스 카드 HTML 생성"""
     color = get_author_color(space['author'])
     avatar_url = get_avatar_url(space['author'])
@@ -1123,54 +754,30 @@ def generate_space_card_html(space: dict, show_global_rank: bool = True) -> str:
     color_hex = color.replace("#", "")
     fallback_url = f"https://ui-avatars.com/api/?name={initials}&background={color_hex}&color=fff&size=128&bold=true"
     
-    # 랭킹 배지 텍스트
-    if show_global_rank and space['rank'] != 99999:
-        rank_badge = f"""
-        <div style='display: flex; gap: 8px;'>
-            <div style='
-                background: #e0e5ec;
-                border-radius: 12px;
-                padding: 8px 12px;
-                box-shadow: inset 4px 4px 8px #b8bec5, inset -4px -4px 8px #ffffff;
-            '>
-                <span style='color: #5a6677; font-weight: 800; font-size: 14px;'>
-                    🌍 #{space['rank']}
-                </span>
-            </div>
-            <div style='
-                background: {color}22;
-                border-radius: 12px;
-                padding: 8px 12px;
-                border: 2px solid {color};
-            '>
-                <span style='color: {color}; font-weight: 800; font-size: 14px;'>
-                    👤 #{space.get('author_rank', '-')}
-                </span>
-            </div>
+    # 전역 순위 배지
+    if space.get('global_rank'):
+        global_badge = f"""
+        <div style='
+            background: linear-gradient(135deg, #10b981, #059669);
+            border-radius: 12px;
+            padding: 8px 12px;
+        '>
+            <span style='color: white; font-weight: 800; font-size: 14px;'>
+                🌍 #{space['global_rank']}
+            </span>
         </div>
         """
     else:
-        rank_badge = f"""
-        <div style='display: flex; gap: 8px;'>
-            <div style='
-                background: linear-gradient(135deg, #fbbf24, #f59e0b);
-                border-radius: 12px;
-                padding: 8px 12px;
-            '>
-                <span style='color: white; font-weight: 800; font-size: 14px;'>
-                    ⭐ Featured
-                </span>
-            </div>
-            <div style='
-                background: {color}22;
-                border-radius: 12px;
-                padding: 8px 12px;
-                border: 2px solid {color};
-            '>
-                <span style='color: {color}; font-weight: 800; font-size: 14px;'>
-                    👤 #{space.get('author_rank', '-')}
-                </span>
-            </div>
+        global_badge = f"""
+        <div style='
+            background: #e0e5ec;
+            border-radius: 12px;
+            padding: 8px 12px;
+            box-shadow: inset 2px 2px 4px #b8bec5, inset -2px -2px 4px #ffffff;
+        '>
+            <span style='color: #9ca3af; font-weight: 600; font-size: 12px;'>
+                Not in Top 10K
+            </span>
         </div>
         """
     
@@ -1207,7 +814,28 @@ def generate_space_card_html(space: dict, show_global_rank: bool = True) -> str:
             </a>
             
             <!-- 랭킹 배지 -->
-            {rank_badge}
+            <div style='display: flex; gap: 8px; flex-wrap: wrap;'>
+                <div style='
+                    background: linear-gradient(135deg, #6366f1, #4f46e5);
+                    border-radius: 12px;
+                    padding: 8px 12px;
+                '>
+                    <span style='color: white; font-weight: 800; font-size: 14px;'>
+                        🇰🇷 #{space['korean_rank']}
+                    </span>
+                </div>
+                {global_badge}
+                <div style='
+                    background: {color}22;
+                    border-radius: 12px;
+                    padding: 8px 12px;
+                    border: 2px solid {color};
+                '>
+                    <span style='color: {color}; font-weight: 800; font-size: 14px;'>
+                        👤 #{space.get('author_rank', '-')}
+                    </span>
+                </div>
+            </div>
         </div>
         
         <!-- 저자 이름 -->
@@ -1242,7 +870,7 @@ def generate_space_card_html(space: dict, show_global_rank: bool = True) -> str:
         
         <!-- Likes -->
         <p style='color: #6d7b8d; margin-bottom: 16px; font-size: 14px;'>
-            👍 Likes: <strong>{space['likes']:,}</strong>
+            ❤️ Likes: <strong>{space['likes']:,}</strong>
         </p>
         
         <!-- Visit 버튼 -->
@@ -1263,18 +891,22 @@ def generate_space_card_html(space: dict, show_global_rank: bool = True) -> str:
     """
 
 def generate_model_cards_html(models: list) -> str:
-    """모델 카드 HTML 생성 - 프로필 사진 + 계정별 순위 포함"""
+    """모델 카드 HTML 생성"""
+    
+    global_ranked = [m for m in models if m.get('global_rank')]
+    
     html = f"""
     <div style='padding: 20px; background: #e0e5ec; border-radius: 20px;
                 box-shadow: inset 4px 4px 8px #b8bec5, inset -4px -4px 8px #ffffff;'>
         <h2 style='color: #5a6677; text-shadow: 2px 2px 4px #ffffff, -1px -1px 3px rgba(0,0,0,0.1); 
                    margin-bottom: 20px; font-size: 24px;'>
-            🤖 Korean Models in Global Top 3000 (Found: {len(models)})
+            🤖 Korean Models Leaderboard (Total: {len(models)} | Global Ranked: {len(global_ranked)})
         </h2>
         <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(320px, 1fr)); gap: 20px;'>
     """
     
-    for model in models:
+    # 상위 100개만 표시
+    for model in models[:100]:
         color = get_author_color(model['author'])
         avatar_url = get_avatar_url(model['author'])
         
@@ -1282,6 +914,33 @@ def generate_model_cards_html(models: list) -> str:
         color_hex = color.replace("#", "")
         fallback_url = f"https://ui-avatars.com/api/?name={initials}&background={color_hex}&color=fff&size=128&bold=true"
         
+        # 전역 순위 배지
+        if model.get('global_rank'):
+            global_badge = f"""
+            <div style='
+                background: linear-gradient(135deg, #10b981, #059669);
+                border-radius: 12px;
+                padding: 8px 12px;
+            '>
+                <span style='color: white; font-weight: 800; font-size: 14px;'>
+                    🌍 #{model['global_rank']}
+                </span>
+            </div>
+            """
+        else:
+            global_badge = f"""
+            <div style='
+                background: #e0e5ec;
+                border-radius: 12px;
+                padding: 8px 12px;
+                box-shadow: inset 2px 2px 4px #b8bec5, inset -2px -2px 4px #ffffff;
+            '>
+                <span style='color: #9ca3af; font-weight: 600; font-size: 12px;'>
+                    Not in Top 10K
+                </span>
+            </div>
+            """
+        
         html += f"""
         <div style='
             background: #e0e5ec;
@@ -1291,9 +950,8 @@ def generate_model_cards_html(models: list) -> str:
             transition: all 0.3s ease;
             border-left: 4px solid {color};
         '>
-            <!-- 헤더: 프로필 사진 + 랭킹 -->
+            <!-- 헤더 -->
             <div style='display: flex; align-items: center; justify-content: space-between; margin-bottom: 16px;'>
-                <!-- 프로필 사진 -->
                 <a href='https://huggingface.co/{model["author"]}' target='_blank' style='text-decoration: none;'>
                     <div style='
                         width: 56px;
@@ -1314,18 +972,17 @@ def generate_model_cards_html(models: list) -> str:
                     </div>
                 </a>
                 
-                <!-- 랭킹 배지 -->
-                <div style='display: flex; gap: 8px;'>
+                <div style='display: flex; gap: 8px; flex-wrap: wrap;'>
                     <div style='
-                        background: #e0e5ec;
+                        background: linear-gradient(135deg, #6366f1, #4f46e5);
                         border-radius: 12px;
                         padding: 8px 12px;
-                        box-shadow: inset 4px 4px 8px #b8bec5, inset -4px -4px 8px #ffffff;
                     '>
-                        <span style='color: #5a6677; font-weight: 800; font-size: 14px;'>
-                            🌍 #{model['rank']}
+                        <span style='color: white; font-weight: 800; font-size: 14px;'>
+                            🇰🇷 #{model['korean_rank']}
                         </span>
                     </div>
+                    {global_badge}
                     <div style='
                         background: {color}22;
                         border-radius: 12px;
@@ -1339,45 +996,24 @@ def generate_model_cards_html(models: list) -> str:
                 </div>
             </div>
             
-            <!-- 저자 이름 -->
             <a href='https://huggingface.co/{model["author"]}' target='_blank' 
                style='text-decoration: none;'>
-                <p style='
-                    color: {color};
-                    font-weight: 700;
-                    font-size: 14px;
-                    margin: 0 0 8px 0;
-                    display: flex;
-                    align-items: center;
-                    gap: 6px;
-                '>
+                <p style='color: {color}; font-weight: 700; font-size: 14px; margin: 0 0 8px 0;'>
                     👤 {model['author']}
                 </p>
             </a>
             
-            <!-- Model ID -->
             <h3 style='color: #5a6677; margin: 8px 0; font-size: 15px; word-break: break-all;'>
                 {model['id']}
             </h3>
             
-            <!-- 타이틀 -->
-            <h4 style='
-                color: {color};
-                margin: 8px 0 16px 0;
-                font-size: 13px;
-                text-shadow: 1px 1px 2px #ffffff;
-                opacity: 0.9;
-            '>{model.get('title', 'N/A')}</h4>
-            
-            <!-- Downloads & Likes -->
             <p style='color: #6d7b8d; margin-bottom: 8px; font-size: 14px;'>
                 ⬇️ Downloads: <strong>{model['downloads']:,}</strong>
             </p>
             <p style='color: #6d7b8d; margin-bottom: 16px; font-size: 14px;'>
-                👍 Likes: <strong>{model['likes']:,}</strong>
+                ❤️ Likes: <strong>{model['likes']:,}</strong>
             </p>
             
-            <!-- Visit 버튼 -->
             <a href='{model['url']}' target='_blank' style='
                 display: inline-block;
                 padding: 12px 24px;
@@ -1387,7 +1023,6 @@ def generate_model_cards_html(models: list) -> str:
                 border-radius: 50px;
                 font-weight: 700;
                 box-shadow: 6px 6px 12px #b8bec5, -6px -6px 12px #ffffff;
-                transition: all 0.2s ease;
             '>
                 Visit Model 🔗
             </a>
@@ -1401,27 +1036,32 @@ def generate_model_cards_html(models: list) -> str:
 # 📈 통계 차트 생성
 # ═══════════════════════════════════════════════════════════
 
-def create_pie_chart(df: pd.DataFrame, total_count: int, type_name: str = "Spaces"):
-    """파이 차트 생성"""
+def create_pie_chart(df: pd.DataFrame, type_name: str = "Spaces"):
+    """파이 차트 생성 - 전역 순위 유무"""
     try:
-        korean_count = len(df) if not df.empty else 0
-        other_count = total_count - korean_count
+        if df.empty:
+            return create_error_plot()
+        
+        global_col = 'Global Rank'
+        if global_col not in df.columns:
+            return create_error_plot()
         
-        limit_label = "2400" if type_name == "Spaces" else "3000"
+        ranked = len(df[df[global_col] != "N/A"])
+        not_ranked = len(df) - ranked
         
         fig = go.Figure(data=[go.Pie(
-            labels=[f'Korean {type_name}', f'Other {type_name}'],
-            values=[korean_count, other_count],
+            labels=['Global Top 10K', 'Not in Top 10K'],
+            values=[ranked, not_ranked],
             hole=.4,
-            marker_colors=['#6366f1', '#a5b4fc'],
+            marker_colors=['#10b981', '#9ca3af'],
             textinfo='percent+value',
             textfont=dict(color='#5a6677'),
-            hovertemplate="<b>%{label}</b><br>Count: %{value}<br>Percentage: %{percent}<extra></extra>"
+            hovertemplate="<b>%{label}</b><br>Count: %{value}<br>%{percent}<extra></extra>"
         )])
         
         fig.update_layout(
             title={
-                'text': f"📊 Korean vs Global {type_name} (Top {limit_label})",
+                'text': f"📊 Korean {type_name} Global Ranking Status",
                 'font': {'size': 16, 'color': '#5a6677'}
             },
             showlegend=True,
@@ -1429,7 +1069,6 @@ def create_pie_chart(df: pd.DataFrame, total_count: int, type_name: str = "Space
             paper_bgcolor='#e0e5ec',
             plot_bgcolor='#e0e5ec',
             font=dict(color='#6d7b8d'),
-            legend=dict(font=dict(color='#6d7b8d')),
             margin=dict(l=20, r=20, t=50, b=20)
         )
         
@@ -1448,7 +1087,7 @@ def create_bar_chart(df: pd.DataFrame, type_name: str = "Spaces"):
         if id_col not in df.columns:
             return create_error_plot()
         
-        registrations = df[id_col].apply(lambda x: x.split('/')[0] if '/' in str(x) else str(x)).value_counts()
+        registrations = df[id_col].apply(lambda x: x.split('/')[0] if '/' in str(x) else str(x)).value_counts().head(20)
         
         colors = [get_author_color(author) for author in registrations.index]
         
@@ -1463,7 +1102,7 @@ def create_bar_chart(df: pd.DataFrame, type_name: str = "Spaces"):
         
         fig.update_layout(
             title={
-                'text': f"👥 {type_name} by Creator",
+                'text': f"👥 Top 20 Creators by {type_name} Count",
                 'font': {'size': 16, 'color': '#5a6677'}
             },
             xaxis_title="Creator",
@@ -1492,10 +1131,10 @@ def refresh_all_data():
         spaces_results = get_spaces_data()
         models_results = get_models_data()
         
-        spaces_pie = create_pie_chart(spaces_results[2], 2400, "Spaces")
+        spaces_pie = create_pie_chart(spaces_results[2], "Spaces")
         spaces_bar = create_bar_chart(spaces_results[2], "Spaces")
         
-        models_pie = create_pie_chart(models_results[2], 3000, "Models")
+        models_pie = create_pie_chart(models_results[2], "Models")
         models_bar = create_bar_chart(models_results[2], "Models")
         
         return [
@@ -1641,41 +1280,19 @@ table td {
     border-radius: 20px !important;
     overflow: hidden !important;
 }
-
-.markdown-text {
-    background: transparent !important;
-}
-
-.row, .column {
-    gap: 20px !important;
-}
-
-.dataframe {
-    border-radius: 15px !important;
-    overflow: hidden !important;
-}
-
-.profile-avatar {
-    transition: transform 0.2s ease, box-shadow 0.2s ease;
-}
-
-.profile-avatar:hover {
-    transform: scale(1.05);
-    box-shadow: 6px 6px 12px var(--neu-shadow-dark), -6px -6px 12px var(--neu-shadow-light) !important;
-}
 """
 
 def create_org_legend_html():
-    """조직 범례 HTML 생성 - 프로필 사진 포함"""
+    """조직 범례 HTML 생성"""
     
     categories = {
         "🏢 대기업": [
-            {"color": "#03C75A", "name": "NAVER", "authors": ["naver-hyperclovax", "naver-ellm", "naver", "navervision", "naver-clova-ix", "naver-clova-ocr", "naver-ai"]},
-            {"color": "#FFD200", "name": "Kakao", "authors": ["kakaocorp", "kakaobrain", "kakaobank"]},
-            {"color": "#1428A0", "name": "Samsung", "authors": ["SamsungResearch", "SamsungSDS-Research", "SamsungSAILMontreal"]},
+            {"color": "#03C75A", "name": "NAVER", "authors": ["naver-hyperclovax", "naver-ellm", "naver"]},
+            {"color": "#FFD200", "name": "Kakao", "authors": ["kakaocorp", "kakaobrain"]},
+            {"color": "#1428A0", "name": "Samsung", "authors": ["SamsungResearch"]},
             {"color": "#D70026", "name": "SK Telecom", "authors": ["skt", "skt-ai"]},
-            {"color": "#A50034", "name": "LG AI", "authors": ["LGAI-EXAONE", "lg-ai"]},
-            {"color": "#FF6B00", "name": "NCSOFT", "authors": ["NCSOFT", "nc-ai-consortium", "NC-AI-consortium-VAETKI"]},
+            {"color": "#A50034", "name": "LG AI", "authors": ["LGAI-EXAONE"]},
+            {"color": "#FF6B00", "name": "NCSOFT", "authors": ["NCSOFT"]},
             {"color": "#E11D48", "name": "KRAFTON", "authors": ["KRAFTON"]},
         ],
         "🚀 AI 스타트업": [
@@ -1683,23 +1300,13 @@ def create_org_legend_html():
             {"color": "#2563EB", "name": "VIDraft", "authors": ["VIDraft"]},
             {"color": "#1D4ED8", "name": "Ginigen", "authors": ["ginigen", "ginipick"]},
             {"color": "#F59E0B", "name": "Trillion Labs", "authors": ["trillionlabs"]},
-            {"color": "#8B5CF6", "name": "Moreh/MOTIF", "authors": ["Motif-Technologies", "moreh"]},
-            {"color": "#0EA5E9", "name": "TELEPIX", "authors": ["telepix"]},
-            {"color": "#EC4899", "name": "Supertone", "authors": ["Supertone"]},
-            {"color": "#84CC16", "name": "HAERAE", "authors": ["HAERAE-HUB", "KOREAson"]},
             {"color": "#C026D3", "name": "Nari Labs", "authors": ["nari-labs"]},
-            {"color": "#DB2777", "name": "Onoma AI", "authors": ["OnomaAIResearch"]},
-            {"color": "#EF4444", "name": "K-intelligence", "authors": ["K-intelligence"]},
-            {"color": "#F97316", "name": "LDCC", "authors": ["LDCC"]},
         ],
         "🎓 연구기관": [
-            {"color": "#10B981", "name": "ETRI", "authors": ["etri-vilab", "etri-xainlp", "etri-lirs", "etri"]},
+            {"color": "#10B981", "name": "ETRI", "authors": ["etri-vilab", "etri"]},
             {"color": "#14B8A6", "name": "KAIST", "authors": ["kaist-ai", "kaist-nlp"]},
-            {"color": "#06B6D4", "name": "SNU", "authors": ["snunlp", "snu-ai"]},
+            {"color": "#06B6D4", "name": "SNU", "authors": ["snunlp"]},
             {"color": "#7C3AED", "name": "NLP AI Lab", "authors": ["nlpai-lab"]},
-            {"color": "#9333EA", "name": "Bllossom", "authors": ["Bllossom", "MLP-KTLim"]},
-            {"color": "#E11D48", "name": "Dnotitia", "authors": ["dnotitia"]},
-            {"color": "#F43F5E", "name": "Maywell", "authors": ["maywell"]},
         ],
         "👥 커뮤니티": [
             {"color": "#3B82F6", "name": "openfree", "authors": ["openfree"]},
@@ -1712,20 +1319,9 @@ def create_org_legend_html():
             {"color": "#EC4899", "name": "cutechicken", "authors": ["cutechicken"]},
             {"color": "#F472B6", "name": "kolaslab", "authors": ["kolaslab"]},
             {"color": "#FB7185", "name": "immunobiotech", "authors": ["immunobiotech"]},
-            {"color": "#F87171", "name": "yokoha", "authors": ["yokoha"]},
-            {"color": "#FBBF24", "name": "gunship999", "authors": ["gunship999"]},
-            {"color": "#A3E635", "name": "keeperballon", "authors": ["keeperballon"]},
-            {"color": "#22D3EE", "name": "shuja666", "authors": ["shuja666"]},
         ],
     }
     
-    # 모든 저자의 아바타 미리 로드
-    all_authors_for_legend = []
-    for orgs in categories.values():
-        for org in orgs:
-            all_authors_for_legend.extend(org["authors"])
-    preload_avatars(all_authors_for_legend)
-    
     html = """
     <details style='background: #e0e5ec; border-radius: 20px; 
                 box-shadow: 8px 8px 16px #b8bec5, -8px -8px 16px #ffffff; margin: 20px 0;'>
@@ -1744,12 +1340,6 @@ def create_org_legend_html():
         """
         for org in orgs:
             main_author = org["authors"][0]
-            avatar_url = get_avatar_url(main_author)
-            
-            initials = main_author[:2].upper()
-            color_hex = org["color"].replace("#", "")
-            fallback_url = f"https://ui-avatars.com/api/?name={initials}&background={color_hex}&color=fff&size=64&bold=true"
-            
             html += f"""
             <a href='https://huggingface.co/{main_author}' target='_blank' style='text-decoration: none;'>
                 <div style='
@@ -1764,32 +1354,13 @@ def create_org_legend_html():
                     font-weight: 600;
                     transition: all 0.2s ease;
                     cursor: pointer;
-                ' onmouseover="this.style.boxShadow='2px 2px 4px #b8bec5, -2px -2px 4px #ffffff'"
-                   onmouseout="this.style.boxShadow='4px 4px 8px #b8bec5, -4px -4px 8px #ffffff'">
-                    <!-- 프로필 이미지 -->
-                    <div style='
-                        width: 28px;
-                        height: 28px;
-                        border-radius: 50%;
-                        margin-right: 8px;
-                        overflow: hidden;
-                        border: 2px solid {org["color"]};
-                        box-shadow: inset 2px 2px 4px rgba(0,0,0,0.1);
-                        background: linear-gradient(145deg, #f0f5fc, #d0d5dc);
-                    '>
-                        <img src='{avatar_url}' 
-                             alt='{org["name"]}' 
-                             style='width: 100%; height: 100%; object-fit: cover;'
-                             onerror="this.onerror=null; this.src='{fallback_url}';">
-                    </div>
-                    <!-- 색상 표시 점 -->
+                '>
                     <span style='
                         width: 10px;
                         height: 10px;
                         border-radius: 50%;
                         background: {org["color"]};
                         margin-right: 6px;
-                        box-shadow: inset 1px 1px 2px rgba(0,0,0,0.2);
                     '></span>
                     {org["name"]}
                 </div>
@@ -1809,46 +1380,45 @@ with gr.Blocks(title="Korean HuggingFace Leaderboard", head=f"<style>{CUSTOM_CSS
     gr.Markdown("""
     # 🤗 Korean HuggingFace Leaderboard
     
-    **HuggingFace의 Spaces(Top 2400)와 Models(Top 3000) 실시간 인기 순위에서 한국 관련 항목을 분석합니다.**
+    **한국 관련 HuggingFace Spaces와 Models의 실시간 인기 순위를 분석합니다.**
     
-    - 🌍 **Global Rank**: HuggingFace 전체 순위
+    - 🇰🇷 **Korean Rank**: 한국 저자 내 순위 (likes/downloads 기준)
+    - 🌍 **Global Rank**: HuggingFace 전체 Top 10,000 내 순위
     - 👤 **Author Rank**: 해당 계정 내 순위
-    - ⭐ **Featured**: Top 2400에는 없지만 주목할 만한 스페이스
     
     © 한국인공지능진흥협회 / 문의: arxivgpt@gmail.com
     """)
     
-    # 조직 범례
     gr.HTML(create_org_legend_html())
     
     refresh_btn = gr.Button("🔄 새로 고침", variant="primary", elem_classes=["neu-button"])
     
-    with gr.Tab("🚀 Spaces Trending (Top 2400 + Featured)"):
-        spaces_plot = gr.Plot(label="Spaces Rankings (Top 2400)")
+    with gr.Tab("🚀 Spaces Trending"):
+        spaces_plot = gr.Plot(label="Spaces by Likes")
         with gr.Row():
             with gr.Column(scale=1):
-                spaces_pie_chart = gr.Plot(label="Distribution")
+                spaces_pie_chart = gr.Plot(label="Global Ranking Status")
             with gr.Column(scale=2):
                 spaces_bar_chart = gr.Plot(label="By Creator")
         spaces_info = gr.HTML()
         spaces_df = gr.DataFrame(
-            headers=["Global Rank", "Author Rank", "Space ID", "Title", "Likes", "Source", "URL"],
-            datatype=["str", "str", "str", "str", "number", "str", "str"],
+            headers=["Korean Rank", "Global Rank", "Author Rank", "Space ID", "Title", "Likes", "URL"],
+            datatype=["str", "str", "str", "str", "str", "number", "str"],
             row_count=(10, "dynamic"),
             wrap=True
         )
     
-    with gr.Tab("🤖 Models Trending (Top 3000)"):
-        models_plot = gr.Plot(label="Models Rankings (Top 3000)")
+    with gr.Tab("🤖 Models Trending"):
+        models_plot = gr.Plot(label="Models by Downloads")
         with gr.Row():
             with gr.Column(scale=1):
-                models_pie_chart = gr.Plot(label="Distribution")
+                models_pie_chart = gr.Plot(label="Global Ranking Status")
             with gr.Column(scale=2):
                 models_bar_chart = gr.Plot(label="By Creator")
         models_info = gr.HTML()
         models_df = gr.DataFrame(
-            headers=["Global Rank", "Author Rank", "Model ID", "Title", "Downloads", "Likes", "URL"],
-            datatype=["str", "str", "str", "str", "str", "number", "str"],
+            headers=["Korean Rank", "Global Rank", "Author Rank", "Model ID", "Title", "Downloads", "Likes", "URL"],
+            datatype=["str", "str", "str", "str", "str", "str", "number", "str"],
             row_count=(10, "dynamic"),
             wrap=True
         )