import gradio as gr
import spaces
import requests
import pandas as pd
import plotly.graph_objects as go
from datetime import datetime
import os
import asyncio
import aiohttp

# Neumorphism CSS 스타일
CUSTOM_CSS = """
/* ═══════════════════════════════════════════════════════════
   🔘 NEUMORPHISM 스타일
   ═══════════════════════════════════════════════════════════ */
:root {
    --neu-bg: #e0e5ec;
    --neu-shadow-dark: #b8bec5;
    --neu-shadow-light: #ffffff;
    --neu-text: #6d7b8d;
    --neu-text-dark: #5a6677;
    --neu-accent: #FF6B6B;
    --neu-accent-2: #4ECDC4;
}

body, .gradio-container {
    background: var(--neu-bg) !important;
}

.neu-container {
    background: var(--neu-bg);
    padding: 30px;
    border-radius: 25px;
}

.neu-raised {
    background: var(--neu-bg);
    border-radius: 20px;
    box-shadow: 8px 8px 16px var(--neu-shadow-dark), -8px -8px 16px var(--neu-shadow-light);
    padding: 25px;
    margin-bottom: 20px;
}

.neu-inset {
    background: var(--neu-bg);
    border-radius: 15px;
    box-shadow: inset 6px 6px 12px var(--neu-shadow-dark), inset -6px -6px 12px var(--neu-shadow-light);
    padding: 20px;
}

.neu-button, button.primary {
    background: var(--neu-bg) !important;
    border: none !important;
    border-radius: 50px !important;
    padding: 18px 50px !important;
    color: var(--neu-text) !important;
    font-weight: 700 !important;
    box-shadow: 8px 8px 16px var(--neu-shadow-dark), -8px -8px 16px var(--neu-shadow-light) !important;
    transition: all 0.2s ease !important;
}

.neu-button:hover, button.primary:hover {
    box-shadow: 4px 4px 8px var(--neu-shadow-dark), -4px -4px 8px var(--neu-shadow-light) !important;
    transform: translateY(-2px);
}

.neu-button:active, button.primary:active {
    box-shadow: inset 4px 4px 8px var(--neu-shadow-dark), inset -4px -4px 8px var(--neu-shadow-light) !important;
}

.neu-text {
    color: var(--neu-text);
    text-shadow: 2px 2px 4px var(--neu-shadow-light), -1px -1px 3px rgba(0,0,0,0.1);
}

.gradio-container .tabs {
    background: var(--neu-bg) !important;
}

.gradio-container .tab-nav button {
    background: var(--neu-bg) !important;
    border-radius: 15px !important;
    margin: 5px !important;
    box-shadow: 5px 5px 10px var(--neu-shadow-dark), -5px -5px 10px var(--neu-shadow-light) !important;
    color: var(--neu-text) !important;
    font-weight: 600 !important;
}

.gradio-container .tab-nav button.selected {
    box-shadow: inset 4px 4px 8px var(--neu-shadow-dark), inset -4px -4px 8px var(--neu-shadow-light) !important;
    color: var(--neu-accent) !important;
}

#spaces_pie, #models_pie, #spaces_bar, #models_bar {
    min-height: 400px;
    border-radius: 20px;
    box-shadow: 8px 8px 16px var(--neu-shadow-dark), -8px -8px 16px var(--neu-shadow-light);
    background: var(--neu-bg);
    padding: 15px;
}

.card-grid {
    display: grid;
    grid-template-columns: repeat(auto-fill, minmax(320px, 1fr));
    gap: 25px;
    padding: 20px;
}

.space-card {
    background: var(--neu-bg);
    border-radius: 20px;
    padding: 25px;
    box-shadow: 8px 8px 16px var(--neu-shadow-dark), -8px -8px 16px var(--neu-shadow-light);
    transition: transform 0.3s ease, box-shadow 0.3s ease;
}

.space-card:hover {
    transform: translateY(-5px);
    box-shadow: 12px 12px 24px var(--neu-shadow-dark), -12px -12px 24px var(--neu-shadow-light);
}

.visit-link {
    display: inline-block;
    padding: 12px 24px;
    background: var(--neu-bg);
    color: var(--neu-text);
    text-decoration: none;
    border-radius: 50px;
    font-weight: 600;
    box-shadow: 5px 5px 10px var(--neu-shadow-dark), -5px -5px 10px var(--neu-shadow-light);
    transition: all 0.2s ease;
}

.visit-link:hover {
    box-shadow: 3px 3px 6px var(--neu-shadow-dark), -3px -3px 6px var(--neu-shadow-light);
    color: var(--neu-accent);
}

.korea-badge {
    display: inline-block;
    padding: 5px 15px;
    background: linear-gradient(145deg, #FF6B6B, #FF8E8E);
    color: white;
    border-radius: 20px;
    font-size: 12px;
    font-weight: 600;
}

.main-title {
    color: var(--neu-text-dark);
    font-size: 2em;
    font-weight: 800;
    text-shadow: 2px 2px 4px var(--neu-shadow-light), -1px -1px 3px rgba(0,0,0,0.1);
}

::-webkit-scrollbar {
    width: 10px;
}

::-webkit-scrollbar-track {
    background: var(--neu-bg);
    border-radius: 10px;
}

::-webkit-scrollbar-thumb {
    background: linear-gradient(145deg, #c8cdd4, #f0f5fc);
    border-radius: 10px;
}
"""

# ZeroGPU 더미 함수
@spaces.GPU(duration=1)
def dummy_gpu_function():
    return "GPU initialized"

def initialize_app():
    try:
        result = dummy_gpu_function()
        print(f"[ZeroGPU] {result}")
    except Exception as e:
        print(f"[ZeroGPU] Running without GPU: {e}")
    return True

app_initialized = initialize_app()

HF_TOKEN = os.getenv("HF_TOKEN")

# 한국 관련 조직/사용자 계정 목록 (모델 및 스페이스 검색에 사용)
KOREAN_ORGS = [
    "keeperballon", "openfree", "VIDraft", "vidraft", "ginigen", "ginipick", 
    "Heartsync", "heartsync", "seawolf2357", "aiqcamp", "aiqtech", "fantaxy", 
    "fantos", "immunobiotech", "yokoha", "cutechicken", "kolaslab", "gunship999",
    "moreh", "OnomaAIResearch", "LGAI-EXAONE", "naver-hyperclovax", "naver-ai",
    "kakaocorp", "kakaobrain", "kakaobank", "K-intelligence", "skt",
    "SamsungResearch", "SamsungSAILMontreal", "upstage", "NCSOFT", "LDCC",
    "nari-labs", "etri-vilab", "etri-xainlp", "etri-lirs", "ETRI-XAINLP",
    "Bllossom", "MLP-KTLim", "DILAB-HYU", "nlpai-lab", "kyujinpy",
    "beomi", "maywell", "yanolja", "saltlux", "42dot", "Edentns",
    "snunlp", "SKT-AI", "monologg", "cosmoquester", "krevas",
    "EleutherAI", "kaist-ai", "KAERI-MLP", "dnotitia", "OLAResearch"
]

target_models = {
    "K-intelligence/Midm-2.0-Mini-Instruct": "https://huggingface.co/K-intelligence/Midm-2.0-Mini-Instruct",
    "K-intelligence/Midm-2.0-Base-Instruct": "https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct",
    "VIDraft/Qwen3-R1984-30B-A3B": "https://huggingface.co/VIDraft/Qwen3-R1984-30B-A3B",
    "VIDraft/Gemma-3-R1984-1B": "https://huggingface.co/VIDraft/Gemma-3-R1984-1B",
    "VIDraft/Qwen3-R1984-0.6B": "https://huggingface.co/VIDraft/Qwen3-R1984-0.6B",
    "VIDraft/Qwen3-R1984-4B": "https://huggingface.co/VIDraft/Qwen3-R1984-4B",
    "seawolf2357/kim-korea": "https://huggingface.co/seawolf2357/kim-korea",
    "ginipick/QwQ-32B-NF4": "https://huggingface.co/ginipick/QwQ-32B-NF4",
    "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B": "https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B",
    "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B": "https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B",
    "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B": "https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B",
    "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",
    "LGAI-EXAONE/EXAONE-Deep-32B": "https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-32B",
    "LGAI-EXAONE/EXAONE-Deep-2.4B": "https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-2.4B",
    "LGAI-EXAONE/EXAONE-Deep-7.8B": "https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-7.8B",
    "moreh/Llama-3-Motif-102B-Instruct": "https://huggingface.co/moreh/Llama-3-Motif-102B-Instruct",
    "moreh/Llama-3-Motif-102B": "https://huggingface.co/moreh/Llama-3-Motif-102B",
    "moreh/MoMo-72B-lora-1.8.7-DPO": "https://huggingface.co/moreh/MoMo-72B-lora-1.8.7-DPO",
    "OnomaAIResearch/Illustrious-XL-v2.0": "https://huggingface.co/OnomaAIResearch/Illustrious-XL-v2.0",
    "OnomaAIResearch/Illustrious-Lumina-v0.03": "https://huggingface.co/OnomaAIResearch/Illustrious-Lumina-v0.03",
    "OnomaAIResearch/Illustrious-xl-early-release-v0": "https://huggingface.co/OnomaAIResearch/Illustrious-xl-early-release-v0",
    "OnomaAIResearch/Illustrious-XL-v1.0": "https://huggingface.co/OnomaAIResearch/Illustrious-XL-v1.0",
    "OnomaAIResearch/Illustrious-XL-v1.1": "https://huggingface.co/OnomaAIResearch/Illustrious-XL-v1.1",
    "upstage/solar-pro-preview-instruct": "https://huggingface.co/upstage/solar-pro-preview-instruct",
    "NCSOFT/VARCO-VISION-14B": "https://huggingface.co/NCSOFT/VARCO-VISION-14B",
    "NCSOFT/Llama-VARCO-8B-Instruct": "https://huggingface.co/NCSOFT/Llama-VARCO-8B-Instruct",
    "NCSOFT/VARCO-VISION-14B-HF": "https://huggingface.co/NCSOFT/VARCO-VISION-14B-HF",
    "kakaocorp/kanana-nano-2.1b-base": "https://huggingface.co/kakaocorp/kanana-nano-2.1b-base",
    "kakaocorp/kanana-nano-2.1b-instruct": "https://huggingface.co/kakaocorp/kanana-nano-2.1b-instruct",
    "kakaobrain/kogpt": "https://huggingface.co/kakaobrain/kogpt",
    "nari-labs/Dia-1.6B": "https://huggingface.co/nari-labs/Dia-1.6B",
    "Bllossom/llama-3.2-Korean-Bllossom-3B": "https://huggingface.co/Bllossom/llama-3.2-Korean-Bllossom-3B",
    "MLP-KTLim/llama-3-Korean-Bllossom-8B": "https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B",
    "etri-lirs/eagle-3b-preview": "https://huggingface.co/etri-lirs/eagle-3b-preview",
    "etri-xainlp/llama3-8b-dpo_v1": "https://huggingface.co/etri-xainlp/llama3-8b-dpo_v1",
    "etri-xainlp/polyglot-ko-12.8b-instruct": "https://huggingface.co/etri-xainlp/polyglot-ko-12.8b-instruct",
    "LDCC/LDCC-SOLAR-10.7B": "https://huggingface.co/LDCC/LDCC-SOLAR-10.7B",
    "nlpai-lab/KULLM3": "https://huggingface.co/nlpai-lab/KULLM3",
    "nlpai-lab/kullm-polyglot-5.8b-v2": "https://huggingface.co/nlpai-lab/kullm-polyglot-5.8b-v2",
    "yanolja/EEVE-Korean-Instruct-10.8B-v1.0": "https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0",
    "yanolja/Bookworm-10.7B-v0.4-DPO": "https://huggingface.co/yanolja/Bookworm-10.7B-v0.4-DPO",
    "saltlux/Ko-Llama3-Luxia-8B": "https://huggingface.co/saltlux/Ko-Llama3-Luxia-8B",
    "maywell/Llama-3-Ko-8B-Instruct": "https://huggingface.co/maywell/Llama-3-Ko-8B-Instruct",
    "maywell/Synatra-10.7B-v0.4": "https://huggingface.co/maywell/Synatra-10.7B-v0.4",
    "beomi/Llama-3-Open-Ko-8B-Instruct-preview": "https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview",
    "beomi/Yi-Ko-6B": "https://huggingface.co/beomi/Yi-Ko-6B",
    "kyujinpy/Korean-OpenOrca-v3": "https://huggingface.co/kyujinpy/Korean-OpenOrca-v3",
    "kyujinpy/KO-Platypus2-13B": "https://huggingface.co/kyujinpy/KO-Platypus2-13B",
    "dnotitia/DNA-R1": "https://huggingface.co/dnotitia/DNA-R1",
    "dnotitia/Llama-DNA-1.0-8B-Instruct": "https://huggingface.co/dnotitia/Llama-DNA-1.0-8B-Instruct",
    "Edentns/DataVortexS-10.7B-dpo-v1.0": "https://huggingface.co/Edentns/DataVortexS-10.7B-dpo-v1.0",
    "kaist-ai/prometheus-13b-v1.0": "https://huggingface.co/kaist-ai/prometheus-13b-v1.0",
    "KAERI-MLP/llama-3.1-Korean-AtomicGPT-Bllossom-8B": "https://huggingface.co/KAERI-MLP/llama-3.1-Korean-AtomicGPT-Bllossom-8B",
    "DILAB-HYU/koquality-polyglot-12.8b": "https://huggingface.co/DILAB-HYU/koquality-polyglot-12.8b",
    "EleutherAI/polyglot-ko-1.3b": "https://huggingface.co/EleutherAI/polyglot-ko-1.3b",
}

target_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",
    
    # 기업 관련 스페이스
    "nari-labs/Dia-1.6B": "https://huggingface.co/spaces/nari-labs/Dia-1.6B",
    "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_korea_models():
    """Korea 관련 모델 검색"""
    params = {"search": "korea", "full": "True", "config": "True", "limit": 1000}
    try:
        response = requests.get("https://huggingface.co/api/models", headers={'Accept': 'application/json'}, params=params)
        if response.status_code == 200:
            return response.json()
        return []
    except Exception as e:
        print(f"Error fetching Korea models: {str(e)}")
        return []

def get_all_models(limit=3000):
    """모든 모델과 Korea 관련 모델 가져오기"""
    all_models = []
    page_size = 1000
    
    for offset in range(0, limit, page_size):
        params = {'limit': min(page_size, limit - offset), 'full': 'True', 'config': 'True', 'offset': offset}
        response = requests.get("https://huggingface.co/api/models", headers={'Accept': 'application/json'}, params=params)
        if response.status_code == 200:
            all_models.extend(response.json())
        else:
            break
    
    korea_response = requests.get("https://huggingface.co/api/models", headers={'Accept': 'application/json'}, 
                                   params={"search": "korea", "full": "True", "config": "True", "limit": limit})
    if korea_response.status_code == 200:
        korea_models = korea_response.json()
        existing_ids = {model.get('id', '') for model in all_models}
        for model in korea_models:
            if model.get('id', '') not in existing_ids:
                all_models.append(model)
    
    return all_models[:limit]

async def fetch_model_data(session, model_id):
    url = f"https://huggingface.co/api/models/{model_id.strip('/')}"
    try:
        async with session.get(url, headers={'Accept': 'application/json'}) as response:
            if response.status == 200:
                return await response.json()
            return None
    except:
        return None

async def fetch_all_target_models(model_ids):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_model_data(session, model_id) for model_id in model_ids]
        return await asyncio.gather(*tasks)

def create_error_plot():
    fig = go.Figure()
    fig.add_annotation(text="데이터를 불러올 수 없습니다", xref="paper", yref="paper", x=0.5, y=0.5, showarrow=False, font=dict(size=20))
    fig.update_layout(title="Error Loading Data", height=400, paper_bgcolor='#e0e5ec', plot_bgcolor='#e0e5ec')
    return fig

def get_models_data(progress=gr.Progress()):
    def calculate_rank(model_id, all_global_models, korea_models):
        global_rank = next((idx for idx, m in enumerate(all_global_models, 1) if m.get('id', '').strip() == model_id.strip()), None)
        is_korea = any(m.get('id', '').strip() == model_id.strip() for m in korea_models)
        if is_korea:
            korea_rank = next((idx for idx, m in enumerate(korea_models, 1) if m.get('id', '').strip() == model_id.strip()), None)
            if korea_rank:
                return min(global_rank or 3001, korea_rank + 1000), True
        return global_rank if global_rank else 'Not in top 3000', is_korea

    try:
        progress(0, desc="Fetching models...")
        
        all_global_models = get_all_models(limit=3000)
        korea_models = get_korea_models()
        
        fig = go.Figure()
        
        target_model_ids = set(target_models.keys())
        extra_model_ids = set()
        for m in all_global_models:
            model_id = m.get('id', '')
            if isinstance(model_id, str):
                for org in KOREAN_ORGS:
                    if model_id.lower().startswith(org.lower() + "/"):
                        extra_model_ids.add(model_id)
                        break
        
        final_model_ids = target_model_ids.union(extra_model_ids)
        model_results = asyncio.run(fetch_all_target_models(final_model_ids))
        
        filtered_models = []
        for model_id, model_data in zip(final_model_ids, model_results):
            if model_data is not None:
                rank, is_korea = calculate_rank(model_id, all_global_models, korea_models)
                filtered_models.append({
                    'id': model_id, 'global_rank': rank, 'downloads': model_data.get('downloads', 0),
                    'likes': model_data.get('likes', 0), 'title': model_data.get('title', 'No Title'), 'is_korea': is_korea
                })
            else:
                filtered_models.append({
                    'id': model_id, 'global_rank': 'Not in top 3000', 'downloads': 0,
                    'likes': 0, 'title': 'No Title', 'is_korea': False
                })

        filtered_models.sort(key=lambda x: x['global_rank'] if isinstance(x['global_rank'], (int, float)) else float('inf'))
        valid_models = [m for m in filtered_models if isinstance(m['global_rank'], (int, float))]
        
        if valid_models:
            fig.add_trace(go.Bar(
                x=[m['id'] for m in valid_models],
                y=[3001 - m['global_rank'] for m in valid_models],
                text=[f"Rank: #{m['global_rank']}<br>Downloads: {format(m['downloads'], ',')}<br>Likes: {format(m['likes'], ',')}" for m in valid_models],
                textposition='auto',
                marker_color=['rgba(255,107,107,0.8)' if m['is_korea'] else 'rgba(78,205,196,0.8)' for m in valid_models]
            ))
            fig.update_layout(
                title={'text': "🏆 HuggingFace Models Global Rankings", 'font': {'size': 24, 'color': '#5a6677'}},
                xaxis_title="Model ID", yaxis_title="Global Rank",
                yaxis=dict(ticktext=[f"#{i}" for i in range(1, 3001, 100)], tickvals=[3001 - i for i in range(1, 3001, 100)], range=[0, 3000]),
                height=800, showlegend=False, template='plotly_white', xaxis_tickangle=-45,
                paper_bgcolor='rgba(224,229,236,0.5)', plot_bgcolor='rgba(224,229,236,0.3)'
            )

        html_content = f"""
        <div class='neu-container'>
            <h2 class='main-title'>🤖 Models Rankings (Up to #3000)</h2>
            <div class='card-grid'>
        """
        for model in filtered_models:
            rank_display = f"Global Rank #{model['global_rank']}" if isinstance(model['global_rank'], (int, float)) else "Not in top 3000"
            korea_badge = "<span class='korea-badge'>🇰🇷 Korea</span>" if model['is_korea'] else ""
            model_url = target_models.get(model['id'], f"https://huggingface.co/{model['id']}")
            html_content += f"""
            <div class='space-card' style='{"border-left: 4px solid #FF6B6B;" if model["is_korea"] else ""}'>
                <h3 style='color: #5a6677; font-size: 16px;'>{rank_display}</h3>
                <h4 style='color: #6d7b8d; font-size: 14px;'>{model['id']}</h4>
                <p>{korea_badge}</p>
                <p style='color: #6d7b8d;'>⬇️ {format(model['downloads'], ',')} | 👍 {format(model['likes'], ',')}</p>
                <a href='{model_url}' target='_blank' class='visit-link'>Visit 🔗</a>
            </div>"""
        html_content += "</div></div>"
        
        df = pd.DataFrame([{
            'Global Rank': f"#{m['global_rank']}" if isinstance(m['global_rank'], (int, float)) else m['global_rank'],
            'Model ID': m['id'], 'Title': m['title'], 'Downloads': format(m['downloads'], ','),
            'Likes': format(m['likes'], ','), 'Korea': '🇰🇷' if m['is_korea'] else '',
            'URL': target_models.get(m['id'], f"https://huggingface.co/{m['id']}")
        } for m in filtered_models])
        
        progress(1.0, desc="Complete!")
        return fig, html_content, df
    except Exception as e:
        return create_error_plot(), f"<div class='neu-container'><p>Error: {str(e)}</p></div>", pd.DataFrame()

def get_spaces_data(sort_type="trending", progress=gr.Progress()):
    """스페이스 데이터 가져오기"""
    url = "https://huggingface.co/api/spaces"
    params = {'full': 'true', 'limit': 1000}
    if sort_type == "modes":
        params['sort'] = 'likes'
    
    try:
        progress(0, desc=f"Fetching {sort_type} spaces data...")
        response = requests.get(url, params=params)
        response.raise_for_status()
        all_spaces = response.json()
        
        space_ranks = {}
        for idx, space in enumerate(all_spaces, 1):
            space_id = space.get('id', '')
            if space_id in target_spaces or any(space_id.lower().startswith(org.lower() + "/") for org in KOREAN_ORGS):
                space['rank'] = idx
                space_ranks[space_id] = space
        
        spaces = sorted([space_ranks[sid] for sid in space_ranks], key=lambda x: x['rank'])
        
        progress(0.3, desc="Creating visualization...")
        
        fig = go.Figure()
        if spaces:
            fig.add_trace(go.Bar(
                x=[s['id'] for s in spaces],
                y=[s['rank'] for s in spaces],
                text=[f"Rank: {s['rank']}<br>Likes: {s.get('likes', 0)}" for s in spaces],
                textposition='auto',
                marker_color='rgba(158,202,225,0.8)'
            ))
        
        fig.update_layout(
            title={'text': f'🚀 HuggingFace Spaces {sort_type.title()} Rankings (Top 500)', 'font': {'size': 24, 'color': '#5a6677'}},
            xaxis_title='Space ID', yaxis_title='Rank',
            yaxis=dict(autorange='reversed', tickmode='array', ticktext=[str(i) for i in range(1, 501, 20)], tickvals=[i for i in range(1, 501, 20)], range=[1, 500]),
            height=800, showlegend=False, template='plotly_white', xaxis_tickangle=-45,
            paper_bgcolor='rgba(224,229,236,0.5)', plot_bgcolor='rgba(224,229,236,0.3)'
        )
        
        progress(0.6, desc="Creating space cards...")
        
        html_content = f"""
        <div class='neu-container'>
            <h2 class='main-title'>🚀 {sort_type.title()} Rankings</h2>
            <div class='card-grid'>
        """
        for space in spaces:
            space_id = space['id']
            title = space.get('cardData', {}).get('title') or space.get('title', 'No Title')
            likes = space.get('likes', 0)
            space_url = target_spaces.get(space_id, f"https://huggingface.co/spaces/{space_id}")
            html_content += f"""
            <div class='space-card'>
                <h3 style='color: #5a6677;'>Rank #{space['rank']} - {space_id}</h3>
                <h4 style='color: #2980b9; font-size: 1.1em;'>{title}</h4>
                <p style='color: #6d7b8d;'>👍 Likes: {likes}</p>
                <a href='{space_url}' target='_blank' class='visit-link'>Visit Space 🔗</a>
            </div>"""
        html_content += "</div></div>"
        
        df = pd.DataFrame([{
            'Rank': s['rank'], 'Space ID': s['id'],
            'Title': s.get('cardData', {}).get('title') or s.get('title', 'No Title'),
            'Likes': s.get('likes', 0),
            'URL': target_spaces.get(s['id'], f"https://huggingface.co/spaces/{s['id']}")
        } for s in spaces])
        
        progress(1.0, desc="Complete!")
        return fig, html_content, df
    except Exception as e:
        return create_error_plot(), f'<div class="neu-container" style="color: red; padding: 20px;">Error: {str(e)}</div>', pd.DataFrame()

def create_registration_bar_chart(data, type_name="Spaces"):
    try:
        top_limit = 500 if type_name == "Spaces" else 3000
        if isinstance(data, pd.DataFrame):
            if type_name == "Models":
                data = data[data['Global Rank'].apply(lambda x: isinstance(x, str) and x.startswith('#'))]
                data = data[data['Global Rank'].apply(lambda x: int(str(x).replace('#', '')) <= top_limit)]
            else:
                data = data[data['Rank'].apply(lambda x: isinstance(x, (int, float)) and x <= top_limit)]
            id_col = 'Space ID' if type_name == "Spaces" else 'Model ID'
            registrations = data[id_col].apply(lambda x: x.split('/')[0]).value_counts()
        else:
            registrations = pd.Series({})
        
        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} by Creator (Top {top_limit})",
            xaxis_title="Creator ID", yaxis_title="Count",
            showlegend=False, height=400, width=700,
            paper_bgcolor='#e0e5ec', plot_bgcolor='rgba(224,229,236,0.3)'
        )
        return fig
    except:
        return go.Figure()

def create_pie_chart(data, total_count, type_name="Spaces"):
    try:
        top_limit = 500 if type_name == "Spaces" else 3000
        if isinstance(data, pd.DataFrame):
            if type_name == "Models":
                korean_count = len(data[data['Global Rank'].apply(lambda x: isinstance(x, str) and x.startswith('#') and int(x.replace('#', '')) <= top_limit)])
            else:
                korean_count = len(data[data['Rank'].apply(lambda x: isinstance(x, (int, float)) and x <= top_limit)])
        else:
            korean_count = 0
        
        other_count = total_count - korean_count
        
        fig = go.Figure(data=[go.Pie(
            labels=[f'Korean {type_name}', f'Other {type_name}'],
            values=[korean_count, other_count], hole=.3,
            marker_colors=['#FF6B6B', '#4ECDC4'],
            textinfo='percent+value'
        )])
        fig.update_layout(
            title=f"Korean vs Other {type_name} (Top {top_limit})",
            showlegend=True, height=400, width=500,
            paper_bgcolor='#e0e5ec'
        )
        return fig
    except:
        return go.Figure()

def refresh_all_data():
    try:
        spaces_results = get_spaces_data("trending")
        models_results = get_models_data()
        
        spaces_pie = create_pie_chart(spaces_results[2], 500, "Spaces")
        spaces_bar = create_registration_bar_chart(spaces_results[2], "Spaces")
        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: {str(e)}")
        return [None] * 10

# Gradio 앱 정의
with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Soft()) 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: arxivgpt@gmail.com
    """)
    
    gr.Markdown("""
    ### [Hot NEWS] 허깅페이스 선정 12월 'TOP 12'에 한국 'ginipick'의 'FLUXllama'와 'Text3D' 2종이 선정됨
    """)
    
    refresh_btn = gr.Button("🔄 새로 고침", variant="primary", elem_classes=["neu-button"])
    
    with gr.Tab("Spaces Trending"):
        trending_plot = gr.Plot()
        with gr.Row():
            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():
            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", "URL"],
            datatype=["str", "str", "str", "str", "str", "str", "str"],
            row_count=(10, "dynamic")
        )

    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()
        if initial_data[0]:
            trending_plot.value = initial_data[0]
        if initial_data[1]:
            trending_info.value = initial_data[1]
        if initial_data[2] is not None:
            trending_df.value = initial_data[2]
        if initial_data[3]:
            spaces_pie_chart.value = initial_data[3]
        if initial_data[4]:
            spaces_bar_chart.value = initial_data[4]
        if initial_data[5]:
            models_plot.value = initial_data[5]
        if initial_data[6]:
            models_info.value = initial_data[6]
        if initial_data[7] is not None:
            models_df.value = initial_data[7]
        if initial_data[8]:
            models_pie_chart.value = initial_data[8]
        if initial_data[9]:
            models_bar_chart.value = initial_data[9]
    except Exception as e:
        print(f"Error loading initial data: {str(e)}")

demo.launch(server_name="0.0.0.0", server_port=7860, share=False, show_error=True)