Delete app-backup-datasets.py

app-backup-datasets.py

@@ -1,1318 +0,0 @@
-import gradio as gr
-import requests
-import pandas as pd
-import plotly.graph_objects as go
-from datetime import datetime
-import os
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-target_models = {
-    "openfree/flux-lora-korea-palace": "https://huggingface.co/openfree/flux-lora-korea-palace",
-    "seawolf2357/hanbok": "https://huggingface.co/seawolf2357/hanbok",
-    "seawolf2357/ntower": "https://huggingface.co/seawolf2357/ntower",
-
-    "openfree/claude-monet": "https://huggingface.co/openfree/claude-monet",
-
-    "LGAI-EXAONE/EXAONE-3.5-32B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-32B-Instruct",
-    "LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct",
-    "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
-    "ginipick/flux-lora-eric-cat": "https://huggingface.co/ginipick/flux-lora-eric-cat",
-    "seawolf2357/flux-lora-car-rolls-royce": "https://huggingface.co/seawolf2357/flux-lora-car-rolls-royce",
-    "moreh/Llama-3-Motif-102B-Instruct": "https://huggingface.co/moreh/Llama-3-Motif-102B-Instruct",
-    "OnomaAIResearch/Illustrious-xl-early-release-v0": "https://huggingface.co/OnomaAIResearch/Illustrious-xl-early-release-v0",
-    "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",
-    "KAERI-MLP/llama-3.1-Korean-AtomicGPT-Bllossom-8B": "https://huggingface.co/KAERI-MLP/llama-3.1-Korean-AtomicGPT-Bllossom-8B",
-    "dnotitia/Llama-DNA-1.0-8B-Instruct": "https://huggingface.co/dnotitia/Llama-DNA-1.0-8B-Instruct",    
-    "Bllossom/llama-3.2-Korean-Bllossom-3B": "https://huggingface.co/Bllossom/llama-3.2-Korean-Bllossom-3B",
-
-    "unidocs/llama-3.1-8b-komedic-instruct": "https://huggingface.co/unidocs/llama-3.1-8b-komedic-instruct",
-    "unidocs/llama-3.2-3b-komedic-instruct": "https://huggingface.co/unidocs/llama-3.2-3b-komedic-instruct",    
-    "etri-lirs/eagle-3b-preview": "https://huggingface.co/etri-lirs/eagle-3b-preview",
-    "kakaobrain/kogpt": "https://huggingface.co/kakaobrain/kogpt", 
-
-    "Saxo/Linkbricks-Horizon-AI-Korean-Gemma-2-sft-dpo-27B": "https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-Gemma-2-sft-dpo-27B",
-    "AALF/gemma-2-27b-it-SimPO-37K": "https://huggingface.co/AALF/gemma-2-27b-it-SimPO-37K",
-    "nbeerbower/mistral-nemo-wissenschaft-12B": "https://huggingface.co/nbeerbower/mistral-nemo-wissenschaft-12B",
-    "Saxo/Linkbricks-Horizon-AI-Korean-Mistral-Nemo-sft-dpo-12B": "https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-Mistral-Nemo-sft-dpo-12B",
-    "princeton-nlp/gemma-2-9b-it-SimPO": "https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO",
-    "migtissera/Tess-v2.5-Gemma-2-27B-alpha": "https://huggingface.co/migtissera/Tess-v2.5-Gemma-2-27B-alpha",
-    "DeepMount00/Llama-3.1-8b-Ita": "https://huggingface.co/DeepMount00/Llama-3.1-8b-Ita",
-    "cognitivecomputations/dolphin-2.9.3-mistral-nemo-12b": "https://huggingface.co/cognitivecomputations/dolphin-2.9.3-mistral-nemo-12b",
-    "ai-human-lab/EEVE-Korean_Instruct-10.8B-expo": "https://huggingface.co/ai-human-lab/EEVE-Korean_Instruct-10.8B-expo",
-    "VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct": "https://huggingface.co/VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct",
-    "Saxo/Linkbricks-Horizon-AI-Korean-llama-3.1-sft-dpo-8B": "https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-llama-3.1-sft-dpo-8B",
-    "AIDX-ktds/ktdsbaseLM-v0.12-based-on-openchat3.5": "https://huggingface.co/AIDX-ktds/ktdsbaseLM-v0.12-based-on-openchat3.5",
-    "mlabonne/Daredevil-8B-abliterated": "https://huggingface.co/mlabonne/Daredevil-8B-abliterated",
-    "ENERGY-DRINK-LOVE/eeve_dpo-v3": "https://huggingface.co/ENERGY-DRINK-LOVE/eeve_dpo-v3",
-    "migtissera/Trinity-2-Codestral-22B": "https://huggingface.co/migtissera/Trinity-2-Codestral-22B",
-    "Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-rlhf-dpo-8B": "https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-rlhf-dpo-8B",
-    "mlabonne/Daredevil-8B-abliterated-dpomix": "https://huggingface.co/mlabonne/Daredevil-8B-abliterated-dpomix",
-    "yanolja/EEVE-Korean-Instruct-10.8B-v1.0": "https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0",
-    "vicgalle/Configurable-Llama-3.1-8B-Instruct": "https://huggingface.co/vicgalle/Configurable-Llama-3.1-8B-Instruct",
-    "T3Q-LLM/T3Q-LLM1-sft1.0-dpo1.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM1-sft1.0-dpo1.0",
-    "Eurdem/Defne-llama3.1-8B": "https://huggingface.co/Eurdem/Defne-llama3.1-8B",
-    "BAAI/Infinity-Instruct-7M-Gen-Llama3_1-8B": "https://huggingface.co/BAAI/Infinity-Instruct-7M-Gen-Llama3_1-8B",
-    "BAAI/Infinity-Instruct-3M-0625-Llama3-8B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0625-Llama3-8B",
-    "T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0",
-    "BAAI/Infinity-Instruct-7M-0729-Llama3_1-8B": "https://huggingface.co/BAAI/Infinity-Instruct-7M-0729-Llama3_1-8B",
-    "mightbe/EEVE-10.8B-Multiturn": "https://huggingface.co/mightbe/EEVE-10.8B-Multiturn",
-    "hyemijo/omed-llama3.1-8b": "https://huggingface.co/hyemijo/omed-llama3.1-8b",
-    "yanolja/Bookworm-10.7B-v0.4-DPO": "https://huggingface.co/yanolja/Bookworm-10.7B-v0.4-DPO",
-    "algograp-Inc/algograpV4": "https://huggingface.co/algograp-Inc/algograpV4",
-    "lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75": "https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75",
-    "chihoonlee10/T3Q-LLM-MG-DPO-v1.0": "https://huggingface.co/chihoonlee10/T3Q-LLM-MG-DPO-v1.0",
-    "vicgalle/Configurable-Hermes-2-Pro-Llama-3-8B": "https://huggingface.co/vicgalle/Configurable-Hermes-2-Pro-Llama-3-8B",
-    "RLHFlow/LLaMA3-iterative-DPO-final": "https://huggingface.co/RLHFlow/LLaMA3-iterative-DPO-final",
-    "SEOKDONG/llama3.1_korean_v0.1_sft_by_aidx": "https://huggingface.co/SEOKDONG/llama3.1_korean_v0.1_sft_by_aidx",
-    "spow12/Ko-Qwen2-7B-Instruct": "https://huggingface.co/spow12/Ko-Qwen2-7B-Instruct",
-    "BAAI/Infinity-Instruct-3M-0625-Qwen2-7B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0625-Qwen2-7B",
-    "lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half": "https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half",
-    "T3Q-LLM/T3Q-LLM1-CV-v2.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM1-CV-v2.0",
-    "migtissera/Trinity-2-Codestral-22B-v0.2": "https://huggingface.co/migtissera/Trinity-2-Codestral-22B-v0.2",
-    "sinjy1203/EEVE-Korean-Instruct-10.8B-v1.0-Grade-Retrieval": "https://huggingface.co/sinjy1203/EEVE-Korean-Instruct-10.8B-v1.0-Grade-Retrieval",
-    "MaziyarPanahi/Llama-3-8B-Instruct-v0.10": "https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.10",
-    "MaziyarPanahi/Llama-3-8B-Instruct-v0.9": "https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.9",
-    "zhengr/MixTAO-7Bx2-MoE-v8.1": "https://huggingface.co/zhengr/MixTAO-7Bx2-MoE-v8.1",
-    "TIGER-Lab/MAmmoTH2-8B-Plus": "https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus",
-    "OpenBuddy/openbuddy-qwen1.5-14b-v21.1-32k": "https://huggingface.co/OpenBuddy/openbuddy-qwen1.5-14b-v21.1-32k",
-    "haoranxu/Llama-3-Instruct-8B-CPO-SimPO": "https://huggingface.co/haoranxu/Llama-3-Instruct-8B-CPO-SimPO",
-    "Weyaxi/Einstein-v7-Qwen2-7B": "https://huggingface.co/Weyaxi/Einstein-v7-Qwen2-7B",
-    "DKYoon/kosolar-hermes-test": "https://huggingface.co/DKYoon/kosolar-hermes-test",
-    "vilm/Quyen-Pro-v0.1": "https://huggingface.co/vilm/Quyen-Pro-v0.1",
-    "chihoonlee10/T3Q-LLM-MG-v1.0": "https://huggingface.co/chihoonlee10/T3Q-LLM-MG-v1.0",
-    "lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25": "https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25",
-    "ai-human-lab/EEVE-Korean-10.8B-RAFT": "https://huggingface.co/ai-human-lab/EEVE-Korean-10.8B-RAFT",
-    "princeton-nlp/Llama-3-Base-8B-SFT-RDPO": "https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-RDPO",
-    "MaziyarPanahi/Llama-3-8B-Instruct-v0.8": "https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.8",
-    "chihoonlee10/T3Q-ko-solar-dpo-v7.0": "https://huggingface.co/chihoonlee10/T3Q-ko-solar-dpo-v7.0",
-    "jondurbin/bagel-8b-v1.0": "https://huggingface.co/jondurbin/bagel-8b-v1.0",
-    "DeepMount00/Llama-3-8b-Ita": "https://huggingface.co/DeepMount00/Llama-3-8b-Ita",
-    "VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct": "https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
-    "princeton-nlp/Llama-3-Instruct-8B-ORPO-v0.2": "https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-ORPO-v0.2",
-    "AIDX-ktds/ktdsbaseLM-v0.11-based-on-openchat3.5": "https://huggingface.co/AIDX-ktds/ktdsbaseLM-v0.11-based-on-openchat3.5",
-    "princeton-nlp/Llama-3-Base-8B-SFT-KTO": "https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-KTO",
-    "maywell/Mini_Synatra_SFT": "https://huggingface.co/maywell/Mini_Synatra_SFT",
-    "princeton-nlp/Llama-3-Base-8B-SFT-ORPO": "https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-ORPO",
-    "princeton-nlp/Llama-3-Instruct-8B-CPO-v0.2": "https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-CPO-v0.2",
-    "spow12/Qwen2-7B-ko-Instruct-orpo-ver_2.0_wo_chat": "https://huggingface.co/spow12/Qwen2-7B-ko-Instruct-orpo-ver_2.0_wo_chat",
-    "princeton-nlp/Llama-3-Base-8B-SFT-DPO": "https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-DPO",
-    "princeton-nlp/Llama-3-Instruct-8B-ORPO": "https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-ORPO",
-    "lcw99/llama-3-10b-it-kor-extented-chang": "https://huggingface.co/lcw99/llama-3-10b-it-kor-extented-chang",
-    "migtissera/Llama-3-8B-Synthia-v3.5": "https://huggingface.co/migtissera/Llama-3-8B-Synthia-v3.5",
-    "megastudyedu/M-SOLAR-10.7B-v1.4-dpo": "https://huggingface.co/megastudyedu/M-SOLAR-10.7B-v1.4-dpo",
-    "T3Q-LLM/T3Q-LLM-solar10.8-sft-v1.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM-solar10.8-sft-v1.0",
-    "maywell/Synatra-10.7B-v0.4": "https://huggingface.co/maywell/Synatra-10.7B-v0.4",
-    "nlpai-lab/KULLM3": "https://huggingface.co/nlpai-lab/KULLM3",
-    "abacusai/Llama-3-Smaug-8B": "https://huggingface.co/abacusai/Llama-3-Smaug-8B",
-    "gwonny/nox-solar-10.7b-v4-kolon-ITD-5-v2.1": "https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-ITD-5-v2.1",
-    "BAAI/Infinity-Instruct-3M-0625-Mistral-7B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0625-Mistral-7B",
-    "openchat/openchat_3.5": "https://huggingface.co/openchat/openchat_3.5",
-    "T3Q-LLM/T3Q-LLM1-v2.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM1-v2.0",
-    "T3Q-LLM/T3Q-LLM1-CV-v1.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM1-CV-v1.0",
-    "ONS-AI-RESEARCH/ONS-SOLAR-10.7B-v1.1": "https://huggingface.co/ONS-AI-RESEARCH/ONS-SOLAR-10.7B-v1.1",
-    "macadeliccc/Samantha-Qwen-2-7B": "https://huggingface.co/macadeliccc/Samantha-Qwen-2-7B",
-    "openchat/openchat-3.5-0106": "https://huggingface.co/openchat/openchat-3.5-0106",
-    "NousResearch/Nous-Hermes-2-SOLAR-10.7B": "https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B",
-    "UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1": "https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1",
-    "MTSAIR/multi_verse_model": "https://huggingface.co/MTSAIR/multi_verse_model",
-    "gwonny/nox-solar-10.7b-v4-kolon-ITD-5-v2.0": "https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-ITD-5-v2.0",
-    "VIRNECT/llama-3-Korean-8B": "https://huggingface.co/VIRNECT/llama-3-Korean-8B",
-    "ENERGY-DRINK-LOVE/SOLAR_merge_DPOv3": "https://huggingface.co/ENERGY-DRINK-LOVE/SOLAR_merge_DPOv3",
-    "SeaLLMs/SeaLLMs-v3-7B-Chat": "https://huggingface.co/SeaLLMs/SeaLLMs-v3-7B-Chat",
-    "VIRNECT/llama-3-Korean-8B-V2": "https://huggingface.co/VIRNECT/llama-3-Korean-8B-V2",
-    "MLP-KTLim/llama-3-Korean-Bllossom-8B": "https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B",
-    "Magpie-Align/Llama-3-8B-Magpie-Align-v0.3": "https://huggingface.co/Magpie-Align/Llama-3-8B-Magpie-Align-v0.3",
-    "cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2": "https://huggingface.co/cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2",
-    "SkyOrbis/SKY-Ko-Llama3-8B-lora": "https://huggingface.co/SkyOrbis/SKY-Ko-Llama3-8B-lora",
-    "4yo1/llama3-eng-ko-8b-sl5": "https://huggingface.co/4yo1/llama3-eng-ko-8b-sl5",
-    "kimwooglae/WebSquareAI-Instruct-llama-3-8B-v0.5.39": "https://huggingface.co/kimwooglae/WebSquareAI-Instruct-llama-3-8B-v0.5.39",
-    "ONS-AI-RESEARCH/ONS-SOLAR-10.7B-v1.2": "https://huggingface.co/ONS-AI-RESEARCH/ONS-SOLAR-10.7B-v1.2",
-    "lcw99/llama-3-10b-it-kor-extented-chang-pro8": "https://huggingface.co/lcw99/llama-3-10b-it-kor-extented-chang-pro8",
-    "BAAI/Infinity-Instruct-3M-0625-Yi-1.5-9B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0625-Yi-1.5-9B",
-    "migtissera/Tess-2.0-Llama-3-8B": "https://huggingface.co/migtissera/Tess-2.0-Llama-3-8B",
-    "BAAI/Infinity-Instruct-3M-0613-Mistral-7B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0613-Mistral-7B",
-    "yeonwoo780/cydinfo-llama3-8b-lora-v01": "https://huggingface.co/yeonwoo780/cydinfo-llama3-8b-lora-v01",
-    "vicgalle/ConfigurableSOLAR-10.7B": "https://huggingface.co/vicgalle/ConfigurableSOLAR-10.7B",
-    "chihoonlee10/T3Q-ko-solar-jo-v1.0": "https://huggingface.co/chihoonlee10/T3Q-ko-solar-jo-v1.0",
-    "Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.4": "https://huggingface.co/Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.4",
-    "Edentns/DataVortexS-10.7B-dpo-v1.0": "https://huggingface.co/Edentns/DataVortexS-10.7B-dpo-v1.0",
-    "SJ-Donald/SJ-SOLAR-10.7b-DPO": "https://huggingface.co/SJ-Donald/SJ-SOLAR-10.7b-DPO",
-    "lemon-mint/gemma-ko-7b-it-v0.40": "https://huggingface.co/lemon-mint/gemma-ko-7b-it-v0.40",
-    "GyuHyeonWkdWkdMan/naps-llama-3.1-8b-instruct-v0.3": "https://huggingface.co/GyuHyeonWkdWkdMan/naps-llama-3.1-8b-instruct-v0.3",
-    "hyeogi/SOLAR-10.7B-v1.5": "https://huggingface.co/hyeogi/SOLAR-10.7B-v1.5",
-    "etri-xainlp/llama3-8b-dpo_v1": "https://huggingface.co/etri-xainlp/llama3-8b-dpo_v1",
-    "LDCC/LDCC-SOLAR-10.7B": "https://huggingface.co/LDCC/LDCC-SOLAR-10.7B",
-    "chlee10/T3Q-Llama3-8B-Inst-sft1.0": "https://huggingface.co/chlee10/T3Q-Llama3-8B-Inst-sft1.0",
-    "lemon-mint/gemma-ko-7b-it-v0.41": "https://huggingface.co/lemon-mint/gemma-ko-7b-it-v0.41",
-    "chlee10/T3Q-Llama3-8B-sft1.0-dpo1.0": "https://huggingface.co/chlee10/T3Q-Llama3-8B-sft1.0-dpo1.0",
-    "maywell/Synatra-7B-Instruct-v0.3-pre": "https://huggingface.co/maywell/Synatra-7B-Instruct-v0.3-pre",
-    "UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2": "https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2",
-    "hwkwon/S-SOLAR-10.7B-v1.4": "https://huggingface.co/hwkwon/S-SOLAR-10.7B-v1.4",
-    "12thD/ko-Llama-3-8B-sft-v0.3": "https://huggingface.co/12thD/ko-Llama-3-8B-sft-v0.3",
-    "hkss/hk-SOLAR-10.7B-v1.4": "https://huggingface.co/hkss/hk-SOLAR-10.7B-v1.4",
-    "lookuss/test-llilu": "https://huggingface.co/lookuss/test-llilu",
-    "chihoonlee10/T3Q-ko-solar-dpo-v3.0": "https://huggingface.co/chihoonlee10/T3Q-ko-solar-dpo-v3.0",
-    "chihoonlee10/T3Q-ko-solar-dpo-v1.0": "https://huggingface.co/chihoonlee10/T3Q-ko-solar-dpo-v1.0",
-    "lcw99/llama-3-10b-wiki-240709-f": "https://huggingface.co/lcw99/llama-3-10b-wiki-240709-f",
-    "Edentns/DataVortexS-10.7B-v0.4": "https://huggingface.co/Edentns/DataVortexS-10.7B-v0.4",
-    "princeton-nlp/Llama-3-Instruct-8B-KTO": "https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-KTO",
-    "spow12/kosolar_4.1_sft": "https://huggingface.co/spow12/kosolar_4.1_sft",
-    "natong19/Qwen2-7B-Instruct-abliterated": "https://huggingface.co/natong19/Qwen2-7B-Instruct-abliterated",
-    "megastudyedu/ME-dpo-7B-v1.1": "https://huggingface.co/megastudyedu/ME-dpo-7B-v1.1",
-    "01-ai/Yi-1.5-9B-Chat-16K": "https://huggingface.co/01-ai/Yi-1.5-9B-Chat-16K",
-    "Edentns/DataVortexS-10.7B-dpo-v0.1": "https://huggingface.co/Edentns/DataVortexS-10.7B-dpo-v0.1",
-    "Alphacode-AI/AlphaMist7B-slr-v4-slow": "https://huggingface.co/Alphacode-AI/AlphaMist7B-slr-v4-slow",
-    "chihoonlee10/T3Q-ko-solar-sft-dpo-v1.0": "https://huggingface.co/chihoonlee10/T3Q-ko-solar-sft-dpo-v1.0",
-    "hwkwon/S-SOLAR-10.7B-v1.1": "https://huggingface.co/hwkwon/S-SOLAR-10.7B-v1.1",
-    "DopeorNope/Dear_My_best_Friends-13B": "https://huggingface.co/DopeorNope/Dear_My_best_Friends-13B",
-    "GyuHyeonWkdWkdMan/NAPS-llama-3.1-8b-instruct-v0.3.2": "https://huggingface.co/GyuHyeonWkdWkdMan/NAPS-llama-3.1-8b-instruct-v0.3.2",
-    "PathFinderKR/Waktaverse-Llama-3-KO-8B-Instruct": "https://huggingface.co/PathFinderKR/Waktaverse-Llama-3-KO-8B-Instruct",
-    "vicgalle/ConfigurableHermes-7B": "https://huggingface.co/vicgalle/ConfigurableHermes-7B",
-    "maywell/PiVoT-10.7B-Mistral-v0.2": "https://huggingface.co/maywell/PiVoT-10.7B-Mistral-v0.2",
-    "failspy/Meta-Llama-3-8B-Instruct-abliterated-v3": "https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
-    "lemon-mint/gemma-ko-7b-instruct-v0.50": "https://huggingface.co/lemon-mint/gemma-ko-7b-instruct-v0.50",
-    "ENERGY-DRINK-LOVE/leaderboard_inst_v1.3_Open-Hermes_LDCC-SOLAR-10.7B_SFT": "https://huggingface.co/ENERGY-DRINK-LOVE/leaderboard_inst_v1.3_Open-Hermes_LDCC-SOLAR-10.7B_SFT",
-    "maywell/PiVoT-0.1-early": "https://huggingface.co/maywell/PiVoT-0.1-early",
-    "hwkwon/S-SOLAR-10.7B-v1.3": "https://huggingface.co/hwkwon/S-SOLAR-10.7B-v1.3",
-    "werty1248/Llama-3-Ko-8B-Instruct-AOG": "https://huggingface.co/werty1248/Llama-3-Ko-8B-Instruct-AOG",
-    "Alphacode-AI/AlphaMist7B-slr-v2": "https://huggingface.co/Alphacode-AI/AlphaMist7B-slr-v2",
-    "maywell/koOpenChat-sft": "https://huggingface.co/maywell/koOpenChat-sft",
-    "lemon-mint/gemma-7b-openhermes-v0.80": "https://huggingface.co/lemon-mint/gemma-7b-openhermes-v0.80",
-    "VIRNECT/llama-3-Korean-8B-r-v1": "https://huggingface.co/VIRNECT/llama-3-Korean-8B-r-v1",
-    "Alphacode-AI/AlphaMist7B-slr-v1": "https://huggingface.co/Alphacode-AI/AlphaMist7B-slr-v1",
-    "Loyola/Mistral-7b-ITmodel": "https://huggingface.co/Loyola/Mistral-7b-ITmodel",
-    "VIRNECT/llama-3-Korean-8B-r-v2": "https://huggingface.co/VIRNECT/llama-3-Korean-8B-r-v2",
-    "NLPark/AnFeng_v3.1-Avocet": "https://huggingface.co/NLPark/AnFeng_v3.1-Avocet",
-    "maywell/Synatra_TbST11B_EP01": "https://huggingface.co/maywell/Synatra_TbST11B_EP01",
-    "GritLM/GritLM-7B-KTO": "https://huggingface.co/GritLM/GritLM-7B-KTO",
-    "01-ai/Yi-34B-Chat": "https://huggingface.co/01-ai/Yi-34B-Chat",
-    "ValiantLabs/Llama3.1-8B-ShiningValiant2": "https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2",
-    "princeton-nlp/Llama-3-Base-8B-SFT-CPO": "https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-CPO",
-    "hyokwan/hkcode_llama3_8b": "https://huggingface.co/hyokwan/hkcode_llama3_8b",
-    "UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3": "https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3",
-    "yuntaeyang/SOLAR-10.7B-Instructlora_sftt-v1.0": "https://huggingface.co/yuntaeyang/SOLAR-10.7B-Instructlora_sftt-v1.0",
-    "juungwon/Llama-3-cs-LoRA": "https://huggingface.co/juungwon/Llama-3-cs-LoRA",
-    "gangyeolkim/llama-3-chat": "https://huggingface.co/gangyeolkim/llama-3-chat",
-    "mncai/llama2-13b-dpo-v3": "https://huggingface.co/mncai/llama2-13b-dpo-v3",
-    "maywell/Synatra-Zephyr-7B-v0.01": "https://huggingface.co/maywell/Synatra-Zephyr-7B-v0.01",
-    "ENERGY-DRINK-LOVE/leaderboard_inst_v1.3_deup_LDCC-SOLAR-10.7B_SFT": "https://huggingface.co/ENERGY-DRINK-LOVE/leaderboard_inst_v1.3_deup_LDCC-SOLAR-10.7B_SFT",
-    "juungwon/Llama-3-constructionsafety-LoRA": "https://huggingface.co/juungwon/Llama-3-constructionsafety-LoRA",
-    "princeton-nlp/Mistral-7B-Base-SFT-SimPO": "https://huggingface.co/princeton-nlp/Mistral-7B-Base-SFT-SimPO",
-    "moondriller/solar10B-eugeneparkthebestv2": "https://huggingface.co/moondriller/solar10B-eugeneparkthebestv2",
-    "chlee10/T3Q-LLM3-Llama3-sft1.0-dpo1.0": "https://huggingface.co/chlee10/T3Q-LLM3-Llama3-sft1.0-dpo1.0",
-    "Edentns/DataVortexS-10.7B-dpo-v1.7": "https://huggingface.co/Edentns/DataVortexS-10.7B-dpo-v1.7",
-    "gamzadole/llama3_instruct_tuning_without_pretraing": "https://huggingface.co/gamzadole/llama3_instruct_tuning_without_pretraing",
-    "saltlux/Ko-Llama3-Luxia-8B": "https://huggingface.co/saltlux/Ko-Llama3-Luxia-8B",
-    "kimdeokgi/ko-pt-model-test1": "https://huggingface.co/kimdeokgi/ko-pt-model-test1",
-    "maywell/Synatra-11B-Testbench-2": "https://huggingface.co/maywell/Synatra-11B-Testbench-2",
-    "Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO": "https://huggingface.co/Danielbrdz/Barcenas-14b-Phi-3-medium-ORPO",
-    "vicgalle/Configurable-Mistral-7B": "https://huggingface.co/vicgalle/Configurable-Mistral-7B",
-    "ENERGY-DRINK-LOVE/leaderboard_inst_v1.5_LDCC-SOLAR-10.7B_SFT": "https://huggingface.co/ENERGY-DRINK-LOVE/leaderboard_inst_v1.5_LDCC-SOLAR-10.7B_SFT",
-    "beomi/Llama-3-Open-Ko-8B-Instruct-preview": "https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview",
-    "Edentns/DataVortexS-10.7B-dpo-v1.3": "https://huggingface.co/Edentns/DataVortexS-10.7B-dpo-v1.3",
-    "spow12/Llama3_ko_4.2_sft": "https://huggingface.co/spow12/Llama3_ko_4.2_sft",
-    "maywell/Llama-3-Ko-8B-Instruct": "https://huggingface.co/maywell/Llama-3-Ko-8B-Instruct",
-    "T3Q-LLM/T3Q-LLM3-NC-v1.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM3-NC-v1.0",
-    "ehartford/dolphin-2.2.1-mistral-7b": "https://huggingface.co/ehartford/dolphin-2.2.1-mistral-7b",
-    "hwkwon/S-SOLAR-10.7B-SFT-v1.3": "https://huggingface.co/hwkwon/S-SOLAR-10.7B-SFT-v1.3",
-    "sel303/llama3-instruct-diverce-v2.0": "https://huggingface.co/sel303/llama3-instruct-diverce-v2.0",
-    "4yo1/llama3-eng-ko-8b-sl3": "https://huggingface.co/4yo1/llama3-eng-ko-8b-sl3",
-    "hkss/hk-SOLAR-10.7B-v1.1": "https://huggingface.co/hkss/hk-SOLAR-10.7B-v1.1",
-    "Open-Orca/Mistral-7B-OpenOrca": "https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca",
-    "hyokwan/familidata": "https://huggingface.co/hyokwan/familidata",
-    "uukuguy/zephyr-7b-alpha-dare-0.85": "https://huggingface.co/uukuguy/zephyr-7b-alpha-dare-0.85",
-    "gwonny/nox-solar-10.7b-v4-kolon-all-5": "https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-all-5",
-    "shleeeee/mistral-ko-tech-science-v1": "https://huggingface.co/shleeeee/mistral-ko-tech-science-v1",
-    "Deepnoid/deep-solar-eeve-KorSTS": "https://huggingface.co/Deepnoid/deep-solar-eeve-KorSTS",
-    "AIdenU/Mistral-7B-v0.2-ko-Y24_v1.0": "https://huggingface.co/AIdenU/Mistral-7B-v0.2-ko-Y24_v1.0",
-    "tlphams/gollm-tendency-45": "https://huggingface.co/tlphams/gollm-tendency-45",
-    "realPCH/ko_solra_merge": "https://huggingface.co/realPCH/ko_solra_merge",
-    "Cartinoe5930/original-KoRAE-13b": "https://huggingface.co/Cartinoe5930/original-KoRAE-13b",
-    "GAI-LLM/Yi-Ko-6B-dpo-v5": "https://huggingface.co/GAI-LLM/Yi-Ko-6B-dpo-v5",
-    "Minirecord/Mini_DPO_test02": "https://huggingface.co/Minirecord/Mini_DPO_test02",
-    "AIJUUD/juud-Mistral-7B-dpo": "https://huggingface.co/AIJUUD/juud-Mistral-7B-dpo",
-    "gwonny/nox-solar-10.7b-v4-kolon-all-10": "https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-all-10",
-    "jieunhan/TEST_MODEL": "https://huggingface.co/jieunhan/TEST_MODEL",
-    "etri-xainlp/kor-llama2-13b-dpo": "https://huggingface.co/etri-xainlp/kor-llama2-13b-dpo",
-    "ifuseok/yi-ko-playtus-instruct-v0.2": "https://huggingface.co/ifuseok/yi-ko-playtus-instruct-v0.2",
-    "Cartinoe5930/original-KoRAE-13b-3ep": "https://huggingface.co/Cartinoe5930/original-KoRAE-13b-3ep",
-    "Trofish/KULLM-RLHF": "https://huggingface.co/Trofish/KULLM-RLHF",
-    "wkshin89/Yi-Ko-6B-Instruct-v1.0": "https://huggingface.co/wkshin89/Yi-Ko-6B-Instruct-v1.0",
-    "momo/polyglot-ko-12.8b-Chat-QLoRA-Merge": "https://huggingface.co/momo/polyglot-ko-12.8b-Chat-QLoRA-Merge",
-    "PracticeLLM/Custom-KoLLM-13B-v5": "https://huggingface.co/PracticeLLM/Custom-KoLLM-13B-v5",
-    "BAAI/Infinity-Instruct-3M-0625-Yi-1.5-9B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0625-Yi-1.5-9B",
-    "MRAIRR/minillama3_8b_all": "https://huggingface.co/MRAIRR/minillama3_8b_all",
-    "failspy/Phi-3-medium-4k-instruct-abliterated-v3": "https://huggingface.co/failspy/Phi-3-medium-4k-instruct-abliterated-v3",
-    "DILAB-HYU/koquality-polyglot-12.8b": "https://huggingface.co/DILAB-HYU/koquality-polyglot-12.8b",
-    "kyujinpy/Korean-OpenOrca-v3": "https://huggingface.co/kyujinpy/Korean-OpenOrca-v3",
-    "4yo1/llama3-eng-ko-8b": "https://huggingface.co/4yo1/llama3-eng-ko-8b",
-    "4yo1/llama3-eng-ko-8": "https://huggingface.co/4yo1/llama3-eng-ko-8",
-    "4yo1/llama3-eng-ko-8-llama": "https://huggingface.co/4yo1/llama3-eng-ko-8-llama",
-    "PracticeLLM/Custom-KoLLM-13B-v2": "https://huggingface.co/PracticeLLM/Custom-KoLLM-13B-v2",
-    "kyujinpy/KOR-Orca-Platypus-13B-v2": "https://huggingface.co/kyujinpy/KOR-Orca-Platypus-13B-v2",
-    "ghost-x/ghost-7b-alpha": "https://huggingface.co/ghost-x/ghost-7b-alpha",
-    "HumanF-MarkrAI/pub-llama-13B-v6": "https://huggingface.co/HumanF-MarkrAI/pub-llama-13B-v6",
-    "nlpai-lab/kullm-polyglot-5.8b-v2": "https://huggingface.co/nlpai-lab/kullm-polyglot-5.8b-v2",
-    "maywell/Synatra-42dot-1.3B": "https://huggingface.co/maywell/Synatra-42dot-1.3B",
-    "yhkim9362/gemma-en-ko-7b-v0.1": "https://huggingface.co/yhkim9362/gemma-en-ko-7b-v0.1",
-    "yhkim9362/gemma-en-ko-7b-v0.2": "https://huggingface.co/yhkim9362/gemma-en-ko-7b-v0.2",
-    "daekeun-ml/Llama-2-ko-OpenOrca-gugugo-13B": "https://huggingface.co/daekeun-ml/Llama-2-ko-OpenOrca-gugugo-13B",
-    "beomi/Yi-Ko-6B": "https://huggingface.co/beomi/Yi-Ko-6B",
-    "jojo0217/ChatSKKU5.8B": "https://huggingface.co/jojo0217/ChatSKKU5.8B",
-    "Deepnoid/deep-solar-v2.0.7": "https://huggingface.co/Deepnoid/deep-solar-v2.0.7",
-    "01-ai/Yi-1.5-9B": "https://huggingface.co/01-ai/Yi-1.5-9B",
-    "PracticeLLM/Custom-KoLLM-13B-v4": "https://huggingface.co/PracticeLLM/Custom-KoLLM-13B-v4",
-    "nuebaek/komt_mistral_mss_user_0_max_steps_80": "https://huggingface.co/nuebaek/komt_mistral_mss_user_0_max_steps_80",
-    "dltjdgh0928/lsh_finetune_v0.11": "https://huggingface.co/dltjdgh0928/lsh_finetune_v0.11",
-    "shleeeee/mistral-7b-wiki": "https://huggingface.co/shleeeee/mistral-7b-wiki",
-    "nayohan/polyglot-ko-5.8b-Inst": "https://huggingface.co/nayohan/polyglot-ko-5.8b-Inst",
-    "ifuseok/sft-solar-10.7b-v1.1": "https://huggingface.co/ifuseok/sft-solar-10.7b-v1.1",
-    "Junmai/KIT-5.8b": "https://huggingface.co/Junmai/KIT-5.8b",
-    "heegyu/polyglot-ko-3.8b-chat": "https://huggingface.co/heegyu/polyglot-ko-3.8b-chat",
-    "etri-xainlp/polyglot-ko-12.8b-instruct": "https://huggingface.co/etri-xainlp/polyglot-ko-12.8b-instruct",
-    "OpenBuddy/openbuddy-mistral2-7b-v20.3-32k": "https://huggingface.co/OpenBuddy/openbuddy-mistral2-7b-v20.3-32k",
-    "sh2orc/Llama-3-Korean-8B": "https://huggingface.co/sh2orc/Llama-3-Korean-8B",
-    "Deepnoid/deep-solar-eeve-v2.0.0": "https://huggingface.co/Deepnoid/deep-solar-eeve-v2.0.0",
-    "Herry443/Mistral-7B-KNUT-ref": "https://huggingface.co/Herry443/Mistral-7B-KNUT-ref",
-    "heegyu/polyglot-ko-5.8b-chat": "https://huggingface.co/heegyu/polyglot-ko-5.8b-chat",
-    "jungyuko/DAVinCI-42dot_LLM-PLM-1.3B-v1.5.3": "https://huggingface.co/jungyuko/DAVinCI-42dot_LLM-PLM-1.3B-v1.5.3",
-    "DILAB-HYU/KoQuality-Polyglot-5.8b": "https://huggingface.co/DILAB-HYU/KoQuality-Polyglot-5.8b",
-    "Byungchae/k2s3_test_0000": "https://huggingface.co/Byungchae/k2s3_test_0000",
-    "migtissera/Tess-v2.5-Phi-3-medium-128k-14B": "https://huggingface.co/migtissera/Tess-v2.5-Phi-3-medium-128k-14B",
-    "kyujinpy/Korean-OpenOrca-13B": "https://huggingface.co/kyujinpy/Korean-OpenOrca-13B",
-    "kyujinpy/KO-Platypus2-13B": "https://huggingface.co/kyujinpy/KO-Platypus2-13B",
-    "jin05102518/Astral-7B-Instruct-v0.01": "https://huggingface.co/jin05102518/Astral-7B-Instruct-v0.01",
-    "Byungchae/k2s3_test_0002": "https://huggingface.co/Byungchae/k2s3_test_0002",
-    "NousResearch/Nous-Hermes-llama-2-7b": "https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b",
-    "kaist-ai/prometheus-13b-v1.0": "https://huggingface.co/kaist-ai/prometheus-13b-v1.0",
-    "sel303/llama3-diverce-ver1.0": "https://huggingface.co/sel303/llama3-diverce-ver1.0",
-    "NousResearch/Nous-Capybara-7B": "https://huggingface.co/NousResearch/Nous-Capybara-7B",
-    "rrw-x2/KoSOLAR-10.7B-DPO-v1.0": "https://huggingface.co/rrw-x2/KoSOLAR-10.7B-DPO-v1.0",
-    "Edentns/DataVortexS-10.7B-v0.2": "https://huggingface.co/Edentns/DataVortexS-10.7B-v0.2",
-    "Jsoo/Llama3-beomi-Open-Ko-8B-Instruct-preview-test6": "https://huggingface.co/Jsoo/Llama3-beomi-Open-Ko-8B-Instruct-preview-test6",
-    "tlphams/gollm-instruct-all-in-one-v1": "https://huggingface.co/tlphams/gollm-instruct-all-in-one-v1",
-    "Edentns/DataVortexTL-1.1B-v0.1": "https://huggingface.co/Edentns/DataVortexTL-1.1B-v0.1",
-    "richard-park/llama3-pre1-ds": "https://huggingface.co/richard-park/llama3-pre1-ds",
-    "ehartford/samantha-1.1-llama-33b": "https://huggingface.co/ehartford/samantha-1.1-llama-33b",
-    "heegyu/LIMA-13b-hf": "https://huggingface.co/heegyu/LIMA-13b-hf",
-    "heegyu/42dot_LLM-PLM-1.3B-mt": "https://huggingface.co/heegyu/42dot_LLM-PLM-1.3B-mt",
-    "shleeeee/mistral-ko-7b-wiki-neft": "https://huggingface.co/shleeeee/mistral-ko-7b-wiki-neft",
-    "EleutherAI/polyglot-ko-1.3b": "https://huggingface.co/EleutherAI/polyglot-ko-1.3b",
-    "kyujinpy/Ko-PlatYi-6B-gu": "https://huggingface.co/kyujinpy/Ko-PlatYi-6B-gu",
-    "sel303/llama3-diverce-ver1.6": "https://huggingface.co/sel303/llama3-diverce-ver1.6"    
-}
-
-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()
-        else:
-            print(f"Failed to fetch Korea models: {response.status_code}")
-            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  # API의 한 번 요청당 최대 크기
-    
-    # 여러 페이지에 걸쳐 데이터 수집
-    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())
-            print(f"Fetched models {offset+1} to {offset+len(response.json())}")
-        else:
-            print(f"Failed to fetch models at offset {offset}: {response.status_code}")
-            break
-    
-    # Korea 검색 결과도 동일하게 확장
-    korea_params = {
-        "search": "korea",
-        "full": "True",
-        "config": "True",
-        "limit": limit
-    }
-    
-    korea_response = requests.get(
-        "https://huggingface.co/api/models",
-        headers={'Accept': 'application/json'},
-        params=korea_params
-    )
-    
-    if korea_response.status_code == 200:
-        korea_models = korea_response.json()
-        print(f"Fetched {len(korea_models)} Korea-related models")
-        
-        # 중복 제거하면서 Korea 모델 추가
-        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)
-                existing_ids.add(model.get('id', ''))
-    
-    print(f"Total unique models: {len(all_models)}")
-    return all_models[:limit]
-
-def get_models_data(progress=gr.Progress()):
-    def calculate_rank(model_id, all_global_models, korea_models):
-        # 글로벌 순위 확인
-        global_rank = next((idx for idx, m in enumerate(all_global_models, 1) 
-                          if m.get('id', '').strip() == model_id.strip()), None)
-        
-        # Korea 모델인 경우
-        is_korea = any(m.get('id', '').strip() == model_id.strip() for m in korea_models)
-        
-        if is_korea:
-            # Korea 모델 중에서의 순위 확인
-            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...")
-        
-        if not HF_TOKEN:
-            fig = create_error_plot()
-            error_html = """
-            <div style='padding: 20px; background: #fee; border-radius: 10px; margin: 10px 0;'>
-                <h3 style='color: #c00;'>⚠️ API 인증이 필요합니다</h3>
-                <p>HuggingFace API 토큰이 설정되지 않았습니다. 완전한 기능을 사용하기 위해서는 API 토큰이 필요합니다.</p>
-            </div>
-            """
-            empty_df = pd.DataFrame(columns=['Global Rank', 'Model ID', 'Title', 'Downloads', 'Likes', 'Korea Search', 'URL'])
-            return fig, error_html, empty_df
-
-        # 일반 모델과 Korea 관련 모델 모두 가져오기 (3000위까지)
-        all_global_models = get_all_models(limit=3000)
-        korea_models = get_korea_models()
-        
-        print(f"Total global models fetched: {len(all_global_models)}")
-        print(f"Total Korea models fetched: {len(korea_models)}")
-        
-        # 모든 모델 통합 (중복 제거)
-        all_models = all_global_models.copy()
-        existing_ids = {model.get('id', '') for model in all_global_models}
-        
-        added_korea_models = 0
-        for korea_model in korea_models:
-            if korea_model.get('id', '') not in existing_ids:
-                all_models.append(korea_model)
-                existing_ids.add(korea_model.get('id', ''))
-                added_korea_models += 1
-        
-        print(f"Added {added_korea_models} unique Korea models")
-        print(f"Total combined models: {len(all_models)}")
-        
-        # 시각화를 위한 Figure 생성
-        fig = go.Figure()
-        
-        # 순위 정보 수집
-        filtered_models = []
-        for model_id in target_models.keys():
-            try:
-                normalized_id = model_id.strip('/')
-                model_url_api = f"https://huggingface.co/api/models/{normalized_id}"
-                response = requests.get(
-                    model_url_api, 
-                    headers={'Accept': 'application/json'}
-                )
-                
-                if response.status_code == 200:
-                    model_data = response.json()
-                    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
-                    })
-                    
-                    print(f"Model {model_id}: Rank={rank}, 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
-                    })
-            except Exception as e:
-                print(f"Error processing {model_id}: {str(e)}")
-                continue
-
-        # 순위로 정렬
-        filtered_models.sort(key=lambda x: float('inf') if isinstance(x['global_rank'], str) else x['global_rank'])
-        
-        # 시각화 데이터 준비
-        valid_models = [m for m in filtered_models if isinstance(m['global_rank'], (int, float))]
-        
-        if valid_models:
-            ids = [m['id'] for m in valid_models]
-            ranks = [m['global_rank'] for m in valid_models]
-            
-            fig.add_trace(go.Bar(
-                x=ids,
-                y=[3001 - r for r in ranks],  # Y축 범위 3000까지 확장
-                text=[f"Rank: #{r}<br>Downloads: {format(m['downloads'], ',')}<br>Likes: {format(m['likes'], ',')}"
-                      for r, m in zip(ranks, valid_models)],
-                textposition='auto',
-                marker_color=['rgba(255,0,0,0.6)' if m['is_korea'] else 'rgba(0,0,255,0.6)' 
-                            for m in valid_models],
-                opacity=0.8
-            ))
-
-            fig.update_layout(
-                title="HuggingFace Models Global Rankings (Up to #3000)",
-                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
-            )
-
-        # HTML 카드 생성
-        html_content = """
-        <div style='padding: 20px; background: #f5f5f5;'>
-            <h2 style='color: #2c3e50;'>Models Rankings (Up to #3000)</h2>
-            <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 20px;'>
-        """
-        
-        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 = "🇰🇷 Korea Search Result" if model['is_korea'] else ""
-            
-            html_content += f"""
-            <div style='
-                background: white;
-                padding: 20px;
-                border-radius: 10px;
-                box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-                transition: transform 0.2s;
-                {f"border: 2px solid #e74c3c;" if model['is_korea'] else ""}
-            '>
-                <h3 style='color: #34495e;'>{rank_display}</h3>
-                <h4 style='color: #2c3e50;'>{model['id']}</h4>
-                <p style='color: #e74c3c; font-weight: bold;'>{korea_badge}</p>
-                <p style='color: #7f8c8d;'>⬇️ Downloads: {format(model['downloads'], ',')}</p>
-                <p style='color: #7f8c8d;'>👍 Likes: {format(model['likes'], ',')}</p>
-                <a href='{target_models[model['id']]}' 
-                   target='_blank' 
-                   style='
-                    display: inline-block;
-                    padding: 8px 16px;
-                    background: #3498db;
-                    color: white;
-                    text-decoration: none;
-                    border-radius: 5px;
-                    transition: background 0.3s;
-                   '>
-                   Visit Model 🔗
-                </a>
-            </div>
-            """
-        
-        html_content += "</div></div>"
-        
-        # 데이터프레임 생성
-        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 Search': '🇰🇷' if m['is_korea'] else '',
-            'URL': target_models[m['id']]
-        } for m in filtered_models])
-        
-        progress(1.0, desc="Complete!")
-        return fig, html_content, df
-        
-    except Exception as e:
-        print(f"Error in get_models_data: {str(e)}")
-        error_fig = create_error_plot()
-        error_html = f"""
-        <div style='padding: 20px; background: #fee; border-radius: 10px; margin: 10px 0;'>
-            <h3 style='color: #c00;'>⚠️ 오류가 발생했습니다</h3>
-            <p>{str(e)}</p>
-        </div>
-        """
-        empty_df = pd.DataFrame(columns=['Global Rank', 'Model ID', 'Title', 'Downloads', 'Likes', 'Korea Search', 'URL'])
-        return error_fig, error_html, empty_df
-
-        
-# 관심 스페이스 URL 리스트와 정보
-target_spaces = {
-    
-    "kolaslab/Quantum": "https://huggingface.co/spaces/kolaslab/Quantum",
-    "openfree/webtoon": "https://huggingface.co/spaces/openfree/webtoon",
-    "immunobiotech/ChicagoGallery": "https://huggingface.co/spaces/immunobiotech/ChicagoGallery",
-    "immunobiotech/MetropolitanMuseum": "https://huggingface.co/spaces/immunobiotech/MetropolitanMuseum",    
-    "immunobiotech/opensky": "https://huggingface.co/spaces/immunobiotech/opensky",
-    
-    "kolaslab/Audio-Visualizer": "https://huggingface.co/spaces/kolaslab/Audio-Visualizer",
-    "kolaslab/Radio-Learning": "https://huggingface.co/spaces/kolaslab/Radio-Learning",
-    "kolaslab/Future-Gallaxy": "https://huggingface.co/spaces/kolaslab/Future-Gallaxy",    
-    "openfree/ProteinGenesis": "https://huggingface.co/spaces/openfree/ProteinGenesis",
-    "openfree/2025saju": "https://huggingface.co/spaces/openfree/2025saju",
-    "ginigen/Dokdo-membership": "https://huggingface.co/spaces/ginigen/Dokdo-membership",
-    "VIDraft/eum": "https://huggingface.co/spaces/VIDraft/eum",
-    "kolaslab/VisionART": "https://huggingface.co/spaces/kolaslab/VisionART",
-    "aiqtech/FLUX-military": "https://huggingface.co/spaces/aiqtech/FLUX-military",
-    "fantaxy/Rolls-Royce": "https://huggingface.co/spaces/fantaxy/Rolls-Royce",
-    "seawolf2357/flux-korea-hanbok-lora": "https://huggingface.co/spaces/seawolf2357/flux-korea-hanbok-lora",
-    "seawolf2357/flux-korea-palace-lora": "https://huggingface.co/spaces/seawolf2357/flux-korea-palace-lora",
-    "aiqcamp/flux-cat-lora": "https://huggingface.co/spaces/aiqcamp/flux-cat-lora",
-    "gunship999/SexyImages": "https://huggingface.co/spaces/gunship999/SexyImages",
-    "aiqtech/flux-claude-monet-lora": "https://huggingface.co/spaces/aiqtech/flux-claude-monet-lora",
-    "ginigen/CANVAS-o3": "https://huggingface.co/spaces/ginigen/CANVAS-o3",
-    "kolaslab/world-sdr": "https://huggingface.co/spaces/kolaslab/world-sdr",
-    "seawolf2357/3D-Avatar-Generator": "https://huggingface.co/spaces/seawolf2357/3D-Avatar-Generator",
-    "fantaxy/playground25": "https://huggingface.co/spaces/fantaxy/playground25",
-    "openfree/ultpixgen": "https://huggingface.co/spaces/openfree/ultpixgen",
-    "kolaslab/VISION-NIGHT": "https://huggingface.co/spaces/kolaslab/VISION-NIGHT",
-    "kolaslab/FLUX-WEB": "https://huggingface.co/spaces/kolaslab/FLUX-WEB",
-    "seawolf2357/REALVISXL-V5": "https://huggingface.co/spaces/seawolf2357/REALVISXL-V5",
-    "ginipick/Dokdo-multimodal": "https://huggingface.co/spaces/ginipick/Dokdo-multimodal",
-    "ginigen/theater": "https://huggingface.co/spaces/ginigen/theater",
-    "VIDraft/stock": "https://huggingface.co/spaces/VIDraft/stock",
-    "fantos/flxcontrol": "https://huggingface.co/spaces/fantos/flxcontrol",
-    "fantos/textcutobject": "https://huggingface.co/spaces/fantos/textcutobject",
-    "ginipick/FLUX-Prompt-Generator": "https://huggingface.co/spaces/ginipick/FLUX-Prompt-Generator",
-    "fantaxy/flxloraexp": "https://huggingface.co/spaces/fantaxy/flxloraexp",
-    "fantos/flxloraexp": "https://huggingface.co/spaces/fantos/flxloraexp",
-    "seawolf2357/flxloraexp": "https://huggingface.co/spaces/seawolf2357/flxloraexp",
-    "ginipick/flxloraexp": "https://huggingface.co/spaces/ginipick/flxloraexp",
-    "ginipick/FLUX-Prompt-Generator": "https://huggingface.co/spaces/ginipick/FLUX-Prompt-Generator",
-    "ginigen/Dokdo": "https://huggingface.co/spaces/ginigen/Dokdo",
-    "aiqcamp/imagemagic": "https://huggingface.co/spaces/aiqcamp/imagemagic",
-    "openfree/ColorRevive": "https://huggingface.co/spaces/openfree/ColorRevive",
-    "VIDraft/RAGOndevice": "https://huggingface.co/spaces/VIDraft/RAGOndevice",
-    "gunship999/Radar-Bluetooth": "https://huggingface.co/spaces/gunship999/Radar-Bluetooth",
-    "gunship999/WiFi-VISION": "https://huggingface.co/spaces/gunship999/WiFi-VISION",
-    "gunship999/SONAR-Radar": "https://huggingface.co/spaces/gunship999/SONAR-Radar",
-    "aiqcamp/AudioLlama": "https://huggingface.co/spaces/aiqcamp/AudioLlama",    
-    "ginigen/FLUXllama-Multilingual": "https://huggingface.co/spaces/ginigen/FLUXllama-Multilingual",    
-    "ginipick/ginimedi": "https://huggingface.co/spaces/ginipick/ginimedi",
-    "ginipick/ginilaw": "https://huggingface.co/spaces/ginipick/ginilaw",
-    "ginipick/ginipharm": "https://huggingface.co/spaces/ginipick/ginipharm",
-    "ginipick/FitGen": "https://huggingface.co/spaces/ginipick/FitGen",
-    "fantaxy/FLUX-Animations": "https://huggingface.co/spaces/fantaxy/FLUX-Animations",
-    "fantaxy/Remove-Video-Background": "https://huggingface.co/spaces/fantaxy/Remove-Video-Background",
-    "fantaxy/ofai-flx-logo": "https://huggingface.co/spaces/fantaxy/ofai-flx-logo",
-    "fantaxy/flx-pulid": "https://huggingface.co/spaces/fantaxy/flx-pulid",
-    "fantaxy/flx-upscale": "https://huggingface.co/spaces/fantaxy/flx-upscale",
-    "aiqcamp/Fashion-FLUX": "https://huggingface.co/spaces/aiqcamp/Fashion-FLUX",
-    "ginipick/StyleGen": "https://huggingface.co/spaces/ginipick/StyleGen",
-    "openfree/StoryStar": "https://huggingface.co/spaces/openfree/StoryStar",
-    "fantos/x-mas": "https://huggingface.co/spaces/fantos/x-mas",
-    "openfree/Korean-Leaderboard": "https://huggingface.co/spaces/openfree/Korean-Leaderboard",
-    "ginipick/FLUXllama": "https://huggingface.co/spaces/ginipick/FLUXllama",
-    "ginipick/SORA-3D": "https://huggingface.co/spaces/ginipick/SORA-3D",
-    "fantaxy/Sound-AI-SFX": "https://huggingface.co/spaces/fantaxy/Sound-AI-SFX",
-    "fantos/flx8lora": "https://huggingface.co/spaces/fantos/flx8lora",
-    "ginigen/Canvas": "https://huggingface.co/spaces/ginigen/Canvas",
-    "fantaxy/erotica": "https://huggingface.co/spaces/fantaxy/erotica",
-    "ginipick/time-machine": "https://huggingface.co/spaces/ginipick/time-machine",
-    "aiqcamp/FLUX-VisionReply": "https://huggingface.co/spaces/aiqcamp/FLUX-VisionReply",
-    "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",
-    "aiqtech/Particle-Accelerator-Simulation": "https://huggingface.co/spaces/aiqtech/Particle-Accelerator-Simulation",
-    "openfree/GiniGEN": "https://huggingface.co/spaces/openfree/GiniGEN",
-    "kolaslab/3DAudio-Spectrum-Analyzer": "https://huggingface.co/spaces/kolaslab/3DAudio-Spectrum-Analyzer",
-    "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",
-    "fantos/miro-game": "https://huggingface.co/spaces/fantos/miro-game",
-    "kolaslab/shooting": "https://huggingface.co/spaces/kolaslab/shooting",
-    "VIDraft/Mouse-Hackathon": "https://huggingface.co/spaces/VIDraft/Mouse-Hackathon",
-    "aiqmaster/stocksimulation": "https://huggingface.co/spaces/aiqmaster/stocksimulation",
-    "aiqmaster/assetai": "https://huggingface.co/spaces/aiqmaster/assetai",
-    "aiqmaster/stockai": "https://huggingface.co/spaces/aiqmaster/stockai",
-    "cutechicken/TankWar3D": "https://huggingface.co/spaces/cutechicken/TankWar3D",
-    "kolaslab/RC4-EnDecoder": "https://huggingface.co/spaces/kolaslab/RC4-EnDecoder",
-    "kolaslab/simulator": "https://huggingface.co/spaces/kolaslab/simulator",
-    "kolaslab/calculator": "https://huggingface.co/spaces/kolaslab/calculator",
-    "aiqtech/kofaceid": "https://huggingface.co/spaces/aiqtech/kofaceid",
-    "fantaxy/fastvideogena": "https://huggingface.co/spaces/fantaxy/fastvideogen",
-    "fantos/cogvidx": "https://huggingface.co/spaces/fantos/cogvidx",
-    "fantos/flxfashmodel": "https://huggingface.co/spaces/fantos/flxfashmodel",
-    "fantos/kolcontrl": "https://huggingface.co/spaces/fantos/kolcontrl",
-    "fantos/EveryText": "https://huggingface.co/spaces/fantos/EveryText",
-    "aiqtech/cinevid": "https://huggingface.co/spaces/aiqtech/cinevid",
-    "aiqtech/FLUX-Ghibli-Studio-LoRA": "https://huggingface.co/spaces/aiqtech/FLUX-Ghibli-Studio-LoRA",
-    "aiqtech/flxgif": "https://huggingface.co/spaces/aiqtech/flxgif",
-    "aiqtech/imaginpaint": "https://huggingface.co/spaces/aiqtech/imaginpaint",
-
-
-    "upstage/open-ko-llm-leaderboard": "https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard",
-    "LGAI-EXAONE/EXAONE-3.5-Instruct-Demo": "https://huggingface.co/spaces/LGAI-EXAONE/EXAONE-3.5-Instruct-Demo",    
-    "LeeSangHoon/HierSpeech_TTS": "https://huggingface.co/spaces/LeeSangHoon/HierSpeech_TTS",    
-    "etri-vilab/Ko-LLaVA": "https://huggingface.co/spaces/etri-vilab/Ko-LLaVA",
-    "etri-vilab/KOALA": "https://huggingface.co/spaces/etri-vilab/KOALA",
-    "naver-clova-ix/donut-base-finetuned-cord-v2": "https://huggingface.co/spaces/naver-clova-ix/donut-base-finetuned-cord-v2",    
-    "NCSOFT/VARCO_Arena": "https://huggingface.co/spaces/NCSOFT/VARCO_Arena"    
-}
-
-def get_spaces_data(sort_type="trending", progress=gr.Progress()):
-    """스페이스 데이터 가져오기 (trending 또는 modes)"""
-    url = "https://huggingface.co/api/spaces"
-    params = {
-        'full': 'true',
-        'limit': 500
-    }
-    
-    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()
-        
-        # 순위 정보 저장
-        space_ranks = {}
-        for idx, space in enumerate(all_spaces, 1):
-            space_id = space.get('id', '')
-            if space_id in target_spaces:
-                space['rank'] = idx
-                space_ranks[space_id] = space
-        
-        spaces = [space_ranks[space_id] for space_id in space_ranks.keys()]
-        spaces.sort(key=lambda x: x['rank'])
-        
-        progress(0.3, desc="Creating visualization...")
-        
-        # 시각화 생성
-        fig = go.Figure()
-        
-        # 데이터 준비
-        ids = [space['id'] for space in spaces]
-        ranks = [space['rank'] for space in spaces]
-        likes = [space.get('likes', 0) for space in spaces]
-        titles = [space.get('cardData', {}).get('title') or space.get('title', 'No Title') for space in spaces]
-        
-        # 막대 그래프 생성
-        fig.add_trace(go.Bar(
-            x=ids,
-            y=ranks,
-            text=[f"Rank: {r}<br>Title: {t}<br>Likes: {l}" 
-                  for r, t, l in zip(ranks, titles, likes)],
-            textposition='auto',
-            marker_color='rgb(158,202,225)',
-            opacity=0.8
-        ))
-        
-        fig.update_layout(
-            title={
-                'text': f'Hugging Face Spaces {sort_type.title()} Rankings (Top 500)',
-                'y':0.95,
-                'x':0.5,
-                'xanchor': 'center',
-                'yanchor': 'top'
-            },
-            xaxis_title='Space ID',
-            yaxis_title='Rank',
-            yaxis=dict(
-                autorange='reversed',  # Y축을 반전
-                tickmode='array',
-                ticktext=[str(i) for i in range(1, 501, 20)],  # 1부터 400까지 20 간격으로 표시
-                tickvals=[i for i in range(1, 501, 20)],
-                range=[1, 500]  # Y축 범위를 1부터 400까지로 설정
-            ),
-            height=800,
-            showlegend=False,
-            template='plotly_white',
-            xaxis_tickangle=-45
-        )
-        
-        progress(0.6, desc="Creating space cards...")
-        
-        # HTML 카드 생성
-        html_content = f"""
-        <div style='padding: 20px; background: #f5f5f5;'>
-            <h2 style='color: #2c3e50;'>{sort_type.title()} Rankings</h2>
-            <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 20px;'>
-        """
-        
-        for space in spaces:
-            space_id = space['id']
-            rank = space['rank']
-            title = space.get('cardData', {}).get('title') or space.get('title', 'No Title')
-            likes = space.get('likes', 0)
-
-            html_content += f"""
-            <div style='
-                background: white;
-                padding: 20px;
-                border-radius: 10px;
-                box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-                transition: transform 0.2s;
-            '>
-                <h3 style='color: #34495e;'>Rank #{rank} - {space_id}</h3>
-                <h4 style='
-                    color: #2980b9;
-                    margin: 10px 0;
-                    font-size: 1.2em;
-                    font-weight: bold;
-                    text-shadow: 1px 1px 2px rgba(0,0,0,0.1);
-                    background: linear-gradient(to right, #3498db, #2980b9);
-                    -webkit-background-clip: text;
-                    -webkit-text-fill-color: transparent;
-                    padding: 5px 0;
-                '>{title}</h4>
-                <p style='color: #7f8c8d; margin-bottom: 10px;'>👍 Likes: {likes}</p>
-                <a href='{target_spaces[space_id]}' 
-                   target='_blank' 
-                   style='
-                    display: inline-block;
-                    padding: 8px 16px;
-                    background: #3498db;
-                    color: white;
-                    text-decoration: none;
-                    border-radius: 5px;
-                    transition: background 0.3s;
-                   '>
-                   Visit Space 🔗
-                </a>
-            </div>
-            """
-        
-        html_content += "</div></div>"
-        
-        # 데이터프레임 생성
-        df = pd.DataFrame([{
-            'Rank': space['rank'],
-            'Space ID': space['id'],
-            'Title': space.get('cardData', {}).get('title') or space.get('title', 'No Title'),
-            'Likes': space.get('likes', 0),
-            'URL': target_spaces[space['id']]
-        } for space in spaces])
-        
-        progress(1.0, desc="Complete!")
-        return fig, html_content, df
-        
-    except Exception as e:
-        print(f"Error in get_spaces_data: {str(e)}")
-        error_html = f'<div style="color: red; padding: 20px;">Error: {str(e)}</div>'
-        error_plot = create_error_plot()
-        return error_plot, error_html, pd.DataFrame()
-
-
-def create_trend_visualization(spaces_data):
-    if not spaces_data:
-        return create_error_plot()
-    
-    fig = go.Figure()
-    
-    # 순위 데이터 준비
-    ranks = []
-    for idx, space in enumerate(spaces_data, 1):
-        space_id = space.get('id', '')
-        if space_id in target_spaces:
-            ranks.append({
-                'id': space_id,
-                'rank': idx,
-                'likes': space.get('likes', 0),
-                'title': space.get('title', 'N/A'),
-                'views': space.get('views', 0)
-            })
-    
-    if not ranks:
-        return create_error_plot()
-    
-    # 순위별로 정렬
-    ranks.sort(key=lambda x: x['rank'])
-    
-    # 플롯 데이터 생성
-    ids = [r['id'] for r in ranks]
-    rank_values = [r['rank'] for r in ranks]
-    likes = [r['likes'] for r in ranks]
-    views = [r['views'] for r in ranks]
-    
-    # 막대 그래프 생성
-    fig.add_trace(go.Bar(
-        x=ids,
-        y=rank_values,
-        text=[f"Rank: {r}<br>Likes: {l}<br>Views: {v}" for r, l, v in zip(rank_values, likes, views)],
-        textposition='auto',
-        marker_color='rgb(158,202,225)',
-        opacity=0.8
-    ))
-    
-    fig.update_layout(
-        title={
-            'text': 'Current Trending Ranks (All Target Spaces)',
-            'y':0.95,
-            'x':0.5,
-            'xanchor': 'center',
-            'yanchor': 'top'
-        },
-        xaxis_title='Space ID',
-        yaxis_title='Trending Rank',
-        yaxis_autorange='reversed',
-        height=800,
-        showlegend=False,
-        template='plotly_white',
-        xaxis_tickangle=-45
-    )
-    
-    return fig
-
-# 토큰이 없는 경우를 위한 대체 함수
-def get_trending_spaces_without_token():
-    try:
-        url = "https://huggingface.co/api/spaces"
-        params = {
-            'sort': 'likes',
-            'direction': -1,
-            'limit': 500,
-            'full': 'true'
-        }
-        
-        response = requests.get(url, params=params)
-        
-        if response.status_code == 200:
-            return response.json()
-        else:
-            print(f"API 요청 실패 (토큰 없음): {response.status_code}")
-            print(f"Response: {response.text}")
-            return None
-    except Exception as e:
-        print(f"API 호출 중 에러 발생 (토큰 없음): {str(e)}")
-        return None
-
-# API 토큰 설정 및 함수 선택
-if not HF_TOKEN:
-    get_trending_spaces = get_trending_spaces_without_token
-
-
-
-def create_error_plot():
-    fig = go.Figure()
-    fig.add_annotation(
-        text="데이터를 불러올 수 없습니다.\n(API 인증이 필요합니다)",
-        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
-    )
-    return fig
-
-
-def create_space_info_html(spaces_data):
-    if not spaces_data:
-        return "<div style='padding: 20px;'><h2>데이터를 불러오는데 실패했습니다.</h2></div>"
-    
-    html_content = """
-    <div style='padding: 20px;'>
-    <h2 style='color: #2c3e50;'>Current Trending Rankings</h2>
-    <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 20px;'>
-    """
-    
-    # 모든 target spaces를 포함하도록 수정
-    for space_id in target_spaces.keys():
-        space_info = next((s for s in spaces_data if s.get('id') == space_id), None)
-        if space_info:
-            rank = next((idx for idx, s in enumerate(spaces_data, 1) if s.get('id') == space_id), 'N/A')
-            html_content += f"""
-            <div style='
-                background: white;
-                padding: 20px;
-                border-radius: 10px;
-                box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-                transition: transform 0.2s;
-            '>
-                <h3 style='color: #34495e;'>#{rank} - {space_id}</h3>
-                <p style='color: #7f8c8d;'>👍 Likes: {space_info.get('likes', 'N/A')}</p>
-                <p style='color: #7f8c8d;'>👀 Views: {space_info.get('views', 'N/A')}</p>
-                <p style='color: #2c3e50;'>{space_info.get('title', 'N/A')}</p>
-                <p style='color: #7f8c8d; font-size: 0.9em;'>{space_info.get('description', 'N/A')[:100]}...</p>
-                <a href='{target_spaces[space_id]}' 
-                   target='_blank' 
-                   style='
-                    display: inline-block;
-                    padding: 8px 16px;
-                    background: #3498db;
-                    color: white;
-                    text-decoration: none;
-                    border-radius: 5px;
-                    transition: background 0.3s;
-                   '>
-                   Visit Space 🔗
-                </a>
-            </div>
-            """
-        else:
-            html_content += f"""
-            <div style='
-                background: #f8f9fa;
-                padding: 20px;
-                border-radius: 10px;
-                box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-            '>
-                <h3 style='color: #34495e;'>{space_id}</h3>
-                <p style='color: #7f8c8d;'>Not in trending</p>
-                <a href='{target_spaces[space_id]}' 
-                   target='_blank' 
-                   style='
-                    display: inline-block;
-                    padding: 8px 16px;
-                    background: #95a5a6;
-                    color: white;
-                    text-decoration: none;
-                    border-radius: 5px;
-                   '>
-                   Visit Space 🔗
-                </a>
-            </div>
-            """
-    
-    html_content += "</div></div>"
-    return html_content
-
-def create_data_table(spaces_data):
-    if not spaces_data:
-        return pd.DataFrame()
-    
-    rows = []
-    for idx, space in enumerate(spaces_data, 1):
-        space_id = space.get('id', '')
-        if space_id in target_spaces:
-            rows.append({
-                'Rank': idx,
-                'Space ID': space_id,
-                'Likes': space.get('likes', 'N/A'),
-                'Title': space.get('title', 'N/A'),
-                'URL': target_spaces[space_id]
-            })
-    
-    return pd.DataFrame(rows)
-
-def refresh_data():
-    spaces_data = get_trending_spaces()
-    if spaces_data:
-        plot = create_trend_visualization(spaces_data)
-        info = create_space_info_html(spaces_data)
-        df = create_data_table(spaces_data)
-        return plot, info, df
-    else:
-        return create_error_plot(), "<div>API 인증이 필요합니다.</div>", pd.DataFrame()
-
-
-
-def create_registration_bar_chart(data, type_name="Spaces"):
-    try:
-        # TOP 기준 설정
-        top_limit = 500 if type_name == "Spaces" else 3000
-        
-        # DataFrame인 경우 처리
-        if isinstance(data, pd.DataFrame):
-            if type_name == "Models":
-                # 3000위 이내의 모델만 필터링
-                data = data[data['Global Rank'].apply(lambda x: isinstance(x, (int, float)) or (isinstance(x, str) and x.startswith('#')))]
-                data = data[data['Global Rank'].apply(lambda x: int(str(x).replace('#', '')) if isinstance(x, str) else x) <= top_limit]
-            elif type_name == "Spaces":
-                # 500위 이내의 스페이스만 필터링
-                data = data[data['Rank'].apply(lambda x: isinstance(x, (int, float))) & (data['Rank'] <= top_limit)]
-            
-            # ID 컬럼 선택
-            id_column = 'Space ID' if type_name == "Spaces" else 'Model ID'
-            registrations = data[id_column].apply(lambda x: x.split('/')[0]).value_counts()
-        else:
-            # 리스트나 다른 형태의 데이터인 경우 처리
-            registrations = {}
-            for item in data:
-                if isinstance(item, dict):
-                    rank = item.get('global_rank' if type_name == "Models" else 'rank')
-                    if isinstance(rank, str) or rank > top_limit:
-                        continue
-                    creator = item.get('id', '').split('/')[0]
-                    registrations[creator] = registrations.get(creator, 0) + 1
-            registrations = pd.Series(registrations)
-        
-        # 정렬된 데이터 준비
-        registrations = registrations.sort_values(ascending=False)
-        
-        fig = go.Figure(data=[go.Bar(
-            x=registrations.index,
-            y=registrations.values,
-            text=registrations.values,
-            textposition='auto',
-            marker_color='#FF6B6B'
-        )])
-        
-        fig.update_layout(
-            title=f"Korean {type_name} Registrations by Creator (Top {top_limit})",
-            xaxis_title="Creator ID",
-            yaxis_title="Number of Registrations",
-            showlegend=False,
-            height=400,
-            width=700
-        )
-        
-        return fig
-    except Exception as e:
-        print(f"Error in create_registration_bar_chart: {str(e)}")
-        return go.Figure()
-
-def create_pie_chart(data, total_count, type_name="Spaces"):
-    try:
-        # TOP 기준 설정
-        top_limit = 500 if type_name == "Spaces" else 3000
-        
-        # DataFrame인 경우 처리
-        if isinstance(data, pd.DataFrame):
-            if type_name == "Models":
-                # 3000위 이내의 모델만 필터링
-                data = data[data['Global Rank'].apply(lambda x: isinstance(x, (int, float)) or (isinstance(x, str) and x.startswith('#')))]
-                data = data[data['Global Rank'].apply(lambda x: int(str(x).replace('#', '')) if isinstance(x, str) else x) <= top_limit]
-            elif type_name == "Spaces":
-                # 500위 이내의 스페이스만 필터링
-                data = data[data['Rank'].apply(lambda x: isinstance(x, (int, float))) & (data['Rank'] <= top_limit)]
-            korean_count = len(data)
-        else:
-            # 리스트나 다른 형태의 데이터인 경우 처리
-            if type_name == "Models":
-                korean_count = sum(1 for item in data if isinstance(item.get('global_rank'), (int, float)) and item.get('global_rank') <= top_limit)
-            else:
-                korean_count = sum(1 for item in data if isinstance(item.get('rank'), (int, float)) and item.get('rank') <= top_limit)
-        
-        other_count = total_count - korean_count
-        
-        fig = go.Figure(data=[go.Pie(
-            labels=[f'Korean {type_name} in Top {top_limit}', f'Other {type_name} in Top {top_limit}'],
-            values=[korean_count, other_count],
-            hole=.3,
-            marker_colors=['#FF6B6B', '#4ECDC4'],
-            textinfo='percent+value',
-            hovertemplate="<b>%{label}</b><br>" +
-                         "Count: %{value}<br>" +
-                         "Percentage: %{percent}<br>"
-        )])
-        
-        fig.update_layout(
-            title=f"Korean vs Other {type_name} Distribution (Top {top_limit})",
-            showlegend=True,
-            height=400,
-            width=500
-        )
-        
-        return fig
-    except Exception as e:
-        print(f"Error in create_pie_chart: {str(e)}")
-        return go.Figure()
-
-def refresh_all_data():
-    spaces_results = get_spaces_data("trending")
-    models_results = get_models_data()
-    
-    # Spaces 차트 생성
-    spaces_pie = create_pie_chart(spaces_results[2], 500, "Spaces")
-    spaces_bar = create_registration_bar_chart(spaces_results[2], "Spaces")
-    
-    # Models 차트 생성
-    models_pie = create_pie_chart(models_results[2], 3000, "Models")
-    models_bar = create_registration_bar_chart(models_results[2], "Models")
-    
-    return [
-        spaces_results[0], spaces_results[1], spaces_results[2],
-        spaces_pie, spaces_bar,
-        models_results[0], models_results[1], models_results[2],
-        models_pie, models_bar
-    ]
-
-
-with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css="""
-    #spaces_pie, #models_pie {
-        min-height: 400px;
-        border-radius: 10px;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-    }
-    #spaces_bar, #models_bar {
-        min-height: 400px;
-        border-radius: 10px;
-        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-    }
-""") as demo:
-
-    gr.Markdown("""
-    # 🤗 허깅페이스 '한국(언어) 리더보드'
-    HuggingFace가 제공하는 Spaces와 Models 실시간 인기 순위 반영하여 '한국인(기업/언어)'의 리스트(공개,검색,리더보드 등)만 분석. (c)'한국인공지능진흥협회' / 요청: [email protected]
-    """)
-    
-    # 이미지와 설명 추가
-    gr.Markdown("""
-    ### [Hot NEWS] 허깅페이스 선정 12월 'TOP 12'에 한국 'ginipick'의 'FLUXllama'와 'Text3D' 2종이 선정됨
-    """)
-    gr.Image("HF-TOP12.png", show_label=False)
-    
-    # 새로 고침 버튼 (기존 코드)
-    refresh_btn = gr.Button("🔄 새로 고침", variant="primary")
-    
-
-    with gr.Tab("Spaces Trending"):
-        trending_plot = gr.Plot()
-        with gr.Row():
-            # 원형 그래프와 막대 그래프를 위한 컨테이너 추가
-            with gr.Column(scale=1):
-                spaces_pie_chart = gr.Plot(
-                    label="Korean Spaces Distribution",
-                    elem_id="spaces_pie"
-                )
-            with gr.Column(scale=2):
-                spaces_bar_chart = gr.Plot(
-                    label="Registrations by Creator",
-                    elem_id="spaces_bar"
-                )
-        trending_info = gr.HTML()
-        trending_df = gr.DataFrame(
-            headers=["Rank", "Space ID", "Title", "Likes", "URL"],
-            datatype=["number", "str", "str", "number", "str"],
-            row_count=(10, "dynamic")
-        )
-    
-    with gr.Tab("Models Trending"):
-        models_plot = gr.Plot()
-        with gr.Row():
-            # 원형 그래프와 막대 그래프를 위한 컨테이너 추가
-            with gr.Column(scale=1):
-                models_pie_chart = gr.Plot(
-                    label="Korean Models Distribution",
-                    elem_id="models_pie"
-                )
-            with gr.Column(scale=2):
-                models_bar_chart = gr.Plot(
-                    label="Registrations by Creator",
-                    elem_id="models_bar"
-                )
-        models_info = gr.HTML()
-        models_df = gr.DataFrame(
-            headers=["Global Rank", "Model ID", "Title", "Downloads", "Likes", "Korea Search", "URL"],
-            datatype=["str", "str", "str", "str", "str", "str", "str"],
-            row_count=(10, "dynamic")
-        )
-
-    def refresh_all_data():
-        try:
-            spaces_results = get_spaces_data("trending")
-            models_results = get_models_data()
-            
-            # Spaces 차트 생성
-            spaces_pie = create_pie_chart(spaces_results[2], 500, "Spaces")
-            spaces_bar = create_registration_bar_chart(spaces_results[2], "Spaces")
-            
-            # Models 차트 생성
-            models_pie = create_pie_chart(models_results[2], 3000, "Models")
-            models_bar = create_registration_bar_chart(models_results[2], "Models")
-            
-            return [
-                spaces_results[0], spaces_results[1], spaces_results[2],
-                spaces_pie, spaces_bar,
-                models_results[0], models_results[1], models_results[2],
-                models_pie, models_bar
-            ]
-        except Exception as e:
-            print(f"Error in refresh_all_data: {str(e)}")
-            # 에러 발생 시 기본값 반환
-            return [None] * 10
-
-    # 새로고침 버튼 클릭 이벤트 핸들러
-    refresh_btn.click(
-        fn=refresh_all_data,
-        outputs=[
-            trending_plot, trending_info, trending_df,
-            spaces_pie_chart, spaces_bar_chart,
-            models_plot, models_info, models_df,
-            models_pie_chart, models_bar_chart
-        ]
-    )
-    
-    # 초기 데이터 로드
-    try:
-        initial_data = refresh_all_data()
-        
-        # 초기값 설정
-        trending_plot.value = initial_data[0]
-        trending_info.value = initial_data[1]
-        trending_df.value = initial_data[2]
-        spaces_pie_chart.value = initial_data[3]
-        spaces_bar_chart.value = initial_data[4]
-        models_plot.value = initial_data[5]
-        models_info.value = initial_data[6]
-        models_df.value = initial_data[7]
-        models_pie_chart.value = initial_data[8]
-        models_bar_chart.value = initial_data[9]
-    except Exception as e:
-        print(f"Error loading initial data: {str(e)}")
-        gr.Warning("초기 데이터 로드 중 오류가 발생했습니다.")
-
-# Gradio 앱 실행
-demo.launch(
-    server_name="0.0.0.0",
-    server_port=7860,
-    share=False,
-    show_error=True
-)