app.py

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",
    "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",

    
    "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",
    
    "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=1000):
    """모든 모델과 Korea 관련 모델 가져오기"""
    all_models = []
    
    # 1. 일반 모델 리스트 가져오기
    params = {
        "limit": limit,
        "full": "True",
        "config": "True"
    }
    
    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 {len(all_models)} general models")
    
    # 2. Korea 검색 결과 가져오기
    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', ''))
    
    # 3. Korean 검색 결과 가져오기
    korean_params = {
        "search": "korean",
        "full": "True",
        "config": "True",
        "limit": limit
    }
    
    korean_response = requests.get(
        "https://huggingface.co/api/models",
        headers={'Accept': 'application/json'},
        params=korean_params
    )
    
    if korean_response.status_code == 200:
        korean_models = korean_response.json()
        print(f"Fetched {len(korean_models)} Korean-related models")
        
        # 중복 제거하면서 Korean 모델 추가
        for model in korean_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()):
    """모델 데이터 가져오기"""
    try:
        progress(0, desc="Fetching models...")
        
        # 모델 가져오기
        all_global_models = get_all_models(limit=1000)
        print(f"Actually fetched models count: {len(all_global_models)}")
        
        # API 응답 순서를 순위로 사용하여 순위 맵 생성
        rank_map = {}
        for rank, model in enumerate(all_global_models, 1):
            model_id = model.get('id', '').strip()
            rank_map[model_id] = {
                'rank': rank,
                'likes': model.get('likes', 0),
                'downloads': model.get('downloads', 0),
                'title': model.get('title', 'No Title')
            }
            print(f"Rank {rank}: {model_id}")

        # target_models의 순위 확인 및 정보 수집
        filtered_models = []
        for model_id in target_models.keys():
            try:
                # 개별 모델 API 호출
                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()
                    api_id = model_data.get('id', '').strip()
                    
                    # API 응답 순서에서 순위 찾기
                    rank_info = rank_map.get(api_id)
                    
                    model_info = {
                        'id': model_id,
                        'global_rank': rank_info['rank'] if rank_info else 'Not in top 1000',
                        'downloads': model_data.get('downloads', 0),
                        'likes': model_data.get('likes', 0),
                        'title': model_data.get('title', 'No Title')
                    }
                    filtered_models.append(model_info)
                    print(f"Model {model_id}: Rank={model_info['global_rank']}, Downloads={model_info['downloads']}, Likes={model_info['likes']}")
                else:
                    filtered_models.append({
                        'id': model_id,
                        'global_rank': 'Not in top 1000',
                        'downloads': 0,
                        'likes': 0,
                        'title': 'No Title'
                    })
            except Exception as e:
                print(f"Error processing {model_id}: {str(e)}")
                filtered_models.append({
                    'id': model_id,
                    'global_rank': 'Not in top 1000',
                    'downloads': 0,
                    'likes': 0,
                    'title': 'No Title'
                })
        
        # 순위로 정렬
        filtered_models.sort(key=lambda x: float('inf') if isinstance(x['global_rank'], str) else x['global_rank'])
        
        progress(0.3, desc="Creating visualization...")
        
        # 시각화 생성
        fig = go.Figure()
        
        # 순위권 내 모델만 필터링하여 시각화
        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]
            likes = [m['likes'] for m in valid_models]
            downloads = [m['downloads'] for m in valid_models]
            
            # Y축 값을 반전 (높은 순위가 위로 가도록)
            y_values = [1001 - r for r in ranks]
            
            fig.add_trace(go.Bar(
                x=ids,
                y=y_values,
                text=[f"Global Rank: #{r}<br>Downloads: {format(d, ',')}<br>Likes: {format(l, ',')}" 
                      for r, d, l in zip(ranks, downloads, likes)],
                textposition='auto',
                marker_color='rgb(158,202,225)',
                opacity=0.8
            ))
            
            fig.update_layout(
                title={
                    'text': 'Hugging Face Models Global Rankings',
                    'y':0.95,
                    'x':0.5,
                    'xanchor': 'center',
                    'yanchor': 'top'
                },
                xaxis_title='Model ID',
                yaxis_title='Global Rank',
                yaxis=dict(
                    ticktext=[f"#{i}" for i in range(1, 1001, 50)],
                    tickvals=[1001 - i for i in range(1, 1001, 50)],
                    range=[0, 1000]
                ),
                height=800,
                showlegend=False,
                template='plotly_white',
                xaxis_tickangle=-45
            )
        
        progress(0.6, desc="Creating model cards...")
        
        # HTML 카드 생성
        html_content = """
        <div style='padding: 20px; background: #f5f5f5;'>
            <h2 style='color: #2c3e50;'>Models Global Rankings</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 1000"
            
            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_display}</h3>
                <h4 style='color: #2c3e50;'>{model['id']}</h4>
                <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'], ','),
            '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)}")
        return create_error_plot(), f"<div>에러 발생: {str(e)}</div>", pd.DataFrame()

        
# 관심 스페이스 URL 리스트와 정보
target_spaces = {

    "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",
    "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",

    "cutechicken/TankWar3D": "https://huggingface.co/spaces/cutechicken/TankWar3D",
    "kolaslab/RC4-EnDecoder": "https://huggingface.co/spaces/kolaslab/RC4-EnDecoder",
    "kolaslab/simulator": "https://huggingface.co/spaces/kolaslab/simulator",
    "kolaslab/calculator": "https://huggingface.co/spaces/kolaslab/calculator",
    "etri-vilab/Ko-LLaVA": "https://huggingface.co/spaces/etri-vilab/Ko-LLaVA",
    "etri-vilab/KOALA": "https://huggingface.co/spaces/etri-vilab/KOALA",
    "naver-clova-ix/donut-base-finetuned-cord-v2": "https://huggingface.co/spaces/naver-clova-ix/donut-base-finetuned-cord-v2",    
    
    "NCSOFT/VARCO_Arena": "https://huggingface.co/spaces/NCSOFT/VARCO_Arena"
}

def get_spaces_data(sort_type="trending", progress=gr.Progress()):
    """스페이스 데이터 가져오기 (trending 또는 modes)"""
    url = "https://huggingface.co/api/spaces"
    params = {
        'full': 'true',
        'limit': 400
    }
    
    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 400)',
                '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, 401, 20)],  # 1부터 400까지 20 간격으로 표시
                tickvals=[i for i in range(1, 401, 20)],
                range=[1, 400]  # 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': 400,
            '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()

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🤗 허깅페이스 '한국(언어) 리더보드'
    HuggingFace가 제공하는 Spaces와 Models 실시간 인기 순위를 반영: 한국인(기업)이 공개, 한국 'LLM 리더보드' 및 TAG 등을 참고해 리스트 갱신. 신규 등록 요청: [email protected]
    """)
    
    # 새로 고침 버튼을 상단으로 이동하고 한글로 변경
    refresh_btn = gr.Button("🔄 새로 고침", variant="primary")
    
    with gr.Tab("Spaces Trending"):
        trending_plot = gr.Plot()
        trending_info = gr.HTML()
        trending_df = gr.DataFrame()
    
    with gr.Tab("Models Trending"):
        models_plot = gr.Plot()
        models_info = gr.HTML()
        models_df = gr.DataFrame()
    
    def refresh_all_data():
        spaces_results = get_spaces_data("trending")
        models_results = get_models_data()
        return [*spaces_results, *models_results]
    
    refresh_btn.click(
        refresh_all_data,
        outputs=[
            trending_plot, trending_info, trending_df,
            models_plot, models_info, models_df
        ]
    )
    
    # 초기 데이터 로드
    spaces_results = get_spaces_data("trending")
    models_results = get_models_data()
    
    trending_plot.value, trending_info.value, trending_df.value = spaces_results
    models_plot.value, models_info.value, models_df.value = models_results
    
    
# Gradio 앱 실행
demo.launch(
    server_name="0.0.0.0",
    server_port=7860,
    share=False
)