import gradio as gr import requests import pandas as pd import plotly.graph_objects as go from datetime import datetime import os import asyncio import aiohttp HF_TOKEN = os.getenv("HF_TOKEN") target_models = { "K-intelligence/Midm-2.0-Mini-Instruct": "https://huggingface.co/K-intelligence/Midm-2.0-Mini-Instruct", "K-intelligence/Midm-2.0-Base-Instruct": "https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct", "VIDraft/Qwen3-R1984-30B-A3B": "https://huggingface.co/VIDraft/Qwen3-R1984-30B-A3B", "VIDraft/Gemma-3-R1984-1B": "https://huggingface.co/VIDraft/Gemma-3-R1984-1B", "VIDraft/Qwen3-R1984-0.6B": "https://huggingface.co/VIDraft/Qwen3-R1984-0.6B", "VIDraft/Qwen3-R1984-4B": "https://huggingface.co/VIDraft/Qwen3-R1984-4B", "seawolf2357/kim-korea": "https://huggingface.co/seawolf2357/kim-korea", "seawolf2357/audrey-hepburn": "https://huggingface.co/seawolf2357/audrey-hepburn", "seawolf2357/test-bag4": "https://huggingface.co/seawolf2357/test-bag4", "seawolf2357/blingone-lani": "https://huggingface.co/seawolf2357/blingone-lani", "seawolf2357/test-hood": "https://huggingface.co/seawolf2357/test-hood", "seawolf2357/shinyoonbok": "https://huggingface.co/seawolf2357/shinyoonbok", "openfree/lee-min-ho": "https://huggingface.co/openfree/lee-min-ho", "openfree/bruce-lee": "https://huggingface.co/openfree/bruce-lee", "openfree/leonardo-dicaprio": "https://huggingface.co/openfree/leonardo-dicaprio", "seawolf2357/nsfw-detection": "https://huggingface.co/seawolf2357/nsfw-detection", "Heartsync/Flux-NSFW-uncensored": "https://huggingface.co/Heartsync/Flux-NSFW-uncensored", "Heartsync/NSFW-Uncensored": "https://huggingface.co/Heartsync/NSFW-Uncensored", "ginipick/QwQ-32B-NF4": "https://huggingface.co/ginipick/QwQ-32B-NF4", "openfree/winslow-homer": "https://huggingface.co/openfree/winslow-homer", "openfree/van-gogh": "https://huggingface.co/openfree/van-gogh", "openfree/paul-cezanne": "https://huggingface.co/openfree/paul-cezanne", "openfree/pierre-auguste-renoir": "https://huggingface.co/openfree/pierre-auguste-renoir", "fantos/QwQ-32B-bnb-4bit": "https://huggingface.co/fantos/QwQ-32B-bnb-4bit", "openfree/myt-flux-fantasy": "https://huggingface.co/openfree/myt-flux-fantasy", "ginipick/Gemma-3-R1984-4B": "https://huggingface.co/ginipick/Gemma-3-R1984-4B", "VIDraft/Gemma-3-R1984-4B": "https://huggingface.co/VIDraft/Gemma-3-R1984-4B", "ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503": "https://huggingface.co/ginigen/Private-BitSix-Mistral-Small-3.1-24B-Instruct-2503", "openfree/QwQ-R1984-32B-Q4_K_M-GGUF": "https://huggingface.co/openfree/QwQ-R1984-32B-Q4_K_M-GGUF", "openfree/QwQ-R1984-32B-Q8_0-GGUF": "https://huggingface.co/openfree/QwQ-R1984-32B-Q8_0-GGUF", "VIDraft/QwQ-R1984-32B": "https://huggingface.co/VIDraft/QwQ-R1984-32B", "openfree/QwQ-32B-Q8_0-GGUF": "https://huggingface.co/openfree/QwQ-32B-Q8_0-GGUF", "openfree/QwQ-32B-Q4_K_M-GGUF": "https://huggingface.co/openfree/QwQ-32B-Q4_K_M-GGUF", "openfree/Llama-3_3-Nemotron-Super-49B-v1-Q4_K_M-GGUF": "https://huggingface.co/openfree/Llama-3_3-Nemotron-Super-49B-v1-Q4_K_M-GGUF", "openfree/Llama-3_3-Nemotron-Super-49B-v1-Q6_K-GGUF": "https://huggingface.co/openfree/Llama-3_3-Nemotron-Super-49B-v1-Q6_K-GGUF", "openfree/Mistral-Small-3.1-24B-Instruct-2503-Q4_K_M-GGUF": "https://huggingface.co/openfree/Mistral-Small-3.1-24B-Instruct-2503-Q4_K_M-GGUF", "openfree/Mistral-Small-3.1-24B-Instruct-2503-Q6_K-GGUF": "https://huggingface.co/openfree/Mistral-Small-3.1-24B-Instruct-2503-Q6_K-GGUF", "openfree/Mistral-Small-3.1-24B-Instruct-2503-Q8_0-GGUF": "https://huggingface.co/openfree/Mistral-Small-3.1-24B-Instruct-2503-Q8_0-GGUF", "openfree/Qwen2.5-VL-32B-Instruct-Q4_K_M-GGUF": "https://huggingface.co/openfree/Qwen2.5-VL-32B-Instruct-Q4_K_M-GGUF", "openfree/Qwen2.5-VL-32B-Instruct-Q8_0-GGUF": "https://huggingface.co/openfree/Qwen2.5-VL-32B-Instruct-Q8_0-GGUF", "openfree/morgenster": "https://huggingface.co/openfree/morgenstern", "openfree/Gemma-3-R1984-27B-Q4_K_M-GGUF": "https://huggingface.co/openfree/Gemma-3-R1984-27B-Q4_K_M-GGUF", "VIDraft/Gemma-3-R1984-12B": "https://huggingface.co/VIDraft/Gemma-3-R1984-12B", "openfree/Gemma-3-R1984-12B-Q8_0-GGUF": "https://huggingface.co/openfree/Gemma-3-R1984-12B-Q8_0-GGUF", "openfree/Gemma-3-R1984-12B-Q6_K-GGUF": "https://huggingface.co/openfree/Gemma-3-R1984-12B-Q6_K-GGUF", "openfree/Gemma-3-R1984-27B-Q8_0-GGUF": "https://huggingface.co/openfree/Gemma-3-R1984-27B-Q8_0-GGUF", "openfree/Gemma-3-R1984-27B-Q6_K-GGUF": "https://huggingface.co/openfree/Gemma-3-R1984-27B-Q6_K-GGUF", "openfree/flux-chatgpt-ghibli-lora": "https://huggingface.co/openfree/flux-chatgpt-ghibli-lora", "openfree/string-sandal": "https://huggingface.co/openfree/string-sandal", "openfree/voice-crown-necklace": "https://huggingface.co/openfree/voice-crown-necklace", "openfree/boris-yeltsin": "https://huggingface.co/openfree/boris-yeltsin", "VIDraft/Gemma-3-R1984-27B": "https://huggingface.co/VIDraft/Gemma-3-R1984-27B", "openfree/president-k-dj": "https://huggingface.co/openfree/president-k-dj", "openfree/president-pjh": "https://huggingface.co/openfree/president-pjh", "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/pepe": "https://huggingface.co/openfree/pepe", "openfree/korea-president-yoon": "https://huggingface.co/openfree/korea-president-yoon", "seawolf2357/flux-lora-military-artillery-k9": "https://huggingface.co/seawolf2357/flux-lora-military-artillery-k9", "openfree/claude-monet": "https://huggingface.co/openfree/claude-monet", "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", "kanana-nano-2.1b-embedding": "https://huggingface.co/kakaocorp/kanana-nano-2.1b-embedding", "kakaocorp/kanana-nano-2.1b-base": "https://huggingface.co/kakaocorp/kanana-nano-2.1b-base", "kakaocorp/kanana-nano-2.1b-instruct": "https://huggingface.co/kakaocorp/kanana-nano-2.1b-instruct", "JungZoona/T3Q-qwen2.5-14b-v1.0-e3": "https://huggingface.co/JungZoona/T3Q-qwen2.5-14b-v1.0-e3", "OnomaAIResearch/Illustrious-XL-v2.0": "https://huggingface.co/OnomaAIResearch/Illustrious-XL-v2.0", "OnomaAIResearch/Illustrious-Lumina-v0.03": "https://huggingface.co/OnomaAIResearch/Illustrious-Lumina-v0.03", "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B": "https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-0.5B", "naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B": "https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Text-Instruct-1.5B", "naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B": "https://huggingface.co/naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B", "dnotitia/DNA-R1": "https://huggingface.co/dnotitia/DNA-R1 ", "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", "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", "kakaocorp/kanana-nano-2.1b-base": "https://huggingface.co/kakaocorp/kanana-nano-2.1b-base", "LGAI-EXAONE/EXAONE-Deep-32B": "https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-32B", "LGAI-EXAONE/EXAONE-Deep-2.4B": "https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-2.4B", "LGAI-EXAONE/EXAONE-Deep-7.8B": "https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-7.8B", "OLAResearch/OLAF2-14B": "https://huggingface.co/OLAResearch/OLAF2-14B", "moreh/Llama-3-Motif-102B": "https://huggingface.co/moreh/Llama-3-Motif-102B", "moreh/MoMo-72B-lora-1.8.7-DPO": "https://huggingface.co/moreh/MoMo-72B-lora-1.8.7-DPO", "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", "OnomaAIResearch/Illustrious-XL-v1.0": "https://huggingface.co/OnomaAIResearch/Illustrious-XL-v1.0", "OnomaAIResearch/Illustrious-XL-v1.1": "https://huggingface.co/OnomaAIResearch/Illustrious-XL-v1.1", "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", "nari-labs/Dia-1.6B": "https://huggingface.co/nari-labs/Dia-1.6B", "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": 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"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 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_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") 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', '')) else: print(f"Failed to fetch Korea models: {korea_response.status_code}") print(f"Total unique models: {len(all_models)}") return all_models[:limit] async def fetch_model_data(session, model_id): url = f"https://huggingface.co/api/models/{model_id.strip('/')}" try: async with session.get(url, headers={'Accept': 'application/json'}) as response: if response.status == 200: return await response.json() else: print(f"Failed to fetch {model_id}: {response.status}") return None except Exception as e: print(f"Error fetching {model_id}: {e}") return None async def fetch_all_target_models(model_ids): async with aiohttp.ClientSession() as session: tasks = [fetch_model_data(session, model_id) for model_id in model_ids] results = await asyncio.gather(*tasks) return results def get_models_data(progress=gr.Progress()): def calculate_rank(model_id, all_global_models, korea_models): global_rank = next((idx for idx, m in enumerate(all_global_models, 1) if m.get('id', '').strip() == model_id.strip()), None) is_korea = any(m.get('id', '').strip() == model_id.strip() for m in korea_models) if is_korea: korea_rank = next((idx for idx, m in enumerate(korea_models, 1) if m.get('id', '').strip() == model_id.strip()), None) if korea_rank: return min(global_rank or 3001, korea_rank + 1000), True return global_rank if global_rank else 'Not in top 3000', is_korea try: progress(0, desc="Fetching models...") if not HF_TOKEN: fig = create_error_plot() error_html = """

⚠️ API 인증이 필요합니다

HuggingFace API 토큰이 설정되지 않았습니다. 완전한 기능을 사용하기 위해서는 API 토큰이 필요합니다.

""" empty_df = pd.DataFrame(columns=['Global Rank', 'Model ID', 'Title', 'Downloads', 'Likes', 'Korea Search', 'URL']) return fig, error_html, empty_df # 전체 모델(상위 3000) + Korea 검색 모델 목록 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)}") 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)}") fig = go.Figure() # --------------------------- # 1) 기존 target_models + 2) openfree/ 계정에 해당하는 모델 ID 모두 포함 # --------------------------- target_model_ids = set(target_models.keys()) # 전체 목록 중 'openfree/' 로 시작하는 모델도 추출해서 포함 extra_accounts = ["keeperballon/", "openfree/", "vidraft/", "ginigen/", "heartsync/", "seawolf2357/", "aiqcamp/", "aiqtech/", "fantaxy/", "fantos/", "immunobiotech/", "yokoha/", "cutechicken/", "kolaslab/","kolaslab/", "gunship999/" ] extra_model_ids = { m['id'] for m in all_global_models if isinstance(m.get('id'), str) and any(m['id'].startswith(prefix) for prefix in extra_accounts) } # 최종적으로 rank 계산할 대상 final_model_ids = target_model_ids.union(extra_model_ids) # 해당 모델들의 상세정보 비동기 호출 model_results = asyncio.run(fetch_all_target_models(final_model_ids)) filtered_models = [] for model_id, model_data in zip(final_model_ids, model_results): if model_data is not None: rank, is_korea = calculate_rank(model_id, all_global_models, korea_models) filtered_models.append({ 'id': model_id, 'global_rank': rank, 'downloads': model_data.get('downloads', 0), 'likes': model_data.get('likes', 0), 'title': model_data.get('title', 'No Title'), 'is_korea': is_korea }) # 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 }) # rank 기준 정렬 def rank_value(x): if isinstance(x['global_rank'], str): return float('inf') return x['global_rank'] filtered_models.sort(key=rank_value) 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], text=[f"Rank: #{r}
Downloads: {format(m['downloads'], ',')}
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_content = """

Models Rankings (Up to #3000)

""" 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 "" # target_models 딕셔너리에 없을 수 있으므로 get() 사용 model_url = target_models.get(model['id'], f"https://huggingface.co/{model['id']}") html_content += f"""

{rank_display}

{model['id']}

{korea_badge}

⬇️ Downloads: {format(model['downloads'], ',')}

👍 Likes: {format(model['likes'], ',')}

Visit Model 🔗
""" html_content += "
" 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 '', # 혹시 target_models 딕셔너리에 없을 경우 직접 HF URL 'URL': target_models.get(m['id'], f"https://huggingface.co/{m['id']}") } for m in filtered_models]) progress(1.0, desc="Complete!") return fig, html_content, df except Exception as e: print(f"Error in get_models_data: {str(e)}") error_fig = create_error_plot() error_html = f"""

⚠️ 오류가 발생했습니다

{str(e)}

""" empty_df = pd.DataFrame(columns=['Global Rank', 'Model ID', 'Title', 'Downloads', 'Likes', 'Korea Search', 'URL']) return error_fig, error_html, empty_df target_spaces = { "ginigen/Flux-Kontext-Style": "https://huggingface.co/spaces/ginigen/Flux-Kontext-Style", "cutechicken/3D-Airforce-Simulator": "https://huggingface.co/spaces/cutechicken/3D-Airforce-Simulator", "ginipick/Private-AI": "https://huggingface.co/spaces/ginipick/Private-AI", "openfree/Best-AI": "https://huggingface.co/spaces/openfree/Best-AI", "aiqtech/Heatmap-Leaderboard": "https://huggingface.co/spaces/aiqtech/Heatmap-Leaderboard", "VIDraft/DNA-CASINO": "https://huggingface.co/spaces/VIDraft/DNA-CASINO", "ginigen/Seedance-Free": "https://huggingface.co/spaces/ginigen/Seedance-Free", "openfree/K-AI": "https://huggingface.co/spaces/openfree/K-AI", "aiqtech/SOMA-Oriental": "https://huggingface.co/spaces/aiqtech/SOMA-Oriental", "ginigen/Flux-Kontext-FaceLORA": "https://huggingface.co/spaces/ginigen/Flux-Kontext-FaceLORA", "VIDraft/SOMA-AGI": "https://huggingface.co/spaces/VIDraft/SOMA-AGI", "openfree/AI": "https://huggingface.co/spaces/openfree/AI", "Heartsync/VEO3-RealTime": "https://huggingface.co/spaces/Heartsync/VEO3-RealTime", "openfree/MOUSE-Workflow": "https://huggingface.co/spaces/openfree/MOUSE-Workflow", "fantaxy/YTB-TEST": "https://huggingface.co/spaces/fantaxy/YTB-TEST", "openfree/Open-GAMMA": "https://huggingface.co/spaces/openfree/Open-GAMMA", "ginigen/VEO3-Free-mirror": "https://huggingface.co/spaces/ginigen/VEO3-Free-mirror", "ginigen/VEO3-Free": "https://huggingface.co/spaces/ginigen/VEO3-Free", "ginigen/VEO3-Directorss": "https://huggingface.co/spaces/ginigen/VEO3-Directorss", "Heartsync/WAN2-1-fast-T2V-FusioniX2": "https://huggingface.co/spaces/Heartsync/WAN2-1-fast-T2V-FusioniX2", "Heartsync/WAN2-1-fast-T2V-FusioniX": "https://huggingface.co/spaces/Heartsync/WAN2-1-fast-T2V-FusioniX", "VIDraft/voice-trans": "https://huggingface.co/spaces/VIDraft/voice-trans", "VIDraft/Gemma-3-R1984-1B-0613": "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-1B-0613", "VIDraft/Robo-Beam": "https://huggingface.co/spaces/VIDraft/Robo-Beam", "Heartsync/NSFW-Uncensored-REAL": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-REAL", "Heartsync/FREE-NSFW-HUB": "https://huggingface.co/spaces/Heartsync/FREE-NSFW-HUB", "fantaxy/fantasy-novel": "https://huggingface.co/spaces/fantaxy/fantasy-novel", "VIDraft/Gemma-3-R1984-1B": "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-1B", "openfree/Chart-GPT": "https://huggingface.co/spaces/openfree/Chart-GPT", "Heartsync/Novel-NSFW": "https://huggingface.co/spaces/Heartsync/Novel-NSFW", "ginigen/FLUX-Ghibli-LoRA2": "https://huggingface.co/spaces/ginigen/FLUX-Ghibli-LoRA2", "Heartsync/WAN-VIDEO-AUDIO": "https://huggingface.co/spaces/Heartsync/WAN-VIDEO-AUDIO", "ginigen/deepseek-r1-0528-API": "https://huggingface.co/spaces/ginigen/deepseek-r1-0528-API", "Heartsync/wan2-1-fast-security": "https://huggingface.co/spaces/Heartsync/wan2-1-fast-security", "ginigen/Flux-VIDEO": "https://huggingface.co/spaces/ginigen/Flux-VIDEO", "aiqcamp/REMOVAL-TEXT-IMAGE": "https://huggingface.co/spaces/aiqcamp/REMOVAL-TEXT-IMAGE", "aiqcamp/deepseek-r1-0528-qwen3-8b": "https://huggingface.co/spaces/aiqcamp/deepseek-r1-0528-qwen3-8b", "aiqcamp/deepseek-r1-0528": "https://huggingface.co/spaces/aiqcamp/deepseek-r1-0528", "VIDraft/BAGEL-Websearch": "https://huggingface.co/spaces/VIDraft/BAGEL-Websearch", "aiqcamp/Mistral-Devstral-API": "https://huggingface.co/spaces/aiqcamp/Mistral-Devstral-API", "VIDraft/Mistral-RAG-BitSix": "https://huggingface.co/spaces/VIDraft/Mistral-RAG-BitSix", "ginigen/3D-LLAMA-V1": "https://huggingface.co/spaces/ginigen/3D-LLAMA-V1", "Heartsync/NSFW-Uncensored-video2": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video2", "Heartsync/NSFW-Uncensored-video": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video", "seawolf2357/PDF-text-extractor": "https://huggingface.co/spaces/seawolf2357/PDF-text-extractor", "ginipick/Change-Hair": "https://huggingface.co/spaces/ginipick/Change-Hair", "VIDraft/Voice-Clone-Podcast": "https://huggingface.co/spaces/VIDraft/Voice-Clone-Podcast", "ginipick/PDF-EXAM": "https://huggingface.co/spaces/ginipick/PDF-EXAM", "openfree/Naming": "https://huggingface.co/spaces/openfree/Naming", "Heartsync/adult": "https://huggingface.co/spaces/Heartsync/adult", "Heartsync/hwp": "https://huggingface.co/spaces/Heartsync/hwp", "VIDraft/ACE-Singer": "https://huggingface.co/spaces/VIDraft/ACE-Singer", "openfree/Live-Podcast": "https://huggingface.co/spaces/openfree/Live-Podcast", "Heartsync/kim-korea": "https://huggingface.co/spaces/Heartsync/kim-korea", "openfree/AI-Podcast": "https://huggingface.co/spaces/openfree/AI-Podcast", "ginipick/NH-Korea": "https://huggingface.co/spaces/ginipick/NH-Korea", "VIDraft/NH-Prediction": "https://huggingface.co/spaces/VIDraft/NH-Prediction", "ginipick/AI-BOOK": "https://huggingface.co/spaces/ginipick/AI-BOOK", "openfree/Web-Manager": "https://huggingface.co/spaces/openfree/Web-Manager", "openfree/Game-Gallery": "https://huggingface.co/spaces/openfree/Game-Gallery", "openfree/Vibe-Game": "https://huggingface.co/spaces/openfree/Vibe-Game", "ginipick/IDEA-DESIGN": "https://huggingface.co/spaces/ginipick/IDEA-DESIGN", "openfree/Cycle-Navigator": "https://huggingface.co/spaces/openfree/Cycle-Navigator", "openfree/Pulsar-Display": "https://huggingface.co/spaces/openfree/Pulsar-Display", "openfree/RPG-GAME": "https://huggingface.co/spaces/openfree/RPG-GAME", "openfree/Evolution-Simulation": "https://huggingface.co/spaces/openfree/Evolution-Simulation", "openfree/DreamO-video": "https://huggingface.co/spaces/openfree/DreamO-video", "ginigen/3D-VIDEO": "https://huggingface.co/spaces/ginigen/3D-VIDEO", "ginipick/Pharmacy": "https://huggingface.co/spaces/ginipick/Pharmacy", "immunobiotech/Gemini-MICHELIN-kr": "https://huggingface.co/spaces/immunobiotech/Gemini-MICHELIN-kr", "immunobiotech/Gemini-MICHELIN": "https://huggingface.co/spaces/immunobiotech/Gemini-MICHELIN", "openfree/Battle-Royale-GAME": "https://huggingface.co/spaces/openfree/Battle-Royale-GAME", "Heartsync/NSFW-detection": "https://huggingface.co/spaces/Heartsync/NSFW-detection", "openfree/repo-graph": "https://huggingface.co/spaces/openfree/repo-graph", "Heartsync/NSFW-Uncensored-photo": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-photo", "Heartsync/NSFW-Uncensored": "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored", "aiqcamp/ENGLISH-Speaking-Scoring": "https://huggingface.co/spaces/aiqcamp/ENGLISH-Speaking-Scoring", "openfree/Chain-of-Recursive-Thoughts": "https://huggingface.co/spaces/openfree/Chain-of-Recursive-Thoughts", "aiqcamp/SEO-checker": "https://huggingface.co/spaces/aiqcamp/SEO-checker", "openfree/qwen3-30b-a3b-research": "https://huggingface.co/spaces/openfree/qwen3-30b-a3b-research", "openfree/qwen3-235b-a22b-research": "https://huggingface.co/spaces/openfree/qwen3-235b-a22b-research", "ginigen/Mistral-Perflexity": "https://huggingface.co/spaces/ginigen/Mistral-Perflexity", "aiqcamp/gemini-2.5-flash-preview": "https://huggingface.co/spaces/aiqcamp/gemini-2.5-flash-preview", "aiqcamp/MCP-kokoro": "https://huggingface.co/spaces/aiqcamp/MCP-kokoro", "aiqcamp/Llama-4-Maverick-17B-search": "https://huggingface.co/spaces/aiqcamp/Llama-4-Maverick-17B-search", "aiqcamp/facefusion3": "https://huggingface.co/spaces/aiqcamp/facefusion3", "ginigen/Every-Text": "https://huggingface.co/spaces/ginigen/Every-Text", "Heartsync/flux-lora-homer": "https://huggingface.co/spaces/Heartsync/flux-lora-homer", "fantos/flux-lora-gogh": "https://huggingface.co/spaces/fantos/flux-lora-gogh", "fantos/flux-lora-cezanne": "https://huggingface.co/spaces/fantos/flux-lora-cezanne", "ginigen/flux-lora-renoir": "https://huggingface.co/spaces/ginigen/flux-lora-renoir", "Heartsync/NSFW-image": "https://huggingface.co/spaces/Heartsync/NSFW-image", "openfree/svg": "https://huggingface.co/spaces/openfree/svg", "VIDraft/phi-4-reasoning-plus": "https://huggingface.co/spaces/VIDraft/phi-4-reasoning-plus", "fantaxy/musepose": "https://huggingface.co/spaces/fantaxy/musepose", "ginigen/VisualCloze": "https://huggingface.co/spaces/ginigen/VisualCloze", "ginigen/framepack-i2v": "https://huggingface.co/spaces/ginigen/framepack-i2v", "VIDraft/FramePack_rotate_landscape": "https://huggingface.co/spaces/VIDraft/FramePack_rotate_landscape", "VIDraft/Flex-preview": "https://huggingface.co/spaces/VIDraft/Flex-preview", "Heartsync/Character": "https://huggingface.co/spaces/Heartsync/Character", "Heartsync/image-ip-composer": "https://huggingface.co/spaces/Heartsync/image-ip-composer", "Heartsync/FLite-Texture-ULTRA": "https://huggingface.co/spaces/Heartsync/FLite-Texture-ULTRA", "VIDraft/Local-RAG-llama-3-8b": "https://huggingface.co/spaces/VIDraft/Local-RAG-llama-3-8b", "fantos/Physics-Playground": "https://huggingface.co/spaces/fantos/Physics-Playground", "fantos/EveryText": "https://huggingface.co/spaces/fantos/EveryText", "ginipick/GLM-4-32B-0414": "https://huggingface.co/spaces/ginipick/GLM-4-32B-0414", "ginipick/Qwen3-30B-A3B": "https://huggingface.co/spaces/ginipick/Qwen3-30B-A3B", "ginipick/Qwen3-32B": "https://huggingface.co/spaces/ginipick/Qwen3-32B", "ginipick/DeepSeek-Prover-V2-671B": "https://huggingface.co/spaces/ginipick/DeepSeek-Prover-V2-671B", "ginipick/Qwen3-235B-A22B": "https://huggingface.co/spaces/ginipick/Qwen3-235B-A22B", "keeperballon/vibe-coding-tetris": "https://huggingface.co/spaces/keeperballon/vibe-coding-tetris", "keeperballon/multi-llm": "https://huggingface.co/spaces/keeperballon/multi-llm", "VIDraft/Qwen3-8B": "https://huggingface.co/spaces/VIDraft/Qwen3-8B", "VIDraft/Qwen3-1_7B": "https://huggingface.co/spaces/VIDraft/Qwen3-1_7B", "VIDraft/Qwen3-4B": "https://huggingface.co/spaces/VIDraft/Qwen3-4B", "VIDraft/Qwen3-0_6B": "https://huggingface.co/spaces/VIDraft/Qwen3-0_6B", "VIDraft/Qwen3": "https://huggingface.co/spaces/VIDraft/Qwen3", "ginigen/interior-design": "https://huggingface.co/spaces/ginigen/interior-design", "yokoha/CSV-Parquet-Convertors": "https://huggingface.co/spaces/yokoha/CSV-Parquet-Convertors", "yokoha/CSV": "https://huggingface.co/spaces/yokoha/CSV", "yokoha/3D-Audio-visuals": "https://huggingface.co/spaces/yokoha/3D-Audio-visuals", "aiqtech/flux-animated-style": "https://huggingface.co/spaces/aiqtech/flux-animated-style", "ginigen/interior-design": "https://huggingface.co/spaces/ginigen/interior-design", "ginigen/perflexity-clone": "https://huggingface.co/spaces/ginigen/perflexity-clone", "ginigen/blogger": "https://huggingface.co/spaces/ginigen/blogger", "openfree/Badge": "https://huggingface.co/spaces/openfree/Badge", "Heartsync/Papers-Leaderboard": "https://huggingface.co/spaces/Heartsync/Papers-Leaderboard", "aiqtech/LLM-Token-Visual": "https://huggingface.co/spaces/aiqtech/LLM-Token-Visual", "ginipick/AgentX-Papers": "https://huggingface.co/spaces/ginipick/AgentX-Papers", "openfree/ZeroGPU-Leader": "https://huggingface.co/spaces/openfree/ZeroGPU-Leader", "VIDraft/ThinkFlow-llama": "https://huggingface.co/spaces/VIDraft/ThinkFlow-llama", "VIDraft/Agentic-AI-CHAT": "https://huggingface.co/spaces/VIDraft/Agentic-AI-CHAT", "ginigen/multimodal-chat-mbti-korea": "https://huggingface.co/spaces/ginigen/multimodal-chat-mbti-korea", "Heartsync/multimodal-chat-mbti-INTJ": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-INTJ", "Heartsync/multimodal-chat-mbti-INTP": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-INTP", "Heartsync/multimodal-chat-mbti-ENTJ": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ENTJ", "Heartsync/multimodal-chat-mbti-ENTP": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ENTP", "Heartsync/multimodal-chat-mbti-INFJ": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-INFJ", "Heartsync/multimodal-chat-mbti-INFP": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-INFP", "Heartsync/multimodal-chat-mbti-ENFJ": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ENFJ", "Heartsync/multimodal-chat-mbti-ENFP": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ENFP", "Heartsync/multimodal-chat-mbti-ISTJ": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ISTJ", "Heartsync/multimodal-chat-mbti-INFJ": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ISFJ", "Heartsync/multimodal-chat-mbti-ESTJ": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ESTJ", "Heartsync/multimodal-chat-mbti-ESFJ": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ESFJ", "Heartsync/multimodal-chat-mbti-ISTP": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ISTP", "Heartsync/multimodal-chat-mbti-ISFP": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ISFP", "Heartsync/multimodal-chat-mbti-ESTP": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ESTP", "Heartsync/multimodal-chat-mbti-ESFP": "https://huggingface.co/spaces/Heartsync/multimodal-chat-mbti-ESFP", "VIDraft/Gemma-3-R1984-4B": "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-4B", "VIDraft/Gemma-3-R1984-12B": "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-12B", "cutechicken/World-of-Tank-GAME": "https://huggingface.co/spaces/cutechicken/World-of-Tank-GAME", "openfree/Llama-4-Maverick-17B-Research": "https://huggingface.co/spaces/openfree/Llama-4-Maverick-17B-Research", "openfree/Llama-4-Scout-17B-Research": "https://huggingface.co/spaces/openfree/Llama-4-Scout-17B-Research", "openfree/Llama-4-Maverick-17B-Research-korea": "https://huggingface.co/spaces/openfree/Llama-4-Maverick-17B-Research-korea", "openfree/Llama-4-Scout-17B-Research-korea": "https://huggingface.co/spaces/openfree/Llama-4-Scout-17B-Research-korea", "fantaxy/Model-Leaderboard": "https://huggingface.co/spaces/fantaxy/Model-Leaderboard", "seawolf2357/Ghibli-Multilingual-Text-rendering": "https://huggingface.co/spaces/seawolf2357/Ghibli-Multilingual-Text-rendering", "openfree/IMAGE2DOT": "https://huggingface.co/spaces/openfree/IMAGE2DOT", "openfree/drqxab": "https://huggingface.co/spaces/openfree/drqxab", "openfree/Minesweeper-Game": "https://huggingface.co/spaces/openfree/Minesweeper-Game", "ginigen/FLUX-Open-Ghibli-Studio": "https://huggingface.co/spaces/ginigen/FLUX-Open-Ghibli-Studio", "VIDraft/Open-Meme-Studio": "https://huggingface.co/spaces/VIDraft/Open-Meme-Studio", "ginigen/Ghibli-Meme-Studio": "https://huggingface.co/spaces/ginigen/Ghibli-Meme-Studio", "ginigen/Private-BitSix-Mistral": "https://huggingface.co/spaces/ginigen/Private-BitSix-Mistral", "VIDraft/FLUX-cat-lora": "https://huggingface.co/spaces/VIDraft/FLUX-cat-lora", "VIDraft/Polaroid-Style": "https://huggingface.co/spaces/VIDraft/Polaroid-Style", "VIDraft/Multilingual-Text-add-Images": "https://huggingface.co/spaces/VIDraft/Multilingual-Text-add-Images", "ginigen/FLUX-Text-Tree-Image": "https://huggingface.co/spaces/ginigen/FLUX-Text-Tree-Image", "VIDraft/ReSize-Image-Outpainting": "https://huggingface.co/spaces/VIDraft/ReSize-Image-Outpainting", "VIDraft/Fashion-Fit": "https://huggingface.co/spaces/VIDraft/Fashion-Fit", "ginigen/SFX-Sound-magic": "https://huggingface.co/spaces/ginigen/SFX-Sound-magic", "openfree/Gemma-3-R1984-12B": "https://huggingface.co/spaces/openfree/Gemma-3-R1984-12B", "openfree/Stock-Trading-Analysis": "https://huggingface.co/spaces/openfree/Stock-Trading-Analysis", "aiqtech/Contributors-Leaderboard": "https://huggingface.co/spaces/aiqtech/Contributors-Leaderboard", "VIDraft/Gemma-3-R1984-27B": "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-27B", "openfree/Deepseek-v3-0324-Research": "https://huggingface.co/spaces/openfree/Deepseek-v3-0324-Research", "openfree/Deepseek-v3-0324-Research-korea": "https://huggingface.co/spaces/openfree/Deepseek-v3-0324-Research-korea", "openfree/Korean-Exam-Leaderboard": "https://huggingface.co/spaces/openfree/Korean-Exam-Leaderboard", "ginigen/Every-Text": "https://huggingface.co/spaces/ginigen/Every-Text", "openfree/Idea-Transformer": "https://huggingface.co/spaces/openfree/Idea-Transformer", "VIDraft/Unique3D": "https://huggingface.co/spaces/VIDraft/Unique3D", "openfree/Compare-RAG-CHAT": "https://huggingface.co/spaces/openfree/Compare-RAG-CHAT", "ginigen/Light-amplification": "https://huggingface.co/spaces/ginigen/Light-amplification", "ginipick/handspew": "https://huggingface.co/spaces/ginipick/handspew", "openfree/AgentX-Travel": "https://huggingface.co/spaces/openfree/AgentX-Travel", "VIDraft/stable-diffusion-3.5-large-turboX": "https://huggingface.co/spaces/VIDraft/stable-diffusion-3.5-large-turboX", "VIDraft/tight-inversion-pulid-demo": "https://huggingface.co/spaces/VIDraft/tight-inversion-pulid-demo", "openfree/SonicOrbit": "https://huggingface.co/spaces/openfree/SonicOrbit", "openfree/FACE-DETECTOR": "https://huggingface.co/spaces/openfree/FACE-DETECTOR", "VIDraft/Space-Leaderboard": "https://huggingface.co/spaces/VIDraft/Space-Leaderboard", "ginipick/3D-GAME": "https://huggingface.co/spaces/ginipick/3D-GAME", "ginipick/Flipper-Code-Generato": "https://huggingface.co/spaces/ginipick/Flipper-Code-Generator", "ginipick/Baby-Name-Generator": "https://huggingface.co/spaces/ginipick/Baby-Name-Generator", "ginipick/secreen-capture-OCR": "https://huggingface.co/spaces/ginipick/secreen-capture-OCR", "ginipick/face-pose-detect": "https://huggingface.co/spaces/ginipick/face-pose-detect", "ginigen/Graph-Mind": "https://huggingface.co/spaces/ginigen/Graph-Mind", "openfree/deepseek_r1_API": "https://huggingface.co/spaces/openfree/deepseek_r1_API", "ginipick/Tech_Hangman_Game": 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"heartsync/assetai": "https://huggingface.co/spaces/heartsync/assetai", "heartsync/stockai": "https://huggingface.co/spaces/heartsync/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": 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"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': 1000 } if sort_type == "modes": params['sort'] = 'likes' try: progress(0, desc=f"Fetching {sort_type} spaces data...") response = requests.get(url, params=params) response.raise_for_status() all_spaces = response.json() # 여기서 순위(rank)는 단순히 응답 배열 순서대로 1부터 매김 # 기본적으로 'sort_type'에 따른 정렬이 되어 있을 것으로 가정. space_ranks = {} for idx, space in enumerate(all_spaces, 1): space_id = space.get('id', '') # 기존 target_spaces + "openfree/" 로 시작하는 스페이스도 rank에 추가 if space_id in target_spaces or any( space_id.startswith(prefix + "/") for prefix in ["keeperballon/", "openfree", "vidraft", "ginigen", "heartsync", "seawolf2357", "aiqcamp", "aiqtech", "fantaxy", "fantos", "immunobiotech", "yokoha", "cutechicken","kolaslab", "gunship999"] ): space['rank'] = idx space_ranks[space_id] = space # space_ranks에 모인 스페이스만 정렬 spaces = [space_ranks[sid] for sid in space_ranks] spaces.sort(key=lambda x: x['rank']) progress(0.3, desc="Creating visualization...") fig = go.Figure() 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] fig.add_trace(go.Bar( x=ids, y=ranks, text=[f"Rank: {r}
Title: {t}
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', tickmode='array', ticktext=[str(i) for i in range(1, 1001, 20)], tickvals=[i for i in range(1, 1001, 20)], range=[1, 500] ), height=800, showlegend=False, template='plotly_white', xaxis_tickangle=-45 ) progress(0.6, desc="Creating space cards...") html_content = f"""

{sort_type.title()} Rankings

""" 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) # 만약 target_spaces에 없으면 기본 주소로 space_url = target_spaces.get(space_id, f"https://huggingface.co/spaces/{space_id}") html_content += f"""

Rank #{rank} - {space_id}

{title}

👍 Likes: {likes}

Visit Space 🔗
""" html_content += "
" 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.get(space['id'], f"https://huggingface.co/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'
Error: {str(e)}
' 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() ranks = [] for idx, space in enumerate(spaces_data, 1): space_id = space.get('id', '') # target_spaces + openfree/ 체크 if space_id in target_spaces or space_id.startswith("openfree/"): 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() ranks.sort(key=lambda x: x['rank']) 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] fig.add_trace(go.Bar( x=ids, y=rank_values, text=[f"Rank: {r}
Likes: {l}
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 + openfree 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 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 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 "

데이터를 불러오는데 실패했습니다.

" html_content = """

Current Trending Rankings

""" # 대상: target_spaces + openfree/로 시작 all_space_keys = set(target_spaces.keys()) for space in spaces_data: space_id = space.get('id') if space_id in all_space_keys or (space_id and space_id.startswith("openfree/")): rank = next((idx for idx, s in enumerate(spaces_data, 1) if s.get('id') == space_id), 'N/A') html_content += f"""

#{rank} - {space_id}

👍 Likes: {space.get('likes', 'N/A')}

👀 Views: {space.get('views', 'N/A')}

{space.get('title', 'N/A')}

{space.get('description', 'N/A')[:100]}...

Visit Space 🔗
""" html_content += "
" return html_content def create_data_table(spaces_data): if not spaces_data: return pd.DataFrame() rows = [] # 대상: target_spaces + openfree/로 시작 for idx, space in enumerate(spaces_data, 1): space_id = space.get('id', '') if space_id in target_spaces or space_id.startswith("openfree/"): rows.append({ 'Rank': idx, 'Space ID': space_id, 'Likes': space.get('likes', 'N/A'), 'Title': space.get('title', 'N/A'), 'URL': target_spaces.get(space_id, f"https://huggingface.co/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(), "
API 인증이 필요합니다.
", pd.DataFrame() def create_registration_bar_chart(data, type_name="Spaces"): try: top_limit = 500 if type_name == "Spaces" else 3000 if isinstance(data, pd.DataFrame): if type_name == "Models": data = data[data['Global Rank'].apply(lambda x: isinstance(x, (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": data = data[data['Rank'].apply(lambda x: isinstance(x, (int, float))) & (data['Rank'] <= top_limit)] id_column = 'Space ID' if type_name == "Spaces" else 'Model ID' registrations = data[id_column].apply(lambda x: x.split('/')[0]).value_counts() else: 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) 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_limit = 500 if type_name == "Spaces" else 3000 if isinstance(data, pd.DataFrame): if type_name == "Models": data = data[data['Global Rank'].apply(lambda x: isinstance(x, (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": data = data[data['Rank'].apply(lambda x: isinstance(x, (int, float))) & (data['Rank'] <= top_limit)] korean_count = len(data) else: 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="%{label}
" + "Count: %{value}
" + "Percentage: %{percent}
" )]) 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_results / models_results 각 결과: # (plot, html, dataframe) # 파이/바 차트 생성 spaces_pie = create_pie_chart(spaces_results[2], 500, "Spaces") spaces_bar = create_registration_bar_chart(spaces_results[2], "Spaces") models_pie = create_pie_chart(models_results[2], 3000, "Models") models_bar = create_registration_bar_chart(models_results[2], "Models") return [ spaces_results[0], spaces_results[1], spaces_results[2], spaces_pie, spaces_bar, models_results[0], models_results[1], models_results[2], models_pie, models_bar ] 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: arxivgpt@gmail.com """) 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) refresh_btn = gr.Button("🔄 새로 고침", variant="primary") with gr.Tab("Spaces Trending"): trending_plot = gr.Plot() with gr.Row(): with gr.Column(scale=1): spaces_pie_chart = gr.Plot( label="Korean Spaces Distribution", elem_id="spaces_pie" ) with gr.Column(scale=2): spaces_bar_chart = gr.Plot( label="Registrations by Creator", elem_id="spaces_bar" ) trending_info = gr.HTML() trending_df = gr.DataFrame( headers=["Rank", "Space ID", "Title", "Likes", "URL"], datatype=["number", "str", "str", "number", "str"], row_count=(10, "dynamic") ) with gr.Tab("Models Trending"): models_plot = gr.Plot() with gr.Row(): with gr.Column(scale=1): models_pie_chart = gr.Plot( label="Korean Models Distribution", elem_id="models_pie" ) with gr.Column(scale=2): models_bar_chart = gr.Plot( label="Registrations by Creator", elem_id="models_bar" ) models_info = gr.HTML() models_df = gr.DataFrame( headers=["Global Rank", "Model ID", "Title", "Downloads", "Likes", "Korea 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_pie = create_pie_chart(spaces_results[2], 500, "Spaces") spaces_bar = create_registration_bar_chart(spaces_results[2], "Spaces") models_pie = create_pie_chart(models_results[2], 3000, "Models") models_bar = create_registration_bar_chart(models_results[2], "Models") return [ spaces_results[0], spaces_results[1], spaces_results[2], spaces_pie, spaces_bar, models_results[0], models_results[1], models_results[2], models_pie, models_bar ] except Exception as e: print(f"Error in refresh_all_data: {str(e)}") return [None] * 10 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 ] ) try: initial_data = refresh_all_data() 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.") demo.launch( server_name="0.0.0.0", server_port=7860, share=False, show_error=True )