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Create app-backup.py

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  1. app-backup.py +924 -0
app-backup.py ADDED
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1
+ import gradio as gr
2
+ import requests
3
+ import pandas as pd
4
+ import plotly.graph_objects as go
5
+ from datetime import datetime
6
+ import os
7
+
8
+ HF_TOKEN = os.getenv("HF_TOKEN")
9
+
10
+ target_models = {
11
+ "openfree/flux-lora-korea-palace": "https://huggingface.co/openfree/flux-lora-korea-palace",
12
+ "seawolf2357/hanbok": "https://huggingface.co/seawolf2357/hanbok",
13
+ "LGAI-EXAONE/EXAONE-3.5-32B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-32B-Instruct",
14
+ "LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct",
15
+ "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
16
+ "ginipick/flux-lora-eric-cat": "https://huggingface.co/ginipick/flux-lora-eric-cat",
17
+ "seawolf2357/flux-lora-car-rolls-royce": "https://huggingface.co/seawolf2357/flux-lora-car-rolls-royce",
18
+
19
+ "moreh/Llama-3-Motif-102B-Instruct": "https://huggingface.co/moreh/Llama-3-Motif-102B-Instruct",
20
+ "moreh/Llama-3-Motif-102B": "https://huggingface.co/moreh/Llama-3-Motif-102B",
21
+
22
+ "Saxo/Linkbricks-Horizon-AI-Korean-Gemma-2-sft-dpo-27B": "https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-Gemma-2-sft-dpo-27B",
23
+ "AALF/gemma-2-27b-it-SimPO-37K": "https://huggingface.co/AALF/gemma-2-27b-it-SimPO-37K",
24
+ "nbeerbower/mistral-nemo-wissenschaft-12B": "https://huggingface.co/nbeerbower/mistral-nemo-wissenschaft-12B",
25
+ "Saxo/Linkbricks-Horizon-AI-Korean-Mistral-Nemo-sft-dpo-12B": "https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-Mistral-Nemo-sft-dpo-12B",
26
+ "princeton-nlp/gemma-2-9b-it-SimPO": "https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO",
27
+ "migtissera/Tess-v2.5-Gemma-2-27B-alpha": "https://huggingface.co/migtissera/Tess-v2.5-Gemma-2-27B-alpha",
28
+ "DeepMount00/Llama-3.1-8b-Ita": "https://huggingface.co/DeepMount00/Llama-3.1-8b-Ita",
29
+ "cognitivecomputations/dolphin-2.9.3-mistral-nemo-12b": "https://huggingface.co/cognitivecomputations/dolphin-2.9.3-mistral-nemo-12b",
30
+ "ai-human-lab/EEVE-Korean_Instruct-10.8B-expo": "https://huggingface.co/ai-human-lab/EEVE-Korean_Instruct-10.8B-expo",
31
+ "VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct": "https://huggingface.co/VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct",
32
+ "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",
33
+ "AIDX-ktds/ktdsbaseLM-v0.12-based-on-openchat3.5": "https://huggingface.co/AIDX-ktds/ktdsbaseLM-v0.12-based-on-openchat3.5",
34
+ "mlabonne/Daredevil-8B-abliterated": "https://huggingface.co/mlabonne/Daredevil-8B-abliterated",
35
+ "ENERGY-DRINK-LOVE/eeve_dpo-v3": "https://huggingface.co/ENERGY-DRINK-LOVE/eeve_dpo-v3",
36
+ "migtissera/Trinity-2-Codestral-22B": "https://huggingface.co/migtissera/Trinity-2-Codestral-22B",
37
+ "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",
38
+ "mlabonne/Daredevil-8B-abliterated-dpomix": "https://huggingface.co/mlabonne/Daredevil-8B-abliterated-dpomix",
39
+ "yanolja/EEVE-Korean-Instruct-10.8B-v1.0": "https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0",
40
+ "vicgalle/Configurable-Llama-3.1-8B-Instruct": "https://huggingface.co/vicgalle/Configurable-Llama-3.1-8B-Instruct",
41
+ "T3Q-LLM/T3Q-LLM1-sft1.0-dpo1.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM1-sft1.0-dpo1.0",
42
+ "Eurdem/Defne-llama3.1-8B": "https://huggingface.co/Eurdem/Defne-llama3.1-8B",
43
+ "BAAI/Infinity-Instruct-7M-Gen-Llama3_1-8B": "https://huggingface.co/BAAI/Infinity-Instruct-7M-Gen-Llama3_1-8B",
44
+ "BAAI/Infinity-Instruct-3M-0625-Llama3-8B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0625-Llama3-8B",
45
+ "T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0",
46
+ "BAAI/Infinity-Instruct-7M-0729-Llama3_1-8B": "https://huggingface.co/BAAI/Infinity-Instruct-7M-0729-Llama3_1-8B",
47
+ "mightbe/EEVE-10.8B-Multiturn": "https://huggingface.co/mightbe/EEVE-10.8B-Multiturn",
48
+ "hyemijo/omed-llama3.1-8b": "https://huggingface.co/hyemijo/omed-llama3.1-8b",
49
+ "yanolja/Bookworm-10.7B-v0.4-DPO": "https://huggingface.co/yanolja/Bookworm-10.7B-v0.4-DPO",
50
+ "algograp-Inc/algograpV4": "https://huggingface.co/algograp-Inc/algograpV4",
51
+ "lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75": "https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top75",
52
+ "chihoonlee10/T3Q-LLM-MG-DPO-v1.0": "https://huggingface.co/chihoonlee10/T3Q-LLM-MG-DPO-v1.0",
53
+ "vicgalle/Configurable-Hermes-2-Pro-Llama-3-8B": "https://huggingface.co/vicgalle/Configurable-Hermes-2-Pro-Llama-3-8B",
54
+ "RLHFlow/LLaMA3-iterative-DPO-final": "https://huggingface.co/RLHFlow/LLaMA3-iterative-DPO-final",
55
+ "SEOKDONG/llama3.1_korean_v0.1_sft_by_aidx": "https://huggingface.co/SEOKDONG/llama3.1_korean_v0.1_sft_by_aidx",
56
+ "spow12/Ko-Qwen2-7B-Instruct": "https://huggingface.co/spow12/Ko-Qwen2-7B-Instruct",
57
+ "BAAI/Infinity-Instruct-3M-0625-Qwen2-7B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0625-Qwen2-7B",
58
+ "lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half": "https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half",
59
+ "T3Q-LLM/T3Q-LLM1-CV-v2.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM1-CV-v2.0",
60
+ "migtissera/Trinity-2-Codestral-22B-v0.2": "https://huggingface.co/migtissera/Trinity-2-Codestral-22B-v0.2",
61
+ "sinjy1203/EEVE-Korean-Instruct-10.8B-v1.0-Grade-Retrieval": "https://huggingface.co/sinjy1203/EEVE-Korean-Instruct-10.8B-v1.0-Grade-Retrieval",
62
+ "MaziyarPanahi/Llama-3-8B-Instruct-v0.10": "https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.10",
63
+ "MaziyarPanahi/Llama-3-8B-Instruct-v0.9": "https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.9",
64
+ "zhengr/MixTAO-7Bx2-MoE-v8.1": "https://huggingface.co/zhengr/MixTAO-7Bx2-MoE-v8.1",
65
+ "TIGER-Lab/MAmmoTH2-8B-Plus": "https://huggingface.co/TIGER-Lab/MAmmoTH2-8B-Plus",
66
+ "OpenBuddy/openbuddy-qwen1.5-14b-v21.1-32k": "https://huggingface.co/OpenBuddy/openbuddy-qwen1.5-14b-v21.1-32k",
67
+ "haoranxu/Llama-3-Instruct-8B-CPO-SimPO": "https://huggingface.co/haoranxu/Llama-3-Instruct-8B-CPO-SimPO",
68
+ "Weyaxi/Einstein-v7-Qwen2-7B": "https://huggingface.co/Weyaxi/Einstein-v7-Qwen2-7B",
69
+ "DKYoon/kosolar-hermes-test": "https://huggingface.co/DKYoon/kosolar-hermes-test",
70
+ "vilm/Quyen-Pro-v0.1": "https://huggingface.co/vilm/Quyen-Pro-v0.1",
71
+ "chihoonlee10/T3Q-LLM-MG-v1.0": "https://huggingface.co/chihoonlee10/T3Q-LLM-MG-v1.0",
72
+ "lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25": "https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual-orpo-borda-top25",
73
+ "ai-human-lab/EEVE-Korean-10.8B-RAFT": "https://huggingface.co/ai-human-lab/EEVE-Korean-10.8B-RAFT",
74
+ "princeton-nlp/Llama-3-Base-8B-SFT-RDPO": "https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-RDPO",
75
+ "MaziyarPanahi/Llama-3-8B-Instruct-v0.8": "https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-v0.8",
76
+ "chihoonlee10/T3Q-ko-solar-dpo-v7.0": "https://huggingface.co/chihoonlee10/T3Q-ko-solar-dpo-v7.0",
77
+ "jondurbin/bagel-8b-v1.0": "https://huggingface.co/jondurbin/bagel-8b-v1.0",
78
+ "DeepMount00/Llama-3-8b-Ita": "https://huggingface.co/DeepMount00/Llama-3-8b-Ita",
79
+ "VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct": "https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
80
+ "princeton-nlp/Llama-3-Instruct-8B-ORPO-v0.2": "https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-ORPO-v0.2",
81
+ "AIDX-ktds/ktdsbaseLM-v0.11-based-on-openchat3.5": "https://huggingface.co/AIDX-ktds/ktdsbaseLM-v0.11-based-on-openchat3.5",
82
+ "princeton-nlp/Llama-3-Base-8B-SFT-KTO": "https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-KTO",
83
+ "maywell/Mini_Synatra_SFT": "https://huggingface.co/maywell/Mini_Synatra_SFT",
84
+ "princeton-nlp/Llama-3-Base-8B-SFT-ORPO": "https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-ORPO",
85
+ "princeton-nlp/Llama-3-Instruct-8B-CPO-v0.2": "https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-CPO-v0.2",
86
+ "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",
87
+ "princeton-nlp/Llama-3-Base-8B-SFT-DPO": "https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT-DPO",
88
+ "princeton-nlp/Llama-3-Instruct-8B-ORPO": "https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-ORPO",
89
+ "lcw99/llama-3-10b-it-kor-extented-chang": "https://huggingface.co/lcw99/llama-3-10b-it-kor-extented-chang",
90
+ "migtissera/Llama-3-8B-Synthia-v3.5": "https://huggingface.co/migtissera/Llama-3-8B-Synthia-v3.5",
91
+ "megastudyedu/M-SOLAR-10.7B-v1.4-dpo": "https://huggingface.co/megastudyedu/M-SOLAR-10.7B-v1.4-dpo",
92
+ "T3Q-LLM/T3Q-LLM-solar10.8-sft-v1.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM-solar10.8-sft-v1.0",
93
+ "maywell/Synatra-10.7B-v0.4": "https://huggingface.co/maywell/Synatra-10.7B-v0.4",
94
+ "nlpai-lab/KULLM3": "https://huggingface.co/nlpai-lab/KULLM3",
95
+ "abacusai/Llama-3-Smaug-8B": "https://huggingface.co/abacusai/Llama-3-Smaug-8B",
96
+ "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",
97
+ "BAAI/Infinity-Instruct-3M-0625-Mistral-7B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0625-Mistral-7B",
98
+ "openchat/openchat_3.5": "https://huggingface.co/openchat/openchat_3.5",
99
+ "T3Q-LLM/T3Q-LLM1-v2.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM1-v2.0",
100
+ "T3Q-LLM/T3Q-LLM1-CV-v1.0": "https://huggingface.co/T3Q-LLM/T3Q-LLM1-CV-v1.0",
101
+ "ONS-AI-RESEARCH/ONS-SOLAR-10.7B-v1.1": "https://huggingface.co/ONS-AI-RESEARCH/ONS-SOLAR-10.7B-v1.1",
102
+ "macadeliccc/Samantha-Qwen-2-7B": "https://huggingface.co/macadeliccc/Samantha-Qwen-2-7B",
103
+ "openchat/openchat-3.5-0106": "https://huggingface.co/openchat/openchat-3.5-0106",
104
+ "NousResearch/Nous-Hermes-2-SOLAR-10.7B": "https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B",
105
+ "UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1": "https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1",
106
+ "MTSAIR/multi_verse_model": "https://huggingface.co/MTSAIR/multi_verse_model",
107
+ "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",
108
+ "VIRNECT/llama-3-Korean-8B": "https://huggingface.co/VIRNECT/llama-3-Korean-8B",
109
+ "ENERGY-DRINK-LOVE/SOLAR_merge_DPOv3": "https://huggingface.co/ENERGY-DRINK-LOVE/SOLAR_merge_DPOv3",
110
+ "SeaLLMs/SeaLLMs-v3-7B-Chat": "https://huggingface.co/SeaLLMs/SeaLLMs-v3-7B-Chat",
111
+ "VIRNECT/llama-3-Korean-8B-V2": "https://huggingface.co/VIRNECT/llama-3-Korean-8B-V2",
112
+ "MLP-KTLim/llama-3-Korean-Bllossom-8B": "https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B",
113
+ "Magpie-Align/Llama-3-8B-Magpie-Align-v0.3": "https://huggingface.co/Magpie-Align/Llama-3-8B-Magpie-Align-v0.3",
114
+ "cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2": "https://huggingface.co/cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2",
115
+ "SkyOrbis/SKY-Ko-Llama3-8B-lora": "https://huggingface.co/SkyOrbis/SKY-Ko-Llama3-8B-lora",
116
+ "4yo1/llama3-eng-ko-8b-sl5": "https://huggingface.co/4yo1/llama3-eng-ko-8b-sl5",
117
+ "kimwooglae/WebSquareAI-Instruct-llama-3-8B-v0.5.39": "https://huggingface.co/kimwooglae/WebSquareAI-Instruct-llama-3-8B-v0.5.39",
118
+ "ONS-AI-RESEARCH/ONS-SOLAR-10.7B-v1.2": "https://huggingface.co/ONS-AI-RESEARCH/ONS-SOLAR-10.7B-v1.2",
119
+ "lcw99/llama-3-10b-it-kor-extented-chang-pro8": "https://huggingface.co/lcw99/llama-3-10b-it-kor-extented-chang-pro8",
120
+ "BAAI/Infinity-Instruct-3M-0625-Yi-1.5-9B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0625-Yi-1.5-9B",
121
+ "migtissera/Tess-2.0-Llama-3-8B": "https://huggingface.co/migtissera/Tess-2.0-Llama-3-8B",
122
+ "BAAI/Infinity-Instruct-3M-0613-Mistral-7B": "https://huggingface.co/BAAI/Infinity-Instruct-3M-0613-Mistral-7B",
123
+ "yeonwoo780/cydinfo-llama3-8b-lora-v01": "https://huggingface.co/yeonwoo780/cydinfo-llama3-8b-lora-v01",
124
+ "vicgalle/ConfigurableSOLAR-10.7B": "https://huggingface.co/vicgalle/ConfigurableSOLAR-10.7B",
125
+ "chihoonlee10/T3Q-ko-solar-jo-v1.0": "https://huggingface.co/chihoonlee10/T3Q-ko-solar-jo-v1.0",
126
+ "Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.4": "https://huggingface.co/Kukedlc/NeuralLLaMa-3-8b-ORPO-v0.4",
127
+ "Edentns/DataVortexS-10.7B-dpo-v1.0": "https://huggingface.co/Edentns/DataVortexS-10.7B-dpo-v1.0",
128
+ "SJ-Donald/SJ-SOLAR-10.7b-DPO": "https://huggingface.co/SJ-Donald/SJ-SOLAR-10.7b-DPO",
129
+ "lemon-mint/gemma-ko-7b-it-v0.40": "https://huggingface.co/lemon-mint/gemma-ko-7b-it-v0.40",
130
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159
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161
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164
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166
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167
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168
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170
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171
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172
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174
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175
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176
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177
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178
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184
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185
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186
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198
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199
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200
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201
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202
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203
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205
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207
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210
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211
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212
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213
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214
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215
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216
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217
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218
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219
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220
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221
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222
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223
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224
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225
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226
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227
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228
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229
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230
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231
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232
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233
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234
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235
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236
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237
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238
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239
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240
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241
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242
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243
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244
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245
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246
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247
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248
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249
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250
+ "jojo0217/ChatSKKU5.8B": "https://huggingface.co/jojo0217/ChatSKKU5.8B",
251
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252
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253
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254
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255
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256
+ "shleeeee/mistral-7b-wiki": "https://huggingface.co/shleeeee/mistral-7b-wiki",
257
+ "nayohan/polyglot-ko-5.8b-Inst": "https://huggingface.co/nayohan/polyglot-ko-5.8b-Inst",
258
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259
+ "Junmai/KIT-5.8b": "https://huggingface.co/Junmai/KIT-5.8b",
260
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261
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262
+ "OpenBuddy/openbuddy-mistral2-7b-v20.3-32k": "https://huggingface.co/OpenBuddy/openbuddy-mistral2-7b-v20.3-32k",
263
+ "sh2orc/Llama-3-Korean-8B": "https://huggingface.co/sh2orc/Llama-3-Korean-8B",
264
+ "Deepnoid/deep-solar-eeve-v2.0.0": "https://huggingface.co/Deepnoid/deep-solar-eeve-v2.0.0",
265
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266
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267
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268
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269
+ "Byungchae/k2s3_test_0000": "https://huggingface.co/Byungchae/k2s3_test_0000",
270
+ "migtissera/Tess-v2.5-Phi-3-medium-128k-14B": "https://huggingface.co/migtissera/Tess-v2.5-Phi-3-medium-128k-14B",
271
+ "kyujinpy/Korean-OpenOrca-13B": "https://huggingface.co/kyujinpy/Korean-OpenOrca-13B",
272
+ "kyujinpy/KO-Platypus2-13B": "https://huggingface.co/kyujinpy/KO-Platypus2-13B",
273
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274
+ "Byungchae/k2s3_test_0002": "https://huggingface.co/Byungchae/k2s3_test_0002",
275
+ "NousResearch/Nous-Hermes-llama-2-7b": "https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b",
276
+ "kaist-ai/prometheus-13b-v1.0": "https://huggingface.co/kaist-ai/prometheus-13b-v1.0",
277
+ "sel303/llama3-diverce-ver1.0": "https://huggingface.co/sel303/llama3-diverce-ver1.0",
278
+ "NousResearch/Nous-Capybara-7B": "https://huggingface.co/NousResearch/Nous-Capybara-7B",
279
+ "rrw-x2/KoSOLAR-10.7B-DPO-v1.0": "https://huggingface.co/rrw-x2/KoSOLAR-10.7B-DPO-v1.0",
280
+ "Edentns/DataVortexS-10.7B-v0.2": "https://huggingface.co/Edentns/DataVortexS-10.7B-v0.2",
281
+ "Jsoo/Llama3-beomi-Open-Ko-8B-Instruct-preview-test6": "https://huggingface.co/Jsoo/Llama3-beomi-Open-Ko-8B-Instruct-preview-test6",
282
+ "tlphams/gollm-instruct-all-in-one-v1": "https://huggingface.co/tlphams/gollm-instruct-all-in-one-v1",
283
+ "Edentns/DataVortexTL-1.1B-v0.1": "https://huggingface.co/Edentns/DataVortexTL-1.1B-v0.1",
284
+ "richard-park/llama3-pre1-ds": "https://huggingface.co/richard-park/llama3-pre1-ds",
285
+ "ehartford/samantha-1.1-llama-33b": "https://huggingface.co/ehartford/samantha-1.1-llama-33b",
286
+ "heegyu/LIMA-13b-hf": "https://huggingface.co/heegyu/LIMA-13b-hf",
287
+ "heegyu/42dot_LLM-PLM-1.3B-mt": "https://huggingface.co/heegyu/42dot_LLM-PLM-1.3B-mt",
288
+ "shleeeee/mistral-ko-7b-wiki-neft": "https://huggingface.co/shleeeee/mistral-ko-7b-wiki-neft",
289
+ "EleutherAI/polyglot-ko-1.3b": "https://huggingface.co/EleutherAI/polyglot-ko-1.3b",
290
+ "kyujinpy/Ko-PlatYi-6B-gu": "https://huggingface.co/kyujinpy/Ko-PlatYi-6B-gu",
291
+ "sel303/llama3-diverce-ver1.6": "https://huggingface.co/sel303/llama3-diverce-ver1.6"
292
+ }
293
+
294
+ # all_models ๊ด€๋ จ ์ฝ”๋“œ ์ œ๊ฑฐํ•˜๊ณ  get_models_data ํ•จ์ˆ˜ ๋‚ด๋ถ€์—์„œ ์ฒ˜๋ฆฌํ•˜๋„๋ก ์ˆ˜์ •
295
+
296
+ def get_models_data(progress=gr.Progress()):
297
+ """๋ชจ๋ธ ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ"""
298
+ def normalize_model_id(model_id):
299
+ """๋ชจ๋ธ ID๋ฅผ ์ •๊ทœํ™”"""
300
+ return model_id.strip().lower()
301
+
302
+ url = "https://huggingface.co/api/models"
303
+
304
+ try:
305
+ progress(0, desc="Fetching models data...")
306
+
307
+ # ์—ฌ๋Ÿฌ ์ •๋ ฌ ๋ฐฉ์‹์œผ๋กœ ์‹œ๋„
308
+ all_found_models = []
309
+ sort_options = [
310
+ {'sort': 'downloads', 'direction': -1},
311
+ {'sort': 'lastModified', 'direction': -1},
312
+ {'sort': 'likes', 'direction': -1}
313
+ ]
314
+
315
+ for sort_params in sort_options:
316
+ params = {
317
+ 'full': 'true',
318
+ 'limit': 1000,
319
+ **sort_params
320
+ }
321
+
322
+ headers = {'Accept': 'application/json'}
323
+
324
+ response = requests.get(url, params=params, headers=headers)
325
+ if response.status_code == 200:
326
+ models = response.json()
327
+ all_found_models.extend(models)
328
+
329
+ # ์ค‘๋ณต ์ œ๊ฑฐ
330
+ seen_ids = set()
331
+ filtered_models = []
332
+ for model in all_found_models:
333
+ model_id = normalize_model_id(model.get('id', ''))
334
+ if model_id not in seen_ids and model_id in [normalize_model_id(tid) for tid in target_models.keys()]:
335
+ seen_ids.add(model_id)
336
+ filtered_models.append(model)
337
+
338
+ # ๋‹ค์šด๋กœ๋“œ ์ˆ˜๋กœ ์ •๋ ฌ
339
+ filtered_models.sort(key=lambda x: x.get('downloads', 0), reverse=True)
340
+
341
+ # ์ˆœ์œ„ ํ• ๋‹น
342
+ for idx, model in enumerate(filtered_models, 1):
343
+ model['rank'] = idx
344
+
345
+ if not filtered_models:
346
+ return create_error_plot(), "<div>์„ ํƒ๋œ ๋ชจ๋ธ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์ฐพ์„ ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค.</div>", pd.DataFrame()
347
+
348
+ progress(0.3, desc="Creating visualization...")
349
+
350
+ # ์‹œ๊ฐํ™” ์ƒ์„ฑ
351
+ fig = go.Figure()
352
+
353
+ # ๋ฐ์ดํ„ฐ ์ค€๋น„
354
+ ids = [model['id'] for model in filtered_models]
355
+ ranks = [model['rank'] for model in filtered_models]
356
+ likes = [model.get('likes', 0) for model in filtered_models]
357
+ downloads = [model.get('downloads', 0) for model in filtered_models]
358
+
359
+ # Y์ถ• ๊ฐ’์„ ๋ฐ˜์ „
360
+ y_values = [1001 - r for r in ranks]
361
+
362
+ # ๋ง‰๋Œ€ ๊ทธ๋ž˜ํ”„ ์ƒ์„ฑ
363
+ fig.add_trace(go.Bar(
364
+ x=ids,
365
+ y=y_values,
366
+ text=[f"Rank: {r}<br>Likes: {l}<br>Downloads: {d}"
367
+ for r, l, d in zip(ranks, likes, downloads)],
368
+ textposition='auto',
369
+ marker_color='rgb(158,202,225)',
370
+ opacity=0.8
371
+ ))
372
+
373
+ fig.update_layout(
374
+ title={
375
+ 'text': 'Hugging Face Models Trending Rankings (Top 1000)',
376
+ 'y':0.95,
377
+ 'x':0.5,
378
+ 'xanchor': 'center',
379
+ 'yanchor': 'top'
380
+ },
381
+ xaxis_title='Model ID',
382
+ yaxis_title='Rank',
383
+ yaxis=dict(
384
+ ticktext=[str(i) for i in range(1, 1001, 50)],
385
+ tickvals=[1001 - i for i in range(1, 1001, 50)],
386
+ range=[0, 1000]
387
+ ),
388
+ height=800,
389
+ showlegend=False,
390
+ template='plotly_white',
391
+ xaxis_tickangle=-45
392
+ )
393
+
394
+ progress(0.6, desc="Creating model cards...")
395
+
396
+ # HTML ์นด๋“œ ์ƒ์„ฑ
397
+ html_content = """
398
+ <div style='padding: 20px; background: #f5f5f5;'>
399
+ <h2 style='color: #2c3e50;'>Models Trending Rankings</h2>
400
+ <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 20px;'>
401
+ """
402
+
403
+ # ์ˆœ์œ„๊ถŒ ๋‚ด ๋ชจ๋ธ ์นด๋“œ ์ƒ์„ฑ
404
+ for model in filtered_models:
405
+ model_id = model['id']
406
+ rank = model['rank']
407
+ likes = model.get('likes', 0)
408
+ downloads = model.get('downloads', 0)
409
+
410
+ html_content += f"""
411
+ <div style='
412
+ background: white;
413
+ padding: 20px;
414
+ border-radius: 10px;
415
+ box-shadow: 0 2px 4px rgba(0,0,0,0.1);
416
+ transition: transform 0.2s;
417
+ '>
418
+ <h3 style='color: #34495e;'>Rank #{rank} - {model_id}</h3>
419
+ <p style='color: #7f8c8d;'>๐Ÿ‘ Likes: {likes}</p>
420
+ <p style='color: #7f8c8d;'>โฌ‡๏ธ Downloads: {downloads}</p>
421
+ <a href='{target_models[model_id]}'
422
+ target='_blank'
423
+ style='
424
+ display: inline-block;
425
+ padding: 8px 16px;
426
+ background: #3498db;
427
+ color: white;
428
+ text-decoration: none;
429
+ border-radius: 5px;
430
+ transition: background 0.3s;
431
+ '>
432
+ Visit Model ๐Ÿ”—
433
+ </a>
434
+ </div>
435
+ """
436
+
437
+ # ์ˆœ์œ„๊ถŒ ๋ฐ– ๋ชจ๋ธ ์นด๋“œ ์ƒ์„ฑ
438
+ for model_id in target_models:
439
+ if model_id not in [m['id'] for m in filtered_models]:
440
+ html_content += f"""
441
+ <div style='
442
+ background: #f8f9fa;
443
+ padding: 20px;
444
+ border-radius: 10px;
445
+ box-shadow: 0 2px 4px rgba(0,0,0,0.1);
446
+ '>
447
+ <h3 style='color: #34495e;'>{model_id}</h3>
448
+ <p style='color: #7f8c8d;'>Not in top 1000</p>
449
+ <a href='{target_models[model_id]}'
450
+ target='_blank'
451
+ style='
452
+ display: inline-block;
453
+ padding: 8px 16px;
454
+ background: #95a5a6;
455
+ color: white;
456
+ text-decoration: none;
457
+ border-radius: 5px;
458
+ '>
459
+ Visit Model ๐Ÿ”—
460
+ </a>
461
+ </div>
462
+ """
463
+
464
+ html_content += "</div></div>"
465
+
466
+ # ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„ ์ƒ์„ฑ
467
+ df_data = []
468
+ # ์ˆœ์œ„๊ถŒ ๋‚ด ๋ชจ๋ธ
469
+ for model in filtered_models:
470
+ df_data.append({
471
+ 'Rank': model['rank'],
472
+ 'Model ID': model['id'],
473
+ 'Likes': model.get('likes', 'N/A'),
474
+ 'Downloads': model.get('downloads', 'N/A'),
475
+ 'URL': target_models[model['id']]
476
+ })
477
+ # ์ˆœ์œ„๊ถŒ ๋ฐ– ๋ชจ๋ธ
478
+ for model_id in target_models:
479
+ if model_id not in [m['id'] for m in filtered_models]:
480
+ df_data.append({
481
+ 'Rank': 'Not in top 1000',
482
+ 'Model ID': model_id,
483
+ 'Likes': 'N/A',
484
+ 'Downloads': 'N/A',
485
+ 'URL': target_models[model_id]
486
+ })
487
+
488
+ df = pd.DataFrame(df_data)
489
+
490
+ progress(1.0, desc="Complete!")
491
+ return fig, html_content, df
492
+
493
+ except Exception as e:
494
+ print(f"Error in get_models_data: {str(e)}")
495
+ return create_error_plot(), f"<div>์—๋Ÿฌ ๋ฐœ์ƒ: {str(e)}</div>", pd.DataFrame()
496
+
497
+ # ๊ด€์‹ฌ ์ŠคํŽ˜์ด์Šค URL ๋ฆฌ์ŠคํŠธ์™€ ์ •๋ณด
498
+ target_spaces = {
499
+
500
+ "openfree/Korean-Leaderboard": "https://huggingface.co/spaces/openfree/Korean-Leaderboard",
501
+ "ginipick/FLUXllama": "https://huggingface.co/spaces/ginipick/FLUXllama",
502
+ "ginipick/SORA-3D": "https://huggingface.co/spaces/ginipick/SORA-3D",
503
+ "fantaxy/Sound-AI-SFX": "https://huggingface.co/spaces/fantaxy/Sound-AI-SFX",
504
+ "fantos/flx8lora": "https://huggingface.co/spaces/fantos/flx8lora",
505
+ "ginigen/Canvas": "https://huggingface.co/spaces/ginigen/Canvas",
506
+ "fantaxy/erotica": "https://huggingface.co/spaces/fantaxy/erotica",
507
+ "ginipick/time-machine": "https://huggingface.co/spaces/ginipick/time-machine",
508
+ "aiqcamp/FLUX-VisionReply": "https://huggingface.co/spaces/aiqcamp/FLUX-VisionReply",
509
+ "openfree/Tetris-Game": "https://huggingface.co/spaces/openfree/Tetris-Game",
510
+ "openfree/everychat": "https://huggingface.co/spaces/openfree/everychat",
511
+ "VIDraft/mouse1": "https://huggingface.co/spaces/VIDraft/mouse1",
512
+ "kolaslab/alpha-go": "https://huggingface.co/spaces/kolaslab/alpha-go",
513
+ "ginipick/text3d": "https://huggingface.co/spaces/ginipick/text3d",
514
+ "openfree/trending-board": "https://huggingface.co/spaces/openfree/trending-board",
515
+ "cutechicken/tankwar": "https://huggingface.co/spaces/cutechicken/tankwar",
516
+ "openfree/game-jewel": "https://huggingface.co/spaces/openfree/game-jewel",
517
+ "VIDraft/mouse-chat": "https://huggingface.co/spaces/VIDraft/mouse-chat",
518
+ "ginipick/AccDiffusion": "https://huggingface.co/spaces/ginipick/AccDiffusion",
519
+ "aiqtech/Particle-Accelerator-Simulation": "https://huggingface.co/spaces/aiqtech/Particle-Accelerator-Simulation",
520
+ "openfree/GiniGEN": "https://huggingface.co/spaces/openfree/GiniGEN",
521
+ "kolaslab/3DAudio-Spectrum-Analyzer": "https://huggingface.co/spaces/kolaslab/3DAudio-Spectrum-Analyzer",
522
+ "openfree/trending-news-24": "https://huggingface.co/spaces/openfree/trending-news-24",
523
+ "ginipick/Realtime-FLUX": "https://huggingface.co/spaces/ginipick/Realtime-FLUX",
524
+ "VIDraft/prime-number": "https://huggingface.co/spaces/VIDraft/prime-number",
525
+ "kolaslab/zombie-game": "https://huggingface.co/spaces/kolaslab/zombie-game",
526
+ "fantos/miro-game": "https://huggingface.co/spaces/fantos/miro-game",
527
+ "kolaslab/shooting": "https://huggingface.co/spaces/kolaslab/shooting",
528
+ "VIDraft/Mouse-Hackathon": "https://huggingface.co/spaces/VIDraft/Mouse-Hackathon",
529
+ "upstage/open-ko-llm-leaderboard": "https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard",
530
+ "LGAI-EXAONE/EXAONE-3.5-Instruct-Demo": "https://huggingface.co/spaces/LGAI-EXAONE/EXAONE-3.5-Instruct-Demo",
531
+ "NCSOFT/VARCO_Arena": "https://huggingface.co/spaces/NCSOFT/VARCO_Arena"
532
+ }
533
+
534
+ def get_spaces_data(sort_type="trending", progress=gr.Progress()):
535
+ """์ŠคํŽ˜์ด์Šค ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ (trending ๋˜๋Š” modes)"""
536
+ url = f"https://huggingface.co/api/spaces"
537
+ params = {
538
+ 'full': 'true',
539
+ 'limit': 300
540
+ }
541
+
542
+ if sort_type == "modes":
543
+ params['sort'] = 'likes' # modes๋Š” ์ข‹์•„์š” ์ˆœ์œผ๋กœ ์ •๋ ฌ
544
+
545
+ try:
546
+ progress(0, desc=f"Fetching {sort_type} spaces data...")
547
+ response = requests.get(url, params=params)
548
+ response.raise_for_status()
549
+ all_spaces = response.json()
550
+
551
+ # ์ˆœ์œ„ ์ •๋ณด ์ €์žฅ
552
+ space_ranks = {space['id']: idx + 1 for idx, space in enumerate(all_spaces)}
553
+
554
+ # target_spaces ํ•„ํ„ฐ๋ง ๋ฐ ์ˆœ์œ„ ์ •๋ณด ํฌํ•จ
555
+ spaces = []
556
+ for space in all_spaces:
557
+ if space.get('id', '') in target_spaces:
558
+ space['rank'] = space_ranks.get(space['id'], 'N/A')
559
+ spaces.append(space)
560
+
561
+ # ์ˆœ์œ„๋ณ„๋กœ ์ •๋ ฌ
562
+ spaces.sort(key=lambda x: x['rank'])
563
+
564
+ progress(0.3, desc="Creating visualization...")
565
+
566
+ # ์‹œ๊ฐํ™” ์ƒ์„ฑ
567
+ fig = go.Figure()
568
+
569
+ # ๋ฐ์ดํ„ฐ ์ค€๋น„
570
+ ids = [space['id'] for space in spaces]
571
+ ranks = [space['rank'] for space in spaces]
572
+ likes = [space.get('likes', 0) for space in spaces]
573
+
574
+ # Y์ถ• ๊ฐ’์„ ๋ฐ˜์ „ (300 - rank + 1)
575
+ y_values = [301 - r for r in ranks] # ์ˆœ์œ„๋ฅผ ๋ฐ˜์ „๋œ ๊ฐ’์œผ๋กœ ๋ณ€ํ™˜
576
+
577
+ # ๋ง‰๋Œ€ ๊ทธ๋ž˜ํ”„ ์ƒ์„ฑ
578
+ fig.add_trace(go.Bar(
579
+ x=ids,
580
+ y=y_values,
581
+ text=[f"Rank: {r}<br>Likes: {l}" for r, l in zip(ranks, likes)],
582
+ textposition='auto',
583
+ marker_color='rgb(158,202,225)',
584
+ opacity=0.8
585
+ ))
586
+
587
+ fig.update_layout(
588
+ title={
589
+ 'text': f'Hugging Face Spaces {sort_type.title()} Rankings (Top 300)',
590
+ 'y':0.95,
591
+ 'x':0.5,
592
+ 'xanchor': 'center',
593
+ 'yanchor': 'top'
594
+ },
595
+ xaxis_title='Space ID',
596
+ yaxis_title='Rank',
597
+ yaxis=dict(
598
+ ticktext=[str(i) for i in range(1, 301, 20)], # 1๋ถ€ํ„ฐ 300๊นŒ์ง€ 20 ๊ฐ„๊ฒฉ
599
+ tickvals=[301 - i for i in range(1, 301, 20)], # ๋ฐ˜์ „๋œ ๊ฐ’
600
+ range=[0, 300] # y์ถ• ๋ฒ”์œ„ ์„ค์ •
601
+ ),
602
+ height=800,
603
+ showlegend=False,
604
+ template='plotly_white',
605
+ xaxis_tickangle=-45
606
+ )
607
+
608
+ progress(0.6, desc="Creating space cards...")
609
+
610
+ # HTML ์นด๋“œ ์ƒ์„ฑ
611
+ html_content = f"""
612
+ <div style='padding: 20px; background: #f5f5f5;'>
613
+ <h2 style='color: #2c3e50;'>{sort_type.title()} Rankings</h2>
614
+ <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 20px;'>
615
+ """
616
+
617
+ for space in spaces:
618
+ space_id = space.get('id', '')
619
+ rank = space.get('rank', 'N/A')
620
+ likes = space.get('likes', 0)
621
+ title = space.get('title', 'No Title')
622
+ description = space.get('description', 'No Description')[:100]
623
+
624
+ html_content += f"""
625
+ <div style='
626
+ background: white;
627
+ padding: 20px;
628
+ border-radius: 10px;
629
+ box-shadow: 0 2px 4px rgba(0,0,0,0.1);
630
+ transition: transform 0.2s;
631
+ '>
632
+ <h3 style='color: #34495e;'>Rank #{rank} - {space_id}</h3>
633
+ <p style='color: #7f8c8d;'>๐Ÿ‘ Likes: {likes}</p>
634
+ <p style='color: #2c3e50;'>{title}</p>
635
+ <p style='color: #7f8c8d; font-size: 0.9em;'>{description}...</p>
636
+ <a href='{target_spaces[space_id]}'
637
+ target='_blank'
638
+ style='
639
+ display: inline-block;
640
+ padding: 8px 16px;
641
+ background: #3498db;
642
+ color: white;
643
+ text-decoration: none;
644
+ border-radius: 5px;
645
+ transition: background 0.3s;
646
+ '>
647
+ Visit Space ๐Ÿ”—
648
+ </a>
649
+ </div>
650
+ """
651
+
652
+ html_content += "</div></div>"
653
+
654
+ # ๋ฐ์ดํ„ฐํ”„๋ ˆ์ž„ ์ƒ์„ฑ
655
+ df = pd.DataFrame([{
656
+ 'Rank': space.get('rank', 'N/A'),
657
+ 'Space ID': space.get('id', ''),
658
+ 'Likes': space.get('likes', 'N/A'),
659
+ 'Title': space.get('title', 'N/A'),
660
+ 'URL': target_spaces[space.get('id', '')]
661
+ } for space in spaces])
662
+
663
+ progress(1.0, desc="Complete!")
664
+ return fig, html_content, df
665
+
666
+ except Exception as e:
667
+ error_html = f'<div style="color: red; padding: 20px;">Error: {str(e)}</div>'
668
+ error_plot = create_error_plot()
669
+ return error_plot, error_html, pd.DataFrame()
670
+
671
+
672
+
673
+
674
+ def create_trend_visualization(spaces_data):
675
+ if not spaces_data:
676
+ return create_error_plot()
677
+
678
+ fig = go.Figure()
679
+
680
+ # ์ˆœ์œ„ ๋ฐ์ดํ„ฐ ์ค€๋น„
681
+ ranks = []
682
+ for idx, space in enumerate(spaces_data, 1):
683
+ space_id = space.get('id', '')
684
+ if space_id in target_spaces:
685
+ ranks.append({
686
+ 'id': space_id,
687
+ 'rank': idx,
688
+ 'likes': space.get('likes', 0),
689
+ 'title': space.get('title', 'N/A'),
690
+ 'views': space.get('views', 0)
691
+ })
692
+
693
+ if not ranks:
694
+ return create_error_plot()
695
+
696
+ # ์ˆœ์œ„๋ณ„๋กœ ์ •๋ ฌ
697
+ ranks.sort(key=lambda x: x['rank'])
698
+
699
+ # ํ”Œ๋กฏ ๋ฐ์ดํ„ฐ ์ƒ์„ฑ
700
+ ids = [r['id'] for r in ranks]
701
+ rank_values = [r['rank'] for r in ranks]
702
+ likes = [r['likes'] for r in ranks]
703
+ views = [r['views'] for r in ranks]
704
+
705
+ # ๋ง‰๋Œ€ ๊ทธ๋ž˜ํ”„ ์ƒ์„ฑ
706
+ fig.add_trace(go.Bar(
707
+ x=ids,
708
+ y=rank_values,
709
+ text=[f"Rank: {r}<br>Likes: {l}<br>Views: {v}" for r, l, v in zip(rank_values, likes, views)],
710
+ textposition='auto',
711
+ marker_color='rgb(158,202,225)',
712
+ opacity=0.8
713
+ ))
714
+
715
+ fig.update_layout(
716
+ title={
717
+ 'text': 'Current Trending Ranks (All Target Spaces)',
718
+ 'y':0.95,
719
+ 'x':0.5,
720
+ 'xanchor': 'center',
721
+ 'yanchor': 'top'
722
+ },
723
+ xaxis_title='Space ID',
724
+ yaxis_title='Trending Rank',
725
+ yaxis_autorange='reversed',
726
+ height=800,
727
+ showlegend=False,
728
+ template='plotly_white',
729
+ xaxis_tickangle=-45
730
+ )
731
+
732
+ return fig
733
+
734
+ # ํ† ํฐ์ด ์—†๋Š” ๊ฒฝ์šฐ๋ฅผ ์œ„ํ•œ ๋Œ€์ฒด ํ•จ์ˆ˜
735
+ def get_trending_spaces_without_token():
736
+ try:
737
+ url = "https://huggingface.co/api/spaces"
738
+ params = {
739
+ 'sort': 'likes',
740
+ 'direction': -1,
741
+ 'limit': 1000,
742
+ 'full': 'true'
743
+ }
744
+
745
+ response = requests.get(url, params=params)
746
+
747
+ if response.status_code == 200:
748
+ return response.json()
749
+ else:
750
+ print(f"API ์š”์ฒญ ์‹คํŒจ (ํ† ํฐ ์—†์Œ): {response.status_code}")
751
+ print(f"Response: {response.text}")
752
+ return None
753
+ except Exception as e:
754
+ print(f"API ํ˜ธ์ถœ ์ค‘ ์—๋Ÿฌ ๋ฐœ์ƒ (ํ† ํฐ ์—†์Œ): {str(e)}")
755
+ return None
756
+
757
+ # API ํ† ํฐ ์„ค์ • ๋ฐ ํ•จ์ˆ˜ ์„ ํƒ
758
+ if not HF_TOKEN:
759
+ get_trending_spaces = get_trending_spaces_without_token
760
+
761
+
762
+
763
+ def create_error_plot():
764
+ fig = go.Figure()
765
+ fig.add_annotation(
766
+ text="๋ฐ์ดํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์˜ฌ ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค.\n(API ์ธ์ฆ์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค)",
767
+ xref="paper",
768
+ yref="paper",
769
+ x=0.5,
770
+ y=0.5,
771
+ showarrow=False,
772
+ font=dict(size=20)
773
+ )
774
+ fig.update_layout(
775
+ title="Error Loading Data",
776
+ height=400
777
+ )
778
+ return fig
779
+
780
+
781
+ def create_space_info_html(spaces_data):
782
+ if not spaces_data:
783
+ return "<div style='padding: 20px;'><h2>๋ฐ์ดํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์˜ค๋Š”๋ฐ ์‹คํŒจํ–ˆ์Šต๋‹ˆ๋‹ค.</h2></div>"
784
+
785
+ html_content = """
786
+ <div style='padding: 20px;'>
787
+ <h2 style='color: #2c3e50;'>Current Trending Rankings</h2>
788
+ <div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 20px;'>
789
+ """
790
+
791
+ # ๋ชจ๋“  target spaces๋ฅผ ํฌํ•จํ•˜๋„๋ก ์ˆ˜์ •
792
+ for space_id in target_spaces.keys():
793
+ space_info = next((s for s in spaces_data if s.get('id') == space_id), None)
794
+ if space_info:
795
+ rank = next((idx for idx, s in enumerate(spaces_data, 1) if s.get('id') == space_id), 'N/A')
796
+ html_content += f"""
797
+ <div style='
798
+ background: white;
799
+ padding: 20px;
800
+ border-radius: 10px;
801
+ box-shadow: 0 2px 4px rgba(0,0,0,0.1);
802
+ transition: transform 0.2s;
803
+ '>
804
+ <h3 style='color: #34495e;'>#{rank} - {space_id}</h3>
805
+ <p style='color: #7f8c8d;'>๐Ÿ‘ Likes: {space_info.get('likes', 'N/A')}</p>
806
+ <p style='color: #7f8c8d;'>๐Ÿ‘€ Views: {space_info.get('views', 'N/A')}</p>
807
+ <p style='color: #2c3e50;'>{space_info.get('title', 'N/A')}</p>
808
+ <p style='color: #7f8c8d; font-size: 0.9em;'>{space_info.get('description', 'N/A')[:100]}...</p>
809
+ <a href='{target_spaces[space_id]}'
810
+ target='_blank'
811
+ style='
812
+ display: inline-block;
813
+ padding: 8px 16px;
814
+ background: #3498db;
815
+ color: white;
816
+ text-decoration: none;
817
+ border-radius: 5px;
818
+ transition: background 0.3s;
819
+ '>
820
+ Visit Space ๐Ÿ”—
821
+ </a>
822
+ </div>
823
+ """
824
+ else:
825
+ html_content += f"""
826
+ <div style='
827
+ background: #f8f9fa;
828
+ padding: 20px;
829
+ border-radius: 10px;
830
+ box-shadow: 0 2px 4px rgba(0,0,0,0.1);
831
+ '>
832
+ <h3 style='color: #34495e;'>{space_id}</h3>
833
+ <p style='color: #7f8c8d;'>Not in trending</p>
834
+ <a href='{target_spaces[space_id]}'
835
+ target='_blank'
836
+ style='
837
+ display: inline-block;
838
+ padding: 8px 16px;
839
+ background: #95a5a6;
840
+ color: white;
841
+ text-decoration: none;
842
+ border-radius: 5px;
843
+ '>
844
+ Visit Space ๐Ÿ”—
845
+ </a>
846
+ </div>
847
+ """
848
+
849
+ html_content += "</div></div>"
850
+ return html_content
851
+
852
+ def create_data_table(spaces_data):
853
+ if not spaces_data:
854
+ return pd.DataFrame()
855
+
856
+ rows = []
857
+ for idx, space in enumerate(spaces_data, 1):
858
+ space_id = space.get('id', '')
859
+ if space_id in target_spaces:
860
+ rows.append({
861
+ 'Rank': idx,
862
+ 'Space ID': space_id,
863
+ 'Likes': space.get('likes', 'N/A'),
864
+ 'Title': space.get('title', 'N/A'),
865
+ 'URL': target_spaces[space_id]
866
+ })
867
+
868
+ return pd.DataFrame(rows)
869
+
870
+ def refresh_data():
871
+ spaces_data = get_trending_spaces()
872
+ if spaces_data:
873
+ plot = create_trend_visualization(spaces_data)
874
+ info = create_space_info_html(spaces_data)
875
+ df = create_data_table(spaces_data)
876
+ return plot, info, df
877
+ else:
878
+ return create_error_plot(), "<div>API ์ธ์ฆ์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค.</div>", pd.DataFrame()
879
+
880
+
881
+ with gr.Blocks(theme=gr.themes.Soft()) as demo:
882
+ gr.Markdown("""
883
+ # ๐Ÿค— ํ—ˆ๊น…ํŽ˜์ด์Šค 'ํ•œ๊ตญ ๋ฆฌ๋”๋ณด๋“œ'
884
+ ์‹ค์‹œ๊ฐ„์œผ๋กœ Hugging Face์˜ Spaces์™€ Models ์ธ๊ธฐ ์ˆœ์œ„๋ฅผ ๋ถ„์„ํ•ฉ๋‹ˆ๋‹ค. ์‹ ๊ทœ ๋“ฑ๋ก ์š”์ฒญ: [email protected]
885
+ """)
886
+
887
+ with gr.Tab("Spaces Trending"):
888
+ trending_plot = gr.Plot()
889
+ trending_info = gr.HTML()
890
+ trending_df = gr.DataFrame()
891
+
892
+ with gr.Tab("Models Trending"):
893
+ models_plot = gr.Plot()
894
+ models_info = gr.HTML()
895
+ models_df = gr.DataFrame()
896
+
897
+ refresh_btn = gr.Button("๐Ÿ”„ Refresh Data", variant="primary")
898
+
899
+ def refresh_all_data():
900
+ spaces_results = get_spaces_data("trending")
901
+ models_results = get_models_data()
902
+ return [*spaces_results, *models_results]
903
+
904
+ refresh_btn.click(
905
+ refresh_all_data,
906
+ outputs=[
907
+ trending_plot, trending_info, trending_df,
908
+ models_plot, models_info, models_df
909
+ ]
910
+ )
911
+
912
+ # ์ดˆ๊ธฐ ๋ฐ์ดํ„ฐ ๋กœ๋“œ
913
+ spaces_results = get_spaces_data("trending")
914
+ models_results = get_models_data()
915
+
916
+ trending_plot.value, trending_info.value, trending_df.value = spaces_results
917
+ models_plot.value, models_info.value, models_df.value = models_results
918
+
919
+ # Gradio ์•ฑ ์‹คํ–‰
920
+ demo.launch(
921
+ server_name="0.0.0.0",
922
+ server_port=7860,
923
+ share=False
924
+ )