Update app.py
Browse files
app.py
CHANGED
@@ -15,7 +15,10 @@ target_models = {
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"LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
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"ginipick/flux-lora-eric-cat": "https://huggingface.co/ginipick/flux-lora-eric-cat",
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"seawolf2357/flux-lora-car-rolls-royce": "https://huggingface.co/seawolf2357/flux-lora-car-rolls-royce",
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"Saxo/Linkbricks-Horizon-AI-Korean-Gemma-2-sft-dpo-27B": "https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-Gemma-2-sft-dpo-27B",
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"AALF/gemma-2-27b-it-SimPO-37K": "https://huggingface.co/AALF/gemma-2-27b-it-SimPO-37K",
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"nbeerbower/mistral-nemo-wissenschaft-12B": "https://huggingface.co/nbeerbower/mistral-nemo-wissenschaft-12B",
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@@ -290,52 +293,46 @@ target_models = {
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def get_models_data(progress=gr.Progress()):
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"""๋ชจ๋ธ ๋ฐ์ดํฐ ๊ฐ์ ธ์ค๊ธฐ"""
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url = "https://huggingface.co/api/models"
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try:
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progress(0, desc="Fetching models data...")
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{'sort': 'downloads', 'direction': -1},
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{'sort': 'lastModified', 'direction': -1},
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{'sort': 'likes', 'direction': -1}
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]
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headers = {
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'Accept': 'application/json'
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'User-Agent': 'Mozilla/5.0'
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}
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**sort_params
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}
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response = requests.get(url, params=params, headers=headers)
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if response.status_code == 200:
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models = response.json()
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all_found_models.extend(models)
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print(f"Found {len(models)} models with {sort_params['sort']} sort")
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if not all_found_models:
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print("No models found from API")
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return create_error_plot(), "<div>๋ชจ๋ธ ๋ฐ์ดํฐ๋ฅผ ๊ฐ์ ธ์ค๋๋ฐ ์คํจํ์ต๋๋ค.</div>", pd.DataFrame()
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print(f"Total unique models: {len(unique_models)}")
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# target_models์ ๋งค์นญ
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filtered_models = []
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filtered_models.append(model)
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print(f"
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print(f"Matched {len(filtered_models)} models out of {len(target_models)} targets")
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@@ -349,25 +346,19 @@ def get_models_data(progress=gr.Progress()):
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# ๋ฐ์ดํฐ ์ค๋น
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ids = [model['id'] for model in filtered_models]
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likes = [model.get('likes', 0) for model in filtered_models]
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#
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sorted_models = sorted(filtered_models, key=lambda x: x.get('downloads', 0), reverse=True)
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for idx, model in enumerate(sorted_models):
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model['rank'] = idx + 1
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ranks = [model['rank'] for model in sorted_models]
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# Y์ถ ๊ฐ์ ๋ฐ์
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y_values = [1001 - r for r in ranks]
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# ๋ง๋ ๊ทธ๋ํ ์์ฑ
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fig.add_trace(go.Bar(
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x=ids,
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y=y_values,
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text=[f"Rank: {r}<br>
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for r,
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textposition='auto',
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marker_color='rgb(158,202,225)',
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opacity=0.8
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@@ -375,7 +366,7 @@ def get_models_data(progress=gr.Progress()):
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fig.update_layout(
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title={
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'text': 'Hugging Face Models Rankings (Top 1000)',
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'y':0.95,
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'x':0.5,
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'xanchor': 'center',
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@@ -399,15 +390,16 @@ def get_models_data(progress=gr.Progress()):
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# HTML ์นด๋ ์์ฑ
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html_content = """
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<div style='padding: 20px; background: #f5f5f5;'>
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<h2 style='color: #2c3e50;'>Models Rankings</h2>
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<div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 20px;'>
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"""
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model_id = model['id']
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rank = model['rank']
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downloads = model.get('downloads', 0)
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likes = model.get('likes', 0)
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html_content += f"""
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<div style='
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@@ -418,8 +410,8 @@ def get_models_data(progress=gr.Progress()):
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transition: transform 0.2s;
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'>
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<h3 style='color: #34495e;'>Rank #{rank} - {model_id}</h3>
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<p style='color: #7f8c8d;'>โฌ๏ธ Downloads: {downloads}</p>
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<p style='color: #7f8c8d;'>๐ Likes: {likes}</p>
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<a href='{target_models[model_id]}'
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target='_blank'
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style='
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@@ -436,16 +428,58 @@ def get_models_data(progress=gr.Progress()):
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</div>
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"""
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html_content += "</div></div>"
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# ๋ฐ์ดํฐํ๋ ์ ์์ฑ
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progress(1.0, desc="Complete!")
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return fig, html_content, df
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"LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
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"ginipick/flux-lora-eric-cat": "https://huggingface.co/ginipick/flux-lora-eric-cat",
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"seawolf2357/flux-lora-car-rolls-royce": "https://huggingface.co/seawolf2357/flux-lora-car-rolls-royce",
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"moreh/Llama-3-Motif-102B-Instruct": "https://huggingface.co/moreh/Llama-3-Motif-102B-Instruct",
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"moreh/Llama-3-Motif-102B": "https://huggingface.co/moreh/Llama-3-Motif-102B",
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"Saxo/Linkbricks-Horizon-AI-Korean-Gemma-2-sft-dpo-27B": "https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-Gemma-2-sft-dpo-27B",
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"AALF/gemma-2-27b-it-SimPO-37K": "https://huggingface.co/AALF/gemma-2-27b-it-SimPO-37K",
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"nbeerbower/mistral-nemo-wissenschaft-12B": "https://huggingface.co/nbeerbower/mistral-nemo-wissenschaft-12B",
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def get_models_data(progress=gr.Progress()):
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"""๋ชจ๋ธ ๋ฐ์ดํฐ ๊ฐ์ ธ์ค๊ธฐ"""
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url = "https://huggingface.co/api/models/trending" # trending API ์ฌ์ฉ
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try:
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progress(0, desc="Fetching models data...")
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params = {
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'full': 'true',
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'limit': 1000
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}
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headers = {
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'Accept': 'application/json'
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}
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response = requests.get(url, params=params, headers=headers)
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if response.status_code != 200:
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print(f"API ์์ฒญ ์คํจ: {response.status_code}")
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print(f"Response: {response.text}")
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return create_error_plot(), "<div>๋ชจ๋ธ ๋ฐ์ดํฐ๋ฅผ ๊ฐ์ ธ์ค๋๋ฐ ์คํจํ์ต๋๋ค.</div>", pd.DataFrame()
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models = response.json()
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print(f"Total models fetched: {len(models)}")
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# target_models์ ๋งค์นญ
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filtered_models = []
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model_ranks = {}
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# ์ ์ฒด ์์ ์ ๋ณด ์ ์ฅ
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for idx, model in enumerate(models, 1):
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model_id = model.get('id', '')
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model_ranks[model_id] = idx
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# target_models ์ค ์์๊ถ ๋ด ๋ชจ๋ธ ํํฐ๋ง
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for model in models:
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if model.get('id', '') in target_models:
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model['rank'] = model_ranks[model.get('id', '')]
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filtered_models.append(model)
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print(f"Found model: {model.get('id', '')} at rank {model['rank']}")
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# ์์๋ก ์ ๋ ฌ
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filtered_models.sort(key=lambda x: x['rank'])
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print(f"Matched {len(filtered_models)} models out of {len(target_models)} targets")
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# ๋ฐ์ดํฐ ์ค๋น
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ids = [model['id'] for model in filtered_models]
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ranks = [model['rank'] for model in filtered_models]
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likes = [model.get('likes', 0) for model in filtered_models]
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downloads = [model.get('downloads', 0) for model in filtered_models]
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# Y์ถ ๊ฐ์ ๋ฐ์ (1000 - rank + 1)
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y_values = [1001 - r for r in ranks]
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# ๋ง๋ ๊ทธ๋ํ ์์ฑ
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fig.add_trace(go.Bar(
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x=ids,
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y=y_values,
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text=[f"Rank: {r}<br>Likes: {l}<br>Downloads: {d}"
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for r, l, d in zip(ranks, likes, downloads)],
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textposition='auto',
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marker_color='rgb(158,202,225)',
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opacity=0.8
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fig.update_layout(
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title={
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'text': 'Hugging Face Models Trending Rankings (Top 1000)',
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'y':0.95,
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'x':0.5,
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'xanchor': 'center',
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# HTML ์นด๋ ์์ฑ
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html_content = """
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<div style='padding: 20px; background: #f5f5f5;'>
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<h2 style='color: #2c3e50;'>Models Trending Rankings</h2>
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<div style='display: grid; grid-template-columns: repeat(auto-fill, minmax(300px, 1fr)); gap: 20px;'>
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"""
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# ์์๊ถ ๋ด ๋ชจ๋ธ ์นด๋ ์์ฑ
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for model in filtered_models:
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model_id = model['id']
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rank = model['rank']
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likes = model.get('likes', 0)
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downloads = model.get('downloads', 0)
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html_content += f"""
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<div style='
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transition: transform 0.2s;
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'>
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<h3 style='color: #34495e;'>Rank #{rank} - {model_id}</h3>
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<p style='color: #7f8c8d;'>๐ Likes: {likes}</p>
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<p style='color: #7f8c8d;'>โฌ๏ธ Downloads: {downloads}</p>
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<a href='{target_models[model_id]}'
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target='_blank'
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style='
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</div>
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"""
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# ์์๊ถ ๋ฐ ๋ชจ๋ธ ์นด๋ ์์ฑ
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for model_id in target_models:
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if model_id not in [m['id'] for m in filtered_models]:
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html_content += f"""
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<div style='
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background: #f8f9fa;
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padding: 20px;
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border-radius: 10px;
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box-shadow: 0 2px 4px rgba(0,0,0,0.1);
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'>
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<h3 style='color: #34495e;'>{model_id}</h3>
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<p style='color: #7f8c8d;'>Not in top 1000</p>
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<a href='{target_models[model_id]}'
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target='_blank'
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style='
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display: inline-block;
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padding: 8px 16px;
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background: #95a5a6;
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color: white;
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text-decoration: none;
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border-radius: 5px;
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'>
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Visit Model ๐
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</a>
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</div>
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"""
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html_content += "</div></div>"
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# ๋ฐ์ดํฐํ๋ ์ ์์ฑ
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df_data = []
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# ์์๊ถ ๋ด ๋ชจ๋ธ
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for model in filtered_models:
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df_data.append({
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'Rank': model['rank'],
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'Model ID': model['id'],
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'Likes': model.get('likes', 'N/A'),
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'Downloads': model.get('downloads', 'N/A'),
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'URL': target_models[model['id']]
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})
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# ์์๊ถ ๋ฐ ๋ชจ๋ธ
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for model_id in target_models:
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if model_id not in [m['id'] for m in filtered_models]:
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df_data.append({
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'Rank': 'Not in top 1000',
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'Model ID': model_id,
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'Likes': 'N/A',
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'Downloads': 'N/A',
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'URL': target_models[model_id]
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})
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df = pd.DataFrame(df_data)
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progress(1.0, desc="Complete!")
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return fig, html_content, df
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