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| import gradio as gr | |
| from huggingface_hub import HfApi | |
| from datetime import datetime, timedelta | |
| import pandas as pd | |
| # Initialize the Hugging Face API | |
| api = HfApi() | |
| def get_recent_models(min_likes, days_ago, filter_string, search_string): | |
| # Calculate the start date for filtering models | |
| start_date = datetime.utcnow() - timedelta(days=days_ago) | |
| # Prepare filter and search substrings | |
| filter_substrings = {sub.strip().lower() for sub in filter_string.split(';') if sub.strip()} | |
| search_substrings = {term.strip().lower() for term in search_string.split(';') if term.strip()} | |
| # Initialize an empty list to store the filtered models | |
| recent_models = [] | |
| # Fetch models sorted by likes in descending order | |
| for model in api.list_models(sort="likes", direction=-1): | |
| if model.likes < min_likes: | |
| # Since models are sorted by likes in descending order, break early | |
| break | |
| created_at_date = model.created_at.replace(tzinfo=None) if model.created_at else None | |
| # Ensure the model meets the date, like, search, and filter criteria | |
| if created_at_date and created_at_date >= start_date: | |
| model_id_lower = model.modelId.lower() | |
| if (not search_substrings or any(term in model_id_lower for term in search_substrings)) and \ | |
| (not filter_substrings or not any(sub in model_id_lower for sub in filter_substrings)): | |
| task = model.pipeline_tag if hasattr(model, "pipeline_tag") else "N/A" | |
| recent_models.append({ | |
| "Model ID": f'<a href="https://huggingface.co/{model.modelId}" target="_blank">{model.modelId}</a>', | |
| "Likes": model.likes, | |
| "Creation Date": created_at_date.strftime("%Y-%m-%d %H:%M"), | |
| "Task": task | |
| }) | |
| # Convert the list of dictionaries to a pandas DataFrame | |
| df = pd.DataFrame(recent_models) | |
| return df | |
| # Define the Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Model Drops Tracker 🚀") | |
| gr.Markdown( | |
| "Overwhelmed by the rapid pace of model releases? 😅 You're not alone! " | |
| "That's exactly why I built this tool. Easily filter recent models from the Hub " | |
| "by setting a minimum number of likes and the number of days since their release. " | |
| "Click on a model to see its card. Use `;` to split filter and search terms." | |
| ) | |
| with gr.Row(): | |
| likes_slider = gr.Slider(minimum=1, maximum=100, step=1, value=5, label="Minimum Likes") | |
| days_slider = gr.Slider(minimum=1, maximum=30, step=1, value=3, label="Days Ago") | |
| with gr.Row(): | |
| filter_text = gr.Text(label="Filter", max_lines=1, placeholder="Exclude models containing these terms (separate by `;`)") | |
| search_text = gr.Text(label="Search", max_lines=1, placeholder="Include only models containing these terms (separate by `;`)") | |
| btn = gr.Button("Run") | |
| with gr.Column(): | |
| df = gr.DataFrame( | |
| headers=["Model ID", "Likes", "Creation Date", "Task"], | |
| wrap=True, | |
| datatype=["html", "number", "str"], | |
| ) | |
| btn.click(fn=get_recent_models, inputs=[likes_slider, days_slider, filter_text, search_text], outputs=df) | |
| if __name__ == "__main__": | |
| demo.launch() |