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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): | |
| # Get the current date and date from `days_ago` days ago | |
| today = datetime.utcnow().replace(tzinfo=None) | |
| start_date = (today - timedelta(days=days_ago)).replace(tzinfo=None) | |
| # Initialize an empty list to store the filtered models | |
| recent_models = [] | |
| filter_substrings = [sub.strip().lower() for sub in filter_string.split(';')] | |
| search_substrings = [term.strip().lower() for term in search_string.split(';')] | |
| # Use a generator to fetch models in batches, sorted by likes in descending order | |
| for model in api.list_models(sort="likes", direction=-1): | |
| if model.likes >= min_likes: | |
| if hasattr(model, "created_at") and model.created_at: | |
| # Ensure created_at is offset-naive | |
| created_at_date = model.created_at.replace(tzinfo=None) | |
| if search_substrings != []: | |
| if any(term in model.modelId.lower() for term in search_substrings): | |
| if filter_substrings != []: | |
| if not any(sub in model.modelId.lower() for sub in filter_substrings): | |
| if created_at_date >= start_date: | |
| 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": model.created_at.strftime("%Y-%m-%d %H:%M"), | |
| "Task": task | |
| }) | |
| else: | |
| if created_at_date >= start_date: | |
| 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": model.created_at.strftime("%Y-%m-%d %H:%M"), | |
| "Task": task | |
| }) | |
| else: | |
| if filter_substrings != []: | |
| if not any(sub in model.modelId.lower() for sub in filter_substrings): | |
| if created_at_date >= start_date: | |
| 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": model.created_at.strftime("%Y-%m-%d %H:%M"), | |
| "Task": task | |
| }) | |
| else: | |
| if created_at_date >= start_date: | |
| 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": model.created_at.strftime("%Y-%m-%d %H:%M"), | |
| "Task": task | |
| }) | |
| else: | |
| # Since the models are sorted by likes in descending order, | |
| # we can stop once we hit a model with 10 or fewer likes | |
| break | |
| # Convert the list of dictionaries to a pandas DataFrame | |
| df = pd.DataFrame(recent_models) | |
| return df | |
| # Define the Gradio interface | |
| def launch_interface(): | |
| iface = gr.Interface( | |
| fn=get_recent_models, | |
| inputs=[ | |
| gr.Slider(minimum=1, maximum=100, step=1, value=5, label="Minimum Likes"), | |
| gr.Slider(minimum=1, maximum=30, step=1, value=3, label="Days Ago"), | |
| gr.Text(label="Filter", max_lines=1), | |
| gr.Text(label="Search", max_lines=1) | |
| ], | |
| outputs=gr.DataFrame( | |
| headers=["Model ID", "Likes", "Creation Date", "Task"], | |
| wrap=True, | |
| datatype=["html", "number", "str"], | |
| ), | |
| title="Model Drops Tracker 🚀", | |
| description="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" | |
| ) | |
| iface.launch() | |
| if __name__ == "__main__": | |
| launch_interface() |