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): # 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 = [] # 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 > 10: 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 created_at_date >= start_date and model.likes >= min_likes: task = model.pipeline_tag if hasattr(model, "pipeline_tag") else "N/A" recent_models.append({ "Model ID": f'{model.modelId}', "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 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.") with gr.Row(): likes_slider = gr.Slider(minimum=0, maximum=100, step=10, value=10, label="Minimum Likes") days_slider = gr.Slider(minimum=1, maximum=7, step=1, value=1, label="Days Ago") 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], outputs=df) if __name__ == "__main__": demo.launch()