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| import gradio as gr | |
| import requests | |
| from huggingface_hub import list_models, list_datasets, list_spaces | |
| from typing import Union | |
| # Helper function to get the total storage for models, datasets, or spaces | |
| def get_total_storage(namespace, resource_type, oauth_token: Union[gr.OAuthToken, None]): | |
| token = oauth_token.token if oauth_token else None | |
| if resource_type == "model": | |
| resources = list(list_models(author=namespace, token=token)) | |
| url_base = "https://huggingface.co/api/models" | |
| elif resource_type == "dataset": | |
| resources = list(list_datasets(author=namespace, token=token)) | |
| url_base = "https://huggingface.co/api/datasets" | |
| elif resource_type == "space": | |
| resources = list(list_spaces(author=namespace, token=token)) | |
| url_base = "https://huggingface.co/api/spaces" | |
| total_storage = 0 | |
| for resource in resources: | |
| resource_id = resource.id | |
| url = f"{url_base}/{resource_id}/treesize/main" | |
| response = requests.get(url) | |
| if response.status_code == 200: | |
| size_info = response.json() | |
| total_storage += size_info.get("size", 0) | |
| return total_storage, len(resources) | |
| def get_report(namespace, oauth_token: Union[gr.OAuthToken, None]): | |
| # Fetch storage and counts for models, datasets, and spaces | |
| model_storage, n_models = get_total_storage(namespace, "model", oauth_token) | |
| dataset_storage, n_datasets = get_total_storage(namespace, "dataset", oauth_token) | |
| space_storage, n_spaces = get_total_storage(namespace, "space", oauth_token) | |
| # Total storage | |
| total_storage = model_storage + dataset_storage + space_storage | |
| total_storage_gb = total_storage / (1024 ** 3) # Convert from bytes to GB | |
| total_storage_tb = total_storage_gb / 1024 # Convert from GB to TB | |
| # Cost calculation (1 TB = 20 USD) | |
| estimated_cost = total_storage_tb * 20 | |
| # Generate a report | |
| report = f""" | |
| ## Hugging Face Storage Report for {namespace} | |
| - **Number of Models**: {n_models} | |
| - **Number of Datasets**: {n_datasets} | |
| - **Number of Spaces**: {n_spaces} | |
| - **Total Storage**: {total_storage_gb:.2f} GB ({total_storage_tb:.2f} TB) | |
| - **Estimated Cost**: ${estimated_cost:.2f} USD (at 1 TB = $20USD) | |
| """ | |
| return report | |
| css = """ | |
| .main_ui_logged_out{opacity: 0.3; pointer-events: none} | |
| """ | |
| # Create Gradio UI | |
| with gr.Blocks(css=css) as demo: | |
| gr.Markdown("# Hugging Face Storage Report") | |
| gr.LoginButton() | |
| namespace = gr.Textbox(label="Enter Namespace (username or org)") | |
| output = gr.Markdown() | |
| # Button to trigger the report generation | |
| report_button = gr.Button("Generate Report") | |
| report_button.click(fn=get_report, inputs=namespace, outputs=output, concurrency_limit=10) | |
| # Launch the Gradio app | |
| demo.launch() | |