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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()
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