Create app.py

app.py

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+import requests
+import gradio as gr
+from huggingface_hub import list_models, list_datasets, list_spaces, login
+
+# Helper function to get the total storage for models, datasets, or spaces
+def get_total_storage(author, resource_type, oauth_token: gr.OAuthToken | None):
+    if resource_type == "model":
+        resources = list_models(author=author, token=oauth_token.token)
+        url_base = "https://huggingface.co/api/models"
+    elif resource_type == "dataset":
+        resources = list_datasets(author=author, token=oauth_token.token)
+        url_base = "https://huggingface.co/api/datasets"
+    elif resource_type == "space":
+        resources = list_spaces(author=author, token=oauth_token.token)
+        url_base = "https://huggingface.co/api/spaces"
+    else:
+        raise ValueError("Invalid resource type. Choose 'model', 'dataset', or 'space'.")
+    
+    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(oauth_token: gr.OAuthToken | None):
+    # Log in to Hugging Face using the provided API token
+    login(api_token)
+    
+    # Replace with the actual author namespace
+    author = "your-namespace"  # Example: 'osanseviero'
+
+    # Fetch storage and counts for models, datasets, and spaces
+    model_storage, n_models = get_total_storage(author, "model")
+    dataset_storage, n_datasets = get_total_storage(author, "dataset")
+    space_storage, n_spaces = get_total_storage(author, "space")
+    
+    # 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 {author}
+
+    - **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 = 1 USD)
+    """
+    
+    return report
+
+# Gradio interface
+def gradio_interface(api_token):
+    report = get_report(api_token)
+    return report
+
+# Create Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# Hugging Face Storage Report")
+    
+    gr.LoginButton
+    
+    output = gr.Markdown()
+    
+    # Button to trigger the report generation
+    report_button = gr.Button("Generate Report")
+    report_button.click(fn=gradio_interface, inputs=None, outputs=output)
+
+# Launch the Gradio app
+demo.launch()