Delete hf-modelf-family-stats-gradio.py

hf-modelf-family-stats-gradio.py

@@ -1,88 +0,0 @@
-import tempfile
-import csv
-import pandas as pd
-import gradio as gr
-from huggingface_hub import HfApi
-from pathlib import Path
-
-def get_model_stats(search_term):
-    # Initialize the API
-    api = HfApi()
-    
-    # Create a temporary file for the CSV
-    temp_dir = tempfile.mkdtemp()
-    output_file = Path(temp_dir) / f"{search_term}_models_alltime.csv"
-    
-    # Get the generator of models with the working sort parameter
-    print(f"Fetching {search_term} models with download statistics...")
-    models_generator = api.list_models(
-        search=search_term, 
-        expand=["downloads", "downloadsAllTime"],  # Get both 30-day and all-time downloads
-        sort="_id"  # Sort by ID to avoid timeout issues
-    )
-    
-    # Create and write to CSV
-    with open(output_file, 'w', newline='', encoding='utf-8') as csvfile:
-        csv_writer = csv.writer(csvfile)
-        # Write header
-        csv_writer.writerow(["Model ID", "Downloads (30 days)", "Downloads (All Time)"])
-        
-        # Process models
-        model_count = 0
-        for model in models_generator:
-            # Write to CSV
-            csv_writer.writerow([
-                getattr(model, 'id', "Unknown"),
-                getattr(model, 'downloads', 0),  # Last 30 days downloads
-                getattr(model, 'downloads_all_time', 0)
-            ])
-            model_count += 1
-    
-    # Read the CSV file into a pandas DataFrame
-    df = pd.read_csv(output_file)
-    
-    # Return both the DataFrame, status message, and the CSV file path
-    return df, f"Found {model_count} models for search term '{search_term}'", str(output_file)
-
-# Create the Gradio interface
-with gr.Blocks(title="Hugging Face Model Statistics") as demo:
-    gr.Markdown("# Hugging Face Model Statistics")
-    gr.Markdown("Enter a search term to find model statistics from Hugging Face Hub")
-    
-    with gr.Row():
-        search_input = gr.Textbox(
-            label="Search Term",
-            placeholder="Enter a model name or keyword (e.g., 'gemma', 'llama')",
-            value="gemma"
-        )
-        search_button = gr.Button("Search")
-    
-    with gr.Row():
-        output_table = gr.Dataframe(
-            headers=["Model ID", "Downloads (30 days)", "Downloads (All Time)"],
-            datatype=["str", "number", "number"],
-            label="Model Statistics"
-        )
-        status_message = gr.Textbox(label="Status")
-    
-    with gr.Row():
-        download_button = gr.Button("Download CSV")
-        csv_file = gr.File(label="CSV File", visible=False)
-    
-    # Store the CSV file path in a state
-    csv_path = gr.State()
-    
-    search_button.click(
-        fn=get_model_stats,
-        inputs=search_input,
-        outputs=[output_table, status_message, csv_path]
-    )
-    
-    download_button.click(
-        fn=lambda x: x,
-        inputs=csv_path,
-        outputs=csv_file
-    )
-
-if __name__ == "__main__":
-    demo.launch()