add counter to total

hf-modelf-family-stats-gradio.py

@@ -0,0 +1,107 @@
+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
+    )
+    
+    # Initialize counters for total downloads
+    total_30day_downloads = 0
+    total_alltime_downloads = 0
+    
+    # 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:
+            # Get download counts
+            downloads_30day = getattr(model, 'downloads', 0)
+            downloads_alltime = getattr(model, 'downloads_all_time', 0)
+            
+            # Add to totals
+            total_30day_downloads += downloads_30day
+            total_alltime_downloads += downloads_alltime
+            
+            # Write to CSV
+            csv_writer.writerow([
+                getattr(model, 'id', "Unknown"),
+                downloads_30day,
+                downloads_alltime
+            ])
+            model_count += 1
+    
+    # Read the CSV file into a pandas DataFrame
+    df = pd.read_csv(output_file)
+    
+    # Create status message with total downloads
+    status_message = (
+        f"Found {model_count} models for search term '{search_term}'\n"
+        f"Total 30-day downloads: {total_30day_downloads:,}\n"
+        f"Total all-time downloads: {total_alltime_downloads:,}"
+    )
+    
+    # Return both the DataFrame, status message, and the CSV file path
+    return df, status_message, 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", lines=3)  # Increased lines to show all stats
+    
+    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()