test 30 days

README.md

@@ -13,7 +13,7 @@ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-
 
 # Hugging Face Model Statistics Search
 
-A web application built with Gradio that allows users to search and analyze model statistics from the Hugging Face Hub. The application provides detailed information about model downloads, trending scores, and allows exporting the data to CSV format.
+A web application built with Gradio that allows users to search and analyze model statistics from the Hugging Face Hub. The application provides detailed information about model downloads and allows exporting the data to CSV format.
 
 ## Features
 
@@ -22,7 +22,6 @@ A web application built with Gradio that allows users to search and analyze mode
   - Model ID
   - Downloads (30 days)
   - Downloads (All Time)
-  - Trending Score
 - Export results to CSV
 - User-friendly web interface
 

hf-modelf-family-stats-gradio.py

@@ -0,0 +1,88 @@
+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()