display scores on load

app.py

@@ -8,7 +8,7 @@ import pandas as pd
 def score_file(csv_file, model_name):
     # Simulate a delay (replace with your actual processing)
     time.sleep(5)
-    # Here is a dummy example output; replace it with your actual scoring logic.
+    # Dummy output; replace with your actual scoring logic.
     result = {
         'individual_scores': {
             'ab': 100.0, 'advanced_geometry': 100.0, 'aiw': 100.0,
@@ -61,37 +61,44 @@ conn.commit()
 def process_file(file_obj, model_name):
     # Run the scoring function
     result = score_file(file_obj, model_name)
-    
+
     # Save the result to the database
     result_json = json.dumps(result)
     total_score = result.get("total_score", 0)
     c.execute("INSERT INTO results (model_name, total_score, result_json) VALUES (?, ?, ?)",
               (model_name, total_score, result_json))
     conn.commit()
+
+    # Retrieve updated records, sorted by total_score descending
+    df = get_saved_results()
     
-    # Retrieve all records, sorted by total_score descending
+    # Return the individual run result and the table of saved results
+    return f"Current Run Result:\n{json.dumps(result, indent=2)}", df
+
+def get_saved_results():
     c.execute("SELECT id, model_name, total_score, timestamp FROM results ORDER BY total_score DESC")
     records = c.fetchall()
     # Create a pandas DataFrame for a nicer display
     df = pd.DataFrame(records, columns=["ID", "Model Name", "Total Score", "Timestamp"])
-    
-    # Return the individual run result and the table of saved results
-    return f"Current Run Result:\n{json.dumps(result, indent=2)}", df
+    return df
 
 with gr.Blocks() as demo:
-    gr.Markdown("# CSV Scoring App")
-    gr.Markdown("Upload a CSV file and enter a model name. The app will process the file with your `score_file` function, store the results, and display all results sorted by total score.")
-    
+    gr.Markdown("# RGBench [WIP]")
+    gr.Markdown("Upload a CSV file with your completed results and enter the name of your model. RGBench will score and save your results.")
+
     with gr.Row():
         file_input = gr.File(label="Upload CSV File", file_types=['.csv'])
         model_input = gr.Textbox(label="Model Name")
-    
+
     run_button = gr.Button("Run Scoring")
-    
+
     result_output = gr.Textbox(label="Score File Result", lines=15)
     table_output = gr.Dataframe(label="All Saved Results (sorted by Total Score)")
-    
+
     run_button.click(fn=process_file, inputs=[file_input, model_input], outputs=[result_output, table_output])
+    
+    # Load the table data on page load
+    demo.load(fn=get_saved_results, inputs=[], outputs=table_output)
 
 demo.launch()