user

app.py

@@ -489,11 +489,17 @@ with gr.Blocks(title="ASR Text Correction Leaderboard") as demo:
     with gr.Row():
         submit_btn = gr.Button("Submit Results")
     
-    def submit_method(name, **values):
+    def submit_method(name, *args):
         if not name:
             return "Please enter a method name", leaderboard
         
-        success = add_user_method(name, values)
+        # Convert args to a dictionary of source:value pairs
+        values = {}
+        for i, source in enumerate(all_sources):
+            if i < len(args):
+                values[source] = args[i]
+        
+        success = add_user_method(name, **values)
         if success:
             updated_df, _ = create_leaderboard()
             return "Method added successfully!", updated_df
@@ -505,7 +511,6 @@ with gr.Blocks(title="ASR Text Correction Leaderboard") as demo:
         return updated_df
     
     # Connect buttons to functions
-    submit_args = [method_name] + list(source_inputs.values())
     submit_btn.click(
         submit_method, 
         inputs=[method_name] + list(source_inputs.values()),
@@ -514,5 +519,36 @@ with gr.Blocks(title="ASR Text Correction Leaderboard") as demo:
     
     refresh_btn.click(refresh_and_report, outputs=[leaderboard])
 
+# Add a new method to the leaderboard
+def add_user_method(name, **values):
+    # Create a new method entry
+    method = {"name": name}
+    
+    # Add values for each source
+    for source, value in values.items():
+        if value and value.strip():
+            try:
+                # Convert to float and ensure it's a percentage
+                float_value = float(value)
+                # Store as decimal (divide by 100 if it's a percentage greater than 1)
+                method[source] = float_value / 100 if float_value > 1 else float_value
+            except ValueError:
+                # Skip invalid values
+                continue
+    
+    # Calculate overall average if we have values
+    if len(method) > 1:  # More than just the name
+        values_list = [v for k, v in method.items() if k != "name" and isinstance(v, (int, float))]
+        if values_list:
+            method["OVERALL"] = np.mean(values_list)
+    
+    # Add to user methods
+    user_methods.append(method)
+    
+    # Save to file
+    save_user_methods()
+    
+    return True
+
 if __name__ == "__main__":
     demo.launch()