Update app.py

app.py

@@ -291,22 +291,22 @@ def update_leaderboard_pro(results):
     try:
         # Update the local leaderboard file
         new_entry_df = pd.DataFrame([new_entry])
-        file_exists = os.path.exists(LEADERBOARD_FILE)
+        file_exists = os.path.exists(LEADERBOARD_FILE_pro)
         
         new_entry_df.to_csv(
-            LEADERBOARD_FILE,
+            LEADERBOARD_FILE_pro,
             mode='a',  # Append mode
             index=False,
             header=not file_exists  # Write header only if the file is new
         )
-        print(f"Leaderboard updated successfully at {LEADERBOARD_FILE}")
+        print(f"Leaderboard updated successfully at {LEADERBOARD_FILE_pro}")
 
         # Push the updated file to the Hugging Face repository using HTTP API
         api = HfApi()
         token = HfFolder.get_token()
         
         api.upload_file(
-            path_or_fileobj=LEADERBOARD_FILE,
+            path_or_fileobj=LEADERBOARD_FILE_pro,
             path_in_repo="leaderboardPro.csv",
             repo_id="SondosMB/Mobile-MMLU",  # Your Space repository
             repo_type="space",
@@ -343,6 +343,19 @@ def load_leaderboard():
             
         })
     return pd.read_csv(LEADERBOARD_FILE)
+
+    def load_leaderboard_pro():
+    if not os.path.exists(LEADERBOARD_FILE_pro) or os.stat(LEADERBOARD_FILE_pro).st_size == 0:
+        return pd.DataFrame({
+            "Model Name": [],
+            "Overall Accuracy": [],
+            "Correct Predictions": [],
+            "Total Questions": [],
+            "Timestamp": [],
+            "Team Name": [],
+            
+        })
+    return pd.read_csv(LEADERBOARD_FILE_pro)
     
 # def evaluate_predictions(prediction_file, model_name, add_to_leaderboard):
 #     try:
@@ -486,7 +499,7 @@ def evaluate_predictions_pro(prediction_file, model_name,Team_name ,add_to_leade
         missing_columns = [col for col in required_columns if col not in predictions_df.columns]
         if missing_columns:
             return (f"Error: Missing required columns in prediction file: {', '.join(missing_columns)}.",
-                    load_leaderboard())
+                    load_leaderboard_pro())
 
         # Validate 'Answer' column in ground truth file
         if 'Answer' not in ground_truth_df.columns:
@@ -1056,14 +1069,14 @@ with gr.Blocks(css=css_tech_theme) as demo:
             )
         with gr.TabItem("🏅 Leaderboard-pro"):
             leaderboard_table = gr.Dataframe(
-                value=load_leaderboard,
+                value=load_leaderboard_pro(),
                 label="Leaderboard",
                 interactive=False,
                 wrap=True,
             )
             refresh_button = gr.Button("Refresh Leaderboard")
             refresh_button.click(
-                lambda: load_leaderboard,
+                lambda: load_leaderboard_pro(),
                 inputs=[],
                 outputs=[leaderboard_table],
             )