enhanced leaderboard

app.py

@@ -399,6 +399,8 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
 
     # Load existing match outcomes and user data from the test split
     users = load_dataset("Jay-Rajput/DIS_IPL_Leads", split="train")
+    # Convert users dataset to pandas DataFrame
+    users_df = pd.DataFrame(users)
 
     # Directly update or add the match outcome
     outcome_exists = False
@@ -418,19 +420,22 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match):
         bid_points = prediction['bid_points']
 
         # Update points based on prediction accuracy
+        points = users_df[user_name][0]['0']
         if predicted_winner == winning_team:
-            users[user_name][0]['0'] += 1000
-            users[user_name][0]['0'] += bid_points
+            points += 1000 + bid_points
             if predicted_motm == man_of_the_match:
-                users[user_name][0]['0'] += 400  # Bonus for both correct predictions
+                points += 400
         else:
-            users[user_name][0]['0'] -= 200 + bid_points  # Penalty for wrong team prediction
+            points -= 200 + bid_points
+
+        # Update user's points in the DataFrame
+        users_df[user_name][0]['0'] = points
 
     users.to_json(USERS_JSON)
     outcomes.to_json(OUTCOMES)
     # Convert the updated DataFrame back to a Hugging Face Dataset and push updates
-    # updated_dataset = Dataset.from_pandas(users)
-    users.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
+    updated_dataset = Dataset.from_pandas(users_df)
+    updated_dataset.push_to_hub("Jay-Rajput/DIS_IPL_Leads", split="train")
     outcomes.push_to_hub("Jay-Rajput/DIS_IPL_Outcomes", split="train")