Update app.py

app.py

@@ -271,27 +271,31 @@ def update_elo_rating(collection, updated_ratings, winner, loser, category):
 
 def generate_leaderboard(collection):
     rows = list(collection.find())
+    categories = set()
+    for row in rows:
+        categories.update(row['categories'].keys())
+    
     leaderboard_data = []
     for row in rows:
         bot_name = row['bot_name']
         original_model = next(entry['original_model'] for entry in chatbots_data if entry['adapter'] == bot_name)
         
-        category_data = []
-        for category, data in row['categories'].items():
-            category_data.append(f"{data['elo_rating']}")
+        category_data = {category: row['categories'].get(category, {}).get('elo_rating', '-') for category in categories}
         
-        total_elo = sum(category['elo_rating'] for category in row['categories'].values())
-        total_games = sum(category['games_played'] for category in row['categories'].values())
+        total_elo = sum(data['elo_rating'] for data in row['categories'].values() if 'elo_rating' in data)
+        total_games = sum(data['games_played'] for data in row['categories'].values() if 'games_played' in data)
         avg_elo = total_elo / len(row['categories']) if len(row['categories']) > 0 else 0
         
-        leaderboard_data.append([original_model, avg_elo, total_games] + category_data)
+        leaderboard_data.append([original_model, avg_elo, total_games] + [category_data[category] for category in categories])
     
-    categories = list(row['categories'].keys())
-    columns = ['Chatbot', 'Avg ELO Score', 'Total Games Played'] + categories
+    columns = ['Chatbot', 'Avg ELO Score', 'Total Games Played'] + list(categories)
     
     leaderboard_data = pd.DataFrame(leaderboard_data, columns=columns)
     leaderboard_data['Avg ELO Score'] = leaderboard_data['Avg ELO Score'].round().astype(int)
     
+    # Replace '-' with empty cells
+    leaderboard_data = leaderboard_data.replace('-', '')
+    
     # Sort by average ELO score in descending order
     leaderboard_data = leaderboard_data.sort_values('Avg ELO Score', ascending=False)