Update app.py

app.py

@@ -71,21 +71,23 @@ def extract_leaderboard_stats(game_name: str) -> pd.DataFrame:
                     "SUM(reward) AS total_rewards " \
                     "FROM game_results"
             df = pd.read_sql_query(query, conn)
+            
+            # Use avg_generation_time from a specific game (e.g., Kuhn Poker)
+            game_query = "SELECT AVG(generation_time) FROM moves WHERE game_name = 'kuhn_poker'"
+            avg_gen_time = conn.execute(game_query).fetchone()[0] or 0
         else:
             query = "SELECT COUNT(DISTINCT episode) AS games_played, " \
                     "SUM(reward) AS total_rewards " \
                     "FROM game_results WHERE game_name = ?"
             df = pd.read_sql_query(query, conn, params=(game_name,))
+            
+            # Fetch average generation time from moves table
+            gen_time_query = "SELECT AVG(generation_time) FROM moves WHERE game_name = ?"
+            avg_gen_time = conn.execute(gen_time_query, (game_name,)).fetchone()[0] or 0
         
         # Keep division by 2 for total rewards
         df["total_rewards"] = df["total_rewards"].fillna(0).astype(float) / 2
         
-        # Fetch average generation time from moves table
-        gen_time_query = """
-            SELECT AVG(generation_time) FROM moves WHERE game_name = ?
-        """
-        avg_gen_time = conn.execute(gen_time_query, (game_name,)).fetchone()[0] or 0
-        
         # Ensure avg_gen_time has decimals
         avg_gen_time = round(avg_gen_time, 3)
         
@@ -105,9 +107,9 @@ def extract_leaderboard_stats(game_name: str) -> pd.DataFrame:
         vs_random_rate = (wins_vs_random / total_vs_random * 100) if total_vs_random > 0 else 0
         
         df.insert(0, "agent_name", model_name)  # Ensure agent_name is the first column
-        df["agent_type"] = agent_type
+        df.insert(1, "agent_type", agent_type)  # Ensure agent_type is second column
         df["avg_generation_time (sec)"] = avg_gen_time
-        df["vs_random"] = round(vs_random_rate, 2)
+        df["win vs_random (%)"] = round(vs_random_rate, 2)
         
         all_stats.append(df)
         conn.close()
@@ -115,7 +117,7 @@ def extract_leaderboard_stats(game_name: str) -> pd.DataFrame:
     leaderboard_df = pd.concat(all_stats, ignore_index=True) if all_stats else pd.DataFrame()
     
     if leaderboard_df.empty:
-        leaderboard_df = pd.DataFrame(columns=["agent_name", "# games", "total rewards", "avg_generation_time (sec)", "win-rate", "vs_random"])
+        leaderboard_df = pd.DataFrame(columns=["agent_name", "agent_type", "# games", "total rewards", "avg_generation_time (sec)", "win-rate", "win vs_random (%)"])
     
     return leaderboard_df
 
@@ -133,7 +135,7 @@ with gr.Blocks() as interface:
         gr.Markdown("# LLM Model Leaderboard\nTrack performance across different games!")
         available_games = get_available_games()
         leaderboard_game_dropdown = gr.Dropdown(available_games, label="Select Game", value="Aggregated Performance")
-        leaderboard_table = gr.Dataframe(headers=["agent_name", "# games", "total rewards", "avg_generation_time (sec)", "win-rate", "vs_random"])
+        leaderboard_table = gr.Dataframe(headers=["agent_name", "agent_type", "# games", "total rewards", "avg_generation_time (sec)", "win-rate", "win vs_random (%)"])
         generate_button = gr.Button("Generate Leaderboard JSON")
         download_component = gr.File(label="Download Leaderboard JSON")
         refresh_button = gr.Button("Refresh Leaderboard")