Trying to restore to working state

app.py

@@ -14,6 +14,7 @@ from typing import Dict
 llm_models = list(LLM_REGISTRY.keys())
 
 # Define game list manually (for now)
+#games_list = list(GAMES_REGISTRY.keys())
 games_list = [
     "rock_paper_scissors",
     "prisoners_dilemma",
@@ -21,12 +22,26 @@ games_list = [
     "connect_four",
     "matching_pennies",
     "kuhn_poker",
-    "Overall Leaderboard"
 ]
 
 # File to persist results
 RESULTS_TRACKER_FILE = "results_tracker.json"
 
+def generate_stats_file(model_name: str):
+    """Generate a JSON file with detailed statistics for the selected LLM model."""
+    file_path = f"{model_name}_stats.json"
+    with open(file_path, "w") as f:
+        json.dump(results_tracker.get(model_name, {}), f, indent=4)
+    return file_path
+
+def provide_download_file(model_name):
+    """Creates a downloadable JSON file with stats for the selected model."""
+    return generate_stats_file(model_name)
+
+def refresh_leaderboard():
+    """Manually refresh the leaderboard."""
+    return calculate_leaderboard(game_dropdown.value)
+
 # Load or initialize the results tracker
 if os.path.exists(RESULTS_TRACKER_FILE):
     with open(RESULTS_TRACKER_FILE, "r") as f:
@@ -43,51 +58,22 @@ def save_results_tracker():
     with open(RESULTS_TRACKER_FILE, "w") as f:
         json.dump(results_tracker, f, indent=4)
 
-def generate_stats_file(model_name: str):
-    """Generate a JSON file with detailed statistics for the selected LLM model."""
-    file_path = f"{model_name}_stats.json"
-    with open(file_path, "w") as f:
-        json.dump(results_tracker.get(model_name, {}), f, indent=4)
-    return file_path
-
-def provide_download_file(model_name):
-    """Creates a downloadable JSON file with stats for the selected model."""
-    return generate_stats_file(model_name)
-
 def calculate_leaderboard(selected_game: str) -> pd.DataFrame:
-    """Generate a structured leaderboard table for the selected game or overall."""
-    leaderboard_df = pd.DataFrame(index=llm_models, 
-                                  columns=["# games", "moves/game", 
+    """Generate a structured leaderboard table for the selected game."""
+    leaderboard_df = pd.DataFrame(index=llm_models,
+                                  columns=["# games", "moves/game",
                                            "illegal-moves", "win-rate", "vs Random"])
-    
+
     for llm in llm_models:
-        if selected_game == "Overall Leaderboard":
-            total_games = 0
-            total_moves = 0
-            total_illegal_moves = 0
-            total_wins = 0
-            total_vs_random = 0
-            for game in games_list[:-1]:
-                game_stats = results_tracker[llm].get(game, {})
-                total_games += game_stats.get("games", 0)
-                total_moves += game_stats.get("moves/game", 0) * game_stats.get("games", 0)
-                total_illegal_moves += game_stats.get("illegal-moves", 0)
-                total_wins += (game_stats.get("win-rate", 0) * game_stats.get("games", 0)) / 100
-                total_vs_random += (game_stats.get("vs Random", 0) * game_stats.get("games", 0)) / 100
-            avg_moves = total_moves / total_games if total_games > 0 else 0
-            avg_win_rate = (total_wins / total_games) * 100 if total_games > 0 else 0
-            avg_vs_random = (total_vs_random / total_games) * 100 if total_games > 0 else 0
-            leaderboard_df.loc[llm] = [total_games, avg_moves, total_illegal_moves, f"{avg_win_rate:.1f}%", f"{avg_vs_random:.1f}%"]
-        else:
-            game_stats = results_tracker[llm].get(selected_game, {})
-            leaderboard_df.loc[llm] = [
-                game_stats.get("games", 0),
-                game_stats.get("moves/game", 0),
-                game_stats.get("illegal-moves", 0),
-                f"{game_stats.get('win-rate', 0):.1f}%",
-                f"{game_stats.get('vs Random', 0):.1f}%"
-            ]
-    
+        game_stats = results_tracker[llm].get(selected_game, {})
+        leaderboard_df.loc[llm] = [
+            game_stats.get("games", 0),
+            game_stats.get("moves/game", 0),
+            game_stats.get("illegal-moves", 0),
+            f"{game_stats.get('win-rate', 0):.1f}%",
+            f"{game_stats.get('vs Random', 0):.1f}%"
+        ]
+
     leaderboard_df = leaderboard_df.reset_index()
     leaderboard_df.rename(columns={"index": "LLM Model"}, inplace=True)
     return leaderboard_df
@@ -110,6 +96,45 @@ def play_game(game_name, player1_type, player2_type, player1_model, player2_mode
         legal_moves = state.legal_actions(current_player)
         board = str(state)
         game_states.append(f"Current Player: {current_player}\nBoard:\n{board}\nLegal Moves: {legal_moves}")
-    
+
     results = simulator.simulate(rounds=int(rounds), log_fn=log_fn)
     return "\n".join(game_states) + f"\nGame Result: {results}"
+
+# Gradio Interface
+with gr.Blocks() as interface:
+    with gr.Tab("Game Arena"):
+        gr.Markdown("# LLM Game Arena\nSelect a game and players to play against LLMs.")
+
+        game_dropdown = gr.Dropdown(choices=games_list, label="Select a Game", value=games_list[0])
+        player1_dropdown = gr.Dropdown(choices=["human", "random_bot", "llm"], label="Player 1 Type", value="llm")
+        player2_dropdown = gr.Dropdown(choices=["human", "random_bot", "llm"], label="Player 2 Type", value="random_bot")
+        player1_model_dropdown = gr.Dropdown(choices=llm_models, label="Player 1 Model", visible=False)
+        player2_model_dropdown = gr.Dropdown(choices=llm_models, label="Player 2 Model", visible=False)
+        rounds_slider = gr.Slider(1, 10, step=1, label="Rounds")
+        result_output = gr.Textbox(label="Game Result")
+
+        play_button = gr.Button("Play Game")
+        play_button.click(
+            play_game,
+            inputs=[game_dropdown, player1_dropdown, player2_dropdown, player1_model_dropdown, player2_model_dropdown, rounds_slider],
+            outputs=result_output,
+        )
+
+    with gr.Tab("Leaderboard"):
+        gr.Markdown("# LLM Model Leaderboard\nTrack performance across different games!")
+
+        game_dropdown = gr.Dropdown(choices=games_list, label="Select Game", value=games_list[0])
+        leaderboard_table = gr.Dataframe(value=calculate_leaderboard(games_list[0]), label="Leaderboard")
+        model_dropdown = gr.Dropdown(choices=llm_models, label="Select LLM Model")
+        download_button = gr.File(label="Download Statistics File")
+        refresh_button = gr.Button("Refresh Leaderboard")
+
+        def update_leaderboard(selected_game):
+            """Updates the leaderboard table based on the selected game."""
+            return calculate_leaderboard(selected_game)
+
+        model_dropdown.change(fn=provide_download_file, inputs=[model_dropdown], outputs=[download_button])
+        game_dropdown.change(fn=update_leaderboard, inputs=[game_dropdown], outputs=[leaderboard_table])
+        refresh_button.click(fn=update_leaderboard, inputs=[game_dropdown], outputs=[leaderboard_table])
+
+interface.launch()