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backend/ai.py

@@ -13,19 +13,17 @@ logger = logging.getLogger("app")
 AI_USER_ID = "ai"
 
 class AIPersonality(Enum):
-  AGGRESSIVE = "aggressive"   # Prefers high attack cards, plays aggressively
-  DEFENSIVE = "defensive"     # Prefers high defense cards, plays conservatively
-  BALANCED = "balanced"       # Mix of offense and defense
-  GREEDY = "greedy"           # Prioritizes high cost cards, willing to sacrifice
-  SWARM = "swarm"             # Prefers low cost cards, fills board quickly
-  CONTROL = "control"         # Focuses on board control and efficiency
-  ARBITRARY = "arbitrary"     # Just does whatever
-  JEBRASKA  = "jebraska"      # Trained neural network plan scorer
+  AGGRESSIVE = "aggressive"
+  DEFENSIVE = "defensive"
+  BALANCED = "balanced"
+  GREEDY = "greedy"           # prioritizes high cost cards, willing to sacrifice
+  SWARM = "swarm"
+  CONTROL = "control"
+  ARBITRARY = "arbitrary"
+  JEBRASKA  = "jebraska"      # trained neural network plan scorer
 
 def get_random_personality() -> AIPersonality:
-  """Returns a random AI personality."""
-  # return random.choice(list(AIPersonality))
-  return AIPersonality.JEBRASKA
+  return random.choice(list(AIPersonality))
 
 def calculate_exact_cost(attack: int, defense: int) -> float:
   """Calculate the exact cost before rounding (matches card.py formula)."""
@@ -130,8 +128,6 @@ def choose_cards(cards: list[Card], difficulty: int, personality: AIPersonality)
   return selected
 
 
-# ==================== Turn planning ====================
-
 @dataclass
 class MovePlan:
   sacrifice_slots: list[int]
@@ -175,7 +171,6 @@ def _plans_for_sacrifice(player, opponent, sacrifice_slots):
 
 
 def generate_plans(player, opponent) -> list[MovePlan]:
-  """Generate diverse candidate move plans covering a range of strategies."""
   plans = []
 
   # Sacrifice n board cards
@@ -189,8 +184,6 @@ def generate_plans(player, opponent) -> list[MovePlan]:
 
   return plans
 
-# ==================== Turn execution ====================
-
 def score_plans_batch(
   plans: list[MovePlan],
   player: PlayerState,
@@ -205,7 +198,7 @@ def score_plans_batch(
     for c in player.hand
   }
 
-  # Build board-state arrays with one Python loop (unavoidable)
+  # Build board-state arrays
   board_atk = np.zeros((n, BOARD_SIZE), dtype=np.float32)
   board_occ = np.zeros((n, BOARD_SIZE), dtype=np.bool_)
   n_sac     = np.zeros(n, dtype=np.float32)
@@ -390,7 +383,6 @@ async def run_ai_turn(game_id: str):
       except Exception:
         pass
 
-  # --- Generate and score candidate plans ---
   best_plan = choose_plan(player, opponent, personality, difficulty)
 
   logger.info(
@@ -398,7 +390,6 @@ async def run_ai_turn(game_id: str):
     f"sac={best_plan.sacrifice_slots} plays={[c.name for c, _ in best_plan.plays]}"
   )
 
-  # --- Execute sacrifices ---
   for slot in best_plan.sacrifice_slots:
     card_slot = player.board[slot]
     if card_slot is None:
@@ -409,7 +400,6 @@ async def run_ai_turn(game_id: str):
     await send_state(state)
     await asyncio.sleep(0.35)
 
-  # --- Execute plays ---
   # Shuffle play order so the AI doesn't always fill slots left-to-right
   plays = list(best_plan.plays)
   random.shuffle(plays)