🐐

backend/ai.py

@@ -1,8 +1,13 @@
 import asyncio
 import random
+import logging
+from dataclasses import dataclass
 from enum import Enum
+from itertools import combinations
 from card import Card
-from game import action_play_card, action_sacrifice, action_end_turn, BOARD_SIZE
+from game import action_play_card, action_sacrifice, action_end_turn, BOARD_SIZE, STARTING_LIFE
+
+logger = logging.getLogger("app")
 
 AI_USER_ID = "ai"
 
@@ -21,244 +26,418 @@ def get_random_personality() -> AIPersonality:
 
 def calculate_exact_cost(attack: int, defense: int) -> float:
   """Calculate the exact cost before rounding (matches card.py formula)."""
-  return min(12.0, max(1.0, ((attack**2 + defense**2)**0.18) / 1.5))
+  return min(11.0, max(1.0, ((attack**2 + defense**2)**0.18) / 1.5))
 
-def get_power_curve_value(card: Card) -> float:
+def get_power_curve_value(card) -> float:
   """
-  Returns how much "above the power curve" a card is.
+  Returns how much above the power curve a card is.
-  Positive values mean the card is better than expected for its cost.
+  Positive values mean the card is a better-than-expected deal for its cost.
   """
   exact_cost = calculate_exact_cost(card.attack, card.defense)
   return exact_cost - card.cost
 
-def get_card_efficiency(card: Card) -> float:
-  """
-  Returns the total stats per cost ratio.
-  Higher is better (more stats for the cost).
-  """
-  if card.cost == 0:
-    return 0
-  return (card.attack + card.defense) / card.cost
-
-def score_card_for_personality(card: Card, personality: AIPersonality) -> float:
-  """
-  Score a card based on how well it fits the AI personality.
-  Higher scores are better fits.
-  """
-  if personality == AIPersonality.AGGRESSIVE:
-    # Prefer high attack, attack > defense
-    attack_bias = card.attack * 1.5
-    return attack_bias + (card.attack - card.defense) * 0.5
-
-  elif personality == AIPersonality.DEFENSIVE:
-    # Prefer high defense, defense > attack
-    defense_bias = card.defense * 1.5
-    return defense_bias + (card.defense - card.attack) * 0.5
-
-  elif personality == AIPersonality.BALANCED:
-    # Prefer balanced stats
-    stat_diff = abs(card.attack - card.defense)
-    balance_score = (card.attack + card.defense) - stat_diff * 0.3
-    return balance_score
-
-  elif personality == AIPersonality.GREEDY:
-    # Prefer high cost cards
-    return card.cost * 2 + (card.attack + card.defense) * 0.5
-
-  elif personality == AIPersonality.SWARM:
-    # Prefer low cost cards
-    low_cost_bonus = (13 - card.cost) * 1.5
-    return low_cost_bonus + (card.attack + card.defense) * 0.3
-
-  elif personality == AIPersonality.CONTROL:
-    # Prefer efficient cards (good stats per cost)
-    efficiency = get_card_efficiency(card)
-    total_stats = card.attack + card.defense
-    return efficiency * 5 + total_stats * 0.2
-
-  elif personality == AIPersonality.ARBITRARY:
-    # Does whatever
-    return random.random()*100
-
-  return card.attack + card.defense
-
-def energy_curve(difficulty: int, personality: AIPersonality) -> tuple[int, int, int]:
-  """Calculate a desired energy curve based on difficulty, personality, and a random factor"""
-
-  # First: cards with cost 1-3
-  # Second: cards with cost 4-6
-  # Third is inferred, and is cards with cost 7+
-  diff_low, diff_mid = [
-    (12, 8), # 1
-    (11, 9), # 2
-    (10, 9), # 3
-    ( 9,10), # 4
-    ( 9, 9), # 5
-    ( 9, 8), # 6
-    ( 8, 9), # 7
-    ( 7,10), # 8
-    ( 7, 9), # 9
-    ( 6, 9), # 10
-  ][difficulty - 1]
-
-  r1 = random.randint(0,20)
-  r2 = random.randint(0,20-r1)
-  pers_low, pers_mid = {
-    AIPersonality.AGGRESSIVE: ( 8,10),
-    AIPersonality.ARBITRARY:  (r1,r2),
-    AIPersonality.BALANCED:   ( 7,10),
-    AIPersonality.CONTROL:    ( 3, 8),
-    AIPersonality.DEFENSIVE:  ( 6, 8),
-    AIPersonality.GREEDY:     ( 3, 7),
-    AIPersonality.SWARM:      (15, 3),
-  }[personality]
-
-  # Blend difficulty (70%) and personality (30%) curves
-  blended_low = diff_low * 0.7 + pers_low * 0.3
-  blended_mid = diff_mid * 0.7 + pers_mid * 0.3
-
-  # Add small random variance (±1)
-  low = int(blended_low + random.uniform(-1, 1))
-  mid = int(blended_mid + random.uniform(-1, 1))
-
-  # Ensure low + mid doesn't exceed 20
-  if low + mid > 20:
-    # Scale down proportionally
-    total = low + mid
-    low = int((low / total) * 20)
-    mid = 20 - low
-    high = 0
-  else:
-    high = 20 - low - mid
-
-  # Apply difficulty constraints
-  if difficulty == 1:
-    # Difficulty 1: absolutely no high-cost cards
-    if high > 0:
-      # Redistribute high cards to low and mid
-      low += high // 2
-      mid += high - (high // 2)
-      high = 0
-
-  # Final bounds checking
-  low = max(0, min(20, low))
-  mid = max(0, min(20 - low, mid))
-  high = max(0, 20 - low - mid)
-
-  return (low, mid, high)
 
 def choose_cards(cards: list[Card], difficulty: int, personality: AIPersonality) -> list[Card]:
-  """
+  BUDGET = 50
-  Choose 20 cards from available cards based on difficulty and personality.
 
-  Difficulty (1-10) affects:
+  logger.info(f"Personality: {personality.value}")
-  - Higher difficulty = prefers cards above the power curve
+  logger.info(f"Difficulty: {difficulty}")
-  - Lower difficulty = prefers low-cost cards for early game playability
+  card_strings = [
-  - Lower difficulty = avoids taking the ridiculously good high-cost cards
+    f"{c.name} {c.cost}"
+    for c in sorted(cards, key=lambda x: x.cost)[::-1][:20]
+  ]
+  logger.info("Cards:\n"+("\n".join(card_strings)))
 
-  Personality affects which types of cards are preferred.
+  # God cards (cost 7-11) are gated by difficulty. Below difficulty 7 they are excluded.
-  """
+  # Each level from 7 upward unlocks a higher cost tier; at difficulty 10 all are allowed.
-  if len(cards) < 20:
+  if difficulty >= 6:
-    return cards
+    max_card_cost = difficulty+1
+  else:
+    max_card_cost = 6
 
-  # Get target energy curve based on difficulty and personality
+  allowed = [c for c in cards if c.cost <= max_card_cost] or list(cards)
-  target_low, target_mid, target_high = energy_curve(difficulty, personality)
 
-  selected = []
+  def card_score(card: Card) -> float:
-  remaining = list(cards)
+    pcv = get_power_curve_value(card)
+    # Normalize pcv to [0, 1].
+    pcv_norm = max(0.0, min(1.0, pcv))
 
-  # Fill each cost bracket by distributing across individual cost levels
+    cost_norm = card.cost / max_card_cost   # [0, 1]; higher = more expensive
-  for cost_min, cost_max, target_count in [(1, 3, target_low), (4, 6, target_mid), (7, 12, target_high)]:
+    total = card.attack + card.defense
-    if target_count == 0:
+    atk_ratio = card.attack / total if total else 0.5
-      continue
 
-    bracket_cards = [c for c in remaining if cost_min <= c.cost <= cost_max]
+    if personality == AIPersonality.AGGRESSIVE:
-    if not bracket_cards:
+      # Prefers high-attack cards; slight bias toward high cost for raw power
-      continue
+      return 0.50 * atk_ratio + 0.30 * pcv_norm + 0.20 * cost_norm
 
-    # Group cards by exact cost
+    if personality == AIPersonality.DEFENSIVE:
-    by_cost = {}
+      # Prefers high-defense cards; same cost bias
-    for card in bracket_cards:
+      return 0.50 * (1.0 - atk_ratio) + 0.30 * pcv_norm + 0.20 * cost_norm
-      if card.cost not in by_cost:
-        by_cost[card.cost] = []
-      by_cost[card.cost].append(card)
 
-    # Distribute target_count across available costs
+    if personality == AIPersonality.GREEDY:
-    available_costs = sorted(by_cost.keys())
+      # Fills budget with the fewest, most expensive cards possible
-    if not available_costs:
+      return 0.70 * cost_norm + 0.30 * pcv_norm
-      continue
 
-    # Calculate how many cards to take from each cost level
+    if personality == AIPersonality.SWARM:
-    per_cost = max(1, target_count // len(available_costs))
+      # Cheap cards
-    remainder = target_count % len(available_costs)
+      return 0.45 * (1.0 - cost_norm) + 0.35 * atk_ratio + 0.20 * pcv_norm
 
-    for cost in available_costs:
+    if personality == AIPersonality.CONTROL:
-      cost_cards = by_cost[cost]
+      # Values efficiency above all: wants cards that are above the power curve,
-      # Score cards at this specific cost level
+      # with a secondary preference for higher cost
-      cost_scores = []
+      return 0.70 * pcv_norm + 0.30 * cost_norm
-      for card in cost_cards:
-        # Base score from personality (but normalize by cost to avoid bias)
-        personality_score = score_card_for_personality(card, personality)
-        # Normalize: divide by cost to make 1-cost and 3-cost comparable
-        # Then multiply by average cost in bracket for scaling
-        avg_bracket_cost = (cost_min + cost_max) / 2
-        normalized_score = (personality_score / max(1, card.cost)) * avg_bracket_cost
 
-        # Power curve bonus
+    if personality == AIPersonality.BALANCED:
-        power_curve = get_power_curve_value(card)
+      # Blends everything: efficiency, cost spread, and a slight attack lean
-        difficulty_factor = (difficulty - 5.5) / 4.5
+      return 0.40 * pcv_norm + 0.35 * cost_norm + 0.15 * atk_ratio + 0.10 * (1.0 - atk_ratio)
-        power_curve_score = power_curve * difficulty_factor * 5
 
-        # For low difficulties, heavily penalize high-cost cards with good stats
+    # ARBITRARY: mostly random at lower difficulties
-        if difficulty <= 4 and card.cost >= 7:
+    return (0.05 * difficulty) * pcv_norm + (1 - (0.05 * difficulty)) * random.random()
-          power_penalty = max(0, power_curve) * -10
-          normalized_score += power_penalty
 
-        total_score = normalized_score + power_curve_score
+  # Higher difficulty -> less noise -> more optimal deck composition
-        cost_scores.append((card, total_score))
+  noise = ((10 - difficulty) / 9.0) * 0.50
 
-      # Sort and take best from this cost level
+  scored = sorted(
-      cost_scores.sort(key=lambda x: x[1], reverse=True)
+    [(card_score(c) + random.gauss(0, noise), c) for c in allowed],
-      # Take per_cost, plus 1 extra if this is one of the remainder slots
+    key=lambda x: x[0],
-      to_take = per_cost
+    reverse=True,
-      if remainder > 0:
+  )
-        to_take += 1
-        remainder -= 1
-      to_take = min(to_take, len(cost_scores))
 
-      for i in range(to_take):
+  # Minimum budget reserved for cheap (cost 1-3) cards to ensure early-game presence.
-        card = cost_scores[i][0]
+  # Without cheap cards the AI will play nothing for the first several turns.
-        selected.append(card)
+  early_budget = {
-        remaining.remove(card)
+    AIPersonality.GREEDY:      4,
-        if len(selected) >= 20:
+    AIPersonality.SWARM:      12,
+    AIPersonality.AGGRESSIVE:  8,
+    AIPersonality.DEFENSIVE:  10,
+    AIPersonality.CONTROL:     8,
+    AIPersonality.BALANCED:   10,
+    AIPersonality.ARBITRARY:   8,
+  }[personality]
+
+  selected: list[Card] = []
+  total_cost = 0
+
+  # First pass: secure early-game cards
+  cheap_spent = 0
+  for _, card in scored:
+    if cheap_spent >= early_budget:
       break
-
+    if card.cost > 3 or total_cost + card.cost > BUDGET:
-      if len(selected) >= 20:
+      continue
-        break
-
-  # Fill remaining slots with best available cards
-  # This handles cases where brackets didn't have enough cards
-  while len(selected) < 20 and remaining:
-    remaining_scores = []
-    for card in remaining:
-      personality_score = score_card_for_personality(card, personality)
-      power_curve = get_power_curve_value(card)
-      difficulty_factor = (difficulty - 5.5) / 4.5
-      power_curve_score = power_curve * difficulty_factor * 5
-
-      # For remaining slots, add a slight preference for lower cost cards
-      # to ensure we have early-game plays
-      cost_penalty = (card.cost - 4) * 0.5  # Neutral at 4, penalty for higher
-
-      total_score = personality_score + power_curve_score - cost_penalty
-      remaining_scores.append((card, total_score))
-
-    remaining_scores.sort(key=lambda x: x[1], reverse=True)
-    card = remaining_scores[0][0]
     selected.append(card)
-    remaining.remove(card)
+    total_cost += card.cost
+    cheap_spent += card.cost
 
-  return selected[:20]
+  # Second pass: fill remaining budget greedily by score
+  taken = {id(c) for c in selected}
+  for _, card in scored:
+    if total_cost >= BUDGET:
+      break
+    if id(card) in taken or total_cost + card.cost > BUDGET:
+      continue
+    selected.append(card)
+    total_cost += card.cost
+
+
+  card_strings = [
+    f"{c.name} {c.cost}"
+    for c in sorted(selected, key=lambda x: x.cost)
+  ]
+  logger.info("Selected:\n"+("\n".join(card_strings)))
+
+  return selected
+
+
+# ==================== Turn planning ====================
+
+@dataclass
+class MovePlan:
+  sacrifice_slots: list[int]
+  plays: list[tuple]   # (CardInstance, board_slot: int)
+  label: str = ""
+
+
+def _affordable_subsets(hand, energy, start=0):
+  """Yield every subset of cards from hand whose total cost fits within energy."""
+  yield []
+  for i in range(start, len(hand)):
+    card = hand[i]
+    if card.cost <= energy:
+      for rest in _affordable_subsets(hand, energy - card.cost, i + 1):
+        yield [card] + rest
+
+
+def _plans_for_sacrifice(player, opponent, sacrifice_slots):
+  """Generate one plan per affordable card subset for a given sacrifice set."""
+  board = list(player.board)
+  energy = player.energy
+
+  for slot in sacrifice_slots:
+    if board[slot] is not None:
+      board[slot] = None
+      energy += 1
+
+  hand = list(player.hand)
+  empty_slots = [i for i, c in enumerate(board) if c is None]
+  en_board = opponent.board
+
+  # For scoring: open enemy slots first so the simulation reflects
+  # direct-damage potential accurately.
+  scoring_slots = (
+    [s for s in empty_slots if en_board[s] is None] +
+    [s for s in empty_slots if en_board[s] is not None]
+  )
+
+  return [
+    MovePlan(
+      sacrifice_slots=list(sacrifice_slots),
+      plays=list(zip(cards, scoring_slots)),
+      label=f"sac{len(sacrifice_slots)}_play{len(cards)}",
+    )
+    for cards in _affordable_subsets(hand, energy)
+  ]
+
+
+def generate_plans(player, opponent) -> list[MovePlan]:
+  """Generate diverse candidate move plans covering a range of strategies."""
+  plans = []
+
+  # Sacrifice n board cards
+  occupied = [s for s in range(BOARD_SIZE) if player.board[s] is not None]
+  for n in range(len(occupied) + 1):
+    for slots in combinations(occupied, n):
+      plans += _plans_for_sacrifice(player, opponent, list(slots))
+
+  # Idle: do nothing
+  plans.append(MovePlan(sacrifice_slots=[], plays=[], label="idle"))
+
+  return plans
+
+
+def score_plan(plan: MovePlan, player, opponent, personality: AIPersonality) -> float:
+  """
+  Score a plan from ~0.0 to ~1.0 based on the projected board state after
+  executing it. Higher is better.
+  """
+  # Simulate board after sacrifices + plays
+  board = list(player.board)
+  energy = player.energy
+  for slot in plan.sacrifice_slots:
+    if board[slot] is not None:
+      board[slot] = None
+      energy += 1
+  for card, slot in plan.plays:
+    board[slot] = card
+
+  en_board = opponent.board
+  enemy_occupied = sum(1 for c in en_board if c is not None)
+
+  # --- Combat metrics ---
+  direct_damage = 0      # AI attacks going straight to opponent life
+  board_damage = 0       # AI attacks hitting enemy cards
+  blocking_slots = 0     # Slots where AI blocks an enemy card
+  cards_destroyed = 0    # Enemy cards the AI would destroy this turn
+  unblocked_incoming = 0 # Enemy attacks that go straight to AI life
+  cards_on_board = 0
+
+  for slot in range(BOARD_SIZE):
+    my = board[slot]
+    en = en_board[slot]
+    if my:
+      cards_on_board += 1
+    if my and en is None:
+      direct_damage += my.attack
+    if my and en:
+      board_damage += my.attack
+      blocking_slots += 1
+      if my.attack >= en.defense:
+        cards_destroyed += 1
+    if not my and en:
+      unblocked_incoming += en.attack
+
+  # --- Normalize to [0, 1] ---
+  # How threatening is the attack relative to what remains of opponent's life?
+  atk_score = min(1.0, direct_damage / max(opponent.life, 1))
+
+  # What fraction of enemy slots are blocked?
+  block_score = (blocking_slots / enemy_occupied) if enemy_occupied > 0 else 1.0
+
+  # What fraction of all slots are filled?
+  cover_score = cards_on_board / BOARD_SIZE
+
+  # What fraction of enemy cards do are destroyed?
+  destroy_score = (cards_destroyed / enemy_occupied) if enemy_occupied > 0 else 0.0
+
+  # How safe is the AI from unblocked hits relative to its own life?
+  threat_score = 1.0 - min(1.0, unblocked_incoming / max(player.life, 1))
+
+  # How many cards compared to the enemy?
+  opponent_cards_left = len(opponent.deck) + len(opponent.hand) + enemy_occupied
+  my_cards_left = len(player.deck) + len(player.hand) + blocking_slots
+  attrition_score = my_cards_left/(my_cards_left + opponent_cards_left)
+
+  # Net value: cost of cards played minus cost of cards sacrificed.
+  n_sac = len(plan.sacrifice_slots)
+  sac_value = sum(player.board[s].cost for s in plan.sacrifice_slots if player.board[s] is not None)
+  play_value = sum(c.cost for c, _ in plan.plays)
+  net_value = play_value - sac_value
+  net_value_norm = max(0.0, min(1.0, (net_value + 10) / 20))
+
+  # Sacrifice penalty. Applied as a flat deduction after personality scoring.
+  sacrifice_penalty = 0.0
+  if n_sac > 0:
+    # Penalty 1: wasted energy. Each sacrifice gives +1 energy; if that energy
+    # goes unspent it was pointless. Weighted heavily.
+    energy_leftover = player.energy + n_sac - play_value
+    wasted_sac_energy = max(0, min(n_sac, energy_leftover))
+    wasted_penalty = wasted_sac_energy / n_sac
+
+    # Penalty 2: low-value swap. Each sacrifice should at minimum unlock a card
+    # that costs more than the one removed (net_value > n_sac means each
+    # sacrifice bought at least one extra cost point). Anything less is a bad trade.
+    swap_penalty = max(0.0, min(1.0, (n_sac - net_value) / max(n_sac, 1)))
+
+    sacrifice_penalty = 0.65 * wasted_penalty + 0.35 * swap_penalty
+
+  # Power curve value of the cards played (are they good value for their cost?)
+  if plan.plays:
+    pcv_scores = [max(0.0, min(1.0, get_power_curve_value(c))) for c, _ in plan.plays]
+    pcv_score = sum(pcv_scores) / len(pcv_scores)
+  else:
+    pcv_score = 0.5
+
+  # --- Personality weights ---
+  if personality == AIPersonality.AGGRESSIVE:
+    # Maximize direct damage
+    score = (
+      0.40 * atk_score +
+      0.10 * block_score +
+      0.10 * cover_score +
+      0.10 * net_value_norm +
+      0.15 * destroy_score +
+      0.05 * attrition_score +
+      0.05 * pcv_score +
+      0.05 * threat_score
+    )
+
+  elif personality == AIPersonality.DEFENSIVE:
+    # Block everything
+    score = (
+      0.05 * atk_score +
+      0.35 * block_score +
+      0.20 * cover_score +
+      0.05 * net_value_norm +
+      0.05 * destroy_score +
+      0.10 * attrition_score +
+      0.05 * pcv_score +
+      0.15 * threat_score
+    )
+
+  elif personality == AIPersonality.SWARM:
+    # Fill the board and press with direct damage
+    score = (
+      0.25 * atk_score +
+      0.10 * block_score +
+      0.35 * cover_score +
+      0.05 * net_value_norm +
+      0.05 * destroy_score +
+      0.10 * attrition_score +
+      0.05 * pcv_score +
+      0.05 * threat_score
+    )
+
+  elif personality == AIPersonality.GREEDY:
+    # High-value card plays, willing to sacrifice weak cards for strong ones
+    score = (
+      0.20 * atk_score +
+      0.05 * block_score +
+      0.10 * cover_score +
+      0.40 * net_value_norm +
+      0.05 * destroy_score +
+      0.05 * attrition_score +
+      0.10 * pcv_score +
+      0.05 * threat_score
+    )
+
+  elif personality == AIPersonality.CONTROL:
+    # Efficiency
+    score = (
+      0.10 * atk_score +
+      0.05 * block_score +
+      0.05 * cover_score +
+      0.20 * net_value_norm +
+      0.05 * destroy_score +
+      0.10 * attrition_score +
+      0.40 * pcv_score +
+      0.05 * threat_score
+    )
+
+  elif personality == AIPersonality.BALANCED:
+    score = (
+      0.10 * atk_score +
+      0.15 * block_score +
+      0.10 * cover_score +
+      0.10 * net_value_norm +
+      0.10 * destroy_score +
+      0.10 * attrition_score +
+      0.15 * pcv_score +
+      0.10 * threat_score
+    )
+
+  else:  # ARBITRARY
+    score = (
+      0.60 * random.random() +
+      0.05 * atk_score +
+      0.05 * block_score +
+      0.05 * cover_score +
+      0.05 * net_value_norm +
+      0.05 * destroy_score +
+      0.05 * attrition_score +
+      0.05 * pcv_score +
+      0.05 * threat_score
+    )
+
+  # --- Context adjustments ---
+
+  # Lethal takes priority regardless of personality
+  if direct_damage >= opponent.life:
+    score = max(score, 0.95)
+
+  if unblocked_incoming >= player.life:
+    score = min(score, 0.05)
+
+  # Against god-card decks: cover all slots so their big cards can't attack freely
+  if opponent.deck_type in ("God Card", "Pantheon"):
+    score = min(1.0, score + 0.08 * cover_score)
+
+  # Against aggro/rush: need to block more urgently
+  if opponent.deck_type in ("Aggro", "Rush"):
+    score = min(1.0, score + 0.06 * block_score + 0.04 * threat_score)
+
+  # Against wall decks: direct damage matters more than destroying cards
+  if opponent.deck_type == "Wall":
+    score = min(1.0, score + 0.06 * atk_score)
+
+  # Press the advantage when opponent is low on life
+  if opponent.life < STARTING_LIFE * 0.3:
+    score = min(1.0, score + 0.06 * atk_score)
+
+  # Prioritize survival when low on life
+  if player.life < STARTING_LIFE * 0.3:
+    score = min(1.0, score + 0.06 * threat_score + 0.04 * block_score)
+
+  # Opponent running low on cards: keep a card on board for attrition win condition
+  if opponent_cards_left <= 5 and cards_on_board > 0:
+    score = min(1.0, score + 0.05)
+
+  # Apply sacrifice penalty last so it can override all other considerations.
+  score = max(0.0, score - sacrifice_penalty)
+
+  return score
+
+
+# ==================== Turn execution ====================
 
 async def run_ai_turn(game_id: str):
   from game_manager import (
@@ -281,46 +460,78 @@ async def run_ai_turn(game_id: str):
   await asyncio.sleep(calculate_combat_animation_time(state.last_combat_events))
 
   player = state.players[AI_USER_ID]
+  opponent = state.players[human_id]
+  difficulty = state.ai_difficulty
+  personality = (
+    AIPersonality(state.ai_personality)
+    if state.ai_personality
+    else AIPersonality.BALANCED
+  )
 
   ws = connections[game_id].get(human_id)
-  async def send_state(state):
+
+  async def send_state(s):
     if ws:
       try:
-        await ws.send_json({
+        await ws.send_json({"type": "state", "state": serialize_state(s, human_id)})
-          "type": "state",
-          "state": serialize_state(state, human_id),
-        })
       except Exception:
         pass
 
-  most_expensive_in_hand = max((c.cost for c in player.hand), default=0)
+  async def send_sacrifice_anim(instance_id):
-  if player.energy < most_expensive_in_hand:
-    for slot in range(BOARD_SIZE):
-      slot_card = player.board[slot]
-      if slot_card is not None and player.energy + slot_card.cost <= most_expensive_in_hand:
     if ws:
       try:
-            await ws.send_json({
+        await ws.send_json({"type": "sacrifice_animation", "instance_id": instance_id})
-              "type": "sacrifice_animation",
-              "instance_id": slot_card.instance_id,
-            })
       except Exception:
         pass
+
+  # --- Generate and score candidate plans ---
+  plans = generate_plans(player, opponent)
+
+  if difficulty <= 2:
+    # Actively bad
+    scored = [(score_plan(p, player, opponent, personality) + random.gauss(0, 0.15*difficulty), p)
+              for p in plans]
+    best_plan = min(scored, key=lambda x: x[0])[1]
+  elif difficulty == 3:
+    # Fully random
+    best_plan = random.choice(plans)
+  else:
+    noise = max(0.0, ((8 - difficulty) / 6.0) * 0.30)
+    scored = [(score_plan(p, player, opponent, personality) + random.gauss(0, noise), p)
+              for p in plans]
+    best_plan = max(scored, key=lambda x: x[0])[1]
+
+  logger.info(
+    f"AI turn: d={difficulty} p={personality.value} plan={best_plan.label} plans={len(plans)} " +
+    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:
+      continue
+    await send_sacrifice_anim(card_slot.instance_id)
     await asyncio.sleep(0.65)
     action_sacrifice(state, slot)
     await send_state(state)
     await asyncio.sleep(0.35)
 
-  play_order = list(range(BOARD_SIZE))
+  # --- Execute plays ---
-  random.shuffle(play_order)
+  # Shuffle play order so the AI doesn't always fill slots left-to-right
-  for slot in play_order:
+  plays = list(best_plan.plays)
+  random.shuffle(plays)
+
+  for card, slot in plays:
+    # Re-look up hand index each time (hand shrinks as cards are played)
+    hand_idx = next((i for i, c in enumerate(player.hand) if c is card), None)
+    if hand_idx is None:
+      continue
     if player.board[slot] is not None:
       continue
-    affordable = [i for i, c in enumerate(player.hand) if c.cost <= player.energy]
+    if card.cost > player.energy:
-    if not affordable:
+      continue
-      break
+    action_play_card(state, hand_idx, slot)
-    best = max(affordable, key=lambda i: player.hand[i].cost)
-    action_play_card(state, best, slot)
     await send_state(state)
     await asyncio.sleep(0.5)
 

backend/alembic/env.py

@@ -1,3 +1,6 @@
+from dotenv import load_dotenv
+load_dotenv()
+
 from logging.config import fileConfig
 
 from sqlalchemy import engine_from_config

backend/alembic/versions/cd7ebb9b11bd_add_ai_used_to_cards.py

@@ -0,0 +1,34 @@
+"""add ai_used to cards
+
+Revision ID: cd7ebb9b11bd
+Revises: adee6bcc23e1
+Create Date: 2026-03-19 18:23:14.422342
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = 'cd7ebb9b11bd'
+down_revision: Union[str, Sequence[str], None] = 'adee6bcc23e1'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column('cards', sa.Column('ai_used', sa.Boolean(), nullable=True))
+    # ### end Alembic commands ###
+    op.execute("UPDATE cards SET ai_used = false")
+    op.alter_column('cards', 'ai_used', nullable=False)
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column('cards', 'ai_used')
+    # ### end Alembic commands ###

backend/card.py

@@ -90,6 +90,7 @@ class Card(NamedTuple):
 
 WIKIDATA_INSTANCE_TYPE_MAP = {
   "Q5":         CardType.person,        # human
+  "Q95074":     CardType.person,        # character
   "Q215627":    CardType.person,        # person
   "Q15632617":  CardType.person,        # fictional human
   "Q22988604":  CardType.person,        # fictional human
@@ -141,6 +142,7 @@ WIKIDATA_INSTANCE_TYPE_MAP = {
   "Q23442":     CardType.location,      # island
   "Q82794":     CardType.location,      # geographic region
   "Q34442":     CardType.location,      # road
+  "Q486972":    CardType.location,      # human settlement
   "Q192611":    CardType.location,      # electoral unit
   "Q398141":    CardType.location,      # school district
   "Q133056":    CardType.location,      # mountain pass
@@ -149,6 +151,7 @@ WIKIDATA_INSTANCE_TYPE_MAP = {
   "Q7930989":   CardType.location,      # city/town
   "Q1250464":   CardType.location,      # realm
   "Q3146899":   CardType.location,      # diocese of the catholic church
+  "Q12076836":  CardType.location,      # administrative territorial entity of a single country
   "Q35145263":  CardType.location,      # natural geographic object
   "Q15642541":  CardType.location,      # human-geographic territorial entity
 
@@ -179,6 +182,7 @@ WIKIDATA_INSTANCE_TYPE_MAP = {
   "Q13406554":  CardType.event,         # sports competition
   "Q15275719":  CardType.event,         # recurring event
   "Q27968055":  CardType.event,         # recurring event edition
+  "Q15091377":  CardType.event,         # cycling race
   "Q114609228": CardType.event,         # recurring sporting event
 
   "Q7278":      CardType.group,         # political party
@@ -216,6 +220,7 @@ WIKIDATA_INSTANCE_TYPE_MAP = {
   "Q12140":     CardType.science_thing, # medication
   "Q11276":     CardType.science_thing, # globular cluster
   "Q83373":     CardType.science_thing, # quasar
+  "Q177719":    CardType.science_thing, # medical diagnosis
   "Q898273":    CardType.science_thing, # protein domain
   "Q134808":    CardType.science_thing, # vaccine
   "Q168845":    CardType.science_thing, # star cluster
@@ -224,7 +229,8 @@ WIKIDATA_INSTANCE_TYPE_MAP = {
   "Q1840368":   CardType.science_thing, # cloud type
   "Q2154519":   CardType.science_thing, # astrophysical x-ray source
   "Q17444909":  CardType.science_thing, # astronomical object type
-  "Q12089225":  CardType.science_thing, # Mineral species
+  "Q12089225":  CardType.science_thing, # mineral species
+  "Q55640599":  CardType.science_thing, # group of chemical entities
   "Q113145171": CardType.science_thing, # type of chemical entity
 
   "Q1420":      CardType.vehicle,       # car
@@ -519,7 +525,7 @@ async def _get_card_async(client: httpx.AsyncClient, page_title: str|None = None
     text=text,
     attack=attack,
     defense=defense,
-    cost=min(12,max(1,int(((attack**2+defense**2)**0.18)/1.5)))
+    cost=min(11,max(1,int(((attack**2+defense**2)**0.18)/1.5)))
   )
 
 async def _get_cards_async(size: int) -> list[Card]:
@@ -542,7 +548,7 @@ def generate_card(title: str) -> Card|None:
 
 # Cards helper function
 def compute_deck_type(cards: list) -> str | None:
-  if len(cards) < 20:
+  if len(cards) == 0:
     return None
   avg_atk = sum(c.attack for c in cards) / len(cards)
   avg_def = sum(c.defense for c in cards) / len(cards)

backend/database_functions.py

@@ -11,10 +11,10 @@ from database import SessionLocal
 
 logger = logging.getLogger("app")
 
-POOL_MINIMUM = 500
+POOL_MINIMUM = 1000
-POOL_TARGET = 1000
+POOL_TARGET = 2000
 POOL_BATCH_SIZE = 10
-POOL_SLEEP = 5.0
+POOL_SLEEP = 4.0
 
 pool_filling = False
 
@@ -25,8 +25,9 @@ async def fill_card_pool():
     return
 
   db: Session = SessionLocal()
+  while True:
     try:
-    unassigned = db.query(CardModel).filter(CardModel.user_id == None).count()
+      unassigned = db.query(CardModel).filter(CardModel.user_id == None, CardModel.ai_used == False).count()
       logger.info(f"Card pool has {unassigned} unassigned cards")
       if unassigned >= POOL_MINIMUM:
         logger.info("Pool sufficiently stocked, skipping fill")

backend/email_utils.py

@@ -21,7 +21,7 @@ def send_password_reset_email(to_email: str, username: str, reset_token: str):
           </a>
         </p>
         <p>This link expires in 1 hour. If you didn't request this, you can safely ignore this email.</p>
-        <p style="color: #888; font-size: 13px;">— WikiTCG</p>
+        <p style="color: #888; font-size: 13px;">- WikiTCG</p>
       </div>
     """,
   })

backend/game.py

@@ -253,7 +253,8 @@ def action_sacrifice(state: GameState, slot: int) -> str | None:
   if card is None:
     return "No card in that slot"
 
-  player.energy += card.cost
+  # player.energy += card.cost
+  player.energy += 1
   player.board[slot] = None
   return None
 

backend/game_manager.py

@@ -56,15 +56,21 @@ def record_game_result(state: GameState, db: Session):
   loser_deck_id = active_deck_ids.get(loser_id_str)
 
   if AI_USER_ID not in [winner_id_str, loser_id_str]:
+    if winner_deck_id:
       deck = db.query(DeckModel).filter(DeckModel.id == uuid.UUID(winner_deck_id)).first()
       if deck:
         deck.times_played += 1
         deck.wins += 1
+    else:
+      logger.warning(f"record_game_result: no deck_id found for winner {winner_id_str}")
 
+    if loser_deck_id:
       deck = db.query(DeckModel).filter(DeckModel.id == uuid.UUID(loser_deck_id)).first()
       if deck:
         deck.times_played += 1
         deck.losses += 1
+    else:
+      logger.warning(f"record_game_result: no deck_id found for loser {loser_id_str}")
 
   db.commit()
 
@@ -251,6 +257,7 @@ async def handle_action(game_id: str, user_id: str, message: dict, db: Session):
             db.commit()
         except Exception as e:
           logger.warning(f"Failed to increment times_played for card {card_instance.card_id}: {e}")
+          db.rollback()
   elif action == "sacrifice":
     slot = message.get("slot")
     if slot is None:
@@ -297,7 +304,7 @@ async def handle_action(game_id: str, user_id: str, message: dict, db: Session):
 
 DISCONNECT_GRACE_SECONDS = 15
 
-async def handle_disconnect(game_id: str, user_id: str, db: Session):
+async def handle_disconnect(game_id: str, user_id: str):
   await asyncio.sleep(DISCONNECT_GRACE_SECONDS)
 
   # Check if game still exists and player hasn't reconnected
@@ -318,7 +325,12 @@ async def handle_disconnect(game_id: str, user_id: str, db: Session):
   )
   state.phase = "end"
 
+  from database import SessionLocal
+  db = SessionLocal()
+  try:
     record_game_result(state, db)
+  finally:
+    db.close()
 
   # Notify the remaining player
   winner_ws = connections[game_id].get(winner_id)

backend/main.py

@@ -149,7 +149,7 @@ async def open_pack(request: Request, user: UserModel = Depends(get_current_user
 
   cards = (
     db.query(CardModel)
-    .filter(CardModel.user_id == None)
+    .filter(CardModel.user_id == None, CardModel.ai_used == False)
     .limit(5)
     .all()
   )
@@ -191,6 +191,7 @@ def get_decks(user: UserModel = Depends(get_current_user), db: Session = Depends
       "id": str(deck.id),
       "name": deck.name,
       "card_count": len(cards),
+      "total_cost": sum(card.cost for card in cards),
       "times_played": deck.times_played,
       "wins": deck.wins,
       "losses": deck.losses,
@@ -215,7 +216,7 @@ def update_deck(deck_id: str, body: dict, user: UserModel = Depends(get_current_
     deck.name = body["name"]
   if "card_ids" in body:
     db.query(DeckCardModel).filter(DeckCardModel.deck_id == deck.id).delete()
-    for card_id in body["card_ids"][:20]:
+    for card_id in body["card_ids"]:
       db.add(DeckCardModel(deck_id=deck.id, card_id=uuid.UUID(card_id)))
     if deck.times_played > 0:
       deck.wins = 0
@@ -265,9 +266,10 @@ async def queue_endpoint(websocket: WebSocket, deck_id: str, db: Session = Depen
     await websocket.close(code=1008)
     return
 
-  card_count = db.query(DeckCardModel).filter(DeckCardModel.deck_id == deck.id).count()
+  card_ids = [dc.card_id for dc in db.query(DeckCardModel).filter(DeckCardModel.deck_id == deck.id).all()]
-  if card_count < 20:
+  total_cost = db.query(func.sum(CardModel.cost)).filter(CardModel.id.in_(card_ids)).scalar() or 0
-    await websocket.send_json({"type": "error", "message": "Deck must have 20 cards"})
+  if total_cost == 0 or total_cost > 50:
+    await websocket.send_json({"type": "error", "message": "Deck total cost must be between 1 and 50"})
     await websocket.close(code=1008)
     return
 
@@ -318,7 +320,7 @@ async def game_endpoint(websocket: WebSocket, game_id: str, db: Session = Depend
   except WebSocketDisconnect:
     if game_id in connections:
       connections[game_id].pop(user_id, None)
-      asyncio.create_task(handle_disconnect(game_id, user_id, db))
+      asyncio.create_task(handle_disconnect(game_id, user_id))
 
 @app.get("/profile")
 def get_profile(user: UserModel = Depends(get_current_user), db: Session = Depends(get_db)):
@@ -441,28 +443,27 @@ async def start_solo_game(deck_id: str, difficulty: int = 5, user: UserModel = D
   if not deck:
     raise HTTPException(status_code=404, detail="Deck not found")
 
-  card_count = db.query(DeckCardModel).filter(DeckCardModel.deck_id == deck.id).count()
+  card_ids = [dc.card_id for dc in db.query(DeckCardModel).filter(DeckCardModel.deck_id == deck.id).all()]
-  if card_count < 20:
+  total_cost = db.query(func.sum(CardModel.cost)).filter(CardModel.id.in_(card_ids)).scalar() or 0
-    raise HTTPException(status_code=400, detail="Deck must have 20 cards")
+  if total_cost == 0 or total_cost > 50:
+    raise HTTPException(status_code=400, detail="Deck total cost must be between 1 and 50")
 
   player_cards = load_deck_cards(deck_id, str(user.id), db)
   if player_cards is None:
     raise HTTPException(status_code=503, detail="Couldn't load deck")
 
   ai_cards = db.query(CardModel).filter(
-    CardModel.user_id == None
+    CardModel.user_id == None,
-  ).order_by(func.random()).limit(100).all()
+  ).order_by(func.random()).limit(500).all()
 
-  if len(ai_cards) < 20:
+  if len(ai_cards) == 0:
     raise HTTPException(status_code=503, detail="Not enough cards in pool for AI deck")
 
   for card in ai_cards:
-    db.delete(card)
+    card.ai_used = True
-
   db.commit()
 
   game_id = create_solo_game(str(user.id), user.username, player_cards, ai_cards, deck_id, difficulty)
-
   asyncio.create_task(fill_card_pool())
 
   return {"game_id": game_id}
@@ -488,7 +489,7 @@ def reset_password(req: ResetPasswordRequest, user: UserModel = Depends(get_curr
 @app.post("/auth/forgot-password")
 def forgot_password(req: ForgotPasswordRequest, db: Session = Depends(get_db)):
   user = db.query(UserModel).filter(UserModel.email == req.email).first()
-  # Always return success even if email not found — prevents user enumeration
+  # Always return success even if email not found. Prevents user enumeration
   if user:
     token = secrets.token_urlsafe(32)
     user.reset_token = token

backend/models.py

@@ -41,6 +41,7 @@ class Card(Base):
   created_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.now)
   times_played: Mapped[int] = mapped_column(Integer, default=0, nullable=False)
   reported: Mapped[bool] = mapped_column(Boolean, default=False, nullable=False)
+  ai_used: Mapped[bool] = mapped_column(Boolean, default=False, nullable=False)
 
   user: Mapped["User | None"] = relationship(back_populates="cards")
   deck_cards: Mapped[list["DeckCard"]] = relationship(back_populates="card")

backend/test_game.py

@@ -1,3 +1,6 @@
+from dotenv import load_dotenv
+load_dotenv()
+
 from game import (
   GameState, PlayerState, CardInstance, CombatEvent, GameResult,
   create_game, resolve_combat, check_win_condition,
@@ -222,7 +225,7 @@ class TestSacrifice:
     err = action_sacrifice(state, slot=0)
     assert err is None
     assert state.players["p1"].board[0] is None
-    assert state.players["p1"].energy == 4
+    assert state.players["p1"].energy == 2
 
   def test_sacrifice_empty_slot(self):
     state = make_game(p1_energy=3)
@@ -249,10 +252,10 @@ class TestSacrifice:
     cheap = make_card(name="Cheap", cost=3)
     expensive = make_card(name="Expensive", cost=5)
     board = [None] + [cheap] + [None] * (BOARD_SIZE - 2)
-    state = make_game(p1_board=board, p1_hand=[expensive], p1_energy=2)
+    state = make_game(p1_board=board, p1_hand=[expensive], p1_energy=4)
     err1 = action_play_card(state, hand_index=0, slot=0)
     assert err1 is not None
-    assert err1 == "Not enough energy (have 2, need 5)"
+    assert err1 == "Not enough energy (have 4, need 5)"
 
     action_sacrifice(state, slot=1)
     assert state.players["p1"].energy == 5

frontend/src/routes/+layout.svelte

@@ -12,6 +12,7 @@
 {/if}
 
 <svelte:head>
+	<title>WikiTCG</title>
 	<link rel="icon" href={favicon} />
 </svelte:head>
 

frontend/src/routes/cards/+page.svelte

@@ -30,7 +30,7 @@
 
   let sortAsc = $state(true);
   let costMin = $state(1);
-  let costMax = $state(12);
+  let costMax = $state(11);
 
   let filtered = $derived.by(() => {
     let result = allCards.filter(c =>
@@ -236,14 +236,14 @@
         <div class="filter-group">
           <div class="filter-group-header">
             <span class="filter-group-label">Cost</span>
-            <button class="select-all" onclick={() => { costMin = 1; costMax = 12; }}>Reset</button>
+            <button class="select-all" onclick={() => { costMin = 1; costMax = 11; }}>Reset</button>
           </div>
           <div class="cost-range">
             <span class="range-label">Min: {costMin}</span>
-            <input type="range" min="1" max="12" bind:value={costMin}
+            <input type="range" min="1" max="11" bind:value={costMin}
               oninput={() => { if (costMin > costMax) costMax = costMin; }} />
             <span class="range-label">Max: {costMax}</span>
-            <input type="range" min="1" max="12" bind:value={costMax}
+            <input type="range" min="1" max="11" bind:value={costMax}
               oninput={() => { if (costMax < costMin) costMin = costMax; }} />
           </div>
         </div>

frontend/src/routes/decks/+page.svelte

@@ -75,6 +75,7 @@
         <tr>
           <th>Name</th>
           <th>Cards</th>
+          <th>Cost</th>
           <th>Type</th>
           <th>Played</th>
           <th>W / L</th>
@@ -87,8 +88,9 @@
           {@const wr = winRate(deck)}
           <tr>
             <td class="deck-name">{deck.name}</td>
-            <td class="deck-count" class:incomplete={deck.card_count < 20}>
+            <td class="deck-count">{deck.card_count}</td>
-              {deck.card_count}/20
+            <td class="deck-cost" class:over-budget={deck.total_cost > 50}>
+              {deck.total_cost}/50
             </td>
             <td class="deck-type">
               <DeckTypeBadge deckType={deck.deck_type} />
@@ -100,14 +102,14 @@
                 <span class="separator"> / </span>
                 <span class="losses">{deck.losses}</span>
               {:else}
-                <span class="no-data">—</span>
+                <span class="no-data">-</span>
               {/if}
             </td>
             <td class="deck-stat">
               {#if wr !== null}
                 <span class:good-wr={wr >= 50} class:bad-wr={wr < 50}>{wr}%</span>
               {:else}
-                <span class="no-data">—</span>
+                <span class="no-data">-</span>
               {/if}
             </td>
             <td class="deck-actions">
@@ -144,7 +146,7 @@
         <p class="popup-body">
           Are you sure you want to delete <strong>{deleteConfirm.name}</strong>?
           {#if deleteConfirm.times_played > 0}
-            This deck has been played {deleteConfirm.times_played} time{deleteConfirm.times_played === 1 ? '' : 's'} — its stats will be preserved in your profile history.
+            This deck has been played {deleteConfirm.times_played} time{deleteConfirm.times_played === 1 ? '' : 's'}. Its stats will be preserved in your profile history.
           {/if}
         </p>
         <div class="popup-actions">
@@ -240,6 +242,14 @@
   }
 
   .deck-count {
+    font-family: 'Cinzel', serif;
+    font-size: 13px;
+    font-weight: 700;
+    color: rgba(240, 180, 80, 0.6);
+    width: 60px;
+  }
+
+  .deck-cost {
     font-family: 'Cinzel', serif;
     font-size: 13px;
     font-weight: 700;
@@ -247,7 +257,7 @@
     width: 60px;
   }
 
-  .deck-count.incomplete { color: #c85050; }
+  .deck-cost.over-budget { color: #c85050; }
 
   .deck-type { width: 90px; }
 

frontend/src/routes/decks/[id]/+page.svelte

@@ -26,7 +26,7 @@
   let selectedRarities = $state(new Set(RARITIES));
   let selectedTypes = $state(new Set(TYPES));
   let costMin = $state(1);
-  let costMax = $state(12);
+  let costMax = $state(11);
   let filtersOpen = $state(false);
 
   function label(str) {
@@ -74,12 +74,17 @@
   function toggleAllRarities() { selectedRarities = allRaritiesSelected() ? new Set() : new Set(RARITIES); }
   function toggleAllTypes() { selectedTypes = allTypesSelected() ? new Set() : new Set(TYPES); }
 
+  const selectedCost = $derived(
+    allCards.filter(c => selectedIds.has(c.id)).reduce((sum, c) => sum + c.cost, 0)
+  );
+
   function toggleCard(id) {
     const s = new Set(selectedIds);
     if (s.has(id)) {
       s.delete(id);
     } else {
-      if (s.size >= 20) return;
+      const card = allCards.find(c => c.id === id);
+      if (card && selectedCost + card.cost > 50) return;
       s.add(id);
     }
     selectedIds = s;
@@ -151,8 +156,8 @@
       {/if}
 
       <div class="header-right">
-        <span class="card-counter" class:full={selectedIds.size === 20} class:empty={selectedIds.size === 0}>
+        <span class="card-counter" class:full={selectedCost === 50} class:over={selectedCost > 50} class:empty={selectedIds.size === 0}>
-          {selectedIds.size}/20
+          {selectedIds.size} cards · {selectedCost}/50
         </span>
         <button class="done-btn" onclick={save} disabled={saving}>
           {saving ? 'Saving...' : 'Done'}
@@ -171,7 +176,7 @@
 
       <button class="filter-toggle" onclick={() => filtersOpen = !filtersOpen}>
         {filtersOpen ? 'Hide filters' : 'Filter'}
-        {#if selectedRarities.size < RARITIES.length || selectedTypes.size < TYPES.length || costMin > 1 || costMax < 12}
+        {#if selectedRarities.size < RARITIES.length || selectedTypes.size < TYPES.length || costMin > 1 || costMax < 11}
           <span class="filter-dot"></span>
         {/if}
       </button>
@@ -212,13 +217,13 @@
         <div class="filter-group">
           <div class="filter-group-header">
             <span class="filter-group-label">Cost</span>
-            <button class="select-all" onclick={() => { costMin = 1; costMax = 12; }}>Reset</button>
+            <button class="select-all" onclick={() => { costMin = 1; costMax = 11; }}>Reset</button>
           </div>
           <div class="cost-range">
             <span class="range-label">Min: {costMin}</span>
-            <input type="range" min="1" max="12" bind:value={costMin} oninput={() => { if (costMin > costMax) costMax = costMin; }} />
+            <input type="range" min="1" max="11" bind:value={costMin} oninput={() => { if (costMin > costMax) costMax = costMin; }} />
             <span class="range-label">Max: {costMax}</span>
-            <input type="range" min="1" max="12" bind:value={costMax} oninput={() => { if (costMax < costMin) costMin = costMax; }} />
+            <input type="range" min="1" max="11" bind:value={costMax} oninput={() => { if (costMax < costMin) costMin = costMax; }} />
           </div>
         </div>
       </div>
@@ -235,7 +240,7 @@
         <button
           class="card-wrap"
           class:selected={selectedIds.has(card.id)}
-          class:disabled={!selectedIds.has(card.id) && selectedIds.size >= 20}
+          class:disabled={!selectedIds.has(card.id) && selectedCost + card.cost > 50}
           onclick={() => toggleCard(card.id)}
         >
           <Card {card} noHover={true} />
@@ -317,11 +322,12 @@
     font-family: 'Cinzel', serif;
     font-size: 14px;
     font-weight: 700;
-    color: #c85050;
+    color: rgba(240, 180, 80, 0.7);
     transition: color 0.2s;
   }
 
   .card-counter.full  { color: #6aaa6a; }
+  .card-counter.over  { color: #c85050; }
   .card-counter.empty { color: rgba(240, 180, 80, 0.3); }
 
   .done-btn {

frontend/src/routes/how-to-play/+page.svelte

@@ -18,7 +18,7 @@
 
   const annotations = [
     { number: 1, label: "Name", description: "The name of the Wikipedia article this card was generated from." },
-    { number: 2, label: "Type", description: "The category of the subject — Person, Location, Artwork, etc." },
+    { number: 2, label: "Type", description: "The category of the subject. Person, Location, Artwork, etc." },
     { number: 3, label: "Rarity badge", description: "Rarity is determined by the article's WikiRank quality score. From lowest to highest: Common, Uncommon, Rare, Super Rare, Epic, and Legendary." },
     { number: 4, label: "Wikipedia link", description: "Opens the Wikipedia article this card was generated from." },
     { number: 5, label: "Cost bubbles", description: "How much energy it costs to play this card. Derived from the card's attack and defense stats." },
@@ -29,14 +29,14 @@
 
   // Annotation positions as percentage of card width/height
   const markerPositions = [
-    { number: 1, x: 15, y: 3 },    // name — top center
+    { number: 1, x: 15, y: 3 },    // name. top center
-    { number: 2, x: 75, y: 3 },    // type badge — top right
+    { number: 2, x: 75, y: 3 },    // type badge. top right
-    { number: 3, x: 14, y: 20 },   // rarity badge — top left of image
+    { number: 3, x: 14, y: 20 },   // rarity badge. top left of image
-    { number: 4, x: 85, y: 20 },   // wiki link — top right of image
+    { number: 4, x: 85, y: 20 },   // wiki link. top right of image
-    { number: 5, x: 15, y: 53 },   // cost bubbles — bottom left of image
+    { number: 5, x: 15, y: 53 },   // cost bubbles. bottom left of image
-    { number: 6, x: 50, y: 73 },   // text — middle
+    { number: 6, x: 50, y: 73 },   // text. middle
-    { number: 7, x: 15, y: 88 },   // attack — bottom left
+    { number: 7, x: 15, y: 88 },   // attack. bottom left
-    { number: 8, x: 85, y: 88 },   // defense — bottom right
+    { number: 8, x: 85, y: 88 },   // defense. bottom right
   ];
 </script>
 
@@ -102,6 +102,22 @@
       </div>
     </section>
 
+    <section class="section">
+      <h2 class="section-title">Building a Deck</h2>
+      <div class="rules-grid">
+        <div class="rule-card">
+          <div class="rule-icon">✦</div>
+          <h3 class="rule-title">Cost Limit</h3>
+          <p class="rule-body">Your deck's total cost, the sum of all card costs, must be 50 or less. You can't queue for a game with an empty deck or one that exceeds the limit.</p>
+        </div>
+        <div class="rule-card">
+          <div class="rule-icon">🃏</div>
+          <h3 class="rule-title">No Card Limit</h3>
+          <p class="rule-body">There is no minimum or maximum number of cards. On the extreme ends, you can have just 4 11-cost cards, or 50 1-cost cards.</p>
+        </div>
+      </div>
+    </section>
+
     <section class="section">
       <h2 class="section-title">Taking a Turn</h2>
       <div class="rules-grid">
@@ -123,7 +139,7 @@
         <div class="rule-card">
           <div class="rule-icon">🗡</div>
           <h3 class="rule-title">Sacrificing</h3>
-          <p class="rule-body">Click the dagger icon to enter sacrifice mode, then click one of your cards to remove it from play and recover its energy cost. Use this to afford expensive cards.</p>
+          <p class="rule-body">Click the dagger icon to enter sacrifice mode, then click one of your cards to remove it from play and recover 1 energy. Use this to afford expensive cards.</p>
         </div>
       </div>
     </section>

frontend/src/routes/play/+page.svelte

@@ -21,6 +21,19 @@
   let gameState = $state(null);
   let myId = $state('');
 
+  let viewingBoard = $state(false);
+  let showDifficultyModal = $state(false);
+  let selectedDifficulty = $state(5);
+
+  const difficultyLabel = $derived(
+    selectedDifficulty <= 2  ? 'Throws the game' :
+    selectedDifficulty === 3 ? 'Fully random' :
+    selectedDifficulty <= 5  ? 'Beginner' :
+    selectedDifficulty <= 7  ? 'Intermediate' :
+    selectedDifficulty <= 9  ? 'Advanced' :
+                               'Expert'
+  );
+
   let selectedHandIndex = $state(null);
   let combatAnimating = $state(false);
   let lunging = $state(new Set());
@@ -69,7 +82,7 @@
       method: 'POST'
     });
     if (!res.ok) {
-      // Server rejected the claim — game may have ended another way
+      // Server rejected the claim. Game may have ended another way
       const err = await res.json();
       console.warn('Timeout claim rejected:', err.detail);
     }
@@ -78,8 +91,8 @@
   $effect(() => {
     if (!gameState || combatAnimating) return;
     displayedLife = {
-      [gameState.you.user_id]: gameState.you.life,
+      [gameState.you.user_id]: Math.max(0, gameState.you.life),
-      [gameState.opponent.user_id]: gameState.opponent.life,
+      [gameState.opponent.user_id]: Math.max(0, gameState.opponent.life),
     };
   });
 
@@ -113,7 +126,7 @@
   });
 
   function joinQueue() {
-    if (!selectedDeckId || selectedDeck?.card_count < 20) return;
+    if (!selectedDeckId || selectedDeck?.total_cost === 0 || selectedDeck?.total_cost > 50) return;
     error = '';
     phase = 'queuing';
     queueWs = new WebSocket(`${WS_URL}/ws/queue?deck_id=${selectedDeckId}`);
@@ -168,7 +181,7 @@
     combatAnimating = true;
 
     // The attacker is whoever was active when end_turn was called.
-    // After end_turn resolves, active_player_id switches — so we look
+    // After end_turn resolves, active_player_id switches. So we look
     // at who is NOT the current active player to find the attacker,
     // unless the game just ended (result is set), in which case
     // active_player_id hasn't switched yet.
@@ -267,10 +280,11 @@
   }
 
   async function joinSolo() {
-    if (!selectedDeckId || selectedDeck?.card_count < 20) return;
+    if (!selectedDeckId || selectedDeck?.total_cost === 0 || selectedDeck?.total_cost > 50) return;
+    showDifficultyModal = false;
     error = '';
     phase = 'queuing';
-    const res = await apiFetch(`${API_URL}/game/solo?deck_id=${selectedDeckId}&difficulty=5`, {
+    const res = await apiFetch(`${API_URL}/game/solo?deck_id=${selectedDeckId}&difficulty=${selectedDifficulty}`, {
       method: 'POST'
     });
     if (!res.ok) {
@@ -297,16 +311,16 @@
           <label class="deck-label" for="deck">Choose your deck</label>
           <select id="deck" bind:value={selectedDeckId}>
             {#each decks as deck}
-              <option value={deck.id}>{deck.name} ({deck.card_count}/20)</option>
+              <option value={deck.id}>{deck.name} ({deck.card_count} cards, cost {deck.total_cost})</option>
             {/each}
           </select>
         </div>
-        <p class="error">{selectedDeck && selectedDeck.card_count < 20 ? `Deck must have 20 cards (${selectedDeck.card_count}/20)` : ''}</p>
+        <p class="error">{selectedDeck && (selectedDeck.total_cost === 0 || selectedDeck.total_cost > 50) ? `Deck cost must be between 1 and 50 (current: ${selectedDeck.total_cost})` : ''}</p>
         <div class="lobby-buttons">
-          <button class="play-btn" onclick={joinQueue} disabled={!selectedDeckId || selectedDeck?.card_count < 20}>
+          <button class="play-btn" onclick={joinQueue} disabled={!selectedDeckId || selectedDeck?.total_cost === 0 || selectedDeck?.total_cost > 50}>
             Find Opponent
           </button>
-          <button class="play-btn solo-btn" onclick={joinSolo} disabled={!selectedDeckId || selectedDeck?.card_count < 20}>
+          <button class="play-btn solo-btn" onclick={() => showDifficultyModal = true} disabled={!selectedDeckId || selectedDeck?.total_cost === 0 || selectedDeck?.total_cost > 50}>
             vs. Computer
           </button>
         </div>
@@ -320,7 +334,7 @@
       <button class="cancel-btn" onclick={() => { queueWs?.close(); phase = 'idle'; }}>Cancel</button>
     </div>
 
-  {:else if phase === 'playing' && gameState}
+  {:else if (phase === 'playing' || (phase === 'ended' && viewingBoard)) && gameState}
     <div class="game">
 
       <div class="sidebar left-sidebar">
@@ -374,7 +388,7 @@
 
         <div class="divider">
           <span class="turn-indicator" class:my-turn={isMyTurn}>
-            {isMyTurn ? 'Your turn' : `${opp.username}'s turn`}
+            {phase === 'ended' ? 'Game Ended' : isMyTurn ? 'Your turn' : `${opp.username}'s turn`}
           </span>
           {#if secondsRemaining <= TIMER_WARNING}
             <span class="turn-timer" class:urgent={secondsRemaining <= 10}>
@@ -420,7 +434,9 @@
       </div>
 
       <div class="sidebar right-sidebar">
-        {#if isMyTurn && !combatAnimating}
+        {#if phase === 'ended'}
+          <button class="end-turn-btn" onclick={() => viewingBoard = false}>Go Back</button>
+        {:else if isMyTurn && !combatAnimating}
           <button class="end-turn-btn" onclick={endTurn}>End Turn</button>
         {/if}
       </div>
@@ -453,7 +469,36 @@
         {gameState.result.winner_id === myId ? 'Victory' : 'Defeat'}
       </h1>
       <p class="lobby-hint">{gameState.result.reason}</p>
-      <button class="play-btn" onclick={() => { gameState = null; phase = 'idle'; }}>Go back</button>
+      <div class="lobby-buttons">
+        <button class="play-btn" onclick={() => viewingBoard = true}>View Board</button>
+        <button class="play-btn" onclick={() => { gameState = null; phase = 'idle'; }}>Go Back</button>
+      </div>
+    </div>
+  {/if}
+
+  {#if showDifficultyModal}
+    <div class="modal-backdrop" onclick={() => showDifficultyModal = false}>
+      <div class="modal" onclick={(e) => e.stopPropagation()}>
+        <h2 class="modal-title">Choose Difficulty</h2>
+        <div class="difficulty-display">
+          <span class="difficulty-number">{selectedDifficulty}</span>
+          <span class="difficulty-label">{difficultyLabel}</span>
+        </div>
+        <input
+          class="difficulty-slider"
+          type="range"
+          min="1"
+          max="10"
+          bind:value={selectedDifficulty}
+        />
+        <div class="difficulty-ticks">
+          <span>1</span><span>10</span>
+        </div>
+        <div class="modal-buttons">
+          <button class="cancel-btn" onclick={() => showDifficultyModal = false}>Cancel</button>
+          <button class="play-btn" onclick={joinSolo}>Start Game</button>
+        </div>
+      </div>
     </div>
   {/if}
 
@@ -1040,6 +1085,82 @@
   .hand-card.unaffordable { filter: grayscale(0.7) brightness(0.55); }
   .hand-card:disabled { cursor: default; }
 
+  /* ── Difficulty modal ── */
+  .modal-backdrop {
+    position: fixed;
+    inset: 0;
+    background: rgba(0, 0, 0, 0.7);
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    z-index: 200;
+  }
+
+  .modal {
+    background: #110d04;
+    border: 1.5px solid #6b4c1e;
+    border-radius: 10px;
+    padding: 2rem 2.5rem;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    gap: 1.25rem;
+    min-width: 300px;
+  }
+
+  .modal-title {
+    font-family: 'Cinzel', serif;
+    font-size: 20px;
+    font-weight: 700;
+    color: #f0d080;
+    margin: 0;
+    letter-spacing: 0.08em;
+  }
+
+  .difficulty-display {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    gap: 0.25rem;
+  }
+
+  .difficulty-number {
+    font-family: 'Cinzel', serif;
+    font-size: 48px;
+    font-weight: 900;
+    color: #f0d080;
+    line-height: 1;
+  }
+
+  .difficulty-label {
+    font-family: 'Crimson Text', serif;
+    font-size: 16px;
+    font-style: italic;
+    color: rgba(240, 180, 80, 0.6);
+  }
+
+  .difficulty-slider {
+    width: 100%;
+    accent-color: #c8861a;
+    cursor: pointer;
+  }
+
+  .difficulty-ticks {
+    width: 100%;
+    display: flex;
+    justify-content: space-between;
+    font-family: 'Cinzel', serif;
+    font-size: 10px;
+    color: rgba(240, 180, 80, 0.4);
+    margin-top: -0.75rem;
+  }
+
+  .modal-buttons {
+    display: flex;
+    gap: 0.75rem;
+    margin-top: 0.25rem;
+  }
+
   /* ── Toast ── */
   .toast {
     position: fixed;

frontend/src/routes/profile/+page.svelte

@@ -60,7 +60,7 @@
         <div class="stat-card">
           <span class="stat-label">Win Rate</span>
           <span class="stat-value" class:good-wr={profile.win_rate >= 50} class:bad-wr={profile.win_rate !== null && profile.win_rate < 50}>
-            {profile.win_rate !== null ? `${profile.win_rate}%` : '—'}
+            {profile.win_rate !== null ? `${profile.win_rate}%` : '-'}
           </span>
         </div>
       </div>