diff --git a/backend/.env.example b/backend/.env.example index 88e888b..a55434c 100644 --- a/backend/.env.example +++ b/backend/.env.example @@ -4,4 +4,7 @@ RESEND_API_KEY= # from resend.com dashboard EMAIL_FROM= # e.g. noreply@yourdomain.com FRONTEND_URL= # e.g. https://yourdomain.com CORS_ORIGINS= # comma-separated, e.g. https://yourdomain.com -WIKIRANK_USER_AGENT= # e.g. WikiTCG/1.0 (you@email.com \ No newline at end of file +WIKIRANK_USER_AGENT= # e.g. WikiTCG/1.0 (you@email.com) +STRIPE_SECRET_KEY= # from stripe dashboard +STRIPE_PUBLISHABLE_KEY= # from stripe dashboard +STRIPE_WEBHOOK_SECRET= # from stripe dashboard \ No newline at end of file diff --git a/backend/ai.py b/backend/ai.py index ef5c03f..9ca9265 100644 --- a/backend/ai.py +++ b/backend/ai.py @@ -19,16 +19,17 @@ class AIPersonality(Enum): 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 - SHOCKER = "shocker" # Cheap high-defense walls + a few powerful high-attack finishers ARBITRARY = "arbitrary" # Just does whatever + JEBRASKA = "jebraska" # Trained neural network plan scorer def get_random_personality() -> AIPersonality: """Returns a random AI personality.""" - return random.choice(list(AIPersonality)) + # return random.choice(list(AIPersonality)) + return AIPersonality.JEBRASKA def calculate_exact_cost(attack: int, defense: int) -> float: """Calculate the exact cost before rounding (matches card.py formula).""" - return min(11.0, max(1.0, ((attack**2 + defense**2)**0.18) / 1.5)) + return min(10.0, max(1.0, ((attack**2 + defense**2)**0.18) / 1.5)) def get_power_curve_value(card) -> float: """ @@ -54,7 +55,7 @@ def choose_cards(cards: list[Card], difficulty: int, personality: AIPersonality) defn = np.array([c.defense for c in allowed], dtype=np.float32) cost = np.array([c.cost for c in allowed], dtype=np.float32) - exact_cost = np.minimum(11.0, np.maximum(1.0, ((atk**2 + defn**2)**0.18) / 1.5)) + exact_cost = np.minimum(10.0, np.maximum(1.0, ((atk**2 + defn**2)**0.18) / 1.5)) pcv_norm = np.clip(exact_cost - cost, 0.0, 1.0) cost_norm = cost / max_card_cost totals = atk + defn @@ -78,21 +79,14 @@ def choose_cards(cards: list[Card], difficulty: int, personality: AIPersonality) elif personality == AIPersonality.CONTROL: # Small cost_norm keeps flavour without causing severe deck shrinkage at D10 scores = 0.85 * pcv_norm + 0.15 * cost_norm - elif personality == AIPersonality.BALANCED: + elif personality in (AIPersonality.BALANCED, AIPersonality.JEBRASKA): scores = 0.60 * pcv_norm + 0.25 * atk_ratio + 0.15 * (1.0 - atk_ratio) - elif personality == AIPersonality.SHOCKER: - # Both cheap walls and expensive finishers want high attack. - # (1-cost_norm) drives first-pass cheap-card selection; pcv_norm drives second-pass finishers. - # defense_ok zeros out cards with defense==1 on the first term so fragile walls are excluded. - # cost-11 cards have pcv=0 so they score near-zero and never shrink the deck. - scores = atk_ratio * (1.0 - cost_norm) * def_not_one + atk_ratio * pcv_norm else: # ARBITRARY - w = 0.05 * difficulty + w = 0.09 * difficulty scores = w * pcv_norm + (1.0 - w) * np.random.random(len(allowed)).astype(np.float32) # Small noise floor at D10 prevents fully deterministic deck building. - # A locked-in deck loses every game against counters; tiny randomness avoids this. - noise = max(0.03, (10 - difficulty) / 9.0) * 0.50 + noise = (max(0,12 - difficulty)**2) * 0.008 scores = scores + np.random.normal(0, noise, len(allowed)).astype(np.float32) order = np.argsort(-scores) @@ -105,7 +99,7 @@ def choose_cards(cards: list[Card], difficulty: int, personality: AIPersonality) AIPersonality.DEFENSIVE: 15, # raised: stable cheap-card base across difficulty levels AIPersonality.CONTROL: 8, AIPersonality.BALANCED: 25, # spread the deck across all cost levels - AIPersonality.SHOCKER: 15, # ~15 cost-1 shields, then expensive attackers fill remaining budget + AIPersonality.JEBRASKA: 25, # same as balanced AIPersonality.ARBITRARY: 8, }[personality] @@ -303,15 +297,11 @@ def score_plans_batch( score = (0.12 * atk_score + 0.13 * block_score + 0.15 * cover_score + 0.10 * net_value_norm + 0.12 * destroy_score + 0.15 * attrition_score + 0.12 * pcv_score + 0.11 * threat_score) - elif personality == AIPersonality.SHOCKER: - score = (0.25 * destroy_score + 0.33 * cover_score + 0.18 * atk_score + - 0.05 * block_score + 0.8 * attrition_score + 0.02 * threat_score + - 0.05 * net_value_norm + 0.04 * pcv_score) else: # ARBITRARY - score = (0.60 * np.random.random(n).astype(np.float32) + - 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) + score = (0.50 * np.random.random(n).astype(np.float32) + + 0.06 * atk_score + 0.06 * block_score + 0.08 * cover_score + + 0.05 * net_value_norm + 0.06 * destroy_score + + 0.08 * attrition_score + 0.06 * pcv_score + 0.05 * threat_score) # --- Context adjustments --- score = np.where(direct_damage >= opponent.life, np.maximum(score, 0.95), score) @@ -333,12 +323,25 @@ def score_plans_batch( return np.maximum(0.0, score - sac_penalty) -async def choose_plan(player: PlayerState, opponent: PlayerState, personality: AIPersonality, difficulty: int) -> MovePlan: +def choose_plan(player: PlayerState, opponent: PlayerState, personality: AIPersonality, difficulty: int) -> MovePlan: plans = generate_plans(player, opponent) - scores = score_plans_batch(plans, player, opponent, personality) + if personality == AIPersonality.JEBRASKA: + from nn import NeuralNet + import os + _weights = os.path.join(os.path.dirname(__file__), "nn_weights.json") + if not hasattr(choose_plan, "_neural_net"): + choose_plan._neural_net = NeuralNet.load(_weights) if os.path.exists(_weights) else None + net = choose_plan._neural_net + if net is not None: + from nn import extract_plan_features + scores = net.forward(extract_plan_features(plans, player, opponent)) + else: # fallback to BALANCED if weights not found + scores = score_plans_batch(plans, player, opponent, AIPersonality.BALANCED) + else: + scores = score_plans_batch(plans, player, opponent, personality) - noise_scale = (max(0,11 - difficulty)**2) * 0.01 - 0.01 + noise_scale = ((max(0,12 - difficulty)**2) - 4) * 0.008 noise = np.random.normal(0, noise_scale, len(scores)).astype(np.float32) return plans[int(np.argmax(scores + noise))] @@ -388,7 +391,7 @@ async def run_ai_turn(game_id: str): pass # --- Generate and score candidate plans --- - best_plan = await choose_plan(player, opponent, personality, difficulty) + best_plan = choose_plan(player, opponent, personality, difficulty) logger.info( f"AI turn: d={difficulty} p={personality.value} plan={best_plan.label} " + diff --git a/backend/alembic/versions/a9f2d4e7c301_add_shards_to_users.py b/backend/alembic/versions/a9f2d4e7c301_add_shards_to_users.py new file mode 100644 index 0000000..068b262 --- /dev/null +++ b/backend/alembic/versions/a9f2d4e7c301_add_shards_to_users.py @@ -0,0 +1,25 @@ +"""add shards to users + +Revision ID: a9f2d4e7c301 +Revises: f3a1c8e2b950 +Create Date: 2026-03-25 00:00:00.000000 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +revision: str = 'a9f2d4e7c301' +down_revision: Union[str, Sequence[str], None] = 'f3a1c8e2b950' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + op.add_column('users', sa.Column('shards', sa.Integer(), nullable=False, server_default='0')) + + +def downgrade() -> None: + op.drop_column('users', 'shards') diff --git a/backend/alembic/versions/f3a1c8e2b950_add_email_verification.py b/backend/alembic/versions/f3a1c8e2b950_add_email_verification.py new file mode 100644 index 0000000..dcfb559 --- /dev/null +++ b/backend/alembic/versions/f3a1c8e2b950_add_email_verification.py @@ -0,0 +1,30 @@ +"""add email verification + +Revision ID: f3a1c8e2b950 +Revises: adee6bcc23e1 +Create Date: 2026-03-25 00:00:00.000000 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +revision: str = 'f3a1c8e2b950' +down_revision: Union[str, Sequence[str], None] = 'cd7ebb9b11bd' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + # Existing users are considered verified so they aren't locked out. + op.add_column('users', sa.Column('email_verified', sa.Boolean(), nullable=False, server_default='true')) + op.add_column('users', sa.Column('email_verification_token', sa.String(), nullable=True)) + op.add_column('users', sa.Column('email_verification_token_expires_at', sa.DateTime(), nullable=True)) + + +def downgrade() -> None: + op.drop_column('users', 'email_verification_token_expires_at') + op.drop_column('users', 'email_verification_token') + op.drop_column('users', 'email_verified') diff --git a/backend/card.py b/backend/card.py index 2a0e0b1..c1bbc2c 100644 --- a/backend/card.py +++ b/backend/card.py @@ -113,6 +113,7 @@ WIKIDATA_INSTANCE_TYPE_MAP = { "Q734698": CardType.artwork, # collectible card game "Q506240": CardType.artwork, # television film "Q738377": CardType.artwork, # student newspaper + "Q2031291": CardType.artwork, # musical release "Q1259759": CardType.artwork, # miniseries "Q3305213": CardType.artwork, # painting "Q3177859": CardType.artwork, # dedicated deck card game @@ -121,6 +122,7 @@ WIKIDATA_INSTANCE_TYPE_MAP = { "Q1761818": CardType.artwork, # advertising campaign "Q1446621": CardType.artwork, # recital "Q1868552": CardType.artwork, # local newspaper + "Q3244175": CardType.artwork, # tabletop game "Q63952888": CardType.artwork, # anime television series "Q47461344": CardType.artwork, # written work "Q71631512": CardType.artwork, # tabletop role-playing game supplement @@ -151,11 +153,14 @@ WIKIDATA_INSTANCE_TYPE_MAP = { "Q7930989": CardType.location, # city/town "Q1250464": CardType.location, # realm "Q3146899": CardType.location, # diocese of the catholic church + "Q17350442": CardType.location, # venue + "Q23764314": CardType.location, # sports location "Q12076836": CardType.location, # administrative territorial entity of a single country "Q35145263": CardType.location, # natural geographic object "Q15642541": CardType.location, # human-geographic territorial entity "Q16521": CardType.life_form, # taxon + "Q38829": CardType.life_form, # breed "Q310890": CardType.life_form, # monotypic taxon "Q23038290": CardType.life_form, # fossil taxon "Q12045585": CardType.life_form, # cattle breed @@ -183,6 +188,7 @@ WIKIDATA_INSTANCE_TYPE_MAP = { "Q15275719": CardType.event, # recurring event "Q27968055": CardType.event, # recurring event edition "Q15091377": CardType.event, # cycling race + "Q87267404": CardType.event, # formula race "Q114609228": CardType.event, # recurring sporting event "Q7278": CardType.group, # political party @@ -225,15 +231,23 @@ WIKIDATA_INSTANCE_TYPE_MAP = { "Q134808": CardType.science_thing, # vaccine "Q168845": CardType.science_thing, # star cluster "Q1491746": CardType.science_thing, # galaxy group + "Q2465832": CardType.science_thing, # branch of science "Q1341811": CardType.science_thing, # astronomical maser "Q1840368": CardType.science_thing, # cloud type "Q2154519": CardType.science_thing, # astrophysical x-ray source + "Q3132741": CardType.science_thing, # substellar object + "Q15636229": CardType.science_thing, # surgery procedure + "Q11862829": CardType.science_thing, # academic discipline + "Q78088984": CardType.science_thing, # study type "Q17444909": CardType.science_thing, # astronomical object type + "Q24034552": CardType.science_thing, # mathematical concept "Q12089225": CardType.science_thing, # mineral species "Q55640599": CardType.science_thing, # group of chemical entities + "Q119459661": CardType.science_thing, # scientific activity "Q113145171": CardType.science_thing, # type of chemical entity "Q1420": CardType.vehicle, # car + "Q42889": CardType.vehicle, # vehicle "Q11446": CardType.vehicle, # ship "Q43193": CardType.vehicle, # truck "Q25956": CardType.vehicle, # space station @@ -252,12 +266,15 @@ WIKIDATA_INSTANCE_TYPE_MAP = { "Q928235": CardType.vehicle, # sloop-of-war "Q391022": CardType.vehicle, # research vessel "Q202527": CardType.vehicle, # minesweeper + "Q1229765": CardType.vehicle, # watercraft + "Q2031121": CardType.vehicle, # warship "Q1185562": CardType.vehicle, # light aircraft carrier "Q7233751": CardType.vehicle, # post ship "Q3231690": CardType.vehicle, # automobile model "Q1428357": CardType.vehicle, # submarine class "Q1499623": CardType.vehicle, # destroyer escort "Q4818021": CardType.vehicle, # attack submarine + "Q15141321": CardType.vehicle, # train service "Q19832486": CardType.vehicle, # locomotive class "Q23866334": CardType.vehicle, # motorcycle model "Q29048322": CardType.vehicle, # vehicle model @@ -267,8 +284,12 @@ WIKIDATA_INSTANCE_TYPE_MAP = { "Q43229": CardType.organization, # organization "Q47913": CardType.organization, # intelligence agency "Q35535": CardType.organization, # police + "Q740752": CardType.organization, # transport company "Q4830453": CardType.organization, # business "Q4671277": CardType.organization, # academic institution + "Q2659904": CardType.organization, # government organization + + "Q686822": CardType.other, # bill (written work) } import asyncio @@ -399,9 +420,14 @@ async def _infer_card_type_async(client: httpx.AsyncClient, entity_id: str) -> t if superclass_qid2 in WIKIDATA_INSTANCE_TYPE_MAP: return WIKIDATA_INSTANCE_TYPE_MAP[superclass_qid2], superclass_qid2, language_count - # Fallback: coordinate location - if "P625" in claims: - return CardType.location, (qids[0] if qids else ""), language_count + # Fallback: classify by presence of specific claims + CLAIMS_FALLBACK = { + "P625": CardType.location, # coordinate location + "P437": CardType.artwork, # distribution format + } + for prop, fallback_type in CLAIMS_FALLBACK.items(): + if prop in claims: + return fallback_type, (qids[0] if qids else ""), language_count return CardType.other, (qids[0] if qids != [] else ""), language_count @@ -525,7 +551,7 @@ async def _get_card_async(client: httpx.AsyncClient, page_title: str|None = None text=text, attack=attack, defense=defense, - cost=min(11,max(1,int(((attack**2+defense**2)**0.18)/1.5))) + cost=min(10,max(1,int(((attack**2+defense**2)**0.18)/1.5))) ) async def _get_cards_async(size: int) -> list[Card]: diff --git a/backend/config.py b/backend/config.py index 918ccc3..3275b1e 100644 --- a/backend/config.py +++ b/backend/config.py @@ -14,6 +14,9 @@ JWT_SECRET_KEY = require("JWT_SECRET_KEY") DATABASE_URL = require("DATABASE_URL") RESEND_API_KEY = require("RESEND_API_KEY") EMAIL_FROM = require("EMAIL_FROM") +STRIPE_SECRET_KEY = require("STRIPE_SECRET_KEY") +STRIPE_PUBLISHABLE_KEY = require("STRIPE_PUBLISHABLE_KEY") +STRIPE_WEBHOOK_SECRET = require("STRIPE_WEBHOOK_SECRET") # Optional with sensible defaults for local dev FRONTEND_URL = optional("FRONTEND_URL", "http://localhost:5173") diff --git a/backend/database_functions.py b/backend/database_functions.py index fdbcb6c..7831881 100644 --- a/backend/database_functions.py +++ b/backend/database_functions.py @@ -68,8 +68,9 @@ BOOSTER_COOLDOWN_HOURS = 5 def check_boosters(user: UserModel, db: Session) -> tuple[int, datetime|None]: if user.boosters_countdown is None: - user.boosters = 5 - db.commit() + if user.boosters < BOOSTER_MAX: + user.boosters = BOOSTER_MAX + db.commit() return (user.boosters, user.boosters_countdown) now = datetime.now() diff --git a/backend/email_utils.py b/backend/email_utils.py index ea339d9..96547b7 100644 --- a/backend/email_utils.py +++ b/backend/email_utils.py @@ -2,6 +2,31 @@ import resend import os from config import RESEND_API_KEY, EMAIL_FROM, FRONTEND_URL +def send_verification_email(to_email: str, username: str, token: str): + resend.api_key = RESEND_API_KEY + verify_url = f"{FRONTEND_URL}/verify-email?token={token}" + + resend.Emails.send({ + "from": EMAIL_FROM, + "to": to_email, + "subject": "Verify your WikiTCG email", + "html": f""" +
+

Welcome to WikiTCG

+

Hi {username},

+

Please verify your email address to complete your registration:

+

+ + Verify Email + +

+

This link expires in 24 hours.

+

- WikiTCG

+
+ """, + }) + + def send_password_reset_email(to_email: str, username: str, reset_token: str): resend.api_key = RESEND_API_KEY reset_url = f"{FRONTEND_URL}/forgot-password/reset?token={reset_token}" diff --git a/backend/game.py b/backend/game.py index 24f8999..9205924 100644 --- a/backend/game.py +++ b/backend/game.py @@ -56,7 +56,7 @@ class PlayerState: def draw_to_full(self): """Draw cards until hand has HAND_SIZE cards or deck is empty.""" while len(self.hand) < HAND_SIZE and self.deck: - self.hand.append(self.deck.pop(0)) + self.hand.append(self.deck.pop()) def refill_energy(self): self.energy = self.energy_cap diff --git a/backend/give_card.py b/backend/give_card.py new file mode 100644 index 0000000..5eaca50 --- /dev/null +++ b/backend/give_card.py @@ -0,0 +1,63 @@ +""" +Give a user a specific card generated from a Wikipedia page title. + +Usage: + python give_card.py + +Example: + python give_card.py nikolaj "Marie Curie" +""" + +import sys +import asyncio +from dotenv import load_dotenv +load_dotenv() + +from database import SessionLocal +from models import User as UserModel, Card as CardModel +from card import _get_specific_card_async +import uuid + + +async def main(username: str, page_title: str) -> None: + db = SessionLocal() + try: + user = db.query(UserModel).filter(UserModel.username == username).first() + if not user: + print(f"Error: user '{username}' not found") + sys.exit(1) + + print(f"Generating card for '{page_title}'...") + card = await _get_specific_card_async(page_title) + if not card: + print(f"Error: could not generate a card for '{page_title}'") + sys.exit(1) + + db_card = CardModel( + id=uuid.uuid4(), + user_id=user.id, + name=card.name, + image_link=card.image_link or None, + card_rarity=card.card_rarity.name, + card_type=card.card_type.name, + text=card.text or None, + attack=card.attack, + defense=card.defense, + cost=card.cost, + ) + db.add(db_card) + db.commit() + + print(f"Gave '{card.name}' ({card.card_rarity.name} {card.card_type.name}) to {username}") + print(f" ATK {card.attack} DEF {card.defense} Cost {card.cost}") + + finally: + db.close() + + +if __name__ == "__main__": + if len(sys.argv) != 3: + print(__doc__) + sys.exit(1) + + asyncio.run(main(sys.argv[1], sys.argv[2])) diff --git a/backend/main.py b/backend/main.py index 7165368..6b0da49 100644 --- a/backend/main.py +++ b/backend/main.py @@ -33,9 +33,16 @@ from game_manager import ( queue, queue_lock, QueueEntry, try_match, handle_action, connections, active_games, serialize_state, handle_disconnect, handle_timeout_claim, load_deck_cards, create_solo_game ) +from trade_manager import ( + trade_queue, trade_queue_lock, TradeQueueEntry, try_trade_match, + handle_trade_action, active_trades, handle_trade_disconnect, + serialize_trade, +) from card import compute_deck_type, _get_specific_card_async -from email_utils import send_password_reset_email -from config import CORS_ORIGINS +from email_utils import send_password_reset_email, send_verification_email +from config import CORS_ORIGINS, STRIPE_SECRET_KEY, STRIPE_PUBLISHABLE_KEY, STRIPE_WEBHOOK_SECRET, FRONTEND_URL +import stripe +stripe.api_key = STRIPE_SECRET_KEY logger = logging.getLogger("app") @@ -82,6 +89,11 @@ app.add_middleware( allow_headers=["*"], ) +try: + from disposable_email_domains import blocklist as _disposable_blocklist +except ImportError: + _disposable_blocklist: set[str] = set() + def validate_register(username: str, email: str, password: str) -> str | None: if not username.strip(): return "Username is required" @@ -89,6 +101,9 @@ def validate_register(username: str, email: str, password: str) -> str | None: return "Username must be 16 characters or fewer" if not re.match(r"^[^\s@]+@[^\s@]+\.[^\s@]+$", email): return "Please enter a valid email" + domain = email.split("@")[-1].lower() + if domain in _disposable_blocklist: + return "Disposable email addresses are not allowed" if len(password) < 8: return "Password must be at least 8 characters" if len(password) > 256: @@ -104,15 +119,23 @@ def register(req: RegisterRequest, db: Session = Depends(get_db)): raise HTTPException(status_code=400, detail="Username already taken") if db.query(UserModel).filter(UserModel.email == req.email).first(): raise HTTPException(status_code=400, detail="Email already registered") + verification_token = secrets.token_urlsafe(32) user = UserModel( id=uuid.uuid4(), username=req.username, email=req.email, password_hash=hash_password(req.password), + email_verified=False, + email_verification_token=verification_token, + email_verification_token_expires_at=datetime.now() + timedelta(hours=24), ) db.add(user) db.commit() - return {"message": "User created"} + try: + send_verification_email(req.email, req.username, verification_token) + except Exception as e: + logger.error(f"Failed to send verification email: {e}") + return {"message": "Account created. Please check your email to verify your account."} @app.post("/login") def login(form: OAuth2PasswordRequestForm = Depends(), db: Session = Depends(get_db)): @@ -126,8 +149,9 @@ def login(form: OAuth2PasswordRequestForm = Depends(), db: Session = Depends(get } @app.get("/boosters") -def get_boosters(user: UserModel = Depends(get_current_user), db: Session = Depends(get_db)) -> tuple[int,datetime|None]: - return check_boosters(user, db) +def get_boosters(user: UserModel = Depends(get_current_user), db: Session = Depends(get_db)): + count, countdown = check_boosters(user, db) + return {"count": count, "countdown": countdown, "email_verified": user.email_verified} @app.get("/cards") def get_cards(user: UserModel = Depends(get_current_user), db: Session = Depends(get_db)): @@ -142,6 +166,9 @@ def get_cards(user: UserModel = Depends(get_current_user), db: Session = Depends @app.post("/open_pack") @limiter.limit("10/minute") async def open_pack(request: Request, user: UserModel = Depends(get_current_user), db: Session = Depends(get_db)): + if not user.email_verified: + raise HTTPException(status_code=403, detail="You must verify your email before opening packs") + check_boosters(user, db) if user.boosters == 0: @@ -322,6 +349,72 @@ async def game_endpoint(websocket: WebSocket, game_id: str, db: Session = Depend connections[game_id].pop(user_id, None) asyncio.create_task(handle_disconnect(game_id, user_id)) +@app.websocket("/ws/trade/queue") +async def trade_queue_endpoint(websocket: WebSocket, db: Session = Depends(get_db)): + await websocket.accept() + + token = await websocket.receive_text() + user_id = decode_access_token(token) + if not user_id: + await websocket.close(code=1008) + return + + user = db.query(UserModel).filter(UserModel.id == uuid.UUID(user_id)).first() + if not user: + await websocket.close(code=1008) + return + if not user.email_verified: + await websocket.send_json({"type": "error", "message": "You must verify your email before trading."}) + await websocket.close(code=1008) + return + + entry = TradeQueueEntry(user_id=user_id, username=user.username, websocket=websocket) + + async with trade_queue_lock: + trade_queue.append(entry) + + await websocket.send_json({"type": "queued"}) + await try_trade_match() + + try: + while True: + await websocket.receive_text() + except WebSocketDisconnect: + async with trade_queue_lock: + trade_queue[:] = [e for e in trade_queue if e.user_id != user_id] + + +@app.websocket("/ws/trade/{trade_id}") +async def trade_endpoint(websocket: WebSocket, trade_id: str, db: Session = Depends(get_db)): + await websocket.accept() + + token = await websocket.receive_text() + user_id = decode_access_token(token) + if not user_id: + await websocket.close(code=1008) + return + + session = active_trades.get(trade_id) + if not session or user_id not in session.offers: + await websocket.close(code=1008) + return + + session.connections[user_id] = websocket + + await websocket.send_json({ + "type": "state", + "state": serialize_trade(session, user_id), + }) + + try: + while True: + data = await websocket.receive_json() + await handle_trade_action(trade_id, user_id, data, db) + except WebSocketDisconnect: + session.connections.pop(user_id, None) + asyncio.create_task(handle_trade_disconnect(trade_id, user_id)) + + @app.get("/profile") def get_profile(user: UserModel = Depends(get_current_user), db: Session = Depends(get_db)): total_games = user.wins + user.losses @@ -343,9 +436,11 @@ def get_profile(user: UserModel = Depends(get_current_user), db: Session = Depen return { "username": user.username, "email": user.email, + "email_verified": user.email_verified, "created_at": user.created_at, "wins": user.wins, "losses": user.losses, + "shards": user.shards, "win_rate": round((user.wins / total_games) * 100) if total_games > 0 else None, "most_played_deck": { "name": most_played_deck.name, @@ -360,6 +455,160 @@ def get_profile(user: UserModel = Depends(get_current_user), db: Session = Depen } if most_played_card else None, } +class ShatterRequest(BaseModel): + card_ids: list[str] + +@app.post("/shards/shatter") +def shatter_cards(req: ShatterRequest, user: UserModel = Depends(get_current_user), db: Session = Depends(get_db)): + if not req.card_ids: + raise HTTPException(status_code=400, detail="No cards selected") + try: + parsed_ids = [uuid.UUID(cid) for cid in req.card_ids] + except ValueError: + raise HTTPException(status_code=400, detail="Invalid card IDs") + + cards = db.query(CardModel).filter( + CardModel.id.in_(parsed_ids), + CardModel.user_id == user.id, + ).all() + + if len(cards) != len(parsed_ids): + raise HTTPException(status_code=400, detail="Some cards are not in your collection") + + total = sum(c.cost for c in cards) + + for card in cards: + db.query(DeckCardModel).filter(DeckCardModel.card_id == card.id).delete() + db.delete(card) + + user.shards += total + db.commit() + return {"shards": user.shards, "gained": total} + +# Shard packages sold for real money. +# price_oere is in Danish øre (1 DKK = 100 øre). Stripe minimum is 250 øre. +SHARD_PACKAGES = { + "s1": {"base": 100, "bonus": 0, "shards": 100, "price_oere": 1000, "price_label": "10 DKK"}, + "s2": {"base": 250, "bonus": 50, "shards": 300, "price_oere": 2500, "price_label": "25 DKK"}, + "s3": {"base": 500, "bonus": 200, "shards": 700, "price_oere": 5000, "price_label": "50 DKK"}, + "s4": {"base": 1000, "bonus": 600, "shards": 1600, "price_oere": 10000, "price_label": "100 DKK"}, + "s5": {"base": 2500, "bonus": 2000, "shards": 4500, "price_oere": 25000, "price_label": "250 DKK"}, + "s6": {"base": 5000, "bonus": 5000, "shards": 10000, "price_oere": 50000, "price_label": "500 DKK"}, +} + +class StripeCheckoutRequest(BaseModel): + package_id: str + +@app.post("/store/stripe/checkout") +def create_stripe_checkout(req: StripeCheckoutRequest, user: UserModel = Depends(get_current_user)): + package = SHARD_PACKAGES.get(req.package_id) + if not package: + raise HTTPException(status_code=400, detail="Invalid package") + session = stripe.checkout.Session.create( + payment_method_types=["card"], + line_items=[{ + "price_data": { + "currency": "dkk", + "product_data": {"name": f"WikiTCG Shards — {package['price_label']}"}, + "unit_amount": package["price_oere"], + }, + "quantity": 1, + }], + mode="payment", + success_url=f"{FRONTEND_URL}/store?payment=success", + cancel_url=f"{FRONTEND_URL}/store", + metadata={"user_id": str(user.id), "shards": str(package["shards"])}, + ) + return {"url": session.url} + +@app.post("/stripe/webhook") +async def stripe_webhook(request: Request, db: Session = Depends(get_db)): + payload = await request.body() + sig = request.headers.get("stripe-signature", "") + try: + event = stripe.Webhook.construct_event(payload, sig, STRIPE_WEBHOOK_SECRET) + except stripe.error.SignatureVerificationError: # type: ignore + raise HTTPException(status_code=400, detail="Invalid signature") + + if event["type"] == "checkout.session.completed": + data = event["data"]["object"] + user_id = data.get("metadata", {}).get("user_id") + shards = data.get("metadata", {}).get("shards") + if user_id and shards: + user = db.query(UserModel).filter(UserModel.id == uuid.UUID(user_id)).first() + if user: + user.shards += int(shards) + db.commit() + + return {"ok": True} + +@app.get("/store/config") +def store_config(): + return { + "publishable_key": STRIPE_PUBLISHABLE_KEY, + "shard_packages": SHARD_PACKAGES, + } + +STORE_PACKAGES = { + 1: 15, + 5: 65, + 10: 120, + 25: 260, +} + +class StoreBuyRequest(BaseModel): + quantity: int + +class BuySpecificCardRequest(BaseModel): + wiki_title: str + +SPECIFIC_CARD_COST = 1000 + +@app.post("/store/buy-specific-card") +@limiter.limit("10/hour") +async def buy_specific_card(request: Request, req: BuySpecificCardRequest, user: UserModel = Depends(get_current_user), db: Session = Depends(get_db)): + if user.shards < SPECIFIC_CARD_COST: + raise HTTPException(status_code=400, detail="Not enough shards") + + card = await _get_specific_card_async(req.wiki_title) + if card is None: + raise HTTPException(status_code=404, detail="Could not generate a card for that Wikipedia page") + + db_card = CardModel( + name=card.name, + image_link=card.image_link, + card_rarity=card.card_rarity.name, + card_type=card.card_type.name, + text=card.text, + attack=card.attack, + defense=card.defense, + cost=card.cost, + user_id=user.id, + ) + db.add(db_card) + user.shards -= SPECIFIC_CARD_COST + db.commit() + db.refresh(db_card) + + return { + **{c.name: getattr(db_card, c.name) for c in db_card.__table__.columns}, + "card_rarity": db_card.card_rarity, + "card_type": db_card.card_type, + "shards": user.shards, + } + +@app.post("/store/buy") +def store_buy(req: StoreBuyRequest, user: UserModel = Depends(get_current_user), db: Session = Depends(get_db)): + cost = STORE_PACKAGES.get(req.quantity) + if cost is None: + raise HTTPException(status_code=400, detail="Invalid package") + if user.shards < cost: + raise HTTPException(status_code=400, detail="Not enough shards") + user.shards -= cost + user.boosters += req.quantity + db.commit() + return {"shards": user.shards, "boosters": user.boosters} + @app.post("/cards/{card_id}/report") def report_card(card_id: str, user: UserModel = Depends(get_current_user), db: Session = Depends(get_db)): card = db.query(CardModel).filter( @@ -382,8 +631,8 @@ async def refresh_card(request: Request, card_id: str, user: UserModel = Depends if not card: raise HTTPException(status_code=404, detail="Card not found") - if user.last_refresh_at and datetime.now() - user.last_refresh_at < timedelta(hours=48): - remaining = (user.last_refresh_at + timedelta(hours=48)) - datetime.now() + if user.last_refresh_at and datetime.now() - user.last_refresh_at < timedelta(hours=2): + remaining = (user.last_refresh_at + timedelta(hours=2)) - datetime.now() hours = int(remaining.total_seconds() // 3600) minutes = int((remaining.total_seconds() % 3600) // 60) raise HTTPException( @@ -417,7 +666,7 @@ async def refresh_card(request: Request, card_id: str, user: UserModel = Depends def refresh_status(user: UserModel = Depends(get_current_user)): if not user.last_refresh_at: return {"can_refresh": True, "next_refresh_at": None} - next_refresh = user.last_refresh_at + timedelta(hours=48) + next_refresh = user.last_refresh_at + timedelta(hours=2) can_refresh = datetime.now() >= next_refresh return { "can_refresh": can_refresh, @@ -516,6 +765,35 @@ def reset_password_with_token(req: ResetPasswordWithTokenRequest, db: Session = db.commit() return {"message": "Password updated"} +@app.get("/auth/verify-email") +def verify_email(token: str, db: Session = Depends(get_db)): + user = db.query(UserModel).filter(UserModel.email_verification_token == token).first() + if not user or not user.email_verification_token_expires_at or user.email_verification_token_expires_at < datetime.now(): + raise HTTPException(status_code=400, detail="Invalid or expired verification link") + user.email_verified = True + user.email_verification_token = None + user.email_verification_token_expires_at = None + db.commit() + return {"message": "Email verified"} + +class ResendVerificationRequest(BaseModel): + email: str + +@app.post("/auth/resend-verification") +def resend_verification(req: ResendVerificationRequest, db: Session = Depends(get_db)): + user = db.query(UserModel).filter(UserModel.email == req.email).first() + # Always return success to prevent user enumeration + if user and not user.email_verified: + token = secrets.token_urlsafe(32) + user.email_verification_token = token + user.email_verification_token_expires_at = datetime.now() + timedelta(hours=24) + db.commit() + try: + send_verification_email(user.email, user.username, token) + except Exception as e: + logger.error(f"Failed to resend verification email: {e}") + return {"message": "If that email is registered and unverified, you will receive a new verification link shortly"} + class RefreshRequest(BaseModel): refresh_token: str diff --git a/backend/models.py b/backend/models.py index daca59a..2e8cd2e 100644 --- a/backend/models.py +++ b/backend/models.py @@ -17,9 +17,13 @@ class User(Base): boosters_countdown: Mapped[datetime | None] = mapped_column(DateTime, nullable=True) wins: Mapped[int] = mapped_column(Integer, default=0, nullable=False) losses: Mapped[int] = mapped_column(Integer, default=0, nullable=False) + shards: Mapped[int] = mapped_column(Integer, default=0, nullable=False) last_refresh_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True) reset_token: Mapped[str | None] = mapped_column(String, nullable=True) reset_token_expires_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True) + email_verified: Mapped[bool] = mapped_column(Boolean, default=False, nullable=False) + email_verification_token: Mapped[str | None] = mapped_column(String, nullable=True) + email_verification_token_expires_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True) cards: Mapped[list["Card"]] = relationship(back_populates="user") decks: Mapped[list["Deck"]] = relationship(back_populates="user") diff --git a/backend/nn.py b/backend/nn.py new file mode 100644 index 0000000..789373b --- /dev/null +++ b/backend/nn.py @@ -0,0 +1,269 @@ +import numpy as np +import json + +# Layout: [state(8) | my_board(15) | opp_board(15) | plan(3) | result_board(15) | opp_deck_type(8)] +N_FEATURES = 64 + +_DECK_TYPES = ["Balanced", "Aggro", "Wall", "Rush", "Control", "God Card", "Pantheon", "Unplayable"] +_DECK_TYPE_IDX = {dt: i for i, dt in enumerate(_DECK_TYPES)} + +_MAX_ATK = 50.0 +_MAX_DEF = 100.0 +_MAX_DECK = 30.0 + + +def _softmax(x: np.ndarray) -> np.ndarray: + e = np.exp(x - x.max()) + return e / e.sum() + + +class NeuralNet: + """ + Fully-connected plan scorer: n_features → 64 → 32 → 1 + Pure numpy so it can be pickled into worker processes. + Optimizer: Adam. + """ + + def __init__(self, n_features: int = N_FEATURES, hidden: tuple = (64, 32), seed: int | None = None): + rng = np.random.RandomState(seed) + sizes = [n_features] + list(hidden) + [1] + + self.weights: list[np.ndarray] = [] + self.biases: list[np.ndarray] = [] + self.m_w: list[np.ndarray] = [] + self.v_w: list[np.ndarray] = [] + self.m_b: list[np.ndarray] = [] + self.v_b: list[np.ndarray] = [] + self.t = 0 + + for fan_in, fan_out in zip(sizes, sizes[1:]): + w = rng.randn(fan_in, fan_out).astype(np.float32) * np.sqrt(2.0 / fan_in) + b = np.zeros(fan_out, dtype=np.float32) + self.weights.append(w) + self.biases.append(b) + self.m_w.append(np.zeros_like(w)) + self.v_w.append(np.zeros_like(w)) + self.m_b.append(np.zeros_like(b)) + self.v_b.append(np.zeros_like(b)) + + self._acts: list[np.ndarray] = [] + self._pre_acts: list[np.ndarray] = [] + + def forward(self, X: np.ndarray) -> np.ndarray: + """X: (n, n_features) → scores: (n,)""" + h = X.astype(np.float32) + self._acts = [h] + self._pre_acts = [] + for i, (W, b) in enumerate(zip(self.weights, self.biases)): + z = h @ W + b + self._pre_acts.append(z) + h = np.maximum(0.0, z) if i < len(self.weights) - 1 else z + self._acts.append(h) + return h.squeeze(-1) + + def backward(self, upstream: np.ndarray) -> tuple[list, list]: + """ + upstream: (n,) — dJ/d(scores), gradient for ascent. + Returns (grads_w, grads_b). + """ + n = len(upstream) + delta = upstream[:, None] # (n, 1) + grads_w = [None] * len(self.weights) + grads_b = [None] * len(self.biases) + for i in range(len(self.weights) - 1, -1, -1): + h_in = self._acts[i] # (n, in_size) + grads_w[i] = h_in.T @ delta / n + grads_b[i] = delta.mean(axis=0) + if i > 0: + delta = (delta @ self.weights[i].T) * (self._pre_acts[i - 1] > 0) + return grads_w, grads_b + + def adam_update(self, grads_w: list, grads_b: list, + lr: float = 1e-3, beta1: float = 0.9, + beta2: float = 0.999, eps: float = 1e-8, + grad_clip: float = 1.0) -> None: + # Global gradient norm clipping + all_grads = [g for g in grads_w + grads_b if g is not None] + global_norm = np.sqrt(sum(np.sum(g * g) for g in all_grads)) + if global_norm > grad_clip: + scale = grad_clip / global_norm + grads_w = [g * scale for g in grads_w] + grads_b = [g * scale for g in grads_b] + + self.t += 1 + bc1 = 1 - beta1 ** self.t + bc2 = 1 - beta2 ** self.t + for i, (gw, gb) in enumerate(zip(grads_w, grads_b)): + self.m_w[i] = beta1 * self.m_w[i] + (1 - beta1) * gw + self.v_w[i] = beta2 * self.v_w[i] + (1 - beta2) * gw * gw + self.weights[i] += lr * (self.m_w[i] / bc1) / (np.sqrt(self.v_w[i] / bc2) + eps) + + self.m_b[i] = beta1 * self.m_b[i] + (1 - beta1) * gb + self.v_b[i] = beta2 * self.v_b[i] + (1 - beta2) * gb * gb + self.biases[i] += lr * (self.m_b[i] / bc1) / (np.sqrt(self.v_b[i] / bc2) + eps) + + def save(self, path: str) -> None: + data = { + "weights": [w.tolist() for w in self.weights], + "biases": [b.tolist() for b in self.biases], + "m_w": [m.tolist() for m in self.m_w], + "v_w": [v.tolist() for v in self.v_w], + "m_b": [m.tolist() for m in self.m_b], + "v_b": [v.tolist() for v in self.v_b], + "t": self.t, + } + with open(path, "w") as f: + json.dump(data, f) + + @classmethod + def load(cls, path: str) -> "NeuralNet": + with open(path) as f: + data = json.load(f) + net = cls.__new__(cls) + net.weights = [np.array(w, dtype=np.float32) for w in data["weights"]] + net.biases = [np.array(b, dtype=np.float32) for b in data["biases"]] + net.m_w = [np.array(m, dtype=np.float32) for m in data["m_w"]] + net.v_w = [np.array(v, dtype=np.float32) for v in data["v_w"]] + net.m_b = [np.array(m, dtype=np.float32) for m in data["m_b"]] + net.v_b = [np.array(v, dtype=np.float32) for v in data["v_b"]] + net.t = data["t"] + net._acts = [] + net._pre_acts = [] + return net + + +# ==================== Feature extraction ==================== + +def extract_plan_features(plans: list, player, opponent) -> np.ndarray: + """ + Returns (n_plans, N_FEATURES) float32 array. + Layout: [state(8) | my_board(15) | opp_board(15) | plan(3) | result_board(15)] + """ + from game import BOARD_SIZE, HAND_SIZE, MAX_ENERGY_CAP, STARTING_LIFE + + n = len(plans) + + # ---- state (same for every plan) ---- + state = np.array([ + player.life / STARTING_LIFE, + opponent.life / STARTING_LIFE, + player.energy / MAX_ENERGY_CAP, + player.energy_cap / MAX_ENERGY_CAP, + len(player.hand) / HAND_SIZE, + len(opponent.hand) / HAND_SIZE, + len(player.deck) / _MAX_DECK, + len(opponent.deck) / _MAX_DECK, + ], dtype=np.float32) + + # ---- current boards (same for every plan) ---- + my_board = np.zeros(BOARD_SIZE * 3, dtype=np.float32) + opp_board = np.zeros(BOARD_SIZE * 3, dtype=np.float32) + for slot in range(BOARD_SIZE): + c = player.board[slot] + if c is not None: + my_board[slot * 3] = c.attack / _MAX_ATK + my_board[slot * 3 + 1] = c.defense / _MAX_DEF + my_board[slot * 3 + 2] = 1.0 + c = opponent.board[slot] + if c is not None: + opp_board[slot * 3] = c.attack / _MAX_ATK + opp_board[slot * 3 + 1] = c.defense / _MAX_DEF + opp_board[slot * 3 + 2] = 1.0 + + # ---- per-plan features ---- + plan_part = np.zeros((n, 3 + BOARD_SIZE * 3), dtype=np.float32) + for idx, plan in enumerate(plans): + # simulate board result + result = list(player.board) + for slot in plan.sacrifice_slots: + result[slot] = None + for card, slot in plan.plays: + result[slot] = card + + total_cost = sum(c.cost for c, _ in plan.plays) if plan.plays else 0 + plan_part[idx, 0] = len(plan.sacrifice_slots) / BOARD_SIZE + plan_part[idx, 1] = len(plan.plays) / HAND_SIZE + plan_part[idx, 2] = total_cost / (MAX_ENERGY_CAP + BOARD_SIZE) + + for slot in range(BOARD_SIZE): + c = result[slot] + if c is not None: + plan_part[idx, 3 + slot * 3] = c.attack / _MAX_ATK + plan_part[idx, 3 + slot * 3 + 1] = c.defense / _MAX_DEF + plan_part[idx, 3 + slot * 3 + 2] = 1.0 + + # ---- opponent deck type one-hot (same for every plan) ---- + opp_deck_oh = np.zeros(len(_DECK_TYPES), dtype=np.float32) + opp_deck_oh[_DECK_TYPE_IDX.get(opponent.deck_type, 0)] = 1.0 + + state_t = np.tile(state, (n, 1)) + my_board_t = np.tile(my_board, (n, 1)) + opp_board_t = np.tile(opp_board, (n, 1)) + opp_deck_t = np.tile(opp_deck_oh, (n, 1)) + + return np.concatenate([state_t, my_board_t, opp_board_t, plan_part, opp_deck_t], axis=1) + + +# ==================== Neural player ==================== + +class NeuralPlayer: + """ + Wraps a NeuralNet for use in game simulation. + In training mode, samples plans stochastically and records the trajectory + for a REINFORCE update after the game ends. + In inference mode, picks the highest-scoring plan deterministically. + """ + + def __init__(self, net: NeuralNet, training: bool = False, temperature: float = 1.0): + self.net = net + self.training = training + self.temperature = temperature + self.trajectory: list[tuple[np.ndarray, int]] = [] # (features, chosen_idx) + + def choose_plan(self, player, opponent): + from ai import generate_plans + plans = generate_plans(player, opponent) + features = extract_plan_features(plans, player, opponent) + scores = self.net.forward(features) + + if self.training: + probs = _softmax((scores / self.temperature).astype(np.float64)) + probs = np.clip(probs, 1e-10, None) + probs /= probs.sum() + chosen_idx = int(np.random.choice(len(plans), p=probs)) + self.trajectory.append((features, chosen_idx)) + else: + chosen_idx = int(np.argmax(scores)) + + return plans[chosen_idx] + + def compute_grads(self, outcome: float) -> tuple[list, list] | None: + """ + Computes averaged REINFORCE gradients for this trajectory without updating weights. + outcome: centered reward (win/loss minus baseline). + Returns (grads_w, grads_b), or None if trajectory is empty. + """ + if not self.trajectory: + return None + + acc_gw = [np.zeros_like(w) for w in self.net.weights] + acc_gb = [np.zeros_like(b) for b in self.net.biases] + + for features, chosen_idx in self.trajectory: + scores = self.net.forward(features) + probs = _softmax(scores.astype(np.float64)).astype(np.float32) + upstream = -probs.copy() + upstream[chosen_idx] += 1.0 + upstream *= outcome + gw, gb = self.net.backward(upstream) + for i in range(len(acc_gw)): + acc_gw[i] += gw[i] + acc_gb[i] += gb[i] + + n = len(self.trajectory) + for i in range(len(acc_gw)): + acc_gw[i] /= n + acc_gb[i] /= n + + self.trajectory.clear() + return acc_gw, acc_gb diff --git a/backend/nn_weights.json b/backend/nn_weights.json new file mode 100644 index 0000000..bff454e --- /dev/null +++ b/backend/nn_weights.json @@ -0,0 +1 @@ +{"weights": [[[0.1382819414138794, -0.12735465168952942, 0.30544814467430115, 0.11696126312017441, -0.0727081373333931, 0.03625354543328285, 0.15646593272686005, 0.21366779506206512, -0.18071255087852478, 0.1654590368270874, -0.17012932896614075, 0.017178768292069435, 0.018158772960305214, -0.26755937933921814, -0.157888263463974, 0.07931747287511826, -0.033913590013980865, 0.2294197380542755, -0.48908671736717224, -0.2872749865055084, 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simulate_game( Player 1 always goes first. Returns "p1", "p2", or None if the game exceeds MAX_TURNS. - - Designed to be awaited inside asyncio.gather() to run many games concurrently. """ deck1 = choose_cards(cards, difficulty1, personality1) deck2 = choose_cards(cards, difficulty2, personality2) @@ -152,7 +151,7 @@ async def simulate_game( player = state.players[active_id] opponent = state.players[state.opponent_id(active_id)] - plan = await choose_plan(player, opponent, personality, difficulty) + plan = choose_plan(player, opponent, personality, difficulty) for slot in plan.sacrifice_slots: if player.board[slot] is not None: @@ -189,11 +188,11 @@ def _init_worker(cards: list[Card]) -> None: def _run_game_sync(args: tuple) -> str | None: """Synchronous entry point for a worker process.""" d1, p1_name, d2, p2_name = args - return asyncio.run(simulate_game( + return simulate_game( _worker_cards, d1, AIPersonality(p1_name), d2, AIPersonality(p2_name), - )) + ) # ==================== Tournament ==================== @@ -290,26 +289,188 @@ async def run_tournament( return wins +def _sprt_check(wins: int, total: int, log_win: float, log_loss: float, log_B: float) -> bool: + """ + Return True when the SPRT has reached a decision for this matchup. + + Tests H0: win_rate = 0.5 vs H1: win_rate = p_decisive (or 1-p_decisive). + log_win = log(p_decisive / 0.5) + log_loss = log((1 - p_decisive) / 0.5) + + LLR drifts slowly for near-50% matchups and quickly for lopsided ones. + Decided when LLR crosses ±log_B. + """ + llr = wins * log_win + (total - wins) * log_loss + return llr >= log_B or llr <= -log_B + + +async def run_tournament_adaptive( + cards: list[Card], + difficulties: list[int] | None = None, + min_games: int = 5, + max_games: int = 200, + p_decisive: float = 0.65, + alpha: float = 0.05, +) -> tuple[dict[tuple[int, int], int], dict[tuple[int, int], int]]: + """ + Like run_tournament but allocates games adaptively. + + Each ordered pair (i, j) plays until SPRT decides one player is dominant + (win rate ≥ p_decisive with confidence 1-alpha) or max_games is reached. + Close matchups play more games; lopsided ones stop early. + + Returns (wins, played): + wins[(i, j)] — how many games player i won as first player against j + played[(i, j)] — how many games were played for that pair + + Each round, all currently-undecided pairs play one game in parallel across + all CPU cores, preserving full parallelism while adapting per-pair budgets. + """ + players = _all_players(difficulties) + n = len(players) + all_pairs = [(i, j) for i in range(n) for j in range(n)] + + wins: dict[tuple[int, int], int] = {pair: 0 for pair in all_pairs} + played: dict[tuple[int, int], int] = {pair: 0 for pair in all_pairs} + decided: set[tuple[int, int]] = set() + + # Precompute SPRT constants (H0: p=0.5, H1: p=p_decisive) + log_B = math.log((1 - alpha) / alpha) + log_win = math.log(p_decisive / 0.5) + log_loss = math.log((1 - p_decisive) / 0.5) + + def make_args(i: int, j: int) -> tuple: + p1, d1 = players[i] + p2, d2 = players[j] + return (d1, p1.value, d2, p2.value) + + n_workers = os.cpu_count() or 1 + loop = asyncio.get_running_loop() + total_played = 0 + max_possible = len(all_pairs) * max_games + + print( + f"Adaptive tournament: {n} players, {len(all_pairs)} pairs, " + f"SPRT p_decisive={p_decisive} alpha={alpha}, " + f"min={min_games} max={max_games} games/pair\n" + f"Worst case: {max_possible:,} games across {n_workers} workers" + ) + + with ProcessPoolExecutor( + max_workers=n_workers, + initializer=_init_worker, + initargs=(cards,), + ) as executor: + round_num = 0 + while True: + pending = [ + pair for pair in all_pairs + if pair not in decided and played[pair] < max_games + ] + if not pending: + break + + round_num += 1 + batch = [(i, j, make_args(i, j)) for (i, j) in pending] + futures = [ + loop.run_in_executor(executor, _run_game_sync, args) + for _, _, args in batch + ] + results = await asyncio.gather(*futures) + + newly_decided = 0 + for (i, j, _), winner in zip(batch, results): + played[(i, j)] += 1 + if winner == PLAYER1_ID: + wins[(i, j)] += 1 + total_played += 1 + + if (played[(i, j)] >= min_games + and _sprt_check(wins[(i, j)], played[(i, j)], log_win, log_loss, log_B)): + decided.add((i, j)) + newly_decided += 1 + + remaining = len(all_pairs) - len(decided) + pct = total_played / max_possible * 100 + print( + f" Round {round_num:3d}: {len(pending):5d} games, " + f"+{newly_decided:4d} decided, " + f"{remaining:5d} pairs left, " + f"{total_played:,} total ({pct:.1f}% of worst case)", + end="\r", flush=True, + ) + + savings = max_possible - total_played + print( + f"\nFinished: {total_played:,} games played " + f"(saved {savings:,} vs fixed, " + f"{savings / max_possible * 100:.1f}% reduction)" + ) + print( + f"Early decisions: {len(decided)}/{len(all_pairs)} pairs " + f"({len(decided) / len(all_pairs) * 100:.1f}%)" + ) + + return wins, played + + +def compute_bradley_terry( + wins: dict[tuple[int, int], int], + n: int, + played: dict[tuple[int, int], int] | None = None, + games_per_matchup: int | None = None, + iterations: int = 1000, +) -> list[float]: + """ + Compute Bradley-Terry strength parameters for all n players. + + For each pair (i, j): w_ij wins for i, w_ji wins for j. + Iteratively updates: strength[i] = sum_j(w_ij) / sum_j((w_ij+w_ji) / (s[i]+s[j])) + + Returns a list of strength values indexed by player. Unlike Elo, this is + path-independent and converges to a unique maximum-likelihood solution. + """ + w: list[list[int]] = [[0] * n for _ in range(n)] + for (i, j), p1_wins in wins.items(): + g = played[(i, j)] if played is not None else games_per_matchup + if g: + w[i][j] += p1_wins + w[j][i] += g - p1_wins + + strength = [1.0] * n + for _ in range(iterations): + new_strength = [0.0] * n + for i in range(n): + wins_i = sum(w[i][j] for j in range(n) if j != i) + denom = sum( + (w[i][j] + w[j][i]) / (strength[i] + strength[j]) + for j in range(n) + if j != i and (w[i][j] + w[j][i]) > 0 + ) + new_strength[i] = wins_i / denom if denom > 0 else strength[i] + # Normalize so the mean stays at 1.0 + mean = sum(new_strength) / n + strength = [s / mean for s in new_strength] + + return strength + + def rank_players( wins: dict[tuple[int, int], int], - games_per_matchup: int, players: list[tuple[AIPersonality, int]], + played: dict[tuple[int, int], int] | None = None, + games_per_matchup: int | None = None, ) -> list[int]: """ - Rank player indices by total wins (as first + second player combined). - Returns indices sorted worst-to-best. + Rank player indices by Bradley-Terry strength. Returns indices sorted worst-to-best. + + Provide either `played` (adaptive tournament) or `games_per_matchup` (fixed). """ - n = len(players) - total_wins = [0] * n + if played is None and games_per_matchup is None: + raise ValueError("Provide either played or games_per_matchup") - for (i, j), p1_wins in wins.items(): - if i == j: - continue # self-matchups are symmetric; skip to avoid double-counting - p2_wins = games_per_matchup - p1_wins - total_wins[i] += p1_wins - total_wins[j] += p2_wins - - return sorted(range(n), key=lambda k: total_wins[k]) + ratings = compute_bradley_terry(wins, len(players), played=played, games_per_matchup=games_per_matchup) + return sorted(range(len(players)), key=lambda i: ratings[i]) TOURNAMENT_RESULTS_PATH = os.path.join(os.path.dirname(__file__), "tournament_results.json") @@ -317,43 +478,63 @@ TOURNAMENT_RESULTS_PATH = os.path.join(os.path.dirname(__file__), "tournament_re def save_tournament( wins: dict[tuple[int, int], int], - games_per_matchup: int, players: list[tuple[AIPersonality, int]], path: str = TOURNAMENT_RESULTS_PATH, + played: dict[tuple[int, int], int] | None = None, + games_per_matchup: int | None = None, ): data = { - "games_per_matchup": games_per_matchup, "players": [ {"personality": p.value, "difficulty": d} for p, d in players ], "wins": {f"{i},{j}": w for (i, j), w in wins.items()}, } + if played is not None: + data["played"] = {f"{i},{j}": g for (i, j), g in played.items()} + if games_per_matchup is not None: + data["games_per_matchup"] = games_per_matchup with open(path, "w", encoding="utf-8") as f: json.dump(data, f, indent=2) print(f"Tournament results saved to {path}") -def load_tournament(path: str = TOURNAMENT_RESULTS_PATH) -> tuple[dict[tuple[int, int], int], int, list[tuple[AIPersonality, int]]]: - """Returns (wins, games_per_matchup, players).""" +def load_tournament( + path: str = TOURNAMENT_RESULTS_PATH, +) -> tuple[ + dict[tuple[int, int], int], + dict[tuple[int, int], int] | None, + int | None, + list[tuple[AIPersonality, int]], +]: + """Returns (wins, played, games_per_matchup, players). + + `played` is None for legacy fixed-game files (use games_per_matchup instead). + `games_per_matchup` is None for adaptive files (use played instead). + """ with open(path, "r", encoding="utf-8") as f: data = json.load(f) - wins = { - (int(k.split(",")[0]), int(k.split(",")[1])): v - for k, v in data["wins"].items() - } + + def parse_pair_dict(d: dict) -> dict[tuple[int, int], int]: + return {(int(k.split(",")[0]), int(k.split(",")[1])): v for k, v in d.items()} + + wins = parse_pair_dict(data["wins"]) + played = parse_pair_dict(data["played"]) if "played" in data else None + games_per_matchup = data.get("games_per_matchup") players = [ (AIPersonality(p["personality"]), p["difficulty"]) for p in data["players"] ] - return wins, data["games_per_matchup"], players + return wins, played, games_per_matchup, players def draw_grid( wins: dict[tuple[int, int], int], - games_per_matchup: int = 5, players: list[tuple[AIPersonality, int]] | None = None, output_path: str = "tournament_grid.png", + played: dict[tuple[int, int], int] | None = None, + games_per_matchup: int | None = None, + ranked: list[int] | None = None, ): """ Draw a heatmap grid of tournament results. @@ -370,19 +551,28 @@ def draw_grid( import matplotlib.colors as mcolors import numpy as np + if played is None and games_per_matchup is None: + raise ValueError("Provide either played or games_per_matchup") + if players is None: players = _all_players() n = len(players) - ranked = rank_players(wins, games_per_matchup, players) # worst-to-best indices + if ranked is None: + ranked = rank_players(wins, players, played=played, games_per_matchup=games_per_matchup) labels = [_player_label(*players[i]) for i in ranked] - # Build value matrix: (p1_wins - p2_wins) / games_per_matchup ∈ [-1, 1], NaN on diagonal + def games(i: int, j: int) -> int: + return_value = played[(i, j)] if played is not None else games_per_matchup + return return_value if return_value is not None else 0 + + # Build value matrix: (p1_wins - p2_wins) / total_games ∈ [-1, 1] matrix = np.full((n, n), np.nan) for row, i in enumerate(ranked): for col, j in enumerate(ranked): + g = games(i, j) p1_wins = wins.get((i, j), 0) - matrix[row, col] = (p1_wins - (games_per_matchup - p1_wins)) / games_per_matchup + matrix[row, col] = (p1_wins - (g - p1_wins)) / g if g > 0 else 0.0 cell_size = 0.22 fig_size = n * cell_size + 3 @@ -398,8 +588,9 @@ def draw_grid( # × marks for sweeps for row, i in enumerate(ranked): for col, j in enumerate(ranked): + g = games(i, j) p1_wins = wins.get((i, j), 0) - if p1_wins == games_per_matchup or p1_wins == 0: + if p1_wins == g or p1_wins == 0: ax.text(col, row, "×", ha="center", va="center", fontsize=5, color="black", fontweight="bold", zorder=3) @@ -427,14 +618,26 @@ def draw_grid( if __name__ == "__main__": - import sys - - GAMES_PER_MATCHUP = 10 - - difficulties = list(range(6, 11)) + difficulties = list(range(7, 11)) card_pool = get_simulation_cards() players = _all_players(difficulties) - wins = asyncio.run(run_tournament(card_pool, games_per_matchup=GAMES_PER_MATCHUP, difficulties=difficulties)) - save_tournament(wins, games_per_matchup=GAMES_PER_MATCHUP, players=players) - draw_grid(wins, games_per_matchup=GAMES_PER_MATCHUP, players=players) + wins, played = asyncio.run(run_tournament_adaptive( + card_pool, + difficulties=difficulties, + min_games=20, + max_games=1000, + p_decisive=0.65, + alpha=0.05, + )) + save_tournament(wins, players=players, played=played) + + ratings = compute_bradley_terry(wins, len(players), played=played) + ranked = sorted(range(len(players)), key=lambda i: ratings[i]) # worst-to-best + draw_grid(wins, players=players, played=played, ranked=ranked) + + print("\nFinal Elo ratings (best to worst):") + for rank, i in enumerate(reversed(ranked), 1): + personality, difficulty = players[i] + label = _player_label(personality, difficulty) + print(f" {rank:2d}. {label:<12} {ratings[i]:.1f}") diff --git a/backend/trade_manager.py b/backend/trade_manager.py new file mode 100644 index 0000000..d70c531 --- /dev/null +++ b/backend/trade_manager.py @@ -0,0 +1,283 @@ +import asyncio +import uuid +import logging +from dataclasses import dataclass, field +from fastapi import WebSocket +from sqlalchemy.orm import Session + +from models import Card as CardModel, DeckCard as DeckCardModel + +logger = logging.getLogger("app") + +## Storage + +@dataclass +class TradeOffer: + username: str + cards: list[dict] = field(default_factory=list) + accepted: bool = False + +@dataclass +class TradeSession: + trade_id: str + offers: dict[str, TradeOffer] # user_id -> TradeOffer + connections: dict[str, WebSocket] = field(default_factory=dict) + +active_trades: dict[str, TradeSession] = {} + +@dataclass +class TradeQueueEntry: + user_id: str + username: str + websocket: WebSocket + +trade_queue: list[TradeQueueEntry] = [] +trade_queue_lock = asyncio.Lock() + +## Serialization + +def serialize_card_model(card: CardModel) -> dict: + return { + "id": str(card.id), + "name": card.name, + "card_rarity": card.card_rarity, + "card_type": card.card_type, + "image_link": card.image_link, + "attack": card.attack, + "defense": card.defense, + "cost": card.cost, + "text": card.text, + "created_at": card.created_at.isoformat() if card.created_at else None, + } + +def serialize_trade(session: TradeSession, perspective_user_id: str) -> dict: + partner_id = next(uid for uid in session.offers if uid != perspective_user_id) + my_offer = session.offers[perspective_user_id] + their_offer = session.offers[partner_id] + return { + "trade_id": session.trade_id, + "partner_username": their_offer.username, + "my_offer": { + "cards": my_offer.cards, + "accepted": my_offer.accepted, + }, + "their_offer": { + "cards": their_offer.cards, + "accepted": their_offer.accepted, + }, + } + +## Broadcasting + +async def broadcast_trade(session: TradeSession) -> None: + for user_id, ws in list(session.connections.items()): + try: + await ws.send_json({ + "type": "state", + "state": serialize_trade(session, user_id), + }) + except Exception: + pass + +## Matchmaking + +async def try_trade_match() -> None: + async with trade_queue_lock: + if len(trade_queue) < 2: + return + # Guard: same user queued twice + if trade_queue[0].user_id == trade_queue[1].user_id: + return + p1 = trade_queue.pop(0) + p2 = trade_queue.pop(0) + + trade_id = str(uuid.uuid4()) + session = TradeSession( + trade_id=trade_id, + offers={ + p1.user_id: TradeOffer(username=p1.username), + p2.user_id: TradeOffer(username=p2.username), + }, + connections={ + p1.user_id: p1.websocket, + p2.user_id: p2.websocket, + }, + ) + active_trades[trade_id] = session + + for entry in [p1, p2]: + try: + await entry.websocket.send_json({"type": "trade_start", "trade_id": trade_id}) + except Exception: + pass + +## Action handling + +async def handle_trade_action( + trade_id: str, + user_id: str, + message: dict, + db: Session, +) -> None: + session = active_trades.get(trade_id) + if not session: + return + + action = message.get("type") + ws = session.connections.get(user_id) + + if action == "update_offer": + card_ids = message.get("card_ids", []) + + if card_ids: + try: + parsed_ids = [uuid.UUID(cid) for cid in card_ids] + except ValueError: + if ws: + await ws.send_json({"type": "error", "message": "Invalid card IDs"}) + return + + db_cards = db.query(CardModel).filter( + CardModel.id.in_(parsed_ids), + CardModel.user_id == uuid.UUID(user_id), + ).all() + + if len(db_cards) != len(card_ids): + if ws: + await ws.send_json({"type": "error", "message": "Some cards are not in your collection"}) + return + + # Preserve the order of card_ids + card_map = {str(c.id): c for c in db_cards} + ordered = [card_map[cid] for cid in card_ids if cid in card_map] + session.offers[user_id].cards = [serialize_card_model(c) for c in ordered] + else: + session.offers[user_id].cards = [] + + # Any offer change unaccepts both sides + for offer in session.offers.values(): + offer.accepted = False + + await broadcast_trade(session) + + elif action == "accept": + either_has_cards = any(len(o.cards) > 0 for o in session.offers.values()) + if not either_has_cards: + return + + # Validate ownership of offered cards one more time + my_offer = session.offers[user_id] + if my_offer.cards: + owned_count = db.query(CardModel).filter( + CardModel.id.in_([uuid.UUID(c["id"]) for c in my_offer.cards]), + CardModel.user_id == uuid.UUID(user_id), + ).count() + if owned_count != len(my_offer.cards): + if ws: + await ws.send_json({"type": "error", "message": "Some offered cards are no longer in your collection"}) + return + + my_offer.accepted = True + + if all(o.accepted for o in session.offers.values()): + await _complete_trade(trade_id, db) + else: + await broadcast_trade(session) + + elif action == "unaccept": + session.offers[user_id].accepted = False + await broadcast_trade(session) + +## Trade completion + +async def _complete_trade(trade_id: str, db: Session) -> None: + session = active_trades.get(trade_id) + if not session: + return + + # Re-check that both sides are still accepted and have a non-empty offer. + # A last-second unaccept or offer change (race or client bug) should abort. + if not all(o.accepted for o in session.offers.values()): + await broadcast_trade(session) + return + if not any(len(o.cards) > 0 for o in session.offers.values()): + for offer in session.offers.values(): + offer.accepted = False + await broadcast_trade(session) + return + + user_ids = list(session.offers.keys()) + u1, u2 = user_ids[0], user_ids[1] + cards_u1 = session.offers[u1].cards # u1 gives these to u2 + cards_u2 = session.offers[u2].cards # u2 gives these to u1 + + # Final ownership double-check before writing + def verify(from_id: str, card_dicts: list[dict]) -> bool: + if not card_dicts: + return True + card_uuids = [uuid.UUID(c["id"]) for c in card_dicts] + count = db.query(CardModel).filter( + CardModel.id.in_(card_uuids), + CardModel.user_id == uuid.UUID(from_id), + ).count() + return count == len(card_uuids) + + if not verify(u1, cards_u1) or not verify(u2, cards_u2): + db.rollback() + for ws in list(session.connections.values()): + try: + await ws.send_json({ + "type": "error", + "message": "Trade failed: ownership check failed. Offers have been reset.", + }) + except Exception: + pass + for offer in session.offers.values(): + offer.accepted = False + await broadcast_trade(session) + return + + # Transfer ownership and clear deck relationships + for cid_str in [c["id"] for c in cards_u1]: + cid = uuid.UUID(cid_str) + card = db.query(CardModel).filter(CardModel.id == cid).first() + if card: + card.user_id = uuid.UUID(u2) + db.query(DeckCardModel).filter(DeckCardModel.card_id == cid).delete() + + for cid_str in [c["id"] for c in cards_u2]: + cid = uuid.UUID(cid_str) + card = db.query(CardModel).filter(CardModel.id == cid).first() + if card: + card.user_id = uuid.UUID(u1) + db.query(DeckCardModel).filter(DeckCardModel.card_id == cid).delete() + + db.commit() + + active_trades.pop(trade_id, None) + + for ws in list(session.connections.values()): + try: + await ws.send_json({"type": "trade_complete"}) + except Exception: + pass + +## Disconnect handling + +async def handle_trade_disconnect(trade_id: str, user_id: str) -> None: + session = active_trades.get(trade_id) + if not session: + return + + active_trades.pop(trade_id, None) + + for uid, ws in list(session.connections.items()): + if uid == user_id: + continue + try: + await ws.send_json({ + "type": "error", + "message": "Your trade partner disconnected. Trade cancelled.", + }) + except Exception: + pass diff --git a/backend/train_nn.py b/backend/train_nn.py new file mode 100644 index 0000000..cb29613 --- /dev/null +++ b/backend/train_nn.py @@ -0,0 +1,205 @@ +import os +import random +import uuid +import numpy as np +from collections import deque +from dotenv import load_dotenv +load_dotenv() + +from card import compute_deck_type +from ai import AIPersonality, choose_cards, choose_plan +from game import PlayerState, GameState, action_play_card, action_sacrifice, action_end_turn +from simulate import get_simulation_cards, _make_instances, MAX_TURNS +from nn import NeuralNet, NeuralPlayer + +NN_WEIGHTS_PATH = os.path.join(os.path.dirname(__file__), "nn_weights.json") + +P1 = "p1" +P2 = "p2" + +FIXED_PERSONALITIES = [p for p in AIPersonality if p != AIPersonality.ARBITRARY] + + +# ==================== Game runner ==================== + +def _build_player(pid: str, name: str, cards: list, difficulty: int, personality: AIPersonality) -> PlayerState: + deck = choose_cards(cards, difficulty, personality) + instances = _make_instances(deck) + random.shuffle(instances) + p = PlayerState( + user_id=pid, username=name, + deck_type=compute_deck_type(deck) or "Balanced", + deck=instances, + ) + return p + + +def run_episode( + p1_state: PlayerState, + p2_state: PlayerState, + p1_ctrl, # (player, opponent) -> MovePlan + p2_ctrl, # (player, opponent) -> MovePlan +) -> str | None: + """Returns winner_id (P1 or P2) or None on timeout.""" + p1_state.increment_energy_cap() + p2_state.increment_energy_cap() + p1_state.refill_energy() + p1_state.draw_to_full() + + state = GameState( + game_id=str(uuid.uuid4()), + players={P1: p1_state, P2: p2_state}, + player_order=[P1, P2], + active_player_id=P1, + phase="main", + turn=1, + ) + ctrls = {P1: p1_ctrl, P2: p2_ctrl} + + for _ in range(MAX_TURNS): + if state.result: + break + active_id = state.active_player_id + player = state.players[active_id] + opponent = state.players[state.opponent_id(active_id)] + + plan = ctrls[active_id](player, opponent) + + for slot in plan.sacrifice_slots: + if player.board[slot] is not None: + action_sacrifice(state, slot) + + plays = list(plan.plays) + random.shuffle(plays) + for card, slot in plays: + hand_idx = next((i for i, c in enumerate(player.hand) if c is card), None) + if hand_idx is None or player.board[slot] is not None or card.cost > player.energy: + continue + action_play_card(state, hand_idx, slot) + + action_end_turn(state) + + return state.result.winner_id if state.result else None + + +# ==================== Training loop ==================== + +def train( + n_episodes: int = 20_000, + self_play_start: int = 5_000, + self_play_max_frac: float = 0.4, + lr: float = 1e-3, + opp_difficulty: int = 10, + temperature: float = 1.0, + batch_size: int = 50, + save_every: int = 5_000, + save_path: str = NN_WEIGHTS_PATH, +) -> NeuralNet: + cards = get_simulation_cards() + + if os.path.exists(save_path): + print(f"Resuming from {save_path}") + net = NeuralNet.load(save_path) + else: + print("Initializing new network") + net = NeuralNet(seed=42) + + recent_outcomes: deque[int] = deque(maxlen=1000) # rolling window for win rate display + baseline = 0.0 # EMA of recent outcomes; subtracted before each update + baseline_alpha = 0.99 # decay — roughly a 100-episode window + + batch_gw = [np.zeros_like(w) for w in net.weights] + batch_gb = [np.zeros_like(b) for b in net.biases] + batch_count = 0 + + for episode in range(1, n_episodes + 1): + # Ramp self-play fraction linearly from 0 to self_play_max_frac + if episode >= self_play_start: + progress = (episode - self_play_start) / max(1, n_episodes - self_play_start) + self_play_prob = self_play_max_frac * progress + else: + self_play_prob = 0.0 + + # Randomly decide who goes first (NN is always P1 for simplicity) + nn_goes_first = random.random() < 0.5 + + if random.random() < self_play_prob: + # ---- Self-play ---- + nn1 = NeuralPlayer(net, training=True, temperature=temperature) + nn2 = NeuralPlayer(net, training=True, temperature=temperature) + + p1_state = _build_player(P1, "NN1", cards, 10, AIPersonality.BALANCED) + p2_state = _build_player(P2, "NN2", cards, 10, AIPersonality.BALANCED) + + if not nn_goes_first: + p1_state, p2_state = p2_state, p1_state + + winner = run_episode(p1_state, p2_state, nn1.choose_plan, nn2.choose_plan) + p1_outcome = 1.0 if winner == P1 else -1.0 + baseline = baseline_alpha * baseline + (1 - baseline_alpha) * p1_outcome + + for player_grads in [nn1.compute_grads(p1_outcome - baseline), + nn2.compute_grads(-p1_outcome - baseline)]: + if player_grads is not None: + gw, gb = player_grads + for i in range(len(batch_gw)): + batch_gw[i] += gw[i] + batch_gb[i] += gb[i] + batch_count += 1 + + else: + # ---- NN vs fixed opponent ---- + opp_personality = random.choice(FIXED_PERSONALITIES) + nn_player = NeuralPlayer(net, training=True, temperature=temperature) + opp_ctrl = lambda p, o, pers=opp_personality, diff=opp_difficulty: choose_plan(p, o, pers, diff) + + if nn_goes_first: + nn_id = P1 + p1_state = _build_player(P1, "NN", cards, 10, AIPersonality.BALANCED) + p2_state = _build_player(P2, "OPP", cards, opp_difficulty, opp_personality) + winner = run_episode(p1_state, p2_state, nn_player.choose_plan, opp_ctrl) + else: + nn_id = P2 + p1_state = _build_player(P1, "OPP", cards, opp_difficulty, opp_personality) + p2_state = _build_player(P2, "NN", cards, 10, AIPersonality.BALANCED) + winner = run_episode(p1_state, p2_state, opp_ctrl, nn_player.choose_plan) + + nn_outcome = 1.0 if winner == nn_id else -1.0 + player_grads = nn_player.compute_grads(nn_outcome - baseline) + baseline = baseline_alpha * baseline + (1 - baseline_alpha) * nn_outcome + + if player_grads is not None: + gw, gb = player_grads + for i in range(len(batch_gw)): + batch_gw[i] += gw[i] + batch_gb[i] += gb[i] + batch_count += 1 + + recent_outcomes.append(1 if winner == nn_id else 0) + + if batch_count >= batch_size: + for i in range(len(batch_gw)): + batch_gw[i] /= batch_count + batch_gb[i] /= batch_count + net.adam_update(batch_gw, batch_gb, lr=lr) + batch_gw = [np.zeros_like(w) for w in net.weights] + batch_gb = [np.zeros_like(b) for b in net.biases] + batch_count = 0 + + if episode % 1000 == 0 or episode == n_episodes: + wr = sum(recent_outcomes) / len(recent_outcomes) if recent_outcomes else 0.0 + print(f"[{episode:>6}/{n_episodes}] win rate (last {len(recent_outcomes)}): {wr:.1%} " + f"self-play frac: {self_play_prob:.0%}", flush=True) + + if episode % save_every == 0: + net.save(save_path) + print(f" → saved to {save_path}") + + net.save(save_path) + wr = sum(recent_outcomes) / len(recent_outcomes) if recent_outcomes else 0.0 + print(f"Done. Final win rate (last {len(recent_outcomes)}): {wr:.1%}") + return net + + +if __name__ == "__main__": + train() diff --git a/frontend/src/lib/CardSelector.svelte b/frontend/src/lib/CardSelector.svelte new file mode 100644 index 0000000..e5cce3c --- /dev/null +++ b/frontend/src/lib/CardSelector.svelte @@ -0,0 +1,454 @@ + + +
+ {#if showFooter && onclose} +
+ {selectedIds.size} card{selectedIds.size === 1 ? '' : 's'} selected + +
+ {/if} +
+
+ Sort by + {#each [['name','Name'],['cost','Cost'],['attack','Attack'],['defense','Defense'],['rarity','Rarity']] as [val, lbl]} + + {/each} + + + + +
+ + {#if filtersOpen} +
+
+
+ Rarity + +
+
+ {#each RARITIES as r} + + {/each} +
+
+ +
+
+ Type + +
+
+ {#each TYPES as t} + + {/each} +
+
+ +
+
+ Cost + +
+
+ Min: {costMin} + { if (costMin > costMax) costMax = costMin; }} /> + Max: {costMax} + { if (costMax < costMin) costMin = costMax; }} /> +
+
+
+ {/if} +
+ + {#if filtered.length === 0} +

No cards match your filters.

+ {:else} +
+ {#each filtered as card (card.id)} + + {/each} +
+ {/if} + +
+ + diff --git a/frontend/src/lib/header.svelte b/frontend/src/lib/header.svelte index 2a0995d..a5f571a 100644 --- a/frontend/src/lib/header.svelte +++ b/frontend/src/lib/header.svelte @@ -9,7 +9,8 @@ { href: '/cards', label: 'Cards' }, { href: '/decks', label: 'Decks' }, { href: '/play', label: 'Play' }, - { href: '/how-to-play', label: 'How to Play' }, + { href: '/trade', label: 'Trade' }, + { href: '/store', label: 'Store' }, ]; function close() { menuOpen = false; } diff --git a/frontend/src/routes/+layout.svelte b/frontend/src/routes/+layout.svelte index 328e2ea..cd9325b 100644 --- a/frontend/src/routes/+layout.svelte +++ b/frontend/src/routes/+layout.svelte @@ -5,15 +5,35 @@ import { page } from '$app/state'; let { children } = $props(); - -{#if !['auth', 'forgot-password'].some(p => page.url.pathname.startsWith(`/${p}`))} -
-{/if} + const showHeader = $derived(!['auth', 'forgot-password'].some(p => page.url.pathname.startsWith(`/${p}`))); + WikiTCG -{@render children()} +
+ {#if showHeader} +
+ {/if} +
+ {@render children()} +
+
+ + diff --git a/frontend/src/routes/+page.svelte b/frontend/src/routes/+page.svelte index a7d152e..6853f34 100644 --- a/frontend/src/routes/+page.svelte +++ b/frontend/src/routes/+page.svelte @@ -9,6 +9,7 @@ let loading = $state(false); let boosters = $state(null); let countdown = $state(null); + let emailVerified = $state(true); let countdownDisplay = $state(''); let countdownInterval = null; @@ -27,9 +28,10 @@ async function fetchBoosters() { const res = await apiFetch(`${API_URL}/boosters`); if (res.status === 401) { goto('/auth'); return; } - const [count, countdownTs] = await res.json(); - boosters = count; - countdown = countdownTs ? new Date(countdownTs) : null; + const data = await res.json(); + boosters = data.count; + countdown = data.countdown ? new Date(data.countdown) : null; + emailVerified = data.email_verified; startCountdown(); } @@ -140,7 +142,9 @@ - {#if boosters !== null && boosters > 0 && phase === 'idle'} + {#if !emailVerified} +

Verify your email to begin opening packs

+ {:else if boosters !== null && boosters > 0 && phase === 'idle'}
{/each} + + +
Min: {costMin} - { if (costMin > costMax) costMax = costMin; }} /> Max: {costMax} - { if (costMax < costMin) costMin = costMax; }} />
@@ -327,6 +337,22 @@ flex-wrap: wrap; } + .search-input { + font-family: 'Crimson Text', serif; + font-size: 15px; + background: rgba(255,255,255,0.04); + border: 1px solid rgba(107, 76, 30, 0.4); + border-radius: 4px; + color: #f0d080; + padding: 5px 10px; + outline: none; + width: 220px; + margin-left: auto; + transition: border-color 0.15s; + } + .search-input:focus { border-color: #c8861a; } + .search-input::placeholder { color: rgba(240, 180, 80, 0.3); } + .toolbar-label { font-family: 'Cinzel', serif; font-size: 11px; @@ -374,7 +400,7 @@ color: rgba(240, 180, 80, 0.6); padding: 4px 10px; cursor: pointer; - margin-left: auto; + margin-left: 0.5rem; position: relative; transition: all 0.15s; } @@ -530,6 +556,7 @@ align-items: center; justify-content: center; z-index: 100; + backdrop-filter: blur(6px); } .card-popup { diff --git a/frontend/src/routes/decks/[id]/+page.svelte b/frontend/src/routes/decks/[id]/+page.svelte index f70e4e9..84371d4 100644 --- a/frontend/src/routes/decks/[id]/+page.svelte +++ b/frontend/src/routes/decks/[id]/+page.svelte @@ -1,10 +1,10 @@
@@ -34,7 +46,17 @@
{profile.username[0].toUpperCase()}

{profile.username}

- + + {#if !profile.email_verified} + + {/if}

Member since {new Date(profile.created_at).toLocaleDateString('en-GB', { year: 'numeric', month: 'long', day: 'numeric' })}

@@ -43,6 +65,15 @@
+
+ ◈ + {profile.shards} + Shards + shatter cards +
+ +
+

Stats

@@ -164,6 +195,37 @@ margin: 0; } + .unverified-badge { + font-family: 'Cinzel', serif; + font-size: 9px; + font-weight: 700; + letter-spacing: 0.06em; + text-transform: uppercase; + color: #c87830; + border: 1px solid rgba(200, 120, 48, 0.4); + border-radius: 3px; + padding: 1px 5px; + vertical-align: middle; + margin-left: 6px; + } + + .resend-btn { + font-family: 'Crimson Text', serif; + font-size: 13px; + font-style: italic; + color: rgba(240, 180, 80, 0.5); + background: none; + border: none; + padding: 0; + cursor: pointer; + text-decoration: underline; + transition: color 0.15s; + text-align: left; + } + + .resend-btn:hover:not(:disabled) { color: rgba(240, 180, 80, 0.8); } + .resend-btn:disabled { cursor: default; opacity: 0.6; } + .joined { font-family: 'Crimson Text', serif; font-size: 13px; @@ -206,6 +268,53 @@ .reset-link:hover { color: rgba(240, 180, 80, 0.7); } + .shards-row { + display: flex; + align-items: center; + gap: 0.5rem; + } + + .shards-link { + font-family: 'Cinzel', serif; + font-size: 10px; + font-weight: 700; + letter-spacing: 0.06em; + text-transform: uppercase; + color: rgba(126, 207, 207, 0.6); + border: 1px solid rgba(126, 207, 207, 0.3); + border-radius: 4px; + padding: 3px 8px; + text-decoration: none; + margin-top: 4px; + margin-left: 0.5rem; + transition: color 0.15s, border-color 0.15s; + } + .shards-link:hover { color: #7ecfcf; border-color: rgba(126, 207, 207, 0.7); } + + .shards-icon { + font-size: 22px; + color: #7ecfcf; + position: relative; + top: -0.1em; + } + + .shards-value { + font-family: 'Cinzel', serif; + font-size: 28px; + font-weight: 700; + color: #7ecfcf; + } + + .shards-label { + font-family: 'Cinzel', serif; + font-size: 11px; + font-weight: 700; + letter-spacing: 0.1em; + text-transform: uppercase; + color: rgba(126, 207, 207, 0.5); + margin-top: 4px; + } + .section-divider { height: 1px; background: rgba(107, 76, 30, 0.3); diff --git a/frontend/src/routes/shards/+page.svelte b/frontend/src/routes/shards/+page.svelte new file mode 100644 index 0000000..20c2898 --- /dev/null +++ b/frontend/src/routes/shards/+page.svelte @@ -0,0 +1,288 @@ + + +
+
+

Shards

+ {#if shards !== null} +
+ ◈ + {shards} + Shards +
+ {/if} +

+ Shatter cards you no longer need to recover shards equal to their cost. + Shattered cards are permanently destroyed. +

+

You can also buy shards in the store.

+
+ + {#if result} +
+ ◈ + +{result.gained} shards gained + +
+ {/if} + +
+ + + {#if selectedIds.size > 0} + + {/if} +
+
+ +{#if selectorOpen} +
+ { selectorOpen = false; }} + /> +
+{/if} + + diff --git a/frontend/src/routes/store/+page.svelte b/frontend/src/routes/store/+page.svelte new file mode 100644 index 0000000..08b0bd4 --- /dev/null +++ b/frontend/src/routes/store/+page.svelte @@ -0,0 +1,1048 @@ + + +
+
+

Store

+ {#if shards !== null} +
+ ◈ + {shards} + Shards + shatter cards +
+ {/if} +
+ +
+ {#each packages as pkg} + {@const canAfford = shards !== null && shards >= pkg.cost} + {@const isFlashing = flash?.quantity === pkg.quantity} +
+ {#if pkg.discount} +
{pkg.discount}
+ {/if} + +
+ {#each Array(fanCount(pkg.quantity)) as _, i} +
+
+
+
+ +
+ {/each} +
+ +

{pkg.quantity} {pkg.quantity === 1 ? 'Pack' : 'Packs'}

+ + +
+ {/each} +
+ +
+ +
+
+
+
+ +
+
+

Specific Card

+

Choose exactly which card you want. Enter the title of a Wikipedia article and we'll generate a card for it.

+ + {#if shards !== null && shards < SPECIFIC_CARD_COST} +

You need {SPECIFIC_CARD_COST - shards} more shards.

+ {/if} +
+
+
+
+ +
+ +
+

Buy Shards

+

Shards can be used to buy booster packs above.

+ + {#if paymentSuccess} +
Payment successful — shards have been added to your account.
+ {/if} + +
+ {#each shardPackages as pkg} +
+ {#if pkg.bonus > 0} +
◈{pkg.bonus}
BONUS
+ {/if} + ◈ {pkg.shards} + {pkg.price_label} + +
+ {/each} +
+
+
+ +{#if specificPhase !== 'idle'} +
+ + {#if specificPhase === 'input'} +
+

Order a Specific Card

+

Enter the title of a Wikipedia article exactly as it appears.

+ e.key === 'Enter' && buySpecificCard()} + autofocus + /> + {#if specificError} +

{specificError}

+ {/if} +
+ + +
+
+ + {:else} +
+
+
+
+
+ +
+
+
+ {#if specificCard} + + {/if} +
+
+
+ +

+ Your card is being generated… +

+ + +
+ {/if} + +
+{/if} + + diff --git a/frontend/src/routes/trade/+page.svelte b/frontend/src/routes/trade/+page.svelte new file mode 100644 index 0000000..776c70e --- /dev/null +++ b/frontend/src/routes/trade/+page.svelte @@ -0,0 +1,560 @@ + + +
+ {#if phase === 'idle'} +
+

Trade

+

Exchange cards with another player

+ {#if error} +

{error}

+ {/if} + +
+ + {:else if phase === 'queuing'} +
+ +

Searching for a trade partner...

+ +
+ + {:else if phase === 'trading'} +
+ {#if selectorOpen} +
+ +
+ {/if} + +
+
+
+ Your Offer + {#if myOffer.accepted} + Accepted ✓ + {/if} +
+
+ {#if myOffer.cards.length === 0} +
+

No cards offered yet

+
+ {:else} +
+ {#each myOffer.cards as card (card.id)} +
+ +
+ {/each} +
+ {/if} +
+
+ +
+ +
+
+ {partnerUsername || 'Partner'}'s Offer + {#if theirOffer.accepted} + Accepted ✓ + {/if} +
+
+ {#if theirOffer.cards.length === 0} +
+

No cards offered yet

+
+ {:else} +
+ {#each theirOffer.cards as card (card.id)} +
+ +
+ {/each} +
+ {/if} +
+
+
+ +
+ + + {#if error} + {error} + {/if} + + +
+
+ + {:else if phase === 'complete'} +
+
⇄
+

Trade Complete!

+

Your cards have been exchanged.

+ + View Your Cards +
+ {/if} +
+ + diff --git a/frontend/src/routes/verify-email/+page.svelte b/frontend/src/routes/verify-email/+page.svelte new file mode 100644 index 0000000..bf3db62 --- /dev/null +++ b/frontend/src/routes/verify-email/+page.svelte @@ -0,0 +1,108 @@ + + +
+
+ {#if status === 'verifying'} +

Verifying...

+

Please wait while we verify your email.

+ {:else if status === 'success'} +

Email Verified

+

Your email has been confirmed. You can now open packs and trade cards.

+ + {:else} +

Verification Failed

+

{errorMessage}

+

Your link may have expired. You can request a new one from your profile.

+ + {/if} +
+
+ +