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# handler.py
#
# Custom handler for HF Inference Endpoints.
#
# When HF Inference Endpoints detects a handler.py
# in the repo root, it uses this class to serve
# predictions instead of the default Transformers
# pipeline.
#
# Expected repo layout:
#
# your-username/chess-model/
# ├── handler.py ← this file
# ├── config_model.json ← lists your Python modules + main
# ├── config_data.json ← lists data files + zone_db key
# ├── chess_mcvs.py ← your UCT search / eval module
# └── requirements.txt ← runtime deps (numpy, huggingface_hub)
#
# config_model.json example:
#
# {
# "files": ["matrix_model.py", "abc_model.py", "chess_mcvs.py"],
# "main": "chess_mcvs"
# }
#
# config_data.json example:
#
# {
# "files": ["chess_zone_db.npz"],
# "zone_db": "chess_zone_db.npz"
# }
#
# Set the HF_DATA_REPO_ID environment variable to
# the HF dataset repo holding your data files.
#
# Request format (sent by Agladiator):
#
# POST /
# {
# "inputs": {
# "fen": "BBBBBBBB/BBBBBBBB/8/8/8/8/WWWWWWWW/WWWWWWWW w",
# "player": "w",
# "game_type": "chess"
# }
# }
#
# Response format (what you must return):
#
# {"move": "a2a3"}
#
# ──────────────────────────────────────────────
from __future__ import annotations
import importlib.util
import json
import logging
import os
import random
import sys
from pathlib import Path
import numpy as np
log = logging.getLogger(__name__)
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
# Chess board constants (used by the minimal legal-move generator)
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
BOARD_SIZE = 8
_FILES = "abcdefgh"
_RANKS = "12345678"
WHITE, BLACK = "w", "b"
EMPTY = "."
class EndpointHandler:
"""Custom handler for chess AI on HF Inference Endpoints.
Lifecycle:
1. ``__init__`` is called once when the endpoint container starts.
Use it to load your model artifacts (modules, zone DB, etc.).
2. ``__call__`` is called for every inference request.
It receives the parsed JSON body and must return a dict.
"""
def __init__(self, path: str):
"""Load model artefacts from *path* (the cloned repo directory).
Steps:
1. Read ``config_model.json`` to discover module files and main module.
2. Dynamically import each listed module.
3. Read ``config_data.json``, download data files, load zone DB.
4. Initialise the search engine.
"""
self.path = Path(path)
self.predict_fn = None
self.zone_db = None
self.module = None
# ── 1. Read config_model.json ───────────
model_config_path = self.path / "config_model.json"
if not model_config_path.exists():
log.warning("No config_model.json found in %s", path)
model_config: dict = {}
else:
with open(model_config_path) as fh:
model_config = json.load(fh)
main_module_name = model_config.get("main", "")
# ── 2. Import listed modules ────────────
# Add repo dir to sys.path so relative imports work.
if str(self.path) not in sys.path:
sys.path.insert(0, str(self.path))
for mod_file in model_config.get("files", []):
mod_name = mod_file.removesuffix(".py")
mod_path = self.path / mod_file
if not mod_path.exists():
log.warning("Module file %s not found — skipping.", mod_file)
continue
spec = importlib.util.spec_from_file_location(mod_name, str(mod_path))
mod = importlib.util.module_from_spec(spec)
sys.modules[mod_name] = mod
spec.loader.exec_module(mod)
log.info("Loaded module: %s", mod_name)
# Mark the module matching "main" as the primary module.
if mod_name == main_module_name:
self.module = mod
# ── 3. Read config_data.json & load zone DB ──
data_config_path = self.path / "config_data.json"
if not data_config_path.exists():
log.warning("No config_data.json found in %s", path)
data_config: dict = {}
else:
with open(data_config_path) as fh:
data_config = json.load(fh)
data_repo = os.environ.get("HF_DATA_REPO_ID", "")
downloaded_files: dict[str, str] = {}
if data_repo:
try:
from huggingface_hub import hf_hub_download
except Exception:
hf_hub_download = None
log.warning(
"huggingface_hub not installed — cannot download data files. Add it to requirements.txt"
)
if hf_hub_download is not None:
for data_file in data_config.get("files", []):
try:
local_path = hf_hub_download(
repo_id=data_repo,
filename=data_file,
repo_type="dataset",
)
downloaded_files[data_file] = local_path
log.info("Downloaded %s from %s", data_file, data_repo)
except Exception:
log.warning(
"Failed to download %s from %s — skipping.",
data_file,
data_repo,
)
else:
log.warning(
"HF_DATA_REPO_ID not set — skipping data file downloads."
)
zone_db_filename = data_config.get("zone_db", "")
if zone_db_filename and zone_db_filename in downloaded_files:
self.zone_db = np.load(
downloaded_files[zone_db_filename], allow_pickle=True
)
log.info("Zone DB loaded from data repo %s", data_repo)
elif zone_db_filename:
local_zone = self.path / zone_db_filename
if local_zone.exists():
self.zone_db = np.load(str(local_zone), allow_pickle=True)
log.info("Zone DB loaded from local file %s", local_zone)
else:
log.warning("No zone DB found for '%s'.", zone_db_filename)
# ── 4. Initialise the predictor ─────────
self._init_predictor()
log.info("EndpointHandler initialised successfully.")
# ──────────────────────────────────────────
# Predictor initialisation
# ──────────────────────────────────────────
def _init_predictor(self):
"""Discover and configure the prediction function from the
loaded module. Tries several common patterns:
1. ``module.get_move(fen, player, zone_db=...)``
2. ``module.UCTSearcher(zone_db=...).search(fen, player)``
3. ``module.predict(fen, player)``
"""
if self.module is None:
log.warning("No module loaded — predictions will use random fallback.")
return
# Pattern 1: module exposes a top-level get_move()
if hasattr(self.module, "get_move"):
raw_fn = self.module.get_move
self.predict_fn = lambda fen, player: raw_fn(
fen, player, zone_db=self.zone_db
)
log.info("Using module.get_move() for predictions.")
return
# Pattern 2: module exposes a UCTSearcher class
if hasattr(self.module, "UCTSearcher"):
searcher_cls = self.module.UCTSearcher
kwargs = {}
if self.zone_db is not None:
kwargs["zone_db"] = self.zone_db
searcher = searcher_cls(**kwargs)
self.predict_fn = lambda fen, player: searcher.search(fen, player)
log.info("Using module.UCTSearcher for predictions.")
return
# Pattern 3: module exposes a predict()
if hasattr(self.module, "predict"):
raw_fn = self.module.predict
self.predict_fn = lambda fen, player: raw_fn(fen, player)
log.info("Using module.predict() for predictions.")
return
log.warning(
"Module %s has no get_move / UCTSearcher / predict — "
"predictions will use random fallback.",
self.module.__name__,
)
# ──────────────────────────────────────────
# Inference entry point
# ──────────────────────────────────────────
def __call__(self, data: dict) -> dict:
"""Handle an inference request.
Expected *data*::
{"inputs": {"fen": "...", "player": "w"}}
Returns::
{"move": "a2a3"}
Falls back to a random legal move if the model fails.
"""
inputs = data.get("inputs", data)
fen = inputs.get("fen", "")
player = inputs.get("player", WHITE)
if not fen:
return {"error": "Missing 'fen' in request."}
# Try model prediction
if self.predict_fn is not None:
try:
move = self.predict_fn(fen, player)
if move and isinstance(move, str):
return {"move": move}
except Exception:
log.exception("Model prediction failed for FEN=%s", fen)
# Fallback: random legal move
moves = self._legal_moves(fen)
if not moves:
return {"error": "No legal moves available.", "move": "0000"}
# Prefer captures (diagonal moves in chess)
captures = [m for m in moves if m[0] != m[2]]
move = random.choice(captures) if captures else random.choice(moves)
return {"move": move}
# ──────────────────────────────────────────
# Minimal legal-move generator (self-contained)
# ──────────────────────────────────────────
@staticmethod
def _legal_moves(fen: str) -> list[str]:
"""Return a list of pseudo-legal UCI moves for a chess FEN.
This minimal generator supports pawn moves (including promotions
and en-passant), knights, bishops, rooks, queens, kings and
basic castling availability. It does not perform check detection
and therefore may return some pseudo-legal moves in checked positions.
"""
parts = fen.strip().split()
board_part = parts[0] if parts else ""
turn = parts[1] if len(parts) > 1 else WHITE
castling = parts[2] if len(parts) > 2 else "-"
ep_square = parts[3] if len(parts) > 3 else "-"
# Parse FEN → grid (8 ranks, top (rank 8) → bottom (rank 1))
grid: list[list[str]] = []
for rank_str in board_part.split("/"):
row: list[str] = []
for ch in rank_str:
if ch.isdigit():
row.extend([EMPTY] * int(ch))
else:
row.append(ch)
# normalize row length
if len(row) != BOARD_SIZE:
row = (row + [EMPTY] * BOARD_SIZE)[:BOARD_SIZE]
grid.append(row)
while len(grid) < BOARD_SIZE:
grid.append([EMPTY] * BOARD_SIZE)
side_white = turn == WHITE
def is_enemy(ch: str) -> bool:
return ch != EMPTY and ch.isupper() != side_white
def is_own(ch: str) -> bool:
return ch != EMPTY and ch.isupper() == side_white
moves: list[str] = []
# En-passant target square (if any)
ep_r = ep_c = None
if ep_square and ep_square != "-":
try:
ep_c = _FILES.index(ep_square[0])
ep_r = BOARD_SIZE - int(ep_square[1])
except Exception:
ep_r = ep_c = None
start_row = 6 if side_white else 1
last_row = 0 if side_white else 7
direction = -1 if side_white else 1
for r in range(BOARD_SIZE):
for c in range(BOARD_SIZE):
ch = grid[r][c]
if ch == EMPTY:
continue
if ch.isupper() != side_white:
continue
piece = ch.lower()
from_sq = f"{_FILES[c]}{_RANKS[BOARD_SIZE - 1 - r]}"
if piece == "p":
nr = r + direction
# forward one
if 0 <= nr < BOARD_SIZE and grid[nr][c] == EMPTY:
# promotion
if nr == last_row:
for promo in ("q", "r", "b", "n"):
moves.append(
f"{from_sq}{_FILES[c]}{_RANKS[BOARD_SIZE - 1 - nr]}{promo}"
)
else:
moves.append(
f"{from_sq}{_FILES[c]}{_RANKS[BOARD_SIZE - 1 - nr]}"
)
# double move from start
if r == start_row:
nr2 = r + 2 * direction
if 0 <= nr2 < BOARD_SIZE and grid[nr2][c] == EMPTY:
moves.append(
f"{from_sq}{_FILES[c]}{_RANKS[BOARD_SIZE - 1 - nr2]}"
)
# captures (including en-passant)
for dc in (-1, 1):
nc = c + dc
if nc < 0 or nc >= BOARD_SIZE:
continue
if 0 <= nr < BOARD_SIZE:
target = grid[nr][nc]
if is_enemy(target):
if nr == last_row:
for promo in ("q", "r", "b", "n"):
moves.append(
f"{from_sq}{_FILES[nc]}{_RANKS[BOARD_SIZE - 1 - nr]}{promo}"
)
else:
moves.append(
f"{from_sq}{_FILES[nc]}{_RANKS[BOARD_SIZE - 1 - nr]}"
)
# en-passant capture
if ep_r is not None and ep_r == nr and ep_c == nc:
cap_r = r
cap_c = nc
if (
0 <= cap_r < BOARD_SIZE
and grid[cap_r][cap_c].lower() == "p"
and is_enemy(grid[cap_r][cap_c])
):
moves.append(
f"{from_sq}{_FILES[nc]}{_RANKS[BOARD_SIZE - 1 - nr]}"
)
elif piece == "n":
for dr, dc in (
(-2, -1),
(-2, 1),
(-1, -2),
(-1, 2),
(1, -2),
(1, 2),
(2, -1),
(2, 1),
):
nr = r + dr
nc = c + dc
if 0 <= nr < BOARD_SIZE and 0 <= nc < BOARD_SIZE:
target = grid[nr][nc]
if not is_own(target):
moves.append(
f"{from_sq}{_FILES[nc]}{_RANKS[BOARD_SIZE - 1 - nr]}"
)
elif piece in ("b", "r", "q"):
directions = []
if piece in ("b", "q"):
directions += [(-1, -1), (-1, 1), (1, -1), (1, 1)]
if piece in ("r", "q"):
directions += [(-1, 0), (1, 0), (0, -1), (0, 1)]
for dr, dc in directions:
nr = r + dr
nc = c + dc
while 0 <= nr < BOARD_SIZE and 0 <= nc < BOARD_SIZE:
target = grid[nr][nc]
if target == EMPTY:
moves.append(
f"{from_sq}{_FILES[nc]}{_RANKS[BOARD_SIZE - 1 - nr]}"
)
else:
if is_enemy(target):
moves.append(
f"{from_sq}{_FILES[nc]}{_RANKS[BOARD_SIZE - 1 - nr]}"
)
break
nr += dr
nc += dc
elif piece == "k":
for dr in (-1, 0, 1):
for dc in (-1, 0, 1):
if dr == 0 and dc == 0:
continue
nr = r + dr
nc = c + dc
if 0 <= nr < BOARD_SIZE and 0 <= nc < BOARD_SIZE:
target = grid[nr][nc]
if not is_own(target):
moves.append(
f"{from_sq}{_FILES[nc]}{_RANKS[BOARD_SIZE - 1 - nr]}"
)
# Basic castling checks (does not verify check conditions)
if side_white:
if "K" in castling and r == 7 and c == 4:
if grid[7][5] == EMPTY and grid[7][6] == EMPTY and grid[7][7] != EMPTY and grid[7][7].isupper():
moves.append(f"{from_sq}g1")
if "Q" in castling and r == 7 and c == 4:
if (
grid[7][3] == EMPTY
and grid[7][2] == EMPTY
and grid[7][1] == EMPTY
and grid[7][0] != EMPTY
and grid[7][0].isupper()
):
moves.append(f"{from_sq}c1")
else:
if "k" in castling and r == 0 and c == 4:
if grid[0][5] == EMPTY and grid[0][6] == EMPTY and grid[0][7] != EMPTY and grid[0][7].islower():
moves.append(f"{from_sq}g8")
if "q" in castling and r == 0 and c == 4:
if (
grid[0][3] == EMPTY
and grid[0][2] == EMPTY
and grid[0][1] == EMPTY
and grid[0][0] != EMPTY
and grid[0][0].islower()
):
moves.append(f"{from_sq}c8")
return moves
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