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Inference Engine — Akinator-style, context-aware, guaranteed-to-terminate AI.
"""
import logging
from typing import List, Dict, Optional
import uuid
from .question_selector import QuestionSelector
from .probability_manager import ProbabilityManager
from .confidence_calculator import ConfidenceCalculator
from algorithms.information_gain import InformationGain
from algorithms.bayesian_network import BayesianNetwork
from models.game_state import GameState
from models.item_model import Item
from config import GAME_CONFIG
from services.firebase_service import FirebaseService
logger = logging.getLogger(__name__)
class InferenceEngine:
"""Main AI Engine — Akinator-style, guaranteed termination."""
def __init__(self):
self.question_selector = QuestionSelector()
self.probability_manager = ProbabilityManager()
self.confidence_calculator = ConfidenceCalculator()
self.information_gain = InformationGain()
self.bayesian_network = BayesianNetwork()
self.firebase_service = FirebaseService()
self.active_games: Dict[str, GameState] = {}
self.session_stats = {
'games_played': 0,
'successful_guesses': 0,
'average_questions': 0,
}
logger.info("InferenceEngine ready (v3.2 — context-aware, guaranteed termination)")
# ── Game lifecycle ────────────────────────────────────────────────────────
def start_new_game(self, category: str, items: List[Dict],
questions: List[Dict]) -> GameState:
session_id = str(uuid.uuid4())
item_objects = [Item.from_dict({**d, 'probability': 0.0}) for d in items]
init_prob = 1.0 / len(item_objects) if item_objects else 0.0
for item in item_objects:
item.probability = init_prob
game_state = GameState(
session_id=session_id,
category=category,
items=item_objects,
questions=questions,
)
self.active_games[session_id] = game_state
self.bayesian_network.build_network(item_objects, questions)
self.question_selector.calculate_feature_importance(item_objects, questions)
self.firebase_service.save_game_state(game_state)
logger.info(f"Game started: {session_id} | {len(item_objects)} items | "
f"{len(questions)} questions")
return game_state
def get_game_state(self, session_id: str) -> Optional[GameState]:
if session_id in self.active_games:
return self.active_games[session_id]
data = self.firebase_service.load_game_state(session_id)
if data:
try:
gs = GameState.from_dict(data)
self.active_games[session_id] = gs
self.bayesian_network.build_network(gs.items, gs.questions)
self.question_selector.calculate_feature_importance(gs.items, gs.questions)
return gs
except Exception as e:
logger.error(f"Failed to rebuild GameState {session_id}: {e}")
return None
# ── Question flow ─────────────────────────────────────────────────────────
def get_next_question(self, game_state: GameState) -> Optional[Dict]:
if self._should_stop_asking(game_state):
logger.info(f"[{game_state.session_id}] Stop condition met before question select.")
return None
active_items = game_state.get_active_items()
available_questions = game_state.get_available_questions()
if not active_items or not available_questions:
logger.info(f"[{game_state.session_id}] No items or questions left → guess.")
return None
question = self.question_selector.select_best_question(
available_questions=available_questions,
active_items=active_items,
bayesian_network=self.bayesian_network,
game_state_history=game_state.answer_history,
)
if question is None:
logger.info(
f"[{game_state.session_id}] Selector found no useful question "
f"({len(active_items)} items remain) → triggering guess."
)
return None
game_state.mark_question_asked(question)
self.firebase_service.save_game_state(game_state)
logger.info(
f"[{game_state.session_id}] Q{game_state.questions_asked}: "
f"{question['question']}"
)
return question
# ── Answer processing ─────────────────────────────────────────────────────
def process_answer(self, game_state: GameState, answer: str) -> Dict:
if not game_state.current_question:
raise ValueError("No active question to answer.")
question = game_state.current_question
game_state.record_answer(answer)
active_items = game_state.get_active_items()
for item in active_items:
item.probability = self.probability_manager.update_item_probability(
item, question, answer
)
self.probability_manager.normalize_probabilities(game_state.items)
self.probability_manager.soft_filter(game_state.items)
self.bayesian_network.update_beliefs(question, answer, game_state.items)
current_active = game_state.get_active_items()
confidence = self.confidence_calculator.calculate(current_active)
top_item = game_state.get_top_prediction()
should_stop = self._should_stop_asking(game_state)
self.firebase_service.save_game_state(game_state)
logger.info(
f"[{game_state.session_id}] Answer={answer} | "
f"active={len(current_active)} | conf={confidence:.1f}% | "
f"stop={should_stop}"
)
return {
'confidence': confidence,
'active_items_count': len(current_active),
'top_prediction': top_item.to_dict() if top_item else None,
'should_stop': should_stop,
}
# ── Final prediction ──────────────────────────────────────────────────────
def get_final_prediction(self, game_state: GameState) -> Dict:
top_item = game_state.get_top_prediction()
active_items = game_state.get_active_items()
confidence = self.confidence_calculator.calculate(active_items)
if top_item:
sorted_items = sorted(active_items, key=lambda x: x.probability, reverse=True)
alternatives = [i.to_dict() for i in sorted_items[1:4]]
self.firebase_service.log_game_result(
game_state, top_item.name, confidence, False, "Final Guess"
)
self._update_session_stats(
game_state,
confidence >= GAME_CONFIG['confidence_threshold_stage_3'],
)
else:
alternatives = []
self.active_games.pop(game_state.session_id, None)
return {
'prediction': top_item.to_dict() if top_item else None,
'confidence': int(confidence),
'alternatives': alternatives,
'questions_asked': game_state.questions_asked,
'total_items': len(game_state.items),
'remaining_items': len(active_items),
}
# ── Stopping logic ────────────────────────────────────────────────────────
def _should_stop_asking(self, game_state: GameState) -> bool:
active_items = game_state.get_active_items()
active_count = len(active_items)
force_at = GAME_CONFIG.get('force_guess_at_items', 2)
if active_count <= force_at:
logger.info(
f"[{game_state.session_id}] Force-guess: "
f"{active_count} item(s) remain."
)
return True
if not game_state.get_available_questions():
logger.info(f"[{game_state.session_id}] No available questions left.")
return True
confidence = self.confidence_calculator.calculate(active_items)
return self.confidence_calculator.should_make_guess(
confidence,
game_state.questions_asked,
active_items_count=active_count,
)
# ── Session statistics ────────────────────────────────────────────────────
def _update_session_stats(self, game_state: GameState, success: bool):
self.session_stats['games_played'] += 1
if success:
self.session_stats['successful_guesses'] += 1
games = self.session_stats['games_played']
prev_avg = self.session_stats['average_questions']
new_avg = ((prev_avg * (games - 1)) + game_state.questions_asked) / games
self.session_stats['average_questions'] = new_avg
def get_session_stats(self) -> Dict:
games = self.session_stats['games_played']
success = self.session_stats['successful_guesses']
return {
'games_played': games,
'successful_guesses': success,
'success_rate': (success / games * 100) if games > 0 else 0,
'average_questions': self.session_stats['average_questions'],
}
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