Add services module: implemented ProfileService, GameService, and AnalysisService for user data, game management, and strategy analysis. Enhanced with schema-aware response handling and LLM-friendly output formatting.
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Yûki VACHOT
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5 changed files with 658 additions and 106 deletions
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"""Analysis and statistics calculations."""
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"""
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Analysis service for game statistics and strategy optimization.
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from typing import List
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This service provides comprehensive analysis capabilities with
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dynamic data handling and LLM-friendly output formatting.
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"""
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from ..models.game import Game
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import logging
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from dataclasses import dataclass, field
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from typing import Optional
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from ..api.client import GeoGuessrClient
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from ..models.Game import Game
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from ..monitoring.schema_manager import schema_registry
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from .game_service import GameService
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from .profile_service import ProfileService
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logger = logging.getLogger(__name__)
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@dataclass
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class GameAnalysis:
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"""Analysis results for a set of games."""
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games_analyzed: int = 0
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total_score: int = 0
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average_score: float = 0.0
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total_rounds: int = 0
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perfect_rounds: int = 0
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perfect_round_percentage: float = 0.0
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average_distance_meters: float = 0.0
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average_time_seconds: float = 0.0
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best_game_score: int = 0
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worst_game_score: int = 0
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score_trend: str = "stable" # improving, declining, stable
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weak_areas: list = field(default_factory=list)
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strong_areas: list = field(default_factory=list)
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def to_dict(self) -> dict:
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"""Convert to dictionary."""
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return {
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"games_analyzed": self.games_analyzed,
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"total_score": self.total_score,
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"average_score": round(self.average_score, 2),
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"total_rounds": self.total_rounds,
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"perfect_rounds": self.perfect_rounds,
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"perfect_round_percentage": round(self.perfect_round_percentage, 2),
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"average_distance_meters": round(self.average_distance_meters, 2),
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"average_time_seconds": round(self.average_time_seconds, 2),
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"best_game_score": self.best_game_score,
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"worst_game_score": self.worst_game_score,
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"score_trend": self.score_trend,
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"weak_areas": self.weak_areas,
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"strong_areas": self.strong_areas,
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}
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class AnalysisService:
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"""Service for analyzing game data."""
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"""Service for game analysis and strategy optimization."""
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def __init__(
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self,
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client: GeoGuessrClient,
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game_service: Optional[GameService] = None,
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profile_service: Optional[ProfileService] = None,
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):
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self.client = client
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self.game_service = game_service or GameService(client)
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self.profile_service = profile_service or ProfileService(client)
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@staticmethod
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def calculate_statistics(games: List[Game]) -> dict:
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"""Calculate aggregate statistics from games."""
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def analyze_games(games: list[Game]) -> GameAnalysis:
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"""
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Analyze a list of games and calculate statistics.
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Args:
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games: List of Game objects to analyze
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Returns:
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GameAnalysis with computed statistics
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"""
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if not games:
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return {"games_analyzed": 0, "total_score": 0, "average_score": 0, "perfect_rounds": 0}
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return GameAnalysis()
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total_score = sum(g.total_score for g in games)
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total_rounds = sum(len(g.rounds) for g in games)
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perfect_rounds = sum(1 for g in games for r in g.rounds if r.score == 5000)
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all_rounds = [r for g in games for r in g.rounds]
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total_rounds = len(all_rounds)
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perfect_rounds = sum(1 for r in all_rounds if r.score == 5000)
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return {
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"games_analyzed": len(games),
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"total_score": total_score,
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"average_score": total_score / len(games),
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"total_rounds": total_rounds,
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"perfect_rounds": perfect_rounds,
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"perfect_round_percentage": (
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# Calculate averages
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avg_distance = (
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sum(r.distance_meters for r in all_rounds) / total_rounds
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if total_rounds > 0
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else 0
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)
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avg_time = (
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sum(r.time_seconds for r in all_rounds) / total_rounds
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if total_rounds > 0
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else 0
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)
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# Find best and worst
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scores = [g.total_score for g in games]
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best_score = max(scores) if scores else 0
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worst_score = min(scores) if scores else 0
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# Determine trend (simple moving average comparison)
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trend = "stable"
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if len(games) >= 4:
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first_half = sum(g.total_score for g in games[: len(games) // 2]) / (
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len(games) // 2
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)
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second_half = sum(g.total_score for g in games[len(games) // 2 :]) / (
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len(games) - len(games) // 2
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)
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if second_half > first_half * 1.05:
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trend = "improving"
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elif second_half < first_half * 0.95:
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trend = "declining"
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# Identify weak/strong areas based on scores
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weak_areas = []
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strong_areas = []
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for game in games:
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for round_guess in game.rounds:
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if round_guess.score < 2000:
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weak_areas.append({
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"game": game.token,
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"round": round_guess.round_number,
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"score": round_guess.score,
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"distance": round_guess.distance_meters,
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})
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elif round_guess.score >= 4500:
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strong_areas.append({
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"game": game.token,
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"round": round_guess.round_number,
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"score": round_guess.score,
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})
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return GameAnalysis(
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games_analyzed=len(games),
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total_score=total_score,
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average_score=total_score / len(games),
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total_rounds=total_rounds,
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perfect_rounds=perfect_rounds,
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perfect_round_percentage=(
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(perfect_rounds / total_rounds * 100) if total_rounds > 0 else 0
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),
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average_distance_meters=avg_distance,
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average_time_seconds=avg_time,
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best_game_score=best_score,
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worst_game_score=worst_score,
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score_trend=trend,
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weak_areas=weak_areas[:10], # Limit to 10
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strong_areas=strong_areas[:10],
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)
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async def analyze_recent_games(
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self,
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count: int = 10,
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session_token: Optional[str] = None,
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) -> dict:
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"""
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Analyze recent games and provide statistics summary.
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Args:
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count: Number of recent games to analyze
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session_token: Optional session token
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Returns:
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Dictionary with analysis results and raw game data
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"""
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games = await self.game_service.get_recent_games(count, session_token)
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analysis = self.analyze_games(games)
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return {
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"analysis": analysis.to_dict(),
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"games": [g.to_dict() for g in games],
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"schema_info": {
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"endpoints_used": ["/v4/feed/private", "/v3/games/{token}"],
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"available_schemas": schema_registry.get_available_endpoints(),
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},
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}
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async def get_performance_summary(
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self,
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session_token: Optional[str] = None,
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) -> dict:
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"""
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Get a comprehensive performance summary.
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Combines profile stats, achievements, season info, and recent game analysis.
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"""
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results = {
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"profile": None,
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"stats": None,
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"season": None,
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"recent_games_analysis": None,
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"explorer": None,
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"objectives": None,
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"api_status": schema_registry.get_schema_summary(),
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"errors": [],
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}
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# Get comprehensive profile
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try:
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results["profile"] = await self.profile_service.get_comprehensive_profile(
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session_token
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)
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except Exception as e:
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results["errors"].append(f"Profile: {str(e)}")
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# Get season stats
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try:
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stats, response = await self.game_service.get_season_stats(session_token)
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results["season"] = {
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"data": {
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"rank": stats.rank,
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"rating": stats.rating,
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"games_played": stats.games_played,
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"division": stats.division,
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},
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"raw_fields": response.available_fields,
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}
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except Exception as e:
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results["errors"].append(f"Season: {str(e)}")
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# Analyze recent games
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try:
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results["recent_games_analysis"] = await self.analyze_recent_games(
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5, session_token
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)
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except Exception as e:
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results["errors"].append(f"Recent games: {str(e)}")
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# Get explorer progress
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try:
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response = await self.client.get(
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self._create_endpoint("/v3/explorer"), session_token
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)
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if response.is_success:
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results["explorer"] = response.summarize()
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except Exception as e:
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results["errors"].append(f"Explorer: {str(e)}")
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# Get objectives
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try:
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response = await self.client.get(
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self._create_endpoint("/v4/objectives"), session_token
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)
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if response.is_success:
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results["objectives"] = response.summarize()
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except Exception as e:
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results["errors"].append(f"Objectives: {str(e)}")
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return results
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async def get_strategy_recommendations(
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self,
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session_token: Optional[str] = None,
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) -> dict:
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"""
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Generate strategy recommendations based on performance analysis.
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This method analyzes the user's gameplay patterns and provides
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actionable recommendations for improvement.
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"""
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# Get recent games for analysis
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games = await self.game_service.get_recent_games(20, session_token)
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analysis = self.analyze_games(games)
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recommendations = []
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# Analyze perfect round rate
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if analysis.perfect_round_percentage < 20:
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recommendations.append({
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"category": "accuracy",
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"priority": "high",
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"recommendation": "Focus on improving pinpoint accuracy",
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"detail": f"Your perfect round rate is {analysis.perfect_round_percentage:.1f}%. "
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"Practice with familiar maps to build confidence.",
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})
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# Analyze time usage
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if analysis.average_time_seconds < 30:
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recommendations.append({
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"category": "time_management",
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"priority": "medium",
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"recommendation": "Consider taking more time per round",
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"detail": f"Average time: {analysis.average_time_seconds:.0f}s. "
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"Taking a bit more time can improve accuracy.",
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})
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# Analyze score trend
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if analysis.score_trend == "declining":
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recommendations.append({
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"category": "consistency",
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"priority": "high",
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"recommendation": "Your scores are trending downward",
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"detail": "Consider taking breaks and reviewing your weak areas.",
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})
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# Check for weak areas pattern
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if len(analysis.weak_areas) > 5:
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recommendations.append({
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"category": "practice",
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"priority": "medium",
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"recommendation": "Practice specific regions",
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"detail": f"You had {len(analysis.weak_areas)} rounds under 2000 points. "
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"Consider using region-specific practice maps.",
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})
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return {
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"analysis_summary": {
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"games_analyzed": analysis.games_analyzed,
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"average_score": round(analysis.average_score, 0),
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"trend": analysis.score_trend,
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"perfect_rate": f"{analysis.perfect_round_percentage:.1f}%",
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},
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"recommendations": recommendations,
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"data_sources": {
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"endpoints_used": schema_registry.get_available_endpoints(),
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"last_updated": schema_registry.get_schema_summary()
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.get("endpoints", {})
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.get("/v4/feed/private", {})
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.get("last_updated"),
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},
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}
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@staticmethod
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def _create_endpoint(path: str):
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"""Create simple endpoint info for raw requests."""
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from ..api.endpoints import EndpointInfo
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return EndpointInfo(path=path, description=f"Request to {path}")
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