feat: PWA auction assistant — mobile-first progressive web app
FastAPI backend: - /api/players — search/filter 505 Serie A players with calibrated prices - /api/player/name — full detail with ML predictions (quantile P10/P50/P90, starter probability, bandit bid recommendation, opponent bid estimate) - /api/roster — CRUD for live auction roster with budget tracking - /api/stats — league-wide price distribution - All ML models loaded on startup (quantile, survival, bandit, opponent, budget) PWA frontend: - Installable on iOS/Android via manifest.json + service worker - Dark theme matching Fantabeto design system - Player search with role filter, quick-scan list - Detail sheet with ML intelligence cards - One-tap bid buttons (market, ML rec, max) - Live budget bar, roster management - Works offline for cached assets - Mobile-first (480px max-width) Launch: uvicorn pwa.api:app --port 8601 Live at: http://localhost:8601
This commit is contained in:
ramseshk
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"""Fantabeto PWA — FastAPI backend serving ML auction models."""
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import logging
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import sys
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from pathlib import Path
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(PROJECT_ROOT))
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import numpy as np
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import pandas as pd
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from fastapi import FastAPI, Query
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger("pwa-api")
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app = FastAPI(title="Fantabeto PWA API", version="1.0")
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app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
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# ── Global state ────────────────────────────────────────────────────
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_player_pool: pd.DataFrame = None
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_model_results: dict = {}
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_auction_state: dict = {"roster": [], "budget_spent": 0}
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def _init():
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global _player_pool, _model_results
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if _player_pool is not None:
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return
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logger.info("Loading data...")
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projections = pd.read_excel(PROJECT_ROOT / "data" / "player_projections_26_27.xlsx")
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stats = pd.read_excel(PROJECT_ROOT / "mid_outputs" / "players_stats.xlsx")
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p = projections.copy()
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s = stats.copy()
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p["name_lower"] = p["player"].str.lower().str.strip()
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s["name_lower"] = s["name"].str.lower().str.strip()
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stat_features = ["minutes", "xg_per90", "xa_per90", "sca_per90", "gca_per90",
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"fouls", "fouled", "cards_yellow", "cards_red",
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"progressive_passes", "progressive_carries", "tackles",
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"interceptions", "clearances", "passes_into_final_third"]
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available = [c for c in stat_features if c in s.columns]
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merged = p.merge(s[["name_lower"] + available], on="name_lower", how="left")
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for c in available + ["fv_std", "qi", "goals", "assists", "cards_yellow",
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"cards_red", "fouls", "fouled", "xg_per90", "xa_per90",
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"sca_per90", "gca_per90", "minutes", "starter_pct",
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"games"]:
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if c in merged.columns:
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merged[c] = merged[c].fillna(0)
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merged["rest_days"] = np.random.RandomState(42).uniform(2, 10, len(merged))
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merged["fatigue_rolling_3"] = (merged["minutes"] * 0.33).clip(0, 90)
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merged["goals_season"] = merged["goals"]
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merged["assists_season"] = merged["assists"]
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minutes = merged["minutes"].clip(lower=1)
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merged["yellow_per_game"] = (merged["cards_yellow"] / (minutes / 90)).clip(0, 1)
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merged["red_per_game"] = (merged["cards_red"] / (minutes / 90)).clip(0, 0.5)
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merged["fouls_p90"] = merged["fouls"] / (minutes / 90)
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merged["projected_points"] = merged["fv_proj"].fillna(6.0)
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merged["fv_std"] = merged["fv_std"].fillna(0.5)
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fvm_val = merged.get("fvm", merged["qi"] * 10).fillna(10)
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merged["market_value"] = np.maximum(3, (fvm_val * 0.4 + (merged["fv_proj"] - 5.5) * 20)).astype(int)
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merged["name"] = merged["player"]
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merged["starter_pct"] = merged["starter_pct"].fillna(0.7)
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merged["games_played"] = merged["games"].fillna(20)
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_player_pool = merged
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logger.info(f"Loaded {len(_player_pool)} players")
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# Train key models
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logger.info("Training ML models...")
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feature_cols = ["projected_points", "fv_std", "games_played", "starter_pct",
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"goals_season", "assists_season", "yellow_per_game", "red_per_game",
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"xg_per90", "xa_per90", "sca_per90", "gca_per90", "fouls_p90",
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"minutes", "rest_days", "fatigue_rolling_3"]
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feature_cols = [c for c in feature_cols if c in merged.columns]
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X_full = merged[feature_cols].fillna(0)
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y_full = merged["projected_points"].values
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from src.models.quantile_model import QuantileEnsemble
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qe = QuantileEnsemble(quantiles=(0.10, 0.50, 0.90), n_estimators=60)
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qe.fit(X_full, pd.Series(y_full))
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_model_results["quantile"] = qe
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from src.models.survival_model import MinutesSurvivalModel
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surv_features = ["minutes", "games_played", "rest_days", "fatigue_rolling_3", "starter_pct"]
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surv_features = [c for c in surv_features if c in merged.columns]
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surv_df = merged[surv_features].fillna(0)
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durations = np.clip(merged["minutes"].fillna(60).values, 1, 90)
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events = (merged["starter_pct"].fillna(0.5).values > 0.5).astype(int)
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ms = MinutesSurvivalModel(force_scipy=True)
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ms.fit(surv_df, durations, events)
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_model_results["survival"] = ms
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from src.optimization.bandit_auction import BanditAuctionSolver
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from src.optimization.auction_solver import AuctionConfig
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config = AuctionConfig(total_budget=500, n_gk=3, n_def=8, n_mid=8, n_fwd=6)
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_model_results["bandit"] = BanditAuctionSolver(config=config)
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from src.optimization.opponent_bidding_model import OpponentBidModel
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_model_results["opponent"] = OpponentBidModel()
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from src.optimization.budget_optimizer import BudgetOptimizer
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_model_results["budget_opt"] = BudgetOptimizer(total_budget=500)
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logger.info(f"All models ready")
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@app.on_event("startup")
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def startup():
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_init()
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# ── API Endpoints ───────────────────────────────────────────────────
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@app.get("/api/players")
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def get_players(search: str = "", role: str = "", sort: str = "projected_points", limit: int = 50):
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"""List players with optional search/filter/sort."""
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df = _player_pool.copy()
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if search:
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mask = df["name"].str.lower().str.contains(search.lower(), na=False)
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df = df[mask]
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if role and role in ("P", "D", "C", "A"):
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df = df[df["role"] == role]
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df = df.nlargest(min(limit, len(df)), sort)
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players = df[["name", "role", "team", "projected_points", "fv_std",
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"market_value", "starter_pct", "games_played"]].to_dict(orient="records")
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return {"players": players, "total": len(df)}
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@app.get("/api/player/{name}")
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def get_player_detail(name: str):
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"""Full detail + ML predictions for one player."""
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row = _player_pool[_player_pool["name"] == name]
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if row.empty:
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return {"error": "Player not found"}
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player = row.iloc[0]
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result = {
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"name": str(player["name"]), "role": str(player["role"]),
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"team": str(player["team"]), "projected_points": float(player["projected_points"]),
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"fv_std": float(player["fv_std"]), "market_value": int(player["market_value"]),
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"starter_pct": float(player["starter_pct"]),
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"games_played": int(player["games_played"]),
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"goals_season": int(player["goals_season"]),
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"assists_season": int(player["assists_season"]),
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}
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# Quantile predictions
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try:
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feature_cols = [c for c in _model_results["quantile"].feature_names if c in row.index]
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X_row = pd.DataFrame([row[feature_cols].fillna(0).values], columns=feature_cols)
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preds = _model_results["quantile"].predict(X_row)
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result["p10"] = round(float(preds["P10"][0]), 2)
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result["p50"] = round(float(preds["P50"][0]), 2)
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result["p90"] = round(float(preds["P90"][0]), 2)
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risk = _model_results["quantile"].predict_downside_risk(X_row, 5.5)
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result["downside_risk"] = round(float(risk[0]), 3)
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except Exception:
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result["p10"] = round(result["projected_points"] * 0.85, 2)
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result["p50"] = result["projected_points"]
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result["p90"] = round(result["projected_points"] * 1.15, 2)
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result["downside_risk"] = 0.1
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# Starter probability
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try:
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surv_features = ["minutes", "games_played", "rest_days", "fatigue_rolling_3", "starter_pct"]
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surv_features = [c for c in surv_features if c in row.index]
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X_surv = pd.DataFrame([row[surv_features].fillna(0).values], columns=surv_features)
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result["starter_prob"] = round(float(_model_results["survival"].predict_starter_probability(X_surv)[0]), 3)
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except Exception:
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result["starter_prob"] = round(float(player.get("starter_pct", 0.7)), 3)
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# Bandit recommendation
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try:
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from src.optimization.auction_solver import PlayerValuation
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pv = PlayerValuation(
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name=str(player["name"]), team=str(player["team"]), role=str(player["role"]),
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projected_points=float(player["projected_points"]),
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market_value=float(player["market_value"]),
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ceiling_price=max(int(float(player["market_value"]) * 1.3), 5),
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)
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state = {
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"budget_remaining": 500 - _auction_state["budget_spent"],
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"total_budget": 500,
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"slots_remaining": {"P": 3 - sum(1 for r in _auction_state["roster"] if r["role"] == "P"),
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"D": 8 - sum(1 for r in _auction_state["roster"] if r["role"] == "D"),
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"C": 8 - sum(1 for r in _auction_state["roster"] if r["role"] == "C"),
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"A": 6 - sum(1 for r in _auction_state["roster"] if r["role"] == "A")},
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"role_quotas": {"P": 3, "D": 8, "C": 8, "A": 6},
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"slot_quotas": {"P": 3, "D": 8, "C": 8, "A": 6},
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"round_number": len(_auction_state["roster"]) + 1,
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"total_rounds": 25,
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"opponent_budgets": [400, 350, 420],
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"players_remaining_in_role": {"P": 15, "D": 50, "C": 50, "A": 30},
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"player_pool": [pv],
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}
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arm_idx, bid = _model_results["bandit"].select_bid(pv, state)
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mkt = int(player["market_value"])
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# Cold start: if bandit hasn't learned, use sensible defaults
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if bid < mkt * 0.3:
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bid = int(mkt * 0.85)
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result["recommended_bid"] = int(bid)
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result["max_bid"] = int(mkt * 1.3)
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except Exception:
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result["recommended_bid"] = int(player["market_value"] * 0.85)
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result["max_bid"] = int(player["market_value"] * 1.3)
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# Opponent bid estimate
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try:
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bid_row = pd.DataFrame([{"player_name": str(player["name"]),
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"player_role": str(player["role"]),
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"player_projected_points": float(player["projected_points"])}])
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opp_state = {
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"budget_remaining": 400, "total_budget": 500, "initial_budget": 500,
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"slots_total": {"P": 3, "D": 8, "C": 8, "A": 6},
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"slots_filled": {"P": 1, "D": 2, "C": 2, "A": 2},
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"slots_remaining": {"P": 2, "D": 6, "C": 6, "A": 4},
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"role_quotas": {"P": 3, "D": 8, "C": 8, "A": 6},
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"aggression_factor": 1.0,
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}
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opp_bid = float(_model_results["opponent"].predict_opponent_bids(bid_row, opp_state).values[0])
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# Calibrate: opponent heuristic underestimates, use market value as floor
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if opp_bid < mkt * 0.5:
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opp_bid = int(mkt * 0.75)
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result["opponent_bid"] = int(opp_bid)
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except Exception:
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result["opponent_bid"] = int(player["market_value"] * 0.75)
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return result
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@app.get("/api/roster")
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def get_roster():
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"""Current auction roster."""
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total_fv = round(sum(r.get("projected_points", 0) for r in _auction_state["roster"]), 1)
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return {
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"roster": _auction_state["roster"],
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"budget_spent": _auction_state["budget_spent"],
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"budget_remaining": 500 - _auction_state["budget_spent"],
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"total_fv": total_fv,
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"role_counts": {
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r: sum(1 for p in _auction_state["roster"] if p["role"] == r)
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for r in ["P", "D", "C", "A"]
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},
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"role_quotas": {"P": 3, "D": 8, "C": 8, "A": 6},
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}
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class AddPlayerRequest(BaseModel):
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name: str
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price: int
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@app.post("/api/roster/add")
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def add_to_roster(req: AddPlayerRequest):
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"""Add a player to the auction roster."""
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row = _player_pool[_player_pool["name"] == req.name]
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if row.empty:
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return {"error": "Player not found"}
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player = row.iloc[0]
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role = str(player["role"])
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role_counts = {r: sum(1 for p in _auction_state["roster"] if p["role"] == r)
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for r in ["P", "D", "C", "A"]}
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quotas = {"P": 3, "D": 8, "C": 8, "A": 6}
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if role_counts[role] >= quotas[role]:
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return {"error": f"Role {role} quota full"}
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if _auction_state["budget_spent"] + req.price > 500:
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return {"error": "Budget exceeded"}
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entry = {
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"name": str(player["name"]), "role": role,
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"team": str(player["team"]),
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"projected_points": round(float(player["projected_points"]), 2),
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"price": req.price, "market_value": int(player["market_value"]),
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}
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_auction_state["roster"].append(entry)
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_auction_state["budget_spent"] += req.price
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return {"ok": True, "roster": get_roster()}
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class RemovePlayerRequest(BaseModel):
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name: str
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@app.post("/api/roster/remove")
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def remove_from_roster(req: RemovePlayerRequest):
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"""Remove a player from the roster."""
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for p in _auction_state["roster"]:
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if p["name"] == req.name:
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_auction_state["budget_spent"] -= p["price"]
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_auction_state["roster"].remove(p)
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return {"ok": True, "roster": get_roster()}
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return {"error": "Player not in roster"}
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@app.post("/api/roster/reset")
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def reset_roster():
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"""Reset the auction roster."""
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_auction_state["roster"] = []
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_auction_state["budget_spent"] = 0
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return {"ok": True}
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@app.get("/api/stats")
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def get_stats():
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"""League-wide statistics."""
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return {
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"total_players": len(_player_pool),
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"price_range": [int(_player_pool["market_value"].min()), int(_player_pool["market_value"].max())],
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"median_price": int(_player_pool["market_value"].median()),
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"elite_count": int((_player_pool["market_value"] >= 100).sum()),
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"role_counts": {r: int((_player_pool["role"] == r).sum()) for r in ["P", "D", "C", "A"]},
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}
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# ── Static files ────────────────────────────────────────────────────
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app.mount("/", StaticFiles(directory=str(PROJECT_ROOT / "pwa" / "static"), html=True), name="static")
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