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+# ─────────────────────────────────────────────
+# Stage 1: Build React frontend
+# ─────────────────────────────────────────────
+FROM node:20-alpine AS frontend-builder
+
+WORKDIR /app/frontend
+
+# Install dependencies first (layer cache)
+COPY frontend/package.json frontend/package-lock.json* ./
+RUN npm ci
+
+# Copy source and build
+COPY frontend/ .
+RUN npm run build
+
+
+# ─────────────────────────────────────────────
+# Stage 2: Python backend (production image)
+# ─────────────────────────────────────────────
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install Python dependencies
+COPY backend/requirements.txt ./requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy backend and shared source
+COPY backend/ ./backend/
+COPY models/ ./models/
+
+# Copy compiled React app from Stage 1
+COPY --from=frontend-builder /app/frontend/dist ./frontend/dist
+
+# HF Spaces requires non-root user with uid 1000
+RUN useradd -m -u 1000 appuser
+USER appuser
+
+# HF Spaces requires port 7860
+EXPOSE 7860
+
+CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "7860"]

backend/__init__.py

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+# hace que backend/ sea un paquete Python

backend/config.py

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+"""
+backend/config.py
+─────────────────────────────────────────────
+Configuración centralizada del backend.
+Variables de entorno, paths y constantes.
+─────────────────────────────────────────────
+"""
+
+import os
+from pathlib import Path
+
+# ─────────────────────────────────────────────
+# Paths
+# ─────────────────────────────────────────────
+PROJECT_ROOT = Path(__file__).parent.parent
+MODELS_DIR = PROJECT_ROOT / "models"
+
+# Model files
+XGBOOST_MODEL_PATH = MODELS_DIR / "xgboost.pkl"
+LSTM_MODEL_PATH    = MODELS_DIR / "lstm.pt"
+LOGREG_MODEL_PATH  = MODELS_DIR / "logistic_regression.pkl"
+SCALER_PATH        = MODELS_DIR / "scaler_lr.pkl"
+
+# ─────────────────────────────────────────────
+# Riot API
+# ─────────────────────────────────────────────
+
+RIOT_API_KEY: str = os.environ.get("HF_SECRET")
+
+if not RIOT_API_KEY:
+    raise ValueError("HF_SECRET no está configurado en el entorno")
+
+# Riot API routing
+RIOT_REGION = "americas"  # Regional routing for Match-V5
+RIOT_PLATFORM = "na1"     # Platform routing
+RIOT_BASE_URL = f"https://{RIOT_REGION}.api.riotgames.com"
+RIOT_PLATFORM_URL = f"https://{RIOT_PLATFORM}.api.riotgames.com"
+
+# ─────────────────────────────────────────────
+# Model constants (must match training)
+# ─────────────────────────────────────────────
+LSTM_HIDDEN_DIM = 64
+LSTM_DROPOUT = 0.3
+LSTM_MAX_SEQ_LEN = 20
+SEED = 42
+
+# ─────────────────────────────────────────────
+# Feature columns (80 features, exact order from training)
+# Excludes: match_id, blue_win
+# ─────────────────────────────────────────────
+FEATURE_COLUMNS = [
+    "minute",
+    "blue_totalGold", "red_totalGold", "goldDiff",
+    "blue_totalXP", "red_totalXP", "xpDiff",
+    "blue_totalLevel", "red_totalLevel", "levelDiff",
+    "blue_totalMinions", "red_totalMinions", "minionsDiff",
+    "blue_totalDmgChamp", "red_totalDmgChamp", "dmgChampDiff",
+    "blue_totalDmgTaken", "red_totalDmgTaken", "dmgTakenDiff",
+    "blue_kills", "red_kills", "killsDiff",
+    "blue_assists", "red_assists", "assistsDiff",
+    "blue_wardsPlaced", "red_wardsPlaced", "wardsPlacedDiff",
+    "blue_wardsKilled", "red_wardsKilled", "wardsKilledDiff",
+    "blue_dragons", "red_dragons", "dragonsDiff",
+    "blue_barons", "red_barons", "baronsDiff",
+    "blue_towers", "red_towers", "towersDiff",
+    "blue_inhibitors", "red_inhibitors", "inhibitorsDiff",
+    "blue_heralds", "red_heralds", "heraldsDiff",
+    "blue_grubs", "red_grubs", "grubsDiff",
+    "first_blood", "first_tower", "first_dragon", "first_baron",
+    "blue_baron_active", "red_baron_active",
+    "blue_goldPerMin", "red_goldPerMin", "blue_killsPerMin",
+    "gold_ratio", "xp_ratio",
+    "blue_kda", "red_kda",
+    "goldDiff_3min_delta", "xpDiff_3min_delta", "killsDiff_3min_delta",
+    "blue_dragons_weighted", "red_dragons_weighted",
+    "blue_gold_share",
+    "blue_dmg_efficiency", "red_dmg_efficiency", "dmg_efficiency_diff",
+    "blue_obj_conversion", "red_obj_conversion", "obj_conversion_diff",
+    "blue_vision_density", "red_vision_density", "vision_density_diff",
+    "blue_teamwork_score", "red_teamwork_score", "teamwork_diff",
+]

backend/main.py

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+"""
+backend/main.py
+─────────────────────────────────────────────
+FastAPI application — Rift Breakdown Backend
+
+Carga los 3 modelos ML al startup y expone endpoints
+para predicción de win probability minuto-a-minuto.
+─────────────────────────────────────────────
+"""
+
+import logging
+import os
+from contextlib import asynccontextmanager
+from pathlib import Path
+
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+
+from backend.routers import predictions
+from backend.services.model_service import model_service
+
+# ─────────────────────────────────────────────
+# Logging
+# ─────────────────────────────────────────────
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s | %(name)s | %(levelname)s | %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+
+# ─────────────────────────────────────────────
+# Lifespan (startup / shutdown)
+# ─────────────────────────────────────────────
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Load models once on startup, release on shutdown."""
+    logger.info("🚀 Starting Rift Breakdown backend…")
+    try:
+        model_service.load_models()
+        logger.info("✅ All models loaded — server ready.")
+    except Exception as e:
+        logger.error("❌ Model loading failed: %s", e, exc_info=True)
+        # Server starts anyway but /predict will return 503
+    yield
+    logger.info("🛑 Shutting down Rift Breakdown backend.")
+
+
+# ─────────────────────────────────────────────
+# App
+# ─────────────────────────────────────────────
+app = FastAPI(
+    title="Rift Breakdown — LoL Win Probability Predictor",
+    version="0.2.0",
+    description="Post-match analysis with 3 ML models (XGBoost, LSTM, LogReg)",
+    lifespan=lifespan,
+    # Docs bajo /api para no colisionar con React SPA
+    docs_url="/api/docs",
+    redoc_url="/api/redoc",
+    openapi_url="/api/openapi.json",
+)
+
+# ─────────────────────────────────────────────
+# CORS (Secured for Production)
+# ─────────────────────────────────────────────
+allowed_origins_env = os.environ.get(
+    "ALLOWED_ORIGINS", 
+    "http://localhost:5173,http://127.0.0.1:5173,http://localhost:3000,https://huggingface.co"
+)
+origins = [origin.strip() for origin in allowed_origins_env.split(",") if origin.strip()]
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["GET", "POST", "OPTIONS"],
+    allow_headers=["Content-Type", "Authorization", "Accept"],
+)
+
+# ─────────────────────────────────────────────
+# Routers
+# ─────────────────────────────────────────────
+app.include_router(predictions.router, prefix="/api/v1", tags=["predictions"])
+
+
+# ─────────────────────────────────────────────
+# Health check
+# ─────────────────────────────────────────────
+@app.get("/api/health", tags=["health"])
+def health_check():
+    return {
+        "status": "ok",
+        "version": "0.2.0",
+        "models_loaded": model_service.is_loaded,
+    }
+
+
+DIST = Path(__file__).parent.parent / "frontend" / "dist"
+
+if DIST.exists():
+    # Archivos estáticos (JS, CSS, imágenes)
+    app.mount("/assets", StaticFiles(directory=DIST / "assets"), name="assets")
+
+    @app.get("/{full_path:path}", include_in_schema=False)
+    def serve_spa(full_path: str):
+        """Catch-all: cualquier ruta no-API sirve el index.html de React."""
+        return FileResponse(DIST / "index.html")

backend/requirements.txt

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+# ─────────────────────────────────────────────
+# backend/requirements.txt
+# Solo inferencia — el entrenamiento se hace en Colab.
+# ─────────────────────────────────────────────
+
+# Web server
+fastapi==0.115.0
+uvicorn[standard]==0.30.6
+
+# Data
+numpy==1.26.4
+pandas==2.2.2
+
+# ML — solo carga de modelos serializados
+scikit-learn==1.5.2
+xgboost==2.1.1
+joblib==1.4.2
+
+# Deep Learning — CPU only (no GPU en HF Spaces gratuito)
+# Ahorra ~1.5 GB vs versión CUDA
+--extra-index-url https://download.pytorch.org/whl/cpu
+torch==2.4.0+cpu
+requests
+pydantic

backend/routers/__init__.py

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+# hace que routers/ sea un paquete Python

backend/routers/predictions.py

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+"""
+backend/routers/predictions.py
+─────────────────────────────────────────────
+Endpoints para predicción de win probability.
+
+POST /api/v1/predict/{match_id}  → Predicciones minuto-a-minuto
+GET  /api/v1/match/{match_id}/events → Eventos del timeline
+GET  /api/v1/health/models       → Estado de carga de modelos
+─────────────────────────────────────────────
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, List, Optional
+
+from fastapi import APIRouter, HTTPException
+
+from backend.config import FEATURE_COLUMNS
+from backend.services.model_service import model_service
+from backend.services.riot_service import (
+    RiotAPIError,
+    extract_events,
+    fetch_match_data,
+    fetch_match_timeline,
+    parse_timeline_to_features,
+    validate_match_id,
+)
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+from pydantic import BaseModel
+
+
+class PredictionResponse(BaseModel):
+    match_id: str
+    blue_win: Optional[int] = None
+    minutes: List[int]
+    predictions: Dict[str, List[float]]
+    events: List[Dict[str, Any]]
+    metadata: Dict[str, Any]
+
+
+class EventsResponse(BaseModel):
+    match_id: str
+    events: List[Dict[str, Any]]
+
+
+class ModelsStatusResponse(BaseModel):
+    loaded: bool
+    models: List[str]
+
+
+# ─────────────────────────────────────────────
+# Endpoints
+# ─────────────────────────────────────────────
+
+@router.post(
+    "/predict/{match_id}",
+    response_model=PredictionResponse,
+    summary="Predict win probability minute-by-minute",
+)
+async def predict_match(match_id: str):
+    """
+    Recibe un match_id (ej: NA1_1234567890), descarga los datos
+    de la Riot API, parsea el timeline a features tabulares, y
+    retorna las predicciones de los 3 modelos minuto-a-minuto.
+    """
+    # 1. Validate match_id format
+    if not validate_match_id(match_id):
+        raise HTTPException(
+            status_code=400,
+            detail=f"Invalid match_id format: '{match_id}'. Expected format: NA1_1234567890",
+        )
+
+    # 2. Check models are loaded
+    if not model_service.is_loaded:
+        raise HTTPException(
+            status_code=503,
+            detail="Models not loaded yet. Please wait for server initialization.",
+        )
+
+    # 3. Fetch data from Riot API
+    try:
+        match_data = fetch_match_data(match_id)
+        timeline_data = fetch_match_timeline(match_id)
+    except RiotAPIError as e:
+        if e.status_code == 403:
+            raise HTTPException(
+                status_code=403,
+                detail="Riot API key is invalid or expired. Check HF_SECRET / RIOT_API_KEY.",
+            )
+        elif e.status_code == 404:
+            raise HTTPException(
+                status_code=404,
+                detail=f"Match not found: {match_id}",
+            )
+        elif e.status_code == 429:
+            raise HTTPException(
+                status_code=429,
+                detail="Riot API rate limit exceeded. Please wait and try again.",
+            )
+        else:
+            raise HTTPException(
+                status_code=502,
+                detail=f"Riot API returned error {e.status_code}: {e.message}",
+            )
+
+    # 4. Parse timeline → features
+    try:
+        features_df = parse_timeline_to_features(match_data, timeline_data)
+    except Exception as e:
+        logger.error("Feature parsing failed for %s: %s", match_id, e, exc_info=True)
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to parse match data: {str(e)}",
+        )
+
+    if features_df.empty:
+        raise HTTPException(
+            status_code=422,
+            detail=f"No timeline data available for match {match_id}",
+        )
+
+    # 5. Run predictions
+    try:
+        predictions = model_service.predict(features_df)
+    except Exception as e:
+        logger.error("Prediction failed for %s: %s", match_id, e, exc_info=True)
+        raise HTTPException(
+            status_code=500,
+            detail=f"Model prediction failed: {str(e)}",
+        )
+
+    # 6. Extract events for frontend timeline
+    events = extract_events(timeline_data)
+
+    # 7. Build response
+    # blue_win comes from match_data directly — it is never stored as a column in features_df
+    blue_win = 1 if match_data["info"]["participants"][0]["win"] else 0
+
+    return PredictionResponse(
+        match_id=match_id,
+        blue_win=blue_win,
+        minutes=features_df["minute"].tolist(),
+        predictions=predictions,
+        events=events,
+        metadata={
+            "total_minutes": len(features_df),
+            "n_features": len(FEATURE_COLUMNS),
+            "models_used": ["xgboost", "lstm", "logreg"],
+        },
+    )
+
+
+@router.get(
+    "/match/{match_id}/events",
+    response_model=EventsResponse,
+    summary="Get match events for timeline",
+)
+async def get_match_events(match_id: str):
+    """
+    Retorna solo los eventos del timeline (kills, objectives,
+    structures, captures) para el feed del frontend.
+    """
+    if not validate_match_id(match_id):
+        raise HTTPException(
+            status_code=400,
+            detail=f"Invalid match_id format: '{match_id}'.",
+        )
+
+    try:
+        timeline_data = fetch_match_timeline(match_id)
+    except RiotAPIError as e:
+        if e.status_code == 404:
+            raise HTTPException(status_code=404, detail=f"Match not found: {match_id}")
+        raise HTTPException(status_code=502, detail=f"Riot API error: {e.message}")
+
+    events = extract_events(timeline_data)
+    return EventsResponse(match_id=match_id, events=events)
+
+
+@router.get(
+    "/models/status",
+    response_model=ModelsStatusResponse,
+    summary="Check model loading status",
+)
+async def models_status():
+    """Estado de carga de los modelos ML."""
+    return ModelsStatusResponse(
+        loaded=model_service.is_loaded,
+        models=["xgboost", "lstm", "logreg"],
+    )

backend/services/model_service.py

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+"""
+backend/services/model_service.py
+─────────────────────────────────────────────
+Carga, gestión e inferencia de los 3 modelos finales:
+  1. XGBoost  (Optuna, EXP-C)  – tabular, fast
+  2. LSTM + Self-Attention (EXP-E) – sequential, best AUC
+  3. Logistic Regression         – calibrated baseline
+
+Diseñado para carga única al startup de FastAPI.
+─────────────────────────────────────────────
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Dict, List, Optional
+
+import joblib
+import numpy as np
+import pandas as pd
+import torch
+import torch.nn as nn
+
+from backend.config import (
+    FEATURE_COLUMNS,
+    LOGREG_MODEL_PATH,
+    LSTM_DROPOUT,
+    LSTM_HIDDEN_DIM,
+    LSTM_MAX_SEQ_LEN,
+    LSTM_MODEL_PATH,
+    SCALER_PATH,
+    XGBOOST_MODEL_PATH,
+)
+
+logger = logging.getLogger(__name__)
+
+# ─────────────────────────────────────────────
+# LSTM Architecture (must match training exactly)
+# ─────────────────────────────────────────────
+
+class LSTMWithAttention(nn.Module):
+    """LSTM + Self-Attention para predicción de win probability.
+
+    Replica exacta de la arquitectura entrenada en notebook 08 (EXP-E).
+    """
+
+    def __init__(self, input_dim: int, hidden_dim: int, dropout: float = 0.3):
+        super().__init__()
+        self.lstm = nn.LSTM(input_dim, hidden_dim, batch_first=True)
+        self.attention = nn.Linear(hidden_dim, 1)
+        self.dropout = nn.Dropout(dropout)
+        self.fc = nn.Linear(hidden_dim, 1)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        out, _ = self.lstm(x)                                    # (B, T, H)
+        attn_w = torch.softmax(self.attention(out), dim=1)       # (B, T, 1)
+        context = (attn_w * out).sum(dim=1)                      # (B, H)
+        return self.fc(self.dropout(context))
+
+
+# ─────────────────────────────────────────────
+# Model Service (Singleton-ish via module state)
+# ─────────────────────────────────────────────
+
+class ModelService:
+    """Servicio centralizado de carga e inferencia.
+
+    Se inicializa una sola vez al arrancar la app y mantiene
+    los 3 modelos en memoria para inferencia rápida.
+    """
+
+    def __init__(self):
+        self._xgb_model = None
+        self._lstm_model: Optional[LSTMWithAttention] = None
+        self._logreg_model = None
+        self._scaler = None
+        self._device = "cpu"  # No GPU en HF Spaces gratuito
+        self._loaded = False
+
+    @property
+    def is_loaded(self) -> bool:
+        return self._loaded
+
+    # ── Load ──────────────────────────────────────────────────
+
+    def load_models(self) -> None:
+        """Carga los 3 modelos desde disco. Llamar una sola vez."""
+        if self._loaded:
+            logger.info("Models already loaded — skipping.")
+            return
+
+        logger.info("Loading ML models…")
+
+        # 1. XGBoost
+        try:
+            self._xgb_model = joblib.load(XGBOOST_MODEL_PATH)
+            logger.info("✅ XGBoost loaded from %s", XGBOOST_MODEL_PATH)
+        except Exception as e:
+            logger.error("❌ Failed to load XGBoost: %s", e)
+            raise
+
+        # 2. Logistic Regression + Scaler
+        try:
+            self._logreg_model = joblib.load(LOGREG_MODEL_PATH)
+            self._scaler = joblib.load(SCALER_PATH)
+            logger.info("✅ LogReg + Scaler loaded")
+        except Exception as e:
+            logger.error("❌ Failed to load LogReg/Scaler: %s", e)
+            raise
+
+        # 3. LSTM + Self-Attention
+        try:
+            input_dim = len(FEATURE_COLUMNS)
+            self._lstm_model = LSTMWithAttention(
+                input_dim=input_dim,
+                hidden_dim=LSTM_HIDDEN_DIM,
+                dropout=LSTM_DROPOUT,
+            )
+            checkpoint = torch.load(
+                LSTM_MODEL_PATH,
+                map_location=self._device,
+                weights_only=False,
+            )
+            # El modelo fue guardado como un diccionario con metadatos
+            if "model_state_dict" in checkpoint:
+                self._lstm_model.load_state_dict(checkpoint["model_state_dict"])
+            else:
+                self._lstm_model.load_state_dict(checkpoint)
+            self._lstm_model.to(self._device)
+            self._lstm_model.eval()
+            logger.info("✅ LSTM loaded from %s (device=%s)", LSTM_MODEL_PATH, self._device)
+        except Exception as e:
+            logger.error("❌ Failed to load LSTM: %s", e)
+            raise
+
+        self._loaded = True
+        logger.info("All models loaded successfully.")
+
+    # ── Predict ───────────────────────────────────────────────
+
+    def predict(self, features_df: pd.DataFrame) -> Dict[str, List[float]]:
+        """Genera predicciones minuto-a-minuto con los 3 modelos.
+
+        Args:
+            features_df: DataFrame con columnas == FEATURE_COLUMNS.
+                          Cada fila es un minuto de la partida.
+
+        Returns:
+            Dict con keys "xgboost", "lstm", "logreg", cada uno
+            una lista de floats (probabilidad de blue_win por minuto).
+        """
+        if not self._loaded:
+            raise RuntimeError("Models not loaded. Call load_models() first.")
+
+        # Validate columns
+        missing = set(FEATURE_COLUMNS) - set(features_df.columns)
+        if missing:
+            raise ValueError(f"Missing features in input: {missing}")
+
+        # Ensure correct column order
+        X = features_df[FEATURE_COLUMNS].astype(np.float32)
+
+        results: Dict[str, List[float]] = {}
+
+        # ── XGBoost (tabular, row-by-row) ─────────────────────
+        try:
+            xgb_probs = self._xgb_model.predict_proba(X)[:, 1]
+            results["xgboost"] = xgb_probs.tolist()
+        except Exception as e:
+            logger.error("XGBoost prediction failed: %s", e)
+            results["xgboost"] = []
+
+        # ── Logistic Regression (scaled, row-by-row) ──────────
+        try:
+            X_scaled = self._scaler.transform(X)
+            logreg_probs = self._logreg_model.predict_proba(X_scaled)[:, 1]
+            results["logreg"] = logreg_probs.tolist()
+        except Exception as e:
+            logger.error("LogReg prediction failed: %s", e)
+            results["logreg"] = []
+
+        # ── LSTM (sequence, padded to MAX_SEQ_LEN) ────────────
+        try:
+            results["lstm"] = self._predict_lstm(X.values)
+        except Exception as e:
+            logger.error("LSTM prediction failed: %s", e)
+            results["lstm"] = []
+
+        return results
+
+    def _predict_lstm(self, X_arr: np.ndarray) -> List[float]:
+        """Inferencia LSTM minuto-a-minuto (acumulativa).
+
+        Para cada minuto t, construimos la secuencia [0..t] (max 20 min),
+        la pasamos por el modelo, y obtenemos P(blue_win) hasta ese punto.
+        Esto simula cómo se usaría en producción: la partida avanza y
+        el modelo ve la secuencia acumulada.
+        """
+        n_minutes = len(X_arr)
+        probs: List[float] = []
+
+        for t in range(n_minutes):
+            # Secuencia acumulada hasta minuto t (inclusive)
+            seq = X_arr[:t + 1]
+
+            # Truncar a MAX_SEQ_LEN (tomar los últimos N minutos)
+            if len(seq) > LSTM_MAX_SEQ_LEN:
+                seq = seq[-LSTM_MAX_SEQ_LEN:]
+
+            # Post-pad with zeros after real data — matches training exactly
+            # (pad_sequences in notebook fills [:l] then leaves zeros at the end)
+            seq_len = len(seq)
+            if seq_len < LSTM_MAX_SEQ_LEN:
+                pad = np.zeros(
+                    (LSTM_MAX_SEQ_LEN - seq_len, seq.shape[1]),
+                    dtype=np.float32,
+                )
+                seq = np.concatenate([seq, pad], axis=0)
+
+            # (1, MAX_SEQ_LEN, n_features)
+            tensor = torch.tensor(seq, dtype=torch.float32).unsqueeze(0).to(self._device)
+
+            with torch.no_grad():
+                logit = self._lstm_model(tensor).squeeze()
+                prob = torch.sigmoid(logit).item()
+
+            probs.append(prob)
+
+        return probs
+
+
+# ── Module-level singleton ────────────────────────────────────
+model_service = ModelService()

backend/services/riot_service.py

@@ -0,0 +1,523 @@
+"""
+backend/services/riot_service.py
+─────────────────────────────────────────────
+Servicio de comunicación con la Riot API (Match-V5).
+  - Descarga match summary + timeline
+  - Parsea los datos a features tabulares minuto-a-minuto
+  - Retorna el DataFrame listo para inferencia
+
+Diseñado para funcionar con API key vía HF_SECRET.
+─────────────────────────────────────────────
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+import requests
+
+from backend.config import (
+    FEATURE_COLUMNS,
+    RIOT_API_KEY,
+    RIOT_BASE_URL,
+    RIOT_PLATFORM_URL,
+)
+
+logger = logging.getLogger(__name__)
+
+# ─────────────────────────────────────────────
+# Riot API client
+# ─────────────────────────────────────────────
+
+class RiotAPIError(Exception):
+    """Custom exception for Riot API errors."""
+
+    def __init__(self, status_code: int, message: str):
+        self.status_code = status_code
+        self.message = message
+        super().__init__(f"Riot API error {status_code}: {message}")
+
+
+def _riot_headers() -> Dict[str, str]:
+    """Build headers with the API key."""
+    return {
+        "X-Riot-Token": RIOT_API_KEY,
+        "Accept": "application/json",
+    }
+
+
+def validate_match_id(match_id: str) -> bool:
+    """Validate that match_id has the expected format (e.g., NA1_1234567890)."""
+    if not match_id or "_" not in match_id:
+        return False
+    parts = match_id.split("_", 1)
+    return len(parts) == 2 and parts[1].isdigit()
+
+
+def fetch_match_data(match_id: str) -> Dict[str, Any]:
+    """Fetch match summary from Riot Match-V5 API.
+
+    GET /lol/match/v5/matches/{matchId}
+    """
+    url = f"{RIOT_BASE_URL}/lol/match/v5/matches/{match_id}"
+    logger.info("Fetching match data: %s", url)
+
+    resp = requests.get(url, headers=_riot_headers(), timeout=15)
+    if resp.status_code != 200:
+        raise RiotAPIError(resp.status_code, resp.text[:300])
+
+    return resp.json()
+
+
+def fetch_match_timeline(match_id: str) -> Dict[str, Any]:
+    """Fetch match timeline from Riot Match-V5 API.
+
+    GET /lol/match/v5/matches/{matchId}/timeline
+    """
+    url = f"{RIOT_BASE_URL}/lol/match/v5/matches/{match_id}/timeline"
+    logger.info("Fetching timeline: %s", url)
+
+    resp = requests.get(url, headers=_riot_headers(), timeout=15)
+    if resp.status_code != 200:
+        raise RiotAPIError(resp.status_code, resp.text[:300])
+
+    return resp.json()
+
+
+# ─────────────────────────────────────────────
+# Timeline → Tabular Feature Parser
+# ─────────────────────────────────────────────
+
+def _safe_get(d: dict, *keys, default=0):
+    """Safely traverse nested dicts."""
+    for k in keys:
+        if isinstance(d, dict):
+            d = d.get(k, default)
+        else:
+            return default
+    return d
+
+
+def parse_timeline_to_features(
+    match_data: Dict[str, Any],
+    timeline_data: Dict[str, Any],
+) -> pd.DataFrame:
+    """Parse Riot timeline JSON into the 80-feature tabular format.
+
+    This replicates the logic from 02_data_processing_and_eda.ipynb
+    and 06_advanced_features.ipynb.
+
+    Returns a DataFrame with one row per minute, columns == FEATURE_COLUMNS.
+    """
+    match_id = match_data["metadata"]["matchId"]
+    info = match_data["info"]
+
+    # Determine blue_win from match summary
+    # Participants 0-4 are blue team (teamId=100)
+    blue_win = 1 if info["participants"][0]["win"] else 0
+
+    # Identify blue (1-5) and red (6-10) participant IDs
+    blue_pids = list(range(1, 6))
+    red_pids = list(range(6, 11))
+
+    frames = timeline_data["info"]["frames"]
+    rows: List[Dict[str, Any]] = []
+
+    # Track cumulative events
+    blue_kills_total = 0
+    red_kills_total = 0
+    blue_assists_total = 0
+    red_assists_total = 0
+    blue_dragons = 0
+    red_dragons = 0
+    blue_barons = 0
+    red_barons = 0
+    blue_towers = 0
+    red_towers = 0
+    blue_inhibitors = 0
+    red_inhibitors = 0
+    blue_heralds = 0
+    red_heralds = 0
+    blue_grubs = 0
+    red_grubs = 0
+    blue_wards_placed = 0
+    red_wards_placed = 0
+    blue_wards_killed = 0
+    red_wards_killed = 0
+
+    # First-event flags
+    first_blood_team = 0
+    first_tower_team = 0
+    first_dragon_team = 0
+    first_baron_team = 0
+
+    # Baron active tracking (last 3 min)
+    blue_baron_times: List[int] = []
+    red_baron_times: List[int] = []
+
+    for frame_idx, frame in enumerate(frames):
+        minute = frame_idx  # frame 0 = minute 0
+
+        # Process events in this frame
+        events = frame.get("events", [])
+        for event in events:
+            etype = event.get("type", "")
+
+            if etype == "CHAMPION_KILL":
+                killer_id = event.get("killerId", 0)
+                if killer_id in blue_pids:
+                    blue_kills_total += 1
+                    # Count assists for blue
+                    assisting = event.get("assistingParticipantIds", [])
+                    blue_assists_total += len([a for a in assisting if a in blue_pids])
+                elif killer_id in red_pids:
+                    red_kills_total += 1
+                    assisting = event.get("assistingParticipantIds", [])
+                    red_assists_total += len([a for a in assisting if a in red_pids])
+
+                # First blood
+                if first_blood_team == 0:
+                    if killer_id in blue_pids:
+                        first_blood_team = 1
+                    elif killer_id in red_pids:
+                        first_blood_team = 2
+
+            elif etype == "ELITE_MONSTER_KILL":
+                killer_team = event.get("killerTeamId", 0)
+                monster = event.get("monsterType", "")
+
+                if monster == "DRAGON":
+                    if killer_team == 100:
+                        blue_dragons += 1
+                        if first_dragon_team == 0:
+                            first_dragon_team = 1
+                    elif killer_team == 200:
+                        red_dragons += 1
+                        if first_dragon_team == 0:
+                            first_dragon_team = 2
+
+                elif monster == "BARON_NASHOR":
+                    if killer_team == 100:
+                        blue_barons += 1
+                        blue_baron_times.append(minute)
+                        if first_baron_team == 0:
+                            first_baron_team = 1
+                    elif killer_team == 200:
+                        red_barons += 1
+                        red_baron_times.append(minute)
+                        if first_baron_team == 0:
+                            first_baron_team = 2
+
+                elif monster == "RIFTHERALD":
+                    if killer_team == 100:
+                        blue_heralds += 1
+                    elif killer_team == 200:
+                        red_heralds += 1
+
+                elif monster == "HORDE":  # Voidgrubs
+                    if killer_team == 100:
+                        blue_grubs += 1
+                    elif killer_team == 200:
+                        red_grubs += 1
+
+            elif etype == "BUILDING_KILL":
+                building = event.get("buildingType", "")
+                team_id = event.get("teamId", 0)  # team that LOST the building
+
+                if building == "TOWER_BUILDING":
+                    if team_id == 200:  # Red lost → Blue destroyed
+                        blue_towers += 1
+                        if first_tower_team == 0:
+                            first_tower_team = 1
+                    elif team_id == 100:
+                        red_towers += 1
+                        if first_tower_team == 0:
+                            first_tower_team = 2
+
+                elif building == "INHIBITOR_BUILDING":
+                    if team_id == 200:
+                        blue_inhibitors += 1
+                    elif team_id == 100:
+                        red_inhibitors += 1
+
+            elif etype == "WARD_PLACED":
+                creator = event.get("creatorId", 0)
+                if creator in blue_pids:
+                    blue_wards_placed += 1
+                elif creator in red_pids:
+                    red_wards_placed += 1
+
+            elif etype == "WARD_KILL":
+                killer = event.get("killerId", 0)
+                if killer in blue_pids:
+                    blue_wards_killed += 1
+                elif killer in red_pids:
+                    red_wards_killed += 1
+
+        # ── Participant frame stats ───────────────────────────
+        participant_frames = frame.get("participantFrames", {})
+
+        blue_gold = sum(_safe_get(participant_frames, str(pid), "totalGold") for pid in blue_pids)
+        red_gold = sum(_safe_get(participant_frames, str(pid), "totalGold") for pid in red_pids)
+        blue_xp = sum(_safe_get(participant_frames, str(pid), "xp") for pid in blue_pids)
+        red_xp = sum(_safe_get(participant_frames, str(pid), "xp") for pid in red_pids)
+        blue_level = sum(_safe_get(participant_frames, str(pid), "level") for pid in blue_pids)
+        red_level = sum(_safe_get(participant_frames, str(pid), "level") for pid in red_pids)
+        blue_minions = sum(
+            _safe_get(participant_frames, str(pid), "minionsKilled")
+            + _safe_get(participant_frames, str(pid), "jungleMinionsKilled")
+            for pid in blue_pids
+        )
+        red_minions = sum(
+            _safe_get(participant_frames, str(pid), "minionsKilled")
+            + _safe_get(participant_frames, str(pid), "jungleMinionsKilled")
+            for pid in red_pids
+        )
+        blue_dmg_champ = sum(
+            _safe_get(participant_frames, str(pid), "damageStats", "totalDamageDoneToChampions")
+            for pid in blue_pids
+        )
+        red_dmg_champ = sum(
+            _safe_get(participant_frames, str(pid), "damageStats", "totalDamageDoneToChampions")
+            for pid in red_pids
+        )
+        blue_dmg_taken = sum(
+            _safe_get(participant_frames, str(pid), "damageStats", "totalDamageTaken")
+            for pid in blue_pids
+        )
+        red_dmg_taken = sum(
+            _safe_get(participant_frames, str(pid), "damageStats", "totalDamageTaken")
+            for pid in red_pids
+        )
+
+        # Baron active flags (active if baron killed within last 3 min)
+        blue_baron_active = 1 if any(t >= minute - 3 and t <= minute for t in blue_baron_times) else 0
+        red_baron_active = 1 if any(t >= minute - 3 and t <= minute for t in red_baron_times) else 0
+
+        # ── Build row with 67 original features ──────────────
+        safe_minute = minute + 1  # avoid /0
+
+        row = {
+            "match_id": match_id,
+            "minute": minute,
+            "blue_win": blue_win,
+            "blue_totalGold": blue_gold,
+            "red_totalGold": red_gold,
+            "goldDiff": blue_gold - red_gold,
+            "blue_totalXP": blue_xp,
+            "red_totalXP": red_xp,
+            "xpDiff": blue_xp - red_xp,
+            "blue_totalLevel": blue_level,
+            "red_totalLevel": red_level,
+            "levelDiff": blue_level - red_level,
+            "blue_totalMinions": blue_minions,
+            "red_totalMinions": red_minions,
+            "minionsDiff": blue_minions - red_minions,
+            "blue_totalDmgChamp": blue_dmg_champ,
+            "red_totalDmgChamp": red_dmg_champ,
+            "dmgChampDiff": blue_dmg_champ - red_dmg_champ,
+            "blue_totalDmgTaken": blue_dmg_taken,
+            "red_totalDmgTaken": red_dmg_taken,
+            "dmgTakenDiff": blue_dmg_taken - red_dmg_taken,
+            "blue_kills": blue_kills_total,
+            "red_kills": red_kills_total,
+            "killsDiff": blue_kills_total - red_kills_total,
+            "blue_assists": blue_assists_total,
+            "red_assists": red_assists_total,
+            "assistsDiff": blue_assists_total - red_assists_total,
+            "blue_wardsPlaced": blue_wards_placed,
+            "red_wardsPlaced": red_wards_placed,
+            "wardsPlacedDiff": blue_wards_placed - red_wards_placed,
+            "blue_wardsKilled": blue_wards_killed,
+            "red_wardsKilled": red_wards_killed,
+            "wardsKilledDiff": blue_wards_killed - red_wards_killed,
+            "blue_dragons": blue_dragons,
+            "red_dragons": red_dragons,
+            "dragonsDiff": blue_dragons - red_dragons,
+            "blue_barons": blue_barons,
+            "red_barons": red_barons,
+            "baronsDiff": blue_barons - red_barons,
+            "blue_towers": blue_towers,
+            "red_towers": red_towers,
+            "towersDiff": blue_towers - red_towers,
+            "blue_inhibitors": blue_inhibitors,
+            "red_inhibitors": red_inhibitors,
+            "inhibitorsDiff": blue_inhibitors - red_inhibitors,
+            "blue_heralds": blue_heralds,
+            "red_heralds": red_heralds,
+            "heraldsDiff": blue_heralds - red_heralds,
+            "blue_grubs": blue_grubs,
+            "red_grubs": red_grubs,
+            "grubsDiff": blue_grubs - red_grubs,
+            "first_blood": first_blood_team,
+            "first_tower": first_tower_team,
+            "first_dragon": first_dragon_team,
+            "first_baron": first_baron_team,
+            "blue_baron_active": blue_baron_active,
+            "red_baron_active": red_baron_active,
+        }
+
+        rows.append(row)
+
+    # ── Build DataFrame and add engineered features ──────────
+    df = pd.DataFrame(rows)
+
+    if df.empty:
+        return df
+
+    # EDA features (12)
+    df["blue_goldPerMin"] = df["blue_totalGold"] / (df["minute"] + 1)
+    df["red_goldPerMin"] = df["red_totalGold"] / (df["minute"] + 1)
+    df["blue_killsPerMin"] = df["blue_kills"] / (df["minute"] + 1)
+
+    df["gold_ratio"] = df["blue_totalGold"] / (df["red_totalGold"] + 1)
+    df["xp_ratio"] = df["blue_totalXP"] / (df["red_totalXP"] + 1)
+
+    df["blue_kda"] = (df["blue_kills"] + df["blue_assists"]) / (df["red_kills"] + 1)
+    df["red_kda"] = (df["red_kills"] + df["red_assists"]) / (df["blue_kills"] + 1)
+
+    for col in ["goldDiff", "xpDiff", "killsDiff"]:
+        df[f"{col}_3min_delta"] = df[col].diff(periods=3).fillna(0)
+
+    df["blue_dragons_weighted"] = df["blue_dragons"] * (1 + df["minute"] / 30)
+    df["red_dragons_weighted"] = df["red_dragons"] * (1 + df["minute"] / 30)
+
+    # Grandmaster features (13)
+    total_gold = df["blue_totalGold"] + df["red_totalGold"] + 1
+    df["blue_gold_share"] = df["blue_totalGold"] / total_gold
+
+    df["blue_dmg_efficiency"] = df["blue_totalDmgChamp"] / (df["blue_totalGold"] + 1)
+    df["red_dmg_efficiency"] = df["red_totalDmgChamp"] / (df["red_totalGold"] + 1)
+    df["dmg_efficiency_diff"] = df["blue_dmg_efficiency"] - df["red_dmg_efficiency"]
+
+    blue_obj = df["blue_towers"] + df["blue_dragons"] + df["blue_barons"] + df["blue_heralds"] + (df["blue_grubs"] / 3)
+    red_obj = df["red_towers"] + df["red_dragons"] + df["red_barons"] + df["red_heralds"] + (df["red_grubs"] / 3)
+    df["blue_obj_conversion"] = blue_obj / (df["blue_kills"] + 1)
+    df["red_obj_conversion"] = red_obj / (df["red_kills"] + 1)
+    df["obj_conversion_diff"] = df["blue_obj_conversion"] - df["red_obj_conversion"]
+
+    df["blue_vision_density"] = df["blue_wardsPlaced"] / (df["minute"] + 1)
+    df["red_vision_density"] = df["red_wardsPlaced"] / (df["minute"] + 1)
+    df["vision_density_diff"] = df["blue_vision_density"] - df["red_vision_density"]
+
+    df["blue_teamwork_score"] = df["blue_assists"] / (df["blue_kills"] + 1)
+    df["red_teamwork_score"] = df["red_assists"] / (df["red_kills"] + 1)
+    df["teamwork_diff"] = df["blue_teamwork_score"] - df["red_teamwork_score"]
+
+    return df
+
+
+def extract_events(timeline_data: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """Extract key events from timeline for the frontend feed.
+
+    Returns a list of event dicts with: minute, type, subtype, description.
+    """
+    events_out: List[Dict[str, Any]] = []
+    blue_pids = set(range(1, 6))
+    red_pids = set(range(6, 11))
+
+    frames = timeline_data.get("info", {}).get("frames", [])
+
+    for frame_idx, frame in enumerate(frames):
+        minute = frame_idx
+
+        for event in frame.get("events", []):
+            etype = event.get("type", "")
+            entry: Optional[Dict[str, Any]] = None
+
+            if etype == "CHAMPION_KILL":
+                # Determine which team gained the advantage from the kill
+                killer = event.get("killerId", 0)
+                victim = event.get("victimId", 0)
+                if killer in blue_pids:
+                    team = "Blue"
+                elif killer in red_pids:
+                    team = "Red"
+                else:
+                    # If killed by tower/minion, the team opposite to the victim gets the 'credit'
+                    team = "Red" if victim in blue_pids else "Blue"
+
+                entry = {
+                    "minute": minute,
+                    "clock": f"{minute:02d}:00",
+                    "type": "kills",
+                    "subtype": "kill",
+                    "text": f"champion kill (killer: P{killer}, victim: P{victim})",
+                    "team": team,
+                }
+
+            elif etype == "ELITE_MONSTER_KILL":
+                killer_team = event.get("killerTeamId", 0)
+                monster = event.get("monsterType", "")
+                monster_sub = event.get("monsterSubType", "")
+                team = "Blue" if killer_team == 100 else "Red"
+
+                if monster == "DRAGON":
+                    entry = {
+                        "minute": minute,
+                        "clock": f"{minute:02d}:00",
+                        "type": "objectives",
+                        "subtype": "dragon",
+                        "text": f"slayed {monster_sub or 'Dragon'}",
+                        "team": team,
+                    }
+                elif monster == "BARON_NASHOR":
+                    entry = {
+                        "minute": minute,
+                        "clock": f"{minute:02d}:00",
+                        "type": "objectives",
+                        "subtype": "baron",
+                        "text": f"slayed Baron Nashor",
+                        "team": team,
+                    }
+                elif monster == "RIFTHERALD":
+                    entry = {
+                        "minute": minute,
+                        "clock": f"{minute:02d}:00",
+                        "type": "objectives",
+                        "subtype": "herald",
+                        "text": f"captured Rift Herald",
+                        "team": team,
+                    }
+                elif monster == "HORDE":
+                    entry = {
+                        "minute": minute,
+                        "clock": f"{minute:02d}:00",
+                        "type": "objectives",
+                        "subtype": "grubs",
+                        "text": f"killed Voidgrubs",
+                        "team": team,
+                    }
+
+            elif etype == "BUILDING_KILL":
+                building = event.get("buildingType", "")
+                team_id = event.get("teamId", 0)  # team that LOST
+                team = "Blue" if team_id == 200 else "Red"
+                lane = event.get("laneType", "")
+
+                if building == "TOWER_BUILDING":
+                    entry = {
+                        "minute": minute,
+                        "clock": f"{minute:02d}:00",
+                        "type": "structures",
+                        "subtype": "tower",
+                        "text": f"destroyed {lane} tower",
+                        "team": team,
+                    }
+                elif building == "INHIBITOR_BUILDING":
+                    entry = {
+                        "minute": minute,
+                        "clock": f"{minute:02d}:00",
+                        "type": "structures",
+                        "subtype": "inhibitor",
+                        "text": f"destroyed {lane} inhibitor",
+                        "team": team,
+                    }
+
+            if entry:
+                events_out.append(entry)
+
+    return events_out

frontend/.gitignore

@@ -0,0 +1,24 @@
+# Logs
+logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+pnpm-debug.log*
+lerna-debug.log*
+
+node_modules
+dist
+dist-ssr
+*.local
+
+# Editor directories and files
+.vscode/*
+!.vscode/extensions.json
+.idea
+.DS_Store
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

frontend/eslint.config.js

@@ -0,0 +1,21 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import { defineConfig, globalIgnores } from 'eslint/config'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{js,jsx}'],
+    extends: [
+      js.configs.recommended,
+      reactHooks.configs.flat.recommended,
+      reactRefresh.configs.vite,
+    ],
+    languageOptions: {
+      globals: globals.browser,
+      parserOptions: { ecmaFeatures: { jsx: true } },
+    },
+  },
+])

frontend/index.html

@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>frontend</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.jsx"></script>
+  </body>
+</html>

frontend/package-lock.json

@@ -0,0 +1,2458 @@
+{
+  "name": "frontend",
+  "version": "0.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "frontend",
+      "version": "0.0.0",
+      "dependencies": {
+        "react": "^19.2.5",
+        "react-dom": "^19.2.5"
+      },
+      "devDependencies": {
+        "@eslint/js": "^10.0.1",
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+}

frontend/package.json

@@ -0,0 +1,27 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "lint": "eslint .",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "react": "^19.2.5",
+    "react-dom": "^19.2.5"
+  },
+  "devDependencies": {
+    "@eslint/js": "^10.0.1",
+    "@types/react": "^19.2.14",
+    "@types/react-dom": "^19.2.3",
+    "@vitejs/plugin-react": "^6.0.1",
+    "eslint": "^10.2.1",
+    "eslint-plugin-react-hooks": "^7.1.1",
+    "eslint-plugin-react-refresh": "^0.5.2",
+    "globals": "^17.5.0",
+    "vite": "^8.0.10"
+  }
+}

frontend/src/App.css

@@ -0,0 +1,820 @@
+.view-shell {
+  min-height: 100vh;
+  width: min(1200px, 92vw);
+  margin: 0 auto;
+  padding: clamp(2rem, 3vw, 3.5rem) 0;
+  display: grid;
+  place-items: center;
+}
+
+.rise {
+  animation: riseIn 700ms cubic-bezier(0.2, 0.9, 0.2, 1) both;
+}
+
+.landing-shell {
+  position: relative;
+}
+
+.landing-card {
+  width: min(860px, 100%);
+  background: linear-gradient(180deg, var(--bg-panel) 0%, rgba(13, 13, 17, 0.9) 100%);
+  border: 1px solid var(--line-soft);
+  border-radius: var(--radius-lg);
+  box-shadow: var(--shadow-depth);
+  padding: clamp(1.5rem, 3vw, 3.5rem);
+  text-align: center;
+}
+
+.eyebrow {
+  margin-bottom: 0.9rem;
+  color: var(--text-muted);
+  text-transform: uppercase;
+  letter-spacing: 0.15em;
+  font-size: 0.78rem;
+  font-weight: 700;
+}
+
+.centered {
+  text-align: center;
+}
+
+.landing-title {
+  font-size: clamp(2rem, 5vw, 3.5rem);
+  line-height: 0.95;
+  margin-bottom: 1rem;
+}
+
+.landing-subtitle {
+  max-width: 68ch;
+  color: var(--text-muted);
+  font-size: clamp(1rem, 2vw, 1.18rem);
+  margin-bottom: 1.5rem;
+  margin-inline: auto;
+}
+
+.landing-metrics {
+  display: flex;
+  flex-wrap: wrap;
+  justify-content: center;
+  gap: 0.7rem;
+  margin-bottom: 1.6rem;
+}
+
+.landing-metrics span {
+  border: 1px solid var(--line-soft);
+  background: rgba(255, 255, 255, 0.02);
+  border-radius: 999px;
+  padding: 0.35rem 0.8rem;
+  font-size: 0.82rem;
+  color: var(--text-muted);
+}
+
+.match-form {
+  display: grid;
+  gap: 0.55rem;
+  text-align: left;
+}
+
+.input-label {
+  color: var(--text-muted);
+  font-size: 0.88rem;
+  text-transform: uppercase;
+  letter-spacing: 0.09em;
+}
+
+.input-row {
+  display: grid;
+  grid-template-columns: 1fr auto;
+  gap: 0.6rem;
+  padding: 0.5rem;
+  border: 1px solid var(--line-soft);
+  border-radius: var(--radius-md);
+  background: var(--bg-panel-soft);
+  transition: border-color 240ms ease, box-shadow 240ms ease;
+}
+
+.input-row:focus-within {
+  border-color: var(--line-strong);
+  box-shadow: 0 0 0 3px rgba(198, 167, 105, 0.18);
+}
+
+.input-row.has-error {
+  border-color: rgba(196, 88, 88, 0.7);
+  box-shadow: 0 0 0 3px rgba(196, 88, 88, 0.2);
+}
+
+.match-input {
+  border: 0;
+  background: transparent;
+  color: var(--text-strong);
+  font-size: 1.05rem;
+  padding: 0.75rem 0.85rem;
+  outline: none;
+}
+
+.match-input::placeholder {
+  color: var(--text-subtle);
+}
+
+.primary-btn,
+.ghost-btn {
+  border: 0;
+  cursor: pointer;
+  white-space: nowrap;
+  transition: transform 220ms ease, box-shadow 220ms ease, background-color 220ms ease;
+}
+
+.primary-btn {
+  border-radius: var(--radius-sm);
+  padding: 0.75rem 1rem;
+  background: linear-gradient(120deg, #9f8552, #c6a769 42%, #e4d1a7);
+  background-size: 220% 220%;
+  color: #121215;
+  font-weight: 800;
+}
+
+.primary-btn:hover {
+  transform: translateY(-1px);
+  box-shadow: 0 10px 24px rgba(198, 167, 105, 0.35);
+  animation: rainbowShift 2.1s linear infinite;
+}
+
+.primary-btn:focus-visible,
+.ghost-btn:focus-visible,
+.match-input:focus-visible {
+  outline: 2px solid var(--accent-primary);
+  outline-offset: 2px;
+}
+
+.helper-text {
+  color: var(--text-subtle);
+  font-size: 0.84rem;
+}
+
+.helper-text.error {
+  color: var(--accent-danger);
+}
+
+.dashboard-shell {
+  align-items: start;
+  padding-top: 2.3rem;
+  gap: 1.2rem;
+}
+
+.dashboard-header {
+  width: 100%;
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  gap: 0.8rem;
+  margin-bottom: 1rem;
+}
+
+.ghost-btn {
+  padding: 0.68rem 1rem;
+  border-radius: 999px;
+  border: 1px solid var(--line-soft);
+  color: var(--text-muted);
+  background: rgba(255, 255, 255, 0.02);
+}
+
+.ghost-btn:hover {
+  transform: translateY(-1px);
+  border-color: var(--line-strong);
+  color: var(--text-strong);
+}
+
+.target-match {
+  margin: 0;
+  color: var(--text-muted);
+  font-size: 0.9rem;
+}
+
+.header-left {
+  display: flex;
+  align-items: center;
+  gap: 1.2rem;
+}
+
+.winner-badge {
+  display: flex;
+  flex-direction: column;
+  align-items: flex-end;
+  padding: 0.5rem 1.2rem;
+  border-radius: var(--radius-md);
+  background: rgba(255, 255, 255, 0.03);
+  border: 1px solid var(--line-soft);
+}
+
+.winner-label {
+  font-size: 0.65rem;
+  text-transform: uppercase;
+  letter-spacing: 0.12em;
+  color: var(--text-muted);
+}
+
+.winner-name {
+  font-family: var(--font-display);
+  font-size: 1.15rem;
+  font-weight: 800;
+}
+
+.winner-badge.blue-win {
+  border-color: rgba(25, 215, 255, 0.4);
+  box-shadow: 0 0 20px rgba(25, 215, 255, 0.15);
+}
+
+.winner-badge.blue-win .winner-name {
+  color: var(--accent-blue-team);
+  text-shadow: 0 0 12px rgba(25, 215, 255, 0.4);
+}
+
+.winner-badge.red-win {
+  border-color: rgba(255, 95, 122, 0.4);
+  box-shadow: 0 0 20px rgba(255, 95, 122, 0.15);
+}
+
+.winner-badge.red-win .winner-name {
+  color: var(--accent-red-team);
+  text-shadow: 0 0 12px rgba(255, 95, 122, 0.4);
+}
+
+.target-match strong {
+  color: var(--accent-primary);
+  font-family: var(--font-display);
+  font-size: 0.98rem;
+}
+
+.dashboard-grid {
+  width: 100%;
+  display: grid;
+  grid-template-columns: 1fr;
+  gap: 1.2rem;
+}
+
+.panel {
+  border-radius: var(--radius-lg);
+  border: 1px solid var(--line-soft);
+  background: linear-gradient(180deg, rgba(19, 19, 24, 0.88) 0%, rgba(10, 10, 13, 0.9) 100%);
+  box-shadow: var(--shadow-depth);
+  padding: clamp(1.1rem, 2.2vw, 1.8rem);
+}
+
+.panel-kicker {
+  color: var(--text-muted);
+  text-transform: uppercase;
+  letter-spacing: 0.11em;
+  font-size: 0.75rem;
+  font-weight: 700;
+}
+
+.primary-panel h2 {
+  margin-top: 0.45rem;
+  font-size: clamp(1.22rem, 2vw, 2rem);
+}
+
+.probability-panel {
+  max-width: 760px;
+  margin: 0 auto;
+  width: 100%;
+}
+
+.probability-panel h2 {
+  text-align: center;
+}
+
+.headline-metric {
+  margin-top: 0.9rem;
+  display: flex;
+  align-items: baseline;
+  gap: 0.8rem;
+}
+
+.metric-value {
+  font-family: var(--font-display);
+  color: var(--accent-primary);
+  font-size: clamp(2rem, 6vw, 3.5rem);
+  line-height: 1;
+}
+
+.metric-caption {
+  color: var(--text-muted);
+  font-size: 0.94rem;
+}
+
+.duel-probability {
+  margin-top: 0.8rem;
+  display: grid;
+  grid-template-columns: repeat(2, minmax(0, 1fr));
+  gap: 0.55rem;
+}
+
+.duel-card {
+  border-radius: var(--radius-sm);
+  border: 1px solid rgba(255, 255, 255, 0.12);
+  padding: 0.65rem 0.75rem;
+  background: rgba(255, 255, 255, 0.03);
+}
+
+.duel-card p {
+  font-size: 0.76rem;
+  color: var(--text-muted);
+  text-transform: uppercase;
+  letter-spacing: 0.08em;
+}
+
+.duel-card strong {
+  display: inline-block;
+  margin-top: 0.2rem;
+  font-family: var(--font-display);
+  font-size: 1.1rem;
+}
+
+.duel-card.blue strong {
+  color: var(--accent-blue-team);
+}
+
+.duel-card.red strong {
+  color: var(--accent-red-team);
+}
+
+.status-row {
+  margin-top: 0.5rem;
+  display: inline-flex;
+  align-items: center;
+  gap: 0.45rem;
+  border: 1px solid var(--line-soft);
+  border-radius: 999px;
+  padding: 0.32rem 0.7rem;
+  color: var(--text-muted);
+  font-size: 0.82rem;
+}
+
+.live-dot {
+  width: 8px;
+  height: 8px;
+  border-radius: 999px;
+  background: var(--accent-danger);
+  animation: pulseLive 1.4s ease-in-out infinite;
+}
+
+.analysis-controls {
+  margin-top: 1rem;
+  display: grid;
+  gap: 0.55rem;
+}
+
+.timeline-panel h3 {
+  margin-top: 0.35rem;
+  font-size: 1.25rem;
+}
+
+.playback-btn {
+  justify-self: start;
+}
+
+.slider-label {
+  color: var(--text-muted);
+  font-size: 0.84rem;
+}
+
+.minute-slider {
+  width: 100%;
+  accent-color: var(--accent-primary);
+  cursor: pointer;
+}
+
+.chart-wrap {
+  margin-top: 0.9rem;
+  padding: 0.7rem;
+  border-radius: var(--radius-md);
+  border: 1px solid var(--line-soft);
+  background: rgba(255, 255, 255, 0.02);
+}
+
+.timeline-feed-scroll {
+  margin-top: 0.9rem;
+  max-height: 320px;
+  overflow-y: auto;
+  padding-right: 0.3rem;
+  scroll-behavior: smooth;
+}
+
+.timeline-feed-scroll::-webkit-scrollbar {
+  width: 8px;
+}
+
+.timeline-feed-scroll::-webkit-scrollbar-track {
+  background: rgba(255, 255, 255, 0.04);
+  border-radius: 999px;
+}
+
+.timeline-feed-scroll::-webkit-scrollbar-thumb {
+  background: rgba(198, 167, 105, 0.45);
+  border-radius: 999px;
+}
+
+.probability-chart {
+  width: 100%;
+  height: 220px;
+}
+
+.chart-line {
+  fill: none;
+  stroke-width: 2.4;
+  stroke-linecap: round;
+  stroke-linejoin: round;
+}
+
+.chart-line.xgboost {
+  stroke: var(--accent-success);
+}
+
+.chart-line.lstm {
+  stroke: var(--accent-secondary);
+}
+
+.chart-line.logistic {
+  stroke: #ffe138;
+  stroke-width: 1.8;
+  stroke-dasharray: 4 2;
+}
+
+.chart-indicator {
+  stroke: var(--text-strong);
+  stroke-opacity: 0.5;
+  stroke-dasharray: 5 5;
+}
+
+.chart-event-line {
+  stroke-width: 2;
+  stroke-dasharray: 4 4;
+  opacity: 0.8;
+}
+
+.chart-event-line.kills {
+  stroke: #ff5f7a;
+}
+
+.chart-event-line.objectives {
+  stroke: #19d7ff;
+}
+
+.chart-event-line.captures {
+  stroke: #6cf3a2;
+}
+
+.chart-event-line.structures {
+  stroke: #f0932b;
+}
+
+.chart-axis {
+  margin-top: 0.35rem;
+  display: flex;
+  justify-content: space-between;
+  color: var(--text-subtle);
+  font-size: 0.76rem;
+}
+
+.models-grid {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 0.7rem;
+}
+
+.models-grid-above {
+  margin-top: -0.2rem;
+}
+
+.model-card {
+  border: 1px solid rgba(255, 255, 255, 0.08);
+  border-radius: var(--radius-md);
+  padding: 0.8rem;
+  background: rgba(255, 255, 255, 0.015);
+}
+
+.model-kicker {
+  margin-bottom: 0.35rem;
+  font-size: 0.68rem;
+  color: var(--text-subtle);
+  text-transform: uppercase;
+  letter-spacing: 0.1em;
+  font-weight: 700;
+}
+
+.model-card h3 {
+  font-size: 0.84rem;
+  margin-bottom: 0.5rem;
+  min-height: 2.1em;
+}
+
+.model-value {
+  font-size: 1.4rem;
+  font-family: var(--font-display);
+}
+
+.model-dual-values {
+  display: grid;
+  gap: 0.15rem;
+}
+
+.model-value.blue {
+  color: var(--accent-blue-team);
+  font-size: 1.06rem;
+}
+
+.model-value.red {
+  color: var(--accent-red-team);
+  font-size: 1.06rem;
+}
+
+.model-track {
+  margin-top: 0.42rem;
+  height: 6px;
+  border-radius: 999px;
+  background: rgba(255, 95, 122, 0.25);
+  overflow: hidden;
+}
+
+.model-fill {
+  height: 100%;
+  transition: width 800ms cubic-bezier(0.22, 1, 0.36, 1);
+  background: linear-gradient(90deg, rgba(25, 215, 255, 0.35), var(--accent-blue-team));
+}
+
+.timeline-list {
+  display: grid;
+  gap: 0.6rem;
+}
+
+.feed-placeholder {
+  color: var(--text-subtle);
+  font-size: 0.85rem;
+  border: 1px dashed var(--line-soft);
+  border-radius: var(--radius-sm);
+  padding: 0.75rem;
+}
+
+.filter-row {
+  margin-top: 0.45rem;
+  margin-bottom: 0.7rem;
+  display: flex;
+  flex-wrap: wrap;
+  gap: 0.45rem;
+}
+
+.filter-chip {
+  border: 1px solid var(--line-soft);
+  background: rgba(255, 255, 255, 0.02);
+  color: var(--text-muted);
+  border-radius: 999px;
+  padding: 0.3rem 0.7rem;
+  font-size: 0.74rem;
+  text-transform: uppercase;
+  letter-spacing: 0.07em;
+  cursor: pointer;
+}
+
+.filter-chip.active {
+  color: var(--text-strong);
+  background: rgba(255, 255, 255, 0.08);
+}
+
+.timeline-item {
+  border: 0;
+  text-align: left;
+  width: 100%;
+  cursor: pointer;
+  border-left: 2px solid rgba(255, 255, 255, 0.16);
+  padding: 0.5rem 0.8rem;
+  background: rgba(255, 255, 255, 0.02);
+  border-radius: 0 var(--radius-sm) var(--radius-sm) 0;
+  opacity: 0.5;
+  transition: opacity 240ms ease, border-color 240ms ease, transform 240ms ease;
+}
+
+.timeline-item.active {
+  opacity: 1;
+  border-color: var(--accent-primary);
+  transform: translateX(4px);
+}
+
+.timeline-item.latest-item {
+  animation: latestAppear 520ms cubic-bezier(0.2, 0.9, 0.3, 1);
+}
+
+.timeline-item:hover {
+  opacity: 0.86;
+}
+
+.event-type-tag {
+  margin-top: 0.3rem;
+  display: inline-block;
+  border-radius: 999px;
+  padding: 0.18rem 0.5rem;
+  border: 1px solid rgba(255, 255, 255, 0.14);
+  color: var(--text-subtle);
+  font-size: 0.68rem;
+  text-transform: uppercase;
+  letter-spacing: 0.08em;
+}
+
+.turning-points-definition {
+  margin-top: 0.9rem;
+  border-top: 1px dashed var(--line-soft);
+  padding-top: 0.7rem;
+}
+
+.turning-panel h3 {
+  margin-top: 0.45rem;
+  margin-bottom: 0.8rem;
+  font-size: 1.15rem;
+}
+
+.turning-grid {
+  display: grid;
+  grid-template-columns: repeat(2, minmax(0, 1fr));
+  gap: 0.55rem;
+  max-height: 420px;
+  overflow-y: auto;
+  padding-right: 0.5rem;
+}
+
+.turning-grid::-webkit-scrollbar {
+  width: 6px;
+}
+
+.turning-grid::-webkit-scrollbar-track {
+  background: rgba(255, 255, 255, 0.02);
+  border-radius: 999px;
+}
+
+.turning-grid::-webkit-scrollbar-thumb {
+  background: rgba(198, 167, 105, 0.3);
+  border-radius: 999px;
+}
+
+.turning-card {
+  border: 0;
+  text-align: left;
+  width: 100%;
+  cursor: pointer;
+  border-left: 2px solid rgba(255, 255, 255, 0.16);
+  padding: 0.5rem 0.8rem;
+  background: rgba(255, 255, 255, 0.02);
+  border-radius: 0 var(--radius-sm) var(--radius-sm) 0;
+  opacity: 0.62;
+  transition: opacity 240ms ease, border-color 240ms ease, transform 240ms ease;
+}
+
+.turning-card.active {
+  opacity: 1;
+  border-color: var(--accent-primary);
+  transform: translateX(-3px);
+}
+
+.definition-title {
+  margin-bottom: 0.32rem;
+  color: var(--text-strong);
+  font-size: 0.84rem;
+  font-weight: 700;
+}
+
+.turning-points-definition p {
+  font-size: 0.82rem;
+  color: var(--text-muted);
+}
+
+.timeline-clock {
+  font-family: var(--font-display);
+  font-size: 0.96rem;
+  color: var(--text-strong);
+  text-shadow: 0 0 10px rgba(198, 167, 105, 0.35);
+}
+
+.timeline-text {
+  margin-top: 0.15rem;
+  color: var(--text-muted);
+  font-size: 0.89rem;
+}
+
+.team-blue {
+  color: var(--accent-blue-team);
+  font-weight: 700;
+}
+
+.team-red {
+  color: var(--accent-red-team);
+  font-weight: 700;
+}
+
+@keyframes riseIn {
+  from {
+    opacity: 0;
+    transform: translateY(16px);
+  }
+  to {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+
+@keyframes pulseLive {
+  0%,
+  100% {
+    box-shadow: 0 0 0 0 rgba(196, 88, 88, 0.42);
+  }
+  60% {
+    box-shadow: 0 0 0 8px rgba(196, 88, 88, 0);
+  }
+}
+
+@keyframes latestAppear {
+  0% {
+    opacity: 0;
+    transform: translateY(10px);
+  }
+  100% {
+    opacity: 1;
+    transform: translateY(0);
+  }
+}
+
+@keyframes rainbowShift {
+  0% {
+    background-position: 0% 50%;
+    filter: hue-rotate(0deg);
+  }
+  100% {
+    background-position: 220% 50%;
+    filter: hue-rotate(360deg);
+  }
+}
+
+@media (max-width: 980px) {
+  .dashboard-grid {
+    grid-template-columns: 1fr;
+  }
+
+  .models-grid {
+    grid-template-columns: 1fr 1fr;
+  }
+
+  .playback-btn {
+    justify-self: stretch;
+  }
+
+  .duel-probability {
+    grid-template-columns: 1fr;
+  }
+
+  .turning-grid {
+    grid-template-columns: 1fr;
+    grid-template-rows: none;
+  }
+}
+
+@media (max-width: 720px) {
+  .view-shell {
+    width: min(94vw, 1200px);
+  }
+
+  .input-row {
+    grid-template-columns: 1fr;
+  }
+
+  .primary-btn {
+    width: 100%;
+  }
+
+  .models-grid {
+    grid-template-columns: 1fr;
+  }
+
+  .dashboard-header {
+    flex-direction: column;
+    align-items: flex-start;
+  }
+}
+
+@media (prefers-reduced-motion: reduce) {
+  .rise,
+  .live-dot {
+    animation: none;
+  }
+
+  .model-fill,
+  .timeline-item,
+  .primary-btn,
+  .ghost-btn {
+    transition: none;
+  }
+
+  .timeline-item.latest-item {
+    animation: none;
+  }
+
+  .primary-btn:hover {
+    animation: none;
+  }
+}

frontend/src/App.jsx

@@ -0,0 +1,526 @@
+import { useEffect, useMemo, useRef, useState } from 'react';
+import './App.css';
+
+const MATCH_DURATION = 70;
+const SUPPORTED_REGION = 'US only';
+const API_ROUTING_LABEL = 'NA1 platform + AMERICAS match routing';
+
+// ─── Input sanitization ───────────────────────────────────────────────────────
+// Accepted format: one or more uppercase letters/digits, an underscore, then
+// one or more digits only.  Examples: NA1_1234567890  EUW1_9876543210
+const MATCH_ID_REGEX = /^[A-Z0-9]+_\d+$/;
+const MATCH_ID_MAX_LEN = 30;
+
+const sanitizeMatchId = (raw) =>
+  raw
+    .toUpperCase()
+    .replace(/[^A-Z0-9_]/g, '')
+    .replace(/_{2,}/g, '_')
+    .slice(0, MATCH_ID_MAX_LEN);
+
+// ─── Constants ────────────────────────────────────────────────────────────────
+const MODEL_META = [
+  { key: 'xgboost', label: 'XGBoost', short: 'Teamfight Pattern', colorClass: 'xgboost' },
+  { key: 'lstm', label: 'LSTM', short: 'Momentum Curve', colorClass: 'lstm' },
+  { key: 'logreg', label: 'Logistic Regression', short: 'Stability Baseline', colorClass: 'logistic' },
+];
+
+const EVENT_FILTERS = ['all', 'kills', 'objectives', 'structures'];
+const EVENT_TYPE_COLORS = {
+  all: '#c6a769',
+  kills: '#ff5f7a',
+  objectives: '#19d7ff',
+  structures: '#f0932b',
+};
+
+// ─── Utilities ────────────────────────────────────────────────────────────────
+// eslint-disable-next-line no-unused-vars
+const clamp = (value, min, max) => Math.min(Math.max(value, min), max);
+
+// ─── Components ───────────────────────────────────────────────────────────────
+function AnimatedBackground() {
+  return (
+    <div className="background-container" aria-hidden="true">
+      <div className="bg-grid" />
+      <div className="bg-noise" />
+      <div className="bg-scanline" />
+      <div className="bg-wave" />
+      <div className="bg-orb orb-left" />
+      <div className="bg-orb orb-right" />
+      <div className="bg-vignette" />
+    </div>
+  );
+}
+
+function LandingView({ matchId, onMatchIdChange, onSimulate, error, isLoading, fetchError }) {
+  return (
+    <section className="view-shell landing-shell">
+      <article className="landing-card rise">
+        <p className="eyebrow centered">Post-Match Analyzer</p>
+        <h1 className="landing-title centered">Rift Breakdown</h1>
+        <p className="landing-subtitle">
+          Analyze a finished match and inspect how the win probability evolved through each key moment.
+        </p>
+
+        <div className="landing-metrics" role="presentation">
+          <span>Region available: {SUPPORTED_REGION}</span>
+          <span>{API_ROUTING_LABEL}</span>
+          <span>Player-friendly insights</span>
+          <span>Timeline breakdown</span>
+        </div>
+
+        <form className="match-form" onSubmit={onSimulate}>
+          <label htmlFor="match-id" className="input-label">
+            Match ID
+          </label>
+
+          <div className={`input-row ${error ? 'has-error' : ''}`}>
+            <input
+              id="match-id"
+              type="text"
+              className="match-input"
+              placeholder="NA1_1234567890"
+              value={matchId}
+              onChange={(event) => onMatchIdChange(sanitizeMatchId(event.target.value))}
+              autoComplete="off"
+              spellCheck="false"
+              inputMode="text"
+              aria-invalid={error ? 'true' : 'false'}
+              aria-describedby="match-help"
+              disabled={isLoading}
+            />
+            <button type="submit" className="primary-btn" disabled={isLoading}>
+              {isLoading ? 'Analyzing...' : 'Analyze Match'}
+            </button>
+          </div>
+
+          <p
+            id="match-help"
+            className={`helper-text ${error || fetchError ? 'error' : ''}`}
+          >
+            {error
+              ? 'Match ID must follow the format REGION_DIGITS — e.g. NA1_1234567890.'
+              : fetchError
+                ? fetchError
+                : 'Use a completed US match ID (NA routing only). Format: NA1_1234567890.'}
+          </p>
+        </form>
+      </article>
+    </section>
+  );
+}
+
+function ModelCard({ model, value }) {
+  const redValue = 100 - value;
+  return (
+    <article className={`model-card ${model.colorClass}`}>
+      <p className="model-kicker">{model.short}</p>
+      <h3>{model.label}</h3>
+      <div className="model-dual-values">
+        <p className="model-value blue">Blue: {value.toFixed(1)}%</p>
+        <p className="model-value red">Red: {redValue.toFixed(1)}%</p>
+      </div>
+      <div className="model-track" aria-hidden="true">
+        <div className="model-fill" style={{ width: `${value}%` }} />
+      </div>
+    </article>
+  );
+}
+
+function ProbabilityChart({ history, minute, selectedFilter, events }) {
+  const chartWidth = 760;
+  const chartHeight = 220;
+  const maxIndex = history.length - 1;
+
+  const toX = (index) => (index / maxIndex) * chartWidth;
+  const toY = (value) => chartHeight - (value / 100) * chartHeight;
+
+  const buildPath = (modelKey) =>
+    history
+      .map((entry, index) => `${index === 0 ? 'M' : 'L'} ${toX(index)} ${toY(entry[modelKey])}`)
+      .join(' ');
+
+  const indicatorX = toX(minute);
+
+  const highlightedEvents = events.filter(
+    (event) =>
+      (selectedFilter === 'all' || event.type === selectedFilter) &&
+      event.minute <= minute
+  );
+
+  const maxMark = history.length > 0 ? history[history.length - 1].minute : MATCH_DURATION;
+  const axisMarks = [0, Math.round(maxMark / 3), Math.round((maxMark * 2) / 3), maxMark];
+
+  return (
+    <section className="chart-wrap">
+      <svg
+        className="probability-chart"
+        viewBox={`0 0 ${chartWidth} ${chartHeight}`}
+        preserveAspectRatio="none"
+        role="img"
+        aria-label="Win probability chart by minute"
+      >
+        <path d={buildPath('xgboost')} className="chart-line xgboost" />
+        <path d={buildPath('lstm')} className="chart-line lstm" />
+        <path d={buildPath('logreg')} className="chart-line logistic" />
+
+        {highlightedEvents.map((event, index) => (
+          <line
+            key={`chart-event-${index}`}
+            x1={toX(event.minute)} y1="0"
+            x2={toX(event.minute)} y2={chartHeight}
+            className={`chart-event-line ${event.type}`}
+          />
+        ))}
+
+        <line
+          x1={indicatorX} y1="0"
+          x2={indicatorX} y2={chartHeight}
+          className="chart-indicator"
+        />
+      </svg>
+
+      <div className="chart-axis">
+        {axisMarks.map((mark) => (
+          <span key={mark}>{mark}m</span>
+        ))}
+      </div>
+    </section>
+  );
+}
+
+function DashboardView({
+  matchId,
+  minute,
+  maxDuration,
+  probabilities,
+  history,
+  events,
+  isPlaying,
+  selectedFilter,
+  onFilterChange,
+  onTogglePlayback,
+  onSetMinute,
+  onBack,
+  blueWin,
+}) {
+  const feedScrollRef = useRef(null);
+  const previousActiveCountRef = useRef(0);
+
+  const visibleEvents = events.filter((event) => selectedFilter === 'all' || event.type === selectedFilter);
+  const activeFeedEvents = visibleEvents
+    .filter((event) => event.minute <= minute)
+    .sort((a, b) => b.minute - a.minute);
+  const matrixEvents = [...visibleEvents].sort((a, b) => a.minute - b.minute);
+
+  useEffect(() => {
+    if (!feedScrollRef.current) return;
+    if (activeFeedEvents.length > previousActiveCountRef.current) {
+      feedScrollRef.current.scrollTo({ top: 0, behavior: 'smooth' });
+    }
+    previousActiveCountRef.current = activeFeedEvents.length;
+  }, [activeFeedEvents.length, minute, selectedFilter]);
+
+  return (
+    <section className="view-shell dashboard-shell rise">
+      <header className="dashboard-header">
+        <div className="header-left">
+          <button type="button" className="ghost-btn" onClick={onBack}>
+            Back to Landing
+          </button>
+          <p className="target-match">
+            Reviewing <strong>{matchId}</strong>
+          </p>
+        </div>
+
+        {blueWin !== null && (
+          <div className={`winner-badge ${blueWin ? 'blue-win' : 'red-win'}`}>
+            <span className="winner-label">Match Winner</span>
+            <span className="winner-name">{blueWin ? 'Blue Team' : 'Red Team'}</span>
+          </div>
+        )}
+      </header>
+
+      <main className="dashboard-grid">
+        <section className="panel primary-panel probability-panel">
+          <h2>Match Analysis — Blue vs Red Win Probability</h2>
+        </section>
+
+        <div className="models-grid models-grid-above">
+          {MODEL_META.map((model) => (
+            <ModelCard key={model.key} model={model} value={probabilities[model.key]} />
+          ))}
+        </div>
+
+        <section className="panel timeline-panel">
+          <p className="panel-kicker">Timeline & Playback</p>
+          <h3>Match Flow</h3>
+
+          <div className="status-row">
+            <span className="live-dot" />
+            <span>Minute {String(minute).padStart(2, '0')}</span>
+          </div>
+
+          <div className="analysis-controls">
+            <button type="button" className="ghost-btn playback-btn" onClick={onTogglePlayback}>
+              {isPlaying ? 'Pause' : 'Play'} Timeline
+            </button>
+            <label htmlFor="minute-range" className="slider-label">
+              Time Window: {minute}m
+            </label>
+            <input
+              id="minute-range"
+              className="minute-slider"
+              type="range"
+              min="0"
+              max={maxDuration}
+              value={minute}
+              onChange={(event) => onSetMinute(Number(event.target.value))}
+            />
+          </div>
+
+          <ProbabilityChart
+            history={history}
+            minute={minute}
+            selectedFilter={selectedFilter}
+            events={events}
+          />
+
+          <div className="timeline-feed-scroll" ref={feedScrollRef}>
+            <div className="timeline-list">
+              {activeFeedEvents.length === 0 ? (
+                <p className="feed-placeholder">
+                  No active events yet. Press play or move the slider forward.
+                </p>
+              ) : (
+                activeFeedEvents.map((event, index) => (
+                  <button
+                    key={`feed-${index}`}
+                    type="button"
+                    className={`timeline-item active ${index === 0 ? 'latest-item' : ''}`}
+                    onClick={() => onSetMinute(event.minute)}
+                  >
+                    <p className="timeline-clock">{event.clock}</p>
+                    <p className="timeline-text">
+                      <span className={event.team === 'Blue' ? 'team-blue' : 'team-red'}>
+                        {event.team}
+                      </span>{' '}
+                      {event.text}
+                    </p>
+                    <span className="event-type-tag">{event.type}</span>
+                  </button>
+                ))
+              )}
+            </div>
+          </div>
+        </section>
+
+        <section className="panel turning-panel">
+          <p className="panel-kicker">Key Turning Points</p>
+          <h3>Event Matrix</h3>
+
+          <div className="filter-row" role="tablist" aria-label="Filter event types">
+            {EVENT_FILTERS.map((filter) => (
+              <button
+                key={filter}
+                type="button"
+                className={`filter-chip ${selectedFilter === filter ? 'active' : ''}`}
+                onClick={() => onFilterChange(filter)}
+                style={
+                  selectedFilter === filter
+                    ? { borderColor: EVENT_TYPE_COLORS[filter], color: EVENT_TYPE_COLORS[filter] }
+                    : undefined
+                }
+              >
+                {filter}
+              </button>
+            ))}
+          </div>
+
+          <div className="turning-grid" aria-live="polite">
+            {matrixEvents.map((event, index) => {
+              const isActive = minute >= event.minute;
+              return (
+                <button
+                  key={`matrix-${index}`}
+                  type="button"
+                  className={`turning-card ${isActive ? 'active' : ''}`}
+                  onClick={() => onSetMinute(event.minute)}
+                >
+                  <p className="timeline-clock">{event.clock}</p>
+                  <p className="timeline-text">
+                    <span className={event.team === 'Blue' ? 'team-blue' : 'team-red'}>
+                      {event.team}
+                    </span>{' '}
+                    {event.text}
+                  </p>
+                  <span className="event-type-tag">{event.type}</span>
+                </button>
+              );
+            })}
+          </div>
+        </section>
+      </main>
+    </section>
+  );
+}
+
+// ─── Root ─────────────────────────────────────────────────────────────────────
+function App() {
+  const [view, setView] = useState('landing');
+  const [matchId, setMatchId] = useState('');
+  const [showInputError, setShowInputError] = useState(false);
+  const [isLoading, setIsLoading] = useState(false);
+  const [fetchError, setFetchError] = useState(null);
+  const [matchData, setMatchData] = useState(null);
+  const [timeMin, setTimeMin] = useState(0);
+  const [isPlaying, setIsPlaying] = useState(false);
+  const [selectedFilter, setSelectedFilter] = useState('all');
+
+  // ── Derived data ────────────────────────────────────────────────────────────
+  const history = useMemo(() => {
+    if (!matchData) return [];
+    const { minutes, predictions } = matchData;
+
+    return minutes.map((minute, i) => {
+      const getProb = (key) => {
+        const raw = predictions[key];
+        if (raw === undefined || raw === null) return 50;
+        if (!Array.isArray(raw)) return raw <= 1.0 ? raw * 100 : raw;
+        if (raw.length === 0) return 50;
+        const val = raw[i] !== undefined ? raw[i] : raw[raw.length - 1];
+        return val <= 1.0 ? val * 100 : val;
+      };
+
+      return {
+        minute,
+        xgboost: getProb('xgboost'),
+        lstm: getProb('lstm'),
+        logreg: getProb('logreg'),
+      };
+    });
+  }, [matchData]);
+
+  const maxDuration = history.length > 0 ? history[history.length - 1].minute : MATCH_DURATION;
+
+  const probabilities = useMemo(() => {
+    if (!history.length || timeMin === 0) return { xgboost: 0, lstm: 0, logreg: 0 };
+    const entry = history.find((h) => h.minute === timeMin) || history[history.length - 1];
+    return { xgboost: entry.xgboost, lstm: entry.lstm, logreg: entry.logreg };
+  }, [history, timeMin]);
+
+  const events = useMemo(() => matchData?.events || [], [matchData]);
+
+  // ── Playback ticker ─────────────────────────────────────────────────────────
+  useEffect(() => {
+    if (!isPlaying || view !== 'dashboard') return undefined;
+
+    const interval = setInterval(() => {
+      setTimeMin((prev) => {
+        if (prev >= maxDuration) {
+          setIsPlaying(false);
+          return prev;
+        }
+        return prev + 1;
+      });
+    }, 1200);
+
+    return () => clearInterval(interval);
+  }, [isPlaying, view, maxDuration]);
+
+  // ── Handlers ────────────────────────────────────────────────────────────────
+  const handleMatchIdChange = (value) => {
+    setMatchId(value);
+    // Clear the validation error as soon as the input becomes valid
+    if (showInputError && MATCH_ID_REGEX.test(value)) {
+      setShowInputError(false);
+    }
+  };
+
+  const handleSimulate = async (event) => {
+    event.preventDefault();
+
+    const value = matchId.trim();
+
+    // Validate against the strict regex — not just "non-empty"
+    if (!value || !MATCH_ID_REGEX.test(value)) {
+      setShowInputError(true);
+      return;
+    }
+
+    setShowInputError(false);
+    setIsLoading(true);
+    setFetchError(null);
+
+    try {
+      const isDev = window.location.hostname === 'localhost' || window.location.hostname === '127.0.0.1';
+      const apiUrl = import.meta.env.VITE_API_URL || (isDev ? 'http://localhost:8000' : '');
+
+      const response = await fetch(`${apiUrl}/api/v1/predict/${value}`, {
+        method: 'POST',
+        headers: {
+          'Accept': 'application/json'
+        }
+      });
+
+      if (!response.ok) {
+        const errorData = await response.json().catch(() => ({}));
+        throw new Error(errorData.detail || `Server error: ${response.status}`);
+      }
+
+      const data = await response.json();
+      setMatchData(data);
+      setView('dashboard');
+      setTimeMin(0);
+      setIsPlaying(true);
+    } catch (err) {
+      setFetchError(err.message);
+    } finally {
+      setIsLoading(false);
+    }
+  };
+
+  const handleBackToLanding = () => {
+    setView('landing');
+    setIsPlaying(false);
+  };
+
+  // ── Render ──────────────────────────────────────────────────────────────────
+  return (
+    <>
+      <AnimatedBackground />
+
+      {view === 'landing' ? (
+        <LandingView
+          matchId={matchId}
+          onMatchIdChange={handleMatchIdChange}
+          onSimulate={handleSimulate}
+          error={showInputError}
+          isLoading={isLoading}
+          fetchError={fetchError}
+        />
+      ) : (
+        <DashboardView
+          matchId={matchId}
+          minute={timeMin}
+          maxDuration={maxDuration}
+          probabilities={probabilities}
+          history={history}
+          events={events}
+          isPlaying={isPlaying}
+          selectedFilter={selectedFilter}
+          onFilterChange={setSelectedFilter}
+          onTogglePlayback={() => setIsPlaying((current) => !current)}
+          onSetMinute={(minute) => {
+            setTimeMin(minute);
+            setIsPlaying(false);
+          }}
+          onBack={handleBackToLanding}
+          blueWin={matchData?.blue_win ?? null}
+        />
+      )}
+    </>
+  );
+}
+
+export default App;

frontend/src/index.css

@@ -0,0 +1,199 @@
+@import url('https://fonts.googleapis.com/css2?family=Cinzel:wght@500;700;800&family=Manrope:wght@400;500;700&display=swap');
+
+:root {
+  --bg-main: #09090d;
+  --bg-panel: rgba(17, 17, 22, 0.84);
+  --bg-panel-soft: rgba(17, 17, 22, 0.64);
+  --text-strong: #f4f0ea;
+  --text-muted: #b2a998;
+  --text-subtle: #807a71;
+  --accent-primary: #c6a769;
+  --accent-secondary: #20b7ff;
+  --accent-danger: #ff4d6d;
+  --accent-success: #6cf3a2;
+  --accent-blue-team: #19d7ff;
+  --accent-red-team: #ff5f7a;
+  --line-soft: rgba(198, 167, 105, 0.22);
+  --line-strong: rgba(198, 167, 105, 0.45);
+  --shadow-depth: 0 24px 80px rgba(0, 0, 0, 0.45);
+  --radius-lg: 24px;
+  --radius-md: 16px;
+  --radius-sm: 10px;
+  --font-display: 'Cinzel', Georgia, serif;
+  --font-body: 'Manrope', sans-serif;
+}
+
+*,
+*::before,
+*::after {
+  box-sizing: border-box;
+}
+
+html,
+body {
+  margin: 0;
+  min-height: 100%;
+}
+
+body {
+  background: var(--bg-main);
+  color: var(--text-strong);
+  font-family: var(--font-body);
+  line-height: 1.5;
+  overflow-x: hidden;
+}
+
+#root {
+  min-height: 100vh;
+}
+
+h1,
+h2,
+h3,
+h4,
+h5,
+h6 {
+  margin: 0;
+  font-family: var(--font-display);
+  letter-spacing: 0.02em;
+  text-transform: uppercase;
+}
+
+p {
+  margin: 0;
+}
+
+button,
+input {
+  font: inherit;
+}
+
+.background-container {
+  position: fixed;
+  inset: 0;
+  z-index: -1;
+  overflow: hidden;
+  background:
+    radial-gradient(1200px 650px at 8% 12%, rgba(25, 215, 255, 0.38), transparent 60%),
+    radial-gradient(1000px 600px at 92% 78%, rgba(255, 95, 122, 0.28), transparent 60%),
+    linear-gradient(160deg, #07070a 0%, #0e0f16 52%, #0b0b0f 100%);
+}
+
+.bg-noise,
+.bg-vignette,
+.bg-grid,
+.bg-scanline,
+.bg-wave,
+.bg-orb {
+  position: absolute;
+  inset: 0;
+  pointer-events: none;
+}
+
+.bg-noise {
+  opacity: 0.32;
+  background-image: radial-gradient(rgba(244, 240, 234, 0.15) 0.5px, transparent 0.5px);
+  background-size: 3px 3px;
+  mix-blend-mode: soft-light;
+  animation: noiseDrift 16s steps(8) infinite;
+}
+
+.bg-vignette {
+  background: radial-gradient(circle at center, transparent 35%, rgba(0, 0, 0, 0.52) 100%);
+}
+
+.bg-grid {
+  opacity: 0.34;
+  background-image:
+    linear-gradient(rgba(198, 167, 105, 0.1) 1px, transparent 1px),
+    linear-gradient(90deg, rgba(198, 167, 105, 0.1) 1px, transparent 1px);
+  background-size: 52px 52px;
+  mask-image: radial-gradient(circle at center, black 40%, transparent 100%);
+}
+
+.bg-scanline {
+  opacity: 0.26;
+  background: linear-gradient(180deg, transparent 0%, rgba(25, 215, 255, 0.38) 50%, transparent 100%);
+  transform: translateY(-100%);
+  animation: scanFall 7.5s linear infinite;
+}
+
+.bg-wave {
+  opacity: 0.34;
+  background:
+    radial-gradient(600px 120px at 25% 70%, rgba(25, 215, 255, 0.35), transparent 70%),
+    radial-gradient(700px 140px at 75% 28%, rgba(198, 167, 105, 0.27), transparent 70%);
+  filter: blur(8px);
+  animation: waveShift 14s ease-in-out infinite;
+}
+
+.bg-orb {
+  inset: auto;
+  border-radius: 50%;
+  filter: blur(24px);
+  animation: orbFloat 18s ease-in-out infinite;
+}
+
+.bg-orb.orb-left {
+  width: min(40vw, 460px);
+  height: min(40vw, 460px);
+  left: -8vw;
+  top: 18vh;
+  background: radial-gradient(circle at center, rgba(25, 215, 255, 0.4), rgba(25, 215, 255, 0));
+}
+
+.bg-orb.orb-right {
+  width: min(50vw, 560px);
+  height: min(50vw, 560px);
+  right: -12vw;
+  bottom: -12vh;
+  background: radial-gradient(circle at center, rgba(255, 95, 122, 0.35), rgba(255, 95, 122, 0));
+  animation-delay: -8s;
+}
+
+@keyframes orbFloat {
+  0%,
+  100% {
+    transform: translate3d(0, 0, 0) scale(1);
+  }
+  50% {
+    transform: translate3d(4vw, -2vh, 0) scale(1.1);
+  }
+}
+
+@keyframes noiseDrift {
+  0% {
+    transform: translate(0, 0);
+  }
+  100% {
+    transform: translate(-12px, 9px);
+  }
+}
+
+@keyframes scanFall {
+  0% {
+    transform: translateY(-100%);
+  }
+  100% {
+    transform: translateY(100%);
+  }
+}
+
+@keyframes waveShift {
+  0%,
+  100% {
+    transform: translate3d(0, 0, 0);
+  }
+  50% {
+    transform: translate3d(0, -2vh, 0) scale(1.05);
+  }
+}
+
+@media (prefers-reduced-motion: reduce) {
+  .bg-noise,
+  .bg-wave,
+  .bg-scanline,
+  .bg-orb {
+    animation: none;
+  }
+}

frontend/src/main.jsx

@@ -0,0 +1,10 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.jsx'
+
+createRoot(document.getElementById('root')).render(
+  <StrictMode>
+    <App />
+  </StrictMode>,
+)

frontend/vite.config.js

@@ -0,0 +1,7 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [react()],
+})

models/logistic_regression.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77305d2c094e44e8c72b357f41dcdf902cbafb2ae1574fe07acaecdbecca6488
+size 1519

models/lstm.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8e783fce87cd691d2b55e6b4e3e0905f62f3b6ac2e58f3a5a6083ebc6d5cbb6e
+size 155517

models/scaler_lr.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23845ffd9ab5c334c3059e0a539c5784115f89d923a7d830e7d7d9eb71b126ff
+size 4471

models/xgboost.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ef0adb85a4324e3b4debd5fdf4d92dafecc417ed1dcafd9636a91ce9293f85a
+size 949324