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bb21b5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | """FastAPI app for F1 pit-stop model inference."""
from __future__ import annotations
import io
from typing import Any
import pandas as pd
from fastapi import FastAPI, File, HTTPException, UploadFile
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel, Field
from src import config
from src.inference import (
RAW_INPUT_COLUMNS,
load_sklearn_model,
predict_records,
prepare_features,
)
class PredictRequest(BaseModel):
records: list[dict[str, Any]] = Field(..., min_length=1)
class PredictResponse(BaseModel):
predictions: list[int]
class DashboardRow(BaseModel):
driver: str
lap: int
compound: str
stint: int
tyreLife: int
lapTime: float
lapDelta: float
cumDeg: float
raceProg: float
position: int
posChange: int
isStintStart: bool
class DashboardPredictRequest(BaseModel):
race: str
year: int
rows: list[DashboardRow] = Field(..., min_length=1)
class DashboardPrediction(BaseModel):
driver: str
lap: int
pPit: float
pred: int
class DashboardPredictResponse(BaseModel):
predictions: list[DashboardPrediction]
app = FastAPI(title="F1 Pit-Stop Prediction API")
_sklearn_model: Any = None
COMPOUND_MAP = {"S": "SOFT", "M": "MEDIUM", "H": "HARD", "I": "INTERMEDIATE", "W": "WET"}
def _get_sklearn_model() -> Any:
global _sklearn_model
if _sklearn_model is None:
_sklearn_model = load_sklearn_model()
return _sklearn_model
@app.get("/health")
def health() -> dict[str, str]:
return {"status": "ok"}
@app.post("/predict", response_model=PredictResponse)
def predict(request: PredictRequest) -> PredictResponse:
try:
predictions = predict_records(request.records)
except ValueError as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
return PredictResponse(predictions=predictions)
@app.post("/predict/dashboard", response_model=DashboardPredictResponse)
def predict_dashboard(request: DashboardPredictRequest) -> DashboardPredictResponse:
rows = request.rows
raw = pd.DataFrame({
"id": [f"{r.driver}_{r.lap}" for r in rows],
"Driver": [r.driver for r in rows],
"Compound": [COMPOUND_MAP.get(r.compound, r.compound) for r in rows],
"Race": [request.race] * len(rows),
"Year": [request.year] * len(rows),
"PitStop": [int(r.isStintStart) for r in rows],
"LapNumber": [r.lap for r in rows],
"Stint": [r.stint for r in rows],
"TyreLife": [r.tyreLife for r in rows],
"Position": [r.position for r in rows],
"LapTime (s)": [r.lapTime for r in rows],
"LapTime_Delta": [r.lapDelta for r in rows],
"Cumulative_Degradation": [r.cumDeg for r in rows],
"RaceProgress": [r.raceProg for r in rows],
"Position_Change": [r.posChange for r in rows],
})
try:
model = _get_sklearn_model()
features = prepare_features(raw)
probs = model.predict_proba(features)[:, 1]
preds = (probs >= 0.5).astype(int)
except ValueError as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
return DashboardPredictResponse(predictions=[
DashboardPrediction(driver=r.driver, lap=r.lap, pPit=float(p), pred=int(d))
for r, p, d in zip(rows, probs, preds)
])
@app.post("/predict/csv")
async def predict_csv(file: UploadFile = File(...)) -> dict[str, Any]:
contents = await file.read()
if not contents:
raise HTTPException(status_code=400, detail="Empty file")
try:
raw = pd.read_csv(io.BytesIO(contents))
except Exception as exc:
raise HTTPException(status_code=400, detail=f"Could not parse CSV: {exc}") from exc
try:
model = _get_sklearn_model()
features = prepare_features(raw)
probs = model.predict_proba(features)[:, 1]
preds = (probs >= 0.5).astype(int)
except ValueError as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
out = raw.copy()
out["pPit"] = probs
out["pred"] = preds
f1 = None
if config.TARGET_COL in raw.columns:
from sklearn.metrics import f1_score
try:
f1 = float(f1_score(raw[config.TARGET_COL].astype(int), preds, average="macro"))
except Exception:
f1 = None
return {
"count": int(len(out)),
"f1_macro": f1,
"expected_columns": RAW_INPUT_COLUMNS,
"rows": out.to_dict(orient="records"),
}
app.mount("/", StaticFiles(directory="dashboard", html=True), name="static")
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