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44cd54e 42029e4 | 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 | """FastAPI application - real-time + batch scoring with Prometheus metrics."""
from __future__ import annotations
import time
from contextlib import asynccontextmanager
from datetime import datetime, timezone
from fastapi import FastAPI, HTTPException, Response
from fastapi.middleware.cors import CORSMiddleware
from prometheus_client import (
Counter,
Gauge,
Histogram,
generate_latest,
CONTENT_TYPE_LATEST,
)
from ..utils.logging import get_logger, setup_logging
from .schemas import (
BatchScoreRequest,
BatchScoreResponse,
HealthResponse,
LoanApplication,
ScoreResponse,
)
from .service import ScoringService
# ---------------------------------------------------------------------- #
# Metrics
# ---------------------------------------------------------------------- #
PREDICT_REQUESTS = Counter(
"loanguard_predict_requests_total",
"Number of scoring requests",
["endpoint", "decision"],
)
PREDICT_LATENCY = Histogram(
"loanguard_predict_latency_seconds",
"Latency of scoring requests",
["endpoint"],
buckets=(0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0),
)
SCORE_HIST = Histogram(
"loanguard_fraud_score",
"Distribution of fraud scores",
buckets=tuple(i / 20 for i in range(21)),
)
FEATURE_PSI = Gauge(
"loanguard_feature_psi",
"Population stability index for a feature vs. training distribution",
["feature"],
)
# ---------------------------------------------------------------------- #
# Lifespan
# ---------------------------------------------------------------------- #
service = ScoringService()
boot_time = time.time()
log = get_logger("api")
@asynccontextmanager
async def lifespan(app: FastAPI): # noqa: D401
setup_logging()
log.info("LoanGuard API booting...")
service.load()
yield
log.info("LoanGuard API shutting down")
# ---------------------------------------------------------------------- #
# App
# ---------------------------------------------------------------------- #
app = FastAPI(
title="LoanGuard Fraud Scoring API",
description="Real-time fraud risk scoring for loan applications.",
version="0.1.0",
lifespan=lifespan,
)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
# ---------------------------------------------------------------------- #
# Routes
# ---------------------------------------------------------------------- #
@app.get("/", tags=["meta"])
def root():
return {
"service": "loanguard",
"version": app.version,
"endpoints": ["/health", "/score", "/score/batch", "/metrics", "/docs"],
}
@app.get("/health", response_model=HealthResponse, tags=["meta"])
def health():
status = "ok" if service.is_ready else "degraded"
return HealthResponse(
status=status, # type: ignore[arg-type]
model_loaded=service.is_ready,
model_version=service.model_version if service.is_ready else None,
uptime_seconds=time.time() - boot_time,
)
@app.post("/score", response_model=ScoreResponse, tags=["scoring"])
def score(app_in: LoanApplication):
if not service.is_ready:
raise HTTPException(status_code=503, detail="Model not loaded yet.")
with PREDICT_LATENCY.labels(endpoint="score").time():
try:
result = service.score_one(app_in)
except Exception as exc:
log.exception("Scoring failed")
raise HTTPException(status_code=500, detail=str(exc)) from exc
PREDICT_REQUESTS.labels(endpoint="score", decision=result.decision).inc()
SCORE_HIST.observe(result.fraud_score)
return result
@app.post("/score/batch", response_model=BatchScoreResponse, tags=["scoring"])
def score_batch(req: BatchScoreRequest):
if not service.is_ready:
raise HTTPException(status_code=503, detail="Model not loaded yet.")
if len(req.applications) > 5000:
raise HTTPException(status_code=413, detail="Batch too large (max 5000).")
with PREDICT_LATENCY.labels(endpoint="score_batch").time():
results = service.score_many(req.applications)
for r in results:
PREDICT_REQUESTS.labels(endpoint="score_batch", decision=r.decision).inc()
SCORE_HIST.observe(r.fraud_score)
return BatchScoreResponse(
scored_at=datetime.now(timezone.utc),
model_version=service.model_version,
results=results,
)
@app.post("/reload", tags=["meta"])
def reload_artifacts():
"""Reload model artifacts (e.g. after a new training run)."""
service.load()
return {"reloaded": True, "ready": service.is_ready}
@app.get("/metrics", tags=["meta"])
def metrics():
return Response(content=generate_latest(), media_type=CONTENT_TYPE_LATEST)
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