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4937cba | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | from __future__ import annotations
import json
import logging
import time
from dataclasses import dataclass, field
from threading import Lock
from typing import Annotated
from uuid import uuid4
from fastapi import Depends, FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse
from api.schemas import (
BatchPredictionRequest,
BatchPredictionResponse,
HealthResponse,
MetricsResponse,
PredictionResponse,
Transaction,
)
from api.service import InferenceService, load_inference_service
logger = logging.getLogger("api")
if not logger.handlers:
logging.basicConfig(level=logging.INFO)
@dataclass
class MonitoringState:
total_requests: int = 0
error_count: int = 0
total_predictions: int = 0
fraud_predictions: int = 0
total_latency_ms: float = 0.0
_lock: Lock = field(default_factory=Lock)
def record_request(self, *, latency_ms: float, status_code: int) -> None:
with self._lock:
self.total_requests += 1
self.total_latency_ms += latency_ms
if status_code >= 400:
self.error_count += 1
def record_predictions(self, predictions: list[dict[str, object]]) -> None:
fraud_count = sum(1 for p in predictions if bool(p.get("is_fraud")))
with self._lock:
self.total_predictions += len(predictions)
self.fraud_predictions += fraud_count
def snapshot(self) -> dict[str, float | int]:
with self._lock:
avg_latency = self.total_latency_ms / self.total_requests if self.total_requests else 0.0
error_rate = self.error_count / self.total_requests if self.total_requests else 0.0
fraud_rate = (
self.fraud_predictions / self.total_predictions if self.total_predictions else 0.0
)
return {
"total_requests": self.total_requests,
"error_count": self.error_count,
"error_rate": float(error_rate),
"total_predictions": self.total_predictions,
"fraud_predictions": self.fraud_predictions,
"fraud_prediction_rate": float(fraud_rate),
"avg_latency_ms": float(avg_latency),
}
app = FastAPI(title="Fraud Detection API", version="0.3.0")
monitoring_state = MonitoringState()
@app.middleware("http")
async def add_observability(request: Request, call_next):
request_id = request.headers.get("X-Request-ID", str(uuid4()))
start = time.perf_counter()
status_code = 500
try:
response = await call_next(request)
status_code = response.status_code
except Exception:
latency_ms = (time.perf_counter() - start) * 1000
monitoring_state.record_request(latency_ms=latency_ms, status_code=status_code)
logger.exception(
json.dumps(
{
"event": "request_error",
"request_id": request_id,
"path": request.url.path,
"method": request.method,
"latency_ms": round(latency_ms, 2),
}
)
)
raise
latency_ms = (time.perf_counter() - start) * 1000
monitoring_state.record_request(latency_ms=latency_ms, status_code=status_code)
response.headers["X-Process-Time-Ms"] = f"{latency_ms:.2f}"
response.headers["X-Request-ID"] = request_id
logger.info(
json.dumps(
{
"event": "request_complete",
"request_id": request_id,
"path": request.url.path,
"method": request.method,
"status_code": status_code,
"latency_ms": round(latency_ms, 2),
}
)
)
return response
def get_inference_service() -> InferenceService:
try:
return load_inference_service()
except FileNotFoundError as exc:
raise HTTPException(status_code=503, detail=str(exc)) from exc
ServiceDep = Annotated[InferenceService, Depends(get_inference_service)]
@app.exception_handler(ValueError)
async def value_error_handler(_: Request, exc: ValueError) -> JSONResponse:
return JSONResponse(status_code=400, content={"detail": str(exc)})
@app.get("/health", response_model=HealthResponse)
def health(service: ServiceDep) -> HealthResponse:
return HealthResponse(
status="ok",
model_loaded=True,
model_path=str(service.model_path),
preprocessor_path=str(service.preprocessor_path),
threshold=service.threshold,
)
@app.get("/metrics", response_model=MetricsResponse)
def metrics() -> MetricsResponse:
return MetricsResponse(**monitoring_state.snapshot())
@app.post("/predict", response_model=PredictionResponse)
def predict(transaction: Transaction, service: ServiceDep) -> PredictionResponse:
output = service.predict_records([transaction.model_dump()])[0]
monitoring_state.record_predictions([output])
logger.info(
json.dumps(
{
"event": "prediction",
"prediction_count": 1,
"fraud_predictions": int(output["is_fraud"]),
"avg_probability": round(float(output["fraud_probability"]), 6),
"threshold": float(output["threshold"]),
}
)
)
return PredictionResponse(**output)
@app.post("/predict/batch", response_model=BatchPredictionResponse)
def predict_batch(request: BatchPredictionRequest, service: ServiceDep) -> BatchPredictionResponse:
predictions = service.predict_records([record.model_dump() for record in request.transactions])
monitoring_state.record_predictions(predictions)
fraud_count = sum(1 for row in predictions if row["is_fraud"])
avg_probability = sum(float(row["fraud_probability"]) for row in predictions) / len(predictions)
logger.info(
json.dumps(
{
"event": "prediction_batch",
"prediction_count": len(predictions),
"fraud_predictions": fraud_count,
"avg_probability": round(avg_probability, 6),
"threshold": float(predictions[0]["threshold"]),
}
)
)
return BatchPredictionResponse(predictions=[PredictionResponse(**row) for row in predictions])
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