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File size: 1,917 Bytes
7d2a23e 6a290f8 7d2a23e | 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 | from pathlib import Path
from fastapi import FastAPI, HTTPException
from .registry import BundleConfigError, ModelRegistry, RequestValidationError
from .schemas import PredictionRequest
def create_app(bundle_root: Path) -> FastAPI:
registry = ModelRegistry(bundle_root)
app = FastAPI(
title="SQuADDS ML Inference API",
version="0.1.0",
description=(
"HTTP API for running inference against ML models trained in "
"ML_qubit_design and packaged for the SQuADDS Hugging Face Space."
),
)
@app.get("/")
def root() -> dict:
return {
"service": "SQuADDS ML Inference API",
"docs": "/docs",
"models_endpoint": "/models",
"predict_endpoint": "/predict",
}
@app.get("/health")
def health() -> dict:
return {
"status": "ok",
"available_models": registry.available_model_ids(),
"bundle_root": str(bundle_root),
}
@app.get("/models")
def list_models() -> dict:
return {"models": registry.describe_models()}
@app.post("/predict")
def predict(request: PredictionRequest) -> dict:
try:
payload = registry.predict(
model_id=request.model_id,
inputs=request.inputs,
include_scaled_outputs=request.options.include_scaled_outputs,
)
except RequestValidationError as exc:
raise HTTPException(status_code=400, detail=str(exc)) from exc
except BundleConfigError as exc:
raise HTTPException(status_code=500, detail=str(exc)) from exc
except Exception as exc:
raise HTTPException(
status_code=500,
detail=f"Unexpected inference error for model '{request.model_id}': {exc}",
) from exc
return payload
return app
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