| """Local FastAPI service: STEP-file-in, classification-out. One process, models loaded once. |
| |
| Designed to be launched by a C# host app as a child process. The service binds to |
| 127.0.0.1 only; never exposes a network-facing port. |
| |
| Startup protocol: |
| - Bind a free port (default: OS-assigned via --port 0). |
| - Once models are loaded and the server is accepting requests, write |
| `READY port=<PORT>` to stdout (single line). The host reads that line to learn |
| where to connect. |
| - On `POST /shutdown` (or SIGTERM), drain in-flight requests and exit. |
| |
| Endpoints: |
| GET /health -> service / model state |
| POST /classify {step_path} -> single STEP classification |
| POST /classify_batch {paths} -> multiple in one call (sequential, single-process) |
| POST /shutdown -> graceful exit |
| """ |
| from __future__ import annotations |
| import argparse |
| import os |
| import sys |
| import tempfile |
| import threading |
| import time |
| from pathlib import Path |
| from typing import List, Optional |
|
|
| from fastapi import FastAPI, HTTPException |
| from pydantic import BaseModel |
| import uvicorn |
|
|
| from heg_brep import ( |
| DEFAULT_PASS1_MODEL, DEFAULT_ELBOW_MODEL, DEFAULT_TEE_MODEL, |
| ) |
| from heg_brep.inference import LoadedModel, TwoPassClassifier |
| from heg_brep.extraction import extract_step_to_npz |
|
|
| |
| |
| |
| import pipeline.extract_brep_extractor_data_from_step |
|
|
|
|
| class State: |
| classifier: Optional[TwoPassClassifier] = None |
| started_at: float = 0.0 |
| device: str = "cpu" |
| pass2_min_conf: float = 0.85 |
| pass2_tau: float = 0.0 |
|
|
|
|
| app = FastAPI(title="HEG BRep component identification") |
|
|
|
|
| class ClassifyRequest(BaseModel): |
| step_path: str |
| |
| npz_keep_dir: Optional[str] = None |
|
|
|
|
| class ClassifyBatchRequest(BaseModel): |
| step_paths: List[str] |
| npz_keep_dir: Optional[str] = None |
|
|
|
|
| @app.get("/health") |
| def health(): |
| return { |
| "status": "ok", |
| "models_loaded": State.classifier is not None, |
| "device": State.device, |
| "uptime_sec": round(time.time() - State.started_at, 2), |
| } |
|
|
|
|
| def _classify_one(step_path: str, npz_keep_dir: Optional[str]) -> dict: |
| if State.classifier is None: |
| raise HTTPException(status_code=503, detail="models not loaded yet") |
| sp = Path(step_path).expanduser() |
| if not sp.exists(): |
| return {"step_path": str(sp), "status": "error", "error": "step_path not found"} |
| out_dir = Path(npz_keep_dir).expanduser().resolve() if npz_keep_dir \ |
| else Path(tempfile.mkdtemp(prefix="heg_brep_npz_")) |
| try: |
| npz = extract_step_to_npz(sp, out_dir) |
| except Exception as exc: |
| return {"step_path": str(sp), "status": "extraction_failed", "error": str(exc)[:500]} |
| try: |
| result = State.classifier.classify_npz(npz) |
| except Exception as exc: |
| return {"step_path": str(sp), "status": "inference_failed", "error": str(exc)[:500], |
| "npz_path": str(npz)} |
| result.update({"step_path": str(sp), "status": "ok", "npz_path": str(npz)}) |
| return result |
|
|
|
|
| @app.post("/classify") |
| def classify(req: ClassifyRequest): |
| return _classify_one(req.step_path, req.npz_keep_dir) |
|
|
|
|
| @app.post("/classify_batch") |
| def classify_batch(req: ClassifyBatchRequest): |
| return {"results": [_classify_one(p, req.npz_keep_dir) for p in req.step_paths]} |
|
|
|
|
| @app.post("/shutdown") |
| def shutdown(): |
| threading.Thread(target=lambda: (time.sleep(0.2), os._exit(0)), daemon=True).start() |
| return {"status": "shutting_down"} |
|
|
|
|
| def _load_models(args) -> None: |
| pass1 = LoadedModel(Path(args.pass1_model), device=args.device) |
| elbow = LoadedModel(Path(args.elbow_model), device=args.device) if Path(args.elbow_model).exists() else None |
| tee = LoadedModel(Path(args.tee_model), device=args.device) if Path(args.tee_model).exists() else None |
| State.classifier = TwoPassClassifier( |
| pass1=pass1, elbow=elbow, tee=tee, |
| pass2_min_conf=args.pass2_min_conf, pass2_tau=args.pass2_tau, |
| ) |
| State.device = args.device |
| State.pass2_min_conf = args.pass2_min_conf |
| State.pass2_tau = args.pass2_tau |
|
|
|
|
| def parse_args() -> argparse.Namespace: |
| ap = argparse.ArgumentParser(description="HEG BRep classification service") |
| ap.add_argument("--host", default="127.0.0.1") |
| ap.add_argument("--port", type=int, default=0, |
| help="Port to bind. 0 = let the OS pick. The chosen port is " |
| "printed to stdout as a single line `READY port=<N>`.") |
| ap.add_argument("--pass1_model", default=str(DEFAULT_PASS1_MODEL)) |
| ap.add_argument("--elbow_model", default=str(DEFAULT_ELBOW_MODEL)) |
| ap.add_argument("--tee_model", default=str(DEFAULT_TEE_MODEL)) |
| ap.add_argument("--device", default="cpu", choices=["cpu", "cuda"]) |
| ap.add_argument("--pass2_min_conf", type=float, default=0.85) |
| ap.add_argument("--pass2_tau", type=float, default=0.0) |
| return ap.parse_args() |
|
|
|
|
| def main() -> int: |
| args = parse_args() |
| State.started_at = time.time() |
| print(f"[heg_brep] loading models from:", file=sys.stderr) |
| print(f" pass1: {args.pass1_model}", file=sys.stderr) |
| print(f" elbow: {args.elbow_model}", file=sys.stderr) |
| print(f" tee : {args.tee_model}", file=sys.stderr) |
| print(f" device: {args.device}", file=sys.stderr) |
| t0 = time.time() |
| _load_models(args) |
| print(f"[heg_brep] models loaded in {time.time() - t0:.1f}s", file=sys.stderr) |
|
|
| |
| import socket |
| sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) |
| sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) |
| sock.bind((args.host, int(args.port))) |
| bound_port = sock.getsockname()[1] |
| print(f"READY port={bound_port}", flush=True) |
|
|
| config = uvicorn.Config(app=app, host=args.host, port=bound_port, log_level="warning") |
| server = uvicorn.Server(config) |
| |
| |
| sock.close() |
| server.run() |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| sys.exit(main()) |
|
|