fix(unraid): expose sanitized inference error detail

servers/unraid/tail2-pose-server/app/tail2/main.py

@@ -41,6 +41,7 @@ class State:
         self.inference_fault = False
         self.ws_source_ok = False
         self.error_code: str | None = None
+        self.error_detail: str | None = None
         self.frame_id = 0
         self.messages = 0
         self.frames_dropped = 0
@@ -97,7 +98,8 @@ def health():
                     "last_frame_age_ms": round(age, 3) if age is not None else None,
                     "actual_width": state.width or None, "actual_height": state.height or None},
             "models": {"detector_ready": state.models_ready, "pose_ready": state.models_ready},
-            "error_code": state.error_code, "uptime_seconds": round(time.monotonic() - state.started, 1)}
+            "error_code": state.error_code, "error_detail": state.error_detail,
+            "uptime_seconds": round(time.monotonic() - state.started, 1)}
 
 
 @app.get("/v1/capabilities", dependencies=[Depends(authorized)])
@@ -144,14 +146,16 @@ def inference_worker(loop: asyncio.AbstractEventLoop) -> None:
     threshold = float(os.getenv("DETECTION_THRESHOLD", ".35")); max_people = int(os.getenv("MAX_PEOPLE", "4"))
     try:
         models.load(); state.models_ready = True; LOG.info("official detector and pose checkpoints loaded")
-    except Exception:
+    except Exception as exc:
-        state.inference_fault = True; state.error_code = "MODEL_LOAD_FAILED"; LOG.exception("model loading failed"); return
+        state.inference_fault = True; state.error_code = "MODEL_LOAD_FAILED"
+        state.error_detail = f"{type(exc).__name__}: {str(exc)[:240]}"; LOG.exception("model loading failed"); return
     while True:
         frame = frames.get(); started_ns = time.time_ns(); perf = time.perf_counter_ns()
         try:
             people, det_ms, pose_ms = models.infer(frame.pixels, threshold, max_people)
             finished_ns = time.time_ns(); total_ms = (time.perf_counter_ns() - perf) / 1e6
-            state.frame_id += 1; state.cuda_inference_ok = True; state.inference_fault = False; state.error_code = None
+            state.frame_id += 1; state.cuda_inference_ok = True; state.inference_fault = False
+            state.error_code = None; state.error_detail = None
             msg = {"schema": "tail2.pose.v1", "stream_id": config["stream_id"], "session_id": state.session_id,
                    "frame_id": state.frame_id, "source_received_unix_ns": frame.received_ns,
                    "inference_started_unix_ns": started_ns, "inference_finished_unix_ns": finished_ns,
@@ -164,8 +168,9 @@ def inference_worker(loop: asyncio.AbstractEventLoop) -> None:
                    "error": None}
             state.inference_ms.append(total_ms); state.messages += 1; state.ws_source_ok = True
             hub.publish_on_loop(loop, msg)
-        except Exception:
+        except Exception as exc:
-            state.inference_fault = True; state.error_code = "INFERENCE_FAILED"; LOG.exception("inference failed")
+            state.inference_fault = True; state.error_code = "INFERENCE_FAILED"
+            state.error_detail = f"{type(exc).__name__}: {str(exc)[:240]}"; LOG.exception("inference failed")
 
 
 @app.on_event("startup")
@@ -178,4 +183,3 @@ async def startup() -> None:
 if __name__ == "__main__":
     uvicorn.run(app, host=os.getenv("API_BIND", "0.0.0.0"), port=int(os.getenv("API_PORT", "18120")),
                 access_log=False, ws_max_size=65536)
-