bug fixes

backend/ai_server.py

@@ -1597,6 +1597,33 @@ def videomae_model_status() -> dict:
     }
 
 
+def detector_model_status(load: bool = False) -> dict:
+    current_model = get_model() if load else model
+    names = getattr(current_model, "names", {}) or {} if current_model is not None else {}
+
+    expected_text = str(DEFAULT_MODEL_PATH)
+    exists = False
+    error = _MODEL_ERROR
+
+    try:
+        expected = resolve_model_path()
+        expected_text = str(expected)
+        exists = True
+    except Exception as exc:
+        if not error:
+            error = str(exc)
+
+    return {
+        "modelAvailable": exists,
+        "modelLoaded": current_model is not None,
+        "model": _MODEL_PATH or (expected_text if exists else ""),
+        "modelError": error,
+        "expectedModel": expected_text,
+        "classCount": len(names),
+        "classes": list(names.values())[:80] if isinstance(names, dict) else names,
+    }
+
+
 @app.post("/predict-batch", dependencies=[Depends(require_ai_server_auth)])
 def predict_batch(req: PredictBatchRequest):
     paths = [str(path).strip() for path in req.paths if str(path).strip()]
@@ -1707,23 +1734,15 @@ def predict_position_clips(req: PredictPositionClipsRequest):
 
 @app.get("/health", dependencies=[Depends(require_ai_server_auth)])
 def health():
-    current_model = get_model()
-    names = getattr(current_model, "names", {}) or {} if current_model is not None else {}
-
     status_payload = {
         "ok": True,
         "ready": True,
-        "modelAvailable": current_model is not None,
-        "model": _MODEL_PATH,
-        "modelError": _MODEL_ERROR,
-        "expectedModel": str(DEFAULT_MODEL_PATH),
         "trainingRoot": str(TRAINING_ROOT),
-        "classCount": len(names),
-        "classes": list(names.values())[:80] if isinstance(names, dict) else names,
         "labelConfig": str(DETECTION_LABELS_PATH) if DETECTION_LABELS_PATH else "",
         "labelError": _LABEL_ERROR,
     }
 
+    status_payload.update(detector_model_status(load=False))
     status_payload.update(pose_model_status())
     status_payload.update(videomae_model_status())
     return status_payload
@@ -1756,23 +1775,15 @@ def reload_model():
     _VIDEOMAE_DEVICE_ACTIVE = ""
     DETECTION_LABELS_PATH = None
 
-    current_model = get_model()
-    names = getattr(current_model, "names", {}) or {} if current_model is not None else {}
-
     status_payload = {
         "ok": True,
         "ready": True,
-        "modelAvailable": current_model is not None,
-        "model": _MODEL_PATH,
-        "modelError": _MODEL_ERROR,
-        "expectedModel": str(DEFAULT_MODEL_PATH),
         "trainingRoot": str(TRAINING_ROOT),
-        "classCount": len(names),
-        "classes": list(names.values())[:80] if isinstance(names, dict) else names,
         "labelConfig": str(DETECTION_LABELS_PATH) if DETECTION_LABELS_PATH else "",
         "labelError": _LABEL_ERROR,
     }
 
+    status_payload.update(detector_model_status(load=True))
     status_payload.update(pose_model_status())
     status_payload.update(videomae_model_status())
     return status_payload

backend/server.go

@@ -633,10 +633,7 @@ func startAIServer(ctx context.Context) (*aiServerProcess, error) {
 		close(proc.done)
 	}()
 
-	waitCtx, cancel := context.WithTimeout(ctx, 30*time.Second)
+	if err := waitForAIServer(ctx, aiServerURL(), 120*time.Second); err != nil {
-	defer cancel()
-
-	if err := waitForAIServer(waitCtx, aiServerURL(), 30*time.Second); err != nil {
 		appLogln("⚠️ AI Server noch nicht bereit:", err)
 		// Nicht hart abbrechen. Deine Analyse kann auf den Fallback gehen.
 		return proc, nil