Validate uploaded YOLO custom workflow

backend/app/acceptance.py

@@ -267,6 +267,19 @@ def _write_acceptance_images(root: Path) -> tuple[Path, Path, Path]:
     return image_path, label_path, result_dir
 
 
+def _relative_to_project(path: Path) -> str:
+    try:
+        return str(path.resolve().relative_to(settings.project_root))
+    except ValueError:
+        return str(path.resolve())
+
+
+def _result_files(root: Path, suffixes: set[str]) -> list[Path]:
+    if not root.exists():
+        return []
+    return sorted(path for path in root.rglob("*") if path.is_file() and path.suffix.lower() in suffixes)
+
+
 def run_model_family_readiness() -> dict[str, Any]:
     """Exercise the model-family runtime stack without launching full training."""
     source = settings.source_root
@@ -501,6 +514,61 @@ def run_live_acceptance(base_url: str = "http://127.0.0.1:8010") -> dict[str, An
         }
     )
 
+    yolo_weight = settings.source_root / "Seg_All_In_One_YoloModel" / "yolo11n-seg.pt"
+    uploaded_image_source = settings.project_root / "var" / "uploads" / "datasets" / dataset_name / "images"
+    predict_name = f"{dataset_name}_predict_smoke"
+    predict = _create_job_and_wait(
+        base_url,
+        "yolo.predict_custom",
+        {
+            "weights": str(yolo_weight),
+            "source": _relative_to_project(uploaded_image_source),
+            "project": "var/custom_yolo_runs",
+            "name": predict_name,
+            "imgsz": 64,
+            "conf": 0.05,
+            "device": "cpu",
+            "exist_ok": True,
+        },
+        timeout=120,
+    )
+    predict_root = settings.project_root / "var" / "custom_yolo_runs" / predict_name
+    predict_outputs = _result_files(predict_root, {".png", ".jpg", ".jpeg"})
+    checks.append(
+        {
+            "name": "uploaded_yolo_predict_job_runner",
+            "passed": predict.get("passed", False) and bool(predict_outputs),
+            "detail": {**predict, "output_count": len(predict_outputs), "outputs": [_relative_to_project(path) for path in predict_outputs[:8]]},
+        }
+    )
+
+    heatmap_name = f"{dataset_name}_heatmap_smoke"
+    heatmap = _create_job_and_wait(
+        base_url,
+        "yolo.heatmap_custom",
+        {
+            "weights": str(yolo_weight),
+            "source": _relative_to_project(uploaded_image_source),
+            "project": "var/custom_yolo_runs",
+            "name": heatmap_name,
+            "model_key": "YOLO11n-seg",
+            "pt_name": "best.pt",
+            "cam_method": "GradCAM",
+            "target_layers": "model.model.model[9]",
+            "limit": 1,
+        },
+        timeout=120,
+    )
+    heatmap_root = settings.project_root / "var" / "custom_yolo_runs" / heatmap_name / "HeartMap_Visual"
+    heatmap_outputs = _result_files(heatmap_root, {".jpg", ".jpeg", ".png"})
+    checks.append(
+        {
+            "name": "uploaded_yolo_heatmap_job_runner",
+            "passed": heatmap.get("passed", False) and len(heatmap_outputs) >= 2,
+            "detail": {**heatmap, "output_count": len(heatmap_outputs), "outputs": [_relative_to_project(path) for path in heatmap_outputs[:8]]},
+        }
+    )
+
     mock = _create_job_and_wait(base_url, "mock.echo", {"message": f"acceptance {run_id}"}, timeout=45)
     mock_log = mock.get("polled", {}).get("job", {}).get("log_tail", "")
     checks.append({"name": "mock_job_runner", "passed": mock.get("passed", False) and f"acceptance {run_id}" in mock_log, "detail": mock})