Expose real train artifacts in dashboard

README.md

@@ -279,9 +279,11 @@ image/txt pair, generates `dataset.yaml`, runs one CPU epoch through
 `yolo.train_custom`, verifies `results.csv` and `best.pt`, then uses that
 trained checkpoint for prediction and GradCAM heatmap jobs. The latest report
 is available from `GET /api/acceptance/real-train/latest` and is shown in the
-coverage panel as `真实短训`. This is heavier than the real-data predict
-acceptance, so run it when you want proof that real uploaded data can create
-loss curves, trained weights, segmentation previews, and heatmap artifacts.
+coverage panel as `真实短训`, including direct links to the generated `best.pt`,
+`results.csv`, prediction preview, and heatmap images. This is heavier than
+the real-data predict acceptance, so run it when you want proof that real
+uploaded data can create loss curves, trained weights, segmentation previews,
+and heatmap artifacts.
 
 For stronger runtime proof, `POST /api/acceptance/deep` runs minimal training
 loops for the three model families: one SegModel optimizer step, one YOLO

backend/app/agents/evaluation_agent.py

@@ -114,6 +114,11 @@ def evaluate_project() -> dict:
         and "real_train_yolo_one_epoch_job_runner" in acceptance_text
         and "real_train_trained_weight_predict_job_runner" in acceptance_text
         and "real_train_trained_weight_heatmap_job_runner" in acceptance_text,
+        "real_train_artifact_links_ui": "AcceptanceArtifactLinks" in frontend_text
+        and "acceptanceArtifacts" in frontend_text
+        and "best_weight" in frontend_text
+        and "results_csv" in frontend_text
+        and "heatmap_outputs" in frontend_text,
         "agent_api": "/api/agents/evaluate" in backend_text and "/api/agents/validate" in backend_text,
         "agent_panel_ui": "runAgentValidation" in frontend_text and "评价建议" in frontend_text and "Validation Agent" in frontend_text,
         "coverage_api": "/api/coverage" in backend_text and coverage["task_build_passed"],
@@ -138,7 +143,7 @@ def evaluate_project() -> dict:
     if coverage["unmapped_user_scripts"]:
         suggestions.append(f"Map remaining user-facing scripts: {len(coverage['unmapped_user_scripts'])}")
     if not suggestions:
-        suggestions.append("Current platform covers the requested control-plane features, uploaded YOLO dataset train/predict/heatmap actions, live uploaded-data YOLO predict/heatmap acceptance, real workspace data acceptance, real short-train acceptance, and synthetic deep training acceptance; next focus is a longer operator-run task on a full dataset.")
+        suggestions.append("Current platform covers the requested control-plane features, uploaded YOLO dataset train/predict/heatmap actions, live uploaded-data YOLO predict/heatmap acceptance, real workspace data acceptance, real short-train acceptance with artifact links, and synthetic deep training acceptance; next focus is a longer operator-run task on a full dataset.")
 
     passed_count = sum(1 for item in checks if item["passed"])
     total_count = max(len(checks), 1)

frontend/src/main.tsx

@@ -169,6 +169,13 @@ type AcceptancePayload = {
   run_id?: string;
   created_at?: string;
   checks?: Array<{ name: string; passed: boolean }>;
+  artifacts?: {
+    train_root?: string;
+    best_weight?: string | null;
+    results_csv?: string | null;
+    predict_outputs?: string[];
+    heatmap_outputs?: string[];
+  };
   model_family_readiness?: {
     passed: boolean;
     warnings: Array<{ name: string; passed: boolean }>;
@@ -367,6 +374,11 @@ function formatBytes(value?: number) {
   return `${next.toFixed(unit > 1 ? 2 : 0)} ${units[unit]}`;
 }
 
+function fileName(path?: string | null) {
+  if (!path) return "";
+  return path.split("/").filter(Boolean).pop() ?? path;
+}
+
 function useData() {
   const [catalog, setCatalog] = useState<Catalog | null>(null);
   const [gpus, setGpus] = useState<GpuPayload | null>(null);
@@ -1195,6 +1207,7 @@ function App() {
                   <span>最近验收：{acceptance?.created_at ?? "尚未运行"} {acceptance?.run_id ? `#${acceptance.run_id}` : ""}</span>
                   <span>真实数据：{realAcceptance?.created_at ?? "尚未运行"} {realAcceptance?.run_id ? `#${realAcceptance.run_id}` : ""}，通过 {realAcceptance?.checks?.filter((item) => item.passed).length ?? 0}/{realAcceptance?.checks?.length ?? 0}</span>
                   <span>真实短训：{realTrainAcceptance?.created_at ?? "尚未运行"} {realTrainAcceptance?.run_id ? `#${realTrainAcceptance.run_id}` : ""}，通过 {realTrainAcceptance?.checks?.filter((item) => item.passed).length ?? 0}/{realTrainAcceptance?.checks?.length ?? 0}</span>
+                  <AcceptanceArtifactLinks artifacts={realTrainAcceptance?.artifacts} />
                   <span>深度验收：{deepAcceptance?.created_at ?? "尚未运行"} {deepAcceptance?.run_id ? `#${deepAcceptance.run_id}` : ""}，通过 {deepAcceptance?.checks?.filter((item) => item.passed).length ?? 0}/{deepAcceptance?.checks?.length ?? 0}</span>
                   <span>模型族 readiness：{acceptance?.model_family_readiness?.checks?.filter((item) => item.passed).length ?? 0}/{acceptance?.model_family_readiness?.checks?.length ?? 0}，warnings {acceptance?.model_family_readiness?.warnings?.length ?? 0}</span>
                 </>
@@ -1595,6 +1608,27 @@ function ResultBrowser({
   );
 }
 
+function AcceptanceArtifactLinks({ artifacts }: { artifacts?: AcceptancePayload["artifacts"] }) {
+  if (!artifacts) return null;
+  const links = [
+    artifacts.best_weight && { label: "best.pt", path: artifacts.best_weight, kind: "weight" },
+    artifacts.results_csv && { label: "loss csv", path: artifacts.results_csv, kind: "curve" },
+    ...(artifacts.predict_outputs ?? []).slice(0, 2).map((path, index) => ({ label: `predict ${index + 1}`, path, kind: "segmentation" })),
+    ...(artifacts.heatmap_outputs ?? []).slice(0, 4).map((path, index) => ({ label: `heatmap ${index + 1}`, path, kind: "heatmap" }))
+  ].filter(Boolean) as Array<{ label: string; path: string; kind: string }>;
+  if (!links.length) return null;
+  return (
+    <div className="acceptanceArtifacts">
+      {links.map((item) => (
+        <a key={`${item.label}-${item.path}`} href={`${API_BASE}/api/artifacts/${item.path}`} target="_blank" rel="noreferrer" title={item.path}>
+          <span>{item.label}</span>
+          <small>{item.kind} · {fileName(item.path)}</small>
+        </a>
+      ))}
+    </div>
+  );
+}
+
 function DatasetQuality({ validation }: { validation: DatasetValidation }) {
   return (
     <div className="qualityBox">

frontend/src/styles.css

@@ -681,6 +681,31 @@ textarea {
   overflow-wrap: anywhere;
 }
 
+.acceptanceArtifacts {
+  display: grid;
+  grid-template-columns: repeat(2, minmax(0, 1fr));
+  gap: 8px;
+}
+
+.acceptanceArtifacts a {
+  min-width: 0;
+  display: grid;
+  gap: 3px;
+  padding: 8px;
+  border-radius: 6px;
+  border: 1px solid var(--line);
+  background: #101310;
+  color: var(--ink);
+  text-decoration: none;
+}
+
+.acceptanceArtifacts span,
+.acceptanceArtifacts small {
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
 .taskCheckList {
   display: grid;
   grid-template-columns: repeat(2, minmax(0, 1fr));