Stream job logs from current offset

README.md

@@ -63,7 +63,10 @@ are parsed into lightweight training curves.
 Job APIs and the SSE log stream also expose structured progress parsed from
 YOLO, MMSeg/MMEngine, SegModel-style epoch logs, and generic tqdm percentages,
 so the queue and live log panel can show stage, epoch/iteration, and percent
-without changing the original training scripts.
+without changing the original training scripts. Starting any web job or
+dataset YOLO shortcut automatically opens its live log; the SSE stream resumes
+from the current log size after the initial tail so existing lines are not
+duplicated in the panel.
 
 The coverage panel calls `GET /api/coverage` and verifies that the user-facing
 scripts from the existing `Seg/` workspace are mapped to web jobs. MMSeg

backend/app/agents/evaluation_agent.py

@@ -59,6 +59,11 @@ def evaluate_project() -> dict:
         and "setResults(resultsNext)" in frontend_text
         and "slice(0, 240)" not in frontend_text,
         "job_progress_ui": "JobProgressBar" in frontend_text and "progressTrack" in frontend_text,
+        "live_log_stream_ui": "EventSource" in frontend_text
+        and "eventSourceRef" in frontend_text
+        and "log_size" in frontend_text
+        and "events?offset=" in frontend_text,
+        "live_log_offset_api": "log_size" in backend_text and "offset: int = Query(0, ge=0)" in backend_text,
         "runtime_readiness_ui": "runtimeReadiness" in frontend_text and "环境就绪" in frontend_text,
         "capability_matrix_ui": "capabilities" in frontend_text and "全功能矩阵" in frontend_text,
         "dataset_api": "/api/datasets" in backend_text and "api_upload_dataset_files" in backend_text,

backend/app/main.py

@@ -39,6 +39,8 @@ app.add_middleware(
 def _job_with_progress(job: dict, include_log_tail: bool = False) -> dict:
     enriched = dict(job)
     max_bytes = 65536 if include_log_tail else 32768
+    log_path = Path(enriched["log_path"])
+    enriched["log_size"] = log_path.stat().st_size if log_path.exists() else 0
     enriched["progress"] = progress_from_log_path(enriched["log_path"], enriched["status"], max_bytes=max_bytes)
     if include_log_tail:
         enriched["log_tail"] = db.log_tail(enriched["log_path"], max_bytes=max_bytes)
@@ -183,9 +185,9 @@ def api_cancel_job(job_id: str) -> dict:
 
 
 @app.get("/api/jobs/{job_id}/events")
-async def api_job_events(job_id: str):
+async def api_job_events(job_id: str, offset: int = Query(0, ge=0)):
     async def stream():
-        last_size = 0
+        last_size = offset
         while True:
             job = db.get_job(job_id)
             if not job:
@@ -196,6 +198,8 @@ async def api_job_events(job_id: str):
             chunk = ""
             if path.exists():
                 size = path.stat().st_size
+                if last_size > size:
+                    last_size = size
                 if size > last_size:
                     with path.open("rb") as handle:
                         handle.seek(last_size)

backend/tests/test_jobs.py

@@ -1,6 +1,38 @@
+from fastapi.testclient import TestClient
+
 from app.jobs import default_conda_env_for_job
+from app.main import _job_with_progress, app
 
 
 def test_mmseg_jobs_use_mmseg_conda_env_by_default():
     assert default_conda_env_for_job("mmseg.train") == "seg_mmcv"
     assert default_conda_env_for_job("segmodel.train") == "seg_smp"
+
+
+def test_job_progress_reports_log_size(tmp_path):
+    log_path = tmp_path / "job.log"
+    log_path.write_text("line one\nline two\n", encoding="utf-8")
+    job = {"id": "job1", "status": "running", "log_path": str(log_path)}
+
+    enriched = _job_with_progress(job, include_log_tail=True)
+
+    assert enriched["log_size"] == log_path.stat().st_size
+    assert enriched["log_tail"] == "line one\nline two\n"
+
+
+def test_job_events_respect_log_offset(tmp_path, monkeypatch):
+    from app import main
+
+    log_path = tmp_path / "job.log"
+    old_chunk = "old chunk\n"
+    log_path.write_text(old_chunk + "new chunk\n", encoding="utf-8")
+
+    def fake_get_job(job_id):
+        return {"id": job_id, "status": "success", "log_path": str(log_path)}
+
+    monkeypatch.setattr(main.db, "get_job", fake_get_job)
+    response = TestClient(app).get(f"/api/jobs/job1/events?offset={len(old_chunk.encode('utf-8'))}")
+
+    assert response.status_code == 200
+    assert "new chunk" in response.text
+    assert "old chunk" not in response.text

frontend/src/main.tsx

@@ -1,4 +1,4 @@
-import React, { useEffect, useMemo, useState } from "react";
+import React, { useEffect, useMemo, useRef, useState } from "react";
 import { createRoot } from "react-dom/client";
 import {
   Activity,
@@ -45,6 +45,7 @@ type Job = {
   started_at?: string;
   finished_at?: string;
   log_tail?: string;
+  log_size?: number;
   params: Record<string, unknown>;
   progress?: JobProgress;
 };
@@ -430,6 +431,11 @@ function App() {
   const [uploadFiles, setUploadFiles] = useState<FileList | null>(null);
   const [agentValidation, setAgentValidation] = useState<ValidationAgentPayload | null>(null);
   const [agentBusy, setAgentBusy] = useState(false);
+  const eventSourceRef = useRef<EventSource | null>(null);
+
+  useEffect(() => () => {
+    eventSourceRef.current?.close();
+  }, []);
 
   const runningCount = jobs.filter((job) => job.status === "running").length;
   const successCount = jobs.filter((job) => job.status === "success").length;
@@ -515,10 +521,11 @@ function App() {
   async function createJob() {
     setBusy(true);
     try {
-      await api<Job>("/api/jobs", {
+      const job = await api<Job>("/api/jobs", {
         method: "POST",
         body: JSON.stringify({ type: taskType, params: JSON.parse(params) })
       });
+      await inspectJob(job);
       await refresh();
     } finally {
       setBusy(false);
@@ -633,13 +640,14 @@ function App() {
     try {
       const generated = await createSelectedYoloYaml();
       if (!generated) return;
-      await api<Job>("/api/jobs", {
+      const job = await api<Job>("/api/jobs", {
         method: "POST",
         body: JSON.stringify({
           type: "yolo.train_custom",
           params: { model: "YOLO11n-seg", data: generated.path, epochs: 10, imgsz: 640, batch: 1, workers: 0, device: "cpu", project: "var/custom_yolo_runs", name: selectedDataset.name, exist_ok: true }
         })
       });
+      await inspectJob(job);
       window.location.hash = "jobs";
       await refresh();
     } finally {
@@ -651,13 +659,14 @@ function App() {
     if (!selectedDataset?.absolute_layout) return;
     setBusy(true);
     try {
-      await api<Job>("/api/jobs", {
+      const job = await api<Job>("/api/jobs", {
         method: "POST",
         body: JSON.stringify({
           type: "yolo.predict_custom",
           params: { weights: customYoloWeightPath(selectedDataset), source: selectedDataset.absolute_layout.images, imgsz: 640, conf: 0.25, device: "cpu", project: "var/custom_yolo_runs", name: `${selectedDataset.name}_predict`, exist_ok: true }
         })
       });
+      await inspectJob(job);
       window.location.hash = "jobs";
       await refresh();
     } finally {
@@ -669,13 +678,14 @@ function App() {
     if (!selectedDataset?.absolute_layout) return;
     setBusy(true);
     try {
-      await api<Job>("/api/jobs", {
+      const job = await api<Job>("/api/jobs", {
         method: "POST",
         body: JSON.stringify({
           type: "yolo.heatmap_custom",
           params: { weights: customYoloWeightPath(selectedDataset), source: selectedDataset.absolute_layout.images, model_key: "YOLO11n-seg", cam_method: "GradCAM", target_layers: "model.model.model[9]", limit: 3, project: "var/custom_yolo_runs", name: `${selectedDataset.name}_heatmap` }
         })
       });
+      await inspectJob(job);
       window.location.hash = "jobs";
       await refresh();
     } finally {
@@ -684,15 +694,24 @@ function App() {
   }
 
   async function inspectJob(job: Job) {
+    eventSourceRef.current?.close();
     const detail = await api<Job>(`/api/jobs/${job.id}`);
     setSelectedJob(detail);
     setLog(detail.log_tail ?? "");
-    const source = new EventSource(`${API_BASE}/api/jobs/${job.id}/events`);
+    const source = new EventSource(`${API_BASE}/api/jobs/${job.id}/events?offset=${detail.log_size ?? 0}`);
+    eventSourceRef.current = source;
     source.onmessage = (event) => {
       const payload = JSON.parse(event.data);
       if (payload.chunk) setLog((prev) => `${prev}${payload.chunk}`);
       setSelectedJob(payload.job);
-      if (["success", "failed", "cancelled"].includes(payload.job.status)) source.close();
+      if (["success", "failed", "cancelled"].includes(payload.job.status)) {
+        source.close();
+        if (eventSourceRef.current === source) eventSourceRef.current = null;
+      }
+    };
+    source.onerror = () => {
+      source.close();
+      if (eventSourceRef.current === source) eventSourceRef.current = null;
     };
   }