Add detection progress bar with staged job polling for DDA compare tab.

app/dda/detect_service.py

@@ -80,8 +80,14 @@ def run_detection_and_save(
     max_size: Optional[int] = None,
     geo_bounds_path: Optional[Path] = None,
     user_id: Optional[int] = None,
+    job_id: Optional[int] = None,
 ) -> dict:
     from ..detection_engine import run_detection
+    from .job_progress import update_job_progress
+
+    def _report(pct: int, stage: str) -> None:
+        if job_id is not None:
+            update_job_progress(job_id, pct, stage)
 
     if user_id:
         from ..auth import get_user_by_id
@@ -94,6 +100,12 @@ def run_detection_and_save(
     if min_region_area is not None:
         min_region_area = int(max(50, min(10000, min_region_area)))
 
+    def _on_engine_progress(engine_pct: int, stage: str) -> None:
+        # Map engine 0–100% into job 15–78%
+        job_pct = 15 + int(engine_pct * 0.63)
+        _report(job_pct, stage)
+
+    _report(15, "Running detection")
     change_mask, result_image, stats, change_regions = run_detection(
         before_pil,
         after_pil,
@@ -103,9 +115,10 @@ def run_detection_and_save(
         detection_sensitivity=detection_sensitivity,
         min_region_area=min_region_area,
         max_size=max_size,
+        on_progress=_on_engine_progress,
     )
 
-    # Save before image at detection resolution (matches overlay coordinates for slider)
+    _report(80, "Saving results")
     from ..detection_engine import preprocess_image, get_detection_max_size
 
     before_for_slider = Image.fromarray(
@@ -178,10 +191,12 @@ def run_detection_and_save(
     db.commit()
     db.refresh(run)
 
+    _report(90, "Preparing report")
     overlay_b64 = base64.b64encode(overlay_path.read_bytes()).decode("utf-8")
     notification_sent = False
     notification_error = None
     if notify_email and notify_email.strip():
+        _report(95, "Sending notification")
         from .config import IS_DDA_MODE, get_public_base_url
         report_url = f"{get_public_base_url()}/dda/reports/{run.id}" if IS_DDA_MODE else ""
         notification_sent, notification_error = send_notification(
@@ -197,6 +212,8 @@ def run_detection_and_save(
             report_url=report_url,
         )
 
+    _report(100, "Complete")
+
     return {
         "id": run.id,
         "title": run.title,

app/dda/job_progress.py

@@ -0,0 +1,47 @@
+"""Persist detection job progress for UI polling."""
+from __future__ import annotations
+
+import json
+import logging
+
+from ..database import SessionLocal
+from .models import DetectionJob
+
+logger = logging.getLogger(__name__)
+
+
+def update_job_progress(job_id: int, pct: int, stage: str) -> None:
+    """Update progress_pct and progress_stage in job params_json."""
+    db = SessionLocal()
+    try:
+        job = db.query(DetectionJob).filter(DetectionJob.id == job_id).first()
+        if not job or job.status not in ("queued", "running"):
+            return
+        params = {}
+        try:
+            params = json.loads(job.params_json or "{}")
+        except json.JSONDecodeError:
+            pass
+        params["progress_pct"] = max(0, min(100, int(pct)))
+        params["progress_stage"] = stage or ""
+        job.params_json = json.dumps(params)
+        db.commit()
+    except Exception as exc:
+        logger.warning("Could not update job %d progress: %s", job_id, exc)
+        db.rollback()
+    finally:
+        db.close()
+
+
+def get_job_progress(params: dict, status: str) -> tuple[int, str]:
+    pct = params.get("progress_pct")
+    stage = params.get("progress_stage") or ""
+    if status == "completed":
+        return 100, stage or "Complete"
+    if status == "failed":
+        return int(pct) if pct is not None else 0, stage or "Failed"
+    if status == "queued":
+        return int(pct) if pct is not None else 0, stage or "Queued"
+    if pct is None:
+        return 10, stage or "Running"
+    return int(pct), stage or "Running"

app/dda/job_runner.py

@@ -17,6 +17,7 @@ from ..models import DetectionRun
 from .config import get_detection_max_side
 from .detect_service import run_detection_and_save
 from .geotiff_io import load_rgb_pil
+from .job_progress import update_job_progress
 from .local_routes import safe_resolve
 from .models import DetectionJob
 
@@ -60,6 +61,7 @@ def _run_job_sync(job_id: int) -> None:
         job.started_at = _utcnow()
         job.error_message = ""
         db.commit()
+        update_job_progress(job_id, 5, "Starting job")
 
         params = _parse_params(job)
         base_path = params.get("base_path", "")
@@ -67,7 +69,9 @@ def _run_job_sync(job_id: int) -> None:
         if not base_path or not comparison_path:
             raise ValueError("Job missing base_path or comparison_path in params_json")
 
+        update_job_progress(job_id, 8, "Loading images")
         before_pil, after_pil, base_file = _load_pair(base_path, comparison_path)
+        update_job_progress(job_id, 12, "Images loaded")
         title = params.get("title") or f"{Path(base_path).name} vs {Path(comparison_path).name}"
         result = run_detection_and_save(
             db,
@@ -85,8 +89,11 @@ def _run_job_sync(job_id: int) -> None:
             max_size=get_detection_max_side(),
             geo_bounds_path=base_file,
             user_id=job.created_by,
+            job_id=job_id,
         )
 
+        update_job_progress(job_id, 100, "Complete")
+
         job.status = "completed"
         job.run_id = result["id"]
         job.completed_at = _utcnow()
@@ -212,7 +219,10 @@ def create_local_folder_job(
 
 
 def job_to_dict(job: DetectionJob, run: Optional[DetectionRun] = None) -> dict:
+    from .job_progress import get_job_progress
+
     params = _parse_params(job)
+    progress_pct, progress_stage = get_job_progress(params, job.status)
     out = {
         "id": job.id,
         "status": job.status,
@@ -223,6 +233,8 @@ def job_to_dict(job: DetectionJob, run: Optional[DetectionRun] = None) -> dict:
         "runId": job.run_id,
         "errorMessage": job.error_message or "",
         "notifyEmail": job.notify_email or "",
+        "progressPct": progress_pct,
+        "progressStage": progress_stage,
         "createdAt": job.created_at.isoformat() if job.created_at else None,
         "startedAt": job.started_at.isoformat() if job.started_at else None,
         "completedAt": job.completed_at.isoformat() if job.completed_at else None,

app/detection_engine.py

@@ -2596,24 +2596,32 @@ def analyze_change_regions(change_mask, image, min_area=400, use_ensemble=True,
 def run_detection(before_pil, after_pil, method="AI-Based Deep Learning",
                   enable_registration=True, enable_normalization=True,
                   detection_sensitivity=0.5, min_region_area=None,
-                  max_size=None):
+                  max_size=None, on_progress=None):
     """Run full detection pipeline; returns change_mask, result_image, stats, regions."""
+    def _prog(pct, stage):
+        if on_progress:
+            on_progress(int(pct), stage)
+
     ms = max_size or get_detection_max_size()
+    _prog(5, "Preprocessing images")
     before_array = preprocess_image(before_pil, max_size=ms)
     after_array = preprocess_image(after_pil, max_size=ms)
 
     registration_ok = False
     reg_meta = {}
     if enable_registration:
+        _prog(20, "Registering images")
         before_array, after_array, registration_ok, reg_meta = register_images(
             before_array, after_array)
     if enable_normalization:
+        _prog(35, "Normalizing radiometry")
         before_array, after_array = normalize_radiometry(before_array, after_array)
 
     alignment_warning = None
     if enable_registration and not registration_ok:
         alignment_warning = ALIGNMENT_WARNING_MSG
 
+    _prog(50, f"Running {method}")
     if method == "AI-Based Deep Learning":
         change_mask, threshold_debug = ai_deep_learning_method(
             before_array, after_array,
@@ -2650,6 +2658,7 @@ def run_detection(before_pil, after_pil, method="AI-Based Deep Learning",
         float(np.sum(change_mask > 127)) / float(total_pixels) if total_pixels else 0.0
     )
 
+    _prog(65, "Analyzing change regions")
     change_regions = analyze_change_regions(
         change_mask,
         after_array,
@@ -2681,12 +2690,14 @@ def run_detection(before_pil, after_pil, method="AI-Based Deep Learning",
             }
 
     total_pixels = int(change_mask.shape[0] * change_mask.shape[1])
+    _prog(85, "Building visualization")
     result_image = visualize_changes(
         before_array, after_array, change_mask,
         regions=change_regions, total_pixels=total_pixels,
     )
     changed_pixels = int(np.sum(change_mask > 127))
     change_pct = (changed_pixels / total_pixels * 100.0) if total_pixels else 0.0
+    _prog(95, "Finalizing results")
 
     stats = {
         "total_pixels": total_pixels,

static/js/dda/compare.js

@@ -297,33 +297,67 @@ function showDetectResult(data) {
   else if (typeof showDdaError === 'function') showDdaError('Result viewer failed to load.');
 }
 
-async function runDetectionWithFallback(form, loadingEl) {
-  const hosted = window.ddaState?.localCfg?.isHosted;
-  if (hosted) {
-    loadingEl.textContent = 'Queuing detection job…';
-    try {
-      const queued = await ddaApi('POST', '/api/dda/jobs', { body: form });
-      return await pollJobUntilDone(queued.jobId, loadingEl);
-    } catch (err) {
-      const msg = String(err.message || '');
-      const useSync = msg.includes('Not Found') || msg.includes('404')
-        || msg.includes('503') || msg.includes('409') || msg.includes('busy');
-      if (!useSync) throw err;
-      loadingEl.textContent = 'Running detection (sync fallback)…';
-    }
+function setDetectProgress(pct, stage) {
+  const fill = document.getElementById('detect-progress-fill');
+  const label = document.getElementById('detect-progress-label');
+  const clamped = Math.max(0, Math.min(100, Number(pct) || 0));
+  if (fill) fill.style.width = `${clamped}%`;
+  const text = stage ? `${stage} — ${clamped}%` : `${clamped}%`;
+  if (label) label.textContent = text;
+}
+
+function showDetectProgress() {
+  const wrap = document.getElementById('detect-progress');
+  wrap?.classList.remove('hidden');
+  setDetectProgress(0, 'Starting detection');
+}
+
+function hideDetectProgress(delayMs = 0) {
+  const hide = () => document.getElementById('detect-progress')?.classList.add('hidden');
+  if (delayMs > 0) setTimeout(hide, delayMs);
+  else hide();
+}
+
+async function runDetectionWithFallback(form) {
+  setDetectProgress(2, 'Queuing detection job');
+  try {
+    const queued = await ddaApi('POST', '/api/dda/jobs', { body: form });
+    return await pollJobUntilDone(queued.jobId);
+  } catch (err) {
+    const msg = String(err.message || '');
+    const useSync = msg.includes('Not Found') || msg.includes('404')
+      || msg.includes('503') || msg.includes('409') || msg.includes('busy');
+    if (!useSync) throw err;
+    return runSyncDetectionWithProgress(form);
   }
-  return ddaApi('POST', '/api/dda/detect/from-library', { body: form }).then((result) => ({ result, jobId: null }));
 }
 
-async function pollJobUntilDone(jobId, loadingEl) {
+async function runSyncDetectionWithProgress(form) {
+  let pct = 5;
+  setDetectProgress(pct, 'Running detection (sync)');
+  const timer = setInterval(() => {
+    pct = Math.min(92, pct + (pct < 50 ? 4 : 2));
+    setDetectProgress(pct, 'Running detection (sync)');
+  }, 1500);
+  try {
+    const result = await ddaApi('POST', '/api/dda/detect/from-library', { body: form });
+    setDetectProgress(100, 'Complete');
+    return { result, jobId: null };
+  } finally {
+    clearInterval(timer);
+  }
+}
+
+async function pollJobUntilDone(jobId) {
   const maxAttempts = 600;
   for (let i = 0; i < maxAttempts; i++) {
     const job = await ddaApi('GET', `/api/dda/jobs/${jobId}`);
     const status = job.status;
-    if (loadingEl) {
-      loadingEl.textContent = `Detection job #${jobId} — ${status}… (${i + 1})`;
-    }
+    const pct = job.progressPct ?? (status === 'queued' ? 0 : 10);
+    const stage = job.progressStage || (status === 'queued' ? 'Queued' : 'Running');
+    setDetectProgress(pct, stage);
     if (status === 'completed') {
+      setDetectProgress(100, 'Complete');
       if (job.result) {
         if (typeof window.refreshDdaNotifications === 'function') window.refreshDdaNotifications();
         return { result: job.result, jobId };
@@ -347,11 +381,9 @@ async function runLibraryDetection() {
     return;
   }
   const btn = document.getElementById('btn-run-job');
-  const loading = document.getElementById('dda-detect-loading');
   if (typeof hideDdaError === 'function') hideDdaError();
   btn.disabled = true;
-  loading?.classList.remove('hidden');
-  loading.textContent = 'Running detection…';
+  showDetectProgress();
 
   const form = new FormData();
   form.append('base_path', compareState.t1.path);
@@ -370,7 +402,7 @@ async function runLibraryDetection() {
   }
 
   try {
-    const { result: data, jobId } = await runDetectionWithFallback(form, loading);
+    const { result: data, jobId } = await runDetectionWithFallback(form);
     if (jobId && typeof window.markDdaJobSeen === 'function') window.markDdaJobSeen(jobId);
     showDetectResult(data);
     if (typeof showDdaSuccess === 'function') {
@@ -383,7 +415,7 @@ async function runLibraryDetection() {
     if (typeof showDdaError === 'function') showDdaError(err.message || 'Detection failed');
   } finally {
     btn.disabled = !(compareState.t1 && compareState.t2);
-    loading?.classList.add('hidden');
+    hideDetectProgress(2000);
   }
 }
 

templates/index_dda.html

@@ -159,7 +159,10 @@
         </div>
         <p class="dim" id="dda-detect-res-hint"></p>
         <button type="button" class="btn btn-primary" id="btn-run-job" disabled>Run Detection</button>
-        <p id="dda-detect-loading" class="dim hidden" style="margin-top:0.75rem">Detection runs as a background job — large GeoTIFFs may take several minutes…</p>
+        <div id="detect-progress" class="dda-upload-progress hidden" style="margin-top:0.75rem">
+          <div class="dda-progress-bar"><div id="detect-progress-fill" class="dda-progress-fill"></div></div>
+          <span id="detect-progress-label" class="dim">Starting detection…</span>
+        </div>
       </div>
     </section>
 
@@ -306,7 +309,7 @@
   <script src="/static/js/dda/tree.js?v=1"></script>
   <script src="/static/js/dda/library.js?v=8"></script>
   <script src="/static/js/dda/result.js?v=6"></script>
-  <script src="/static/js/dda/compare.js?v=9"></script>
+  <script src="/static/js/dda/compare.js?v=10"></script>
   <script src="/static/js/dda/reports.js?v=4"></script>
   <script src="/static/js/dda/notifications.js?v=1"></script>
 </body>