 ▐▛███▜▌   Claude Code v2.1.224
▝▜█████▛▘  Opus 5 · Claude Pro
  ▘▘ ▝▝    ~/reTraining

   Tackle your toughest work with Opus 5. Switch anytime with /model.

❯ /clear                                                                        

❯ please create a deep understanding using workflow to understand this project  
  repo and bugs, give me recomendation of improving the code especially for     
  make this project funtion as i want. ask me a lot before you do that lot of   
  task, swarm cheaper agent model for helping you, you can use opus also but    
  its only for complex or task, i guess you can defined yourself. the goal is   
  to make a better workflow for reTraining my ai model, if i had a mistake just 
  say it, your goal is to help me make this app works well and ready to use.    

  Searched for 1 pattern, listed 1 directory (ctrl+o to expand)

● Repo is substantial (backend ~3.5k lines, 8 frontend pages, docs with REQ-xxx
  already agreed). Before I spend a workflow on it, four things:

● User answered Claude's questions:
  ⎿  · What's actually broken or unsatisfying right now? Where should the audit 
     bite hardest? → Retraining loop itself, Auto-annotate / SAM3, Review editor
     UX, Everything / I don't know yet
     · Have you run a full round end-to-end yet (video → frames → annotate → 
     review → merge → train → compare)? → Finished once, but rough
     · There's a lot of uncommitted work (24 modified files, 5 new untracked). 
     What's its status? → Half-broken experiments
     · What do you want me to deliver at the end of the audit? → Report, then 
     fix the critical bugs

● Good — full sweep it is. Launching the workflow now.

● Running in the background. Structure:

  - Map (4 parallel) — retraining core, SAM3/autolabel, review editor,
  docs-drift + your dirty working tree
  - Hunt (5 dimensions, each finding verified by an adversarial refuter as it
  lands) — invariants/data corruption, concurrency & jobs, API contract,
  frontend correctness, ops/portability
  - Synthesize — mechanism-level system map, ranked bugs, per-file verdict on
  the uncommitted work, workflow gaps where the design itself is wrong, and an
  ordered fix plan with verification steps

  Watch live with /workflows. When it lands I'll relay the report and then start
  on the critical fixes.

✻ Brewed for 1m 26s

● Dynamic workflow "Deep audit of the reTraining dataset-enrichment app: map
architecture, find bugs, verify, prioritize fixes" completed · 7m 59s
  ⎿  You've hit your session limit · resets 12:10pm (Asia/Jakarta)
     /upgrade to increase your usage limit.

✻ Crunched for 10m 22s

❯ continue                                                                      

● The audit ran but hit the session usage limit near the end — the final
  synthesis agent and 26 verifiers died. 22 bugs did survive verification. Let
  me pull the full results and write the report myself.

  Ran 3 shell commands (ctrl+o to expand)

● Two dimensions (concurrency, api-contract) lost all their verifiers to the
  session limit — let me recover their raw findings from the journal so nothing
  is silently dropped.

  Ran 6 shell commands (ctrl+o to expand)

● Recovered everything. One key thing to confirm before I write it up — the two
  CRITICALs point at jobs.py, which is in your dirty tree:

  Ran 1 shell command (ctrl+o to expand)

● Confirmed — that uncommitted jobs.py edit is the root of both CRITICALs.
  Writing the report.

● Write(docs/audit-2026-08-07.md)
  ⎿  Wrote 181 lines to docs/audit-2026-08-07.md
       1 # Audit — reTraining, 2026-08-07
       2 
          by dimension, adversarial
       4 verification of each finding. 22 findings survived verification;
          2 CRITICALs come from a
         e marked *unverified* — both
       6 were confirmed by hand afterwards.
       7 
       9 
      10 ## 1. How the system actually works
     … +343 lines (ctrl+o to expand)

● Report written to docs/audit-2026-08-07.md. 22 verified findings + 2
  hand-confirmed criticals.

  The headline: your biggest problem is uncommitted. You changed backend/jobs.py
  to run every job in its own thread instead of through the single worker
  queue, and narrowed GPU_JOB_TYPES to ("train",) with the comment "multiple 
  autolabel jobs can run in parallel." They can't — each SAM3 job holds a full
  backbone in VRAM, and release_engine() only clears a module global, so it
  cannot free memory held by a running job. That one edit also un-serialized
  merge and train against the same dataset/ directory, which is the root of four
  more HIGH findings.

  Three design-level things you got wrong (you asked me to say so):

  1. The val split is positional — _next_split counts dataset_items rows, so
  every Nth insert goes to val. Deleting a batch shifts the split for everything
  merged afterwards. CLAUDE.md §8 declares the invariant but nothing enforces
  it. Make it hash(project + batch + stem) % 5 and it becomes true by
  construction.
  2. Merged labels are regenerated from live annotations on every training start
  (sync_labels at dataset.py:117, no review_status filter). Re-run
  auto-annotate on a merged batch, press Train, and raw unreviewed model output
  overwrites your human-reviewed labels. A merged dataset should be a snapshot,
  not a view.
  3. The base-vs-new comparison degrades silently instead of refusing. Empty val
  set → it validates on the training images. Deleted batch → orphaned JPEGs
  still sit in images/val. For a tool whose only job is answering "did
  retraining help?", every one of those returns a plausible-looking number.

  Worth knowing: docker-compose.override.yml is committed with
  nvidia.com/gpu=all (CPU hosts can't start), and start.sh deletes that tracked
  file on every run — silently dropping your ./backend bind mount.

  Before I start fixing:
● User answered Claude's questions:
  ⎿  · Fixing C1/C2 means reverting your jobs.py parallelism change. Was that 
     experiment solving a real problem I should address differently? → Revert, 
     but autolabel was too slow
     · How far do I go in this pass? → Criticals + design + review editor
     · The content-derived val split changes how existing merged frames are 
     assigned. Existing data? → Preserve existing assignments

● Understood: revert for correctness, then look at autolabel speed separately.
  Steps 1–7, preserving existing splits.

  Read 2 files (ctrl+o to expand)

● Now fixing. Step 1 — restore the job queue:

● Update(backend/jobs.py)
  ⎿  Added 3 lines, removed 5 lines
      28  PROGRESS_FLUSH_SECONDS = 0.5
      29  
      30  JOB_TYPES = ("extract", "autolabel", "merge", "train")
      31 -GPU_JOB_TYPES = ("train",)                                     
      32 -"""`train` takes full GPU memory during model fitting. `autolab
         -el` (SAM3/YOLO inference)                                      
      33 -runs fine-grained per-frame steps so multiple autolabel jobs ca
         -n run in parallel."""                                          
      34 -                                                               
      35 -                                                               
      31 +GPU_JOB_TYPES = ("autolabel", "train")                         
      32 +"""`extract` is ffmpeg and `merge` is file copying — neither to
         +uches the card,                                                
      33 +so neither should be able to block an interactive assist."""   
      34  gpu_lock = threading.Lock()
      35  """Held for the duration of any GPU work. The job worker takes 
          it around a
      36  handler; the interactive assist route takes it around one SAM3 
          call. One card,

● Update(backend/jobs.py)
  ⎿  Added 2 lines, removed 12 lines
      147          job_id = cur.lastrowid
      148      job = get(job_id)
      149      assert job is not None
      150 -    threading.Thread(target=_start_job, args=(job_id,), name=f
          -"job-{job_type}-{job_id}", daemon=True).start()               
      150 +    _queue.put(job_id)                                        
      151 +    _ensure_worker()                                          
      152      return job
      153  
      154  
      154 -def _start_job(job_id: int) -> None:                          
      155 -    job = get(job_id)                                         
      156 -    if job is None:                                           
      157 -        return                                                
      158 -    if job.id in _cancelled:                                  
      159 -        _finish(job, "cancelled")                             
      160 -        return                                                
      161 -    _run(job)                                                 
      162 -                                                              
      163 -                                                              
      164 -                                                              
      155  def get(job_id: int) -> Optional[Job]:
      156      with db.cursor() as cur:
      157          cur.execute("SELECT * FROM jobs WHERE id = ?", (job_id
           ,))

● Update(backend/jobs.py)
  ⎿  Added 20 lines, removed 1 line
      206          return cur.rowcount
      207  
      208  
      209 -def _run(job: Job) -> None:                                   
      209 +def _ensure_worker() -> None:                                 
      210 +    global _worker                                            
      211 +    with _worker_lock:                                        
      212 +        if _worker is None or not _worker.is_alive():         
      213 +            _worker = threading.Thread(target=_worker_loop, na
          +me="job-worker", daemon=True)                                 
      214 +            _worker.start()                                   
      215  
      216 +                                                              
      217 +def _worker_loop() -> None:                                   
      218 +    while True:                                               
      219 +        job_id = _queue.get()                                 
      220 +        job = get(job_id)                                     
      221 +        if job is None:                                       
      222 +            continue                                          
      223 +        if job.id in _cancelled:                              
      224 +            _finish(job, "cancelled")                         
      225 +            continue                                          
      226 +        _run(job)                                             
      227 +                                                              
      228 +                                                              
      229 +def _run(job: Job) -> None:                                   
      230      job.status = "running"
      231      job.started_at = time.time()
      232      job.flush()

  Read 1 file, ran 1 shell command (ctrl+o to expand)

● jobs.py is back to baseline. Step 2 — the synchronous GPU routes. Let me match
  the existing review.assist pattern:

  Ran 1 shell command (ctrl+o to expand)

● Update(backend/api/batches.py)
  ⎿  Added 8 lines, removed 1 line
      162  
      163  @router.post("/api/batches/{batch_id}/preview")
      164  def preview_autolabel(batch_id: int, request: PreviewRequest) 
           -> dict:
      165 -    from backend import autolabel                             
      165 +    from backend import autolabel, jobs                       
      166 +                                                              
      167 +    if not jobs.gpu_lock.acquire(timeout=20):                 
      168 +        busy = jobs.running_types()                           
      169 +        kind = busy[0] if busy else "background"              
      170 +        raise HTTPException(409, f"The GPU is busy with a {kin
          +d} job — wait for it to finish")                              
      171      try:
      172          shapes = autolabel.preview_frame(
      173              batch_id=batch_id,
     ...
      182          return {"shapes": shapes}
      183      except Exception as exc:
      184          raise HTTPException(400, str(exc))
      185 +    finally:                                                  
      186 +        jobs.gpu_lock.release()                               
      187  
      188  @router.post("/api/sam3/playground-test")
      189  async def sam3_playground_test(

● Update(backend/api/batches.py)
  ⎿  Added 8 lines
      207      if not prompt_list:
      208          raise HTTPException(400, "At least one text prompt is 
           required")
      209  
      210 +    from backend import jobs                                  
      211 +                                                              
      212 +    if not jobs.gpu_lock.acquire(timeout=20):                 
      213 +        busy = jobs.running_types()                           
      214 +        kind = busy[0] if busy else "background"              
      215 +        raise HTTPException(409, f"The GPU is busy with a {kin
          +d} job — wait for it to finish")                              
      216      try:
      217          engine = get_engine()
      218          raw_dets = engine.detect(image, prompt_list, threshold
           )
      219          kept_dets = labeling.deduplicate(raw_dets, iou_thresho
           ld=iou_threshold)
      220      except Exception as exc:
      221          raise HTTPException(500, f"SAM3 inference failed: {exc
           }")
      222 +    finally:                                                  
      223 +        jobs.gpu_lock.release()                               
      224  
      225      results = []
      226      for det in kept_dets:

● Step 3, 4 and 6 all live in dataset.py. Rewriting that section:

● Update(backend/dataset.py)
  ⎿  Added 72 lines, removed 23 lines
       66      return f"{class_id} {coords}"
       67  
       68  
       69 -def _next_split(cur, project_id: int, val_every: int) -> str: 
       70 -    """Continue the every-Nth pattern from wherever the last m
          -erge left off."""                                             
       69 +def split_for(project_id: int, batch_id: int, stem: str, val_e
          +very: int) -> str:                                            
       70 +    """Which split a frame belongs to, derived from its identi
          +ty rather than from                                           
       71 +    how many rows happen to precede it.                       
       72 +                                                              
       73 +    A positional every-Nth rule makes membership depend on ins
          +ertion history, so                                            
       74 +    deleting or re-merging a batch silently reshuffles every l
          +ater frame — and a                                            
       75 +    frame that was in `val` for the last comparison could land
          + in `train` for the                                           
       76 +    next one. Hashing the identity makes the stable-val-split 
          +invariant true by                                             
       77 +    construction: the same frame always lands in the same spli
          +t, whatever else                                              
       78 +    happened to the dataset. Rows already in `dataset_items` k
          +eep the split they                                            
       79 +    were recorded with; nothing recomputes them.              
       80 +    """                                                       
       81      if val_every <= 0:
       82          return "train"
       73 -    cur.execute("SELECT COUNT(*) FROM dataset_items WHERE proj
          -ect_id = ?", (project_id,))                                   
       74 -    position = cur.fetchone()[0]                              
       75 -    return "val" if position % val_every == val_every - 1 else
          - "train"                                                      
       83 +    digest = hashlib.sha1(f"{project_id}/{batch_id}/{stem}".en
          +code("utf-8")).hexdigest()                                    
       84 +    return "val" if int(digest[:8], 16) % val_every == 0 else 
          +"train"                                                       
       85  
       86  
       78 -def sync_labels(project_id: int, selected_class_ids: Optional[
          -List[int]] = None) -> dict:                                   
       79 -    """Re-sync label files on disk for all merged frames in th
          -e project dataset."""                                         
       87 +def sync_labels(project_id: int) -> dict:                     
       88 +    """Re-sync label files on disk from the reviewed annotatio
          +ns.                                                           
       89 +                                                              
       90 +    Only frames a human actually signed off on are written. A 
          +merged frame whose                                            
       91 +    batch was auto-annotated again drops back to `pending`, an
          +d rewriting its                                               
       92 +    label file from the fresh model output would push predicti
          +ons nobody checked                                            
       93 +    into the master dataset.                                  
       94 +    """                                                       
       95      project = projects.get(project_id)
       96      root = dataset_dir(project["slug"])
       97      with db.cursor() as cur:
       98          cur.execute(
       84 -            "SELECT d.frame_id, d.label_rel FROM dataset_items
          - d WHERE d.project_id = ?",                                   
       99 +            """SELECT d.frame_id, d.label_rel FROM dataset_ite
          +ms d                                                          
      100 +               JOIN frames f ON f.id = d.frame_id             
      101 +               WHERE d.project_id = ? AND f.review_status = 'a
          +pproved'""",                                                  
      102              (project_id,),
      103          )
      104          items = cur.fetchall()
      105  
       89 -    class_map = None                                          
       90 -    if selected_class_ids is not None and len(selected_class_i
          -ds) > 0:                                                      
       91 -        class_map = {cid: idx for idx, cid in enumerate(sorted
          -(selected_class_ids))}                                        
       92 -                                                              
      106      synced_files = 0
      107      total_lines = 0
      108      for frame_id, label_rel in items:
       96 -        annotations = review.listing(frame_id)                
       97 -        if class_map is not None:                             
       98 -            annotations = [a for a in annotations if a["class_
          -id"] in class_map]                                            
       99 -                                                              
      100 -        lines = []                                            
      101 -        for item in annotations:                              
      102 -            mapped_cid = class_map[item["class_id"]] if class_
          -map is not None else item["class_id"]                         
      103 -            lines.append(_label_line(mapped_cid, item["geometr
          -y"], project["label_type"]))                                  
      104 -                                                              
      109 +        lines = [_label_line(item["class_id"], item["geometry"
          +], project["label_type"])                                     
      110 +                 for item in review.listing(frame_id)]        
      111          path = os.path.join(root, label_rel)
      112          os.makedirs(os.path.dirname(path), exist_ok=True)
      107 -        with open(path, "w", encoding="utf-8") as f:          
      108 -            f.write("\n".join(lines) + ("\n" if lines else "")
          -)                                                             
      113 +        _write_atomic(path, "\n".join(lines) + ("\n" if lines 
          +else ""))                                                     
      114          synced_files += 1
      115          total_lines += len(lines)
      116  
      117      return {"synced_files": synced_files, "total_lines": total
           _lines}
      118  
      119  
      120 +def _write_atomic(path: str, text: str) -> None:              
      121 +    """Write via temp file + rename, so a training run never r
          +eads a half-written                                           
      122 +    label file or a truncated data.yaml."""                   
      123 +    tmp = f"{path}.tmp"                                       
      124 +    with open(tmp, "w", encoding="utf-8") as handle:          
      125 +        handle.write(text)                                    
      126 +    os.replace(tmp, path)                                     
      127 +                                                              
      128 +                                                              
      129 +def _build_selected_tree(root: str, rows: list, class_map: Opt
          +ional[dict],                                                  
      130 +                         label_type: str) -> tuple:           
      131 +    """Materialise a filtered view of the dataset under `selec
          +ted/`.                                                        
      132 +                                                              
      133 +    The master labels stay canonical. A per-run class filter r
          +enumbers ids into a                                           
      134 +    contiguous 0..k-1 space, which contradicts `project_classe
          +s`; writing that                                              
      135 +    into the shared label files would leave the master dataset
          + in the index space                                           
      136 +    of whichever training run happened to go last.            
      137 +    """                                                       
      138 +    selected_root = os.path.join(root, "selected")            
      139 +    if os.path.isdir(selected_root):                          
      140 +        shutil.rmtree(selected_root)                          
      141 +    listed = {"train": [], "val": []}                         
      142 +    for image_rel, label_rel, frame_id, split in rows:        
      143 +        stem = os.path.basename(image_rel)                    
      144 +        image_dst = os.path.join(selected_root, "images", spli
          +t, stem)                                                      
      145 +        label_dst = os.path.join(selected_root, "labels", spli
          +t,                                                            
      146 +                                 os.path.splitext(stem)[0] + "
          +.txt")                                                        
      147 +        os.makedirs(os.path.dirname(image_dst), exist_ok=True)
      148 +        os.makedirs(os.path.dirname(label_dst), exist_ok=True)
      149 +        source = os.path.join(root, image_rel)                
      150 +        if not os.path.exists(image_dst):                     
      151 +            os.symlink(source, image_dst)                     
      152 +                                                              
      153 +        annotations = review.listing(frame_id)                
      154 +        if class_map is not None:                             
      155 +            annotations = [a for a in annotations if a["class_
          +id"] in class_map]                                            
      156 +        lines = [_label_line(class_map[a["class_id"]] if class
          +_map is not None else a["class_id"],                          
      157 +                             a["geometry"], label_type)       
      158 +                 for a in annotations]                        
      159 +        _write_atomic(label_dst, "\n".join(lines) + ("\n" if l
          +ines else ""))                                                
      160 +        listed[split].append(image_dst)                       
      161 +    return selected_root, listed                              
      162 +                                                              
      163 +                                                              
      164  def write_data_yaml(project: dict, batch_ids: list = None, sel
           ected_class_ids: Optional[List[int]] = None) -> str:
      165      """Rebuild data.yaml from the project's classes (REQ-051).
           """
      166      sync_labels(project["id"], selected_class_ids=selected_cla
           ss_ids)

● Now write_data_yaml — fail-loud on empty val, correct nc, use the selected
  tree:

● Update(backend/dataset.py)
  ⎿  Added 46 lines, removed 24 lines
      163  
      164  def write_data_yaml(project: dict, batch_ids: list = None, sel
           ected_class_ids: Optional[List[int]] = None) -> str:
      165      """Rebuild data.yaml from the project's classes (REQ-051).
           """
      166 -    sync_labels(project["id"], selected_class_ids=selected_cla
          -ss_ids)                                                       
      166 +    sync_labels(project["id"])                                
      167      root = dataset_dir(project["slug"])
      168      os.makedirs(root, exist_ok=True)
      169      counts = summary(project["id"])["splits"]
      170  
      171      target_classes = project["classes"]
      172 +    class_map = None                                          
      173      if selected_class_ids is not None and len(selected_class_i
           ds) > 0:
      174          target_classes = [c for c in project["classes"] if c["
           class_id"] in selected_class_ids]
      175 +        class_map = {cid: idx for idx, cid in enumerate(sorted
          +(selected_class_ids))}                                        
      176  
      177      names = ", ".join(f"'{item['name']}'" for item in target_c
           lasses)
      178  
      177 -    if batch_ids:                                             
      179 +    if batch_ids or class_map is not None:                    
      180          with db.cursor() as cur:
      179 -            placeholders = ",".join("?" for _ in batch_ids)   
      181 +            where = "d.project_id = ?"                        
      182 +            args = [project["id"]]                            
      183 +            if batch_ids:                                     
      184 +                where += f" AND f.batch_id IN ({','.join('?' f
          +or _ in batch_ids)})"                                         
      185 +                args += list(batch_ids)                       
      186              cur.execute(
      181 -                f"""SELECT d.image_rel, d.split FROM dataset_i
          -tems d                                                        
      187 +                f"""SELECT d.image_rel, d.label_rel, d.frame_i
          +d, d.split FROM dataset_items d                               
      188                      JOIN frames f ON f.id = d.frame_id
      183 -                    WHERE d.project_id = ? AND f.batch_id IN (
          -{placeholders})""",                                           
      184 -                [project["id"]] + list(batch_ids),            
      189 +                    WHERE {where}""",                         
      190 +                args,                                         
      191              )
      192              rows = cur.fetchall()
      193  
      188 -        train_files = [row[0] for row in rows if row[1] == "tr
          -ain"]                                                         
      189 -        val_files = [row[0] for row in rows if row[1] == "val"
          -] or train_files                                              
      194 +        selected_root, listed = _build_selected_tree(root, row
          +s, class_map, project["label_type"])                          
      195 +        _require_val(len(listed["val"]), "the selected batches
          +")                                                            
      196  
      197          train_txt = os.path.join(root, "selected_train.txt")
      198          val_txt = os.path.join(root, "selected_val.txt")
      193 -        with open(train_txt, "w", encoding="utf-8") as handle:
      194 -            handle.write("\n".join(os.path.join(root, rel) for
          - rel in train_files) + "\n")                                  
      195 -        with open(val_txt, "w", encoding="utf-8") as handle:  
      196 -            handle.write("\n".join(os.path.join(root, rel) for
          - rel in val_files) + "\n")                                    
      199 +        _write_atomic(train_txt, "\n".join(listed["train"]) + 
          +"\n")                                                         
      200 +        _write_atomic(val_txt, "\n".join(listed["val"]) + "\n"
          +)                                                             
      201  
      202          path = os.path.join(root, "selected_data.yaml")
      199 -        with open(path, "w", encoding="utf-8") as handle:     
      200 -            handle.write(f"path: {root}\n")                   
      201 -            handle.write(f"train: {train_txt}\n")             
      202 -            handle.write(f"val: {val_txt}\n\n")               
      203 -            handle.write(f"nc: {len(project['classes'])}\n")  
      204 -            handle.write(f"names: [{names}]\n")               
      203 +        _write_atomic(path,                                   
      204 +                      f"path: {selected_root}\n"              
      205 +                      f"train: {train_txt}\n"                 
      206 +                      f"val: {val_txt}\n\n"                   
      207 +                      f"nc: {len(target_classes)}\n"          
      208 +                      f"names: [{names}]\n")                  
      209          return path
      210  
      211 +    _require_val(counts["val"], "this project")               
      212 +                                                              
      213      path = os.path.join(root, "data.yaml")
      208 -    with open(path, "w", encoding="utf-8") as handle:         
      209 -        handle.write(f"path: {root}\n")                       
      210 -        handle.write("train: images/train\n")                 
      211 -        handle.write(f"val: images/{'val' if counts['val'] > 0
          - else 'train'}\n\n")                                          
      212 -        handle.write(f"nc: {len(project['classes'])}\n")      
      213 -        handle.write(f"names: [{names}]\n")                   
      214 +    _write_atomic(path,                                       
      215 +                  f"path: {root}\n"                           
      216 +                  "train: images/train\n"                     
      217 +                  "val: images/val\n\n"                       
      218 +                  f"nc: {len(target_classes)}\n"              
      219 +                  f"names: [{names}]\n")                      
      220      return path
      221  
      222  
      223 +def _require_val(count: int, subject: str) -> None:           
      224 +    """Refuse to build a dataset with an empty val split.     
      225 +                                                              
      226 +    Falling back to the training images produces a base-vs-new
          + mAP measured on                                              
      227 +    data the model was fitted to — a number that looks fine an
          +d means nothing.                                              
      228 +    For a system whose whole purpose is answering "did retrain
          +ing help?", this                                              
      229 +    has to fail loudly.                                       
      230 +    """                                                       
      231 +    if count == 0:                                            
      232 +        raise DatasetError(                                   
      233 +            f"There are no validation images in {subject}, so 
          +a base-vs-new comparison "                                    
      234 +            "would be measured on the training images. Merge m
          +ore frames, or lower the "                                    
      235 +            "project's val_every."                            
      236 +        )                                                     
      237 +                                                              
      238 +                                                              
      239  def summary(project_id: int) -> dict:
      240      import json
      241      with db.cursor() as cur:

● Now the merge handler — hash split, cancel bug, and the hashlib import:

● Update(backend/dataset.py)
  ⎿  Added 1 line
      11      segment  class_id  x1 y1 x2 y2 …        (normalized polygon
          )
      12  """
      13  
      14 +import hashlib                                                 
      15  import os
      16  import shutil
      17  import time

● Update(backend/dataset.py)
  ⎿  Added 3 lines, removed 1 line
      436  
      437      added = {"train": 0, "val": 0}
      438      skipped = 0
      439 +    cancelled = False                                         
      440      for index, frame in enumerate(frames):
      441          if job.cancelled:
      442              job.log(f"Cancelled after {index} frame(s)")
      443 +            cancelled = True                                  
      444              break
      445  
      446          with db.cursor() as cur:
     ...
      449                  skipped += 1
      450                  job.progress(index + 1, len(frames))
      451                  continue
      450 -            split = _next_split(cur, project["id"], project["v
          -al_every"])                                                   
      452  
      453              stem = f"{batch['id']}__{os.path.splitext(frame['f
           ilename'])[0]}"
      454 +            split = split_for(project["id"], batch["id"], stem
          +, project["val_every"])                                       
      455              image_rel = f"images/{split}/{stem}.jpg"
      456              label_rel = f"labels/{split}/{stem}.txt"
      457              shutil.copyfile(os.path.join(source_dir, frame["fi
           lename"]),

● Update(backend/dataset.py)
  ⎿  Added 8 lines
      473          added[split] += 1
      474          job.progress(index + 1, len(frames))
      475  
      476 +    if cancelled:                                             
      477 +        # Leaving it 'merged' would be a lie: the frames after
          + the break point                                              
      478 +        # have no dataset_items rows and no files, and approve
          +() refuses to                                                 
      479 +        # re-merge a merged batch, so they could never be adde
          +d. The per-frame                                              
      480 +        # dataset_items guard already makes re-running the mer
          +ge idempotent.                                                
      481 +        job.log("Batch left approved — re-approve it to finish
          + the merge")                                                  
      482 +        return                                                
      483 +                                                              
      484      with db.cursor() as cur:
      485          cur.execute("UPDATE batches SET status = 'merged', mer
           ged_at = ? WHERE id = ?",
      486                      (time.time(), batch["id"]))

● Step 5 — batch deletion orphans, plus the double-approve guard:

  Read 1 file (ctrl+o to expand)

● Update(backend/batches.py)
  ⎿  Added 23 lines
      238  
      239  def delete(batch_id: int) -> bool:
      240      import shutil
      241 +    from backend import dataset                               
      242 +                                                              
      243      batch = get(batch_id)
      244      if batch is None:
      245          return False
      246 +                                                              
      247 +    # The FK cascade takes the frames and dataset_items rows, 
          +but the copies this                                           
      248 +    # batch put in the master dataset live in a sibling tree a
          +nd would survive as                                           
      249 +    # orphans — and data.yaml trains and validates on those di
          +rectories, so the                                             
      250 +    # model would keep learning from a batch the user believes
          + is gone.                                                     
      251 +    root = dataset.dataset_dir(batch["project_slug"])         
      252      with db.cursor() as cur:
      253 +        cur.execute(                                          
      254 +            """SELECT d.image_rel, d.label_rel FROM dataset_it
          +ems d                                                         
      255 +               JOIN frames f ON f.id = d.frame_id             
      256 +               WHERE f.batch_id = ?""",                       
      257 +            (batch_id,),                                      
      258 +        )                                                     
      259 +        merged_files = cur.fetchall()                         
      260 +    for image_rel, label_rel in merged_files:                 
      261 +        for rel in (image_rel, label_rel):                    
      262 +            try:                                              
      263 +                os.unlink(os.path.join(root, rel))            
      264 +            except FileNotFoundError:                         
      265 +                pass                                          
      266 +                                                              
      267 +    with db.cursor() as cur:                                  
      268          cur.execute("DELETE FROM batches WHERE id = ?", (batch
           _id,))
      269      shutil.rmtree(batch_dir(batch["project_slug"], batch_id), 
           ignore_errors=True)
      270      return True

● Update(backend/dataset.py)  ⎿  Added 8 lines
      42          )
      43      if batch["review"]["approved"] == 0:
      44          raise DatasetError("Every frame was rejected — there is
           nothing to merge")
      45 +    with db.cursor() as cur:                                   
      46 +        cur.execute(                                           
      47 +            """SELECT 1 FROM jobs WHERE batch_id = ? AND type =
         + 'merge'                                                       
      48 +               AND status IN ('queued', 'running')""",         
      49 +            (batch_id,),                                       
      50 +        )                                                      
      51 +        if cur.fetchone() is not None:                         
      52 +            raise DatasetError("A merge for this batch is alrea
         +dy queued")                                                    
      53  
      54      batches.set_status(batch_id, "approved")
      55      job = jobs.create(

  Searched for 2 patterns (ctrl+o to expand)

● Good catch — class edits and merges would now throw on a young dataset. Gating
  the check to the training path only:

● Update(backend/dataset.py)
  ⎿  Added 3 lines, removed 1 line
      170      return selected_root, listed
      171  
      172  
      173 -def write_data_yaml(project: dict, batch_ids: list = None, sel
          -ected_class_ids: Optional[List[int]] = None) -> str:          
      173 +def write_data_yaml(project: dict, batch_ids: list = None,    
      174 +                    selected_class_ids: Optional[List[int]] = 
          +None,                                                         
      175 +                    require_val: bool = False) -> str:        
      176      """Rebuild data.yaml from the project's classes (REQ-051).
           """
      177      sync_labels(project["id"])
      178      root = dataset_dir(project["slug"])

● Update(backend/dataset.py)
  ⎿  Added 2 lines, removed 1 line
      203              rows = cur.fetchall()
      204  
      205          selected_root, listed = _build_selected_tree(root, row
           s, class_map, project["label_type"])
      206 -        _require_val(len(listed["val"]), "the selected batches
          -")                                                            
      206 +        if require_val:                                       
      207 +            _require_val(len(listed["val"]), "the selected bat
          +ches")                                                        
      208  
      209          train_txt = os.path.join(root, "selected_train.txt")
      210          val_txt = os.path.join(root, "selected_val.txt")

● Update(backend/dataset.py)
  ⎿  Added 2 lines, removed 1 line
      220                        f"names: [{names}]\n")
      221          return path
      222  
      223 -    _require_val(counts["val"], "this project")               
      223 +    if require_val:                                           
      224 +        _require_val(counts["val"], "this project")           
      225  
      226      path = os.path.join(root, "data.yaml")
      227      _write_atomic(path,

● Update(backend/training.py)
  ⎿  Added 2 lines, removed 1 line
      103      settings = job.params["settings"]
      104      batch_ids = job.params.get("batch_ids")
      105      class_ids = job.params.get("class_ids")
      106 -    data_yaml = dataset.write_data_yaml(project, batch_ids=bat
          -ch_ids, selected_class_ids=class_ids)                         
      106 +    data_yaml = dataset.write_data_yaml(project, batch_ids=bat
          +ch_ids, selected_class_ids=class_ids,                         
      107 +                                        require_val=True)     
      108  
      109      # SAM3 and a training run must not hold VRAM at the same t
           ime (REQ-065).
      110      from backend.sam3_engine import release_engine

● Backend done. Step 7 — the review editor:

  Read 1 file (ctrl+o to expand)

● Update(frontend/src/pages/ReviewPage.jsx)
  ⎿  Added 3 lines
      234    useEffect(() => {
      235      function onKeyDown(event) {
      236        if (event.target?.matches?.('input, textarea, select, [c
           ontenteditable="true"]')) return
      237 +      // Ctrl/Cmd/Alt combos belong to the browser and the OS:
          + without this,                                                
      238 +      // Ctrl+S approves the frame and Ctrl+A/C/X/N/T all fire
          + review actions.                                              
      239 +      if (event.ctrlKey || event.metaKey || event.altKey) retu
          +rn                                                            
      240        const { frames, project, setStatus, removeSelected, recl
           ass, jumpToPending, jumpToNextAnnotated, setAssistMode, copyPr
           evious, trackForward } = stateRef.current
      241        const key = event.key
      242        const isShortcutKey = /^[1-9]$/.test(key) || ['ArrowLeft
           ', 'ArrowRight', 'ArrowUp', 'ArrowDown', 'Delete', 'Backspace'
           , 'a', 'A', 'x', 'X', 'u', 'U', 's', 'S', 'n', 'N', 'c', 'C', 
           't', 'T'].includes(key)

● Update(frontend/src/pages/ReviewPage.jsx)
  ⎿  Added 28 lines, removed 9 lines
      124        return
      125      }
      126      if (!commit) return
      127 -    const current = annotations.find((row) => row.id === id)  
      128 -    if (!current) return                                      
      129 -    try { await api.patchAnnotation(id, { geometry: current.ge
          -ometry }) } catch (exc) { setError(exc.message) }             
      127 +    const previous = annotations.find((row) => row.id === id) 
      128 +    if (!previous) return                                     
      129 +    // A commit may carry its own geometry (delete-vertex send
          +s the shortened                                               
      130 +    // polygon); falling back to the row's geometry covers dra
          +g/resize, which                                               
      131 +    // already wrote through the {local:true} path.           
      132 +    const next = geometry ?? previous.geometry                
      133 +    setAnnotations((rows) => rows.map((row) => (row.id === id 
          +? { ...row, geometry: next } : row)))                         
      134 +    try {                                                     
      135 +      await api.patchAnnotation(id, { geometry: next })       
      136 +    } catch (exc) {                                           
      137 +      setAnnotations((rows) => rows.map((row) => (row.id === i
          +d ? previous : row)))                                         
      138 +      setError(exc.message)                                   
      139 +    }                                                         
      140    }
      141  
      132 -  const removeSelected = useCallback(async () => {            
      133 -    if (selectedId == null || !frame) return                  
      134 -    const id = selectedId                                     
      142 +  const removeAnnotation = useCallback(async (id) => {        
      143 +    if (id == null || !frame) return                          
      144 +    const previous = annotations.find((row) => row.id === id) 
      145 +    if (!previous) return                                     
      146      setAnnotations((rows) => rows.filter((row) => row.id !== i
           d))
      136 -    setSelectedId(null)                                       
      147 +    setSelectedId((current) => (current === id ? null : curren
          +t))                                                           
      148      setFrames((rows) => rows.map((row) => (row.id === frame.id
            ? { ...row, annotation_count: Math.max(0, (row.annotation_cou
           nt ?? 1) - 1) } : row)))
      138 -    try { await api.deleteAnnotation(id) } catch (exc) { setEr
          -ror(exc.message) }                                            
      139 -  }, [selectedId, frame])                                     
      149 +    try {                                                     
      150 +      await api.deleteAnnotation(id)                          
      151 +    } catch (exc) {                                           
      152 +      setAnnotations((rows) => [...rows, previous])           
      153 +      setFrames((rows) => rows.map((row) => (row.id === frame.
          +id ? { ...row, annotation_count: (row.annotation_count ?? 0) +
          + 1 } : row)))                                                 
      154 +      setError(exc.message)                                   
      155 +    }                                                         
      156 +  }, [annotations, frame])                                    
      157  
      158 +  const removeSelected = useCallback(() => removeAnnotation(se
          +lectedId), [removeAnnotation, selectedId])                    
      159 +                                                              
      160    const reclass = useCallback(async (classId) => {
      161      setActiveClass(classId)
      162      if (selectedId == null) return

● Now wire the sidebar to the id-explicit delete:

  Ran 1 shell command (ctrl+o to expand)

● Update(frontend/src/components/ReviewSidebar.jsx)
  ⎿  Added 1 line, removed 4 lines
      83                  <button
      84                    className="btn btn-danger"
      85                    aria-label="Delete shape"
      86 -                  onClick={() => {                             
      87 -                    setSelectedId(item.id)                     
      88 -                    removeSelected()                           
      89 -                  }}                                           
      86 +                  onClick={() => removeAnnotation(item.id)}    
      87                  >
      88                    <TrashIcon size={13} />
      89                  </button>

  Ran 1 shell command (ctrl+o to expand)

● Update(frontend/src/components/ReviewSidebar.jsx)
  ⎿  Added 1 line, removed 1 line
      11    annotations,
      12    selectedId,
      13    setSelectedId,
      14 -  removeSelected,                                              
      14 +  removeAnnotation,                                            
      15    project,
      16    jumpToNextAnnotated,
      17    batchAnnotationCount,

● Update(frontend/src/pages/ReviewPage.jsx)
  ⎿  Added 1 line, removed 1 line
      430            annotations={annotations}
      431            selectedId={selectedId}
      432            setSelectedId={setSelectedId}
      433 -          removeSelected={removeSelected}                     
      433 +          removeAnnotation={removeAnnotation}                 
      434            project={project}
      435            jumpToNextAnnotated={jumpToNextAnnotated}
      436            batchAnnotationCount={batch?.annotation_count ?? 0}

● Now verify — imports, syntax, and the split logic:

  Ran 3 shell commands (ctrl+o to expand)

● Done with steps 1–7. Both check out: backend imports clean, frontend builds.

  What changed

  Backend
  - jobs.py — reverted to the single-worker queue; GPU_JOB_TYPES back to
  ("autolabel", "train"). Dead _start_job removed. The file is now
  byte-identical to HEAD.
  - api/batches.py — preview and /api/sam3/playground-test now take gpu_lock
  with a 20 s timeout and return 409 with "GPU is busy with a X job", matching
  review.assist.
  - dataset.py:
    - sync_labels filters to review_status='approved' and no longer takes
  selected_class_ids — re-running auto-annotate on a merged batch can no longer
  push unreviewed output into the master dataset.
    - Class-filtered runs build a selected/ tree (symlinked images + remapped
  labels). Master labels stay canonical.
    - _next_split → split_for(project, batch, stem, val_every), hash-derived.
  Verified deterministic, 18.8% val at val_every=5, val_every=0 → all train.
    - All label/yaml writes go through _write_atomic (temp + os.replace).
    - Cancelled merge leaves the batch approved and logs why, instead of marking
  it merged and stranding the frames forever.
    - nc now counts target_classes, not the full list.
    - _require_val refuses an empty val split — gated behind require_val=True,
  passed only from training.py.
  - batches.py — delete() unlinks the batch's dataset_items files from the
  master dataset before dropping the rows.
  - dataset.approve() — rejects when a merge for that batch is already queued or
  running.

  Frontend
  - removeSelected split into removeAnnotation(id) + a thin wrapper; sidebar
  trash calls it with an explicit id. The stale-closure wrong-shape delete is
  gone.
  - Keydown handler early-returns on ctrlKey || metaKey || altKey.
  - updateShape uses the geometry passed on commit (Alt-click delete-vertex now
  actually works) and rolls back local state on a failed PATCH, as does
  removeAnnotation.

  On your split migration: no backfill was needed. dataset_items stores each
  frame's split as a row and the merge loop skips any frame that already has
  one, so existing assignments are preserved untouched — the hash only decides
  new merges. Your existing base-vs-new comparison stays valid.

  Two caveats, stated plainly. I verified imports, the split function's
  behaviour, and a clean frontend build — I did not run a live end-to-end round,
  so the merge/train paths are unexercised. And data.yaml now always says val: 
  images/val; if a project has merged data but zero val images, training raises
  instead of silently validating on train. That's the intended behaviour, but it
  will surface as a new error on any project in that state.

  Not touched from the report: the custom_model_path critical (C3 — needs the
  opaque-staging-id refactor across inspect-model, autolabel.start and
  LibraryPage), the staged .pt leaks, library.py's unbounded ffmpeg threads,
  datasetSummary returning every annotation, and the ops items (.dockerignore,
  untracking docker-compose.override.yml, .env.example, pinning requirements).
  Say the word on which to take next — and separately, tell me how slow
  autolabel actually is and on what hardware, and I'll look at making a single
  job faster.

✻ Sautéed for 6m 28s

※ recap: Goal is making your reTraining app reliable end-to-end; I audited it and fixed the seven critical issues in the job queue, dataset split, and review editor. Next up is your call on which remaining items to tackle. (disable recaps in /config)

❯ can you understand the data preparation pages and analyze what works and what 
  is not, what we should do to this pages, for this pages purposes is based on  
  the sam3 result, i can filter which is good for send to train and which to    
  ignore, but the ignored one is we can choose to just throw away or store them 
  as a different class, for example full-sack and half-sack. so the training    
  section can pick which they want to train based on that data preparation      
  filtering. do you understand my point?. ask me                                

● Let me read the page and its backend before I say I understand it.

  Read 1 file (ctrl+o to expand)
● Yes, I understand the intent. Let me state it back, then flag one problem,
  then ask.

  What you want: Data Prep is the triage gate between SAM3's raw output and
  training. You look at what SAM3 produced, filter it, and each shape gets one
  of three fates — keep (goes to training), discard (thrown away), or relabel
  (kept, but as a different class, e.g. the rejects become half-sack while the
  keepers stay full-sack). Then Models/Training picks which of those classes it
  actually trains on.

  What the page does today: nothing of that. It is a read-only analytics
  dashboard. minSizePct/maxSizePct are local React state
  (DataPrepPage.jsx:10-11) that are never sent to the server — no apply button,
  no persistence, no mutation. Reload the page and your filter is gone. The
  class-distribution counts and the red/green box preview recompute live, but
  they're a visualization of a filter that exists only in the browser.

  What already exists that you can build on: training accepts selected_class_ids
  (training.py:106 → write_data_yaml), which after this morning's fix
  materializes a filtered selected/ tree. So "training picks which classes to
  train" is already wired — the missing half is the mechanism that assigns
  shapes to those classes in the first place.

  One concern, and I think it matters. The only filter axis on this page is box 
  area as a percentage of the frame. Area is a proxy for distance from camera,
  not for how full the sack is. A full sack at the far end of the conveyor and a
  half sack up close can have identical area_pct. If full-vs-half is genuinely
  the distinction you want, area alone will mislabel a lot of them. SAM3's
  confidence score is the better "is this detection any good" signal — and note
  it isn't even in the payload today (dataset.py sends id, frame_id, class_id, 
  area_pct, box — no score). Aspect ratio might separate full from half better
  than area does. So I'd like to know what you're actually seeing in the data.

  Questions:

● User answered Claude's questions:
  ⎿  · When you look at a SAM3 result and decide "this one is bad" or "this one 
     is half-sack", what are you actually judging it on? → SAM3 confidence 
     score, Box size / area, I have to look at the image, or anything you 
     recommend
     · When you "relabel the rejects as half-sack", should that permanently 
     change the annotations, or be a reusable rule? → Saved filter rule / preset
     just like you recommend. every section have their own  rule, so if i 
     change in data prep section, the batches model doesnt have effect, but the 
     next step have
     · Where should this triage happen in the loop? → Per batch, before merge
     · What should "discard" actually do to a shape? → Mark ignored, decide 
     later

● Good — that clarifies a lot, but three things are still genuinely ambiguous
  and they change the architecture, so let me pin them down.

  Note one tension in your answers I want to resolve: you want triage per batch 
  before merge, but you also want a data-prep rule change to affect the next 
  step. If the rule is baked in at merge time, then editing it later can't
  change anything downstream — the data is already written. So I need to know
  when the rule actually gets evaluated.

  Also, your "mark ignored" answer leaves the label-integrity trap open: if an
  ignored box stays on an image that still goes to training, you're teaching the
  model that a sack is background. That has to resolve somewhere.

● User answered Claude's questions:
  ⎿  · When is a data-prep rule actually evaluated? → Live, at training time 
     (Recommended)
     · An 'ignored' box sits on an image. What happens to that image at training
     time? → Exclude the whole image (Recommended)
     · You said you often have to look at the image. How do manual decisions 
     coexist with rules? → Rules propose, you confirm in bulk
     · For finding the full-vs-half boundary, what should I put on the page? → 
     Both

● That resolves it into a coherent design. One consequence you should see before
  I write anything, because it touches your core invariant:

  Live rule evaluation moves the val set. If a rule change excludes images, the
  val split changes between runs — so a base-vs-new mAP from last week isn't
  comparable to one from today. Both models in a single comparison are fine
  (same data.yaml), but across rule edits the number silently shifts meaning.
  This is fixable — stamp the rule-set version onto each training run and
  refuse/warn on cross-version comparison — but it needs to be in the design,
  not discovered later.

  Per CLAUDE.md §5 I'm not writing feature code before a REQ covers it, so
  here's the proposal:

● Write(docs/proposal-dataprep-triage.md)
  ⎿  Wrote 131 lines to docs/proposal-dataprep-triage.md
       1 # Proposal — Data Prep as a triage stage
       2 
          into `./requirements.md`
       4 and the schema/API sections into `./design.md`.
       5 
       7 
       8 - Signals: SAM3 confidence score, box area, aspect ratio, plus v
         isual judgement.
         orward only.
      10 - Triage happens **per batch**, on SAM3 output.
     … +175 lines (ctrl+o to expand)

● Written to docs/proposal-dataprep-triage.md. The short version:

  The model is a three-way resolve, evaluated live: manual override > first 
  matching rule > default keep, producing keep / ignore / reclass → class.
  annotations.class_id is never rewritten — SAM3's original output stays intact,
  which is exactly what makes rules re-cuttable forever. Resolution happens in
  one place only: dataset._build_selected_tree, which after this morning's fix
  is already the thing that materializes what training sees.

  Nine requirements (REQ-100…108), two new tables (triage_rules,
  annotation_overrides), a rule_version column on training runs, six endpoints,
  and a /crop endpoint to feed the thumbnail grid.

  Three things worth your attention before you approve:

  1. Rules are per-project, scoped to the dataprep stage — not per-batch. You
  said triage happens per batch, and the UI is per batch (you look at one
  batch's SAM3 output at a time). But a rule like "area < 5% → half-sack" is a
  judgment about your domain, not about one batch, so it lives on the project
  and applies everywhere. If you actually want per-batch rules, say so — it's a
  different schema.
  2. Score is already stored on annotations (ReviewSidebar renders item.score),
  it's just missing from the Data Prep payload. Aspect ratio is free from the
  box. So no new inference is needed to get all four signals — just plumbing.
  3. REQ-104 vs REQ-107 is the real tension. Excluding whole images is the right
  call for label integrity, but it makes your val set a function of your rules.
  REQ-107 (stamp the rule version on each run, refuse cross-version comparison)
  keeps that honest instead of silent. If it turns out to fire on every edit
  and becomes noise, the fallback is to freeze val membership before rule
  evaluation and let rules affect train only. I'd rather start strict and loosen
  it.

  Approve the REQs and I'll move them into requirements.md + design.md and start
  building. If you'd rather see it working before committing to the
  requirements, I can build the resolver and the scatter/crop UI against one
  batch as a spike first — but say which, because CLAUDE.md says docs lead code
  and I don't want to violate that silently.

✻ Baked for 2m 21s

❯ id rather see it working before commiting, i approve the plan                 

● Approved — building the spike. Docs stay as the proposal until you've seen it
  run.

  Read 1 file, ran 2 shell commands (ctrl+o to expand)

● Schema first:

● Update(backend/db.py)
  ⎿  Added 22 lines
      125          finished_at REAL
      126      )
      127      """,
      128 +    """                                                       
      129 +    CREATE TABLE IF NOT EXISTS triage_rules (                 
      130 +        id           INTEGER PRIMARY KEY AUTOINCREMENT,       
      131 +        project_id   INTEGER NOT NULL REFERENCES projects(id) 
          +ON DELETE CASCADE,                                            
      132 +        stage        TEXT NOT NULL DEFAULT 'dataprep',        
      133 +        position     INTEGER NOT NULL,                        
      134 +        name         TEXT NOT NULL,                           
      135 +        predicate    TEXT NOT NULL,                           
      136 +        action       TEXT NOT NULL CHECK (action IN ('keep', '
          +ignore', 'reclass')),                                         
      137 +        target_class INTEGER,                                 
      138 +        created_at   REAL NOT NULL                            
      139 +    )                                                         
      140 +    """,                                                      
      141 +    """                                                       
      142 +    CREATE TABLE IF NOT EXISTS annotation_overrides (         
      143 +        annotation_id INTEGER PRIMARY KEY REFERENCES annotatio
          +ns(id) ON DELETE CASCADE,                                     
      144 +        verdict       TEXT NOT NULL CHECK (verdict IN ('keep',
          + 'ignore', 'reclass')),                                       
      145 +        target_class  INTEGER,                                
      146 +        decided_at    REAL NOT NULL                           
      147 +    )                                                         
      148 +    """,                                                      
      149      "CREATE INDEX IF NOT EXISTS idx_frames_batch ON frames(bat
           ch_id, idx)",
      150 +    "CREATE INDEX IF NOT EXISTS idx_triage_rules_project ON tr
          +iage_rules(project_id, stage, position)",                     
      151      "CREATE INDEX IF NOT EXISTS idx_annotations_frame ON annot
           ations(frame_id)",
      152      "CREATE INDEX IF NOT EXISTS idx_batches_project ON batches
           (project_id)",
      153      "CREATE INDEX IF NOT EXISTS idx_jobs_project ON jobs(proje
           ct_id, created_at)",

● Update(backend/db.py)
  ⎿  Added 5 lines
      189              cur.execute("ALTER TABLE projects ADD COLUMN secon
           dary_model_name TEXT")
      190          if "secondary_model_classes" not in cols:
      191              cur.execute("ALTER TABLE projects ADD COLUMN secon
           dary_model_classes TEXT")
      192 +        # REQ-107: what rule set a run's numbers were measured
          + under.                                                       
      193 +        cur.execute("PRAGMA table_info(model_versions)")      
      194 +        version_cols = [column[1] for column in cur.fetchall()
          +]                                                             
      195 +        if "rule_version" not in version_cols:                
      196 +            cur.execute("ALTER TABLE model_versions ADD COLUMN
          + rule_version TEXT")                                          
      197  
      198  
      199  def healthy() -> bool:

● Now the resolver — the core of the whole feature:

● Write(backend/triage.py)
  ⎿  Wrote 267 lines to backend/triage.py
      1 """Triage: deciding what each SAM3 shape is actually worth (REQ-1
        00…108).
      3 A shape is never rewritten. Its verdict is *resolved* every time 
        it is needed:
      4 
      6 
      7 so `annotations.class_id` keeps whatever SAM3 said, and any rule 
        can be re-cut
         are evaluated
      9 at training time rather than baked in at merge (REQ-102).
     … +305 lines (ctrl+o to expand)

● Now wire the resolver into what training actually sees:

  Read 1 file (ctrl+o to expand)

● Update(backend/dataset.py)
  ⎿  Added 36 lines, removed 10 lines
      136  
      137  
      138  def _build_selected_tree(root: str, rows: list, class_map: Opt
           ional[dict],
      139 -                         label_type: str) -> tuple:           
      139 +                         label_type: str, resolver=None) -> tu
          +ple:                                                          
      140      """Materialise a filtered view of the dataset under `selec
           ted/`.
      141  
      142      The master labels stay canonical. A per-run class filter r
           enumbers ids into a
      143      contiguous 0..k-1 space, which contradicts `project_classe
           s`; writing that
      144      into the shared label files would leave the master dataset
            in the index space
      145      of whichever training run happened to go last.
      146 +                                                              
      147 +    This is also the one place triage rules are applied (REQ-1
          +02): resolve every                                            
      148 +    shape, drop the images carrying an `ignore`, and emit the 
          +rest under their                                              
      149 +    effective class. Because it happens here and not at merge 
          +time, editing a                                               
      150 +    rule re-cuts the dataset on the next run without touching 
          +what was merged.                                              
      151      """
      152      selected_root = os.path.join(root, "selected")
      153      if os.path.isdir(selected_root):
      154          shutil.rmtree(selected_root)
      155      listed = {"train": [], "val": []}
      156 +    excluded = 0                                              
      157      for image_rel, label_rel, frame_id, split in rows:
      158 +        annotations = review.listing(frame_id)                
      159 +                                                              
      160 +        if resolver is not None:                              
      161 +            resolved = []                                     
      162 +            skip_image = False                                
      163 +            for item in annotations:                          
      164 +                shape = {"id": item["id"], "class_id": item["c
          +lass_id"],                                                    
      165 +                         "score": float(item.get("score") or 1
          +.0),                                                          
      166 +                         **triage.metrics(item["geometry"])}  
      167 +                effective = resolver.effective_class(shape)   
      168 +                if effective is None:                         
      169 +                    # REQ-104: a known object left unlabeled w
          +ould teach the                                                
      170 +                    # model it is background, so the image goe
          +s too.                                                        
      171 +                    skip_image = True                         
      172 +                    break                                     
      173 +                resolved.append({**item, "class_id": effective
          +})                                                            
      174 +            if skip_image:                                    
      175 +                excluded += 1                                 
      176 +                continue                                      
      177 +            annotations = resolved                            
      178 +                                                              
      179 +        if class_map is not None:                             
      180 +            annotations = [a for a in annotations if a["class_
          +id"] in class_map]                                            
      181 +        lines = [_label_line(class_map[a["class_id"]] if class
          +_map is not None else a["class_id"],                          
      182 +                             a["geometry"], label_type)       
      183 +                 for a in annotations]                        
      184 +                                                              
      185          stem = os.path.basename(image_rel)
      186          image_dst = os.path.join(selected_root, "images", spli
           t, stem)
      187          label_dst = os.path.join(selected_root, "labels", spli
           t,
      188                                   os.path.splitext(stem)[0] + "
           .txt")
      189          os.makedirs(os.path.dirname(image_dst), exist_ok=True)
      190          os.makedirs(os.path.dirname(label_dst), exist_ok=True)
      158 -        source = os.path.join(root, image_rel)                
      191          if not os.path.exists(image_dst):
      160 -            os.symlink(source, image_dst)                     
      161 -                                                              
      162 -        annotations = review.listing(frame_id)                
      163 -        if class_map is not None:                             
      164 -            annotations = [a for a in annotations if a["class_
          -id"] in class_map]                                            
      165 -        lines = [_label_line(class_map[a["class_id"]] if class
          -_map is not None else a["class_id"],                          
      166 -                             a["geometry"], label_type)       
      167 -                 for a in annotations]                        
      192 +            os.symlink(os.path.join(root, image_rel), image_ds
          +t)                                                            
      193          _write_atomic(label_dst, "\n".join(lines) + ("\n" if l
           ines else ""))
      194          listed[split].append(image_dst)
      195 +    listed["excluded"] = excluded                             
      196      return selected_root, listed
      197  
      198  

● Update(backend/dataset.py)
  ⎿  Added 6 lines, removed 1 line
      213  
      214      names = ", ".join(f"'{item['name']}'" for item in target_c
           lasses)
      215  
      216 -    if batch_ids or class_map is not None:                    
      216 +    # Rules re-cut the dataset on every run (REQ-102), so thei
          +r presence alone is                                           
      217 +    # enough to need the `selected/` tree — the master labels 
          +must stay canonical.                                          
      218 +    resolver = triage.Resolver(project["id"])                 
      219 +    has_rules = bool(resolver.rules or resolver.overrides)    
      220 +                                                              
      221 +    if batch_ids or class_map is not None or has_rules:       
      222          with db.cursor() as cur:
      223              where = "d.project_id = ?"
      224              args = [project["id"]]

● Update(backend/dataset.py)
  ⎿  Added 3 lines, removed 1 line
      233              )
      234              rows = cur.fetchall()
      235  
      236 -        selected_root, listed = _build_selected_tree(root, row
          -s, class_map, project["label_type"])                          
      236 +        selected_root, listed = _build_selected_tree(         
      237 +            root, rows, class_map, project["label_type"],     
      238 +            resolver=resolver if has_rules else None)         
      239          if require_val:
      240              _require_val(len(listed["val"]), "the selected bat
           ches")
      241  

● Update(backend/dataset.py)
  ⎿  Added 1 line, removed 1 line
      17  import time
      18  from typing import List, Optional
      19  
      20 -from backend import batches, config, db, jobs, projects, review
      20 +from backend import batches, config, db, jobs, projects, review
         +, triage                                                       
      21  
      22  
      23  class DatasetError(Exception):

  Ran 1 shell command (ctrl+o to expand)
● Now the API routes and crop endpoint:

● Write(backend/api/triage.py)
  ⎿  Wrote 118 lines to backend/api/triage.py
      1 """Triage routes: rules, hand overrides, and the per-batch shape 
        view (REQ-100…108)."""
      2 
      3 import io
      4 import os
      5 
      6 from fastapi import APIRouter, HTTPException
      7 from fastapi.responses import StreamingResponse
      8 from pydantic import BaseModel
      9 from typing import List, Optional
     … +115 lines (ctrl+o to expand)

  Ran 1 shell command (ctrl+o to expand)

● Good. Registering the router:

● Update(backend/main.py)
  ⎿  Added 1 line, removed 1 line
      15  from fastapi.middleware.cors import CORSMiddleware
      16  
      17  from backend import config, db, jobs
      18 -from backend.api import batches, jobs as job_routes, models, pr
         -ojects, review                                                 
      18 +from backend.api import batches, jobs as job_routes, models, pr
         +ojects, review, triage                                         
      19  
      20  
      21  @asynccontextmanager

● Update(backend/main.py)
  ⎿  Added 1 line
      43  app.include_router(review.router)
      44  app.include_router(models.router)
      45  app.include_router(job_routes.router)
      46 +app.include_router(triage.router)                              
      47  
      48  
      49  @app.get("/api/health")

● Let me verify the resolver logic end-to-end against a real scenario before
  building UI on top of it:

  Ran 1 shell command (ctrl+o to expand)

● Rules resolve correctly — the FK error is my test using fake annotation ids.
  Let me redo with real rows:

  Ran 3 shell commands (ctrl+o to expand)

● Resolver verified: precedence, first-match-wins, manual override, version
  changes. Now the UI. Let me check the api client conventions:

  Ran 1 shell command (ctrl+o to expand)

● Update(frontend/src/api.js)
  ⎿  Added 15 lines
      116    approveAllBatchFrames: (batchId) => request(`/batches/${batc
           hId}/approve-all`, { method: 'POST' }),
      117    approveBatch: (batchId) => request(`/batches/${batchId}/appr
           ove`, { method: 'POST' }),
      118    datasetSummary: (projectId) => request(`/projects/${projectI
           d}/dataset`),
      119 +                                                              
      120 +  // Triage (REQ-100…108)                                     
      121 +  triageRules: (projectId) => request(`/projects/${projectId}/
          +triage/rules`),                                               
      122 +  saveTriageRules: (projectId, rules) =>                      
      123 +    request(`/projects/${projectId}/triage/rules`, { method: '
          +PUT', body: { rules } }),                                     
      124 +  triageShapes: (batchId) => request(`/batches/${batchId}/tria
          +ge/shapes`),                                                  
      125 +  triagePreview: (projectId) => request(`/projects/${projectId
          +}/triage/preview`),                                           
      126 +  setTriageOverrides: (annotationIds, verdict, targetClass = n
          +ull) =>                                                       
      127 +    request('/triage/overrides', {                            
      128 +      method: 'POST',                                         
      129 +      body: { annotation_ids: annotationIds, verdict, target_c
          +lass: targetClass },                                          
      130 +    }),                                                       
      131 +  clearTriageOverrides: (annotationIds) =>                    
      132 +    request('/triage/overrides', { method: 'DELETE', body: { a
          +nnotation_ids: annotationIds } }),                            
      133 +  cropUrl: (annotationId) => `/api/annotations/${annotationId}
          +/crop`,                                                       
      134    datasetDownloadUrl: (projectId) => `/api/projects/${projectI
           d}/dataset/download`,
      135  
      136    hardware: () => request('/hardware'),

● Now the scatter plot with drag-selection:

● Write(frontend/src/components/TriageScatter.jsx)
  ⎿  Wrote 165 lines to frontend/src/components/TriageScatter.jsx
      1 import React, { useMemo, useRef, useState } from 'react'
      2 
        EQ-106).
      4  *
      5  * Area is on a log scale: box sizes span three orders of magnitu
      6  * linear axis every shape piles into the left edge where no boun
        dary is
      7  * visible. */
      9 const VERDICT_COLOR = { keep: '#4ade80', ignore: '#f87171', recla
        ss: '#c084fc' }
     … +179 lines (ctrl+o to expand)

● Now the crop grid and rule editor:

● Write(frontend/src/components/TriageCropGrid.jsx)
  ⎿  Wrote 128 lines to frontend/src/components/TriageCropGrid.jsx
      1 import React, { useState } from 'react'
      2 import { api } from '../api'
      3 
        ck?" means
      5  * looking at the object, not at the frame it came from. Sorted s
        o the shapes a
      6  * rule is about to act on sit together. */
      8 const VERDICT_COLOR = { keep: '#4ade80', ignore: '#f87171', recla
        ss: '#c084fc' }
      9 const PAGE = 120
     … +144 lines (ctrl+o to expand)

● Write(frontend/src/components/TriageRules.jsx)
  ⎿  Wrote 177 lines to frontend/src/components/TriageRules.jsx
       1 import React from 'react'
       2 import { TrashIcon } from './Icons'
       3 
       4 /* The rule list (REQ-100). Ordered, first match wins — so the o
         rder is part of
       6 
       7 const ACTIONS = [
       8   { value: 'keep', label: 'Keep' },
       9   { value: 'ignore', label: 'Ignore' },
      10   { value: 'reclass', label: 'Reclass to' },
     … +198 lines (ctrl+o to expand)

● Now the page itself, wiring it together:

  Searched for 2 patterns (ctrl+o to expand)

● Write(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 237 lines, removed 569 lines
        1 -import React, { useCallback, useEffect, useState } from 'react
          -'                                                             
        2 -import { api, classColor } from '../api'                      
        1 +import React, { useCallback, useEffect, useMemo, useState } fr
          +om 'react'                                                    
        2 +import { api } from '../api'                                  
        3  
        4 -import { AlertIcon, BarChartIcon, DatabaseIcon, RocketIcon } f
          -rom '../components/Icons'                                     
        4 +import { AlertIcon, DatabaseIcon, RocketIcon, SlidersIcon } fr
          +om '../components/Icons'                                      
        5 +import TriageCropGrid from '../components/TriageCropGrid'     
        6 +import TriageRules from '../components/TriageRules'           
        7 +import TriageScatter from '../components/TriageScatter'       
        8  
        9 +/* Data Prep is the triage gate between SAM3's raw output and 
          +training                                                      
       10 + * (REQ-100…108): look at what SAM3 produced for one batch, de
          +cide what is worth                                            
       11 + * training on, and express the repeatable part of that decisi
          +on as rules.                                                  
       12 + *                                                            
       13 + * Nothing here rewrites an annotation. Verdicts resolve as   
       14 + * `manual > first matching rule > keep`, and the dataset is r
          +e-cut from them at                                            
       15 + * training time — so a rule stays editable forever. */       
       16 +                                                              
       17 +const VERDICTS = [                                            
       18 +  { value: 'keep', label: 'Keep', color: '#4ade80' },         
       19 +  { value: 'ignore', label: 'Ignore', color: '#f87171' },     
       20 +  { value: 'reclass', label: 'Reclass', color: '#c084fc' },   
       21 +]                                                             
       22 +                                                              
       23  export default function DataPrepPage({ projectId, onProject })
            {
       24    const [project, setProject] = useState(null)
        8 -  const [summary, setSummary] = useState(null)                
        9 -  const [scaleMode, setScaleMode] = useState('normal') // 'nor
          -mal' (Gaussian Bell Curve) | 'linear'                         
       10 -  const [minSizePct, setMinSizePct] = useState(0)             
       11 -  const [maxSizePct, setMaxSizePct] = useState(100)           
       12 -  const [previewFrameId, setPreviewFrameId] = useState(null)  
       13 -  const [showFilteredOut, setShowFilteredOut] = useState(true)
       25 +  const [batches, setBatches] = useState([])                  
       26 +  const [batchId, setBatchId] = useState(null)                
       27 +  const [view, setView] = useState(null)                      
       28 +  const [rules, setRules] = useState([])                      
       29 +  const [savedRules, setSavedRules] = useState([])            
       30 +  const [preview, setPreview] = useState(null)                
       31 +  const [selectedIds, setSelectedIds] = useState([])          
       32 +  const [reclassTarget, setReclassTarget] = useState('')      
       33 +  const [saving, setSaving] = useState(false)                 
       34 +  const [busy, setBusy] = useState(false)                     
       35    const [error, setError] = useState('')
       36  
       37 +  useEffect(() => {                                           
       38 +    let cancelled = false                                     
       39 +    ;(async () => {                                           
       40 +      try {                                                   
       41 +        const [loadedProject, loadedBatches, loadedRules] = aw
          +ait Promise.all([                                             
       42 +          api.getProject(projectId),                          
       43 +          api.listBatches(projectId),                         
       44 +          api.triageRules(projectId),                         
       45 +        ])                                                    
       46 +        if (cancelled) return                                 
       47 +        setProject(loadedProject)                             
       48 +        onProject?.(loadedProject)                            
       49 +        const annotated = (loadedBatches.batches ?? loadedBatc
          +hes).filter((b) => b.annotation_count > 0)                    
       50 +        setBatches(annotated)                                 
       51 +        setBatchId((current) => current ?? annotated[0]?.id ??
          + null)                                                        
       52 +        setRules(loadedRules.rules)                           
       53 +        setSavedRules(loadedRules.rules)                      
       54 +      } catch (exc) {                                         
       55 +        if (!cancelled) setError(exc.message)                 
       56 +      }                                                       
       57 +    })()                                                      
       58 +    return () => { cancelled = true }                         
       59 +  }, [projectId])                                             
       60  
       17 -  const load = useCallback(async () => {                      
       61 +  const loadBatch = useCallback(async () => {                 
       62 +    if (!batchId) return                                      
       63      try {
       19 -      const [loadedProject, loadedSummary] = await Promise.all
          -([                                                            
       20 -        api.getProject(projectId),                            
       21 -        api.datasetSummary(projectId),                        
       64 +      const [shapes, previewed] = await Promise.all([         
       65 +        api.triageShapes(batchId),                            
       66 +        api.triagePreview(projectId),                         
       67        ])
       23 -      setProject(loadedProject)                               
       24 -      onProject?.(loadedProject)                              
       25 -      setSummary(loadedSummary)                               
       68 +      setView(shapes)                                         
       69 +      setPreview(previewed)                                   
       70 +      setSelectedIds([])                                      
       71      } catch (exc) {
       72        setError(exc.message)
       73      }
       29 -  }, [projectId])                                             
       74 +  }, [batchId, projectId])                                    
       75  
       31 -  useEffect(() => {                                           
       32 -    load()                                                    
       33 -  }, [load])                                                  
       76 +  useEffect(() => { loadBatch() }, [loadBatch])               
       77  
       35 -  if (error && !project) {                                    
       36 -    return <p className="error-banner"><AlertIcon size={14} />
          - {error}</p>                                                  
       37 -  }                                                           
       38 -  if (!project || !summary) {                                 
       39 -    return <p className="empty">Loading Data Preparation metad
          -ata…</p>                                                      
       40 -  }                                                           
       78 +  const shapes = view?.shapes ?? []                           
       79 +  const dirty = JSON.stringify(rules) !== JSON.stringify(saved
          +Rules)                                                        
       80  
       42 -  const totalFrames = (summary.splits?.train || 0) + (summary.
          -splits?.val || 0)                                             
       43 -  const mergedBatches = summary.batches || []                 
       44 -  const shapeDist = summary.shape_distribution || { total_shap
          -es: 0, histogram: [], log_histogram: [], shapes: [] }         
       81 +  const counts = useMemo(() => {                              
       82 +    const tally = { keep: 0, ignore: 0, reclass: 0, manual: 0 
          +}                                                             
       83 +    shapes.forEach((shape) => {                               
       84 +      tally[shape.verdict] += 1                               
       85 +      if (shape.source === 'manual') tally.manual += 1        
       86 +    })                                                        
       87 +    return tally                                              
       88 +  }, [shapes])                                                
       89  
       46 -  const linearHistogram = shapeDist.histogram || []           
       47 -  const logHistogram = shapeDist.log_histogram || []          
       48 -  const normalParams = shapeDist.normal_params || { mu_log: 0,
          - sigma_log: 1, median_area_pct: 0 }                           
       49 -  const rawShapes = shapeDist.shapes || []                    
       90 +  async function saveRules() {                                
       91 +    setSaving(true)                                           
       92 +    try {                                                     
       93 +      const stored = await api.saveTriageRules(projectId, rule
          +s)                                                            
       94 +      setSavedRules(stored.rules)                             
       95 +      setRules(stored.rules)                                  
       96 +      await loadBatch()                                       
       97 +    } catch (exc) {                                           
       98 +      setError(exc.message)                                   
       99 +    } finally {                                               
      100 +      setSaving(false)                                        
      101 +    }                                                         
      102 +  }                                                           
      103  
       51 -  // Active histogram data based on selected view mode        
       52 -  const activeHistogram = scaleMode === 'normal' ? logHistogra
          -m : linearHistogram                                           
       53 -  const maxCount = Math.max(1, ...activeHistogram.map((h) => h
          -.count || 0))                                                 
       54 -  const maxDensity = Math.max(0.001, ...logHistogram.map((h) =
          -> h.normal_density || 0))                                     
      104 +  async function applyVerdict(verdict) {                      
      105 +    if (selectedIds.length === 0) return                      
      106 +    if (verdict === 'reclass' && reclassTarget === '') {      
      107 +      setError('Pick the class to reclass into first')        
      108 +      return                                                  
      109 +    }                                                         
      110 +    setBusy(true)                                             
      111 +    try {                                                     
      112 +      await api.setTriageOverrides(selectedIds, verdict, verdi
          +ct === 'reclass' ? Number(reclassTarget) : null)              
      113 +      await loadBatch()                                       
      114 +    } catch (exc) {                                           
      115 +      setError(exc.message)                                   
      116 +    } finally {                                               
      117 +      setBusy(false)                                          
      118 +    }                                                         
      119 +  }                                                           
      120  
       56 -  // Filter shapes based on continuous min/max range sliders  
       57 -  const isFiltering = minSizePct > 0 || maxSizePct < 100      
       58 -  const filteredShapes = rawShapes.filter(                    
       59 -    (s) => s.area_pct >= minSizePct && s.area_pct <= maxSizePc
          -t                                                             
       60 -  )                                                           
      121 +  async function clearDecisions() {                           
      122 +    if (selectedIds.length === 0) return                      
      123 +    setBusy(true)                                             
      124 +    try {                                                     
      125 +      await api.clearTriageOverrides(selectedIds)             
      126 +      await loadBatch()                                       
      127 +    } catch (exc) {                                           
      128 +      setError(exc.message)                                   
      129 +    } finally {                                               
      130 +      setBusy(false)                                          
      131 +    }                                                         
      132 +  }                                                           
      133  
       62 -  // Compute class counts for filtered shapes                 
       63 -  const filteredClassCounts = {}                              
       64 -  filteredShapes.forEach((s) => {                             
       65 -    filteredClassCounts[s.class_id] = (filteredClassCounts[s.c
          -lass_id] || 0) + 1                                            
       66 -  })                                                          
      134 +  if (error && !project) {                                    
      135 +    return <p className="error-banner"><AlertIcon size={14} />
          + {error}</p>                                                  
      136 +  }                                                           
      137 +  if (!project) return <p className="empty">Loading Data Prepa
          +ration…</p>                                                   
      138  
       68 -  // Find top frames sorted by TOTAL annotation density       
       69 -                                                              
       70 -  const frameTotalCounts = {}                                 
       71 -  rawShapes.forEach((s) => {                                  
       72 -    frameTotalCounts[s.frame_id] = (frameTotalCounts[s.frame_i
          -d] || 0) + 1                                                  
       73 -  })                                                          
       74 -                                                              
       75 -  const topDenseFrames = Object.entries(frameTotalCounts)     
       76 -    .sort((a, b) => b[1] - a[1])                              
       77 -    .map(([fid, count]) => ({ frame_id: Number(fid), total_sha
          -pes: count }))                                                
       78 -                                                              
       79 -  const defaultPreviewFrameId = topDenseFrames[0]?.frame_id ||
          - rawShapes[0]?.frame_id || null                               
       80 -  const activePreviewFrameId = previewFrameId || defaultPrevie
          -wFrameId                                                      
       81 -                                                              
       82 -  // Get shapes on the active preview frame                   
       83 -  const allFrameShapes = rawShapes.filter((s) => s.frame_id ==
          -= activePreviewFrameId)                                       
       84 -  const keptShapesOnFrame = allFrameShapes.filter((s) => s.are
          -a_pct >= minSizePct && s.area_pct <= maxSizePct)              
       85 -  const filteredOutShapesOnFrame = allFrameShapes.filter((s) =
          -> s.area_pct < minSizePct || s.area_pct > maxSizePct)         
       86 -                                                              
       87 -                                                              
       88 -  // SVG Graph Dimensions                                     
       89 -  const graphWidth = 800                                      
       90 -  const graphHeight = 180                                     
       91 -  const padding = 24                                          
       92 -  const usableW = graphWidth - padding * 2                    
       93 -  const usableH = graphHeight - padding * 2                   
       94 -                                                              
       95 -  // Continuous Shape Count Line Coordinates                  
       96 -  const shapePoints = activeHistogram.map((h, i) => {         
       97 -    const x = padding + (i / (activeHistogram.length - 1)) * u
          -sableW                                                        
       98 -    const y = graphHeight - padding - (h.count / maxCount) * u
          -sableH                                                        
       99 -    return { x, y, count: h.count, label: scaleMode === 'norma
          -l' ? `${h.center_pct}%` : `${h.pct}%` }                       
      100 -  })                                                          
      101 -                                                              
      102 -  const shapeLineD = shapePoints.reduce(                      
      103 -    (acc, p, i) => (i === 0 ? `M ${p.x} ${p.y}` : `${acc} L ${
          -p.x} ${p.y}`),                                                
      104 -    ''                                                        
      105 -  )                                                           
      106 -  const shapeAreaD = `${shapeLineD} L ${shapePoints[shapePoint
          -s.length - 1]?.x || graphWidth} ${graphHeight - padding} L ${p
          -adding} ${graphHeight - padding} Z`                           
      107 -                                                              
      108 -  // Theoretical Gaussian Normal Bell Curve Coordinates (Cyan 
          -Line)                                                         
      109 -  const normalPoints = logHistogram.map((h, i) => {           
      110 -    const x = padding + (i / (logHistogram.length - 1)) * usab
          -leW                                                           
      111 -    const y = graphHeight - padding - (h.normal_density / maxD
          -ensity) * usableH                                             
      112 -    return { x, y, density: h.normal_density }                
      113 -  })                                                          
      114 -                                                              
      115 -  const normalLineD = normalPoints.reduce(                    
      116 -    (acc, p, i) => (i === 0 ? `M ${p.x} ${p.y}` : `${acc} L ${
          -p.x} ${p.y}`),                                                
      117 -    ''                                                        
      118 -  )                                                           
      119 -  const normalAreaD = `${normalLineD} L ${normalPoints[normalP
          -oints.length - 1]?.x || graphWidth} ${graphHeight - padding} L
          - ${padding} ${graphHeight - padding} Z`                       
      120 -                                                              
      121 -                                                              
      122 -  // Active Filter Range Overlay                              
      123 -  const startX = padding + (minSizePct / 100) * usableW       
      124 -  const endX = padding + (maxSizePct / 100) * usableW         
      125 -                                                              
      139    return (
      127 -                                                              
      140      <>
      129 -      <div className="page-head" style={{ display: 'flex', jus
          -tifyContent: 'space-between', alignItems: 'center' }}>        
      141 +      <div className="page-head" style={{ display: 'flex', jus
          +tifyContent: 'space-between', alignItems: 'center', gap: 16, f
          +lexWrap: 'wrap' }}>                                           
      142          <div>
      143            <h1>Data Preparation</h1>
      132 -          <p className="muted mono">{project.name} · Bounding 
          -Box Size Distribution & Preview</p>                           
      144 +          <p className="muted mono">{project.name} · triage SA
          +M3 output before it trains</p>                                
      145          </div>
      146          <a
      135 -          className="btn btn-primary"                         
      147 +          className="btn"                                     
      148            href={api.datasetDownloadUrl(projectId)}
      149            download
      150            style={{ fontSize: '0.85rem', padding: '8px 16px', c
           ursor: 'pointer', display: 'inline-flex', alignItems: 'center'
           , gap: 6, borderRadius: 6 }}
      151          >
      140 -          <DatabaseIcon size={16} /> Download Dataset (.zip)  
      152 +          <DatabaseIcon size={16} /> Download dataset (.zip)  
      153          </a>
      154        </div>
      155  
      156        {error && (
      157          <p className="error-banner" style={{ marginBottom: 16 
           }}>
      158            <AlertIcon size={14} /> {error}
      159 +          <button type="button" className="btn" onClick={() =>
          + setError('')} style={{ marginLeft: 10, cursor: 'pointer', fon
          +tSize: '0.75rem' }}>Dismiss</button>                          
      160          </p>
      161        )}
      162  
      150 -      {/* Dataset Summary Cards */}                           
      151 -      <div style={{ display: 'grid', gridTemplateColumns: 'rep
          -eat(auto-fit, minmax(190px, 1fr))', gap: 16, marginBottom: 20 
          -}}>                                                           
      152 -        <div className="panel side-panel" style={{ padding: 16
          - }}>                                                          
      153 -          <span style={{ fontSize: '0.78rem', color: '#a1a1aa'
          -, textTransform: 'uppercase', letterSpacing: '0.05em' }}>Total
          - Master Images</span>                                         
      154 -          <div style={{ fontSize: '1.8rem', fontWeight: 700, c
          -olor: '#f4f4f5', marginTop: 4 }}>{totalFrames}</div>          
      155 -          <p className="hint" style={{ fontSize: '0.78rem', ma
          -rgin: '4px 0 0 0' }}>Across all approved batches</p>          
      163 +      {preview && (                                           
      164 +        <div style={{ display: 'grid', gridTemplateColumns: 'r
          +epeat(auto-fit, minmax(180px, 1fr))', gap: 14, marginBottom: 1
          +8 }}>                                                         
      165 +          <Stat label="Trainable images" value={preview.traina
          +ble_images} hint={`of ${preview.total_images} merged`} accent=
          +"#4ade80" />                                                  
      166 +          <Stat label="Excluded images" value={preview.exclude
          +d_images} hint="carry an ignored shape" accent="#f87171" />   
      167 +          <Stat label="Reclassed shapes" value={preview.reclas
          +s} hint="trained as another class" accent="#c084fc" />        
      168 +          <Stat label="Rule version" value={preview.rule_versi
          +on} hint="stamped on each run" accent="#38bdf8" mono />       
      169          </div>
      170 +      )}                                                      
      171  
      158 -        <div className="panel side-panel" style={{ padding: 16
          -, borderLeft: '3px solid #22c55e' }}>                         
      159 -          <span style={{ fontSize: '0.78rem', color: '#a1a1aa'
          -, textTransform: 'uppercase', letterSpacing: '0.05em' }}>Train
          -ing Set (Train)</span>                                        
      160 -          <div style={{ fontSize: '1.8rem', fontWeight: 700, c
          -olor: '#4ade80', marginTop: 4 }}>{summary.splits?.train || 0}<
          -/div>                                                         
      161 -          <p className="hint" style={{ fontSize: '0.78rem', ma
          -rgin: '4px 0 0 0' }}>80% split for model training</p>         
      162 -        </div>                                                
      172 +      <TriageRules                                            
      173 +        rules={rules}                                         
      174 +        classes={project.classes}                             
      175 +        onChange={setRules}                                   
      176 +        onSave={saveRules}                                    
      177 +        saving={saving}                                       
      178 +        dirty={dirty}                                         
      179 +      />                                                      
      180  
      164 -        <div className="panel side-panel" style={{ padding: 16
          -, borderLeft: '3px solid #38bdf8' }}>                         
      165 -          <span style={{ fontSize: '0.78rem', color: '#a1a1aa'
          -, textTransform: 'uppercase', letterSpacing: '0.05em' }}>Valid
          -ation Set (Val)</span>                                        
      166 -          <div style={{ fontSize: '1.8rem', fontWeight: 700, c
          -olor: '#38bdf8', marginTop: 4 }}>{summary.splits?.val || 0}</d
          -iv>                                                           
      167 -          <p className="hint" style={{ fontSize: '0.78rem', ma
          -rgin: '4px 0 0 0' }}>20% locked split for metrics</p>         
      168 -        </div>                                                
      169 -                                                              
      170 -        <div className="panel side-panel" style={{ padding: 16
          -, borderLeft: '3px solid #a855f7' }}>                         
      171 -          <span style={{ fontSize: '0.78rem', color: '#a1a1aa'
          -, textTransform: 'uppercase', letterSpacing: '0.05em' }}>Total
          - Bounding Boxes</span>                                        
      172 -          <div style={{ fontSize: '1.8rem', fontWeight: 700, c
          -olor: '#c084fc', marginTop: 4 }}>{shapeDist.total_shapes}</div
          ->                                                             
      173 -          <p className="hint" style={{ fontSize: '0.78rem', ma
          -rgin: '4px 0 0 0' }}>Median Area: <strong>{normalParams.median
          -_area_pct}%</strong></p>                                      
      174 -        </div>                                                
      175 -      </div>                                                  
      176 -                                                              
      177 -      {/* Bounding Box Size Distribution Graph */}            
      178 -      <div className="panel table-wrap" style={{ padding: 20, 
          -marginBottom: 20 }}>                                          
      179 -        <div style={{ display: 'flex', justifyContent: 'space-
          -between', alignItems: 'center', marginBottom: 14 }}>          
      180 -          <div>                                               
      181 -            <h2 style={{ fontSize: '1.05rem', margin: 0, displ
          -ay: 'flex', alignItems: 'center', gap: 8 }}>                  
      182 -              <BarChartIcon size={18} /> Bounding Box Size Dis
          -tribution Curve                                               
      183 -            </h2>                                             
      184 -            <p className="hint" style={{ fontSize: '0.8rem', m
          -argin: '4px 0 0 0' }}>                                        
      185 -              Log-Normal Bell Curve N(μ, σ²) showing shape siz
          -e distribution.                                               
      186 -            </p>                                              
      187 -          </div>                                              
      188 -                                                              
      189 -          {/* Scale View Toggle */}                           
      190 -          <div style={{ display: 'flex', gap: 8, alignItems: '
          -center' }}>                                                   
      191 -            <button                                           
      192 -              type="button"                                   
      193 -              className="tag"                                 
      194 -              style={{                                        
      195 -                cursor: 'pointer',                            
      196 -                padding: '6px 12px',                          
      197 -                fontSize: '0.8rem',                           
      198 -                background: scaleMode === 'normal' ? 'rgba(56,
          - 189, 248, 0.2)' : 'rgba(255,255,255,0.05)',                  
      199 -                color: scaleMode === 'normal' ? '#38bdf8' : '#
          -a1a1aa',                                                      
      200 -                border: scaleMode === 'normal' ? '1px solid rg
          -ba(56, 189, 248, 0.5)' : '1px solid rgba(255,255,255,0.1)',   
      201 -                borderRadius: 6                               
      202 -              }}                                              
      203 -              onClick={() => setScaleMode('normal')}          
      181 +      <div className="panel table-wrap" style={{ padding: 18, 
          +marginTop: 18 }}>                                             
      182 +        <div style={{ display: 'flex', justifyContent: 'space-
          +between', alignItems: 'center', gap: 12, flexWrap: 'wrap', mar
          +ginBottom: 14 }}>                                             
      183 +          <h2 style={{ fontSize: '1rem', margin: 0, display: '
          +flex', alignItems: 'center', gap: 8 }}>                       
      184 +            <SlidersIcon size={17} /> Batch triage            
      185 +          </h2>                                               
      186 +          <div style={{ display: 'flex', gap: 10, alignItems: 
          +'center', flexWrap: 'wrap' }}>                                
      187 +            <label className="faint" style={{ fontSize: '0.8re
          +m' }}>Batch:</label>                                          
      188 +            <select                                           
      189 +              value={batchId ?? ''}                           
      190 +              onChange={(event) => setBatchId(Number(event.tar
          +get.value))}                                                  
      191 +              style={{ padding: '4px 10px', fontSize: '0.82rem
          +', background: 'rgba(0,0,0,0.4)', color: '#f4f4f5', border: '1
          +px solid rgba(255,255,255,0.15)', borderRadius: 6, cursor: 'po
          +inter' }}                                                     
      192              >
      205 -              Normal Bell Curve (Log Scale)                   
      206 -            </button>                                         
      207 -            <button                                           
      208 -              type="button"                                   
      209 -              className="tag"                                 
      210 -              style={{                                        
      211 -                cursor: 'pointer',                            
      212 -                padding: '6px 12px',                          
      213 -                fontSize: '0.8rem',                           
      214 -                background: scaleMode === 'linear' ? 'rgba(192
          -, 132, 252, 0.2)' : 'rgba(255,255,255,0.05)',                 
      215 -                color: scaleMode === 'linear' ? '#c084fc' : '#
          -a1a1aa',                                                      
      216 -                border: scaleMode === 'linear' ? '1px solid rg
          -ba(192, 132, 252, 0.5)' : '1px solid rgba(255,255,255,0.1)',  
      217 -                borderRadius: 6                               
      218 -              }}                                              
      219 -              onClick={() => setScaleMode('linear')}          
      220 -            >                                                 
      221 -              Linear Scale (0% - 100%)                        
      222 -            </button>                                         
      223 -          </div>                                              
      224 -        </div>                                                
      225 -                                                              
      226 -        {/* Normal Distribution Stats Banner */}              
      227 -        {scaleMode === 'normal' && (                          
      228 -          <div style={{ display: 'flex', gap: 16, padding: '8p
          -x 14px', background: 'rgba(56, 189, 248, 0.08)', border: '1px 
          -solid rgba(56, 189, 248, 0.2)', borderRadius: 6, marginBottom:
          - 14, fontSize: '0.8rem', color: '#e0f2fe', alignItems: 'center
          -' }}>                                                         
      229 -            <span>Gaussian Normal Fit:</span>                 
      230 -            <span className="mono" style={{ color: '#38bdf8' }
          -}>μ (Log Mean) = {normalParams.mu_log}</span>                 
      231 -            <span className="mono" style={{ color: '#38bdf8' }
          -}>σ (Std Dev) = {normalParams.sigma_log}</span>               
      232 -            <span className="mono" style={{ color: '#4ade80' }
          -}>Median Box Area = {normalParams.median_area_pct}% of frame</
          -span>                                                         
      233 -          </div>                                              
      234 -        )}                                                    
      235 -                                                              
      236 -        {/* SVG Normal Distribution Line Chart */}            
      237 -        <div style={{ background: 'rgba(0,0,0,0.3)', borderRad
          -ius: 8, padding: 12, border: '1px solid rgba(255,255,255,0.06)
          -' }}>                                                         
      238 -          <svg viewBox={`0 0 ${graphWidth} ${graphHeight}`} st
          -yle={{ width: '100%', height: 'auto', display: 'block' }}>    
      239 -            <defs>                                            
      240 -              <linearGradient id="purpleGradient" x1="0" y1="0
          -" x2="0" y2="1">                                              
      241 -                <stop offset="0%" stopColor="#c084fc" stopOpac
          -ity="0.35" />                                                 
      242 -                <stop offset="100%" stopColor="#c084fc" stopOp
          -acity="0.0" />                                                
      243 -              </linearGradient>                               
      244 -              <linearGradient id="cyanGradient" x1="0" y1="0" 
          -x2="0" y2="1">                                                
      245 -                <stop offset="0%" stopColor="#38bdf8" stopOpac
          -ity="0.25" />                                                 
      246 -                <stop offset="100%" stopColor="#38bdf8" stopOp
          -acity="0.0" />                                                
      247 -              </linearGradient>                               
      248 -            </defs>                                           
      249 -                                                              
      250 -            {/* Active Range Overlay */}                      
      251 -            <rect                                             
      252 -              x={startX}                                      
      253 -              y={padding}                                     
      254 -              width={Math.max(2, endX - startX)}              
      255 -              height={usableH}                                
      256 -              fill="rgba(192, 132, 252, 0.15)"                
      257 -              stroke="rgba(192, 132, 252, 0.4)"               
      258 -              strokeWidth="1.5"                               
      259 -            />                                                
      260 -                                                              
      261 -            {/* Shape Distribution Fill & Line (Purple) */}   
      262 -            <path d={shapeAreaD} fill="url(#purpleGradient)" /
          ->                                                             
      263 -            <path d={shapeLineD} fill="none" stroke="#c084fc" 
          -strokeWidth="2.5" strokeLinecap="round" strokeLinejoin="round"
          - />                                                           
      264 -                                                              
      265 -            {/* Gaussian Normal Bell Curve Fill & Line (Cyan) 
          -*/}                                                           
      266 -            {scaleMode === 'normal' && (                      
      267 -              <>                                              
      268 -                <path d={normalAreaD} fill="url(#cyanGradient)
          -" />                                                          
      269 -                <path d={normalLineD} fill="none" stroke="#38b
          -df8" strokeWidth="3" strokeLinecap="round" strokeLinejoin="rou
          -nd" />                                                        
      270 -              </>                                             
      271 -            )}                                                
      272 -                                                              
      273 -            {/* Data Points */}                               
      274 -            {shapePoints.map((p, idx) => (                    
      275 -              <circle                                         
      276 -                key={idx}                                     
      277 -                cx={p.x}                                      
      278 -                cy={p.y}                                      
      279 -                r="3"                                         
      280 -                fill="#f4f4f5"                                
      281 -                stroke="#a855f7"                              
      282 -                strokeWidth="1.5"                             
      283 -              >                                               
      284 -                <title>{`Area: ${p.label}\nShapes: ${p.count}`
          -}</title>                                                     
      285 -              </circle>                                       
      193 +              {batches.length === 0 && <option value="">no ann
          +otated batches</option>}                                      
      194 +              {batches.map((batch) => (                       
      195 +                <option key={batch.id} value={batch.id}>      
      196 +                  {batch.date_label}/{batch.batch_label} · {ba
          +tch.annotation_count} shapes                                  
      197 +                </option>                                     
      198 +              ))}                                             
      199 +            </select>                                         
      200 +            {VERDICTS.map((v) => (                            
      201 +              <span key={v.value} className="tag" style={{ fon
          +tSize: '0.78rem', color: v.color, border: `1px solid ${v.color
          +}55`, background: `${v.color}18` }}>                          
      202 +                {counts[v.value]} {v.label.toLowerCase()}     
      203 +              </span>                                         
      204              ))}
      287 -          </svg>                                              
      288 -                                                              
      289 -          {/* X Axis Labels */}                               
      290 -          <div style={{ display: 'flex', justifyContent: 'spac
          -e-between', marginTop: 8, padding: '0 10px', fontSize: '0.75re
          -m', color: '#a1a1aa', fontFamily: 'monospace' }}>             
      291 -            {scaleMode === 'normal' ? (                       
      292 -              <>                                              
      293 -                <span>0.001% (Tiny)</span>                    
      294 -                <span>0.05%</span>                            
      295 -                <span>0.5% (Median)</span>                    
      296 -                <span>5.0%</span>                             
      297 -                <span>100% (Full Frame)</span>                
      298 -              </>                                             
      299 -            ) : (                                             
      300 -              <>                                              
      301 -                <span>0% Area</span>                          
      302 -                <span>20%</span>                              
      303 -                <span>40%</span>                              
      304 -                <span>60%</span>                              
      305 -                <span>80%</span>                              
      306 -                <span>100% Area</span>                        
      307 -              </>                                             
      205 +            {counts.manual > 0 && (                           
      206 +              <span className="tag" style={{ fontSize: '0.78re
          +m' }}>{counts.manual} by hand</span>                          
      207              )}
      208            </div>
      209          </div>
      210  
      211 +        {batches.length === 0 ? (                             
      212 +          <p className="empty">No auto-annotated batches yet. 
          +Run auto-annotation on a batch first.</p>                     
      213 +        ) : (                                                 
      214 +          <>                                                  
      215 +            <TriageScatter shapes={shapes} selectedIds={select
          +edIds} onSelect={setSelectedIds} />                           
      216  
      313 -        {/* Dual Range Sliders & Presets for Size Filtering */
          -}                                                             
      314 -        <div style={{ marginTop: 20, padding: 16, background: 
          -'rgba(255,255,255,0.03)', borderRadius: 8, border: '1px solid 
          -rgba(255,255,255,0.06)' }}>                                   
      315 -          <div style={{ display: 'flex', justifyContent: 'spac
          -e-between', alignItems: 'center', marginBottom: 12 }}>        
      316 -            <span style={{ fontSize: '0.88rem', fontWeight: 60
          -0, color: '#f4f4f5' }}>                                       
      317 -              Interactive Size Filter Range:                  
      318 -            </span>                                           
      319 -                                                              
      320 -            {/* Quick Filter Presets */}                      
      321 -            <div style={{ display: 'flex', gap: 6, flexWrap: '
          -wrap' }}>                                                     
      322 -              <button                                         
      323 -                type="button"                                 
      324 -                className="tag"                               
      325 -                style={{ cursor: 'pointer', padding: '4px 8px'
          -, fontSize: '0.75rem', background: minSizePct === 0 && maxSize
          -Pct === 100 ? 'rgba(192, 132, 252, 0.25)' : 'rgba(255,255,255,
          -0.05)', color: '#e4e4e7', border: '1px solid rgba(255,255,255,
          -0.1)' }}                                                      
      326 -                onClick={() => { setMinSizePct(0); setMaxSizeP
          -ct(100); }}                                                   
      327 -              >                                               
      328 -                All Sizes                                     
      329 -              </button>                                       
      330 -              <button                                         
      331 -                type="button"                                 
      332 -                className="tag"                               
      333 -                style={{ cursor: 'pointer', padding: '4px 8px'
          -, fontSize: '0.75rem', background: minSizePct === 0 && maxSize
          -Pct === 1 ? 'rgba(192, 132, 252, 0.25)' : 'rgba(255,255,255,0.
          -05)', color: '#e4e4e7', border: '1px solid rgba(255,255,255,0.
          -1)' }}                                                        
      334 -                onClick={() => { setMinSizePct(0); setMaxSizeP
          -ct(1); }}                                                     
      335 -              >                                               
      336 -                Tiny (&lt; 1%)                                
      337 -              </button>                                       
      338 -              <button                                         
      339 -                type="button"                                 
      340 -                className="tag"                               
      341 -                style={{ cursor: 'pointer', padding: '4px 8px'
          -, fontSize: '0.75rem', background: minSizePct === 1 && maxSize
          -Pct === 5 ? 'rgba(192, 132, 252, 0.25)' : 'rgba(255,255,255,0.
          -05)', color: '#e4e4e7', border: '1px solid rgba(255,255,255,0.
          -1)' }}                                                        
      342 -                onClick={() => { setMinSizePct(1); setMaxSizeP
          -ct(5); }}                                                     
      343 -              >                                               
      344 -                Small (1%–5%)                                 
      345 -              </button>                                       
      346 -              <button                                         
      347 -                type="button"                                 
      348 -                className="tag"                               
      349 -                style={{ cursor: 'pointer', padding: '4px 8px'
          -, fontSize: '0.75rem', background: minSizePct === 5 && maxSize
          -Pct === 20 ? 'rgba(192, 132, 252, 0.25)' : 'rgba(255,255,255,0
          -.05)', color: '#e4e4e7', border: '1px solid rgba(255,255,255,0
          -.1)' }}                                                       
      350 -                onClick={() => { setMinSizePct(5); setMaxSizeP
          -ct(20); }}                                                    
      351 -              >                                               
      352 -                Medium (5%–20%)                               
      353 -              </button>                                       
      354 -              <button                                         
      355 -                type="button"                                 
      356 -                className="tag"                               
      357 -                style={{ cursor: 'pointer', padding: '4px 8px'
          -, fontSize: '0.75rem', background: minSizePct === 20 && maxSiz
          -ePct === 100 ? 'rgba(192, 132, 252, 0.25)' : 'rgba(255,255,255
          -,0.05)', color: '#e4e4e7', border: '1px solid rgba(255,255,255
          -,0.1)' }}                                                     
      358 -                onClick={() => { setMinSizePct(20); setMaxSize
          -Pct(100); }}                                                  
      359 -              >                                               
      360 -                Large (&gt; 20%)                              
      361 -              </button>                                       
      362 -            </div>                                            
      363 -          </div>                                              
      364 -                                                              
      365 -          <div style={{ display: 'grid', gridTemplateColumns: 
          -'1fr 1fr', gap: 20, alignItems: 'center' }}>                  
      366 -            <div>                                             
      367 -              <div style={{ display: 'flex', justifyContent: '
          -space-between', alignItems: 'center', marginBottom: 4 }}>     
      368 -                <label style={{ fontSize: '0.78rem', color: '#
          -a1a1aa' }}>Min Box Area (%):</label>                          
      369 -                <input                                        
      370 -                  type="number"                               
      371 -                  min="0"                                     
      372 -                  max="100"                                   
      373 -                  step="0.01"                                 
      374 -                  value={minSizePct}                          
      375 -                  onChange={(e) => setMinSizePct(Math.min(Math
          -.max(0, Number(e.target.value)), maxSizePct))}                
      376 -                  style={{ width: 70, padding: '2px 6px', font
          -Size: '0.8rem', textAlign: 'right', background: 'rgba(0,0,0,0.
          -4)', border: '1px solid rgba(255,255,255,0.15)', borderRadius:
          - 4, color: '#c084fc' }}                                       
      377 -                />                                            
      378 -              </div>                                          
      379 -              <input                                          
      380 -                type="range"                                  
      381 -                min="0"                                       
      382 -                max="100"                                     
      383 -                step="0.01"                                   
      384 -                value={minSizePct}                            
      385 -                onChange={(e) => setMinSizePct(Math.min(Number
          -(e.target.value), maxSizePct))}                               
      386 -                style={{ width: '100%', cursor: 'pointer' }}  
      387 -              />                                              
      388 -            </div>                                            
      389 -                                                              
      390 -            <div>                                             
      391 -              <div style={{ display: 'flex', justifyContent: '
          -space-between', alignItems: 'center', marginBottom: 4 }}>     
      392 -                <label style={{ fontSize: '0.78rem', color: '#
          -a1a1aa' }}>Max Box Area (%):</label>                          
      393 -                <input                                        
      394 -                  type="number"                               
      395 -                  min="0"                                     
      396 -                  max="100"                                   
      397 -                  step="0.01"                                 
      398 -                  value={maxSizePct}                          
      399 -                  onChange={(e) => setMaxSizePct(Math.max(Math
          -.min(100, Number(e.target.value)), minSizePct))}              
      400 -                  style={{ width: 70, padding: '2px 6px', font
          -Size: '0.8rem', textAlign: 'right', background: 'rgba(0,0,0,0.
          -4)', border: '1px solid rgba(255,255,255,0.15)', borderRadius:
          - 4, color: '#c084fc' }}                                       
      401 -                />                                            
      402 -              </div>                                          
      403 -              <input                                          
      404 -                type="range"                                  
      405 -                min="0"                                       
      406 -                max="100"                                     
      407 -                step="0.01"                                   
      408 -                value={maxSizePct}                            
      409 -                onChange={(e) => setMaxSizePct(Math.max(Number
          -(e.target.value), minSizePct))}                               
      410 -                style={{ width: '100%', cursor: 'pointer' }}  
      411 -              />                                              
      412 -            </div>                                            
      413 -          </div>                                              
      414 -        </div>                                                
      415 -      </div>                                                  
      416 -                                                              
      417 -      {/* Most Dense Frame Preview Section */}                
      418 -      {activePreviewFrameId && (                              
      419 -        <div className="panel table-wrap" style={{ padding: 20
          -, marginBottom: 20 }}>                                        
      420 -          <div style={{ display: 'flex', justifyContent: 'spac
          -e-between', alignItems: 'center', marginBottom: 14, flexWrap: 
          -'wrap', gap: 12 }}>                                           
      421 -            <div>                                             
      422 -              <h2 style={{ fontSize: '1.05rem', margin: 0, dis
          -play: 'flex', alignItems: 'center', gap: 8 }}>                
      423 -                Most Dense Frame Preview (Filtered vs Kept Bou
          -nding Boxes)                                                  
      424 -              </h2>                                           
      425 -              <p className="hint" style={{ fontSize: '0.8rem',
          - margin: '4px 0 0 0' }}>                                      
      426 -                Active Filter Range: <strong style={{ color: '
          -#c084fc' }}>{minSizePct}% – {maxSizePct}% Area</strong>. Filte
          -red-out boxes are dimmed in red.                              
      427 -              </p>                                            
      428 -            </div>                                            
      429 -                                                              
      430 -            {/* Dense Frame Dropdown Selector & Counters */}  
      431 -            <div style={{ display: 'flex', gap: 10, alignItems
          -: 'center', flexWrap: 'wrap' }}>                              
      432 -              <label style={{ fontSize: '0.8rem', color: '#a1a
          -1aa' }}>Select Dense Frame:</label>                           
      217 +            <div                                              
      218 +              style={{                                        
      219 +                display: 'flex',                              
      220 +                gap: 10,                                      
      221 +                alignItems: 'center',                         
      222 +                flexWrap: 'wrap',                             
      223 +                margin: '16px 0',                             
      224 +                padding: 12,                                  
      225 +                background: selectedIds.length ? 'rgba(56,189,
          +248,0.08)' : 'rgba(255,255,255,0.03)',                        
      226 +                border: `1px solid ${selectedIds.length ? 'rgb
          +a(56,189,248,0.3)' : 'rgba(255,255,255,0.06)'}`,              
      227 +                borderRadius: 8,                              
      228 +                transition: 'background 150ms ease, border-col
          +or 150ms ease',                                               
      229 +              }}                                              
      230 +            >                                                 
      231 +              <strong style={{ fontSize: '0.85rem' }}>{selecte
          +dIds.length} selected</strong>                                
      232 +              <span className="faint" style={{ fontSize: '0.78
          +rem' }}>— decide by hand (overrides every rule):</span>       
      233 +              {VERDICTS.map((v) => (                          
      234 +                <button                                       
      235 +                  key={v.value}                               
      236 +                  type="button"                               
      237 +                  className="btn"                             
      238 +                  disabled={busy || selectedIds.length === 0} 
      239 +                  onClick={() => applyVerdict(v.value)}       
      240 +                  style={{ cursor: 'pointer', fontSize: '0.8re
          +m', color: v.color, borderColor: `${v.color}55` }}            
      241 +                >                                             
      242 +                  {v.label}                                   
      243 +                </button>                                     
      244 +              ))}                                             
      245                <select
      434 -                value={activePreviewFrameId}                  
      435 -                onChange={(e) => setPreviewFrameId(Number(e.ta
          -rget.value))}                                                 
      436 -                style={{ padding: '4px 10px', fontSize: '0.82r
          -em', background: 'rgba(0,0,0,0.4)', color: '#f4f4f5', border: 
          -'1px solid rgba(255,255,255,0.15)', borderRadius: 6 }}        
      246 +                value={reclassTarget}                         
      247 +                onChange={(event) => setReclassTarget(event.ta
          +rget.value)}                                                  
      248 +                aria-label="Reclass target class"             
      249 +                style={{ padding: '4px 8px', fontSize: '0.8rem
          +', background: 'rgba(0,0,0,0.4)', color: '#e4e4e7', border: '1
          +px solid rgba(255,255,255,0.15)', borderRadius: 4, cursor: 'po
          +inter' }}                                                     
      250                >
      438 -                {topDenseFrames.map((f, idx) => (             
      439 -                  <option key={f.frame_id} value={f.frame_id}>
      440 -                    Frame #{f.frame_id} ({f.total_shapes} tota
          -l boxes) {idx === 0 ? '🔥 [Most Dense]' : ''}                 
      441 -                  </option>                                   
      251 +                <option value="">reclass into…</option>       
      252 +                {project.classes?.map((cls) => (              
      253 +                  <option key={cls.class_id} value={cls.class_
          +id}>{cls.name}</option>                                       
      254                  ))}
      255                </select>
      444 -                                                              
      445 -              <span className="tag" style={{ background: 'rgba
          -(34, 197, 94, 0.2)', color: '#4ade80', border: '1px solid rgba
          -(34, 197, 94, 0.4)', fontSize: '0.8rem' }}>                   
      446 -                {keptShapesOnFrame.length} Kept               
      447 -              </span>                                         
      448 -              <span className="tag" style={{ background: 'rgba
          -(239, 68, 68, 0.15)', color: '#f87171', border: '1px solid rgb
          -a(239, 68, 68, 0.3)', fontSize: '0.8rem' }}>                  
      449 -                {filteredOutShapesOnFrame.length} Filtered Out
      450 -              </span>                                         
      451 -                                                              
      256                <button
      257                  type="button"
      454 -                className="tag"                               
      455 -                style={{                                      
      456 -                  cursor: 'pointer',                          
      457 -                  padding: '4px 10px',                        
      458 -                  fontSize: '0.78rem',                        
      459 -                  background: showFilteredOut ? 'rgba(239, 68,
          - 68, 0.2)' : 'rgba(255,255,255,0.05)',                        
      460 -                  color: showFilteredOut ? '#f87171' : '#a1a1a
          -a',                                                           
      461 -                  border: showFilteredOut ? '1px solid rgba(23
          -9, 68, 68, 0.4)' : '1px solid rgba(255,255,255,0.1)',         
      462 -                  borderRadius: 6                             
      463 -                }}                                            
      464 -                onClick={() => setShowFilteredOut(!showFiltere
          -dOut)}                                                        
      258 +                className="btn"                               
      259 +                disabled={busy || selectedIds.length === 0}   
      260 +                onClick={clearDecisions}                      
      261 +                style={{ cursor: 'pointer', fontSize: '0.8rem'
          +, marginLeft: 'auto' }}                                       
      262                >
      466 -                {showFilteredOut ? 'Hide Filtered Out Boxes' :
          - 'Show Filtered Out Boxes'}                                   
      263 +                Clear hand decisions                          
      264                </button>
      265              </div>
      469 -          </div>                                              
      266  
      471 -          {/* Interactive Frame Canvas Preview */}            
      472 -          <div style={{ position: 'relative', width: '100%', m
          -axWidth: 880, margin: '0 auto', background: '#000', borderRadi
          -us: 8, overflow: 'hidden', border: '1px solid rgba(255,255,255
          -,0.1)' }}>                                                    
      473 -            <img                                              
      474 -              src={`/api/frames/${activePreviewFrameId}/image`
          -}                                                             
      475 -              alt={`Frame ${activePreviewFrameId}`}           
      476 -              style={{ width: '100%', height: 'auto', display:
          - 'block' }}                                                   
      267 +            <TriageCropGrid                                   
      268 +              shapes={shapes}                                 
      269 +              selectedIds={selectedIds}                       
      270 +              onSelect={setSelectedIds}                       
      271 +              classes={project.classes}                       
      272              />
      273 +          </>                                                 
      274 +        )}                                                    
      275 +      </div>                                                  
      276  
      479 -            {/* SVG Bounding Box Overlays showing Kept vs Filt
          -ered Out */}                                                  
      480 -            <svg                                              
      481 -              viewBox="0 0 1 1"                               
      482 -              preserveAspectRatio="none"                      
      483 -              style={{ position: 'absolute', top: 0, left: 0, 
          -width: '100%', height: '100%', pointerEvents: 'none' }}       
      484 -            >                                                 
      485 -              {allFrameShapes.map((s) => {                    
      486 -                if (!s.box) return null                       
      487 -                const isKept = s.area_pct >= minSizePct && s.a
          -rea_pct <= maxSizePct                                         
      488 -                if (!isKept && !showFilteredOut) return null  
      489 -                                                              
      490 -                const [x0, y0, x1, y1] = s.box                
      491 -                const cls = project.classes?.find((c) => c.cla
          -ss_id === s.class_id)                                         
      492 -                const color = isKept ? classColor(s.class_id) 
          -: '#ef4444'                                                   
      493 -                                                              
      494 -                return (                                      
      495 -                  <g key={s.id} opacity={isKept ? 1.0 : 0.45}>
      496 -                    <rect                                     
      497 -                      x={x0}                                  
      498 -                      y={y0}                                  
      499 -                      width={x1 - x0}                         
      500 -                      height={y1 - y0}                        
      501 -                      fill={isKept ? 'rgba(56, 189, 248, 0.2)'
          - : 'rgba(239, 68, 68, 0.08)'}                                 
      502 -                      stroke={color}                          
      503 -                      strokeWidth={isKept ? '0.0035' : '0.002'
          -}                                                             
      504 -                      strokeDasharray={isKept ? 'none' : '0.00
          -4 0.004'}                                                     
      505 -                    />                                        
      506 -                    <text                                     
      507 -                      x={x0 + 0.004}                          
      508 -                      y={Math.max(0.02, y0 - 0.004)}          
      509 -                      fill={isKept ? '#ffffff' : '#f87171'}   
      510 -                      fontSize="0.02"                         
      511 -                      fontWeight={isKept ? 'bold' : 'normal'} 
      512 -                      fontFamily="sans-serif"                 
      513 -                    >                                         
      514 -                      {isKept ? `${cls?.name || `Class ${s.cla
          -ss_id}`} (${s.area_pct}%)` : `❌ Filtered (${s.area_pct}%)`}  
      515 -                    </text>                                   
      516 -                  </g>                                        
      517 -                )                                             
      518 -              })}                                             
      519 -            </svg>                                            
      520 -          </div>                                              
      521 -        </div>                                                
      522 -      )}                                                      
      523 -                                                              
      524 -                                                              
      525 -                                                              
      526 -      <div style={{ display: 'grid', gridTemplateColumns: '1fr
          - 340px', gap: 20, alignItems: 'start' }}>                     
      527 -                                                              
      528 -        {/* Main Section: Class Distribution & Merged Batches 
          -*/}                                                           
      529 -        <div style={{ display: 'flex', flexDirection: 'column'
          -, gap: 20 }}>                                                 
      530 -          {/* Target Classes Summary with Continuous Size Filt
          -er */}                                                        
      531 -          <div className="panel table-wrap" style={{ padding: 
          -18 }}>                                                        
      532 -            <h2 style={{ fontSize: '1rem', marginBottom: 12, d
          -isplay: 'flex', alignItems: 'center', gap: 8 }}>              
      533 -              Target Class Distribution {isFiltering ? `(Filte
          -red: ${minSizePct}% – ${maxSizePct}% Area)` : ''}             
      534 -            </h2>                                             
      535 -            {project.classes?.length === 0 ? (                
      536 -              <p className="hint">No target classes defined fo
          -r this project.</p>                                           
      537 -            ) : (                                             
      538 -              <div style={{ display: 'grid', gridTemplateColum
          -ns: 'repeat(auto-fill, minmax(220px, 1fr))', gap: 12 }}>      
      539 -                {project.classes?.map((cls) => {              
      540 -                  const count = filteredClassCounts[cls.class_
          -id] || 0                                                      
      541 -                  return (                                    
      542 -                    <div                                      
      543 -                      key={cls.class_id}                      
      544 -                      style={{                                
      545 -                        padding: 12,                          
      546 -                        background: 'rgba(0,0,0,0.3)',        
      547 -                        borderRadius: 8,                      
      548 -                        border: isFiltering && count > 0 ? '1p
          -x solid rgba(192, 132, 252, 0.4)' : '1px solid rgba(255,255,25
          -5,0.08)',                                                     
      549 -                      }}                                      
      550 -                    >                                         
      551 -                      <div style={{ display: 'flex', justifyCo
          -ntent: 'space-between', alignItems: 'center' }}>              
      552 -                        <div style={{ fontSize: '0.85rem', fon
          -tWeight: 600, color: '#f4f4f5' }}>{cls.name}</div>            
      553 -                        <span className="tag" style={{ fontSiz
          -e: '0.75rem', background: 'rgba(168, 85, 247, 0.2)', color: '#
          -c084fc', border: '1px solid rgba(168, 85, 247, 0.3)' }}>      
      554 -                          {count} shapes                      
      555 -                        </span>                               
      556 -                      </div>                                  
      557 -                      <div style={{ fontSize: '0.75rem', color
          -: '#a1a1aa', marginTop: 4 }}>                                 
      558 -                        Class ID: <code style={{ color: '#c084
          -fc' }}>{cls.class_id}</code>                                  
      559 -                      </div>                                  
      560 -                      {cls.prompt && (                        
      561 -                        <div style={{ fontSize: '0.75rem', col
          -or: '#38bdf8', marginTop: 4 }}>                               
      562 -                          Prompt: "{cls.prompt}"              
      563 -                        </div>                                
      564 -                      )}                                      
      565 -                    </div>                                    
      566 -                  )                                           
      567 -                })}                                           
      568 -              </div>                                          
      569 -            )}                                                
      570 -          </div>                                              
      571 -                                                              
      572 -                                                              
      573 -          {/* Merged Batches List */}                         
      574 -          <div className="panel table-wrap" style={{ padding: 
          -18 }}>                                                        
      575 -            <h2 style={{ fontSize: '1rem', marginBottom: 12 }}
          ->Approved & Merged Dataset Batches ({mergedBatches.length})</h
          -2>                                                            
      576 -            {mergedBatches.length === 0 ? (                   
      577 -              <p className="empty">No approved batches merged 
          -into dataset yet. Go to Batches page to review & approve.</p> 
      578 -            ) : (                                             
      579 -              <table className="video-table">                 
      580 -                <thead>                                       
      581 -                  <tr>                                        
      582 -                    <th>Batch</th>                            
      583 -                    <th>Date</th>                             
      584 -                    <th>Frames</th>                           
      585 -                    <th>Reviewed</th>                         
      586 -                    <th>Status</th>                           
      587 -                  </tr>                                       
      588 -                </thead>                                      
      589 -                <tbody>                                       
      590 -                  {mergedBatches.map((item) => (              
      591 -                    <tr key={item.id}>                        
      592 -                      <td style={{ fontWeight: 500 }}>{item.ba
          -tch_label}</td>                                               
      593 -                      <td>{item.date_label}</td>              
      594 -                      <td className="num">{item.images}</td>  
      595 -                      <td>                                    
      596 -                        <span className="tag" style={{ backgro
          -und: 'rgba(34, 197, 94, 0.15)', color: '#4ade80', border: '1px
          - solid rgba(34, 197, 94, 0.3)' }}>                            
      597 -                          Approved                            
      598 -                        </span>                               
      599 -                      </td>                                   
      600 -                      <td>                                    
      601 -                        <span className="mono" style={{ fontSi
          -ze: '0.8rem', color: '#a1a1aa' }}>                            
      602 -                          In Master Dataset                   
      603 -                        </span>                               
      604 -                      </td>                                   
      605 -                    </tr>                                     
      606 -                  ))}                                         
      607 -                </tbody>                                      
      608 -              </table>                                        
      609 -            )}                                                
      610 -          </div>                                              
      611 -        </div>                                                
      612 -                                                              
      613 -        {/* Right Sidebar: Next Steps & Quick Actions */}     
      614 -        <div style={{ display: 'flex', flexDirection: 'column'
          -, gap: 16 }}>                                                 
      615 -          <div className="panel side-panel" style={{ border: '
          -1px solid rgba(168, 85, 247, 0.3)' }}>                        
      616 -            <h2 style={{ fontSize: '0.95rem', color: '#c084fc'
          -, marginBottom: 8 }}>Data Preparation Readiness</h2>          
      617 -            <p className="hint" style={{ fontSize: '0.8rem', m
          -arginBottom: 14 }}>                                           
      618 -              Once your dataset and shape sizes are verified, 
          -proceed to fine-tune baseline YOLO models.                    
      619 -            </p>                                              
      620 -            <a                                                
      621 -              className="btn btn-primary"                     
      622 -              href={`#/projects/${projectId}/models`}         
      623 -              style={{ width: '100%', padding: '8px 12px', fon
          -tSize: '0.85rem', cursor: 'pointer', display: 'inline-flex', a
          -lignItems: 'center', justifyContent: 'center', gap: 6, borderR
          -adius: 6 }}                                                   
      624 -            >                                                 
      625 -              <RocketIcon size={16} /> Proceed to Models & Tra
          -ining                                                         
      626 -            </a>                                              
      627 -          </div>                                              
      628 -        </div>                                                
      277 +      <div className="panel side-panel" style={{ marginTop: 18
          +, border: '1px solid rgba(168, 85, 247, 0.3)' }}>             
      278 +        <h2 style={{ fontSize: '0.95rem', color: '#c084fc', ma
          +rginBottom: 8 }}>Ready to train</h2>                          
      279 +        <p className="hint" style={{ fontSize: '0.8rem', margi
          +nBottom: 14 }}>                                               
      280 +          Training re-applies these rules to the master datase
          +t, so what you decide here is what                            
      281 +          the next run sees. Images carrying an ignored shape 
          +are left out whole.                                           
      282 +        </p>                                                  
      283 +        <a                                                    
      284 +          className="btn btn-primary"                         
      285 +          href={`#/projects/${projectId}/models`}             
      286 +          style={{ padding: '8px 12px', fontSize: '0.85rem', c
          +ursor: 'pointer', display: 'inline-flex', alignItems: 'center'
          +, gap: 6, borderRadius: 6 }}                                  
      287 +        >                                                     
      288 +          <RocketIcon size={16} /> Proceed to models &amp; tra
          +ining                                                         
      289 +        </a>                                                  
      290        </div>
      291      </>
      292    )
      293  }
      294  
      634 -                                                              
      635 -                                                              
      295 +function Stat({ label, value, hint, accent, mono }) {         
      296 +  return (                                                    
      297 +    <div className="panel side-panel" style={{ padding: 16, bo
          +rderLeft: `3px solid ${accent}` }}>                           
      298 +      <span style={{ fontSize: '0.75rem', color: '#a1a1aa', te
          +xtTransform: 'uppercase', letterSpacing: '0.05em' }}>{label}</
          +span>                                                         
      299 +      <div className={mono ? 'mono' : undefined} style={{ font
          +Size: mono ? '1.1rem' : '1.8rem', fontWeight: 700, color: acce
          +nt, marginTop: 4 }}>{value}</div>                             
      300 +      <p className="hint" style={{ fontSize: '0.76rem', margin
          +: '4px 0 0 0' }}>{hint}</p>                                   
      301 +    </div>                                                    
      302 +  )                                                           
      303 +}                                                             

  Ran 7 shell commands (ctrl+o to expand)

● Router's fine — my introspection was wrong. Let me just run the server and hit
  it for real:

  Ran 2 shell commands (ctrl+o to expand)

● Real data — batch 36 has 85,520 shapes. That's a good stress test:
  Ran 1 shell command (ctrl+o to expand)

● It works and it's fast, but 21 MB of JSON for one batch — 85k SVG circles
  would lock the browser. That's a real defect, not a nit. Fixing it before I
  call this done:

● Update(backend/triage.py)
  ⎿  Added 46 lines, removed 3 lines
      211      return shapes
      212  
      213  
      214 -def batch_shapes(batch_id: int) -> dict:                      
      215 -    """The triage view of one batch: every shape, scored and r
          -esolved (REQ-106)."""                                         
      214 +SCATTER_POINTS = 4000                                         
      215 +"""How many dots the scatter gets. A real batch runs to ~85k s
          +hapes; every one of                                           
      216 +them as an SVG circle locks the browser, and a boundary betwee
          +n two clusters is                                             
      217 +just as visible in a few thousand points. The verdict tallies 
          +are still counted                                             
      218 +over every shape, so the numbers are never a sample."""       
      219 +                                                              
      220 +                                                              
      221 +def _resolved_shapes(batch_id: int):                          
      222      from backend import batches
      223  
      224      batch = batches.get(batch_id)
     ...
      232      shapes = shapes_for_frames(frame_ids)
      233      for shape in shapes:
      234          shape.update(resolver.verdict(shape))
      235 +    return batch, frame_ids, shapes, resolver                 
      236 +                                                              
      237 +                                                              
      238 +def batch_summary(batch_id: int) -> dict:                     
      239 +    """Verdict tallies over the whole batch, plus a sample to 
          +plot (REQ-106)."""                                            
      240 +    batch, frame_ids, shapes, resolver = _resolved_shapes(batc
          +h_id)                                                         
      241 +                                                              
      242 +    counts = {"keep": 0, "ignore": 0, "reclass": 0, "manual": 
          +0}                                                            
      243 +    for shape in shapes:                                      
      244 +        counts[shape["verdict"]] += 1                         
      245 +        if shape["source"] == "manual":                       
      246 +            counts["manual"] += 1                             
      247 +                                                              
      248 +    # An even stride rather than a random draw: the sample is 
          +stable across                                                 
      249 +    # reloads, so points do not jump around while the user is 
          +reading the plot.                                             
      250 +    stride = max(1, len(shapes) // SCATTER_POINTS)            
      251 +    sample = [                                                
      252 +        {k: shape[k] for k in ("id", "class_id", "score", "are
          +a_pct", "aspect", "verdict", "source")}                       
      253 +        for shape in shapes[::stride][:SCATTER_POINTS]        
      254 +    ]                                                         
      255 +                                                              
      256      return {
      257          "batch_id": batch_id,
      258          "project_id": batch["project_id"],
      259          "frame_count": len(frame_ids),
      233 -        "shapes": shapes,                                     
      260 +        "total_shapes": len(shapes),                          
      261 +        "counts": counts,                                     
      262 +        "sample": sample,                                     
      263 +        "sampled": len(sample) < len(shapes),                 
      264          "rule_version": resolver.version(),
      265      }
      266  
      267  
      268 +def batch_page(batch_id: int, sort: str = "score", offset: int
          + = 0, limit: int = 120) -> dict:                              
      269 +    """One page of shapes for the crop grid, sorted server-sid
          +e so the client                                               
      270 +    never holds the whole batch."""                           
      271 +    if sort not in ("score", "area_pct"):                     
      272 +        raise TriageError(f"Cannot sort by {sort}")           
      273 +    _, _, shapes, _ = _resolved_shapes(batch_id)              
      274 +    shapes.sort(key=lambda shape: shape[sort])                
      275 +    page = shapes[offset:offset + limit]                      
      276 +    for shape in page:                                        
      277 +        shape.pop("box", None)                                
      278 +    return {"total": len(shapes), "offset": offset, "limit": l
          +imit, "shapes": page}                                         
      279 +                                                              
      280 +                                                              
      281  def preview(project_id: int) -> dict:
      282      """What the current rules would do to the whole merged dat
           aset."""
      283      with db.cursor() as cur:

● Update(backend/api/triage.py)
  ⎿  Added 11 lines, removed 3 lines
      49      return {"rules": stored}
      50  
      51  
      52 -@router.get("/api/batches/{batch_id}/triage/shapes")           
      53 -def batch_shapes(batch_id: int) -> dict:                       
      52 +@router.get("/api/batches/{batch_id}/triage/summary")          
      53 +def batch_summary(batch_id: int) -> dict:                      
      54      try:
      55 -        return triage.batch_shapes(batch_id)                   
      55 +        return triage.batch_summary(batch_id)                  
      56      except triage.TriageError as exc:
      57          raise HTTPException(404, str(exc))
      58  
      59  
      60 +@router.get("/api/batches/{batch_id}/triage/shapes")           
      61 +def batch_page(batch_id: int, sort: str = "score", offset: int 
         += 0, limit: int = 120) -> dict:                                
      62 +    try:                                                       
      63 +        return triage.batch_page(batch_id, sort=sort, offset=of
         +fset, limit=min(limit, 500))                                   
      64 +    except triage.TriageError as exc:                          
      65 +        raise HTTPException(400, str(exc))                     
      66 +                                                               
      67 +                                                               
      68  @router.post("/api/triage/overrides")
      69  def set_overrides(body: OverrideRequest) -> dict:
      70      try:

  Ran 4 shell commands (ctrl+o to expand)

● 21 MB → 450 KB. Now let me verify rules actually work against your real data:

  Ran 1 shell command (ctrl+o to expand)

● Rule works — 16,776 of 85,520 shapes ignored. But preview shows 1,390 merged 
  images with 0 shapes total. Let me check whether that's a bug in my code or
  something real about your data:

  Ran 1 shell command (ctrl+o to expand)

● Important find — and it's not my code. Let me clean up my test rule first:

  Ran 1 shell command (ctrl+o to expand)

● Now update the frontend to the paginated endpoints, then a final build:

● Update(frontend/src/api.js)
  ⎿  Added 3 lines, removed 1 line
      121    triageRules: (projectId) => request(`/projects/${projectId}/
           triage/rules`),
      122    saveTriageRules: (projectId, rules) =>
      123      request(`/projects/${projectId}/triage/rules`, { method: '
           PUT', body: { rules } }),
      124 -  triageShapes: (batchId) => request(`/batches/${batchId}/tria
          -ge/shapes`),                                                  
      124 +  triageSummary: (batchId) => request(`/batches/${batchId}/tri
          +age/summary`),                                                
      125 +  triageShapes: (batchId, { sort = 'score', offset = 0, limit 
          += 120 } = {}) =>                                              
      126 +    request(`/batches/${batchId}/triage/shapes?sort=${sort}&of
          +fset=${offset}&limit=${limit}`),                              
      127    triagePreview: (projectId) => request(`/projects/${projectId
           }/triage/preview`),
      128    setTriageOverrides: (annotationIds, verdict, targetClass = n
           ull) =>
      129      request('/triage/overrides', {

● Write(frontend/src/components/TriageCropGrid.jsx)
  ⎿  Added 50 lines, removed 23 lines
       1 -import React, { useState } from 'react'                        
       1 +import React, { useCallback, useEffect, useState } from 'react'
       2  import { api } from '../api'
       3  
       4  /* A wall of cropped shapes (REQ-106). Judging "is this a half 
          sack?" means
       5 - * looking at the object, not at the frame it came from. Sorted
         - so the shapes a                                               
       6 - * rule is about to act on sit together. */                    
       5 + * looking at the object, not at the frame it came from.       
       6 + *                                                             
       7 + * Paged from the server: a real batch holds ~85k shapes, so th
         +e client never                                                 
       8 + * gets to hold them all. Sorting happens server-side for the s
         +ame reason —                                                   
       9 + * "worst 120 by score" has to be chosen from the whole batch, 
         +not from a page. */                                            
      10  
      11  const VERDICT_COLOR = { keep: '#4ade80', ignore: '#f87171', rec
          lass: '#c084fc' }
      12  const PAGE = 120
      13  
      11 -export default function TriageCropGrid({ shapes, selectedIds, o
         -nSelect, classes }) {                                          
      14 +export default function TriageCropGrid({ batchId, selectedIds, 
         +onSelect, classes, reloadKey }) {                              
      15    const [sortBy, setSortBy] = useState('score')
      13 -  const [limit, setLimit] = useState(PAGE)                     
      16 +  const [shapes, setShapes] = useState([])                     
      17 +  const [total, setTotal] = useState(0)                        
      18 +  const [loading, setLoading] = useState(false)                
      19 +  const [error, setError] = useState('')                       
      20  
      15 -  const sorted = [...shapes].sort((a, b) =>                    
      16 -    sortBy === 'score' ? a.score - b.score : a.area_pct - b.are
         -a_pct,                                                         
      21 +  const fetchPage = useCallback(                               
      22 +    async (offset, replace) => {                               
      23 +      if (!batchId) return                                     
      24 +      setLoading(true)                                         
      25 +      try {                                                    
      26 +        const page = await api.triageShapes(batchId, { sort: so
         +rtBy, offset, limit: PAGE })                                   
      27 +        setTotal(page.total)                                   
      28 +        setShapes((rows) => (replace ? page.shapes : [...rows, 
         +...page.shapes]))                                              
      29 +        setError('')                                           
      30 +      } catch (exc) {                                          
      31 +        setError(exc.message)                                  
      32 +      } finally {                                              
      33 +        setLoading(false)                                      
      34 +      }                                                        
      35 +    },                                                         
      36 +    [batchId, sortBy],                                         
      37    )
      18 -  const visible = sorted.slice(0, limit)                       
      38  
      39 +  useEffect(() => { fetchPage(0, true) }, [fetchPage, reloadKey
         +])                                                             
      40 +                                                               
      41    function toggle(id, event) {
      42      if (event.shiftKey) {
      43        onSelect(selectedIds.includes(id) ? selectedIds.filter((x
          ) => x !== id) : [...selectedIds, id])
     ...
      52      <div>
      53        <div style={{ display: 'flex', justifyContent: 'space-bet
          ween', alignItems: 'center', marginBottom: 10, gap: 12, flexWra
          p: 'wrap' }}>
      54          <span className="hint" style={{ fontSize: '0.8rem' }}>
      34 -          Showing {visible.length} of {sorted.length} · click t
         -o select, shift-click to add                                   
      55 +          Showing {shapes.length} of {total} · click to select,
         + shift-click to add                                            
      56          </span>
      57          <div style={{ display: 'flex', gap: 6 }}>
      37 -          {['score', 'area'].map((mode) => (                   
      58 +          {[                                                   
      59 +            { key: 'score', label: 'lowest score' },           
      60 +            { key: 'area_pct', label: 'smallest area' },       
      61 +          ].map((mode) => (                                    
      62              <button
      39 -              key={mode}                                       
      63 +              key={mode.key}                                   
      64                type="button"
      65                className="tag"
      42 -              aria-pressed={sortBy === mode}                   
      43 -              onClick={() => setSortBy(mode)}                  
      66 +              aria-pressed={sortBy === mode.key}               
      67 +              onClick={() => setSortBy(mode.key)}              
      68                style={{
      69                  cursor: 'pointer',
      70                  padding: '4px 10px',
      71                  fontSize: '0.78rem',
      48 -                background: sortBy === mode ? 'rgba(56,189,248,
         -0.2)' : 'rgba(255,255,255,0.05)',                              
      49 -                color: sortBy === mode ? '#38bdf8' : '#a1a1aa',
      50 -                border: `1px solid ${sortBy === mode ? 'rgba(56
         -,189,248,0.5)' : 'rgba(255,255,255,0.1)'}`,                    
      72 +                background: sortBy === mode.key ? 'rgba(56,189,
         +248,0.2)' : 'rgba(255,255,255,0.05)',                          
      73 +                color: sortBy === mode.key ? '#38bdf8' : '#a1a1
         +aa',                                                           
      74 +                border: `1px solid ${sortBy === mode.key ? 'rgb
         +a(56,189,248,0.5)' : 'rgba(255,255,255,0.1)'}`,                
      75                  borderRadius: 6,
      76                  transition: 'background 150ms ease, color 150ms
           ease',
      77                }}
      78              >
      55 -              Sort by {mode === 'score' ? 'lowest score' : 'sma
         -llest area'}                                                   
      79 +              Sort by {mode.label}                             
      80              </button>
      81            ))}
      82          </div>
      83        </div>
      84  
      61 -      {sorted.length === 0 ? (                                 
      62 -        <p className="empty">No shapes on this batch yet — run 
         -auto-annotation first.</p>                                     
      85 +      {error && <p className="error-banner" style={{ marginBott
         +om: 10 }}>{error}</p>}                                         
      86 +                                                               
      87 +      {total === 0 && !loading ? (                             
      88 +        <p className="empty">No shapes on this batch — run auto
         +-annotation first.</p>                                         
      89        ) : (
      90          <div style={{ display: 'grid', gridTemplateColumns: 're
          peat(auto-fill, minmax(104px, 1fr))', gap: 8 }}>
      65 -          {visible.map((shape) => {                            
      91 +          {shapes.map((shape) => {                             
      92              const chosen = selectedIds.includes(shape.id)
      93              return (
      94                <button
     ...
      139          </div>
      140        )}
      141  
      116 -      {limit < sorted.length && (                             
      142 +      {shapes.length < total && (                             
      143          <button
      144            type="button"
      145            className="btn"
      120 -          onClick={() => setLimit((n) => n + PAGE)}           
      146 +          disabled={loading}                                  
      147 +          onClick={() => fetchPage(shapes.length, false)}     
      148            style={{ marginTop: 12, cursor: 'pointer', fontSize:
            '0.82rem' }}
      149          >
      123 -          Load {Math.min(PAGE, sorted.length - limit)} more   
      150 +          {loading ? 'Loading…' : `Load ${Math.min(PAGE, total
          + - shapes.length)} more`}                                     
      151          </button>
      152        )}
      153      </div>

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 4 lines, removed 3 lines
      61    const loadBatch = useCallback(async () => {
      62      if (!batchId) return
      63      try {
      64 -      const [shapes, previewed] = await Promise.all([          
      65 -        api.triageShapes(batchId),                             
      64 +      const [summary, previewed] = await Promise.all([         
      65 +        api.triageSummary(batchId),                            
      66          api.triagePreview(projectId),
      67        ])
      68 -      setView(shapes)                                          
      68 +      setView(summary)                                         
      69        setPreview(previewed)
      70        setSelectedIds([])
      71 +      setReloadKey((n) => n + 1)                               
      72      } catch (exc) {
      73        setError(exc.message)
      74      }

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 1 line
      29    const [savedRules, setSavedRules] = useState([])
      30    const [preview, setPreview] = useState(null)
      31    const [selectedIds, setSelectedIds] = useState([])
      32 +  const [reloadKey, setReloadKey] = useState(0)                
      33    const [reclassTarget, setReclassTarget] = useState('')
      34    const [saving, setSaving] = useState(false)
      35    const [busy, setBusy] = useState(false)

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 4 lines, removed 10 lines
      77  
      78    useEffect(() => { loadBatch() }, [loadBatch])
      79  
      80 -  const shapes = view?.shapes ?? []                            
      80 +  // Counted server-side over every shape in the batch — the sc
         +atter plots a                                                  
      81 +  // sample, but these numbers are never a sample.             
      82 +  const counts = view?.counts ?? { keep: 0, ignore: 0, reclass:
         + 0, manual: 0 }                                                
      83 +  const sample = view?.sample ?? []                            
      84    const dirty = JSON.stringify(rules) !== JSON.stringify(savedR
          ules)
      85  
      83 -  const counts = useMemo(() => {                               
      84 -    const tally = { keep: 0, ignore: 0, reclass: 0, manual: 0 }
      85 -    shapes.forEach((shape) => {                                
      86 -      tally[shape.verdict] += 1                                
      87 -      if (shape.source === 'manual') tally.manual += 1         
      88 -    })                                                         
      89 -    return tally                                               
      90 -  }, [shapes])                                                 
      91 -                                                               
      86    async function saveRules() {
      87      setSaving(true)
      88      try {

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 7 lines, removed 1 line
      208            <p className="empty">No auto-annotated batches yet. 
           Run auto-annotation on a batch first.</p>
      209          ) : (
      210            <>
      211 -            <TriageScatter shapes={shapes} selectedIds={select
          -edIds} onSelect={setSelectedIds} />                           
      211 +            <TriageScatter shapes={sample} selectedIds={select
          +edIds} onSelect={setSelectedIds} />                           
      212 +            {view?.sampled && (                               
      213 +              <p className="hint" style={{ fontSize: '0.78rem'
          +, margin: '4px 0 0' }}>                                       
      214 +                Plotting {sample.length} of {view.total_shapes
          +} shapes — enough to see where the                            
      215 +                clusters split. The counts above cover all of 
          +them.                                                         
      216 +              </p>                                            
      217 +            )}                                                
      218  
      219              <div
      220                style={{

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 2 lines, removed 1 line
      267              </div>
      268  
      269              <TriageCropGrid
      270 -              shapes={shapes}                                 
      270 +              batchId={batchId}                               
      271 +              reloadKey={reloadKey}                           
      272                selectedIds={selectedIds}
      273                onSelect={setSelectedIds}
      274                classes={project.classes}

  Ran 3 shell commands (ctrl+o to expand)

● It's built and working against your real data. Test server stopped, test rule
  removed — your project state is clean.

  Verified against project 5, batch 36 (85,520 real SAM3 shapes):

  - Applied a rule score < 0.45 → ignore. Result: 68,744 keep / 16,776 ignore,
  resolved in ~0.5 s.
  - Manual override on a shape the rule had marked ignore correctly flipped it
  to keep via manual, and rule-version changed (735edd… → c47e02…).
  - First-match-wins confirmed: a shape that is both low-score and tiny gets the
  first rule's verdict, not the second's.
  - Your data's actual distribution: score p05/p50/p95 = 0.37 / 0.61 / 0.82,
  area p05/p50/p95 = 0.16% / 0.74% / 2.38%. That's where you'll be drawing
  boundaries.

  One thing I caught and fixed mid-build: the first version returned 21 MB of 
  JSON for one batch, and 85k SVG circles would have frozen the browser. Split
  into a /summary endpoint (full-batch counts + a stable 4,000-point stride
  sample → 450 KB) and a paged /shapes endpoint sorted server-side. The tallies
  you see are always over all shapes; only the plot is sampled, and the page
  says so.

  Something you need to know about your data — this isn't my code. Your master
  dataset is 1,390 images with zero annotations between them:

  ┌────────┬───────────┬────────┬─────────────┐
  │ batch  │  status   │ frames │ annotations │
  ├────────┼───────────┼────────┼─────────────┤
  │ batch6 │ reviewing │ 1,950  │ 85,520      │
  ├────────┼───────────┼────────┼─────────────┤
  │ batch7 │ merged    │ 1,390  │ 0           │
  └────────┴───────────┴────────┴─────────────┘

  The only batch you've merged is batch7, which has no annotations at all — so
  every label file in dataset/labels/ is empty, and the entire master dataset is
  negative samples. Training on it teaches the model that sacks don't exist.
  Meanwhile batch6, the one carrying all 85k annotations, is still sitting in
  reviewing and has never been merged. My guess is "approve all frames" was used
  on batch7 before auto-annotation ran — approve() only checks that frames are
  approved, never that any of them carry a shape. Worth a guard, and worth
  telling me whether that merge was intentional.

  What's there now: backend/triage.py (resolver, rules, overrides, versioning),
  backend/api/triage.py (7 endpoints incl. /crop), two new tables + rule_version
  on model_versions, and the rebuilt Data Prep page — rule editor, score×area
  scatter with drag-select, crop grid, bulk verdict bar. Resolution plugs into
  _build_selected_tree, so it's the one place that decides what training sees.
  All files under the 400-line limit; frontend builds clean.

  What I have not verified: I tested the API and the build, not the page in a
  browser, and no training run has consumed a rule-filtered dataset yet. The
  REQs are still sitting in docs/proposal-dataprep-triage.md — I'll move them
  into requirements.md and design.md once you've clicked around and confirmed it
  does what you meant.

  Run it and tell me how it feels. The obvious next question is whether score or
  aspect ratio actually separates full-sack from half-sack in your footage —
  the scatter should answer that within a minute of looking at it.

✻ Cooked for 9m 9s

❯ now from where my data preparation data comes? and how to revert or choose    
  which batch we want to do data preparation and pass to models and training.   
  also i still confuse about the flow. because the UI doesnt reflect what you   
  said                                                                          
● Fair — let me check what's actually wired rather than assume.

  Searched for 2 patterns, ran 1 shell command (ctrl+o to expand)

● That explains most of it:

  Ran 1 shell command (ctrl+o to expand)

● Confirmed — you're looking at an hour-old build. Both containers predate
  everything I wrote. The backend has no /triage routes (404) and the frontend
  is serving the old Data Prep page. Nothing I described exists in what's on
  your screen. That's on me for not saying "rebuild to see it."

  Now your actual questions.

  Where does Data Prep's data come from?

  The annotations table — every shape SAM3 wrote for the frames of the batch you
  pick. Not from disk, not from the master dataset. That's why it can show you
  85,520 shapes for batch6 even though batch6 has never been merged.

  But the four stat cards at the top (trainable images, excluded, …) come from
  somewhere else: dataset_items, the merged master dataset. That split is real
  and it's confusing, and it's the second reason the page won't make sense to
  you even after a rebuild. More below.

  The flow, honestly:

  archive → batch → extract frames → SAM3 auto-annotate → review/approve
                                                                ↓
                                                          APPROVE BATCH
                                                                ↓
                                                       merge → master dataset
                                                                ↓
                                            triage rules applied here, at train
  time
                                                                ↓
                                                Models page: pick batches +
  classes → train

  Choosing which batch goes to training already exists — it's on the Models
  page, not Data Prep (ModelsPage.jsx:306, "Select Batches"). But it only lists
  merged batches, because dataset.summary returns WHERE b.status = 'merged'.
  Same for class selection at ModelsPage.jsx:235.

  And here's the gap I have to own. You asked for triage per batch, before 
  merge. I built the UI that way — but I wired enforcement at training time,
  which only ever sees merged data. So right now:

  - batch6 has all 85k shapes, is reviewing, never merged → you can triage it,
  and it will affect nothing
  - batch7 has 0 shapes, is merged → it's the only thing training can see, and
  it's empty

  Triaging batch6 today changes no training run. The page doesn't tell you that.
  That's a genuine design hole between what you asked for and what I built, not
  just a stale container.

  Reverting: nothing I built is destructive. "Clear hand decisions" removes
  overrides for selected shapes; deleting a rule and saving un-applies it;
  annotations.class_id is never rewritten, so any triage decision is reversible
  at any time. What is not currently revertible is a merge — once a batch is
  merged the only way back is deleting the batch (which now also cleans its
  files from the dataset).

  Two things to decide:

● User answered Claude's questions:
  ⎿  · Rebuild the containers so you can actually see it? This restarts the app.
     → yes rebuild every time it needed to reflect the UI 
     · How should triage relate to merging? This is the gap that's confusing 
     you. → Triage decides what gets merged (Recommended)

● Both. First, check nothing's running before I rebuild:

  Ran 1 shell command (ctrl+o to expand)

● Nothing running. Now the merge gate — triage decides what enters the master
  dataset:

● Update(backend/dataset.py)
  ⎿  Added 9 lines
      479      job.progress(0, len(frames))
      480      job.log(f"Merging {len(frames)} approved frame(s) into the
            master dataset")
      481  
      482 +    # Triage gates the merge (REQ-104): a frame carrying an ig
          +nored shape never                                             
      483 +    # enters the master dataset at all, so junk is stopped at 
          +the door rather                                               
      484 +    # than filtered out on every later training run.          
      485 +    resolver = triage.Resolver(project["id"])                 
      486 +    gating = bool(resolver.rules or resolver.overrides)       
      487 +    if gating:                                                
      488 +        job.log(f"Applying {len(resolver.rules)} triage rule(s
          +), version {resolver.version()}")                             
      489 +                                                              
      490      added = {"train": 0, "val": 0}
      491      skipped = 0
      492 +    triaged_out = 0                                           
      493      cancelled = False
      494      for index, frame in enumerate(frames):
      495          if job.cancelled:

● Update(backend/dataset.py)
  ⎿  Added 18 lines
      497              cancelled = True
      498              break
      499  
      500 +        annotations = review.listing(frame["id"])             
      501 +        if gating:                                            
      502 +            resolved = []                                     
      503 +            for item in annotations:                          
      504 +                shape = {"id": item["id"], "class_id": item["c
          +lass_id"],                                                    
      505 +                         "score": float(item.get("score") or 1
          +.0),                                                          
      506 +                         **triage.metrics(item["geometry"])}  
      507 +                effective = resolver.effective_class(shape)   
      508 +                if effective is None:                         
      509 +                    resolved = None                           
      510 +                    break                                     
      511 +                resolved.append({**item, "class_id": effective
          +})                                                            
      512 +            if resolved is None:                              
      513 +                triaged_out += 1                              
      514 +                job.progress(index + 1, len(frames))          
      515 +                continue                                      
      516 +            annotations = resolved                            
      517 +                                                              
      518          with db.cursor() as cur:
      519              cur.execute("SELECT 1 FROM dataset_items WHERE fra
           me_id = ?", (frame["id"],))
      520              if cur.fetchone() is not None:

● Update(backend/dataset.py)
  ⎿  Added 1 line, removed 1 line
      530                              os.path.join(root, image_rel))
      531  
      532              lines = [_label_line(item["class_id"], item["geome
           try"], project["label_type"])
      533 -                     for item in review.listing(frame["id"])] 
      533 +                     for item in annotations]                 
      534              # An approved frame with nothing on it is a negati
           ve sample, and an
      535              # empty .txt is how YOLO spells that (REQ-033).
      536              with open(os.path.join(root, label_rel), "w", enco
           ding="utf-8") as handle:

● Update(backend/dataset.py)
  ⎿  Added 2 lines, removed 1 line
      560      path = write_data_yaml(projects.get(project["id"]))
      561      totals = summary(project["id"])["splits"]
      562      job.log(f"Added {added['train']} train / {added['val']} va
           l"
      563 -            + (f", skipped {skipped} already merged" if skippe
          -d else ""))                                                   
      563 +            + (f", skipped {skipped} already merged" if skippe
          +d else "")                                                    
      564 +            + (f", held back {triaged_out} by triage" if triag
          +ed_out else ""))                                              
      565      job.log(f"Master dataset now {totals['train']} train / {to
           tals['val']} val — {path}")

● Now make the page tell you what will actually happen, per batch:

● Update(backend/triage.py)
  ⎿  Added 3 lines
      240      batch, frame_ids, shapes, resolver = _resolved_shapes(batc
           h_id)
      241  
      242      counts = {"keep": 0, "ignore": 0, "reclass": 0, "manual": 
           0}
      243 +    ignored_frames = set()                                    
      244      for shape in shapes:
      245          counts[shape["verdict"]] += 1
      246          if shape["source"] == "manual":
      247              counts["manual"] += 1
      248 +        if shape["verdict"] == "ignore":                      
      249 +            ignored_frames.add(shape["frame_id"])             
      250  
      251      # An even stride rather than a random draw: the sample is 
           stable across
      252      # reloads, so points do not jump around while the user is 
           reading the plot.

● Update(backend/triage.py)
  ⎿  Added 5 lines
      259      return {
      260          "batch_id": batch_id,
      261          "project_id": batch["project_id"],
      262 +        "status": batch["status"],                            
      263 +        "merged": batch["status"] == "merged",                
      264          "frame_count": len(frame_ids),
      265          "total_shapes": len(shapes),
      266          "counts": counts,
      267 +        # What merging this batch would do right now (REQ-104)
          +.                                                             
      268 +        "frames_held_back": len(ignored_frames),              
      269 +        "frames_would_merge": len(frame_ids) - len(ignored_fra
          +mes),                                                         
      270          "sample": sample,
      271          "sampled": len(sample) < len(shapes),
      272          "rule_version": resolver.version(),

● Now the page needs to state where the batch stands in the flow, since that was
  the core confusion:

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 31 lines, removed 1 line
      156          </p>
      157        )}
      158  
      159 +      {view && (                                              
      160 +        <div                                                  
      161 +          style={{                                            
      162 +            display: 'flex',                                  
      163 +            alignItems: 'center',                             
      164 +            gap: 10,                                          
      165 +            flexWrap: 'wrap',                                 
      166 +            padding: '10px 14px',                             
      167 +            marginBottom: 16,                                 
      168 +            borderRadius: 8,                                  
      169 +            fontSize: '0.82rem',                              
      170 +            background: view.merged ? 'rgba(34,197,94,0.08)' :
          + 'rgba(250,204,21,0.08)',                                     
      171 +            border: `1px solid ${view.merged ? 'rgba(34,197,94
          +,0.3)' : 'rgba(250,204,21,0.3)'}`,                            
      172 +          }}                                                  
      173 +        >                                                     
      174 +          <strong>This batch is “{view.status}”.</strong>     
      175 +          {view.merged ? (                                    
      176 +            <span className="muted">                          
      177 +              It is already in the master dataset. Rules still
          + re-cut it every training run.                                
      178 +            </span>                                           
      179 +          ) : (                                               
      180 +            <span className="muted">                          
      181 +              Nothing here reaches training until the batch is
          + reviewed, approved and merged.                               
      182 +              On merge, <strong>{view.frames_would_merge}</str
          +ong> of {view.frame_count} frames                             
      183 +              would go in and <strong>{view.frames_held_back}<
          +/strong> would be held back.                                  
      184 +            </span>                                           
      185 +          )}                                                  
      186 +        </div>                                                
      187 +      )}                                                      
      188 +                                                              
      189        {preview && (
      190          <div style={{ display: 'grid', gridTemplateColumns: 'r
           epeat(auto-fit, minmax(180px, 1fr))', gap: 14, marginBottom: 1
           8 }}>
      161 -          <Stat label="Trainable images" value={preview.traina
          -ble_images} hint={`of ${preview.total_images} merged`} accent=
          -"#4ade80" />                                                  
      191 +          <Stat label="Trainable images" value={preview.traina
          +ble_images} hint={`of ${preview.total_images} in master datase
          +t`} accent="#4ade80" />                                       
      192            <Stat label="Excluded images" value={preview.exclude
           d_images} hint="carry an ignored shape" accent="#f87171" />
      193            <Stat label="Reclassed shapes" value={preview.reclas
           s} hint="trained as another class" accent="#c084fc" />
      194            <Stat label="Rule version" value={preview.rule_versi
           on} hint="stamped on each run" accent="#38bdf8" mono />

● Now let the page show all annotated batches regardless of merge state, and
  label them:

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 1 line, removed 1 line
      219                {batches.length === 0 && <option value="">no ann
           otated batches</option>}
      220                {batches.map((batch) => (
      221                  <option key={batch.id} value={batch.id}>
      222 -                  {batch.date_label}/{batch.batch_label} · {ba
          -tch.annotation_count} shapes                                  
      222 +                  {batch.date_label}/{batch.batch_label} · {ba
          +tch.annotation_count} shapes · {batch.status}                 
      223                  </option>
      224                ))}
      225              </select>

  Ran 1 shell command (ctrl+o to expand)

● Now rebuilding the containers:

  Ran 3 shell commands (ctrl+o to expand)

● Rebuilt and live — frontend is serving the new bundle (index-Di3kDxxW.js),
  triage endpoints answer 200. But note: batch6 is now merged — it was reviewing
  an hour ago. Let me re-check your dataset:

  Ran 1 shell command (ctrl+o to expand)
● Everything's live and your dataset changed — batch6 merged while we talked:

  Master dataset now: 3,340 images (2,646 train / 694 val — 20.8%, the hash
  split working correctly)
  - batch6 → 1,950 images carrying all 85,520 labels
  - batch7 → 1,390 images with empty label files

  Reload the browser with a hard refresh (Ctrl+Shift+R) — the bundle hash
  changed to index-Di3kDxxW.js, and a cached old bundle is exactly what made the
  page look wrong before.

  What changed so the flow makes sense

  Triage now gates the merge, as you chose. _run_merge resolves every frame
  before copying it: a frame carrying an ignore never enters the master dataset,
  and a reclass is written under its target class in the label file. The job
  log tells you ("held back N by triage"). Rules still re-apply at training
  time, so editing them later re-cuts what's already merged — you get both, and
  the earlier contradiction is gone.

  The page now opens with a status banner that says exactly where the selected
  batch stands: whether it's merged, and for an unmerged one, "on merge, X of Y 
  frames would go in and Z would be held back." That was the missing sentence.
  The batch dropdown shows status too.

  Your flow, concretely:

  1. Batches page → extract frames → auto-annotate (SAM3 writes annotations)
  2. Data Prep → pick that batch, drag on the scatter / click crops, write
  rules, hand-decide the rest
  3. Review → approve frames
  4. Approve batch → merge. Triage applies here — junk never enters
  5. Models page → "Select Batches" + "Select Classes" → train. Rules re-apply,
  so a rule edit changes this run without touching what's merged

  Choosing which batch trains lives on the Models page, not Data Prep — it lists
  merged batches only. That page already worked; it just wasn't obvious it was
  the answer to your question.

  Reverting: rules and hand-decisions are fully reversible (delete the rule and
  save; "Clear hand decisions" for overrides) because annotations.class_id is
  never rewritten. A merge is still the one irreversible step — deleting the
  batch is the only way back, which now also removes its files from the dataset.

  One thing to decide: 1,390 of your 3,340 images (42%) are batch7's empty label
  files. If that footage really is empty conveyor, they're legitimate negative
  samples and help. If those frames actually contain sacks and were merged
  before annotation ran, they're actively teaching the model to miss sacks — and
  at 42% of the dataset that will dominate training. Worth eyeballing a few
  batch7 frames before you train. Tell me which it is and I'll either leave it
  or clean it up.

  I haven't clicked through the page myself — I verified the API against live
  data and the bundle is served. Reload and tell me what's still off.

✻ Baked for 2m 57s

※ recap: Goal is getting your retraining loop reliable, and the Data Prep triage gate is now built, rebuilt into the containers, and live. Next: hard-refresh the browser, click through the page, and tell me whether batch7's 1,390 empty frames are real empty conveyor or unannotated sacks. (disable recaps in /config)

❯ can you create a default rule, or try different filtering approach rule,      
  because i really confuse when try filtering especially in UI                  

● Right — three numeric range boxes with no feedback is a bad way to ask someone
  to find a threshold. Let me fix the approach: presets computed from your
  actual data, plus live impact as you edit.

● Update(backend/triage.py)
  ⎿  Added 123 lines
      286      return {"total": len(shapes), "offset": offset, "limit": l
           imit, "shapes": page}
      287  
      288  
      289 +def _percentile(values: list, fraction: float) -> float:      
      290 +    if not values:                                            
      291 +        return 0.0                                            
      292 +    return values[min(len(values) - 1, int(len(values) * fract
          +ion))]                                                        
      293 +                                                              
      294 +                                                              
      295 +def suggest(batch_id: int) -> dict:                           
      296 +    """Presets with thresholds read off this batch's own distr
          +ibution.                                                      
      297 +                                                              
      298 +    Asking someone to invent "score below 0.45" from nothing i
          +s guesswork. The                                              
      299 +    same question is easy when the number comes from their dat
          +a and the effect                                              
      300 +    is stated: "the weakest 10% of detections — 8,552 shapes".
      301 +    """                                                       
      302 +    _, frame_ids, shapes, _ = _resolved_shapes(batch_id)      
      303 +    if not shapes:                                            
      304 +        return {"presets": [], "stats": {}}                   
      305 +                                                              
      306 +    scores = sorted(shape["score"] for shape in shapes)       
      307 +    areas = sorted(shape["area_pct"] for shape in shapes)     
      308 +    total = len(shapes)                                       
      309 +                                                              
      310 +    def impact(predicate: dict) -> dict:                      
      311 +        matched = [s for s in shapes if _matches(predicate, s)
          +]                                                             
      312 +        frames = {s["frame_id"] for s in matched}             
      313 +        return {"shapes": len(matched), "frames": len(frames)}
      314 +                                                              
      315 +    presets = []                                              
      316 +                                                              
      317 +    weak = round(_percentile(scores, 0.10), 3)                
      318 +    presets.append({                                          
      319 +        "key": "drop-weakest",                                
      320 +        "title": "Ignore the weakest detections",             
      321 +        "blurb": f"SAM3 scored these below {weak} — the bottom
          + 10% of this batch.",                                         
      322 +        "rule": {"name": "low confidence", "predicate": {"scor
          +e": [None, weak]}, "action": "ignore"},                       
      323 +        "impact": impact({"score": [None, weak]}),            
      324 +    })                                                        
      325 +                                                              
      326 +    specks = round(_percentile(areas, 0.05), 3)               
      327 +    presets.append({                                          
      328 +        "key": "drop-specks",                                 
      329 +        "title": "Ignore tiny specks",                        
      330 +        "blurb": f"Boxes smaller than {specks}% of the frame —
          + usually noise, not objects.",                                
      331 +        "rule": {"name": "specks", "predicate": {"area_pct": [
          +None, specks]}, "action": "ignore"},                          
      332 +        "impact": impact({"area_pct": [None, specks]}),       
      333 +    })                                                        
      334 +                                                              
      335 +    median_area = round(_percentile(areas, 0.50), 3)          
      336 +    presets.append({                                          
      337 +        "key": "split-by-size",                               
      338 +        "title": "Split by size into a second class",         
      339 +        "blurb": f"Everything under {median_area}% area (half 
          +this batch) becomes another class — "                         
      340 +                 "pick which one. Size tracks distance from th
          +e camera as much as object type, "                            
      341 +                 "so check the crops before trusting it.",    
      342 +        "rule": {"name": "small ones", "predicate": {"area_pct
          +": [None, median_area]},                                      
      343 +                 "action": "reclass", "target_class": None},  
      344 +        "impact": impact({"area_pct": [None, median_area]}),  
      345 +        "needs_target": True,                                 
      346 +    })                                                        
      347 +                                                              
      348 +    tall = round(_percentile(sorted(s["aspect"] for s in shape
          +s), 0.15), 3)                                                 
      349 +    presets.append({                                          
      350 +        "key": "odd-shapes",                                  
      351 +        "title": "Ignore oddly-shaped boxes",                 
      352 +        "blurb": f"Aspect ratio under {tall} — long thin slive
          +rs, usually a bad mask.",                                     
      353 +        "rule": {"name": "slivers", "predicate": {"aspect": [N
          +one, tall]}, "action": "ignore"},                             
      354 +        "impact": impact({"aspect": [None, tall]}),           
      355 +    })                                                        
      356 +                                                              
      357 +    return {                                                  
      358 +        "presets": presets,                                   
      359 +        "stats": {                                            
      360 +            "total_shapes": total,                            
      361 +            "total_frames": len(frame_ids),                   
      362 +            "score": {"p05": round(_percentile(scores, 0.05), 
          +3),                                                           
      363 +                      "p50": round(_percentile(scores, 0.50), 
          +3),                                                           
      364 +                      "p95": round(_percentile(scores, 0.95), 
          +3)},                                                          
      365 +            "area_pct": {"p05": round(_percentile(areas, 0.05)
          +, 3),                                                         
      366 +                         "p50": round(_percentile(areas, 0.50)
          +, 3),                                                         
      367 +                         "p95": round(_percentile(areas, 0.95)
          +, 3)},                                                        
      368 +        },                                                    
      369 +    }                                                         
      370 +                                                              
      371 +                                                              
      372 +def simulate(batch_id: int, candidate_rules: List[dict]) -> di
          +ct:                                                           
      373 +    """What these rules would do, without saving them.        
      374 +                                                              
      375 +    Editing a threshold and seeing the number move is the whol
          +e difference                                                  
      376 +    between tuning a filter and guessing at one.              
      377 +    """                                                       
      378 +    _, frame_ids, shapes, _ = _resolved_shapes(batch_id)      
      379 +    overrides = Resolver(0).overrides if False else None  # ke
          +pt simple: rules only                                         
      380 +                                                              
      381 +    with db.cursor() as cur:                                  
      382 +        cur.execute("SELECT annotation_id, verdict, target_cla
          +ss FROM annotation_overrides")                                
      383 +        manual = {row[0]: (row[1], row[2]) for row in cur.fetc
          +hall()}                                                       
      384 +                                                              
      385 +    counts = {"keep": 0, "ignore": 0, "reclass": 0}           
      386 +    per_rule = [0] * len(candidate_rules)                     
      387 +    ignored_frames = set()                                    
      388 +                                                              
      389 +    for shape in shapes:                                      
      390 +        if shape["id"] in manual:                             
      391 +            verdict = manual[shape["id"]][0]                  
      392 +        else:                                                 
      393 +            verdict = "keep"                                  
      394 +            for index, rule in enumerate(candidate_rules):    
      395 +                if _matches(rule.get("predicate") or {}, shape
          +):                                                            
      396 +                    verdict = rule["action"]                  
      397 +                    per_rule[index] += 1                      
      398 +                    break                                     
      399 +        counts[verdict] += 1                                  
      400 +        if verdict == "ignore":                               
      401 +            ignored_frames.add(shape["frame_id"])             
      402 +                                                              
      403 +    return {                                                  
      404 +        "total_shapes": len(shapes),                          
      405 +        "counts": counts,                                     
      406 +        "per_rule": per_rule,                                 
      407 +        "frames_held_back": len(ignored_frames),              
      408 +        "frames_would_merge": len(frame_ids) - len(ignored_fra
          +mes),                                                         
      409 +    }                                                         
      410 +                                                              
      411 +                                                              
      412  def preview(project_id: int) -> dict:
      413      """What the current rules would do to the whole merged dat
           aset."""
      414      with db.cursor() as cur:

● Update(backend/triage.py)
  ⎿  Removed 2 lines
      376      between tuning a filter and guessing at one.
      377      """
      378      _, frame_ids, shapes, _ = _resolved_shapes(batch_id)
      379 -    overrides = Resolver(0).overrides if False else None  # ke
          -pt simple: rules only                                         
      380 -                                                              
      379      with db.cursor() as cur:
      380          cur.execute("SELECT annotation_id, verdict, target_cla
           ss FROM annotation_overrides")
      381          manual = {row[0]: (row[1], row[2]) for row in cur.fetc
           hall()}

● Update(backend/api/triage.py)
  ⎿  Added 16 lines
       79      return {"cleared": triage.clear_overrides(body.annotation_
           ids)}
       80  
       81  
       82 +@router.get("/api/batches/{batch_id}/triage/suggest")         
       83 +def suggest(batch_id: int) -> dict:                           
       84 +    try:                                                      
       85 +        return triage.suggest(batch_id)                       
       86 +    except triage.TriageError as exc:                         
       87 +        raise HTTPException(404, str(exc))                    
       88 +                                                              
       89 +                                                              
       90 +@router.post("/api/batches/{batch_id}/triage/simulate")       
       91 +def simulate(batch_id: int, body: RuleList) -> dict:          
       92 +    try:                                                      
       93 +        return triage.simulate(batch_id, [item.model_dump() fo
          +r item in body.rules])                                        
       94 +    except triage.TriageError as exc:                         
       95 +        raise HTTPException(400, str(exc))                    
       96 +                                                              
       97 +                                                              
       98  @router.get("/api/projects/{project_id}/triage/preview")
       99  def preview(project_id: int) -> dict:
      100      return triage.preview(project_id)

  Ran 2 shell commands (ctrl+o to expand)

● Restart isn't enough — the image COPYs backend/, so it needs a rebuild:

  Ran 1 shell command (ctrl+o to expand)

● Presets work. But look closely — this exposes a serious problem with the
  design I recommended:

  "Ignore the weakest 10%" = 8,552 shapes spread across 1,882 of your 1,950 
  frames. Because an ignored shape excludes its whole image, that rule would
  hold back 96% of the batch.

  Your frames average 44 sacks each. At that density, nearly every frame
  contains at least one weak detection, so "exclude the whole image" destroys
  the dataset. Let me build the UI, then we need to fix that.

● Update(frontend/src/api.js)
  ⎿  Added 3 lines
      125    triageShapes: (batchId, { sort = 'score', offset = 0, limit 
           = 120 } = {}) =>
      126      request(`/batches/${batchId}/triage/shapes?sort=${sort}&of
           fset=${offset}&limit=${limit}`),
      127    triagePreview: (projectId) => request(`/projects/${projectId
           }/triage/preview`),
      128 +  triageSuggest: (batchId) => request(`/batches/${batchId}/tri
          +age/suggest`),                                                
      129 +  triageSimulate: (batchId, rules) =>                         
      130 +    request(`/batches/${batchId}/triage/simulate`, { method: '
          +POST', body: { rules } }),                                    
      131    setTriageOverrides: (annotationIds, verdict, targetClass = n
           ull) =>
      132      request('/triage/overrides', {
      133        method: 'POST',

● Write(frontend/src/components/TriagePresets.jsx)
  ⎿  Wrote 59 lines to frontend/src/components/TriagePresets.jsx
       1 import React from 'react'
       3 /* Starting points, with thresholds read off this batch's own di
         stribution and
       4  * the cost stated up front.
       6  * Inventing "score below 0.45" from a blank box is guesswork; p
         icking "the
         ision. */
       8 
       9 export default function TriagePresets({ presets, onAdd, classes,
      10   if (!presets?.length) return null
     … +64 lines (ctrl+o to expand)

● Now live impact per rule as you edit:

● Update(frontend/src/components/TriageRules.jsx)
  ⎿  Added 1 line, removed 1 line
      26    color: '#e4e4e7',
      27  }
      28  
      29 -export default function TriageRules({ rules, classes, onChange,
         - onSave, saving, dirty }) {                                    
      29 +export default function TriageRules({ rules, classes, onChange,
         + onSave, saving, dirty, simulation, children }) {              
      30    function update(index, patch) {
      31      onChange(rules.map((rule, i) => (i === index ? { ...rule, .
          ..patch } : rule)))
      32    }

● Update(frontend/src/components/TriageRules.jsx)
  ⎿  Added 24 lines
       81          decided by hand ignore these rules entirely.
       82        </p>
       83  
       84 +      {children}                                              
       85 +                                                              
       86 +      {simulation && rules.length > 0 && (                    
       87 +        <p                                                    
       88 +          style={{                                            
       89 +            fontSize: '0.82rem',                              
       90 +            padding: '8px 12px',                              
       91 +            marginBottom: 12,                                 
       92 +            borderRadius: 6,                                  
       93 +            background: 'rgba(56,189,248,0.08)',              
       94 +            border: '1px solid rgba(56,189,248,0.25)',        
       95 +          }}                                                  
       96 +        >                                                     
       97 +          As written, these rules keep <strong>{simulation.cou
          +nts.keep.toLocaleString()}</strong> shapes,                   
       98 +          ignore <strong>{simulation.counts.ignore.toLocaleStr
          +ing()}</strong>, reclass{' '}                                 
       99 +          <strong>{simulation.counts.reclass.toLocaleString()}
          +</strong> — and{' '}                                          
      100 +          <strong style={{ color: simulation.frames_held_back 
          +> simulation.frames_would_merge ? '#f87171' : '#4ade80' }}>   
      101 +            {simulation.frames_would_merge.toLocaleString()}  
      102 +          </strong>{' '}                                      
      103 +          of {(simulation.frames_would_merge + simulation.fram
          +es_held_back).toLocaleString()} frames would                  
      104 +          reach training.                                     
      105 +        </p>                                                  
      106 +      )}                                                      
      107 +                                                              
      108        {rules.length === 0 && <p className="empty">No rules — e
           very shape is kept as its own class.</p>}
      109  
      110        <div style={{ display: 'flex', flexDirection: 'column', 
           gap: 10 }}>

● Update(frontend/src/components/TriageRules.jsx)
  ⎿  Added 6 lines
      180                </select>
      181              )}
      182  
      183 +            {simulation?.per_rule?.[index] !== undefined && ( 
      184 +              <span className="mono faint" style={{ fontSize: 
          +'0.75rem' }}>                                                 
      185 +                matches {simulation.per_rule[index].toLocaleSt
          +ring()}                                                       
      186 +              </span>                                         
      187 +            )}                                                
      188 +                                                              
      189              <span style={{ marginLeft: 'auto', display: 'flex'
           , gap: 4 }}>
      190                <button type="button" className="btn" aria-label
           ="Move rule up" onClick={() => move(index, -1)} disabled={inde
           x === 0} style={{ cursor: 'pointer', padding: '2px 8px' }}>↑</
           button>
      191                <button type="button" className="btn" aria-label
           ="Move rule down" onClick={() => move(index, 1)} disabled={ind
           ex === rules.length - 1} style={{ cursor: 'pointer', padding: 
           '2px 8px' }}>↓</button>

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 2 lines
      31    const [selectedIds, setSelectedIds] = useState([])
      32    const [reloadKey, setReloadKey] = useState(0)
      33    const [reclassTarget, setReclassTarget] = useState('')
      34 +  const [presets, setPresets] = useState([])                   
      35 +  const [simulation, setSimulation] = useState(null)           
      36    const [saving, setSaving] = useState(false)
      37    const [busy, setBusy] = useState(false)
      38    const [error, setError] = useState('')

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 22 lines
       79  
       80    useEffect(() => { loadBatch() }, [loadBatch])
       81  
       82 +  useEffect(() => {                                           
       83 +    if (!batchId) return                                      
       84 +    let cancelled = false                                     
       85 +    api.triageSuggest(batchId)                                
       86 +      .then((data) => { if (!cancelled) setPresets(data.preset
          +s) })                                                         
       87 +      .catch(() => { if (!cancelled) setPresets([]) })        
       88 +    return () => { cancelled = true }                         
       89 +  }, [batchId])                                               
       90 +                                                              
       91 +  // Debounced: the point is watching the number move as you d
          +rag a threshold,                                              
       92 +  // and a request per keystroke would just queue up behind it
          +self.                                                         
       93 +  useEffect(() => {                                           
       94 +    if (!batchId) return                                      
       95 +    let cancelled = false                                     
       96 +    const timer = setTimeout(() => {                          
       97 +      api.triageSimulate(batchId, rules)                      
       98 +        .then((data) => { if (!cancelled) setSimulation(data) 
          +})                                                            
       99 +        .catch(() => { if (!cancelled) setSimulation(null) }) 
      100 +    }, 350)                                                   
      101 +    return () => { cancelled = true; clearTimeout(timer) }    
      102 +  }, [batchId, rules])                                        
      103 +                                                              
      104    // Counted server-side over every shape in the batch — the s
           catter plots a
      105    // sample, but these numbers are never a sample.
      106    const counts = view?.counts ?? { keep: 0, ignore: 0, reclass
           : 0, manual: 0 }

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 9 lines, removed 1 line
      226          onSave={saveRules}
      227          saving={saving}
      228          dirty={dirty}
      229 -      />                                                      
      229 +        simulation={simulation}                               
      230 +      >                                                       
      231 +        <TriagePresets                                        
      232 +          presets={presets}                                   
      233 +          classes={project.classes}                           
      234 +          frameCount={view?.frame_count ?? 0}                 
      235 +          onAdd={(rule) => setRules((current) => [...current, 
          +rule])}                                                       
      236 +        />                                                    
      237 +      </TriageRules>                                          
      238  
      239        <div className="panel table-wrap" style={{ padding: 18, 
           marginTop: 18 }}>
      240          <div style={{ display: 'flex', justifyContent: 'space-
           between', alignItems: 'center', gap: 12, flexWrap: 'wrap', mar
           ginBottom: 14 }}>

● Update(frontend/src/pages/DataPrepPage.jsx)
  ⎿  Added 1 line
      3  
      4  import { AlertIcon, DatabaseIcon, RocketIcon, SlidersIcon } from
          '../components/Icons'
      5  import TriageCropGrid from '../components/TriageCropGrid'
      6 +import TriagePresets from '../components/TriagePresets'         
      7  import TriageRules from '../components/TriageRules'
      8  import TriageScatter from '../components/TriageScatter'
      9  

  Ran 1 shell command (ctrl+o to expand)
● That's the proof: 68 of 1,950 frames would survive. I need to correct the
  recommendation I gave you earlier.

  Ran 1 shell command (ctrl+o to expand)

● Rebuilt and live (index-4bSN9aPo.js). Hard-refresh the browser.

  What's on the page now, to fix the filtering confusion:

  - Four preset cards with thresholds computed from your batch, each stating its
  cost before you click: "SAM3 scored these below 0.399 — the bottom 10%. 8,552
  shapes · 1,882 frames." Click "Add this rule" and it drops into the editor,
  pre-filled and editable.
  - Live impact, debounced 350 ms: a blue line under the rules saying how many
  shapes are kept/ignored/reclassed and how many frames would actually reach 
  training, plus a matches N counter on each individual rule row. You now watch
  the number move as you drag a threshold instead of guessing.
  - Presets turn their count red when an ignore rule would take most of the
  batch.

  Now the correction. Earlier I recommended "an ignored shape excludes its whole
  image", and you agreed. Your data proves that was wrong advice for your case:

  ┌───────────────┬───────────────────────┬──────────────────────────┐
  │     Rule      │    Shapes ignored     │ Frames reaching training │
  ├───────────────┼───────────────────────┼──────────────────────────┤
  │ score < 0.399 │ 8,552 of 85,520 (10%) │ 68 of 1,950              │
  └───────────────┴───────────────────────┴──────────────────────────┘

  Your frames carry ~44 sacks each. Almost every frame contains at least one
  weak detection, so excluding whole images throws away 96% of the data to
  remove 10% of the boxes. My reasoning was sound for sparse scenes (1–2 objects
  per frame, where an unlabeled object really does dominate that image's
  signal) and wrong for dense ones. At 44 objects per frame, dropping 4 boxes
  leaves 40 correctly labeled — the background-poisoning harm is small and
  local, while the data loss is catastrophic.

  So ignore needs to mean something different for you:

● User answered Claude's questions:
  ⎿  · What should 'ignore' do, now that we can see the cost on your real data? 
     → Drop the box, keep the image (Recommended)
     · Do you want a rule set applied by default to new projects? → No default, 
     presets are enough

● Changing ignore to drop the box and keep the image. One guard I'm adding: if a
  frame loses every shape it had, the image is still excluded — otherwise it
  becomes a false negative teaching the model that a frame full of sacks is
  empty.

● Update(backend/triage.py)
  ⎿  Added 11 lines, removed 4 lines
      12  
      13      keep                  the shape trains as its own class
      14      reclass -> class_id   the shape trains as a different class
           (REQ-105)
      15 -    ignore                the shape does not train, and takes i
         -ts whole image                                                 
      16 -                          with it (REQ-104) — an image with a k
         -nown object left                                               
      17 -                          unlabeled would teach the model that 
         -object is                                                      
      18 -                          background                           
      15 +    ignore                the box is dropped; its image still t
         +rains (REQ-104)                                                
      16 +                                                               
      17 +`ignore` drops the box rather than the image because these fram
         +es are dense —                                                 
      18 +around 44 shapes each. Excluding the whole image was measured a
         +gainst a real                                                  
      19 +batch and cost 96% of it (1,882 of 1,950 frames) to remove 10% 
         +of the boxes.                                                  
      20 +Dropping four boxes out of forty-four leaves the image overwhel
         +mingly correct;                                                
      21 +dropping the image leaves nothing to train on.                 
      22 +                                                               
      23 +The exception is a frame that loses *every* shape it had: an em
         +pty label file                                                 
      24 +says "there is nothing here", and for a frame that was full of 
         +sacks that is a                                                
      25 +lie the model will learn. Those images are excluded.           
      26  """
      27  
      28  import hashlib

● Update(backend/triage.py)
  ⎿  Added 21 lines, removed 1 line
      166                          "source": rule["name"]}
      167          return {"verdict": "keep", "target_class": None, "sour
           ce": "default"}
      168  
      169 +    def resolve_shapes(self, annotations: list) -> Optional[li
          +st]:                                                          
      170 +        """Apply verdicts to one frame's annotations.         
      171 +                                                              
      172 +        Returns the surviving annotations with their effective
          + class, or None                                               
      173 +        when the frame must not train at all — which now happe
          +ns only if every                                              
      174 +        shape was dropped.                                    
      175 +        """                                                   
      176 +        kept = []                                             
      177 +        for item in annotations:                              
      178 +            shape = {"id": item["id"], "class_id": item["class
          +_id"],                                                        
      179 +                     "score": float(item.get("score") or 1.0),
      180 +                     **metrics(item["geometry"])}             
      181 +            effective = self.effective_class(shape)           
      182 +            if effective is None:                             
      183 +                continue                                      
      184 +            kept.append({**item, "class_id": effective})      
      185 +        if annotations and not kept:                          
      186 +            return None                                       
      187 +        return kept                                           
      188 +                                                              
      189      def effective_class(self, shape: dict) -> Optional[int]:
      170 -        """The class this shape trains as, or None when it mus
          -t not train."""                                               
      190 +        """The class this shape trains as, or None when the bo
          +x is dropped."""                                              
      191          resolved = self.verdict(shape)
      192          if resolved["verdict"] == "ignore":
      193              return None

● Now update the two consumers to the new semantics:

● Update(backend/dataset.py)
  ⎿  Added 4 lines, removed 14 lines
      158          annotations = review.listing(frame_id)
      159  
      160          if resolver is not None:
      161 -            resolved = []                                     
      162 -            skip_image = False                                
      163 -            for item in annotations:                          
      164 -                shape = {"id": item["id"], "class_id": item["c
          -lass_id"],                                                    
      165 -                         "score": float(item.get("score") or 1
          -.0),                                                          
      166 -                         **triage.metrics(item["geometry"])}  
      167 -                effective = resolver.effective_class(shape)   
      168 -                if effective is None:                         
      169 -                    # REQ-104: a known object left unlabeled w
          -ould teach the                                                
      170 -                    # model it is background, so the image goe
          -s too.                                                        
      171 -                    skip_image = True                         
      172 -                    break                                     
      173 -                resolved.append({**item, "class_id": effective
          -})                                                            
      174 -            if skip_image:                                    
      161 +            resolved = resolver.resolve_shapes(annotations)   
      162 +            if resolved is None:                              
      163 +                # Every shape was dropped. An empty label file
          + would tell the                                               
      164 +                # model this frame is empty, which it is not (
          +REQ-104).                                                     
      165                  excluded += 1
      166                  continue
      167              annotations = resolved

● Update(backend/dataset.py)
  ⎿  Added 1 line, removed 10 lines
      489  
      490          annotations = review.listing(frame["id"])
      491          if gating:
      492 -            resolved = []                                     
      493 -            for item in annotations:                          
      494 -                shape = {"id": item["id"], "class_id": item["c
          -lass_id"],                                                    
      495 -                         "score": float(item.get("score") or 1
          -.0),                                                          
      496 -                         **triage.metrics(item["geometry"])}  
      497 -                effective = resolver.effective_class(shape)   
      498 -                if effective is None:                         
      499 -                    resolved = None                           
      500 -                    break                                     
      501 -                resolved.append({**item, "class_id": effective
          -})                                                            
      492 +            resolved = resolver.resolve_shapes(annotations)   
      493              if resolved is None:
      494                  triaged_out += 1
      495                  job.progress(index + 1, len(frames))

● Now the reporting has to count frames the new way — only frames losing all
  shapes are held back:

● Update(backend/triage.py)
  ⎿  Added 7 lines, removed 3 lines
      267      batch, frame_ids, shapes, resolver = _resolved_shapes(batc
           h_id)
      268  
      269      counts = {"keep": 0, "ignore": 0, "reclass": 0, "manual": 
           0}
      270 -    ignored_frames = set()                                    
      270 +    per_frame = {}                                            
      271      for shape in shapes:
      272          counts[shape["verdict"]] += 1
      273          if shape["source"] == "manual":
      274              counts["manual"] += 1
      275 -        if shape["verdict"] == "ignore":                      
      276 -            ignored_frames.add(shape["frame_id"])             
      275 +        total, dropped = per_frame.get(shape["frame_id"], (0, 
          +0))                                                           
      276 +        per_frame[shape["frame_id"]] = (total + 1, dropped + (
          +shape["verdict"] == "ignore"))                                
      277  
      278 +    # Only a frame that loses everything is held back; the res
          +t keep training                                               
      279 +    # with their surviving boxes.                             
      280 +    ignored_frames = {fid for fid, (total, dropped) in per_fra
          +me.items() if total == dropped}                               
      281 +                                                              
      282      # An even stride rather than a random draw: the sample is 
           stable across
      283      # reloads, so points do not jump around while the user is 
           reading the plot.
      284      stride = max(1, len(shapes) // SCATTER_POINTS)

● Update(backend/triage.py)
  ⎿  Added 5 lines, removed 3 lines
      413  
      414      counts = {"keep": 0, "ignore": 0, "reclass": 0}
      415      per_rule = [0] * len(candidate_rules)
      416 -    ignored_frames = set()                                    
      416 +    per_frame = {}                                            
      417  
      418      for shape in shapes:
      419          if shape["id"] in manual:
     ...
      426                      per_rule[index] += 1
      427                      break
      428          counts[verdict] += 1
      429 -        if verdict == "ignore":                               
      430 -            ignored_frames.add(shape["frame_id"])             
      429 +        total, dropped = per_frame.get(shape["frame_id"], (0, 
          +0))                                                           
      430 +        per_frame[shape["frame_id"]] = (total + 1, dropped + (
          +verdict == "ignore"))                                         
      431  
      432 +    ignored_frames = {fid for fid, (total, dropped) in per_fra
          +me.items() if total == dropped}                               
      433 +                                                              
      434      return {
      435          "total_shapes": len(shapes),
      436          "counts": counts,

● Update(backend/triage.py)
  ⎿  Added 5 lines, removed 2 lines
      451      shapes = shapes_for_frames(frame_ids)
      452      counts = {"keep": 0, "ignore": 0, "reclass": 0}
      453      per_class: dict = {}
      454 -    excluded_images = set()                                   
      454 +    per_frame = {}                                            
      455      for shape in shapes:
      456          resolved = resolver.verdict(shape)
      457          counts[resolved["verdict"]] += 1
      458 +        total, dropped = per_frame.get(shape["frame_id"], (0, 
          +0))                                                           
      459 +        per_frame[shape["frame_id"]] = (total + 1, dropped + (
          +resolved["verdict"] == "ignore"))                             
      460          if resolved["verdict"] == "ignore":
      459 -            excluded_images.add(shape["frame_id"])            
      461              continue
      462          effective = resolver.effective_class(shape)
      463          per_class[effective] = per_class.get(effective, 0) + 1
      464  
      465 +    excluded_images = {fid for fid, (total, dropped) in per_fr
          +ame.items() if total == dropped}                              
      466 +                                                              
      467      return {
      468          "total_shapes": len(shapes),
      469          "total_images": len(frame_ids),

  Searched for 1 pattern (ctrl+o to expand)

● Now update the UI wording to match the new meaning, then rebuild:

● Update(frontend/src/components/TriageRules.jsx)
  ⎿  Added 4 lines, removed 4 lines
       94              border: '1px solid rgba(56,189,248,0.25)',
       95            }}
       96          >
       97 -          As written, these rules keep <strong>{simulation.cou
          -nts.keep.toLocaleString()}</strong> shapes,                   
       98 -          ignore <strong>{simulation.counts.ignore.toLocaleStr
          -ing()}</strong>, reclass{' '}                                 
       97 +          As written, these rules keep <strong>{simulation.cou
          +nts.keep.toLocaleString()}</strong> boxes,                    
       98 +          drop <strong>{simulation.counts.ignore.toLocaleStrin
          +g()}</strong>, reclass{' '}                                   
       99            <strong>{simulation.counts.reclass.toLocaleString()}
           </strong> — and{' '}
      100            <strong style={{ color: simulation.frames_held_back 
           > simulation.frames_would_merge ? '#f87171' : '#4ade80' }}>
      101              {simulation.frames_would_merge.toLocaleString()}
      102            </strong>{' '}
      103 -          of {(simulation.frames_would_merge + simulation.fram
          -es_held_back).toLocaleString()} frames would                  
      104 -          reach training.                                     
      103 +          of {(simulation.frames_would_merge + simulation.fram
          +es_held_back).toLocaleString()} frames still                  
      104 +          train. A dropped box leaves its image in; only a fra
          +me that loses every box is held back.                         
      105          </p>
      106        )}
      107  

● Update(frontend/src/components/TriagePresets.jsx)
  ⎿  Added 4 lines, removed 6 lines
      33              >
      34                <strong style={{ fontSize: '0.85rem' }}>{preset.t
          itle}</strong>
      35                <span className="hint" style={{ fontSize: '0.76re
          m', lineHeight: 1.4 }}>{preset.blurb}</span>
      36 -              <span                                            
      37 -                className="mono"                               
      38 -                style={{ fontSize: '0.74rem', color: heavy && p
         -reset.rule.action === 'ignore' ? '#f87171' : '#a1a1aa' }}      
      39 -              >                                                
      40 -                {preset.impact.shapes.toLocaleString()} shapes 
         -· {preset.impact.frames.toLocaleString()} frames               
      41 -                {heavy && preset.rule.action === 'ignore' && ' 
         -— most of the batch'}                                          
      36 +              <span className="mono" style={{ fontSize: '0.74re
         +m', color: '#a1a1aa' }}>                                       
      37 +                {preset.impact.shapes.toLocaleString()} boxes  
      38 +                {preset.rule.action === 'ignore' ? ' dropped' :
         + ' matched'} · touches{' '}                                    
      39 +                {preset.impact.frames.toLocaleString()} frames 
      40                </span>
      41                <button
      42                  type="button"

  Ran 2 shell commands (ctrl+o to expand)

● That's the fix, measured on the same rule and the same data:

  ┌────────────────────────┬──────────────────────────┬───────────────┐
  │                        │ frames reaching training │ boxes dropped │
  ├────────────────────────┼──────────────────────────┼───────────────┤
  │ before (exclude image) │ 68 of 1,950              │ 8,552         │
  ├────────────────────────┼──────────────────────────┼───────────────┤
  │ after (drop box)       │ 1,945 of 1,950           │ 8,552         │
  └────────────────────────┴──────────────────────────┴───────────────┘

  Same 8,552 weak boxes removed, but you keep the dataset. The 5 held-back
  frames are ones where every box was weak — an empty label there would tell the
  model a frame full of sacks is empty, so those are still excluded.

  Rebuilt and live (index-CVeUWQf4.js). Hard-refresh.

  What to do when you open it:

  1. Pick batch6 in the Batch dropdown
  2. Click "Ignore the weakest detections" → it adds a rule pre-filled at score 
  ≤ 0.399
  3. Watch the blue line: "keeps 76,968 boxes, drops 8,552 … 1,945 of 1,950 
  frames still train"
  4. Nudge the threshold up or down — the numbers follow within ~350 ms, and
  each rule row shows its own matches N
  5. Scroll to the crop grid, sorted by lowest score, and look at what you're
  about to drop. If they're real sacks, your threshold is too high
  6. Save rules when the numbers look right

  For full-sack vs half-sack, the "Split by size" preset is the starting point
  (under 0.743% area → another class), but you need a second class in the
  project first — you only have white sack today. And I'd still check the crops
  before trusting it: at your camera angle, area tracks distance as much as
  fullness. If the crop grid shows small boxes that are just distant full sacks,
  size is the wrong signal and aspect ratio is worth trying instead.

  Nothing is saved until you press Save, nothing is destructive, and no rule
  affects training until a batch is merged under it.

  Two things still outstanding from earlier that I haven't touched: the 1,390 
  empty-label images from batch7 (42% of your master dataset — still worth
  deciding), and moving the REQs from the proposal into requirements.md once
  you're happy with how this behaves.

✻ Cooked for 4m 38s

※ recap: You're building a retraining loop; I fixed critical bugs and built the Data Prep triage page, where rules now drop bad boxes instead of whole frames. Next: hard-refresh the browser, pick batch6, and try the preset rules. (disable recaps in /config)

❯ from your understanding, this project is for counting sack into the truck,    
  how should we prepare the dataset, if the sack is covered by someone head but 
  thats is in counting zone, how we prepare that? should i annotate that or     
  not? if that covered by others people                                         

● Good question — and it's the one that actually determines whether your model
  counts correctly. Let me check what consumes the detections first:

  Listed 1 directory (ctrl+o to expand)
● That changes the answer substantially — you're counting with ByteTrack + line 
  crossing on a Jetson, not counting detections per frame. Let me look at the
  counter:

  Ran 1 shell command (ctrl+o to expand)

● Now I can answer properly, because your counting logic constrains the
  annotation policy in a very specific way.

  The decisive detail: LineCrossCounter uses y1, the top edge of the box. A
  track counts when it was seen ABOVE the zone and later BELOW it. So the top 
  edge of your boxes is the measurement instrument. Anything that moves y1
  artificially moves your count.

  That's exactly what a head covering the top of a sack does. If you annotate
  the visible extent only, a head over the sack's top pushes y1 downward by
  50–100 px — the sack looks lower than it is, and can trip above → below early.
  You'd get phantom counts caused by annotation policy, not by model error.

  Your occlusion is already handled elsewhere. cfg/tracker.yaml sets
  track_buffer: 60 — ~2.4 s of lost-track tolerance, and the comment says
  explicitly "survive worker occlusion". The design intent is that the tracker
  coasts through occlusion; the detector is not supposed to see through heads.
  That's the right architecture.

  So my recommendation:

  ┌───────────────────────────────────────────┬───────────┬────────────────┐
  │                 Situation                 │ Annotate? │      Box       │
  ├───────────────────────────────────────────┼───────────┼────────────────┤
  │ Sack fully visible                        │ Yes       │ Visible extent │
  ├───────────────────────────────────────────┼───────────┼────────────────┤
  │ Partly occluded, top edge clearly visible │ Yes       │ Visible extent │
  ├───────────────────────────────────────────┼───────────┼────────────────┤
  │ Top edge hidden (head/arm over the top)   │ No        │ —              │
  ├───────────────────────────────────────────┼───────────┼────────────────┤
  │ Less than ~40% visible                    │ No        │ —              │
  ├───────────────────────────────────────────┼───────────┼────────────────┤
  │ Fully hidden behind a person              │ Never     │ —              │
  └───────────────────────────────────────────┴───────────┴────────────────┘

  The rule is: if you can't see where the sack's top edge is, don't annotate it.
  Let the track coast on track_buffer for those frames. A missing detection
  costs you nothing — ByteTrack holds the ID for 2.4 s and the state machine
  only needs to see the sack below the line at any point in its lifetime. A
  detection with a wrong y1 costs you a miscount.

  And whatever you choose — be consistent. Inconsistent occlusion labeling is
  the single biggest cause of bad detection models. A model trained on
  "sometimes we box occluded sacks, sometimes not" learns to fire at random
  confidence on ambiguous evidence, which produces ID switches and double
  counts.

  A warning about the tool I just built for you. Low SAM3 score correlates
  strongly with occlusion. If you apply "ignore the weakest 10%" as a blanket
  rule, you are systematically deleting your occluded examples — the hard ones —
  and training a model that only knows easy, fully-visible sacks. It will then
  fail precisely where workers stand, which is precisely where your counting
  line is. Use triage to remove boxes that are wrong (slivers, duplicates,
  background objects), not boxes that are merely hard. Hard-but-correct examples
  are the most valuable ones in the set. Check the crop grid before dropping
  anything.

  That also reframes your original full-sack/half-sack idea: I suspect
  "half-sack" in your footage is often "partially occluded sack", not a
  genuinely different object. Those are worth keeping as normal sacks if the top
  edge is visible — not split into a second class.

  To pin the policy down:

● User answered Claude's questions:
  ⎿  · What is the counter actually getting wrong today? → Both, inconsistently
     · Where do workers occlude sacks relative to the counting line? → Varies by
     camera
     · Who annotates, and how consistent can the policy be enforced? → Only me

  Ran 2 shell commands (ctrl+o to expand)

● That surfaces something more important than the annotation question.

  Your counting geometry is hardcoded to one camera. predict.py:1039 sets the
  counting line at y = 330 × scale_y, with the detection polygon and truck ROI
  at fixed pixel coordinates (574, 586, 1418, 1397…), and zones.json holds a
  single palet/truck pair. The only adaptation is a resolution scale factor —
  which corrects for pixel dimensions, not for where the camera is actually
  pointing.

  You told me occlusion varies by camera. If the cameras also differ in angle or
  framing, then a line calibrated for camera A sits in the wrong place for
  camera B — sometimes above the occlusion zone (undercount, sacks never seen
  "below"), sometimes inside it (overcount, y1 jitter tripping the state
  machine). That produces exactly your symptom: both errors, inconsistently, 
  varying by camera. It's also a direct violation of CLAUDE.md §9.

  No amount of retraining fixes a line in the wrong place. I'd put the effort in
  this order:

  1. Get ground truth. Hand-count 2–3 videos per camera. Without it you cannot
  tell whether any change helped — which is the entire premise of this app, and
  right now you have no number to compare against.
  2. Move the geometry into per-camera config. zones.json keyed by camera name,
  with line, detection polygon, truck ROI and dedup_radius per camera. That's a
  contained change to predict.py.
  3. Then annotation policy and retraining.

  Since you annotate alone and mostly correct SAM3, I wrote the policy down so
  it stays consistent across sessions:

● Write(docs/annotation-policy.md)
  ⎿  Wrote 72 lines to docs/annotation-policy.md
       1 # Annotation policy — sack counting
       3 Why this document exists: the model is not the product, **the co
         unt** is. Every rule below
       4 is derived from how `algoritma-batch/src/counting.py` turns boxe
       5 counter and this policy has to change with it.
       6 
       8 
       9 `LineCrossCounter` counts on **`y1` — the top edge of the box** 
         (`counting.py`, "Uses y1
         nted when it was seen ABOVE
     … +89 lines (ctrl+o to expand)

● Written to docs/annotation-policy.md.

  Direct answer to your question: a sack covered by someone's head, inside the
  counting zone — don't annotate it, if the head hides the sack's top edge. Not
  because it isn't a sack, but because your counter measures y1. A box whose top
  edge is a head's outline reports the wrong height, and the state machine can
  fire early. The missing detection costs you nothing: track_buffer: 60 was set
  precisely to bridge that gap and keeps the track ID alive for ~2.4 s.

  If the head covers the bottom or side but the top edge is clear — annotate it
  normally, boxing what you can see. Top edge is the deciding factor, not
  overall visibility.

  Never annotate a sack you can't see at all. That teaches hallucination, which
  becomes ghost tracks and overcounting.

  Three things I'd push back on in how you're framing the problem:

  1. "Both, inconsistently" is rarely an annotation symptom. Simultaneous over-
  and undercounting that varies by camera points at geometry, not model quality.
  Your line is hardcoded at one camera's coordinates.
  2. You have no ground truth. You've retrained at least once with no manual
  count to compare against, so there's no way to know if it helped. That's the
  cheapest, highest-value thing you could do this week — hand-count a few videos
  per camera.
  3. The triage tool I just built can hurt you here. Low SAM3 score ≈ occluded,
  not wrong. Dropping the weakest 10% deletes your occluded examples and makes
  the model worse exactly where workers stand. Use it to remove wrong boxes
  (slivers, duplicates, non-sacks), not hard ones.

  I haven't changed any code for this — it's a policy question and the
  per-camera geometry work sits in algoritma-batch/, which is outside what you
  asked me to touch. Say the word and I'll move the counting geometry into
  per-camera config in zones.json; that's the change most likely to fix your
  actual counting error, and it doesn't need a single new annotation.

✻ Cooked for 2m 33s

※ recap: Goal is a reliable retraining loop for your sack counter; I audited the app, fixed the critical bugs, and built the Data Prep triage tool, now live. Next: hard-refresh the browser and try a preset rule on batch6. (disable recaps in /config)

❯ tolong hati hati saya sudah manual anotasi di page batches simpan backupnya   
  dulu untuk hasil anotasi itu                                                  

● Baik, saya amankan dulu sebelum menyentuh apa pun.

  Ran 2 shell commands (ctrl+o to expand)

● Backup selesai dan terverifikasi — tapi ada yang serius, tolong baca ini dulu.

  Ran 2 shell commands (ctrl+o to expand)

● Aman — backup tersimpan dan terverifikasi. Saya tambahkan satu lapis lagi:
  ekspor JSON yang tidak bergantung pada skema database, supaya kerja tangan
  Anda tetap bisa dipulihkan walau struktur DB berubah.

  Ran 1 shell command (ctrl+o to expand)

● Backup selesai, dua lapis, dua-duanya sudah saya verifikasi isinya (bukan cuma
  "file-nya ada"):

  ┌───────────────────────────────────────────────┬──────────────────────────┐
  │                     File                      │           Isi            │
  ├───────────────────────────────────────────────┼──────────────────────────┤
  │                                               │ Snapshot penuh 50 MB,    │
  │ data/backups/app-20260807-163509.db           │ PRAGMA integrity_check = │
  │                                               │  ok                      │
  ├───────────────────────────────────────────────┼──────────────────────────┤
  │ data/backups/annotations-20260807-163546.json │ 6.324 anotasi (2,6 MB),  │
  │                                               │ bisa dibaca ulang        │
  └───────────────────────────────────────────────┴──────────────────────────┘

  Snapshot DB saya ambil pakai API sqlite3.backup(), jadi konsisten walaupun
  aplikasi sedang jalan (WAL ikut ter-flush). Semua operasi read-only — tidak
  ada yang saya ubah.

  Kerja tangan Anda yang terlindungi: 1.547 anotasi manual, tersebar di 10
  batch:

  batch7 406 · batch013 172 · batch016 168 · batch8 165 · batch12 155 · batch014
  140 · batch9 134 · batch11 94 · batch10 58 · batch015 55

  Tapi ada yang berubah besar sejak terakhir saya lihat, tolong dikonfirmasi.
  Beberapa jam lalu database berisi batch6 dengan 85.520 anotasi dan master
  dataset 3.340 gambar. Sekarang isinya batch yang sama sekali berbeda (id
  45–62), total 6.324 anotasi, dan master dataset kosong — dataset_items = 0,
  dan di disk data/projects/sack/dataset/ tinggal data.yaml, tanpa satu pun
  gambar atau label.

  Kalau Anda memang sengaja menghapus batch lama dan mulai ulang dengan batch
  baru, semuanya normal — perbaikan delete() yang saya buat tadi memang
  membersihkan file dataset ikut terhapus, dan itu yang terjadi. Frame mentahnya
  masih utuh (226 MB di data/projects/sack/batches).

  Tapi kalau 85.520 anotasi itu tidak Anda hapus sengaja, tolong bilang sekarang
  — jangan jalankan apa pun dulu. Backup yang barusan saya ambil isinya kondisi
  sesudah perubahan itu, jadi tidak memuat data lama. Saya perlu cek apakah
  masih ada sisa yang bisa diselamatkan sebelum tertimpa.

  Satu catatan: karena dataset_items = 0, saat ini belum ada apa pun yang bisa
  dilatih — semua batch masih reviewing atau extracted, belum ada yang di-merge.

  Saya berhenti di sini dan tidak menyentuh kode lagi sampai Anda konfirmasi
  soal data lama itu. Kalau nanti mau lanjut, saya sarankan backup otomatis
  sebelum tiap operasi merusak (delete batch, delete class, merge) — supaya
  tidak bergantung pada saya ingat mengambilnya.

✻ Cooked for 1m 20s

❯ oke sekarang move ke code, untuk yang di approve itu disimpan dan yang tidak  
  di approve = ignore, lalu tambahkan tombol download dataset yang sudah di     
  anotasi ini jadikan dalam zip agar user bisa download dan backup sendiri      

● Dua hal: (1) approve = simpan, non-approve = ignore, (2) tombol download ZIP
  dataset hasil anotasi. Saya lihat kode terkait dulu.

  Searched for 2 patterns, ran 1 shell command (ctrl+o to expand)

● Sekarang perubahan (1) — approve = simpan, sisanya ignore:

● Update(backend/dataset.py)
  ⎿  Added 4 lines, removed 6 lines
      35          raise DatasetError("No such batch")
      36      if batch["status"] == "merged":
      37          raise DatasetError("This batch is already in the master
           dataset")
      38 -    if batch["review"]["pending"] > 0:                         
      39 -        raise DatasetError(                                    
      40 -            f"{batch['review']['pending']} frame(s) still need 
         -a decision before this "                                       
      41 -            "batch can be approved"                            
      42 -        )                                                      
      38      if batch["review"]["approved"] == 0:
      44 -        raise DatasetError("Every frame was rejected — there is
         - nothing to merge")                                            
      39 +        raise DatasetError("No frame in this batch is approved 
         +— there is nothing to merge")                                  
      40 +    # Frames that are not approved — rejected or never looked a
         +t — are simply                                                 
      41 +    # left behind. Only what the user signed off on enters the 
         +dataset, so a                                                  
      42 +    # partly-reviewed batch can be merged for the part that is 
         +done.                                                          
      43      with db.cursor() as cur:
      44          cur.execute(
      45              """SELECT 1 FROM jobs WHERE batch_id = ? AND type =
           'merge'
● Karena sekarang merge bisa sebagian, batch yang sudah merged harus bisa
  di-merge lagi untuk frame yang baru di-approve — kalau tidak, sisanya terkunci
  selamanya:

● Update(backend/dataset.py)
  ⎿  Added 5 lines, removed 2 lines
      33      batch = batches.get(batch_id)
      34      if batch is None:
      35          raise DatasetError("No such batch")
      36 -    if batch["status"] == "merged":                            
      37 -        raise DatasetError("This batch is already in the master
         - dataset")                                                     
      36 +    if batch["status"] == "merged" and _unmerged_approved(batch
         +_id) == 0:                                                     
      37 +        # Re-merging is allowed, because a partly-reviewed batc
         +h can be merged                                                
      38 +        # for the part that was done and finished later. Only r
         +efuse when there                                               
      39 +        # is genuinely nothing new to add.                     
      40 +        raise DatasetError("Every approved frame in this batch 
         +is already in the master dataset")                             
      41      if batch["review"]["approved"] == 0:
      42          raise DatasetError("No frame in this batch is approved 
          — there is nothing to merge")
      43      # Frames that are not approved — rejected or never looked a
          t — are simply

● Update(backend/dataset.py)
  ⎿  Added 12 lines
      63      return job.to_dict()
      64  
      65  
      66 +def _unmerged_approved(batch_id: int) -> int:                  
      67 +    """Approved frames of this batch that are not in the datase
         +t yet."""                                                      
      68 +    with db.cursor() as cur:                                   
      69 +        cur.execute(                                           
      70 +            """SELECT COUNT(*) FROM frames f                   
      71 +               LEFT JOIN dataset_items d ON d.frame_id = f.id  
      72 +               WHERE f.batch_id = ? AND f.review_status = 'appr
         +oved' AND d.id IS NULL""",                                     
      73 +            (batch_id,),                                       
      74 +        )                                                      
      75 +        return cur.fetchone()[0]                               
      76 +                                                               
      77 +                                                               
      78  def _label_line(class_id: int, geometry: dict, label_type: str)
           -> str:
      79      if label_type == "bbox":
      80          x0, y0, x1, y1 = review.to_box(geometry)

● Sekarang (2) — export ZIP dataset hasil anotasi, tanpa perlu merge dulu:

● Write(backend/export.py)
  ⎿  Wrote 147 lines to backend/export.py
       1 """Export annotated frames as a plain YOLO dataset the user can 
       2 
       3 This is deliberately independent of the master dataset. A batch 
         can be exported
         cause the point
       5 is that the user owns a copy of their own work and does not have
       6 application to keep it.
       7 
       8 Layout inside the zip:
      10     images/<batch>__<frame>.jpg
     … +163 lines (ctrl+o to expand)

● Update(backend/api/triage.py)
  ⎿  Added 17 lines
      100      return triage.preview(project_id)
      101  
      102  
      103 +@router.get("/api/projects/{project_id}/export")              
      104 +def export_annotated(project_id: int, batch_ids: str = "", app
          +roved_only: bool = False,                                     
      105 +                     include_empty: bool = False):            
      106 +    """Download annotated frames as a YOLO zip, merged or not 
          +— the user's own backup."""                                   
      107 +    from fastapi.responses import FileResponse                
      108 +                                                              
      109 +    from backend import export                                
      110 +                                                              
      111 +    ids = [int(part) for part in batch_ids.split(",") if part.
          +strip().isdigit()]                                            
      112 +    try:                                                      
      113 +        path = export.build_zip(project_id, ids or None,      
      114 +                                approved_only=approved_only, i
          +nclude_empty=include_empty)                                   
      115 +    except export.ExportError as exc:                         
      116 +        raise HTTPException(400, str(exc))                    
      117 +    return FileResponse(path, media_type="application/zip", fi
          +lename=os.path.basename(path))                                
      118 +                                                              
      119 +                                                              
      120  @router.get("/api/annotations/{annotation_id}/crop")
      121  def crop(annotation_id: int, pad: float = 0.08):
      122      """The shape itself, cropped out of its frame — the crop g
           rid judges objects,

  Ran 1 shell command (ctrl+o to expand)

● Sekarang tombolnya di UI:

● Update(frontend/src/api.js)
  ⎿  Added 8 lines
      136    clearTriageOverrides: (annotationIds) =>
      137      request('/triage/overrides', { method: 'DELETE', body: { a
           nnotation_ids: annotationIds } }),
      138    cropUrl: (annotationId) => `/api/annotations/${annotationId}
           /crop`,
      139 +  exportUrl: (projectId, { batchIds = [], approvedOnly = false
          +, includeEmpty = false } = {}) => {                           
      140 +    const params = new URLSearchParams()                      
      141 +    if (batchIds.length) params.set('batch_ids', batchIds.join
          +(','))                                                        
      142 +    if (approvedOnly) params.set('approved_only', 'true')     
      143 +    if (includeEmpty) params.set('include_empty', 'true')     
      144 +    const query = params.toString()                           
      145 +    return `/api/projects/${projectId}/export${query ? `?${que
          +ry}` : ''}`                                                   
      146 +  },                                                          
      147    datasetDownloadUrl: (projectId) => `/api/projects/${projectI
           d}/dataset/download`,
      148  
      149    hardware: () => request('/hardware'),

  Read 1 file (ctrl+o to expand)

● Update(frontend/src/pages/BatchesPage.jsx)
  ⎿  Added 18 lines, removed 7 lines
       93            <p className="muted mono">{project.name}</p>
       94          </div>
       95          {batches.length > 0 && (
       96 -          <button                                             
       97 -            className="btn btn-primary"                       
       98 -            style={{ fontSize: '0.85rem', padding: '8px 16px',
          - cursor: 'pointer', display: 'inline-flex', alignItems: 'cente
          -r', gap: 6, borderRadius: 6 }}                                
       99 -            onClick={handleAutoAnnotateAllHeader}             
      100 -          >                                                   
      101 -            <ZapIcon size={16} /> Auto-Annotate All Batches ({
          -batches.length})                                              
      102 -          </button>                                           
       96 +          <div style={{ display: 'flex', gap: 8, alignItems: '
          +center', flexWrap: 'wrap' }}>                                 
       97 +            <a                                                
       98 +              className="btn"                                 
       99 +              href={api.exportUrl(projectId)}                 
      100 +              download                                        
      101 +              title="Download every annotated frame in this pr
          +oject as a YOLO dataset — your own backup, no merge needed"   
      102 +              style={{ fontSize: '0.85rem', padding: '8px 16px
          +', cursor: 'pointer', display: 'inline-flex', alignItems: 'cen
          +ter', gap: 6, borderRadius: 6 }}                              
      103 +            >                                                 
      104 +              <DatabaseIcon size={16} /> Download Annotations 
          +(.zip)                                                        
      105 +            </a>                                              
      106 +            <button                                           
      107 +              className="btn btn-primary"                     
      108 +              style={{ fontSize: '0.85rem', padding: '8px 16px
          +', cursor: 'pointer', display: 'inline-flex', alignItems: 'cen
          +ter', gap: 6, borderRadius: 6 }}                              
      109 +              onClick={handleAutoAnnotateAllHeader}           
      110 +            >                                                 
      111 +              <ZapIcon size={16} /> Auto-Annotate All Batches 
          +({batches.length})                                            
      112 +            </button>                                         
      113 +          </div>                                              
      114          )}
      115        </div>
      116  

  Searched for 1 pattern (ctrl+o to expand)

● Update(frontend/src/pages/BatchesPage.jsx)
  ⎿  Added 1 line, removed 1 line
      1  import React, { useCallback, useEffect, useRef, useState } from 
         'react'
      2  import { api } from '../api'
      3 -import { AlertIcon, ZapIcon } from '../components/Icons'        
      3 +import { AlertIcon, DatabaseIcon, ZapIcon } from '../components/
        +Icons'                                                          
      4  import { BatchList, ActiveJobsBanner } from './LibraryPage'
      5  
      6  export default function BatchesPage({ projectId, onProject }) {

❯ tambahkan juga upload zip untuk upload hasil backup agar bisa dilanjutkan     

● Baik, saya tambahkan juga import ZIP-nya. Lanjut dulu di file yang sama:

● Update(backend/export.py)
  ⎿  Added 148 lines
      145      path = shutil.make_archive(archive_base, "zip", staging)
      146      shutil.rmtree(staging, ignore_errors=True)
      147      return path
      148 +                                                              
      149 +                                                              
      150 +# ---- restore -----------------------------------------------
          +-------------                                                 
      151 +                                                              
      152 +def _safe_members(archive) -> list:                           
      153 +    """Only plain files under images/ and labels/.            
      154 +                                                              
      155 +    A zip can name `../../etc/passwd`; extracting one blindly 
          +writes wherever it                                            
      156 +    says. Every member is checked rather than trusted.        
      157 +    """                                                       
      158 +    picked = []                                               
      159 +    for member in archive.infolist():                         
      160 +        if member.is_dir():                                   
      161 +            continue                                          
      162 +        name = member.filename.replace("\\", "/")             
      163 +        if name.startswith("/") or ".." in name.split("/"):   
      164 +            continue                                          
      165 +        if name.startswith(("images/", "labels/")) or name == 
          +"MANIFEST.json":                                              
      166 +            picked.append((name, member))                     
      167 +    return picked                                             
      168 +                                                              
      169 +                                                              
      170 +def _points_from_label(parts: List[str], label_type: str) -> O
          +ptional[dict]:                                                
      171 +    values = [float(v) for v in parts]                        
      172 +    if label_type == "bbox":                                  
      173 +        if len(values) != 4:                                  
      174 +            return None                                       
      175 +        cx, cy, w, h = values                                 
      176 +        return {"type": "bbox",                               
      177 +                "points": [cx - w / 2, cy - h / 2, cx + w / 2,
          + cy + h / 2]}                                                 
      178 +    if len(values) < 6 or len(values) % 2:                    
      179 +        return None                                           
      180 +    return {"type": "polygon",                                
      181 +            "points": [[values[i], values[i + 1]] for i in ran
          +ge(0, len(values), 2)]}                                       
      182 +                                                              
      183 +                                                              
      184 +def restore_zip(project_id: int, zip_path: str, batch_label: s
          +tr = "") -> dict:                                             
      185 +    """Load an exported zip back in as a fresh batch, ready to
          + keep reviewing.                                              
      186 +                                                              
      187 +    The frames land in a new batch rather than being merged ba
          +ck into the ones                                              
      188 +    they came from: the originals may still exist, and silentl
          +y overwriting a                                               
      189 +    batch the user is working in would destroy the very work t
          +his feature is                                                
      190 +    meant to protect.                                         
      191 +    """                                                       
      192 +    import zipfile                                            
      193 +                                                              
      194 +    from PIL import Image                                     
      195 +                                                              
      196 +    project = projects.get(project_id)                        
      197 +    if project is None:                                       
      198 +        raise ExportError("No such project")                  
      199 +                                                              
      200 +    by_name = {item["name"]: item["class_id"] for item in proj
          +ect["classes"]}                                               
      201 +    stamp = time.strftime("%Y%m%d-%H%M%S")                    
      202 +    label = batch_label or f"restored-{stamp}"                
      203 +                                                              
      204 +    with zipfile.ZipFile(zip_path) as archive:                
      205 +        members = _safe_members(archive)                      
      206 +        names = {name for name, _ in members}                 
      207 +        if not any(name.startswith("images/") for name in name
          +s):                                                           
      208 +            raise ExportError("This zip has no images/ folder 
          +— is it an export from this app?")                            
      209 +                                                              
      210 +        manifest = {}                                         
      211 +        if "MANIFEST.json" in names:                          
      212 +            manifest = json.loads(archive.read("MANIFEST.json"
          +))                                                            
      213 +        source_type = manifest.get("label_type", project["labe
          +l_type"])                                                     
      214 +        if source_type != project["label_type"]:              
      215 +            raise ExportError(                                
      216 +                f"This export holds {source_type} labels but t
          +he project is "                                               
      217 +                f"{project['label_type']} — importing it would
          + produce wrong shapes"                                        
      218 +            )                                                 
      219 +                                                              
      220 +        # Classes come back by name, so an id that shifted sin
          +ce the export does                                            
      221 +        # not silently relabel every shape.                   
      222 +        remap = {}                                            
      223 +        for item in manifest.get("classes", []):              
      224 +            if item["name"] in by_name:                       
      225 +                remap[item["class_id"]] = by_name[item["name"]
          +]                                                             
      226 +            else:                                             
      227 +                raise ExportError(                            
      228 +                    f"The export uses class '{item['name']}', 
          +which this project does not "                                 
      229 +                    "have. Add the class first, then import." 
      230 +                )                                             
      231 +                                                              
      232 +        with db.cursor() as cur:                              
      233 +            cur.execute(                                      
      234 +                """INSERT INTO batches (project_id, video_path
          +, date_label, batch_label,                                    
      235 +                                        start_sec, end_sec, fp
          +s, status, created_at)                                        
      236 +                   VALUES (?, '', 'restored', ?, 0, 0, 0, 'ext
          +racted', ?)""",                                               
      237 +                (project_id, label, time.time()),             
      238 +            )                                                 
      239 +            batch_id = cur.lastrowid                          
      240 +                                                              
      241 +        target_dir = batches.frames_dir(project["slug"], batch
          +_id)                                                          
      242 +        os.makedirs(target_dir, exist_ok=True)                
      243 +                                                              
      244 +        restored, shapes, skipped = 0, 0, 0                   
      245 +        image_members = sorted(n for n in names if n.startswit
          +h("images/"))                                                 
      246 +        for index, name in enumerate(image_members):          
      247 +            stem = os.path.splitext(os.path.basename(name))[0]
      248 +            if not stem:                                      
      249 +                continue                                      
      250 +            filename = f"{stem}.jpg"                          
      251 +            destination = os.path.join(target_dir, filename)  
      252 +            with archive.open(name) as source, open(destinatio
          +n, "wb") as handle:                                           
      253 +                shutil.copyfileobj(source, handle)            
      254 +                                                              
      255 +            try:                                              
      256 +                with Image.open(destination) as image:        
      257 +                    width, height = image.size                
      258 +            except Exception:                                 
      259 +                os.unlink(destination)                        
      260 +                skipped += 1                                  
      261 +                continue                                      
      262 +                                                              
      263 +            with db.cursor() as cur:                          
      264 +                cur.execute(                                  
      265 +                    """INSERT INTO frames (batch_id, idx, file
          +name, width, height, review_status)                           
      266 +                       VALUES (?, ?, ?, ?, ?, 'pending')""",  
      267 +                    (batch_id, index, filename, width, height)
          +,                                                             
      268 +                )                                             
      269 +                frame_id = cur.lastrowid                      
      270 +                                                              
      271 +                label_name = f"labels/{stem}.txt"             
      272 +                if label_name in names:                       
      273 +                    for line in archive.read(label_name).decod
          +e("utf-8").splitlines():                                      
      274 +                        parts = line.split()                  
      275 +                        if len(parts) < 5:                    
      276 +                            continue                          
      277 +                        geometry = _points_from_label(parts[1:
          +], project["label_type"])                                     
      278 +                        if geometry is None:                  
      279 +                            continue                          
      280 +                        class_id = remap.get(int(parts[0]), in
          +t(parts[0]))                                                  
      281 +                        cur.execute(                          
      282 +                            """INSERT INTO annotations (frame_
          +id, class_id, geometry, score,                                
      283 +                                                        source
          +, created_at)                                                 
      284 +                               VALUES (?, ?, ?, 1.0, 'manual',
          + ?)""",                                                       
      285 +                            (frame_id, class_id, json.dumps(ge
          +ometry), time.time()),                                        
      286 +                        )                                     
      287 +                        shapes += 1                           
      288 +            restored += 1                                     
      289 +                                                              
      290 +    with db.cursor() as cur:                                  
      291 +        cur.execute("UPDATE batches SET frame_count = ?, statu
          +s = 'reviewing' WHERE id = ?",                                
      292 +                    (restored, batch_id))                     
      293 +                                                              
      294 +    return {"batch_id": batch_id, "batch_label": label, "frame
          +s": restored,                                                 
      295 +            "shapes": shapes, "skipped": skipped}             

● Update(backend/api/triage.py)
  ⎿  Added 22 lines
      117      return FileResponse(path, media_type="application/zip", fi
           lename=os.path.basename(path))
      118  
      119  
      120 +@router.post("/api/projects/{project_id}/import")             
      121 +async def import_annotated(project_id: int, file: UploadFile =
          + File(...),                                                   
      122 +                           batch_label: str = Form("")) -> dic
          +t:                                                            
      123 +    """Load a previously exported zip back in, as a new batch 
          +to keep working on."""                                        
      124 +    import tempfile                                           
      125 +                                                              
      126 +    from backend import export                                
      127 +                                                              
      128 +    staged = tempfile.NamedTemporaryFile(suffix=".zip", delete
          +=False)                                                       
      129 +    try:                                                      
      130 +        shutil.copyfileobj(file.file, staged)                 
      131 +        staged.close()                                        
      132 +        return export.restore_zip(project_id, staged.name, bat
          +ch_label=batch_label)                                         
      133 +    except export.ExportError as exc:                         
      134 +        raise HTTPException(400, str(exc))                    
      135 +    except Exception as exc:                                  
      136 +        raise HTTPException(400, f"Could not read that zip: {e
          +xc}")                                                         
      137 +    finally:                                                  
      138 +        if os.path.exists(staged.name):                       
      139 +            os.unlink(staged.name)                            
      140 +                                                              
      141 +                                                              
      142  @router.get("/api/annotations/{annotation_id}/crop")
      143  def crop(annotation_id: int, pad: float = 0.08):
      144      """The shape itself, cropped out of its frame — the crop g
           rid judges objects,

● Update(backend/api/triage.py)
  ⎿  Added 2 lines, removed 1 line
       2  
       3  import io
       4  import os
       5 +import shutil                                                  
       6  
       6 -from fastapi import APIRouter, HTTPException                   
       7 +from fastapi import APIRouter, File, Form, HTTPException, Uploa
         +dFile                                                          
       8  from fastapi.responses import StreamingResponse
       9  from pydantic import BaseModel
      10  from typing import List, Optional

● Sekarang tombol upload di UI:

● Update(frontend/src/api.js)
  ⎿  Added 6 lines
      136    clearTriageOverrides: (annotationIds) =>
      137      request('/triage/overrides', { method: 'DELETE', body: { a
           nnotation_ids: annotationIds } }),
      138    cropUrl: (annotationId) => `/api/annotations/${annotationId}
           /crop`,
      139 +  importAnnotations: (projectId, zipFile, batchLabel = '') => 
          +{                                                             
      140 +    const form = new FormData()                               
      141 +    form.append('file', zipFile)                              
      142 +    form.append('batch_label', batchLabel)                    
      143 +    return request(`/projects/${projectId}/import`, { method: 
          +'POST', form })                                               
      144 +  },                                                          
      145    exportUrl: (projectId, { batchIds = [], approvedOnly = false
           , includeEmpty = false } = {}) => {
      146      const params = new URLSearchParams()
      147      if (batchIds.length) params.set('batch_ids', batchIds.join
           (','))

  Read 1 file (ctrl+o to expand)

● Update(frontend/src/pages/BatchesPage.jsx)
  ⎿  Added 24 lines
       8    const [batches, setBatches] = useState([])
       9    const [jobs, setJobs] = useState([])
      10    const [error, setError] = useState('')
      11 +  const [importing, setImporting] = useState(false)            
      12 +  const [notice, setNotice] = useState('')                     
      13 +  const importInput = useRef(null)                             
      14  
      15 +  async function handleImport(event) {                         
      16 +    const file = event.target.files?.[0]                       
      17 +    event.target.value = ''                                    
      18 +    if (!file) return                                          
      19 +    setImporting(true)                                         
      20 +    setError('')                                               
      21 +    setNotice('')                                              
      22 +    try {                                                      
      23 +      const result = await api.importAnnotations(projectId, fil
         +e)                                                             
      24 +      setNotice(                                               
      25 +        `Restored "${result.batch_label}" — ${result.frames} fr
         +ame(s), ${result.shapes} annotation(s)`                        
      26 +        + (result.skipped ? `, ${result.skipped} skipped` : '')
         +,                                                              
      27 +      )                                                        
      28 +      loadBatches()                                            
      29 +    } catch (exc) {                                            
      30 +      setError(exc.message)                                    
      31 +    } finally {                                                
      32 +      setImporting(false)                                      
      33 +    }                                                          
      34 +  }                                                            
      35 +                                                               
      36    const loadBatches = useCallback(() => {
      37      api.listBatches(projectId).then((payload) => setBatches(pay
          load.batches)).catch(() => {})
      38    }, [projectId])

● Update(frontend/src/pages/BatchesPage.jsx)
  ⎿  Added 19 lines, removed 3 lines
      116            <h1>Batches ({batches.length})</h1>
      117            <p className="muted mono">{project.name}</p>
      118          </div>
      119 -        {batches.length > 0 && (                              
      120 -          <div style={{ display: 'flex', gap: 8, alignItems: '
          -center', flexWrap: 'wrap' }}>                                 
      121 -            <a                                                
      119 +        <div style={{ display: 'flex', gap: 8, alignItems: 'ce
          +nter', flexWrap: 'wrap' }}>                                   
      120 +          <input                                              
      121 +            ref={importInput}                                 
      122 +            type="file"                                       
      123 +            accept=".zip"                                     
      124 +            onChange={handleImport}                           
      125 +            style={{ display: 'none' }}                       
      126 +          />                                                  
      127 +          <button                                             
      128 +            className="btn"                                   
      129 +            disabled={importing}                              
      130 +            onClick={() => importInput.current?.click()}      
      131 +            title="Restore a previously downloaded annotation 
          +zip as a new batch"                                           
      132 +            style={{ fontSize: '0.85rem', padding: '8px 16px',
          + cursor: importing ? 'wait' : 'pointer', display: 'inline-flex
          +', alignItems: 'center', gap: 6, borderRadius: 6 }}           
      133 +          >                                                   
      134 +            <UploadIcon size={16} /> {importing ? 'Restoring…'
          + : 'Restore from .zip'}                                       
      135 +          </button>                                           
      136 +          {batches.length > 0 && (                            
      137 +            <                                                 
      138                className="btn"
      139                href={api.exportUrl(projectId)}
      140                download

● Edit itu merusak JSX-nya. Saya perbaiki blok itu utuh:

  Read 1 file (ctrl+o to expand)
● Update(frontend/src/pages/BatchesPage.jsx)
  ⎿  Added 24 lines, removed 18 lines
      134              <UploadIcon size={16} /> {importing ? 'Restoring…'
            : 'Restore from .zip'}
      135            </button>
      136            {batches.length > 0 && (
      137 -            <                                                 
      138 -              className="btn"                                 
      139 -              href={api.exportUrl(projectId)}                 
      140 -              download                                        
      141 -              title="Download every annotated frame in this pr
          -oject as a YOLO dataset — your own backup, no merge needed"   
      142 -              style={{ fontSize: '0.85rem', padding: '8px 16px
          -', cursor: 'pointer', display: 'inline-flex', alignItems: 'cen
          -ter', gap: 6, borderRadius: 6 }}                              
      143 -            >                                                 
      144 -              <DatabaseIcon size={16} /> Download Annotations 
          -(.zip)                                                        
      145 -            </a>                                              
      146 -            <button                                           
      147 -              className="btn btn-primary"                     
      148 -              style={{ fontSize: '0.85rem', padding: '8px 16px
          -', cursor: 'pointer', display: 'inline-flex', alignItems: 'cen
          -ter', gap: 6, borderRadius: 6 }}                              
      149 -              onClick={handleAutoAnnotateAllHeader}           
      150 -            >                                                 
      151 -              <ZapIcon size={16} /> Auto-Annotate All Batches 
          -({batches.length})                                            
      152 -            </button>                                         
      153 -          </div>                                              
      154 -        )}                                                    
      137 +            <>                                                
      138 +              <a                                              
      139 +                className="btn"                               
      140 +                href={api.exportUrl(projectId)}               
      141 +                download                                      
      142 +                title="Download every annotated frame in this 
          +project as a YOLO dataset — your own backup, no merge needed" 
      143 +                style={{ fontSize: '0.85rem', padding: '8px 16
          +px', cursor: 'pointer', display: 'inline-flex', alignItems: 'c
          +enter', gap: 6, borderRadius: 6 }}                            
      144 +              >                                               
      145 +                <DatabaseIcon size={16} /> Download Annotation
          +s (.zip)                                                      
      146 +              </a>                                            
      147 +              <button                                         
      148 +                className="btn btn-primary"                   
      149 +                style={{ fontSize: '0.85rem', padding: '8px 16
          +px', cursor: 'pointer', display: 'inline-flex', alignItems: 'c
          +enter', gap: 6, borderRadius: 6 }}                            
      150 +                onClick={handleAutoAnnotateAllHeader}         
      151 +              >                                               
      152 +                <ZapIcon size={16} /> Auto-Annotate All Batche
          +s ({batches.length})                                          
      153 +              </button>                                       
      154 +            </>                                               
      155 +          )}                                                  
      156 +        </div>                                                
      157        </div>
      158  
      159 +      {notice && (                                            
      160 +        <p className="hint" style={{ marginBottom: 12, color: 
          +'#4ade80' }}>{notice}</p>                                     
      161 +      )}                                                      
      162 +                                                              
      163        <ActiveJobsBanner jobs={activeJobs} onCancel={cancelJob}
            />
      164  
      165        {batches.length === 0 ? (

● Update(frontend/src/pages/BatchesPage.jsx)
  ⎿  Added 1 line, removed 1 line
      1  import React, { useCallback, useEffect, useRef, useState } from 
         'react'
      2  import { api } from '../api'
      3 -import { AlertIcon, DatabaseIcon, ZapIcon } from '../components/
        -Icons'                                                          
      3 +import { AlertIcon, DatabaseIcon, UploadIcon, ZapIcon } from '..
        +/components/Icons'                                              
      4  import { BatchList, ActiveJobsBanner } from './LibraryPage'
      5  
      6  export default function BatchesPage({ projectId, onProject }) {

  Ran 1 shell command (ctrl+o to expand)

● Rebuild container, lalu saya uji export→import benar-benar bolak-balik:

  Ran 1 shell command (ctrl+o to expand)

● Export jalan: 932 gambar, 6.324 anotasi, 187 MB. Sekarang uji import-nya
  benar-benar bisa dipulihkan:

  Ran 1 shell command (ctrl+o to expand)

● Sama persis. Sekarang saya cek geometri kotaknya benar-benar utuh, bukan cuma
  jumlahnya cocok:

  Ran 1 shell command (ctrl+o to expand)

● Utuh — 0 shape berbeda, selisih koordinat terbesar 0,001 pixel (itu cuma
  pembulatan 6 desimal di format YOLO). Sekarang saya hapus batch uji supaya
  data Anda bersih:

  Ran 1 shell command (ctrl+o to expand)

● Satu masalah yang saya lihat: tiap klik download menulis file ZIP 187 MB di
  folder proyek dan tidak pernah dihapus. Sepuluh kali klik = 1,9 GB sampah.
  Saya perbaiki:

● Update(backend/export.py)
  ⎿  Added 11 lines, removed 3 lines
      139      with open(os.path.join(staging, "MANIFEST.json"), "w", enc
           oding="utf-8") as handle:
      140          json.dump(manifest, handle, indent=2)
      141  
      142 +    project_root = config.project_dir(project["slug"])        
      143 +    # Each export is ~200 MB. Without this, every click leaves
          + another copy                                                 
      144 +    # behind and the project folder grows without bound.      
      145 +    for stale in os.listdir(project_root):                    
      146 +        if stale.startswith("export-") and stale.endswith(".zi
          +p"):                                                          
      147 +            try:                                              
      148 +                os.unlink(os.path.join(project_root, stale))  
      149 +            except OSError:                                   
      150 +                pass                                          
      151 +                                                              
      152      stamp = time.strftime("%Y%m%d-%H%M%S")
      143 -    archive_base = os.path.join(config.project_dir(project["sl
          -ug"]),                                                        
      144 -                                f"export-{stamp}")            
      145 -    path = shutil.make_archive(archive_base, "zip", staging)  
      153 +    path = shutil.make_archive(os.path.join(project_root, f"ex
          +port-{stamp}"), "zip", staging)                               
      154      shutil.rmtree(staging, ignore_errors=True)
      155      return path
      156  

  Ran 2 shell commands (ctrl+o to expand)

● Selesai dan sudah diuji dengan data asli Anda.

  1. Approve = simpan, tidak approve = ignore

  approve() tidak lagi menolak batch yang masih ada frame pending. Sekarang
  hanya frame ber-status approved yang masuk dataset; rejected dan pending
  ditinggalkan begitu saja. Jadi batch yang baru direview separuh tetap bisa
  di-merge untuk bagian yang sudah selesai.

  Konsekuensinya saya tangani juga: batch yang sudah merged sekarang boleh 
  di-merge ulang untuk frame yang baru Anda approve belakangan. Kalau tidak,
  sisa frame di batch itu akan terkunci selamanya — dan itu justru merugikan
  karena merge sebagian sekarang jadi hal biasa. Penolakan hanya terjadi kalau
  memang tidak ada frame approved baru.

  2. Download ZIP — tombol "Download Annotations (.zip)" di halaman Batches.
  Isinya images/, labels/ (format YOLO), data.yaml, classes.txt, dan
  MANIFEST.json. Ini tidak bergantung pada merge — semua frame yang punya
  anotasi ikut, apa pun status batch-nya. Frame tanpa anotasi sengaja dilewati
  (176 frame), karena file label kosong artinya "tidak ada objek di sini", dan
  untuk frame yang sekadar belum dianotasi itu klaim yang salah.

  3. Upload ZIP — tombol "Restore from .zip" di halaman yang sama. Hasilnya
  masuk sebagai batch baru (restored-<tanggal>), bukan menimpa batch yang ada —
  menimpa batch yang sedang Anda kerjakan justru akan menghancurkan pekerjaan
  yang mau dilindungi fitur ini.

  Hasil uji pulang-pergi dengan data Anda:

  ┌────────────────────────────┬───────────────────────────────────┐
  │                            │                                   │
  ├────────────────────────────┼───────────────────────────────────┤
  │ Export                     │ 932 gambar, 6.324 anotasi, 187 MB │
  ├────────────────────────────┼───────────────────────────────────┤
  │ Import kembali             │ 932 frame, 6.324 anotasi, 0 gagal │
  ├────────────────────────────┼───────────────────────────────────┤
  │ Shape yang berbeda         │ 0                                 │
  ├────────────────────────────┼───────────────────────────────────┤
  │ Selisih koordinat terbesar │ 0,001 pixel pada frame 1920px     │
  └────────────────────────────┴───────────────────────────────────┘

  Batch uji sudah saya hapus. Data Anda sekarang: 10 batch, 6.324 anotasi, 1.547
  manual — persis seperti sebelum saya mulai.

  Tiga hal yang saya putuskan sendiri, tolong dikoreksi kalau salah:

  - Kelas dicocokkan berdasarkan nama, bukan angka. Kalau urutan kelas berubah
  setelah export, class_id lama bisa menunjuk kelas yang salah — dengan nama,
  itu tidak mungkin terjadi. Kalau ada kelas di ZIP yang belum ada di proyek,
  import ditolak dengan pesan jelas, tidak diam-diam dibuat.
  - Isi ZIP divalidasi terhadap zip slip (anggota bernama ../../etc/passwd
  diabaikan), dan label_type bbox/polygon dicek supaya tidak menghasilkan bentuk
  salah.
  - Anotasi hasil restore ditandai source='manual', jadi tidak akan tertimpa
  kalau auto-annotate dijalankan lagi di batch itu.

  Satu bug yang saya temukan sambil menguji dan sudah diperbaiki: tiap klik
  download menulis ZIP 187 MB ke folder proyek dan tidak pernah menghapusnya.
  Sepuluh klik = 1,9 GB terbuang. Sekarang export lama dibersihkan sebelum yang
  baru dibuat. File sisa dari pengujian tadi sudah saya hapus.

  Container sudah di-rebuild (index-BhGV3ZDV.js). Hard-refresh browser dulu
  sebelum mencoba.

✻ Cogitated for 4m 49s

❯ oke sekarang di table buatkan tombol untuk memilih mana yang akan di "approve 
  batch" dan masuk ke proses selanjutnya, untuk posisi ganti dengan auto        
  annotate karena itu tombol ada 2 di satu pages                                

● Saya lihat dulu tabelnya dan tombol yang dobel itu.

  Searched for 1 pattern, read 1 file, ran 1 shell command (ctrl+o to expand)

● batch.review.approved tersedia — jadi tombolnya bisa tahu mana yang layak.
  Saya ganti tombol dobel itu:

● Update(frontend/src/pages/LibraryPage.jsx)
  ⎿  Added 17 lines, removed 8 lines
      152        <div className="panel table-wrap" style={{ marginTop: 16
           , padding: '16px' }}>
      153          <div style={{ display: 'flex', justifyContent: 'space-
           between', alignItems: 'center', marginBottom: 14, padding: '0 
           4px' }}>
      154            <h3 style={{ margin: 0, fontSize: '1rem', color: '#e
           4e4e7' }}>Batches ({batches.length})</h3>
      155 -          <button                                             
      156 -            className="btn btn-primary"                       
      157 -            style={{ fontSize: '0.85rem', padding: '6px 14px',
          - cursor: 'pointer', display: 'inline-flex', alignItems: 'cente
          -r', gap: 6, borderRadius: 6 }}                                
      158 -            onClick={handleAutoAnnotateAll}                   
      159 -          >                                                   
      160 -            <ZapIcon size={14} /> Auto-Annotate All Batches ({
          -batches.length})                                              
      161 -          </button>                                           
      162 -                                                              
      155 +          <div style={{ display: 'flex', gap: 10, alignItems: 
          +'center' }}>                                                  
      156 +            {selectedIds.length > 0 && (                      
      157 +              <span className="faint" style={{ fontSize: '0.8r
          +em' }}>                                                       
      158 +                {approvableSelected.length} of {selectedIds.le
          +ngth} selected can be merged                                  
      159 +              </span>                                         
      160 +            )}                                                
      161 +            <button                                           
      162 +              className="btn btn-primary"                     
      163 +              disabled={approving || approvableSelected.length
          + === 0}                                                       
      164 +              title="Approve the selected batches and merge th
          +eir approved frames into the master dataset"                  
      165 +              style={{ fontSize: '0.85rem', padding: '6px 14px
          +', cursor: approvableSelected.length === 0 ? 'not-allowed' : '
          +pointer', display: 'inline-flex', alignItems: 'center', gap: 6
          +, borderRadius: 6 }}                                          
      166 +              onClick={approveSelected}                       
      167 +            >                                                 
      168 +              <CheckIcon size={14} />                         
      169 +              {approving ? 'Approving…' : `Approve & Merge Sel
          +ected (${approvableSelected.length})`}                        
      170 +            </button>                                         
      171 +          </div>                                              
      172          </div>
      173          <table className="video-table">
      174  

● Sekarang kolom checkbox di tabel dan logikanya:

● Update(frontend/src/pages/LibraryPage.jsx)
  ⎿  Added 12 lines
      174  
      175            <thead>
      176            <tr>
      177 +            <th style={{ width: 34 }}>                        
      178 +              <input                                          
      179 +                type="checkbox"                               
      180 +                aria-label="Select every batch that can be mer
          +ged"                                                          
      181 +                checked={approvable.length > 0 && approvableSe
          +lected.length === approvable.length}                          
      182 +                onChange={(event) =>                          
      183 +                  setSelectedIds(event.target.checked ? approv
          +able.map((b) => b.id) : [])                                   
      184 +                }                                             
      185 +                disabled={approvable.length === 0}            
      186 +                style={{ cursor: approvable.length === 0 ? 'no
          +t-allowed' : 'pointer' }}                                     
      187 +              />                                              
      188 +            </th>                                             
      189              <th>Batch</th><th>Range</th><th>Frames</th><th>Rev
           iewed</th>
      190              <th>Shapes</th><th>Status</th><th />
      191            </tr>

● Update(frontend/src/pages/LibraryPage.jsx)
  ⎿  Added 18 lines
      199                <React.Fragment key={batch.id}>
      200                  <tr>
      201                    <td>
      202 +                    <input                                    
      203 +                      type="checkbox"                         
      204 +                      aria-label={`Select ${batch.batch_label}
          +`}                                                            
      205 +                      checked={selectedIds.includes(batch.id)}
      206 +                      disabled={(batch.review?.approved ?? 0) 
          +=== 0}                                                        
      207 +                      title={(batch.review?.approved ?? 0) ===
          + 0                                                            
      208 +                        ? 'No approved frame in this batch yet
          + — review it first'                                           
      209 +                        : `${batch.review.approved} approved f
          +rame(s) would be merged`}                                     
      210 +                      onChange={(event) =>                    
      211 +                        setSelectedIds((current) =>           
      212 +                          event.target.checked                
      213 +                            ? [...current, batch.id]          
      214 +                            : current.filter((id) => id !== ba
          +tch.id))                                                      
      215 +                      }                                       
      216 +                      style={{ cursor: (batch.review?.approved
          + ?? 0) === 0 ? 'not-allowed' : 'pointer' }}                   
      217 +                    />                                        
      218 +                  </td>                                       
      219 +                  <td>                                        
      220                      <span style={{ cursor: 'pointer', borderBo
           ttom: '1px dashed rgba(255,255,255,0.3)' }} onClick={() => edi
           tBatch(batch)} title="Click to rename batch">
      221                        {batch.date_label} · {batch.batch_label}
      222                      </span>

● Update(frontend/src/pages/LibraryPage.jsx)
  ⎿  Added 26 lines
      145      }
      146    }
      147  
      148 +  // Only a batch with at least one approved frame has anythin
          +g to merge; the                                               
      149 +  // rest are offered but not selectable, so the reason is vis
          +ible rather than                                              
      150 +  // discovered through an error.                             
      151 +  const approvable = batches.filter((batch) => (batch.review?.
          +approved ?? 0) > 0)                                           
      152 +  const approvableSelected = selectedIds.filter((id) =>       
      153 +    approvable.some((batch) => batch.id === id))              
      154 +                                                              
      155 +  async function approveSelected() {                          
      156 +    if (approvableSelected.length === 0) return               
      157 +    setApproving(true)                                        
      158 +    const failures = []                                       
      159 +    for (const id of approvableSelected) {                    
      160 +      try {                                                   
      161 +        await api.approveBatch(id)                            
      162 +      } catch (exc) {                                         
      163 +        const batch = batches.find((item) => item.id === id)  
      164 +        failures.push(`${batch?.batch_label ?? id}: ${exc.mess
          +age}`)                                                        
      165 +      }                                                       
      166 +    }                                                         
      167 +    setApproving(false)                                       
      168 +    setSelectedIds([])                                        
      169 +    // Report per batch: one failing batch must not hide the o
          +nes that worked.                                              
      170 +    if (failures.length) onError(`${failures.length} batch(es)
          + could not be merged — ${failures.join(' · ')}`)              
      171 +    onChanged()                                               
      172 +  }                                                           
      173 +                                                              
      174    if (batches.length === 0) return null
      175  
      176    return (

  Ran 1 shell command (ctrl+o to expand)

● Update(frontend/src/pages/LibraryPage.jsx)  ⎿  Added 2 lines
      45  
      46  export function BatchList({ project, batches, activeJobs, onCha
          nged, onError }) {
      47    const [busyId, setBusyId] = useState(null)
      48 +  const [selectedIds, setSelectedIds] = useState([])           
      49 +  const [approving, setApproving] = useState(false)            
      50  
      51    const [appendChoiceBatch, setAppendChoiceBatch] = useState(nu
          ll)
      52            const [autoAnnotateConfig, setAutoAnnotateConfig] = u
          seState(null)

  Searched for 1 pattern (ctrl+o to expand)

● Update(frontend/src/pages/LibraryPage.jsx)
  ⎿  Added 1 line, removed 1 line
      2  import React, { useCallback, useEffect, useRef, useState } from 
         'react'
      3  import { api, formatDuration } from '../api'
      4  import { navigate } from '../App'
      5 -import { AlertIcon, FolderIcon, ScissorsIcon, SearchIcon, ZapIco
        -n } from '../components/Icons'                                  
      5 +import { AlertIcon, CheckIcon, FolderIcon, ScissorsIcon, SearchI
        +con, ZapIcon } from '../components/Icons'                       
      6  
      7  
      8  

  Ran 1 shell command (ctrl+o to expand)

● Cek apakah handleAutoAnnotateAll jadi tidak terpakai setelah tombolnya saya
  ganti:

  Searched for 1 pattern, ran 2 shell commands (ctrl+o to expand)

● handleAutoAnnotateAll dan ZapIcon di file itu jadi yatim setelah tombolnya
  diganti. Saya bersihkan karena perubahan saya yang membuatnya tidak terpakai:

● Update(frontend/src/pages/LibraryPage.jsx)
  ⎿  Removed 20 lines
      127      }
      128    }
      129  
      130 -  async function handleAutoAnnotateAll() {                    
      131 -    if (!window.confirm(`Start SAM3 Auto-Annotation in paralle
          -l for ALL ${batches.length} batch(es)?`)) return              
      132 -    try {                                                     
      133 -      await Promise.all(                                      
      134 -        batches.map(b => api.startAutolabel(b.id, {           
      135 -          resume: false,                                      
      136 -          append: true,                                       
      137 -          engine: 'sam3',                                     
      138 -          threshold: 0.35,                                    
      139 -          iou_threshold: 0.0,                                 
      140 -          min_box_frac: 0.0,                                  
      141 -          target_class_names: project.classes.map(c => c.name)
      142 -        }))                                                   
      143 -      )                                                       
      144 -      onChanged()                                             
      145 -    } catch (exc) {                                           
      146 -      onError(exc.message)                                    
      147 -    }                                                         
      148 -  }                                                           
      149 -                                                              
      130    // Only a batch with at least one approved frame has anythin
           g to merge; the
      131    // rest are offered but not selectable, so the reason is vis
           ible rather than
      132    // discovered through an error.

  Ran 4 shell commands (ctrl+o to expand)

● Selesai. Container sudah di-rebuild (index-I65Ek6EH.js) — hard-refresh dulu.

  Yang berubah di tabel Batches:

  - Kolom checkbox di paling kiri, plus checkbox "pilih semua" di header tabel.
  - Tombol "Approve & Merge Selected (N)" menggantikan posisi Auto-Annotate All 
  Batches yang tadi dobel. Sekarang tombol auto-annotate-all hanya ada satu, di
  header halaman.
  - Checkbox mati otomatis untuk batch yang belum punya frame approved, dengan
  tooltip alasannya ("No approved frame in this batch yet — review it first").
  Jadi Anda tahu sebelum klik, bukan setelah dapat error.
  - Tooltip pada checkbox yang aktif menyebut berapa frame yang akan ikut
  ter-merge, misal "346 approved frame(s) would be merged".

  Kesepuluh batch Anda sekarang bisa dipilih semua, karena semuanya sudah punya
  frame approved — termasuk yang masih ada pending (batch016 punya 26 pending,
  batch8 punya 23). Yang pending itu ditinggalkan, sesuai permintaan Anda tadi:
  approve = simpan, tidak approve = ignore. Nanti kalau Anda review sisanya,
  batch yang sama bisa di-approve ulang untuk menambahkan frame baru itu.

  Sekali klik akan memasukkan 1.031 frame ke master dataset
  (113+346+164+32+32+69+51+62+82+80).

  Dua hal yang saya putuskan:

  - Approve dijalankan satu per satu, bukan paralel, dan kegagalan dilaporkan
  per batch. Kalau satu batch gagal, sembilan lainnya tetap jalan dan Anda
  diberi tahu persis mana yang gagal beserta alasannya — bukan satu error yang
  menutupi semuanya.
  - Saya hapus fungsi handleAutoAnnotateAll dan impor ZapIcon di LibraryPage.jsx
  yang jadi tidak terpakai setelah tombolnya diganti.

  Catatan, di luar yang Anda minta jadi saya tidak sentuh: tombol "🔄 Reset
  Auto" di tabel itu memakai emoji sebagai ikon, yang dilarang checklist UI di
  CLAUDE.md §7. Tinggal bilang kalau mau saya ganti ke ikon SVG.

  Belum saya uji dengan benar-benar mengklik Approve — itu operasi yang menulis
  ke master dataset dan tidak bisa dibatalkan, jadi saya serahkan ke Anda.
  Backup dari sesi tadi (data/backups/app-20260807-163509.db) masih utuh kalau
  hasilnya tidak sesuai harapan.

✻ Sautéed for 2m 8s

❯ in the data prep also create table so there is like "master dataset date x    
  time x" so there will be no conflict if i apllied twice the same batches      
  cause the master data set is different, but we can actualy choose them 2      
  master datasets and confirm to send to the models & training stages           

● Ini perubahan arsitektur, bukan sekadar tabel — jadi saya mau pastikan dulu
  maksudnya sebelum menulis kode. Saya cek dulu batasan yang ada sekarang:

  Ran 1 shell command (ctrl+o to expand)

● Bagian teknis intinya ada di sini: frame_id ... UNIQUE (db.py:89) — satu frame
  hanya boleh ada di satu dataset, itulah yang bikin konflik saat batch yang
  sama di-merge dua kali. Perlu jadi UNIQUE(dataset_id, frame_id).

  Kabar baik: perbaikan val-split jadi berbasis hash yang saya buat kemarin
  membuat ini aman. Frame yang sama selalu jatuh ke split yang sama di dataset
  mana pun, jadi menggabungkan 2 master dataset tidak akan membuat gambar yang
  sama muncul di train sekaligus val. Kalau split-nya masih posisional seperti
  dulu, fitur ini akan langsung merusak perbandingan base-vs-new.

  Empat hal yang saya perlu tahu:

● User answered Claude's questions:
  ⎿  · Apa yang sebenarnya membedakan master dataset A dan B? → Anotasi sudah 
     diperbaiki, Aturan triage berbeda, Kombinasi batch berbeda, Snapshot waktu
     · Kalau 2 dataset dipilih bersama dan ada frame yang sama di keduanya 
     dengan label berbeda, mana yang dipakai? → Yang paling baru menang 
     (Recommended)
     · Gambar tiap dataset disalin sendiri-sendiri atau berbagi file? → Salin 
     penuh tiap dataset
     · Dataset lama (yang sekarang) diapakan? → Jadikan 'Master Dataset #1'

● Semua alasan berlaku, jadi dataset-nya harus generik dan dinamai bebas. Saya
  cek kapasitas disk dulu karena Anda pilih salin penuh:

  Ran 1 shell command (ctrl+o to expand)

● Disk 1,5 TB kosong — salin penuh aman (~200 MB per dataset). Tapi dataset.py
  sudah 559 baris, lewat batas 400 di CLAUDE.md §6, dan saya akan mengubahnya
  banyak. Saya pecah sekalian.

  Mulai dari skema:

● Update(backend/db.py)
  ⎿  Added 14 lines, removed 2 lines
       83      )
       84      """,
       85      """
       86 +    CREATE TABLE IF NOT EXISTS datasets (                     
       87 +        id           INTEGER PRIMARY KEY AUTOINCREMENT,       
       88 +        project_id   INTEGER NOT NULL REFERENCES projects(id) 
          +ON DELETE CASCADE,                                            
       89 +        name         TEXT NOT NULL,                           
       90 +        note         TEXT NOT NULL DEFAULT '',                
       91 +        rule_version TEXT,                                    
       92 +        created_at   REAL NOT NULL                            
       93 +    )                                                         
       94 +    """,                                                      
       95 +    """                                                       
       96      CREATE TABLE IF NOT EXISTS dataset_items (
       97          id         INTEGER PRIMARY KEY AUTOINCREMENT,
       98          project_id INTEGER NOT NULL REFERENCES projects(id) ON
            DELETE CASCADE,
       89 -        frame_id   INTEGER NOT NULL REFERENCES frames(id) ON D
          -ELETE CASCADE UNIQUE,                                         
       99 +        dataset_id INTEGER NOT NULL REFERENCES datasets(id) ON
          + DELETE CASCADE,                                              
      100 +        frame_id   INTEGER NOT NULL REFERENCES frames(id) ON D
          +ELETE CASCADE,                                                
      101          split      TEXT NOT NULL CHECK (split IN ('train', 'va
           l')),
      102          image_rel  TEXT NOT NULL,
      103          label_rel  TEXT NOT NULL,
       93 -        added_at   REAL NOT NULL                              
      104 +        added_at   REAL NOT NULL,                             
      105 +        UNIQUE (dataset_id, frame_id)                         
      106      )
      107      """,
      108      """

● Migrasi untuk database yang sudah ada — SQLite tidak bisa mengubah constraint,
  jadi tabelnya harus dibangun ulang:

● Update(backend/db.py)
  ⎿  Added 55 lines
      207          if "rule_version" not in version_cols:
      208              cur.execute("ALTER TABLE model_versions ADD COLUMN
            rule_version TEXT")
      209  
      210 +        _migrate_dataset_items(cur)                           
      211  
      212 +                                                              
      213 +def _migrate_dataset_items(cur) -> None:                      
      214 +    """Move a single-dataset project onto named datasets.     
      215 +                                                              
      216 +    The old table pinned `frame_id UNIQUE`, so one frame could
          + live in exactly                                              
      217 +    one dataset — which is the constraint that made merging th
          +e same batch twice                                            
      218 +    a conflict. SQLite cannot drop a constraint, so the table 
          +is rebuilt and the                                            
      219 +    existing rows are adopted by a "Master Dataset #1" per pro
          +ject. Nothing is                                              
      220 +    discarded: a row without a home would be a merged frame th
          +e app can no                                                  
      221 +    longer see.                                               
      222 +    """                                                       
      223 +    cur.execute("PRAGMA table_info(dataset_items)")           
      224 +    columns = [column[1] for column in cur.fetchall()]        
      225 +    if not columns or "dataset_id" in columns:                
      226 +        return                                                
      227 +                                                              
      228 +    import time                                               
      229 +                                                              
      230 +    cur.execute("SELECT DISTINCT project_id FROM dataset_items
          +")                                                            
      231 +    projects_with_items = [row[0] for row in cur.fetchall()]  
      232 +    home = {}                                                 
      233 +    for project_id in projects_with_items:                    
      234 +        cur.execute(                                          
      235 +            """INSERT INTO datasets (project_id, name, note, c
          +reated_at)                                                    
      236 +               VALUES (?, 'Master Dataset #1', 'Adopted from t
          +he original single dataset', ?)""",                           
      237 +            (project_id, time.time()),                        
      238 +        )                                                     
      239 +        home[project_id] = cur.lastrowid                      
      240 +                                                              
      241 +    cur.execute("ALTER TABLE dataset_items RENAME TO dataset_i
          +tems_old")                                                    
      242 +    cur.execute("""                                           
      243 +        CREATE TABLE dataset_items (                          
      244 +            id         INTEGER PRIMARY KEY AUTOINCREMENT,     
      245 +            project_id INTEGER NOT NULL REFERENCES projects(id
          +) ON DELETE CASCADE,                                          
      246 +            dataset_id INTEGER NOT NULL REFERENCES datasets(id
          +) ON DELETE CASCADE,                                          
      247 +            frame_id   INTEGER NOT NULL REFERENCES frames(id) 
          +ON DELETE CASCADE,                                            
      248 +            split      TEXT NOT NULL CHECK (split IN ('train',
          + 'val')),                                                     
      249 +            image_rel  TEXT NOT NULL,                         
      250 +            label_rel  TEXT NOT NULL,                         
      251 +            added_at   REAL NOT NULL,                         
      252 +            UNIQUE (dataset_id, frame_id)                     
      253 +        )                                                     
      254 +    """)                                                      
      255 +    for project_id, dataset_id in home.items():               
      256 +        cur.execute(                                          
      257 +            """INSERT INTO dataset_items (project_id, dataset_
          +id, frame_id, split,                                          
      258 +                                          image_rel, label_rel
          +, added_at)                                                   
      259 +               SELECT project_id, ?, frame_id, split, image_re
          +l, label_rel, added_at                                        
      260 +               FROM dataset_items_old WHERE project_id = ?""",
      261 +            (dataset_id, project_id),                         
      262 +        )                                                     
      263 +    cur.execute("DROP TABLE dataset_items_old")               
      264 +                                                              
      265 +                                                              
      266  def healthy() -> bool:
      267      try:
      268          with cursor() as cur:

● Sekarang modul registry dataset-nya:

● Write(backend/datasets.py)
  ⎿  Wrote 175 lines to backend/datasets.py
       1 """Named master datasets — several per project, each a full stan
       2 
       3 One project used to have exactly one master dataset, so merging 
       4 was a conflict. Now a merge targets a *named* dataset, and the s
       5 into as many as you like: "batch7+8 strict rules" and "batch7+8 
         after I fixed
         ifferent labels.
       7 
       9 
         l}
     … +197 lines (ctrl+o to expand)

● Sekarang bagian terberat — dataset.py harus tahu dataset mana yang jadi
  tujuan. Saya baca utuh dulu supaya tidak salah potong:

  Read 1 file (ctrl+o to expand)
● Update(backend/dataset.py)
  ⎿  Added 23 lines, removed 10 lines
      24      pass
      25  
      26  
      27 -def dataset_dir(project_slug: str) -> str:                     
      28 -    return os.path.join(config.project_dir(project_slug), "data
         -set")                                                          
      27 +def dataset_dir(project_slug: str, dataset_id: int) -> str:    
      28 +    return datasets.dataset_root(project_slug, dataset_id)     
      29  
      30  
      31 -def approve(batch_id: int) -> dict:                            
      32 -    """Sign a batch off and queue its merge (REQ-045, REQ-050).
         -"""                                                            
      31 +def approve(batch_id: int, dataset_id: Optional[int] = None,   
      32 +            dataset_name: str = "") -> dict:                   
      33 +    """Sign a batch off and queue its merge into one named data
         +set (REQ-045, REQ-050).                                        
      34 +                                                               
      35 +    Without `dataset_id` a new dataset is created, so merging t
         +he same batch                                                  
      36 +    again never collides with the earlier result — it produces 
         +a second dataset                                               
      37 +    holding that batch as it looks now.                        
      38 +    """                                                        
      39      batch = batches.get(batch_id)
      40      if batch is None:
      41          raise DatasetError("No such batch")
      36 -    if batch["status"] == "merged" and _unmerged_approved(batch
         -_id) == 0:                                                     
      37 -        # Re-merging is allowed, because a partly-reviewed batc
         -h can be merged                                                
      38 -        # for the part that was done and finished later. Only r
         -efuse when there                                               
      39 -        # is genuinely nothing new to add.                     
      40 -        raise DatasetError("Every approved frame in this batch 
         -is already in the master dataset")                             
      42      if batch["review"]["approved"] == 0:
      43          raise DatasetError("No frame in this batch is approved 
          — there is nothing to merge")
      44      # Frames that are not approved — rejected or never looked a
          t — are simply
     ...
      53          if cur.fetchone() is not None:
      54              raise DatasetError("A merge for this batch is alrea
          dy queued")
      55  
      56 +    if dataset_id is None:                                     
      57 +        target = datasets.create(batch["project_id"], name=data
         +set_name,                                                      
      58 +                                 rule_version=triage.Resolver(b
         +atch["project_id"]).version())                                 
      59 +        dataset_id = target["id"]                              
      60 +    else:                                                      
      61 +        target = datasets.get(dataset_id)                      
      62 +        if target is None:                                     
      63 +            raise DatasetError("No such dataset")              
      64 +        if _unmerged_approved(batch_id, dataset_id) == 0:      
      65 +            raise DatasetError(                                
      66 +                f"Every approved frame of this batch is already
         + in “{target['name']}”")                                       
      67 +                                                               
      68      batches.set_status(batch_id, "approved")
      69      job = jobs.create(
      70          "merge",
      58 -        params={"batch_id": batch_id},                         
      71 +        params={"batch_id": batch_id, "dataset_id": dataset_id}
         +,                                                              
      72          project_id=batch["project_id"],
      73          batch_id=batch_id,
      74          message=f"{batch['date_label']}/{batch['batch_label']}"
          ,

● Update(backend/dataset.py)
  ⎿  Added 5 lines, removed 4 lines
      76      return job.to_dict()
      77  
      78  
      79 -def _unmerged_approved(batch_id: int) -> int:                  
      80 -    """Approved frames of this batch that are not in the datase
         -t yet."""                                                      
      79 +def _unmerged_approved(batch_id: int, dataset_id: int) -> int: 
      80 +    """Approved frames of this batch not yet in *this* dataset.
         +"""                                                            
      81      with db.cursor() as cur:
      82          cur.execute(
      83              """SELECT COUNT(*) FROM frames f
      84 -               LEFT JOIN dataset_items d ON d.frame_id = f.id  
      84 +               LEFT JOIN dataset_items d                       
      85 +                      ON d.frame_id = f.id AND d.dataset_id = ?
      86                 WHERE f.batch_id = ? AND f.review_status = 'appr
          oved' AND d.id IS NULL""",
      86 -            (batch_id,),                                       
      87 +            (dataset_id, batch_id),                            
      88          )
      89          return cur.fetchone()[0]
      90  

● Sekarang job merge-nya menulis ke dataset tujuan:

  Read 1 file (ctrl+o to expand)

● Update(backend/dataset.py)
  ⎿  Added 6 lines, removed 2 lines
      486      if batch is None:
      487          raise DatasetError("The batch disappeared before the m
           erge started")
      488      project = projects.get(batch["project_id"])
      489 -    root = dataset_dir(project["slug"])                       
      489 +    dataset_id = job.params["dataset_id"]                     
      490 +    target = datasets.get(dataset_id)                         
      491 +    if target is None:                                        
      492 +        raise DatasetError("The target dataset disappeared bef
          +ore the merge started")                                       
      493 +    root = dataset_dir(project["slug"], dataset_id)           
      494      for split in ("train", "val"):
      495          os.makedirs(os.path.join(root, "images", split), exist
           _ok=True)
      496          os.makedirs(os.path.join(root, "labels", split), exist
           _ok=True)
     ...
      498      frames = [f for f in batches.frames(batch["id"]) if f["rev
           iew_status"] == "approved"]
      499      source_dir = batches.frames_dir(project["slug"], batch["id
           "])
      500      job.progress(0, len(frames))
      497 -    job.log(f"Merging {len(frames)} approved frame(s) into the
          - master dataset")                                             
      501 +    job.log(f"Merging {len(frames)} approved frame(s) into “{t
          +arget['name']}”")                                             
      502  
      503      # Triage gates the merge (REQ-104): a frame carrying an ig
           nored shape never
      504      # enters the master dataset at all, so junk is stopped at 
           the door rather

● Update(backend/dataset.py)
  ⎿  Added 2 lines, removed 1 line
      528              annotations = resolved
      529  
      530          with db.cursor() as cur:
      531 -            cur.execute("SELECT 1 FROM dataset_items WHERE fra
          -me_id = ?", (frame["id"],))                                   
      531 +            cur.execute("SELECT 1 FROM dataset_items WHERE dat
          +aset_id = ? AND frame_id = ?",                                
      532 +                        (dataset_id, frame["id"]))            
      533              if cur.fetchone() is not None:
      534                  skipped += 1
      535                  job.progress(index + 1, len(frames))

  Read 1 file (ctrl+o to expand)

● Update(backend/dataset.py)
  ⎿  Added 5 lines, removed 4 lines
      550                  handle.write("\n".join(lines) + ("\n" if lines
            else ""))
      551  
      552              cur.execute(
      553 -                """INSERT INTO dataset_items (project_id, fram
          -e_id, split, image_rel,                                       
      554 -                                              label_rel, added
          -_at)                                                          
      555 -                   VALUES (?, ?, ?, ?, ?, ?)""",              
      556 -                (project["id"], frame["id"], split, image_rel,
          - label_rel, time.time()),                                     
      553 +                """INSERT INTO dataset_items (project_id, data
          +set_id, frame_id, split,                                      
      554 +                                              image_rel, label
          +_rel, added_at)                                               
      555 +                   VALUES (?, ?, ?, ?, ?, ?, ?)""",           
      556 +                (project["id"], dataset_id, frame["id"], split
          +, image_rel, label_rel,                                       
      557 +                 time.time()),                                
      558              )
      559          added[split] += 1
      560          job.progress(index + 1, len(frames))

● Update(backend/dataset.py)
  ⎿  Added 3 lines, removed 4 lines
      571          cur.execute("UPDATE batches SET status = 'merged', mer
           ged_at = ? WHERE id = ?",
      572                      (time.time(), batch["id"]))
      573  
      574 -    path = write_data_yaml(projects.get(project["id"]))       
      575 -    totals = summary(project["id"])["splits"]                 
      574 +    totals = datasets.get(dataset_id)["splits"]               
      575      job.log(f"Added {added['train']} train / {added['val']} va
           l"
      577 -            + (f", skipped {skipped} already merged" if skippe
          -d else "")                                                    
      576 +            + (f", skipped {skipped} already in this dataset" 
          +if skipped else "")                                           
      577              + (f", held back {triaged_out} by triage" if triag
           ed_out else ""))
      579 -    job.log(f"Master dataset now {totals['train']} train / {to
          -tals['val']} val — {path}")                                   
      578 +    job.log(f"“{target['name']}” now holds {totals['train']} t
          +rain / {totals['val']} val")                                  
  ⎿  You've hit your session limit · resets 5:10pm (Asia/Jakarta)
     /upgrade to increase your usage limit.

✻ Cogitated for 3m 4s