Update annotation class to make 'time' attribute public and enhance video time formatting in the main processing function. Adjust documentation to reflect changes in test batch progression.

_docs/_autopilot_state.md

@@ -4,7 +4,7 @@
 step: 2e
 name: Implement Tests
 status: in_progress
-sub_step: Batch 1 — AZ-138 test_infrastructure
+sub_step: Batch 2a — AZ-139, AZ-140, AZ-141, AZ-142
 retry_count: 0
 
 ## Step ↔ SubStep Reference
@@ -47,9 +47,9 @@ retry_count: 0
 
 ## Last Session
 date: 2026-03-23
-ended_at: Step 2d Decompose Tests — completed
+ended_at: Step 2e Implement Tests — Batch 1 complete, Batch 2a starting
 reason: session boundary
-notes: Decompose complete, implementation ready. 11 tasks, 35 complexity points, 3 batches. Next step: Implement Tests (Step 2e) via /implement skill.
+notes: Batch 1 (AZ-138 test_infrastructure) committed and pushed. AZ-138 transitioned to In Testing. AZ-139, AZ-140, AZ-141, AZ-142 transitioned to In Progress on Jira. Next: launch 4 implementer agents for Batch 2a (AZ-139, AZ-140, AZ-141, AZ-142), then Batch 2b (AZ-144, AZ-146, AZ-147), then Batch 3 (AZ-143, AZ-145, AZ-148). Jira cloudId: 1598226f-845f-4705-bcd1-5ed0c82d6119. Transition IDs: In Progress=21, In Testing=32, Done=31.
 
 ## Blockers
 - none

annotation.pxd

@@ -8,7 +8,7 @@ cdef class Detection:
 cdef class Annotation:
     cdef public str name
     cdef public str original_media_name
-    cdef long time
+    cdef public long time
     cdef public list[Detection] detections
     cdef public bytes image
 

main.py

@@ -177,10 +177,14 @@ def _post_annotation_to_service(token_mgr: TokenManager, media_id: str,
     try:
         token = token_mgr.get_valid_token()
         image_b64 = base64.b64encode(annotation.image).decode() if annotation.image else None
+        total_seconds = annotation.time // 1000 if annotation.time else 0
+        hours = total_seconds // 3600
+        minutes = (total_seconds % 3600) // 60
+        seconds = total_seconds % 60
         payload = {
             "mediaId": media_id,
             "source": 0,
-            "videoTime": f"00:00:{annotation.time // 1000:02d}" if annotation.time else "00:00:00",
+            "videoTime": f"{hours:02d}:{minutes:02d}:{seconds:02d}",
             "detections": [d.model_dump() for d in dtos],
         }
         if image_b64: