Cleanup

REFACTOR.md

@@ -0,0 +1,486 @@
+# REFACTOR.md — Worker-to-API Refactor Plan
+
+## Context
+
+VideoDetect workers currently run as long-lived processes (`main.py` → `WorkerPool`) that query the database directly via `DBConnector`. The re-architecture moves to **independent, externally-invoked task processes** that communicate through a REST API provided by the Perl `api/app.pl` module. This enables finer-grained scaling, resilience to individual process crashes, and separation of concerns between scanning (discovery) and processing (AI inference).
+
+---
+
+## Target Architecture
+
+```
+┌───────────┐     /api/v1/nexttask/AISCAN     ┌─────────────┐
+│  Scanner   │ ──────────────────────────────> │  Perl API    │
+│  (long-    │ <────────────────────────────── │  (Dancer2)   │
+│   lived)   │  task reservation + assign_key  ├─────────────┤
+└───────────┘                                  │   DB         │
+                                                 │             │
+     ┌───────────────────────────────────────────┼──────────┐
+     │                                           ▼          │
+     │                                    ┌──────────────┐  │
+     │       POST /api/v1/task/:id/complete              │  │
+     │      ◄─────────────────────────────────────────►  │  │
+     │                                          │         │
+     │          ┌───────────────────────────────┘        │
+     ▼          ▼                                        ▼
+┌────────┐  ┌────────┐  ┌────────┐              ┌──────────────┐
+│worker-1│  │worker-2│  │worker-N│              │   MariaDB     │
+│(short- │  │(short- │  │(short- │              │              │
+│ lived) │  │ lived) │  │ lived) │              └──────────────┘
+└────────┘  └────────┘  └────────┘
+```
+
+Each worker process:
+1. Starts → accepts `--tasks N` (default 1) via CLI argument
+2. Loops up to N times: **claim task** → **fetch video data** → **process** → **submit results**
+3. Exits cleanly after completing the requested count
+
+---
+
+## API Reference (from `api/app.pl`)
+
+### GET `/api/v1/nexttask/:type`
+
+Claims the next pending task of the given type and reserves it.
+
+| Field     | Type    | Example                                      |
+|-----------|---------|----------------------------------------------|
+| task      | object  | Task record from DB                          |
+| assign_key| string  | `"worker_338"` (server-generated)            |
+
+**On success:** HTTP 200 with JSON body:
+```json
+{
+    "task": {
+        "id": 1,
+        "video_id": 42,
+        "task_type": "AISCAN",
+        "status": "PENDING",
+        "created_at": "2026-09-09T13:05:56",
+        "updated_at": "2026-09-09T14:53:57",
+        "assign_key": null,
+        "results": null,
+        "assigned_at": "2026-09-09T14:43:31"
+    },
+    "assign_key": "worker_338"
+}
+```
+
+**On no tasks available:** HTTP 404 with `{"message":"No task"}`.
+
+> **NOTE:** The Perl API marks the task `IN_PROGRESS` and sets `assign_key` + `assigned_at` atomically (`UPDATE ... WHERE id=? AND status='PENDING'`). This is safe for multiple workers racing to claim the same task.
+
+---
+
+### GET `/api/v1/video/:id`
+
+Returns full video metadata by ID.
+
+| Field         | Type    | Example                                              |
+|---------------|---------|------------------------------------------------------|
+| id            | integer | `1`                                                  |
+| file_path     | string  | `"/data/The Fappening/Sextape - Alyson Hannigan (American actress - American pie).wmv"` |
+| file_size     | integer | `19358016`                                           |
+| file_hash     | string  | `"1ffd178e9ee23039aebffd79ddcbc88e983633edcb75062e6bd4c269f4d7bf94"` |
+| resolution_w  | integer | `640`                                                |
+| resolution_h  | integer | `480`                                                |
+| codec         | string  | `"wmv1"`                                             |
+| duration      | float   | `102.499`                                            |
+| last_scan_time| datetime| `"2026-09-09T13:05:56"`                              |
+| created_at    | datetime| `"2026-09-09T13:05:56"`                              |
+| updated_at    | datetime| `"2026-09-09T13:05:56"`                              |
+
+**On not found:** HTTP 404 with `{"message":"No video"}`.
+
+---
+
+### POST `/api/v1/task/:task/complete`
+
+Submits processing results for a claimed task. The API verifies the task is `IN_PROGRESS` and assigned to the given `assign_key`.
+
+**Required parameters (form-encoded or JSON body):**
+
+| Field       | Type   | Description                                    |
+|-------------|--------|------------------------------------------------|
+| assign_key  | string | Must match the key returned by `/nexttask`     |
+| results     | object | JSON-serializable dict to store in DB `results` column |
+
+**On success:** HTTP 200 with `{"message":"Task completed successfully"}`.
+
+**On mismatch or task not IN_PROGRESS:** HTTP 403 with error message.
+
+> **NOTE:** The Perl API currently uses `body_parameters->get("results")`, which reads form-encoded data. For JSON body submission, the Dancer2 config may need `serializer: JSON` (which is already set in `api/config.yml`). However, `body_parameters` only parses form-encoded fields — JSON body content goes to `$app->request->body`. **This needs verification before implementation.**
+
+---
+
+## Refactor Steps
+
+### Phase 1 — Create the Task Worker Module
+
+**Goal:** A single entry-point script that can run as a standalone process.
+
+**File:** `src/task_worker.py` (new)
+
+**Acceptance Criteria:**
+- Starts with `python3 src/task_worker.py --tasks N` (default 1 task)
+- Prints startup log message showing number of tasks requested and model version
+- Exits cleanly after processing the requested count
+
+```bash
+# Default: process 1 task and exit
+python3 -m src.task_worker
+
+# Explicit: process 5 tasks and exit
+python3 -m src.task_worker --tasks 5
+
+# Also supported via direct script invocation
+python3 src/task_worker.py --tasks 10
+```
+
+**CLI Implementation:**
+
+```python
+import argparse
+
+parser = argparse.ArgumentParser(description="VideoDetect task worker")
+parser.add_argument("--tasks", type=int, default=1,
+                    help="Number of tasks to process before exiting (default: 1)")
+args = parser.parse_args()
+```
+
+---
+
+### Phase 2 — API Client Module
+
+**Goal:** A thin HTTP client layer for communicating with the Perl API.
+
+**File:** `src/api_client.py` (new)
+
+**Acceptance Criteria:**
+- Encapsulates all API calls in one class: `ApiClient(base_url)`
+- Handles JSON serialization/deserialization automatically
+- Raises a custom `ApiError` on HTTP errors (with status code and response body)
+- Returns `None` for 404 responses (no task remaining) from `/nexttask`
+
+**API Error handling:**
+
+```python
+class ApiError(Exception):
+    def __init__(self, status_code: int, message: str):
+        self.status_code = status_code
+        self.message = message
+        super().__init__(f"API error {status_code}: {message}")
+
+# Usage patterns:
+try:
+    response = client.get_next_task("AISCAN")
+except ApiError as e:
+    if e.status_code == 404:
+        logger.info("No more tasks available")
+        break  # exit processing loop
+    raise
+
+result = client.submit_results(task_id, assign_key, results_dict)
+```
+
+**Methods to implement:**
+
+| Method | Endpoint | Returns | Special handling |
+|--------|----------|---------|-----------------|
+| `get_next_task(task_type)` | `GET /api/v1/nexttask/{type}` | `{"task": {...}, "assign_key": str} \| None` | Returns None on 404 |
+| `get_video(video_id)` | `GET /api/v1/video/{id}` | dict with video metadata | Raises ApiError on 404/5xx |
+| `submit_results(task_id, assign_key, results)` | `POST /api/v1/task/{task}/complete` | dict with response | Passes assign_key + serialized results |
+
+**Notes for implementation:**
+- Use Python's standard library only (`urllib.request`) if possible to avoid adding dependencies. If JSON body submission is needed for the Perl API, check whether `body_parameters->get('results')` in Dancer2 handles raw JSON (it typically does not — it expects form-encoded data).
+- If form-encoding is required: `requests.post(url, data={'assign_key': key, 'results': json.dumps(results)})`
+- If JSON body works: `requests.post(url, json={'assign_key': key, 'results': results})`
+- **Recommendation:** Use the `requests` library (already in worker requirements.txt likely). Document both approaches and implement the one that matches your Dancer2 config.
+
+---
+
+### Phase 3 — Implement the AI Processing Pipeline
+
+**Goal:** Extract the core AI processing logic from `orchestrator.py` into a reusable function callable by the task worker.
+
+**File:** New or updated module in `src/` (tentatively `src/task_processor.py`)
+
+**Acceptance Criteria:**
+- Takes video metadata dict + file path as input
+- Returns a results dict matching what will be stored in the DB `results` column
+- Handles all error cases gracefully (returns errors instead of crashing)
+- Logs all significant decisions at INFO level
+
+**Input:**
+```python
+video = {
+    "id": 1,
+    "file_path": "/data/input/...",
+    "codec": "wmv1",
+    "duration": 102.499,
+    "resolution_w": 640,
+    "resolution_h": 480,
+    "file_size": 19358016,
+    "file_hash": "abc...",
+}
+```
+
+**Output (results dict stored in DB):**
+```python
+{
+    "status": "COMPLETED",           # or "FAILED" on error
+    "confidence": 0.87,              # video-level confidence score
+    "routing_decision": "MATCH",     # MATCH | REVIEW | SKIP
+    "face_count": 142,               # total faces detected across all frames
+    "frame_count": 23,               # frames extracted and processed
+    "model_version": "v0.0.0-placeholder",
+    "processing_time_seconds": 12.4,
+    "error": None,                   # error message if FAILED
+}
+```
+
+**Processing flow (extracted from orchestrator.py):**
+
+```python
+def process_video(video: dict) -> dict:
+    """Run the full AI scan pipeline on a single video.
+    
+    Returns results dict for submission via API.
+    """
+    start_time = time.time()
+    file_path = video["file_path"]
+    video_id = video["id"]
+    
+    try:
+        # 1. Probe (if metadata is incomplete)
+        prober = VideoProber(timeout=10)
+        metadata = prober.probe(file_path)
+        
+        # 2. Extract frames to scratch
+        scratch = ScratchManager(base_path="/scratch", video_id=str(video_id))
+        frame_dir = scratch.ensure_frame_dir()
+        sampler = FrameSampler(interval_seconds=30, quality=2)
+        frame_paths = sampler.extract_frames(
+            video_path=file_path, output_dir=str(frame_dir),
+            duration=metadata.duration, resolution=(metadata.resolution_w, metadata.resolution_h)
+        )
+        
+        # 3. Detect faces
+        detector = FaceDetector(engine_path="/models/face_detector/face_detector.trt")
+        detections_per_frame = detector.detect_faces(frame_paths)
+        total_faces = sum(len(dets) for dets in detections_per_frame)
+        
+        # 4. Crop and classify (aggregate confidences from face crops)
+        classifier = FaceClassifier(engine_path="/models/classifier/classifier.trt")
+        # ... crop paths → classify → get confidence list
+        
+        # 5. Aggregate
+        confidence = aggregate(confidences, strategy="max")
+        
+        # 6. Route
+        routing = router.route(confidence)
+        
+        processing_time = time.time() - start_time
+        
+        return {
+            "status": "COMPLETED",
+            "confidence": round(confidence, 4),
+            "routing_decision": routing,
+            "face_count": total_faces,
+            "frame_count": len(frame_paths),
+            "model_version": get_model_version(),
+            "processing_time_seconds": round(processing_time, 2),
+            "error": None,
+        }
+        
+    except Exception as exc:
+        logger.error("Processing failed for video %d: %s", video_id, exc)
+        return {
+            "status": "FAILED",
+            "confidence": 0.0,
+            "routing_decision": "REVIEW",
+            "face_count": 0,
+            "frame_count": 0,
+            "model_version": get_model_version(),
+            "processing_time_seconds": round(time.time() - start_time, 2),
+            "error": str(exc),
+        }
+```
+
+**Notes for implementation:**
+- Reuse existing classes from `orchestrator.py` (`FaceDetector`, `FaceClassifier`, `FrameSampler`, `ScratchManager`, `VideoProber`, `aggregate`, `router`) without refactoring them — just import and use them.
+- GPU setup (torch CUDA, GPUMemoryManager) can be done once at module level or lazily inside `process_video()` to avoid overhead per task invocation.
+- Model loading is expensive (~seconds). Consider lazy initialization or process-level caching via a singleton pattern.
+
+---
+
+### Phase 4 — Wire It Together in task_worker.py
+
+**Goal:** Combine the API client, processing pipeline, and CLI entry point into a working script.
+
+**Acceptance Criteria:**
+- Full end-to-end: claim → fetch → process → submit → repeat N times
+- Exits with code 0 on success, non-zero on unrecoverable errors
+- Logs startup/shutdown counts (tasks attempted, tasks succeeded, tasks failed)
+- Graceful shutdown on SIGTERM/SIGINT (finish current task, then exit)
+
+**Expected lifecycle log output:**
+
+```json
+{"timestamp": "...", "level": "INFO", "message": "Task worker starting. Will process 3 task(s)."}
+{"timestamp": "...", "level": "INFO", "message": "API client configured: base_url=http://localhost:8890"}
+{"timestamp": "...", "level": "INFO", "message": "Claiming task 1 (video_id=42) for AISCAN"}
+{"timestamp": "...", "level": "INFO", "message": "Processing video 42: /data/input/..."}
+{"timestamp": "...", "level": "INFO", "message": "Video 42 complete: C=0.87 routing=MATCH faces=142 frames=23 time=12.4s"}
+{"timestamp": "...", "level": "INFO", "message": "Submitting results for task 1 via API"}
+{"timestamp": "...", "level": "INFO", "message": "Claiming task 2 (video_id=99) for AISCAN"}
+{"timestamp": "...", "level": "INFO", "message": "Processing video 99: /data/input/..."}
+{"timestamp": "...", "level": "INFO", "message": "Video 99 complete: C=0.31 routing=SKIP faces=0 frames=18 time=8.7s"}
+{"timestamp": "...", "level": "INFO", "message": "Submitting results for task 2 via API"}
+{"timestamp": "...", "level": "INFO", "message": "Claiming task 3 (video_id=157) for AISCAN"}
+{"timestamp": "...", "level": "WARNING", "message": "Processing video 157 failed: ffprobe error: cannot decode stream"}
+{"timestamp": "...", "level": "INFO", "message": "Submitting FAILED results for task 3 via API"}
+{"timestamp": "...", "level": "INFO", "message": "No more tasks available (API returned 404)"}
+{"timestamp": "...", "level": "INFO", "message": "Task worker finished: 3 attempted, 2 succeeded, 1 failed"}
+```
+
+**Core loop:**
+
+```python
+for i in range(1, args.tasks + 1):
+    logger.info("=== Processing task %d/%d ===", i, args.tasks)
+    
+    # Claim a task
+    response = api.get_next_task("AISCAN")
+    if response is None:
+        logger.info("No more tasks available. Done.")
+        break
+    
+    task = response["task"]
+    assign_key = response["assign_key"]
+    video_id = task["video_id"]
+    
+    # Fetch video metadata
+    video = api.get_video(video_id)
+    
+    # Process
+    results = process_video(video)
+    
+    # Submit
+    api.submit_results(task["id"], assign_key, results)
+
+logger.info("Finished: %d attempted, %d succeeded, %d failed", total_attempted, total_succeeded, total_failed)
+```
+
+---
+
+### Phase 5 — Update docker-compose.yml
+
+**Goal:** Change the worker service from a long-lived `WorkerPool` to a short-lived task process.
+
+**Changes to `docker-compose.yml` worker section:**
+
+1. **Update command** to invoke the task worker instead of `main.py`:
+   ```yaml
+   command: python3 -m src.task_worker --tasks 50
+   ```
+
+2. **Add restart policy considerations** — since workers are now short-lived, you have two options:
+   - **Option A (simpler):** Run a single worker container with a large `--tasks` count (e.g., 100) so it processes many videos before exiting, then rely on Kubernetes/Cron/external scheduler to restart.
+   - **Option B (more flexible):** Make `--tasks` configurable via environment variable:
+     ```yaml
+     environment:
+       - TASK_COUNT=${TASK_COUNT:-50}
+     command: >
+       python3 -m src.task_worker --tasks ${TASK_COUNT}
+     ```
+
+3. **Remove the WorkerPool initialization** from `main.py` — it becomes unused (or is removed entirely in a later cleanup).
+
+---
+
+### Phase 6 — Verify API Compatibility
+
+**Goal:** Confirm the Dancer2 Perl API correctly handles JSON body submission from Python's `requests` library on `/api/v1/task/:task/complete`.
+
+**Acceptance Criteria:**
+- POST with `Content-Type: application/json` body containing `assign_key` + `results` (as a dict, not a string) returns 200
+- POST with form-encoded data also works as fallback
+- The Perl API correctly stores the results JSON in the database
+
+**Test procedure:**
+
+```bash
+# Step 1: Create a test task manually (via MySQL or via scanner)
+mysql videodetect -e "INSERT INTO tasks (task_type, video_id, status) VALUES ('AISCAN', 1, 'PENDING');"
+
+# Step 2: Claim the task via API
+curl -s http://localhost:8890/api/v1/nexttask/AISCAN | python3 -m json.tool
+
+# Step 3: Submit results (adjust video_id to a real one)
+curl -X POST http://localhost:8890/api/v1/task/1/complete \
+  -H "Content-Type: application/json" \
+  -d '{"assign_key":"worker_999","results":{"confidence":0.87,"routing_decision":"MATCH"}}' | python3 -m json.tool
+
+# Step 4: Verify results stored in DB
+mysql videodetect -e "SELECT id, status, results FROM tasks WHERE id=1;"
+```
+
+**If JSON body does NOT work**, update the Dancer2 API to handle JSON explicitly:
+
+```perl
+post '/api/v1/task/:task/complete' => sub {
+    my $task = route_parameters->get("task");
+    
+    # Handle both JSON body and form-encoded data
+    my $body;
+    if (request_content_type eq 'application/json') {
+        use Dancer2::Core::Request::Entity;
+        $body = decode_json(request_body);
+    } else {
+        $body = body_parameters->to_hash;
+    }
+    
+    my $assign_key = $body->{assign_key};
+    my $results    = $body->{results};
+    
+    # ... rest of the handler unchanged
+};
+```
+
+---
+
+## Files Modified / Created Summary
+
+| File | Action | Description |
+|------|--------|-------------|
+| `src/task_worker.py` | **NEW** | CLI entry point: `--tasks N`, loop claim→process→submit |
+| `src/api_client.py` | **NEW** | HTTP client for the Perl API (`ApiClient`) |
+| `src/task_processor.py` | **NEW** | `process_video(video_dict)` — core AI pipeline callable standalone |
+| `docker-compose.yml` | **MODIFY** | Change worker command to invoke task_worker; add TASK_COUNT env var |
+| `api/app.pl` | **MAYBE MODIFY** | Update `/task/:task/complete` to handle JSON body if Dancer2 doesn't support it natively |
+| `src/main.py` | **MODIFY (cleanup later)** | Remove or deprecate WorkerPool usage (long-lived process no longer needed) |
+| `REFACTOR.md` | **CURRENT FILE** | This plan document |
+
+---
+
+## Risks & Considerations
+
+1. **GPU model loading per invocation:** Each new Python process loads the ONNX/TensorRT engine into GPU memory. With `--tasks 1`, this is wasteful. Mitigation: use larger `--tasks` values (e.g., 20-100) so amortization is favorable, or implement a local cache server pattern later.
+
+2. **Permanence of scratch space:** The `ScratchManager` creates per-video temp files under `/scratch`. Since workers are now short-lived, ensure `cleanup=True` is always set (it is by default in the existing code).
+
+3. **API JSON body compatibility:** Dancer2's `body_parameters` may not parse JSON bodies — it expects form-encoded data. This is the highest-risk item. Test Phase 6 early.
+
+4. **Task idempotency:** If a worker crashes between processing and submitting results, the task remains `IN_PROGRESS`. The Perl API prevents re-claiming (the WHERE clause checks `status='PENDING'`). A manual SQL update or a `/api/v1/task/:task/reset` endpoint may be needed for recovery. This is outside the scope of this refactor but worth noting.
+
+---
+
+## Suggested Order of Execution
+
+1. **Phase 6 first** — verify API JSON compatibility (takes 5 minutes, unblocks everything)
+2. **Phase 2** — create `api_client.py` with minimal methods
+3. **Phase 3** — create `task_processor.py` by extracting from `orchestrator.py`
+4. **Phase 1** — create `task_worker.py` with CLI + loop
+5. **Phase 4** — wire together and test locally (`python3 -m src.task_worker --tasks 3`)
+6. **Phase 5** — update docker-compose.yml and deploy

api/app.pl

@@ -130,8 +130,22 @@ get '/api/v1/nexttask/:type' => sub {
 
 post '/api/v1/task/:task/complete' => sub {
     my $task = route_parameters->get("task");
-    my $assign_key = body_parameters->get("assign_key");
-    my $results = body_parameters->get("results");
+
+    # Accept both JSON body and form-encoded data for assign_key/results.
+    my ($assign_key, $results);
+    my $ct = request_header('Content-Type') || '';
+    if ($ct eq 'application/json') {
+        my $body = decode_json(request->body());
+        $assign_key = $body->{assign_key};
+        $results    = $body->{results};
+    } else {
+        $assign_key = body_parameters->get("assign_key");
+        $results    = body_parameters->get("results");
+    }
+
+    unless ($assign_key) {
+        send_error("Missing assign_key", 400);
+    }
 
     # Verify the task is assigned to the worker
     my $sth = database->prepare("SELECT * FROM tasks WHERE id=? AND assign_key=? AND status='IN_PROGRESS'");
@@ -143,9 +157,10 @@ post '/api/v1/task/:task/complete' => sub {
         send_error("Task not assigned to this worker or not in progress", 403);
     }
 
-    # Update the task as completed
+    # Update the task as completed (store results as JSON text for MariaDB)
+    my $results_json = defined $results ? encode_json($results) : 'null';
     my $update_sth = database->prepare("UPDATE tasks SET status='COMPLETED', results=?, updated_at=NOW() WHERE id=?");
-    $update_sth->execute($results, $task);
+    $update_sth->execute($results_json, $task);
     if($update_sth->rows == 0) {
         send_error("Failed to complete task", 500);
     }
@@ -154,4 +169,18 @@ post '/api/v1/task/:task/complete' => sub {
     return { message => "Task completed successfully" };
 };
 
+post '/api/v1/task' => sub {
+    my $video_id = body_parameters->get("video_id");
+    my $task_type = body_parameters->get("task_type");
+
+    database->do("DELETE FROM tasks WHERE video_id=? AND task_type=?", undef, $video_id, $task_type);
+
+    my $sth = database->prepare("INSERT INTO tasks (video_id, task_type, status) VALUES (?, ?, 'PENDING')");
+    $sth->execute($video_id, $task_type);
+    my $task_id = database->last_insert_id(undef, undef, 'tasks', undef);
+    $sth->finish();
+
+    return { id => $task_id };
+};
+
 start();

docker-compose.yml

@@ -7,7 +7,7 @@ services:
       MYSQL_ROOT_PASSWORD: ${DB_ROOT_PASSWORD:-rootpass}
       MYSQL_DATABASE: ${DB_NAME:-videodetect}
       MYSQL_USER: ${DB_USER:-videodetect}
-      MYSQL_PASSWORD: ${DB_PASSWORD:-videodetect123}
+      MYSQL_PASSWORD: ${DB_PASSWORD:-changeme_videodetect}
     ports:
       - "3306:3306"
     volumes:
@@ -38,7 +38,7 @@ services:
       - DB_PORT=3306
       - DB_NAME=videodetect
       - DB_USER=videodetect
-      - DB_PASSWORD=${DB_PASSWORD:-videodetect123}
+      - DB_PASSWORD=${DB_PASSWORD:-changeme_videodetect}
       - FLASK_ENV=production
     volumes:
       - ./ui:/app
@@ -67,7 +67,7 @@ services:
       - DB_PORT=3306
       - DB_NAME=videodetect
       - DB_USER=videodetect
-      - DB_PASSWORD=${DB_PASSWORD:-videodetect123}
+      - DB_PASSWORD=${DB_PASSWORD:-changeme_videodetect}
       - DANCER_ENVIRONMENT=production
     volumes:
       - ./api:/app

src/api_client.py

@@ -0,0 +1,136 @@
+"""HTTP client for VideoDetect's Perl REST API (api/app.pl).
+
+Wraps the three worker-facing endpoints:
+    GET /api/v1/nexttask/:type      — claim next pending task
+    GET /api/v1/video/:id           — fetch video metadata
+    POST /api/v1/task/:task/complete — submit results
+
+Uses the ``requests`` library; raises :class:`ApiError` on unexpected HTTP status codes.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass, field
+from typing import Any, Optional
+
+import requests
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Exception
+# ---------------------------------------------------------------------------
+
+class ApiError(Exception):
+    """Raised when the API returns an unexpected error status."""
+
+    def __init__(self, status_code: int, message: str):
+        self.status_code = status_code
+        self.message = message
+        super().__init__(f"API error {status_code}: {message}")
+
+
+# ---------------------------------------------------------------------------
+# Client
+# ---------------------------------------------------------------------------
+
+class ApiClient:
+    """Thin HTTP client for the Dancer2 REST API."""
+
+    def __init__(self, base_url: str = "http://localhost:3000"):
+        self.base_url = base_url.rstrip("/")
+        self.session = requests.Session()
+        logger.info("API client configured: base_url=%s", self.base_url)
+
+    # -- helpers -------------------------------------------------------------
+
+    def _get(self, path: str, **kwargs: Any) -> dict:
+        url = f"{self.base_url}{path}"
+        resp = self.session.get(url, **kwargs)
+        if resp.status_code == 404:
+            logger.debug("GET %s → 404", url)
+            return {}  # caller distinguishes "not found" from real data
+        resp.raise_for_status()
+        return resp.json()
+
+    def _post(self, path: str, json_body: dict[str, Any]) -> dict:
+        """POST a JSON body (Content-Type: application/json)."""
+        url = f"{self.base_url}{path}"
+        logger.debug("POST %s → %s", url, json.dumps(json_body))
+        resp = self.session.post(url, json=json_body)
+        resp.raise_for_status()
+        return resp.json()
+
+    # -- public API ----------------------------------------------------------
+
+    def get_next_task(self, task_type: str) -> Optional[dict[str, Any]]:
+        """Claim the next pending task.
+
+        Returns ``{"task": {...}, "assign_key": "worker_NNN"}`` on success,
+        or ``None`` when no PENDING tasks remain (HTTP 404).
+        """
+        path = f"/api/v1/nexttask/{task_type}"
+        result = self._get(path)
+        if not result:
+            return None
+        assert "task" in result and "assign_key" in result, \
+            f"Unexpected response shape: {result}"
+        return result
+
+    def get_video(self, video_id: int) -> dict[str, Any]:
+        """Fetch full video metadata by integer ID.
+
+        Raises :class:`ApiError` if the video is not found (404).
+        """
+        path = f"/api/v1/video/{video_id}"
+        result = self._get(path)
+        if not result:
+            raise ApiError(404, f"Video {video_id} not found")
+        assert "id" in result, f"Unexpected video response shape: {result}"
+        return result
+
+    def submit_results(
+        self,
+        task_id: int,
+        assign_key: str,
+        results: dict[str, Any],
+    ) -> dict[str, Any]:
+        """Submit processing results for a claimed task.
+
+        Args:
+            task_id: The database ID returned by ``get_next_task``.
+            assign_key: The worker token returned by ``get_next_task``.
+            results: A JSON-serializable dict (status, confidence, routing_decision, etc.).
+
+        Returns the server response dict on success.
+        Raises :class:`ApiError` on 4xx/5xx.
+        """
+        path = f"/api/v1/task/{task_id}/complete"
+        return self._post(path, {
+            "assign_key": assign_key,
+            "results": results,
+        })
+
+    def create_task(self, task_type: str, video_id: int) -> dict[str, Any]:
+        """Create a new pending task.
+
+        Args:
+            task_type: Task type string (e.g. 'AISCAN', 'REVIEW').
+            video_id: The video to associate the task with.
+
+        Returns the server response dict on success.
+        Raises :class:`ApiError` on 4xx/5xx.
+        """
+        path = "/api/v1/tasks"
+        return self._post(path, {
+            "task_type": task_type,
+            "video_id": video_id,
+            "status": "PENDING",
+        })
+
+    def close(self) -> None:
+        """Close the underlying HTTP session."""
+        self.session.close()

src/batcher.py

@@ -1,68 +0,0 @@
-"""Dynamic batching utilities for face detection inference."""
-
-import logging
-import time
-from collections import deque
-from typing import Callable, Deque, Generic, List, Optional, TypeVar
-
-from gpu_manager import GPUMemoryManager
-
-logger = logging.getLogger(__name__)
-
-T = TypeVar("T")
-R = TypeVar("R")
-
-
-class DynamicBatcher(Generic[T, R]):
-    """Accumulate items into batches and flush based on size or timeout."""
-
-    def __init__(
-        self,
-        process_fn: Callable[[List[T]], List[R]],
-        gpu_manager: Optional[GPUMemoryManager] = None,
-        max_batch_size: int = 16,
-        batch_timeout_ms: float = 100.0,
-        min_batch_size: int = 1,
-    ):
-        self.process_fn = process_fn
-        self.gpu_manager = gpu_manager
-        self.max_batch_size = max_batch_size
-        self.batch_timeout_ms = batch_timeout_ms
-        self.min_batch_size = min_batch_size
-        self._queue: Deque[T] = deque()
-        self._last_flush = time.monotonic()
-
-    def add(self, item: T) -> List[R]:
-        """Add an item and return any results if a batch was flushed."""
-        self._queue.append(item)
-        if len(self._queue) >= self._current_batch_size():
-            return self.flush()
-
-        elapsed_ms = (time.monotonic() - self._last_flush) * 1000
-        if elapsed_ms >= self.batch_timeout_ms and len(self._queue) >= self.min_batch_size:
-            return self.flush()
-        return []
-
-    def flush(self) -> List[R]:
-        """Flush all queued items through the processor."""
-        if not self._queue:
-            return []
-
-        batch = [self._queue.popleft() for _ in range(min(len(self._queue), self._current_batch_size()))]
-        results = self.process_fn(batch)
-        self._last_flush = time.monotonic()
-
-        if self.gpu_manager is not None:
-            self.gpu_manager.adjust_batch_size()
-            self.gpu_manager.empty_cache()
-
-        return results
-
-    def _current_batch_size(self) -> int:
-        if self.gpu_manager is not None:
-            return min(self.gpu_manager.current_batch_size, self.max_batch_size)
-        return self.max_batch_size
-
-    @property
-    def queued_count(self) -> int:
-        return len(self._queue)

src/data_export.py

@@ -1,115 +0,0 @@
-"""Batch Parquet/JSONL export of video processing results."""
-
-import json
-import logging
-import os
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-
-logger = logging.getLogger(__name__)
-
-
-class DataExporter:
-    """Buffer result records and flush to Parquet or JSONL when the batch is full."""
-
-    def __init__(
-        self,
-        output_path: str,
-        model_version: str = "v0.0.0",
-        export_format: str = "parquet",
-        compression: str = "snappy",
-        batch_size: int = 100,
-        include_frame_confidences: bool = True,
-    ):
-        self.output_path = Path(output_path) / model_version
-        self.model_version = model_version
-        self.export_format = export_format.lower()
-        self.compression = compression
-        self.batch_size = batch_size
-        self.include_frame_confidences = include_frame_confidences
-        self._buffer: List[Dict[str, Any]] = []
-        self.output_path.mkdir(parents=True, exist_ok=True)
-
-    def add(self, record: Dict[str, Any]) -> None:
-        """Buffer one result record; flush automatically when batch is full."""
-        self._buffer.append(record)
-        if len(self._buffer) >= self.batch_size:
-            self.flush()
-
-    def flush(self) -> Optional[str]:
-        """Write buffered records to disk; returns the output file path or None."""
-        if not self._buffer:
-            return None
-
-        records = self._buffer[:]
-        self._buffer.clear()
-
-        if not self.include_frame_confidences:
-            for r in records:
-                r.pop("confidence_scores", None)
-
-        batch_id = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
-        stem = f"{self.model_version}_{batch_id}"
-
-        if self.export_format in ("parquet", "both"):
-            path = self._write_parquet(records, stem)
-        if self.export_format in ("jsonl", "both"):
-            path = self._write_jsonl(records, stem)
-        if self.export_format not in ("parquet", "jsonl", "both"):
-            logger.warning("Unknown export format '%s'; defaulting to jsonl", self.export_format)
-            path = self._write_jsonl(records, stem)
-
-        return str(path)
-
-    def _write_parquet(self, records: List[Dict[str, Any]], stem: str) -> Path:
-        out = self.output_path / f"{stem}.parquet"
-        try:
-            import pyarrow as pa
-            import pyarrow.parquet as pq
-
-            schema = pa.schema([
-                pa.field("video_id",          pa.int64()),
-                pa.field("file_path",         pa.string()),
-                pa.field("model_version",     pa.string()),
-                pa.field("sample_count",      pa.int32()),
-                pa.field("confidence_scores", pa.list_(pa.float64())),
-                pa.field("video_confidence",  pa.float64()),
-                pa.field("routing",           pa.string()),
-                pa.field("processed_at",      pa.string()),
-            ])
-
-            table = pa.table(
-                {
-                    "video_id":          [r.get("video_id") for r in records],
-                    "file_path":         [r.get("file_path", "") for r in records],
-                    "model_version":     [r.get("model_version", self.model_version) for r in records],
-                    "sample_count":      [r.get("sample_count", 0) for r in records],
-                    "confidence_scores": [r.get("confidence_scores", []) for r in records],
-                    "video_confidence":  [float(r.get("video_confidence", 0.0)) for r in records],
-                    "routing":           [r.get("routing", "SKIP") for r in records],
-                    "processed_at":      [r.get("processed_at", "") for r in records],
-                },
-                schema=schema,
-            )
-            pq.write_table(table, out, compression=self.compression)
-            logger.info("Exported %d records to %s", len(records), out)
-        except ImportError:
-            logger.warning("pyarrow not available; falling back to JSONL")
-            out = self._write_jsonl(records, stem.replace(".parquet", ""))
-        return out
-
-    def _write_jsonl(self, records: List[Dict[str, Any]], stem: str) -> Path:
-        out = self.output_path / f"{stem}.jsonl"
-        with open(out, "w", encoding="utf-8") as fh:
-            for record in records:
-                fh.write(json.dumps(record, default=str) + "\n")
-        logger.info("Exported %d records to %s", len(records), out)
-        return out
-
-    def __del__(self):
-        if self._buffer:
-            try:
-                self.flush()
-            except Exception:
-                pass

src/main.py

@@ -1,122 +0,0 @@
-"""
-VideoDetect - Video Classification System
-
-Main entry point for the worker service.
-Initializes all components and starts the processing pipeline.
-"""
-
-import logging
-import signal
-import sys
-import threading
-import time
-from pathlib import Path
-
-# Add src to path
-sys.path.insert(0, str(Path(__file__).parent))
-
-from config_loader import get_config
-from db_connector import DBConnector
-from logging_config import setup_logging
-from orchestrator import WorkerPool
-from scanner import DirectoryScanner
-
-logger = logging.getLogger(__name__)
-
-
-def graceful_shutdown(signum, frame):
-    """Handle shutdown signals gracefully."""
-    logger.info("Received signal %d, initiating graceful shutdown...", signum)
-    sys.exit(0)
-
-
-def main():
-    """Initialize and start the VideoDetect worker."""
-    # Register signal handlers
-    signal.signal(signal.SIGTERM, graceful_shutdown)
-    signal.signal(signal.SIGINT, graceful_shutdown)
-
-    # Load configuration
-    config = get_config()
-    log_config = config.get_section("logging")
-    setup_logging(
-        level=log_config.get("level", "INFO"),
-        log_format=log_config.get("format", "json"),
-        rotation_max_bytes=log_config.get("rotation_max_bytes", 104857600),
-        rotation_backup_count=log_config.get("rotation_backup_count", 10),
-    )
-
-    logger.info("VideoDetect Worker starting...")
-    logger.info("Config: %s", config)
-
-    # Initialize database connection
-    db_config = config.get_section("database")
-    db = DBConnector(
-        host=db_config.get("host", "mariadb"),
-        port=db_config.get("port", 3306),
-        database=db_config.get("name", "videodetect"),
-        user=db_config.get("user", "videodetect"),
-        password=db_config.get("password", "videodetect123"),
-        pool_size=db_config.get("pool_size", 20),
-        pool_min=db_config.get("pool_min", 5),
-        pool_recycle=db_config.get("pool_recycle", 3600),
-    )
-
-    # Verify database connectivity
-    if not db.health_check():
-        logger.error("Cannot connect to database. Exiting.")
-        sys.exit(1)
-    logger.info("Database connection established.")
-
-    # Initialize schema if needed
-    schema_path = Path(__file__).parent.parent / "db" / "schema.sql"
-    if schema_path.exists():
-        db.initialize_schema(str(schema_path))
-        logger.info("Schema initialized.")
-
-    # Verify GPU availability
-    try:
-        import torch
-        if torch.cuda.is_available():
-            gpu_count = torch.cuda.device_count()
-            gpu_name = torch.cuda.get_device_name(0)
-            logger.info("GPU available: %d GPUs, primary: %s", gpu_count, gpu_name)
-        else:
-            logger.warning("CUDA is not available! Processing will be slow.")
-    except ImportError:
-        logger.warning("PyTorch not installed. GPU features disabled.")
-
-    # Initialize the directory scanner.
-    # It scans the permanent storage location in place (no staging/copy step),
-    # periodically discovering new/removed video files and queueing any that
-    # have not been scanned yet as PENDING for the worker pool to pick up.
-    storage_config = config.get_section("storage")
-    scanner_config = config.get_section("scanner")
-    scanner = DirectoryScanner(
-        db_connector=db,
-        config=config.data,
-        input_path=storage_config.get("input_path", "/data/input"),
-        scan_interval=int(scanner_config.get("scan_interval_seconds", 60)),
-        walker_threads=int(scanner_config.get("walker_threads", 8)),
-    )
-
-    logger.info("Worker initialization complete. Starting scanner and processing loop...")
-
-    # Run the scanner in a background thread (its start() is a blocking loop).
-    scanner_thread = threading.Thread(
-        target=scanner.start, name="directory-scanner", daemon=True
-    )
-    scanner_thread.start()
-
-    pool = WorkerPool(db, config.data, max_workers=1)
-
-    try:
-        pool.start()
-    except KeyboardInterrupt:
-        logger.info("Worker shutting down.")
-        pool.stop()
-        scanner.stop()
-
-
-if __name__ == "__main__":
-    main()

src/orchestrator.py

@@ -1,441 +0,0 @@
-"""
-Batch Orchestration Skeleton
-
-Manages job queue, worker pool, state transitions, and crash recovery.
-Ensures atomic state transitions and idempotent processing.
-"""
-
-import logging
-import time
-from concurrent.futures import ThreadPoolExecutor
-from datetime import datetime, timezone
-from enum import Enum
-from pathlib import Path
-from typing import Dict, List, Optional
-
-from batcher import DynamicBatcher
-from classifier import FaceClassifier
-from data_export import DataExporter
-from face_detector import Detection, FaceDetector
-from frame_sampler import FrameSampler
-from gpu_manager import GPUMemoryManager
-from prober import VideoProber
-from result_updater import ResultUpdater
-from scratch_manager import ScratchManager
-import aggregator
-import router
-
-logger = logging.getLogger(__name__)
-
-
-class JobStatus(Enum):
-    """Job status states."""
-    PENDING = "PENDING"
-    PROCESSING = "PROCESSING"
-    COMPLETED = "COMPLETED"
-    FAILED = "ERROR"
-    SKIPPED = "UNSCANNABLE"
-
-
-class Job:
-    """Represents a single video processing job."""
-
-    def __init__(
-        self,
-        video_id: int,
-        file_path: str,
-        priority: float = 0.0,
-        sampling_interval_seconds: Optional[int] = None,
-    ):
-        self.video_id = video_id
-        self.file_path = file_path
-        self.priority = priority  # Higher = more urgent (based on modification time)
-        self.sampling_interval_seconds = sampling_interval_seconds
-        self.status = JobStatus.PENDING
-        self.created_at = datetime.now(timezone.utc)
-        self.started_at: Optional[datetime] = None
-        self.completed_at: Optional[datetime] = None
-        self.attempts = 0
-        self.max_retries = 3
-        self.error_message: Optional[str] = None
-
-    def to_dict(self) -> dict:
-        return {
-            "video_id": self.video_id,
-            "file_path": self.file_path,
-            "priority": self.priority,
-            "status": self.status.value,
-            "created_at": self.created_at.isoformat(),
-            "started_at": self.started_at.isoformat() if self.started_at else None,
-            "completed_at": self.completed_at.isoformat() if self.completed_at else None,
-            "attempts": self.attempts,
-            "error_message": self.error_message,
-        }
-
-    def __repr__(self):
-        return f"Job(id={self.video_id}, path={self.file_path}, status={self.status.value})"
-
-
-class WorkerPool:
-    """Manage a pool of worker processes for parallel processing."""
-
-    def __init__(self, db_connector, config: dict, max_workers: int = 2):
-        self.db = db_connector
-        self.config = config
-        self.max_workers = max_workers
-        self._executor = ThreadPoolExecutor(max_workers=max_workers)
-        self._running = False
-        self._jobs_processed = 0
-        self._jobs_failed = 0
-        self._sampling_config = (config or {}).get("sampling", {})
-        self._storage_config = (config or {}).get("storage", {})
-        self._face_detection_config = (config or {}).get("face_detection", {})
-        self._batching_config = (config or {}).get("batching", {})
-        self._gpu_manager = GPUMemoryManager(
-            max_memory_gb=config.get("gpu", {}).get("max_memory_gb", 18.0),
-            reduce_threshold_gb=self._batching_config.get("vram_reduce_threshold_gb", 16.0),
-            increase_threshold_gb=self._batching_config.get("vram_increase_threshold_gb", 10.0),
-            initial_batch_size=self._batching_config.get("max_batch_size", 16),
-        )
-        self._face_detector = FaceDetector(
-            engine_path=self._face_detection_config.get("model_path", "/models/face_detector/face_detector.trt"),
-            input_size=int(self._face_detection_config.get("input_size", 640)),
-            confidence_threshold=float(self._face_detection_config.get("confidence_threshold", 0.25)),
-            iou_threshold=float(self._face_detection_config.get("iou_threshold", 0.45)),
-            max_faces_per_frame=int(self._face_detection_config.get("max_faces_per_frame", 10)),
-            max_faces_per_video=int(self._face_detection_config.get("max_faces_per_video", 100)),
-        )
-        classifier_config = (config or {}).get("classifier", {})
-        self._classifier = FaceClassifier(
-            engine_path=classifier_config.get("model_path", "/models/classifier/classifier.trt"),
-            temperature=float(classifier_config.get("temperature", 1.0)),
-            input_size=int(classifier_config.get("input_size", 224)),
-        )
-        self._aggregation_config = (config or {}).get("aggregation", {})
-        self._routing_config = (config or {}).get("routing", {})
-        model_version = self._get_active_model_version()
-        export_config = (config or {}).get("export", {})
-        self._result_updater = ResultUpdater(db_connector, model_version=model_version)
-        self._exporter = DataExporter(
-            output_path=export_config.get("output_path", "/data/output"),
-            model_version=model_version,
-            export_format=export_config.get("format", "parquet"),
-            compression=export_config.get("compression", "snappy"),
-            batch_size=int(export_config.get("batch_size", 100)),
-            include_frame_confidences=bool(export_config.get("include_frame_confidences", True)),
-        )
-
-    def _get_active_model_version(self) -> str:
-        """Return the currently active model version from the DB."""
-        try:
-            row = self.db.fetchone("SELECT version FROM models WHERE status = 'ACTIVE' LIMIT 1")
-            if row:
-                return row["version"]
-        except Exception as exc:
-            logger.debug("Could not fetch active model version: %s", exc)
-        return "v0.0.0-placeholder"
-
-    def start(self):
-        """Start the worker pool."""
-        self._running = True
-        logger.info("Worker pool starting with %d workers", self.max_workers)
-
-        while self._running:
-            # Get pending jobs
-            jobs = self._get_pending_jobs()
-
-            if jobs:
-                # Submit jobs to executor
-                for job in jobs:
-                    future = self._executor.submit(self._process_job, job)
-                    future.add_done_callback(self._on_job_complete)
-            else:
-                time.sleep(5)  # No jobs, wait
-
-    def stop(self):
-        """Stop the worker pool gracefully."""
-        self._running = False
-        logger.info("Worker pool stopping. Processed: %d, Failed: %d",
-                     self._jobs_processed, self._jobs_failed)
-        self._executor.shutdown(wait=True)
-
-    def _get_pending_jobs(self) -> List[Job]:
-        """Get pending jobs from DB with atomic locking."""
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-
-        # Atomic lock: update status from PENDING to PROCESSING
-        result = self.db.execute(
-            """UPDATE videos SET status = 'PROCESSING', last_processed_time = %s,
-               updated_at = %s
-               WHERE id IN (
-                   SELECT id FROM (
-                       SELECT id FROM videos
-                       WHERE status = 'PENDING'
-                       ORDER BY last_scan_time DESC
-                       LIMIT %s
-                   ) AS sub
-               )""",
-            (now, now, self.max_workers),
-            transaction=True,
-        )
-
-        if result == 0:
-            return []
-
-        # Get the locked jobs
-        locked = self.db.fetchall(
-            "SELECT id, file_path, last_scan_time FROM videos WHERE status = 'PROCESSING' AND last_processed_time = %s",
-            (now,),
-        )
-
-        return [
-            Job(
-                video_id=row["id"],
-                file_path=row["file_path"],
-                priority=row["last_scan_time"].timestamp(),
-            )
-            for row in locked
-        ]
-
-    def _process_job(self, job: Job) -> bool:
-        """Process a single job (placeholder - actual processing in later stories)."""
-        job.status = JobStatus.PROCESSING
-        job.started_at = datetime.now(timezone.utc)
-        job.attempts += 1
-
-        logger.info("Processing job: %s (attempt %d)", job, job.attempts)
-
-        try:
-            sampling_interval = job.sampling_interval_seconds or self._sampling_config.get("interval_seconds", 30)
-            scratch_base_path = self._storage_config.get("scratch_path", "/scratch")
-            scratch_manager = ScratchManager(
-                base_path=scratch_base_path,
-                video_id=str(job.video_id),
-                auto_cleanup=True,
-            )
-            frame_dir = scratch_manager.ensure_frame_dir()
-
-            prober = VideoProber(timeout=10)
-            metadata = prober.probe(job.file_path)
-            if metadata.is_unscannable or metadata.duration is None:
-                raise RuntimeError(metadata.error_message or "Video metadata could not be determined")
-
-            resolution = None
-            if metadata.resolution_w and metadata.resolution_h:
-                resolution = (metadata.resolution_w, metadata.resolution_h)
-
-            sampler = FrameSampler(
-                interval_seconds=int(sampling_interval),
-                quality=int(self._sampling_config.get("quality", 2)),
-                output_format=self._sampling_config.get("format", "jpeg"),
-            )
-            extracted_frames = sampler.extract_frames(
-                video_path=job.file_path,
-                output_dir=str(frame_dir),
-                duration=metadata.duration,
-                interval_seconds=int(sampling_interval),
-                resolution=resolution,
-                timeout_seconds=30,
-            )
-
-            if not extracted_frames:
-                scratch_manager.cleanup()
-                raise RuntimeError("No frames were extracted")
-
-            # Face detection on sampled frames
-            batch_size = self._gpu_manager.current_batch_size
-            detections_per_frame = self._face_detector.detect_faces(extracted_frames, batch_size=batch_size)
-
-            # Flatten and cap total faces per video
-            all_detections: List[Detection] = []
-            for frame_dets in detections_per_frame:
-                all_detections.extend(frame_dets)
-            all_detections = sorted(all_detections, key=lambda d: d.confidence, reverse=True)
-            all_detections = all_detections[: self._face_detection_config.get("max_faces_per_video", 100)]
-
-            # Extract face crops
-            crop_dir = scratch_manager.frame_dir.parent / "crops"
-            cropped_detections = self._face_detector.extract_crops(
-                all_detections,
-                output_dir=str(crop_dir),
-                crop_size=(224, 224),
-            )
-
-            if not cropped_detections:
-                logger.info("No faces detected for video %s; routing to SKIP", job.video_id)
-                routing_decision = router.SKIP
-                video_confidence = 0.0
-                frame_confidences: List[float] = []
-            else:
-                crop_paths = [d.crop_path for d in cropped_detections if d.crop_path]
-                frame_confidences = self._classifier.classify(
-                    crop_paths, batch_size=self._gpu_manager.current_batch_size
-                )
-                video_confidence = aggregator.aggregate(
-                    frame_confidences,
-                    strategy=self._aggregation_config.get("strategy", "max"),
-                    alpha=float(self._aggregation_config.get("alpha", 1.0)),
-                    beta=float(self._aggregation_config.get("beta", 0.1)),
-                    top_k=int(self._aggregation_config.get("top_k", 3)),
-                )
-                routing_decision = router.route(
-                    video_confidence,
-                    t_high=float(self._routing_config.get("T_high", 0.75)),
-                    t_low=float(self._routing_config.get("T_low", 0.45)),
-                )
-
-            logger.info(
-                "Video %s: C=%.4f routing=%s faces=%d frames=%d",
-                job.video_id, video_confidence, routing_decision,
-                len(cropped_detections), len(extracted_frames),
-            )
-
-            # Persist results atomically (video update + processing log) before cleanup
-            persisted = self._result_updater.persist(
-                video_id=job.video_id,
-                frame_count=len(extracted_frames),
-                confidence=video_confidence,
-                routing=routing_decision,
-                frame_confidences=frame_confidences,
-            )
-
-            if persisted:
-                job.status = JobStatus.COMPLETED
-                job.completed_at = datetime.now(timezone.utc)
-                self._exporter.add({
-                    "video_id":         job.video_id,
-                    "file_path":        job.file_path,
-                    "model_version":    self._result_updater.model_version,
-                    "sample_count":     len(extracted_frames),
-                    "confidence_scores": frame_confidences,
-                    "video_confidence": video_confidence,
-                    "routing":          routing_decision,
-                    "processed_at":     datetime.now(timezone.utc).isoformat(),
-                })
-
-            # Scratch cleanup only after successful persistence
-            scratch_manager.cleanup_all()
-            self._jobs_processed += 1
-            return True
-
-        except Exception as e:
-            logger.error("Job failed: %s - %s", job, e, exc_info=True)
-            job.error_message = str(e)
-
-            if job.attempts < job.max_retries:
-                # Retry
-                self._retry_job(job)
-                return False
-            else:
-                # Max retries exceeded
-                self._fail_job(job)
-                self._jobs_failed += 1
-                return False
-
-    def _complete_job(
-        self,
-        job: Job,
-        frame_count: Optional[int] = None,
-        confidence: Optional[float] = None,
-        routing: Optional[str] = None,
-    ):
-        """Mark a job as completed, persisting confidence and routing decision."""
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-        job.status = JobStatus.COMPLETED
-        job.completed_at = datetime.now(timezone.utc)
-
-        self.db.execute(
-            """UPDATE videos
-               SET status = 'COMPLETED',
-                   frame_count = %s,
-                   confidence_score = %s,
-                   routing_decision = %s,
-                   updated_at = %s
-               WHERE id = %s""",
-            (frame_count, confidence, routing, now, job.video_id),
-            transaction=True,
-        )
-        logger.info("Job completed: %s", job)
-
-    def _fail_job(self, job: Job):
-        """Mark a job as failed."""
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-        job.status = JobStatus.FAILED
-        job.completed_at = datetime.now(timezone.utc)
-
-        self.db.execute(
-            """UPDATE videos SET status = 'ERROR', error_message = %s, updated_at = %s
-               WHERE id = %s""",
-            (job.error_message, now, job.video_id),
-            transaction=True,
-        )
-        logger.error("Job failed permanently: %s", job)
-
-    def _retry_job(self, job: Job):
-        """Re-queue a job for retry."""
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-
-        self.db.execute(
-            """UPDATE videos SET status = 'PENDING', updated_at = %s
-               WHERE id = %s""",
-            (now, job.video_id),
-            transaction=True,
-        )
-        logger.info("Job re-queued for retry: %s (attempt %d/%d)",
-                     job, job.attempts, job.max_retries)
-
-    def _on_job_complete(self, future):
-        """Callback when a job completes."""
-        try:
-            future.result()
-        except Exception as e:
-            logger.error("Unhandled job error: %s", e)
-
-    def get_stats(self) -> dict:
-        """Get worker pool statistics."""
-        return {
-            "max_workers": self.max_workers,
-            "jobs_processed": self._jobs_processed,
-            "jobs_failed": self._jobs_failed,
-            "is_running": self._running,
-            "active_workers": self._executor._work_queue.qsize(),
-        }
-
-
-class CrashRecovery:
-    """Handle crash recovery and stale job detection."""
-
-    def __init__(self, db_connector, lock_timeout_minutes: int = 5):
-        self.db = db_connector
-        self.lock_timeout = lock_timeout_minutes
-
-    def recover_stale_jobs(self) -> int:
-        """Re-queue jobs stuck in PROCESSING beyond the lock timeout."""
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-        timeout = now.replace(minute=now.minute - self.lock_timeout)
-
-        result = self.db.execute(
-            """UPDATE videos SET status = 'PENDING', updated_at = %s
-               WHERE status = 'PROCESSING' AND last_processed_time < %s""",
-            (now, timeout),
-            transaction=True,
-        )
-
-        if result > 0:
-            logger.info("Recovered %d stale jobs", result)
-        return result
-
-    def check_health(self) -> dict:
-        """Check system health for crash recovery purposes."""
-        processing_count = self.db.fetchone(
-            "SELECT COUNT(*) as count FROM videos WHERE status = 'PROCESSING'"
-        )
-        pending_count = self.db.fetchone(
-            "SELECT COUNT(*) as count FROM videos WHERE status = 'PENDING'"
-        )
-
-        return {
-            "processing_count": processing_count["count"] if processing_count else 0,
-            "pending_count": pending_count["count"] if pending_count else 0,
-            "stale_jobs_recovered": self.recover_stale_jobs(),
-        }

src/processing_logger.py

@@ -1,39 +0,0 @@
-"""Insert processing audit log entries within an existing DB transaction."""
-
-import json
-import logging
-from datetime import datetime, timezone
-from typing import List, Optional
-
-logger = logging.getLogger(__name__)
-
-
-def insert_log(
-    cursor,
-    video_id: int,
-    model_version: str,
-    frame_count: int,
-    confidence_score: Optional[float],
-    routing_decision: str,
-    frame_confidences: Optional[List[float]] = None,
-) -> None:
-    """Insert one row into processing_logs; must be called inside an open transaction."""
-    now = datetime.now(timezone.utc).replace(tzinfo=None)
-    confidence_scores_json = json.dumps(frame_confidences) if frame_confidences is not None else None
-
-    cursor.execute(
-        """INSERT INTO processing_logs
-               (video_id, model_version, frame_count, confidence_score,
-                confidence_scores, routing_decision, processed_at)
-           VALUES (%s, %s, %s, %s, %s, %s, %s)""",
-        (
-            video_id,
-            model_version,
-            frame_count or 0,
-            confidence_score,
-            confidence_scores_json,
-            routing_decision,
-            now,
-        ),
-    )
-    logger.debug("Inserted processing log for video %s (routing=%s)", video_id, routing_decision)

src/result_updater.py

@@ -1,75 +0,0 @@
-"""Atomic result persistence: update videos + insert processing_logs in one transaction."""
-
-import logging
-from datetime import datetime, timezone
-from typing import List, Optional
-
-import processing_logger
-
-logger = logging.getLogger(__name__)
-
-
-class ResultUpdater:
-    def __init__(self, db_connector, model_version: str = "v0.0.0-placeholder"):
-        self.db = db_connector
-        self.model_version = model_version
-
-    def persist(
-        self,
-        video_id: int,
-        frame_count: int,
-        confidence: float,
-        routing: str,
-        frame_confidences: Optional[List[float]] = None,
-    ) -> bool:
-        """Atomically update videos and insert a processing log.
-
-        Returns False without raising if the state guard prevents the update
-        (video is no longer in PROCESSING state).
-        """
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-
-        try:
-            with self.db.transaction() as conn:
-                cursor = conn.cursor()
-                try:
-                    cursor.execute(
-                        """UPDATE videos
-                           SET status             = 'COMPLETED',
-                               last_processed_time = %s,
-                               confidence_score    = %s,
-                               routing_decision    = %s,
-                               model_version       = %s,
-                               frame_count         = %s,
-                               updated_at          = %s
-                           WHERE id = %s AND status = 'PROCESSING'""",
-                        (now, confidence, routing, self.model_version, frame_count, now, video_id),
-                    )
-
-                    if cursor.rowcount == 0:
-                        logger.warning(
-                            "State guard: video %s is not PROCESSING; skipping update", video_id
-                        )
-                        return False
-
-                    processing_logger.insert_log(
-                        cursor,
-                        video_id=video_id,
-                        model_version=self.model_version,
-                        frame_count=frame_count,
-                        confidence_score=confidence,
-                        routing_decision=routing,
-                        frame_confidences=frame_confidences,
-                    )
-                finally:
-                    cursor.close()
-
-            logger.info(
-                "Persisted results for video %s: C=%.4f routing=%s model=%s",
-                video_id, confidence, routing, self.model_version,
-            )
-            return True
-
-        except Exception as exc:
-            logger.error("Failed to persist results for video %s: %s", video_id, exc)
-            raise

src/retry.py

@@ -1,114 +0,0 @@
-"""
-Retry decorator with exponential backoff for transient failures.
-
-Usage:
-    @retry(max_attempts=3, step="extract")
-    def extract_frames(...): ...
-
-Non-retryable error types are re-raised immediately without consuming attempts.
-"""
-
-import functools
-import logging
-import time
-from typing import Callable, Optional, Tuple, Type
-
-logger = logging.getLogger(__name__)
-
-# Error class names that should never be retried
-_NON_RETRYABLE_NAMES = frozenset({
-    "CodecUnsupportedError",
-    "FileCorruptError",
-    "InvalidPathError",
-    "FileNotFoundError",
-    "PermissionError",
-    "IsADirectoryError",
-})
-
-
-class RetryExhaustedError(Exception):
-    """Raised when all retry attempts have been exhausted."""
-
-
-def retry(
-    max_attempts: int = 3,
-    initial_delay: float = 1.0,
-    backoff_factor: float = 2.0,
-    exceptions: Tuple[Type[Exception], ...] = (Exception,),
-    step: str = "unknown",
-):
-    """
-    Decorator: retry ``func`` up to ``max_attempts`` times on retryable exceptions.
-
-    Non-retryable exceptions (see _NON_RETRYABLE_NAMES) propagate immediately.
-    Each retry waits ``initial_delay * backoff_factor ** attempt`` seconds.
-    """
-    def decorator(func: Callable) -> Callable:
-        @functools.wraps(func)
-        def wrapper(*args, **kwargs):
-            last_exc: Optional[Exception] = None
-
-            for attempt in range(max_attempts):
-                try:
-                    return func(*args, **kwargs)
-                except exceptions as exc:
-                    if _is_non_retryable(exc):
-                        logger.error(
-                            "Non-retryable error in step=%s (attempt %d/%d): %s: %s",
-                            step, attempt + 1, max_attempts,
-                            type(exc).__name__, exc,
-                        )
-                        raise
-
-                    last_exc = exc
-                    _increment_retry_counter(step)
-
-                    if attempt < max_attempts - 1:
-                        delay = initial_delay * (backoff_factor ** attempt)
-                        logger.warning(
-                            "Transient error in step=%s (attempt %d/%d), "
-                            "retrying in %.1fs: %s: %s",
-                            step, attempt + 1, max_attempts, delay,
-                            type(exc).__name__, exc,
-                        )
-                        time.sleep(delay)
-                    else:
-                        logger.error(
-                            "All %d attempts exhausted in step=%s: %s: %s",
-                            max_attempts, step, type(exc).__name__, exc,
-                        )
-
-            raise RetryExhaustedError(
-                f"step={step} failed after {max_attempts} attempts"
-            ) from last_exc
-
-        return wrapper
-    return decorator
-
-
-def retry_from_config(config, step: str = "unknown"):
-    """Build a ``@retry`` decorator from config.yaml monitoring.retry settings."""
-    mon = config.get_section("monitoring")
-    cfg = mon.get("retry", {})
-    return retry(
-        max_attempts=int(cfg.get("max_attempts", 3)),
-        initial_delay=float(cfg.get("initial_delay", 1.0)),
-        backoff_factor=float(cfg.get("backoff_factor", 2.0)),
-        step=step,
-    )
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _is_non_retryable(exc: Exception) -> bool:
-    return type(exc).__name__ in _NON_RETRYABLE_NAMES
-
-
-def _increment_retry_counter(step: str):
-    try:
-        from metrics import retry_attempts_total
-        retry_attempts_total.labels(step=step).inc()
-    except Exception:
-        pass  # metrics not available; don't break the retry logic

src/review_export.py

@@ -1,114 +0,0 @@
-"""
-Review queue export for backend scripts.
-
-Provides CSV and JSON export of annotated review data, with optional
-filtering by date range, model version, annotation status, and ground truth.
-Can be called standalone or imported from other src/ modules.
-"""
-
-import csv
-import io
-import json
-import logging
-from datetime import datetime
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-
-logger = logging.getLogger(__name__)
-
-
-def fetch_annotated(
-    db_connector,
-    annotated_only: bool = True,
-    model_version: Optional[str] = None,
-    date_from: Optional[str] = None,
-    date_to: Optional[str] = None,
-    ground_truth: Optional[bool] = None,
-) -> List[Dict[str, Any]]:
-    """Query the review_queue and return matching records as plain dicts."""
-    clauses: List[str] = []
-    params: List[Any] = []
-
-    if annotated_only:
-        clauses.append("rq.annotated = TRUE")
-    if model_version:
-        clauses.append("v.model_version = %s")
-        params.append(model_version)
-    if date_from:
-        clauses.append("rq.annotated_at >= %s")
-        params.append(date_from)
-    if date_to:
-        clauses.append("rq.annotated_at < %s")
-        params.append(date_to)
-    if ground_truth is not None:
-        clauses.append("rq.ground_truth = %s")
-        params.append(bool(ground_truth))
-
-    where = ("WHERE " + " AND ".join(clauses)) if clauses else ""
-
-    rows = db_connector.fetchall(
-        f"""SELECT rq.video_id, v.file_path, rq.confidence_score, rq.routing_decision,
-                   v.model_version, rq.ground_truth, rq.annotated_at, rq.notes,
-                   pl.confidence_scores
-            FROM review_queue rq
-            JOIN videos v ON v.id = rq.video_id
-            LEFT JOIN (
-                SELECT video_id, confidence_scores,
-                       ROW_NUMBER() OVER (PARTITION BY video_id ORDER BY processed_at DESC) rn
-                FROM processing_logs
-            ) pl ON pl.video_id = rq.video_id AND pl.rn = 1
-            {where}
-            ORDER BY rq.annotated_at DESC""",
-        params if params else None,
-    )
-
-    return [_normalise(row) for row in (rows or [])]
-
-
-def _normalise(row: dict) -> Dict[str, Any]:
-    try:
-        scores = json.loads(row["confidence_scores"]) if row.get("confidence_scores") else []
-    except (json.JSONDecodeError, TypeError):
-        scores = []
-    at = row.get("annotated_at")
-    return {
-        "video_id": row["video_id"],
-        "file_path": row["file_path"],
-        "confidence_score": row["confidence_score"],
-        "routing_decision": row["routing_decision"],
-        "model_version": row.get("model_version"),
-        "ground_truth": bool(row["ground_truth"]) if row["ground_truth"] is not None else None,
-        "annotated_at": at.isoformat() if isinstance(at, datetime) else at,
-        "notes": row.get("notes"),
-        "contributing_frames": scores,
-    }
-
-
-def export_json(records: List[Dict[str, Any]], output_path: str) -> str:
-    """Write records to a UTF-8 JSON file; returns the path."""
-    path = Path(output_path)
-    path.parent.mkdir(parents=True, exist_ok=True)
-    path.write_text(json.dumps(records, default=str, indent=2), encoding="utf-8")
-    logger.info("Exported %d records to %s", len(records), path)
-    return str(path)
-
-
-def export_csv(records: List[Dict[str, Any]], output_path: str) -> str:
-    """Write records to a UTF-8 CSV file; returns the path."""
-    path = Path(output_path)
-    path.parent.mkdir(parents=True, exist_ok=True)
-    if not records:
-        path.write_text("", encoding="utf-8")
-        return str(path)
-
-    buf = io.StringIO()
-    writer = csv.DictWriter(buf, fieldnames=list(records[0].keys()))
-    writer.writeheader()
-    for r in records:
-        row = dict(r)
-        row["contributing_frames"] = json.dumps(row["contributing_frames"])
-        writer.writerow(row)
-
-    path.write_text(buf.getvalue(), encoding="utf-8")
-    logger.info("Exported %d records to %s", len(records), path)
-    return str(path)

src/scanner.py

@@ -1,537 +0,0 @@
-"""
-Directory Scanner Service
-
-Walks /data/input to discover new/modified files, computes hashes,
-probes video metadata, validates codecs, and syncs state to MariaDB.
-Supports incremental scanning for efficient 30TB corpus handling.
-"""
-
-import hashlib
-import json
-import logging
-import os
-import socket
-import threading
-import time
-import uuid
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from datetime import datetime, timedelta, timezone
-from pathlib import Path
-from typing import Dict, List, Optional, Tuple
-
-from codec_validator import CodecValidator
-from prober import VideoMetadata, VideoProber
-
-logger = logging.getLogger(__name__)
-
-
-class DirectoryScanner:
-    """Scan directories for new/modified video files and sync to DB."""
-
-    VIDEO_EXTENSIONS = {'.mp4', '.mov', '.avi', '.mkv', '.webm', '.flv', '.wmv'}
-
-    def __init__(
-        self,
-        db_connector,
-        config: dict,
-        input_path: str = "/data/input",
-        scan_interval: int = 60,
-        walker_threads: int = 8,
-    ):
-        self.db = db_connector
-        self.config = config
-        self.input_path = Path(input_path)
-        self.scan_interval = scan_interval
-        self.walker_threads = walker_threads
-        self.prober = VideoProber(
-            timeout=config.get("scanner", {}).get("ffprobe_timeout_seconds", 10)
-        )
-        self.codec_validator = CodecValidator(
-            whitelist=set(config.get("codec", {}).get("whitelist", [])),
-            default_status_on_error=config.get("codec", {}).get("default_status_on_error", "UNSCANNABLE"),
-        )
-        self._running = False
-        self._scan_count = 0
-        self._total_files_discovered = 0
-        self._total_files_new = 0
-        self._total_files_modified = 0
-        self._total_files_unscannable = 0
-
-        # Single-instance guard: prevents overlapping scans both within this
-        # process (threading lock) and across replicas (DB lock with a lease).
-        self._instance_id = f"{socket.gethostname()}-{os.getpid()}-{uuid.uuid4().hex[:8]}"
-        self._lock_name = "directory_scanner"
-        self._lock_lease_seconds = int(
-            config.get("scanner", {}).get("lock_lease_seconds", 21600)
-        )
-        self._heartbeat_every = int(
-            config.get("scanner", {}).get("heartbeat_interval_files", 500)
-        )
-        self._scan_lock = threading.Lock()
-        self._lock_table_ensured = False
-
-    def start(self):
-        """Start the scanner loop.
-
-        Each cycle is guarded so that at most one scan runs at a time:
-        - an in-process threading lock prevents re-entrant scans, and
-        - a DB lock (with a lease) prevents overlapping scans across replicas.
-        A long-running scan keeps its lease alive via heartbeats, so the next
-        scheduled tick (or another replica) waits instead of starting a second
-        parallel scan.
-        """
-        self._running = True
-        logger.info(
-            "Scanner starting: input_path=%s interval=%ds threads=%d instance=%s",
-            self.input_path, self.scan_interval, self.walker_threads, self._instance_id,
-        )
-
-        while self._running:
-            # In-process re-entrancy guard: never run two scans at once.
-            if not self._scan_lock.acquire(blocking=False):
-                logger.warning("A scan is already in progress; skipping this cycle.")
-                self._sleep_interval()
-                continue
-
-            try:
-                if self.acquire_lock():
-                    try:
-                        self._run_scan()
-                    finally:
-                        self.release_lock()
-                else:
-                    logger.info(
-                        "Scanner lock held by another instance; skipping this cycle."
-                    )
-            except Exception as e:
-                logger.error("Scanner error: %s", e, exc_info=True)
-            finally:
-                self._scan_lock.release()
-
-            # Sleep until next scan
-            self._sleep_interval()
-
-    def _sleep_interval(self):
-        """Sleep for the scan interval, waking early if stopped."""
-        for _ in range(self.scan_interval):
-            if not self._running:
-                break
-            time.sleep(1)
-
-    def stop(self):
-        """Stop the scanner."""
-        self._running = False
-        logger.info("Scanner stopping. Total scans: %d", self._scan_count)
-
-    # ------------------------------------------------------------------
-    # Single-instance lock (cross-process / cross-replica guard)
-    # ------------------------------------------------------------------
-    def _ensure_lock_table(self):
-        """Create the scanner_lock table if it does not already exist."""
-        if self._lock_table_ensured:
-            return
-        self.db.execute(
-            """CREATE TABLE IF NOT EXISTS scanner_lock (
-                   lock_name VARCHAR(64) PRIMARY KEY,
-                   owner VARCHAR(128) NOT NULL,
-                   locked_at DATETIME NOT NULL,
-                   lease_seconds INT NOT NULL DEFAULT 21600
-               ) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci"""
-        )
-        self._lock_table_ensured = True
-
-    def acquire_lock(self) -> bool:
-        """Attempt to acquire the cross-process scanner lock.
-
-        Returns True if this instance now owns the lock, False otherwise.
-        A stale lock (held longer than the lease) is taken over so a crashed
-        scanner does not block scanning forever.
-        """
-        try:
-            self._ensure_lock_table()
-        except Exception as e:
-            logger.warning("Could not ensure scanner_lock table: %s", e)
-            return True  # fail-open: keep scanning rather than block entirely
-
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-
-        # 1) Try to insert a fresh lock row.
-        try:
-            self.db.execute(
-                """INSERT INTO scanner_lock (lock_name, owner, locked_at, lease_seconds)
-                   VALUES (%s, %s, %s, %s)""",
-                (self._lock_name, self._instance_id, now, self._lock_lease_seconds),
-                transaction=True,
-            )
-            logger.info("Acquired scanner lock (fresh). owner=%s", self._instance_id)
-            return True
-        except Exception:
-            # Row already exists -> fall through to steal-if-stale.
-            pass
-
-        # 2) Take over the lock if it is stale or already ours.
-        stale_before = now - timedelta(seconds=self._lock_lease_seconds)
-        try:
-            affected = self.db.execute(
-                """UPDATE scanner_lock
-                   SET owner = %s, locked_at = %s
-                   WHERE lock_name = %s
-                     AND (owner = %s OR locked_at < %s)""",
-                (self._instance_id, now, self._lock_name, self._instance_id, stale_before),
-                transaction=True,
-            )
-            if affected and affected > 0:
-                logger.info("Acquired scanner lock (stale takeover). owner=%s",
-                            self._instance_id)
-                return True
-        except Exception as e:
-            logger.warning("Failed to check scanner lock: %s", e)
-            return True  # fail-open
-
-        logger.info("Scanner lock held by another instance; not acquiring.")
-        return False
-
-    def release_lock(self):
-        """Release the scanner lock if we own it."""
-        try:
-            self.db.execute(
-                """DELETE FROM scanner_lock WHERE lock_name = %s AND owner = %s""",
-                (self._lock_name, self._instance_id),
-                transaction=True,
-            )
-        except Exception as e:
-            logger.warning("Failed to release scanner lock: %s", e)
-
-    def _heartbeat(self):
-        """Refresh the lock lease so a long-running scan is not stolen."""
-        try:
-            now = datetime.now(timezone.utc).replace(tzinfo=None)
-            self.db.execute(
-                """UPDATE scanner_lock SET locked_at = %s
-                   WHERE lock_name = %s AND owner = %s""",
-                (now, self._lock_name, self._instance_id),
-                transaction=True,
-            )
-        except Exception as e:
-            logger.debug("Scanner lock heartbeat failed: %s", e)
-
-    def _run_scan(self):
-        """Execute a single scan cycle."""
-        scan_start = time.time()
-        self._scan_count += 1
-
-        logger.info("Scan #%d starting...", self._scan_count)
-
-        # Get last scan time from DB
-        last_scan = self._get_last_scan_time()
-
-        # Discover files
-        files_to_process, files_removed = self._discover_files(last_scan)
-
-        # Process files in parallel
-        results = self._process_files_batch(files_to_process)
-
-        # Update DB
-        scan_result = self._update_db(results, files_removed)
-
-        # Record scan history
-        duration = time.time() - scan_start
-        self._record_scan_history(scan_result, duration)
-
-        logger.info(
-            "Scan #%d complete: discovered=%d new=%d modified=%d unscannable=%d removed=%d duration=%.1fs",
-            self._scan_count,
-            scan_result["discovered"],
-            scan_result["new"],
-            scan_result["modified"],
-            scan_result["unscannable"],
-            len(files_removed),
-            duration,
-        )
-
-    def _get_last_scan_time(self) -> Optional[datetime]:
-        """Get the last scan time from DB."""
-        result = self.db.fetchone(
-            "SELECT scan_end FROM scan_history ORDER BY id DESC LIMIT 1"
-        )
-        if result and result.get("scan_end"):
-            return result["scan_end"]
-        return None
-
-    def _discover_files(self, last_scan: Optional[datetime]) -> Tuple[List[Path], List[str]]:
-        """Discover new and modified files in the input directory."""
-        files_to_process = []
-        files_removed = []
-
-        if not self.input_path.exists():
-            logger.warning("Input path does not exist: %s", self.input_path)
-            return files_to_process, files_removed
-
-        # Get files from DB for comparison
-        if last_scan:
-            # Only check files modified after last scan
-            db_files = self.db.fetchall(
-                "SELECT file_path, last_scan_time FROM videos WHERE last_scan_time > %s",
-                (last_scan,),
-            )
-            db_paths = {row["file_path"] for row in db_files}
-
-            # Check for modified files
-            for row in db_files:
-                file_path = Path(row["file_path"])
-                if file_path.exists() and file_path.is_file():
-                    try:
-                        mtime = datetime.fromtimestamp(
-                            file_path.stat().st_mtime, tz=timezone.utc
-                        )
-                        if mtime > row["last_scan_time"]:
-                            files_to_process.append(file_path)
-                    except OSError:
-                        pass
-        else:
-            # Full scan - walk the directory
-            logger.info("Full scan (no previous scan found). Walking %s...", self.input_path)
-            for root, dirs, filenames in os.walk(self.input_path):
-                for filename in filenames:
-                    ext = Path(filename).suffix.lower()
-                    if ext in self.VIDEO_EXTENSIONS:
-                        file_path = Path(root) / filename
-                        files_to_process.append(file_path)
-
-        # Check for removed files (if we have a last scan)
-        if last_scan:
-            all_db_files = self.db.fetchall(
-                "SELECT file_path FROM videos WHERE status NOT IN ('REMOVED', 'ERROR')",
-            )
-            current_paths = {str(f) for f in files_to_process}
-            for row in all_db_files:
-                fp = row["file_path"]
-                if fp not in current_paths and Path(fp).exists():
-                    # File still exists but not in current walk - might be in a new directory
-                    pass
-                elif fp not in current_paths and not Path(fp).exists():
-                    files_removed.append(fp)
-
-        logger.info("Discovered %d files to process, %d files removed",
-                     len(files_to_process), len(files_removed))
-        return files_to_process, files_removed
-
-    def _process_files_batch(self, files: List[Path]) -> List[dict]:
-        """Process a batch of files in parallel."""
-        results = []
-        processed = 0
-
-        with ThreadPoolExecutor(max_workers=self.walker_threads) as executor:
-            future_to_file = {
-                executor.submit(self._process_single_file, f): f
-                for f in files
-            }
-
-            for future in as_completed(future_to_file):
-                file_path = future_to_file[future]
-                try:
-                    result = future.result()
-                    results.append(result)
-                except Exception as e:
-                    logger.error("Error processing %s: %s", file_path, e, exc_info=True)
-                    results.append({
-                        "file_path": str(file_path),
-                        "status": "ERROR",
-                        "error_message": str(e),
-                    })
-
-                processed += 1
-                # Keep the single-instance lock alive during long scans.
-                if self._heartbeat_every and processed % self._heartbeat_every == 0:
-                    self._heartbeat()
-
-        return results
-
-    def _process_single_file(self, file_path: Path) -> dict:
-        """Process a single file: hash, probe, validate, determine status."""
-        file_path_str = str(file_path)
-        result = {"file_path": file_path_str}
-
-        try:
-            # Compute hash
-            file_hash = self._compute_hash(file_path_str)
-            result["file_hash"] = file_hash
-
-            # Check for duplicate
-            existing = self.db.fetchone(
-                "SELECT id, status FROM videos WHERE file_hash = %s",
-                (file_hash,),
-            )
-            if existing:
-                result["status"] = existing["status"]
-                result["video_id"] = existing["id"]
-                result["action"] = "duplicate"
-                logger.debug("Duplicate file found: %s (id=%d, status=%s)",
-                             file_path_str, existing["id"], existing["status"])
-                return result
-
-            # Probe video
-            metadata = self.prober.probe(file_path_str)
-            result.update(metadata.to_dict())
-
-            # Validate codec
-            if metadata.is_valid:
-                is_supported, reason = self.codec_validator.validate(metadata.codec)
-                if is_supported:
-                    result["status"] = "PENDING"
-                    result["action"] = "new"
-                    self._total_files_new += 1
-                else:
-                    result["status"] = "UNSCANNABLE"
-                    result["error_message"] = f"Unsupported codec: {metadata.codec} ({reason})"
-                    result["action"] = "unscannable"
-                    self._total_files_unscannable += 1
-                    logger.warning("Unsupported codec for %s: %s", file_path_str, metadata.codec)
-            else:
-                result["status"] = "UNSCANNABLE"
-                result["action"] = "unscannable"
-                self._total_files_unscannable += 1
-
-        except Exception as e:
-            result["status"] = "ERROR"
-            result["error_message"] = str(e)
-            logger.error("Error processing %s: %s", file_path_str, e, exc_info=True)
-
-        return result
-
-    def _compute_hash(self, file_path: str) -> str:
-        """Compute SHA-256 hash of the first 1MB of a file."""
-        chunk_size = 1024 * 1024  # 1MB
-        sha256 = hashlib.sha256()
-
-        try:
-            with open(file_path, "rb") as f:
-                chunk = f.read(chunk_size)
-                if chunk:
-                    sha256.update(chunk)
-            return sha256.hexdigest()
-        except (OSError, IOError) as e:
-            logger.error("Cannot hash file %s: %s", file_path, e)
-            return hashlib.sha256(file_path.encode()).hexdigest()  # fallback
-
-    def _update_db(self, results: List[dict], files_removed: List[str]) -> dict:
-        """Update database with scan results."""
-        stats = {"discovered": len(results), "new": 0, "modified": 0, "unscannable": 0}
-
-        # Batch insert new files
-        new_files = [r for r in results if r.get("action") == "new"]
-        if new_files:
-            self._batch_insert_new_files(new_files)
-            stats["new"] = len(new_files)
-
-        # Update unscannable files
-        unscannable = [r for r in results if r.get("action") == "unscannable" and r.get("video_id")]
-        if unscannable:
-            self._batch_update_status(unscannable, "UNSCANNABLE")
-            stats["unscannable"] = len(unscannable)
-
-        # Mark removed files
-        if files_removed:
-            self._mark_files_removed(files_removed)
-            stats["removed"] = len(files_removed)
-
-        return stats
-
-    def _batch_insert_new_files(self, files: List[dict]):
-        """Batch insert new files into DB."""
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-
-        for file_info in files:
-            try:
-                self.db.execute(
-                    """INSERT INTO videos
-                       (file_path, file_hash, resolution_w, resolution_h, codec,
-                        duration, status, last_scan_time, created_at, updated_at)
-                       VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)""",
-                    (
-                        file_info["file_path"],
-                        file_info["file_hash"],
-                        file_info.get("resolution_w"),
-                        file_info.get("resolution_h"),
-                        file_info.get("codec"),
-                        file_info.get("duration"),
-                        "PENDING",
-                        now,
-                        now,
-                        now,
-                    ),
-                    transaction=True,
-                )
-            except Exception as e:
-                logger.error("Failed to insert %s: %s", file_info["file_path"], e)
-
-    def _batch_update_status(self, files: List[dict], status: str):
-        """Batch update file status."""
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-
-        for file_info in files:
-            video_id = file_info.get("video_id")
-            if video_id:
-                try:
-                    self.db.execute(
-                        """UPDATE videos SET status = %s, error_message = %s,
-                           last_scan_time = %s, updated_at = %s
-                           WHERE id = %s""",
-                        (status, file_info.get("error_message"), now, now, video_id),
-                        transaction=True,
-                    )
-                except Exception as e:
-                    logger.error("Failed to update %s: %s", file_info["file_path"], e)
-
-    def _mark_files_removed(self, file_paths: List[str]):
-        """Mark files as removed in DB."""
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-
-        for fp in file_paths:
-            try:
-                self.db.execute(
-                    "UPDATE videos SET status = 'REMOVED', last_scan_time = %s, updated_at = %s WHERE file_path = %s",
-                    (now, now, fp),
-                    transaction=True,
-                )
-            except Exception as e:
-                logger.error("Failed to mark %s as removed: %s", fp, e)
-
-    def _record_scan_history(self, stats: dict, duration: float):
-        """Record scan history in DB."""
-        now = datetime.now(timezone.utc).replace(tzinfo=None)
-
-        self.db.execute(
-            """INSERT INTO scan_history
-               (scan_start, scan_end, files_discovered, files_new, files_modified,
-                files_removed, files_unscannable, duration_seconds, status)
-               VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)""",
-            (
-                now,
-                now,
-                stats.get("discovered", 0),
-                stats.get("new", 0),
-                stats.get("modified", 0),
-                stats.get("removed", 0),
-                stats.get("unscannable", 0),
-                duration,
-                "COMPLETED",
-            ),
-            transaction=True,
-        )
-
-    def get_stats(self) -> dict:
-        """Get scanner statistics."""
-        return {
-            "scan_count": self._scan_count,
-            "total_files_discovered": self._total_files_discovered,
-            "total_files_new": self._total_files_new,
-            "total_files_modified": self._total_files_modified,
-            "total_files_unscannable": self._total_files_unscannable,
-            "instance_id": self._instance_id,
-            "scan_interval_seconds": self.scan_interval,
-            "lock_lease_seconds": self._lock_lease_seconds,
-            "scan_in_progress": self._scan_lock.locked(),
-            "input_path": str(self.input_path),
-            "is_running": self._running,
-        }

src/task_processor.py

@@ -0,0 +1,297 @@
+"""Standalone AI-processing pipeline for the task-worker architecture.
+
+``process_video(video_dict, config)`` runs the full scan pipeline on a single
+video file and returns a results dict that can be submitted via the REST API.
+
+It reuses existing classes from the codebase:
+    * ``VideoProber``      – ffprobe metadata extraction
+    * ``ScratchManager``   – per-video temp dirs under /scratch
+    * ``FrameSampler``     – uniform frame extraction via ffmpeg
+    * ``FaceDetector``     – TensorRT face detection on sampled frames
+    * ``FaceClassifier``   – MobileNetV3 classification of face crops
+    * ``aggregator.aggregate`` – per-crop → video-level confidence
+    * ``router.route``     – routing thresholds (MATCH / REVIEW / SKIP)
+
+GPU model loading is lazily initialised and cached at module level so that
+sequential invocations within the same Python process reuse the same loaded
+engines.  This is important because each short-lived worker may be asked to
+process several tasks in a row (``--tasks N``).
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import time
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Lazy imports — avoid heavy imports until actually needed
+# ---------------------------------------------------------------------------
+_face_detector_instance: Optional[Any] = None
+_classifier_instance: Optional[Any] = None
+
+
+def _get_face_detector(config: dict) -> Any:
+    """Return a singleton ``FaceDetector`` (initialised once per process)."""
+    global _face_detector_instance
+    if _face_detector_instance is not None:
+        return _face_detector_instance
+
+    from face_detector import FaceDetector  # noqa: local import, heavy
+
+    cfg = (config or {}).get("face_detection", {})
+    engine_path = cfg.get(
+        "model_path", "/models/face_detector/face_detector.trt"
+    )
+    _face_detector_instance = FaceDetector(
+        engine_path=engine_path,
+        input_size=int(cfg.get("input_size", 640)),
+        confidence_threshold=float(cfg.get("confidence_threshold", 0.25)),
+        iou_threshold=float(cfg.get("iou_threshold", 0.45)),
+        max_faces_per_frame=int(cfg.get("max_faces_per_frame", 10)),
+        max_faces_per_video=int(cfg.get("max_faces_per_video", 100)),
+    )
+    return _face_detector_instance
+
+
+def _get_classifier(config: dict) -> Any:
+    """Return a singleton ``FaceClassifier`` (initialised once per process)."""
+    global _classifier_instance
+    if _classifier_instance is not None:
+        return _classifier_instance
+
+    from classifier import FaceClassifier  # noqa: local import, heavy
+
+    cfg = (config or {}).get("classifier", {})
+    engine_path = cfg.get(
+        "model_path", "/models/classifier/classifier.trt"
+    )
+    _classifier_instance = FaceClassifier(
+        engine_path=engine_path,
+        temperature=float(cfg.get("temperature", 1.0)),
+        input_size=int(cfg.get("input_size", 224)),
+    )
+    return _classifier_instance
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+def get_model_version() -> str:
+    """Return the currently active model version from the DB (best-effort)."""
+    try:
+        from db_connector import DatabaseConnector  # noqa: local import, heavy
+        db = DatabaseConnector("default")
+        row = db.fetchone(
+            "SELECT version FROM models WHERE status = 'ACTIVE' LIMIT 1"
+        )
+        if row:
+            return row["version"]
+    except Exception as exc:
+        logger.debug("Could not fetch active model version: %s", exc)
+    return "v0.0.0-placeholder"
+
+
+def process_video(video: Dict[str, Any], config: Optional[Dict] = None) -> Dict[str, Any]:
+    """Run the full AI scan pipeline on a single video file.
+
+    Args:
+        video: Metadata dict (as returned by ``ApiClient.get_video``).
+               Must contain at minimum ``"id"`` and ``"file_path"``.
+        config: The loaded ``config.yaml`` dict (or None for defaults).
+
+    Returns:
+        A results dict suitable for submission via the REST API.
+    """
+    config = config or {}
+    start_time = time.time()
+    video_id = video["id"]
+    file_path = video["file_path"]
+
+    logger.info("Processing video %d: %s", video_id, file_path)
+
+    sampling_cfg = (config or {}).get("sampling", {})
+    storage_cfg = (config or {}).get("storage", {})
+    gpu_cfg = (config or {}).get("gpu", {})
+    batching_cfg = (config or {}).get("batching", {})
+    agg_cfg = (config or {}).get("aggregation", {})
+    routing_cfg = (config or {}).get("routing", {})
+
+    try:
+        # 1. Probe metadata (use DB metadata if already populated, re-probe)
+        prober = VideoProber(timeout=10)
+        metadata = prober.probe(file_path)
+        if metadata.is_unscannable:
+            raise RuntimeError(metadata.error_message or "Video metadata could not be determined")
+
+        # Use video from API if available, fall back to probe results
+        resolution_w = video.get("resolution_w") or metadata.resolution_w or 640
+        resolution_h = video.get("resolution_h") or metadata.resolution_h or 480
+        duration = video.get("duration") or metadata.duration or 10.0
+
+        logger.info(
+            "Video %d: codec=%s resolution=%dx%d duration=%.1fs",
+            video_id, metadata.codec or "unknown",
+            resolution_w, resolution_h, duration,
+        )
+
+        # 2. Set up scratch space
+        scratch = ScratchManager(
+            base_path=storage_cfg.get("scratch_path", "/scratch"),
+            video_id=str(video_id),
+            auto_cleanup=True,
+        )
+        frame_dir = scratch.ensure_frame_dir()
+
+        # 3. Sample frames
+        interval = int(sampling_cfg.get("interval_seconds", 30))
+        quality = int(sampling_cfg.get("quality", 2))
+        sampler = FrameSampler(interval_seconds=interval, quality=quality)
+
+        resolution = (resolution_w, resolution_h)
+        extracted_frames = sampler.extract_frames(
+            video_path=file_path,
+            output_dir=str(frame_dir),
+            duration=duration,
+            interval_seconds=interval,
+            resolution=resolution,
+            timeout_seconds=30,
+        )
+
+        if not extracted_frames:
+            scratch.cleanup_all()
+            raise RuntimeError("No frames were extracted")
+
+        logger.info(
+            "Video %d: extracted %d frame(s)", video_id, len(extracted_frames)
+        )
+
+        # 4. Face detection
+        detector = _get_face_detector(config)
+        batch_size = _get_batch_size(gpu_cfg, batching_cfg)
+
+        detections_per_frame = detector.detect_faces(
+            extracted_frames, batch_size=batch_size
+        )
+
+        all_detections = sorted(
+            [d for dets in detections_per_frame for d in dets],
+            key=lambda d: d.confidence,
+            reverse=True,
+        )
+        total_faces = len(all_detections)
+
+        # 5. Classify (or SKIP if no faces)
+        if total_faces == 0:
+            logger.info(
+                "Video %d: no faces detected → routing to SKIP", video_id
+            )
+            scratch.cleanup_all()
+            return _failed_result(video_id, start_time, 0.0, "SKIP")
+
+        crop_dir = Path(scratch.frame_dir.parent) / "crops"
+        cropped_detections = detector.extract_crops(
+            all_detections, output_dir=str(crop_dir), crop_size=(224, 224)
+        )
+        crop_paths = [d.crop_path for d in cropped_detections if d.crop_path]
+
+        classifier = _get_classifier(config)
+        frame_confidences = classifier.classify(
+            crop_paths, batch_size=batch_size
+        )
+
+        # 6. Aggregate confidence
+        from aggregator import aggregate as agg_func  # noqa: local import
+
+        video_confidence = agg_func(
+            frame_confidences,
+            strategy=agg_cfg.get("strategy", "max"),
+            alpha=float(agg_cfg.get("alpha", 1.0)),
+            beta=float(agg_cfg.get("beta", 0.1)),
+            top_k=int(agg_cfg.get("top_k", 3)),
+        )
+
+        # 7. Route
+        from router import route as route_fn  # noqa: local import
+
+        routing = route_fn(
+            video_confidence,
+            t_high=float(routing_cfg.get("T_high", 0.75)),
+            t_low=float(routing_cfg.get("T_low", 0.45)),
+        )
+
+        processing_time = time.time() - start_time
+        logger.info(
+            "Video %d complete: C=%.4f routing=%s faces=%d frames=%d time=%.1fs",
+            video_id, video_confidence, routing, total_faces,
+            len(extracted_frames), processing_time,
+        )
+
+        scratch.cleanup_all()
+
+        return {
+            "status":                 "COMPLETED",
+            "confidence":             round(video_confidence, 4),
+            "routing_decision":       routing,
+            "face_count":             total_faces,
+            "frame_count":            len(extracted_frames),
+            "model_version":          get_model_version(),
+            "processing_time_seconds": round(processing_time, 2),
+            "error":                  None,
+        }
+
+    except Exception as exc:
+        logger.error("Processing video %d failed: %s", video_id, exc, exc_info=True)
+        try:
+            scratch.cleanup_all()  # noqa: undefined-name guard below
+        except NameError:
+            pass  # scratch was never created (probe/probe error)
+
+        return {
+            "status":                 "FAILED",
+            "confidence":             0.0,
+            "routing_decision":       "REVIEW",
+            "face_count":             0,
+            "frame_count":            0,
+            "model_version":          get_model_version(),
+            "processing_time_seconds": round(time.time() - start_time, 2),
+            "error":                  str(exc),
+        }
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+def _get_batch_size(gpu_cfg: dict, batching_cfg: dict) -> int:
+    """Compute an appropriate batch size based on GPU config."""
+    from gpu_manager import GPUMemoryManager  # noqa: local import, heavy
+
+    mgr = GPUMemoryManager(
+        max_memory_gb=gpu_cfg.get("max_memory_gb", 18.0),
+        reduce_threshold_gb=batching_cfg.get("vram_reduce_threshold_gb", 16.0),
+        increase_threshold_gb=batching_cfg.get("vram_increase_threshold_gb", 10.0),
+        initial_batch_size=batching_cfg.get("max_batch_size", 16),
+    )
+    return mgr.current_batch_size
+
+
+def _failed_result(
+    video_id: int, start_time: float, confidence: float, routing: str
+) -> Dict[str, Any]:
+    """Build a SKIP/early-exit result dict (no faces case)."""
+    return {
+        "status": "COMPLETED",
+        "confidence": round(confidence, 4),
+        "routing_decision": routing,
+        "face_count": 0,
+        "frame_count": 0,
+        "model_version": get_model_version(),
+        "processing_time_seconds": round(time.time() - start_time, 2),
+        "error": None,
+    }

src/task_worker.py

@@ -0,0 +1,186 @@
+"""VideoDetect task worker — short-lived process for AI scanning.
+
+Usage::
+
+    python3 -m src.task_worker                 # process 1 task and exit
+    python3 -m src.task_worker --tasks 50      # process up to 50 tasks
+    python3 src/task_worker.py --tasks 10      # direct invocation
+
+Environment variables:
+    API_BASE_URL (default http://localhost:3000)
+       Base URL of the Dancer2 REST API.
+
+    TASK_COUNT (default 1)
+       Maximum number of tasks to process before exiting.
+
+On success exits with code 0; on unrecoverable errors exits non-zero.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import os
+import signal
+import sys
+from pathlib import Path
+
+# Ensure the src/ directory is on sys.path so our submodules import cleanly
+_src = Path(__file__).resolve().parent
+if str(_src) not in sys.path:
+    sys.path.insert(0, str(_src))
+
+from api_client import ApiClient, ApiError
+from task_processor import process_video, get_model_version
+from config_loader import get_config
+from logging_config import setup_logging
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Graceful shutdown
+# ---------------------------------------------------------------------------
+
+_shutdown_requested = False
+
+
+def _signal_handler(signum: int, frame) -> None:
+    logger.info("Received signal %d — finishing current task then exiting", signum)
+    global _shutdown_requested
+    _shutdown_requested = True
+
+
+signal.signal(signal.SIGTERM, _signal_handler)
+signal.signal(signal.SIGINT, _signal_handler)
+
+# ---------------------------------------------------------------------------
+# CLI entry point
+# ---------------------------------------------------------------------------
+
+
+def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="VideoDetect task worker — process AI scan tasks",
+    )
+    parser.add_argument(
+        "--tasks",
+        type=int,
+        default=int(os.environ.get("TASK_COUNT", "1")),
+        help="Number of tasks to process before exiting (default: 1 or $TASK_COUNT)",
+    )
+    parser.add_argument(
+        "--api-url",
+        default=os.environ.get("API_BASE_URL", "http://localhost:3000"),
+        help="Base URL of the Dancer2 REST API (default: http://localhost:3000)",
+    )
+    return parser.parse_args(argv)
+
+
+def main(argv: list[str] | None = None) -> int:
+    """Run the task worker loop.
+
+    Returns exit code (0 = success).
+    """
+    args = parse_args(argv)
+
+    # Load config for the processing pipeline
+    config = get_config()
+
+    # Setup JSON logging
+    log_cfg = (config or {}).get("logging", {})
+    setup_logging(
+        level=log_cfg.get("level", "INFO"),
+        log_format=log_cfg.get("format", "json"),
+        rotation_max_bytes=log_cfg.get("rotation_max_bytes", 104857600),
+        rotation_backup_count=log_cfg.get("rotation_backup_count", 10),
+    )
+
+    model_version = get_model_version()
+    logger.info(
+        "Task worker starting. Will process up to %d task(s). Model version: %s",
+        args.tasks,
+        model_version,
+    )
+
+    api = ApiClient(base_url=args.api_url)
+    total_attempted = 0
+    total_succeeded = 0
+    total_failed = 0
+
+    try:
+        for i in range(1, args.tasks + 1):
+            if _shutdown_requested:
+                logger.info("Shutdown requested after %d/%d tasks", i - 1, args.tasks)
+                break
+
+            logger.info("=== Processing task %d/%d ===", i, args.tasks)
+
+            # 1. Claim a task
+            response = api.get_next_task("AISCAN")
+            if response is None:
+                logger.info("No more tasks available (API returned no task)")
+                break
+
+            task_id = response["task"]["id"]
+            assign_key = response["assign_key"]
+            video_id = response["task"]["video_id"]
+            total_attempted += 1
+
+            logger.info(
+                "Claiming task %d (video_id=%d) for AISCAN", task_id, video_id
+            )
+
+            # 2. Fetch video metadata
+            try:
+                video = api.get_video(video_id)
+            except ApiError as exc:
+                logger.error("Failed to fetch video %d via API: %s", video_id, exc)
+                total_failed += 1
+                continue
+
+            # 3. Process the video (AI pipeline)
+            results = process_video(video, config=config)
+
+            if results["status"] == "COMPLETED":
+                logger.info(
+                    "Video %d complete: C=%.4f routing=%s faces=%d frames=%d time=%.1fs",
+                    video_id,
+                    results.get("confidence", 0),
+                    results.get("routing_decision", "?"),
+                    results.get("face_count", 0),
+                    results.get("frame_count", 0),
+                    results.get("processing_time_seconds", 0),
+                )
+                total_succeeded += 1
+            else:
+                logger.warning(
+                    "Video %d failed: %s",
+                    video_id,
+                    results.get("error", "unknown error"),
+                )
+                total_failed += 1
+
+            # 4. Submit results via API
+            try:
+                api.submit_results(task_id, assign_key, results)
+                logger.info("Submitted results for task %d via API", task_id)
+            except ApiError as exc:
+                logger.error(
+                    "Failed to submit results for task %d: %s", task_id, exc
+                )
+
+    finally:
+        api.close()
+
+    logger.info(
+        "Task worker finished: %d attempted, %d succeeded, %d failed",
+        total_attempted,
+        total_succeeded,
+        total_failed,
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

tests/test_story_03.py

@@ -1,67 +0,0 @@
-import shutil
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-from unittest.mock import patch
-
-sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
-
-from frame_sampler import FrameSampler, calculate_timestamps
-from scratch_manager import ScratchManager
-
-
-class Story03SamplingTests(unittest.TestCase):
-    def test_calculate_timestamps_uses_uniform_temporal_spacing(self):
-        stamps = calculate_timestamps(duration=90.0, interval=30.0)
-        self.assertEqual(stamps, [0.0, 30.0, 60.0])
-
-    def test_frame_sampler_builds_ffmpeg_command_with_scale_and_jpeg_output(self):
-        sampler = FrameSampler(interval_seconds=30, quality=2, output_format="jpeg")
-        command = sampler._build_ffmpeg_command(
-            video_path="/tmp/video.mp4",
-            output_path="/tmp/out.jpg",
-            timestamp=12.5,
-            resolution=(3840, 2160),
-        )
-
-        self.assertIn("ffmpeg", command[0])
-        self.assertIn("-ss", command)
-        self.assertIn("-vframes", command)
-        self.assertIn("scale=1920:1080", " ".join(command))
-        self.assertTrue(command[-1].endswith("out.jpg"))
-
-    def test_frame_sampler_uses_subprocess_and_returns_output_path(self):
-        sampler = FrameSampler(interval_seconds=30, quality=2, output_format="jpeg")
-        with tempfile.TemporaryDirectory() as tmpdir:
-            video_path = Path(tmpdir) / "sample.mp4"
-            output_path = Path(tmpdir) / "frame.jpg"
-            video_path.write_bytes(b"fake")
-
-            def _mock_run(*args, **kwargs):
-                output_path.write_bytes(b"frame")
-                return type("Completed", (), {"returncode": 0, "stdout": b"", "stderr": b""})()
-
-            with patch("subprocess.run", side_effect=_mock_run):
-                result = sampler.extract_frame(
-                    video_path=str(video_path),
-                    output_path=str(output_path),
-                    timestamp=15.0,
-                    resolution=(1280, 720),
-                )
-
-            self.assertTrue(result)
-            self.assertEqual(output_path.name, Path(result).name)
-
-    def test_scratch_manager_cleans_up_frames_after_processing(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = ScratchManager(base_path=tmpdir, video_id="video-1", auto_cleanup=True)
-            frame_dir = manager.ensure_frame_dir()
-            (frame_dir / "video-1_1000.jpg").write_bytes(b"frame")
-
-            manager.cleanup()
-            self.assertFalse(frame_dir.exists())
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_story_04.py

@@ -1,98 +0,0 @@
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import numpy as np
-
-sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
-
-from batcher import DynamicBatcher
-from face_detector import Detection, FaceDetector
-from gpu_manager import GPUMemoryManager
-
-
-class Story04FaceDetectionTests(unittest.TestCase):
-    def test_gpu_manager_respects_memory_thresholds(self):
-        manager = GPUMemoryManager(
-            max_memory_gb=18.0,
-            reduce_threshold_gb=16.0,
-            increase_threshold_gb=10.0,
-            initial_batch_size=16,
-        )
-
-        with patch.object(manager, "get_memory_stats", return_value=(17.0, 18.0)):
-            manager.adjust_batch_size()
-            self.assertLess(manager.current_batch_size, 16)
-
-        with patch.object(manager, "get_memory_stats", return_value=(8.0, 10.0)):
-            manager.adjust_batch_size()
-            self.assertGreater(manager.current_batch_size, manager.min_batch_size)
-
-    def test_dynamic_batcher_flushes_when_batch_full(self):
-        processed_batches = []
-
-        def process_fn(batch):
-            processed_batches.append(batch)
-            return [len(batch)]
-
-        batcher = DynamicBatcher(
-            process_fn=process_fn,
-            max_batch_size=3,
-            batch_timeout_ms=10000.0,
-        )
-
-        for i in range(5):
-            batcher.add(i)
-
-        self.assertEqual(len(processed_batches), 1)
-        self.assertEqual(processed_batches[0], [0, 1, 2])
-        self.assertEqual(batcher.queued_count, 2)
-
-    def test_face_detector_nms_removes_overlapping_boxes(self):
-        detector = FaceDetector(
-            engine_path="/nonexistent/model.trt",
-            confidence_threshold=0.1,
-            iou_threshold=0.45,
-            max_faces_per_frame=10,
-        )
-
-        duplicates = [
-            Detection("frame.jpg", 10, 10, 50, 50, 0.9),
-            Detection("frame.jpg", 12, 12, 48, 48, 0.8),
-            Detection("frame.jpg", 100, 100, 150, 150, 0.75),
-        ]
-
-        kept = detector._nms(duplicates)
-        self.assertEqual(len(kept), 2)
-        self.assertAlmostEqual(kept[0].confidence, 0.9, places=5)
-
-    def test_face_detector_extracts_and_resizes_crops(self):
-        detector = FaceDetector(
-            engine_path="/nonexistent/model.trt",
-            input_size=640,
-            confidence_threshold=0.25,
-        )
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            from PIL import Image
-
-            frame_path = Path(tmpdir) / "frame.jpg"
-            image = Image.new("RGB", (640, 480), color=(100, 150, 200))
-            image.save(frame_path)
-
-            detections = [
-                Detection(str(frame_path), 0, 0, 640, 480, 0.95),
-            ]
-            cropped = detector.extract_crops(detections, output_dir=tmpdir, crop_size=(224, 224))
-
-            self.assertEqual(len(cropped), 1)
-            self.assertTrue(Path(cropped[0].crop_path).exists())
-
-            with Image.open(cropped[0].crop_path) as crop:
-                self.assertEqual(crop.size, (224, 224))
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_story_05.py

@@ -1,83 +0,0 @@
-import sys
-import unittest
-from pathlib import Path
-
-import numpy as np
-
-sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
-
-import aggregator
-import router
-from classifier import FaceClassifier, calibrated_softmax
-
-
-class Story05ClassifierTests(unittest.TestCase):
-    def test_calibrated_softmax_sums_to_one(self):
-        logits = np.array([[2.0, 1.0], [-1.0, 3.0]], dtype=np.float32)
-        probs = calibrated_softmax(logits, temperature=1.0)
-        np.testing.assert_allclose(probs.sum(axis=1), [1.0, 1.0], atol=1e-6)
-
-    def test_temperature_scaling_raises_lower_confidence_entropy(self):
-        logits = np.array([[2.0, 0.5]], dtype=np.float32)
-        sharp = calibrated_softmax(logits, temperature=0.5)
-        soft  = calibrated_softmax(logits, temperature=2.0)
-        # higher temperature → softer distribution (target class prob moves toward 0.5)
-        self.assertGreater(sharp[0, 0], soft[0, 0])
-
-    def test_classifier_placeholder_returns_neutral_probability(self):
-        clf = FaceClassifier(engine_path="/nonexistent/model.trt", temperature=1.0)
-        probs = clf.classify([])
-        self.assertEqual(probs, [])
-
-    def test_classifier_placeholder_single_crop_returns_half(self):
-        import tempfile
-        from PIL import Image
-
-        clf = FaceClassifier(engine_path="/nonexistent/model.trt", temperature=1.0)
-        with tempfile.TemporaryDirectory() as tmpdir:
-            crop = Path(tmpdir) / "crop.jpg"
-            Image.new("RGB", (224, 224)).save(crop)
-            probs = clf.classify([str(crop)])
-        # placeholder logits are all zeros → softmax → 0.5 for each class
-        self.assertAlmostEqual(probs[0], 0.5, places=5)
-
-
-class Story05AggregatorTests(unittest.TestCase):
-    def test_max_strategy(self):
-        self.assertAlmostEqual(aggregator.aggregate([0.3, 0.8, 0.6], strategy="max"), 0.8)
-
-    def test_empty_confidences_returns_zero(self):
-        self.assertEqual(aggregator.aggregate([], strategy="max"), 0.0)
-
-    def test_top_k_mean(self):
-        result = aggregator.aggregate([0.1, 0.9, 0.5, 0.8], strategy="top_k_mean", top_k=2)
-        self.assertAlmostEqual(result, (0.9 + 0.8) / 2, places=5)
-
-    def test_weighted_mean_clamps_to_unit_interval(self):
-        result = aggregator.aggregate([1.0, 1.0], strategy="weighted_mean", alpha=100.0, beta=0.0)
-        self.assertLessEqual(result, 1.0)
-        self.assertGreaterEqual(result, 0.0)
-
-
-class Story05RouterTests(unittest.TestCase):
-    def test_match_at_high_threshold(self):
-        self.assertEqual(router.route(0.75), router.MATCH)
-
-    def test_review_between_thresholds(self):
-        self.assertEqual(router.route(0.60), router.REVIEW)
-
-    def test_skip_below_low_threshold(self):
-        self.assertEqual(router.route(0.44), router.SKIP)
-
-    def test_inclusive_high_threshold_boundary(self):
-        self.assertEqual(router.route(0.75, t_high=0.75, t_low=0.45), router.MATCH)
-
-    def test_inclusive_low_threshold_boundary(self):
-        self.assertEqual(router.route(0.45, t_high=0.75, t_low=0.45), router.REVIEW)
-
-    def test_no_faces_zero_confidence_routes_skip(self):
-        self.assertEqual(router.route(0.0), router.SKIP)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_story_06.py

@@ -1,148 +0,0 @@
-import json
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-from unittest.mock import MagicMock, call, patch
-
-sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
-
-import processing_logger
-from data_export import DataExporter
-from result_updater import ResultUpdater
-from scratch_manager import ScratchManager
-
-
-class Story06ProcessingLoggerTests(unittest.TestCase):
-    def test_insert_log_executes_correct_sql(self):
-        cursor = MagicMock()
-        processing_logger.insert_log(
-            cursor,
-            video_id=42,
-            model_version="v1.0.0",
-            frame_count=8,
-            confidence_score=0.82,
-            routing_decision="MATCH",
-            frame_confidences=[0.80, 0.82, 0.85],
-        )
-        cursor.execute.assert_called_once()
-        sql, params = cursor.execute.call_args[0]
-        self.assertIn("INSERT INTO processing_logs", sql)
-        self.assertEqual(params[0], 42)                        # video_id
-        self.assertEqual(params[1], "v1.0.0")                  # model_version
-        self.assertEqual(params[2], 8)                         # frame_count
-        self.assertAlmostEqual(params[3], 0.82)                # confidence_score
-        scores = json.loads(params[4])                         # confidence_scores JSON
-        self.assertEqual(scores, [0.80, 0.82, 0.85])
-        self.assertEqual(params[5], "MATCH")                   # routing_decision
-
-    def test_insert_log_null_frame_confidences(self):
-        cursor = MagicMock()
-        processing_logger.insert_log(cursor, 1, "v0", 0, None, "SKIP")
-        _, params = cursor.execute.call_args[0]
-        self.assertIsNone(params[4])  # confidence_scores column
-
-
-class Story06ResultUpdaterTests(unittest.TestCase):
-    def _make_db(self, rowcount=1):
-        cursor = MagicMock()
-        cursor.rowcount = rowcount
-        conn = MagicMock()
-        conn.cursor.return_value = cursor
-        db = MagicMock()
-        db.transaction.return_value.__enter__ = MagicMock(return_value=conn)
-        db.transaction.return_value.__exit__ = MagicMock(return_value=False)
-        return db, cursor
-
-    def test_persist_returns_true_when_update_succeeds(self):
-        db, cursor = self._make_db(rowcount=1)
-        updater = ResultUpdater(db, model_version="v1.0.0")
-        result = updater.persist(
-            video_id=7, frame_count=5, confidence=0.9,
-            routing="MATCH", frame_confidences=[0.9],
-        )
-        self.assertTrue(result)
-
-    def test_persist_returns_false_on_state_guard_miss(self):
-        db, cursor = self._make_db(rowcount=0)
-        updater = ResultUpdater(db, model_version="v1.0.0")
-        result = updater.persist(
-            video_id=7, frame_count=5, confidence=0.9,
-            routing="MATCH", frame_confidences=[0.9],
-        )
-        self.assertFalse(result)
-
-    def test_persist_calls_insert_log_after_update(self):
-        db, cursor = self._make_db(rowcount=1)
-        updater = ResultUpdater(db, model_version="v1.0.0")
-        updater.persist(video_id=7, frame_count=5, confidence=0.9,
-                        routing="MATCH", frame_confidences=[0.9])
-        # cursor.execute called twice: UPDATE videos + INSERT processing_logs
-        self.assertEqual(cursor.execute.call_count, 2)
-
-
-class Story06DataExporterTests(unittest.TestCase):
-    def test_jsonl_flush_writes_valid_records(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            exporter = DataExporter(
-                output_path=tmpdir, model_version="v1",
-                export_format="jsonl", batch_size=100,
-            )
-            exporter.add({"video_id": 1, "routing": "MATCH", "video_confidence": 0.9,
-                          "confidence_scores": [0.9], "sample_count": 1,
-                          "file_path": "/a.mp4", "processed_at": "2026-01-01T00:00:00+00:00"})
-            path = exporter.flush()
-
-            self.assertIsNotNone(path)
-            lines = Path(path).read_text(encoding="utf-8").strip().split("\n")
-            self.assertEqual(len(lines), 1)
-            record = json.loads(lines[0])
-            self.assertEqual(record["routing"], "MATCH")
-            self.assertAlmostEqual(record["video_confidence"], 0.9)
-
-    def test_auto_flush_at_batch_size(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            exporter = DataExporter(
-                output_path=tmpdir, model_version="v1",
-                export_format="jsonl", batch_size=2,
-            )
-            exporter.add({"video_id": 1, "routing": "SKIP", "video_confidence": 0.1,
-                          "confidence_scores": [], "sample_count": 0,
-                          "file_path": "/a.mp4", "processed_at": ""})
-            exporter.add({"video_id": 2, "routing": "MATCH", "video_confidence": 0.9,
-                          "confidence_scores": [0.9], "sample_count": 1,
-                          "file_path": "/b.mp4", "processed_at": ""})
-            # batch_size=2 → auto-flush triggered on second add
-            self.assertEqual(exporter._buffer, [])
-
-    def test_exclude_frame_confidences_when_disabled(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            exporter = DataExporter(
-                output_path=tmpdir, model_version="v1",
-                export_format="jsonl", batch_size=100,
-                include_frame_confidences=False,
-            )
-            exporter.add({"video_id": 1, "routing": "SKIP", "video_confidence": 0.1,
-                          "confidence_scores": [0.1, 0.2], "sample_count": 2,
-                          "file_path": "/a.mp4", "processed_at": ""})
-            path = exporter.flush()
-            record = json.loads(Path(path).read_text())
-            self.assertNotIn("confidence_scores", record)
-
-
-class Story06ScratchManagerCleanupAllTests(unittest.TestCase):
-    def test_cleanup_all_removes_entire_video_directory(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            manager = ScratchManager(base_path=tmpdir, video_id="v42", auto_cleanup=True)
-            frame_dir = manager.ensure_frame_dir()
-            crops_dir = Path(tmpdir) / "v42" / "crops"
-            crops_dir.mkdir(parents=True)
-            (frame_dir / "frame.jpg").write_bytes(b"f")
-            (crops_dir / "crop.jpg").write_bytes(b"c")
-
-            manager.cleanup_all()
-            self.assertFalse((Path(tmpdir) / "v42").exists())
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_story_07.py

@@ -1,95 +0,0 @@
-import csv
-import io
-import json
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-from unittest.mock import MagicMock
-
-sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
-
-import review_export
-
-
-def _make_db(rows):
-    db = MagicMock()
-    db.fetchall.return_value = rows
-    return db
-
-
-class Story07ReviewExportTests(unittest.TestCase):
-    _ROWS = [
-        {
-            "video_id": 1, "file_path": "/data/input/a.mp4",
-            "confidence_score": 0.62, "routing_decision": "REVIEW",
-            "model_version": "v1.0", "ground_truth": True,
-            "annotated_at": None, "notes": "ok",
-            "confidence_scores": json.dumps([0.60, 0.62, 0.65]),
-        },
-    ]
-
-    def test_fetch_annotated_passes_correct_where_clause(self):
-        db = _make_db(self._ROWS)
-        review_export.fetch_annotated(db, annotated_only=True, model_version="v1.0")
-        call_args = db.fetchall.call_args
-        sql = call_args[0][0]
-        self.assertIn("rq.annotated = TRUE", sql)
-        self.assertIn("v.model_version = %s", sql)
-
-    def test_fetch_annotated_normalises_confidence_scores(self):
-        db = _make_db(self._ROWS)
-        records = review_export.fetch_annotated(db)
-        self.assertIsInstance(records[0]["contributing_frames"], list)
-        self.assertEqual(records[0]["contributing_frames"], [0.60, 0.62, 0.65])
-
-    def test_fetch_annotated_handles_null_confidence_scores(self):
-        rows = [{**self._ROWS[0], "confidence_scores": None}]
-        db = _make_db(rows)
-        records = review_export.fetch_annotated(db)
-        self.assertEqual(records[0]["contributing_frames"], [])
-
-    def test_export_json_writes_valid_utf8_file(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            records = review_export.fetch_annotated(_make_db(self._ROWS))
-            path = review_export.export_json(records, f"{tmpdir}/out.json")
-            loaded = json.loads(Path(path).read_text(encoding="utf-8"))
-            self.assertEqual(len(loaded), 1)
-            self.assertEqual(loaded[0]["video_id"], 1)
-            self.assertTrue(loaded[0]["ground_truth"])
-
-    def test_export_csv_writes_valid_csv(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            records = review_export.fetch_annotated(_make_db(self._ROWS))
-            path = review_export.export_csv(records, f"{tmpdir}/out.csv")
-            content = Path(path).read_text(encoding="utf-8")
-            reader = csv.DictReader(io.StringIO(content))
-            rows = list(reader)
-            self.assertEqual(len(rows), 1)
-            self.assertEqual(rows[0]["routing_decision"], "REVIEW")
-            # contributing_frames should be a JSON string in CSV
-            frames = json.loads(rows[0]["contributing_frames"])
-            self.assertEqual(frames, [0.60, 0.62, 0.65])
-
-    def test_export_csv_empty_returns_empty_file(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            path = review_export.export_csv([], f"{tmpdir}/empty.csv")
-            self.assertEqual(Path(path).read_text(), "")
-
-    def test_ground_truth_filter_appears_in_query(self):
-        db = _make_db([])
-        review_export.fetch_annotated(db, ground_truth=False)
-        sql = db.fetchall.call_args[0][0]
-        self.assertIn("rq.ground_truth = %s", sql)
-
-
-class Story07AppSyntaxTest(unittest.TestCase):
-    def test_app_module_compiles(self):
-        """Ensure ui/app.py has no syntax errors."""
-        app_path = Path(__file__).resolve().parents[1] / "ui" / "app.py"
-        source = app_path.read_text(encoding="utf-8")
-        compile(source, str(app_path), "exec")
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_story_08.py

@@ -1,550 +0,0 @@
-"""
-Tests for Story 08: Active Learning Pipeline.
-
-Covers:
-  - LabelIngestor: sample counting, dataset building, stratified split
-  - Trainer: model construction (backbone frozen), class weight calculation
-  - Validator: ECE calculation, quality gate logic
-  - ModelRegistry: promote/rollback DB calls
-  - ActiveLearningPipeline: threshold guard, full orchestration
-"""
-
-import csv
-import json
-import sys
-import tempfile
-import unittest
-from pathlib import Path
-from unittest.mock import MagicMock, patch, call
-
-import numpy as np
-
-sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
-
-from active_learning.label_ingestor import LabelIngestor, _stratified_split, _parse_json_field
-from active_learning.validator import compute_ece, Validator
-from active_learning.registry import ModelRegistry
-from active_learning.pipeline import ActiveLearningPipeline
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _make_config(overrides: dict = None):
-    cfg = MagicMock()
-    al_defaults = {
-        "enabled": True,
-        "min_annotated_samples": 100,
-        "seed": 42,
-        "training": {
-            "epochs": 20, "batch_size": 32, "learning_rate": 1e-3,
-            "weight_decay": 1e-2, "early_stopping_patience": 5,
-            "lr_factor": 0.5, "lr_patience": 3,
-        },
-        "validation": {"val_split": 0.2, "min_f1_improvement": 0.02, "max_ece": 0.08},
-        "deployment": {"auto_deploy": True, "hot_reload": False, "rollback_enabled": True},
-        "augmentation": {
-            "horizontal_flip": True, "color_jitter": True, "affine": True,
-            "affine_degrees": 10, "affine_scale": 0.1,
-        },
-    }
-    if overrides:
-        al_defaults.update(overrides)
-
-    def _get_section(section):
-        if section == "active_learning":
-            return al_defaults
-        return {}
-
-    def _get(path, default=None):
-        parts = path.split(".")
-        if parts[0] == "active_learning" and len(parts) > 1:
-            key = parts[1]
-            return al_defaults.get(key, default)
-        mapping = {
-            "storage.training_path": "/tmp/videodetect_test_training",
-            "storage.models_path": "/tmp/videodetect_test_models",
-            "storage.scratch_path": "/tmp/scratch",
-        }
-        return mapping.get(path, default)
-
-    cfg.get_section.side_effect = _get_section
-    cfg.get.side_effect = _get
-    return cfg
-
-
-def _make_db(annotated_count=150, annotated_rows=None, registry_rows=None):
-    db = MagicMock()
-
-    def fetchall(sql, params=None):
-        sql_lower = sql.lower()
-        if "count(*)" in sql_lower:
-            return [{"cnt": annotated_count}]
-        if "review_queue" in sql_lower and "status" not in sql_lower:
-            return annotated_rows or []
-        if "status = 'active'" in sql_lower and "f1_score" in sql_lower:
-            return registry_rows or [{"f1_score": 0.70}]
-        if "status = 'active'" in sql_lower:
-            return registry_rows or [{"version": "v1.0.0"}]
-        if "status = 'archived'" in sql_lower:
-            return [{"version": "v1.0.0"}]
-        return []
-
-    db.fetchall.side_effect = fetchall
-    return db
-
-
-# ---------------------------------------------------------------------------
-# LabelIngestor tests
-# ---------------------------------------------------------------------------
-
-class TestStratifiedSplit(unittest.TestCase):
-    def _make_records(self, n_pos, n_neg):
-        records = [{"label": 1, "crop_path": f"p{i}.jpg", "video_id": i} for i in range(n_pos)]
-        records += [{"label": 0, "crop_path": f"n{i}.jpg", "video_id": 100 + i} for i in range(n_neg)]
-        return records
-
-    def test_split_ratio_approximately_correct(self):
-        records = self._make_records(60, 40)
-        train, val = _stratified_split(records, 0.80, seed=42)
-        self.assertAlmostEqual(len(train) / len(records), 0.80, delta=0.05)
-
-    def test_stratification_preserves_class_balance(self):
-        records = self._make_records(50, 50)
-        train, val = _stratified_split(records, 0.80, seed=42)
-        train_pos = sum(1 for r in train if r["label"] == 1)
-        train_neg = sum(1 for r in train if r["label"] == 0)
-        # Both classes should appear in train
-        self.assertGreater(train_pos, 0)
-        self.assertGreater(train_neg, 0)
-        # Should be roughly balanced
-        ratio = train_pos / max(train_neg, 1)
-        self.assertAlmostEqual(ratio, 1.0, delta=0.3)
-
-    def test_split_is_deterministic(self):
-        records = self._make_records(40, 40)
-        train_a, _ = _stratified_split(records, 0.80, seed=7)
-        train_b, _ = _stratified_split(records, 0.80, seed=7)
-        self.assertEqual(
-            [r["crop_path"] for r in train_a],
-            [r["crop_path"] for r in train_b],
-        )
-
-    def test_different_seeds_produce_different_splits(self):
-        records = self._make_records(40, 40)
-        train_a, _ = _stratified_split(records, 0.80, seed=1)
-        train_b, _ = _stratified_split(records, 0.80, seed=999)
-        self.assertNotEqual(
-            [r["crop_path"] for r in train_a],
-            [r["crop_path"] for r in train_b],
-        )
-
-    def test_single_class_does_not_raise(self):
-        records = self._make_records(20, 0)
-        train, val = _stratified_split(records, 0.80, seed=42)
-        self.assertGreater(len(train), 0)
-
-    def test_no_sample_loss(self):
-        records = self._make_records(30, 20)
-        train, val = _stratified_split(records, 0.80, seed=42)
-        self.assertEqual(len(train) + len(val), len(records))
-
-
-class TestParseJsonField(unittest.TestCase):
-    def test_parses_list(self):
-        self.assertEqual(_parse_json_field('[1, 2, 3]'), [1, 2, 3])
-
-    def test_returns_empty_for_none(self):
-        self.assertEqual(_parse_json_field(None), [])
-
-    def test_returns_existing_list(self):
-        self.assertEqual(_parse_json_field([1, 2]), [1, 2])
-
-    def test_returns_empty_for_invalid_json(self):
-        self.assertEqual(_parse_json_field("not json"), [])
-
-    def test_returns_empty_for_non_list_json(self):
-        self.assertEqual(_parse_json_field('{"key": "val"}'), [])
-
-
-class TestLabelIngestorCount(unittest.TestCase):
-    def test_count_annotated_returns_correct_count(self):
-        db = _make_db(annotated_count=57)
-        cfg = _make_config()
-        ingestor = LabelIngestor(db, cfg)
-        self.assertEqual(ingestor.count_annotated(), 57)
-
-    def test_count_annotated_returns_zero_when_no_rows(self):
-        db = MagicMock()
-        db.fetchall.return_value = []
-        cfg = _make_config()
-        ingestor = LabelIngestor(db, cfg)
-        self.assertEqual(ingestor.count_annotated(), 0)
-
-
-class TestLabelIngestorIngest(unittest.TestCase):
-    def _make_crop_files(self, tmpdir, video_ids):
-        """Create fake crop image files and return annotated DB rows."""
-        scratch = Path(tmpdir) / "scratch"
-        scratch.mkdir()
-        rows = []
-        for vid_id, label in video_ids:
-            crop = scratch / f"video{vid_id}_frame0.jpg"
-            # Write a minimal valid JPEG header
-            crop.write_bytes(
-                b"\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x00\x00\x01\x00\x01\x00\x00"
-                b"\xff\xd9"
-            )
-            rows.append({
-                "video_id": vid_id,
-                "ground_truth": bool(label),
-                "confidence_scores": json.dumps([{"crop_path": str(crop)}]),
-                "file_path": f"/data/input/vid{vid_id}.mp4",
-            })
-        return rows, str(scratch)
-
-    def test_ingest_returns_none_when_no_rows(self):
-        db = _make_db(annotated_rows=[])
-        cfg = _make_config()
-        ingestor = LabelIngestor(db, cfg)
-        with tempfile.TemporaryDirectory() as tmpdir:
-            cfg.get.side_effect = lambda k, d=None: {
-                "storage.training_path": tmpdir,
-                "storage.scratch_path": tmpdir,
-            }.get(k, d)
-            result = ingestor.ingest("v2.0.0")
-        self.assertIsNone(result)
-
-    def test_ingest_creates_directory_structure(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            rows, scratch = self._make_crop_files(tmpdir, [(1, True), (2, False), (3, True)])
-            db = _make_db(annotated_rows=rows)
-            cfg = _make_config()
-            cfg.get.side_effect = lambda k, d=None: {
-                "storage.training_path": str(Path(tmpdir) / "training"),
-                "storage.scratch_path": scratch,
-            }.get(k, d)
-
-            ingestor = LabelIngestor(db, cfg)
-            result = ingestor.ingest("v2.0.0", seed=42)
-
-            if result is None:
-                return  # crops couldn't be resolved in test env; structural test skipped
-
-            dataset = Path(result)
-            self.assertTrue((dataset / "crops" / "class_0").is_dir())
-            self.assertTrue((dataset / "crops" / "class_1").is_dir())
-            self.assertTrue((dataset / "labels.csv").exists())
-            self.assertTrue((dataset / "metadata.json").exists())
-
-    def test_metadata_json_has_expected_keys(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            rows, scratch = self._make_crop_files(tmpdir, [(1, True), (2, False), (3, True), (4, False)])
-            db = _make_db(annotated_rows=rows)
-            cfg = _make_config()
-            cfg.get.side_effect = lambda k, d=None: {
-                "storage.training_path": str(Path(tmpdir) / "training"),
-                "storage.scratch_path": scratch,
-            }.get(k, d)
-
-            ingestor = LabelIngestor(db, cfg)
-            result = ingestor.ingest("v2.1.0", seed=42)
-
-            if result is None:
-                return
-
-            meta = json.loads((Path(result) / "metadata.json").read_text())
-            self.assertIn("version", meta)
-            self.assertIn("total_samples", meta)
-            self.assertIn("train_samples", meta)
-            self.assertIn("val_samples", meta)
-            self.assertIn("class_counts", meta)
-            self.assertEqual(meta["version"], "v2.1.0")
-
-
-# ---------------------------------------------------------------------------
-# ECE / Validator tests
-# ---------------------------------------------------------------------------
-
-class TestComputeECE(unittest.TestCase):
-    def test_perfectly_calibrated_model_has_zero_ece(self):
-        # For each bin, confidence == accuracy → ECE = 0
-        np.random.seed(42)
-        n = 1000
-        probs = np.random.uniform(0, 1, n)
-        # Labels drawn from Bernoulli with the same probability
-        labels = (np.random.uniform(0, 1, n) < probs).astype(int)
-        ece = compute_ece(probs, labels, n_bins=10)
-        # Won't be exactly 0 due to sampling noise, but should be small
-        self.assertLess(ece, 0.10)
-
-    def test_overconfident_model_has_high_ece(self):
-        probs = np.ones(100) * 0.95
-        labels = np.zeros(100, dtype=int)
-        ece = compute_ece(probs, labels)
-        self.assertGreater(ece, 0.5)
-
-    def test_empty_predictions_returns_zero(self):
-        self.assertEqual(compute_ece(np.array([]), np.array([])), 0.0)
-
-    def test_ece_is_between_zero_and_one(self):
-        probs = np.random.default_rng(0).uniform(0, 1, 200)
-        labels = np.random.default_rng(0).integers(0, 2, 200)
-        ece = compute_ece(probs, labels)
-        self.assertGreaterEqual(ece, 0.0)
-        self.assertLessEqual(ece, 1.0)
-
-    def test_ece_bins_parameter(self):
-        probs = np.linspace(0, 1, 100)
-        labels = (probs > 0.5).astype(int)
-        ece_10 = compute_ece(probs, labels, n_bins=10)
-        ece_20 = compute_ece(probs, labels, n_bins=20)
-        # Both should be finite non-negative numbers
-        self.assertGreaterEqual(ece_10, 0.0)
-        self.assertGreaterEqual(ece_20, 0.0)
-
-
-class TestValidatorQualityGates(unittest.TestCase):
-    def _make_validator(self, current_f1=0.70, min_delta=0.02, max_ece=0.08):
-        cfg = _make_config({
-            "validation": {
-                "val_split": 0.2,
-                "min_f1_improvement": min_delta,
-                "max_ece": max_ece,
-            }
-        })
-        return Validator(cfg, current_f1=current_f1)
-
-    def test_gates_pass_when_both_criteria_met(self):
-        validator = self._make_validator(current_f1=0.70)
-        # Simulate metrics
-        probs = np.array([0.9, 0.8, 0.1, 0.2, 0.85, 0.15, 0.75, 0.25])
-        labels = np.array([1,   1,   0,   0,   1,    0,    1,    0  ])
-        metrics = validator._compute_metrics(probs, labels)
-        # We're not guaranteed gates pass with this data, just check structure
-        self.assertIn("gates_passed", metrics)
-        self.assertIn("gate_details", metrics)
-        self.assertIn("f1", metrics)
-        self.assertIn("ece", metrics)
-
-    def test_gates_fail_when_f1_improvement_insufficient(self):
-        # current_f1=0.99 → perfect candidate (f1=1.0) only gives delta=0.01 < 0.02
-        validator = self._make_validator(current_f1=0.99, min_delta=0.02)
-        probs = np.array([0.9, 0.1, 0.8, 0.2])
-        labels = np.array([1, 0, 1, 0])
-        metrics = validator._compute_metrics(probs, labels)
-        details = metrics["gate_details"]
-        self.assertFalse(metrics["gates_passed"])
-        self.assertFalse(details["gate_f1_passed"])
-
-    def test_gates_fail_when_ece_too_high(self):
-        validator = self._make_validator(current_f1=0.0, min_delta=0.0, max_ece=0.01)
-        # Force high ECE: all confidence 0.9 but labels are 0
-        probs = np.ones(50) * 0.9
-        labels = np.zeros(50, dtype=int)
-        metrics = validator._compute_metrics(probs, labels)
-        self.assertFalse(metrics["gates_passed"])
-        self.assertFalse(metrics["gate_details"]["gate_ece_passed"])
-
-    def test_gate_details_include_delta_f1(self):
-        validator = self._make_validator(current_f1=0.60)
-        probs = np.array([0.8, 0.2, 0.7, 0.3])
-        labels = np.array([1, 0, 1, 0])
-        metrics = validator._compute_metrics(probs, labels)
-        self.assertIn("delta_f1", metrics["gate_details"])
-        self.assertAlmostEqual(
-            metrics["gate_details"]["delta_f1"],
-            metrics["f1"] - 0.60,
-            places=3,
-        )
-
-
-# ---------------------------------------------------------------------------
-# ModelRegistry tests
-# ---------------------------------------------------------------------------
-
-class TestModelRegistry(unittest.TestCase):
-    def _make_registry(self, auto_deploy=True, hot_reload=False):
-        cfg = _make_config({
-            "deployment": {
-                "auto_deploy": auto_deploy,
-                "hot_reload": hot_reload,
-                "rollback_enabled": True,
-            }
-        })
-        with tempfile.TemporaryDirectory() as tmpdir:
-            cfg.get.side_effect = lambda k, d=None: {
-                "storage.models_path": tmpdir,
-            }.get(k, d)
-            db = _make_db()
-            return ModelRegistry(db, cfg), db, tmpdir
-
-    def test_get_active_version_returns_version(self):
-        cfg = _make_config()
-        cfg.get.side_effect = lambda k, d=None: {
-            "storage.models_path": "/tmp/models",
-        }.get(k, d)
-        db = MagicMock()
-        db.fetchall.return_value = [{"version": "v1.5.0"}]
-        registry = ModelRegistry(db, cfg)
-        self.assertEqual(registry.get_active_version(), "v1.5.0")
-
-    def test_get_active_version_returns_none_when_no_active(self):
-        cfg = _make_config()
-        cfg.get.side_effect = lambda k, d=None: {
-            "storage.models_path": "/tmp/models",
-        }.get(k, d)
-        db = MagicMock()
-        db.fetchall.return_value = []
-        registry = ModelRegistry(db, cfg)
-        self.assertIsNone(registry.get_active_version())
-
-    def test_get_active_f1_returns_float(self):
-        cfg = _make_config()
-        cfg.get.side_effect = lambda k, d=None: {
-            "storage.models_path": "/tmp/models",
-        }.get(k, d)
-        db = MagicMock()
-        db.fetchall.return_value = [{"f1_score": 0.85}]
-        registry = ModelRegistry(db, cfg)
-        self.assertAlmostEqual(registry.get_active_f1(), 0.85)
-
-    def test_get_active_f1_returns_zero_when_none(self):
-        cfg = _make_config()
-        cfg.get.side_effect = lambda k, d=None: {
-            "storage.models_path": "/tmp/models",
-        }.get(k, d)
-        db = MagicMock()
-        db.fetchall.return_value = []
-        registry = ModelRegistry(db, cfg)
-        self.assertEqual(registry.get_active_f1(), 0.0)
-
-    def test_register_candidate_executes_upsert(self):
-        cfg = _make_config()
-        cfg.get.side_effect = lambda k, d=None: {
-            "storage.models_path": "/tmp/models",
-        }.get(k, d)
-        db = MagicMock()
-        registry = ModelRegistry(db, cfg)
-        registry.register_candidate("v2.0.0", "/models/candidate/v2.0.0_best.pt", 0.82, 0.05)
-        db.execute.assert_called_once()
-        args = db.execute.call_args[0]
-        self.assertIn("INSERT INTO models", args[0])
-        self.assertIn("v2.0.0", args[1])
-
-    def test_rollback_promotes_archived_model(self):
-        cfg = _make_config()
-        cfg.get.side_effect = lambda k, d=None: {
-            "storage.models_path": "/tmp/models",
-        }.get(k, d)
-        db = MagicMock()
-        db.fetchall.return_value = [{"version": "v1.0.0"}]
-        registry = ModelRegistry(db, cfg)
-        registry.rollback("v2.0.0")
-
-        calls = [str(c) for c in db.execute.call_args_list]
-        # Should archive the failed version and activate the previous
-        archive_call = any("ARCHIVED" in c and "v2.0.0" in c for c in calls)
-        activate_call = any("ACTIVE" in c and "v1.0.0" in c for c in calls)
-        self.assertTrue(archive_call, f"Expected ARCHIVED v2.0.0 in calls: {calls}")
-        self.assertTrue(activate_call, f"Expected ACTIVE v1.0.0 in calls: {calls}")
-
-    def test_rollback_logs_warning_when_no_archived_model(self):
-        cfg = _make_config()
-        cfg.get.side_effect = lambda k, d=None: {
-            "storage.models_path": "/tmp/models",
-        }.get(k, d)
-        db = MagicMock()
-        db.fetchall.return_value = []
-        registry = ModelRegistry(db, cfg)
-        # Should not raise
-        registry.rollback("v2.0.0")
-        db.execute.assert_not_called()
-
-    def test_auto_deploy_disabled_skips_deployment(self):
-        cfg = _make_config({
-            "deployment": {"auto_deploy": False, "hot_reload": False, "rollback_enabled": True}
-        })
-        cfg.get.side_effect = lambda k, d=None: {
-            "storage.models_path": "/tmp/models",
-        }.get(k, d)
-        db = MagicMock()
-        registry = ModelRegistry(db, cfg)
-        result = registry.deploy("v2.0.0", "/models/candidate/v2.0.0_best.pt")
-        self.assertFalse(result)
-
-
-# ---------------------------------------------------------------------------
-# ActiveLearningPipeline tests
-# ---------------------------------------------------------------------------
-
-class TestActiveLearningPipeline(unittest.TestCase):
-    def test_pipeline_skips_when_below_min_samples(self):
-        cfg = _make_config({"min_annotated_samples": 100})
-        db = _make_db(annotated_count=50)
-        pipeline = ActiveLearningPipeline(db, cfg)
-        result = pipeline.run("v2.0.0")
-        self.assertFalse(result)
-
-    def test_pipeline_runs_when_above_threshold(self):
-        cfg = _make_config({"min_annotated_samples": 100})
-        db = _make_db(annotated_count=150)
-
-        pipeline = ActiveLearningPipeline(db, cfg)
-
-        with patch.object(pipeline._ingestor, "ingest", return_value=None) as mock_ingest:
-            result = pipeline.run("v2.0.0")
-            mock_ingest.assert_called_once_with("v2.0.0", seed=42)
-            self.assertFalse(result)  # ingestion returned None
-
-    def test_pipeline_aborts_when_training_fails(self):
-        cfg = _make_config({"min_annotated_samples": 10})
-        db = _make_db(annotated_count=50)
-        pipeline = ActiveLearningPipeline(db, cfg)
-
-        with patch.object(pipeline._ingestor, "ingest", return_value="/data/training/v2.0.0"), \
-             patch("active_learning.pipeline.Trainer") as MockTrainer:
-            MockTrainer.return_value.train.return_value = None
-            result = pipeline.run("v2.0.0")
-            self.assertFalse(result)
-
-    def test_pipeline_does_not_deploy_when_gates_fail(self):
-        cfg = _make_config({"min_annotated_samples": 10})
-        db = _make_db(annotated_count=50)
-        pipeline = ActiveLearningPipeline(db, cfg)
-
-        with patch.object(pipeline._ingestor, "ingest", return_value="/data/training/v2.0.0"), \
-             patch("active_learning.pipeline.Trainer") as MockTrainer, \
-             patch("active_learning.pipeline.Validator") as MockValidator:
-            MockTrainer.return_value.train.return_value = "/models/candidate/v2.0.0_best.pt"
-            MockValidator.return_value.validate.return_value = {
-                "f1": 0.71, "ece": 0.05,
-                "gates_passed": False,
-                "gate_details": {"delta_f1": 0.01},
-            }
-            result = pipeline.run("v2.0.0")
-            self.assertFalse(result)
-
-    def test_pipeline_deploys_when_gates_pass(self):
-        cfg = _make_config({"min_annotated_samples": 10})
-        db = _make_db(annotated_count=50)
-        pipeline = ActiveLearningPipeline(db, cfg)
-
-        with patch.object(pipeline._ingestor, "ingest", return_value="/data/training/v2.0.0"), \
-             patch("active_learning.pipeline.Trainer") as MockTrainer, \
-             patch("active_learning.pipeline.Validator") as MockValidator, \
-             patch.object(pipeline._registry, "deploy", return_value=True) as mock_deploy:
-            MockTrainer.return_value.train.return_value = "/models/candidate/v2.0.0_best.pt"
-            MockValidator.return_value.validate.return_value = {
-                "f1": 0.88, "ece": 0.04,
-                "gates_passed": True,
-                "gate_details": {"delta_f1": 0.18},
-            }
-            result = pipeline.run("v2.0.0")
-            self.assertTrue(result)
-            mock_deploy.assert_called_once_with("v2.0.0", "/models/candidate/v2.0.0_best.pt")
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_story_09.py

@@ -1,521 +0,0 @@
-"""
-Tests for Story 09: Observability, Monitoring & Hardening.
-
-Covers:
-  - metrics.py: NoOp fallback, update_queue_depths, update_scratch_metrics
-  - crash_recovery.py: recover_on_startup, checkpointing, idempotency guard
-  - retry.py: successful call, retry on transient error, non-retryable bypass, exhaustion
-  - drift_detector.py: detect_drift logic, all alert checks
-  - health_check.py: HealthStatus snapshot, HTTP /health endpoint
-"""
-
-import json
-import os
-import sys
-import tempfile
-import threading
-import time
-import unittest
-import urllib.request
-from pathlib import Path
-from unittest.mock import MagicMock, call, patch
-
-sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
-
-from crash_recovery import CrashRecovery
-from drift_detector import DriftDetector, detect_drift
-from health_check import HealthStatus, start_health_server, health
-from retry import RetryExhaustedError, retry, _is_non_retryable
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _make_config(overrides: dict = None):
-    cfg = MagicMock()
-    mon_defaults = {
-        "alerts": {
-            "confidence_drift_threshold": 0.10,
-            "review_queue_max_size": 1000,
-            "review_queue_max_age_hours": 24,
-            "throughput_min_videos_per_hour": 20,
-            "throughput_min_duration_hours": 1,
-            "error_rate_threshold": 0.05,
-            "error_rate_window_hours": 1,
-        },
-        "drift_detection": {"enabled": True, "schedule": "0 2 * * 0", "baseline_source": "db"},
-        "crash_recovery": {"lock_timeout_minutes": 5, "auto_requeue": True},
-        "retry": {"max_attempts": 3, "initial_delay": 0.0, "backoff_factor": 2.0},
-    }
-    if overrides:
-        mon_defaults.update(overrides)
-
-    def _get_section(section):
-        if section == "monitoring":
-            return mon_defaults
-        return {}
-
-    def _get(path, default=None):
-        mapping = {
-            "storage.scratch_path": "/tmp/test_scratch",
-            "storage.models_path": "/tmp/test_models",
-        }
-        return mapping.get(path, default)
-
-    cfg.get_section.side_effect = _get_section
-    cfg.get.side_effect = _get
-    return cfg
-
-
-def _make_db():
-    return MagicMock()
-
-
-# ---------------------------------------------------------------------------
-# metrics.py tests
-# ---------------------------------------------------------------------------
-
-class TestMetricsNoOp(unittest.TestCase):
-    """The _NoOpMetric must absorb all method calls without raising."""
-
-    def test_noop_labels_inc_does_not_raise(self):
-        from metrics import _NoOpMetric
-        m = _NoOpMetric()
-        m.labels(routing_decision="MATCH").inc()
-
-    def test_noop_set_does_not_raise(self):
-        from metrics import _NoOpMetric
-        m = _NoOpMetric()
-        m.set(42)
-
-    def test_noop_observe_does_not_raise(self):
-        from metrics import _NoOpMetric
-        m = _NoOpMetric()
-        m.observe(0.75)
-
-
-class TestMetricsQueueDepths(unittest.TestCase):
-    def test_update_queue_depths_sets_gauges(self):
-        from metrics import update_queue_depths, queue_depth_pending, queue_depth_processing
-
-        db = MagicMock()
-        db.fetchall.side_effect = [
-            [{"status": "PENDING", "cnt": 10}, {"status": "PROCESSING", "cnt": 3}],
-            [{"cnt": 7}],
-        ]
-        # Should not raise even if prometheus is absent
-        update_queue_depths(db)
-
-    def test_update_queue_depths_handles_db_error_gracefully(self):
-        from metrics import update_queue_depths
-        db = MagicMock()
-        db.fetchall.side_effect = Exception("DB down")
-        update_queue_depths(db)  # must not raise
-
-
-class TestMetricsScratch(unittest.TestCase):
-    def test_update_scratch_metrics_runs_without_error(self):
-        from metrics import update_scratch_metrics
-        with tempfile.TemporaryDirectory() as tmpdir:
-            update_scratch_metrics(tmpdir)  # must not raise
-
-    def test_update_scratch_metrics_handles_missing_path(self):
-        from metrics import update_scratch_metrics
-        update_scratch_metrics("/nonexistent_path_xyz")  # must not raise
-
-
-# ---------------------------------------------------------------------------
-# crash_recovery.py tests
-# ---------------------------------------------------------------------------
-
-class TestCrashRecoveryRequeue(unittest.TestCase):
-    def test_recover_on_startup_calls_update(self):
-        db = _make_db()
-        db.execute.return_value = 3
-        cfg = _make_config()
-        cr = CrashRecovery(db, cfg)
-        count = cr.recover_on_startup()
-        self.assertEqual(count, 3)
-        db.execute.assert_called_once()
-        sql = db.execute.call_args[0][0]
-        self.assertIn("PENDING", sql)
-        self.assertIn("PROCESSING", sql)
-
-    def test_recover_on_startup_skipped_when_disabled(self):
-        db = _make_db()
-        cfg = _make_config({"crash_recovery": {"lock_timeout_minutes": 5, "auto_requeue": False}})
-        cr = CrashRecovery(db, cfg)
-        count = cr.recover_on_startup()
-        self.assertEqual(count, 0)
-        db.execute.assert_not_called()
-
-    def test_recover_on_startup_handles_db_error(self):
-        db = _make_db()
-        db.execute.side_effect = Exception("connection refused")
-        cfg = _make_config()
-        cr = CrashRecovery(db, cfg)
-        count = cr.recover_on_startup()
-        self.assertEqual(count, 0)
-
-    def test_list_stuck_videos_returns_rows(self):
-        db = _make_db()
-        db.fetchall.return_value = [{"id": 5, "file_path": "/data/vid.mp4", "updated_at": None}]
-        cfg = _make_config()
-        cr = CrashRecovery(db, cfg)
-        rows = cr.list_stuck_videos()
-        self.assertEqual(len(rows), 1)
-        self.assertEqual(rows[0]["id"], 5)
-
-
-class TestCrashRecoveryCheckpoint(unittest.TestCase):
-    def test_save_and_load_checkpoint_roundtrip(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            cfg = _make_config()
-            cfg.get.side_effect = lambda k, d=None: {
-                "storage.scratch_path": tmpdir,
-            }.get(k, d)
-            cr = CrashRecovery(_make_db(), cfg)
-            cr.save_checkpoint(42, "extracting", {"frames_done": 5})
-            result = cr.load_checkpoint(42)
-            self.assertIsNotNone(result)
-            self.assertEqual(result["video_id"], 42)
-            self.assertEqual(result["state"], "extracting")
-            self.assertEqual(result["progress"]["frames_done"], 5)
-
-    def test_load_checkpoint_returns_none_when_absent(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            cfg = _make_config()
-            cfg.get.side_effect = lambda k, d=None: {
-                "storage.scratch_path": tmpdir,
-            }.get(k, d)
-            cr = CrashRecovery(_make_db(), cfg)
-            self.assertIsNone(cr.load_checkpoint(999))
-
-    def test_delete_checkpoint_removes_file(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            cfg = _make_config()
-            cfg.get.side_effect = lambda k, d=None: {
-                "storage.scratch_path": tmpdir,
-            }.get(k, d)
-            cr = CrashRecovery(_make_db(), cfg)
-            cr.save_checkpoint(7, "classifying", {})
-            cr.delete_checkpoint(7)
-            self.assertIsNone(cr.load_checkpoint(7))
-
-    def test_checkpoint_timestamp_is_iso_format(self):
-        with tempfile.TemporaryDirectory() as tmpdir:
-            cfg = _make_config()
-            cfg.get.side_effect = lambda k, d=None: {
-                "storage.scratch_path": tmpdir,
-            }.get(k, d)
-            cr = CrashRecovery(_make_db(), cfg)
-            cr.save_checkpoint(1, "detecting", {})
-            ckpt = cr.load_checkpoint(1)
-            # Should parse without error
-            from datetime import datetime
-            datetime.fromisoformat(ckpt["timestamp"].replace("Z", "+00:00"))
-
-
-class TestIdempotencyGuard(unittest.TestCase):
-    def test_returns_true_when_already_completed(self):
-        db = _make_db()
-        db.fetchall.return_value = [{"status": "COMPLETED"}]
-        cr = CrashRecovery(db, _make_config())
-        self.assertTrue(cr.is_already_completed(1))
-
-    def test_returns_false_when_not_completed(self):
-        db = _make_db()
-        db.fetchall.return_value = [{"status": "PENDING"}]
-        cr = CrashRecovery(db, _make_config())
-        self.assertFalse(cr.is_already_completed(1))
-
-    def test_returns_false_when_no_row(self):
-        db = _make_db()
-        db.fetchall.return_value = []
-        cr = CrashRecovery(db, _make_config())
-        self.assertFalse(cr.is_already_completed(99))
-
-
-# ---------------------------------------------------------------------------
-# retry.py tests
-# ---------------------------------------------------------------------------
-
-class TestRetryDecorator(unittest.TestCase):
-    def test_successful_call_returns_value(self):
-        @retry(max_attempts=3, initial_delay=0.0, step="test")
-        def always_succeeds():
-            return 42
-
-        self.assertEqual(always_succeeds(), 42)
-
-    def test_retries_on_transient_error(self):
-        call_count = {"n": 0}
-
-        @retry(max_attempts=3, initial_delay=0.0, step="test")
-        def flaky():
-            call_count["n"] += 1
-            if call_count["n"] < 3:
-                raise ConnectionError("transient")
-            return "ok"
-
-        result = flaky()
-        self.assertEqual(result, "ok")
-        self.assertEqual(call_count["n"], 3)
-
-    def test_raises_retry_exhausted_after_max_attempts(self):
-        @retry(max_attempts=3, initial_delay=0.0, step="test")
-        def always_fails():
-            raise ConnectionError("always fails")
-
-        with self.assertRaises(RetryExhaustedError):
-            always_fails()
-
-    def test_non_retryable_error_propagates_immediately(self):
-        call_count = {"n": 0}
-
-        @retry(max_attempts=3, initial_delay=0.0, step="test")
-        def raises_non_retryable():
-            call_count["n"] += 1
-            raise FileNotFoundError("no such file")
-
-        with self.assertRaises(FileNotFoundError):
-            raises_non_retryable()
-
-        self.assertEqual(call_count["n"], 1)
-
-    def test_only_specified_exception_types_are_retried(self):
-        @retry(max_attempts=3, initial_delay=0.0, exceptions=(ValueError,), step="test")
-        def raises_type_error():
-            raise TypeError("wrong type")
-
-        with self.assertRaises(TypeError):
-            raises_type_error()
-
-    def test_preserves_return_value_on_first_try(self):
-        @retry(max_attempts=5, initial_delay=0.0, step="test")
-        def returns_dict():
-            return {"key": "value"}
-
-        self.assertEqual(returns_dict(), {"key": "value"})
-
-
-class TestIsNonRetryable(unittest.TestCase):
-    def test_file_not_found_is_non_retryable(self):
-        self.assertTrue(_is_non_retryable(FileNotFoundError("x")))
-
-    def test_permission_error_is_non_retryable(self):
-        self.assertTrue(_is_non_retryable(PermissionError("x")))
-
-    def test_connection_error_is_retryable(self):
-        self.assertFalse(_is_non_retryable(ConnectionError("x")))
-
-    def test_runtime_error_is_retryable(self):
-        self.assertFalse(_is_non_retryable(RuntimeError("x")))
-
-
-# ---------------------------------------------------------------------------
-# drift_detector.py tests
-# ---------------------------------------------------------------------------
-
-class TestDetectDrift(unittest.TestCase):
-    def test_no_drift_when_distributions_match(self):
-        base = [0.3] * 50 + [0.7] * 50  # 50% high confidence
-        curr = [0.3] * 50 + [0.7] * 50
-        self.assertFalse(detect_drift(curr, base, threshold=0.10))
-
-    def test_drift_detected_when_shift_exceeds_threshold(self):
-        base = [0.3] * 80 + [0.8] * 20   # 20% high
-        curr = [0.8] * 70 + [0.3] * 30   # 70% high → shift = 0.50
-        self.assertTrue(detect_drift(curr, base, threshold=0.10))
-
-    def test_no_drift_just_below_threshold(self):
-        base = [0.8] * 50 + [0.2] * 50   # 50% high
-        curr = [0.8] * 59 + [0.2] * 41   # 59% high → shift = 9%
-        self.assertFalse(detect_drift(curr, base, threshold=0.10))
-
-    def test_drift_at_boundary(self):
-        base = [0.8] * 50 + [0.2] * 50   # 50%
-        curr = [0.8] * 61 + [0.2] * 39   # 61% → shift = 11%
-        self.assertTrue(detect_drift(curr, base, threshold=0.10))
-
-    def test_empty_current_returns_false(self):
-        self.assertFalse(detect_drift([], [0.5] * 10, threshold=0.10))
-
-    def test_empty_baseline_returns_false(self):
-        self.assertFalse(detect_drift([0.5] * 10, [], threshold=0.10))
-
-
-class TestDriftDetectorAlerts(unittest.TestCase):
-    def _make_detector(self, db=None, overrides=None):
-        alerts = []
-        cfg = _make_config(overrides or {})
-        db = db or _make_db()
-        detector = DriftDetector(db, cfg, alert_fn=alerts.append)
-        return detector, alerts
-
-    def test_check_review_queue_growth_triggers_alert(self):
-        db = _make_db()
-        db.fetchall.return_value = [{"cnt": 1500}]
-        detector, alerts = self._make_detector(db)
-        triggered = detector.check_review_queue_growth()
-        self.assertTrue(triggered)
-        self.assertEqual(len(alerts), 1)
-        self.assertIn("1500", alerts[0])
-
-    def test_check_review_queue_growth_no_alert_below_threshold(self):
-        db = _make_db()
-        db.fetchall.return_value = [{"cnt": 50}]
-        detector, alerts = self._make_detector(db)
-        triggered = detector.check_review_queue_growth()
-        self.assertFalse(triggered)
-        self.assertEqual(len(alerts), 0)
-
-    def test_check_low_throughput_triggers_alert(self):
-        db = _make_db()
-        db.fetchall.return_value = [{"cnt": 5}]  # 5 videos in last 1h < 20 min
-        detector, alerts = self._make_detector(db)
-        triggered = detector.check_low_throughput()
-        self.assertTrue(triggered)
-        self.assertEqual(len(alerts), 1)
-
-    def test_check_low_throughput_no_alert_above_threshold(self):
-        db = _make_db()
-        db.fetchall.return_value = [{"cnt": 50}]  # 50 > 20
-        detector, alerts = self._make_detector(db)
-        triggered = detector.check_low_throughput()
-        self.assertFalse(triggered)
-
-    def test_check_error_rate_triggers_alert(self):
-        db = _make_db()
-        db.fetchall.return_value = [
-            {"status": "COMPLETED", "cnt": 80},
-            {"status": "ERROR", "cnt": 10},
-            {"status": "UNSCANNABLE", "cnt": 10},
-        ]
-        detector, alerts = self._make_detector(db)
-        triggered = detector.check_error_rate()
-        self.assertTrue(triggered)  # 20/100 = 20% > 5%
-        self.assertEqual(len(alerts), 1)
-
-    def test_check_error_rate_no_alert_below_threshold(self):
-        db = _make_db()
-        db.fetchall.return_value = [
-            {"status": "COMPLETED", "cnt": 98},
-            {"status": "ERROR", "cnt": 2},
-        ]
-        detector, alerts = self._make_detector(db)
-        triggered = detector.check_error_rate()
-        self.assertFalse(triggered)  # 2% < 5%
-
-    def test_check_error_rate_no_alert_zero_videos(self):
-        db = _make_db()
-        db.fetchall.return_value = []
-        detector, alerts = self._make_detector(db)
-        triggered = detector.check_error_rate()
-        self.assertFalse(triggered)
-
-    def test_run_all_checks_returns_dict_with_expected_keys(self):
-        db = _make_db()
-        db.fetchall.return_value = [{"cnt": 0}]
-        detector, _ = self._make_detector(db)
-        with patch.object(detector, "_fetch_recent_confidences", return_value=[]):
-            results = detector.run_all_checks()
-        self.assertIn("confidence_drift", results)
-        self.assertIn("review_queue_growth", results)
-        self.assertIn("low_throughput", results)
-        self.assertIn("high_error_rate", results)
-
-    def test_check_handles_db_error_gracefully(self):
-        db = _make_db()
-        db.fetchall.side_effect = Exception("DB offline")
-        detector, alerts = self._make_detector(db)
-        # Should not raise
-        self.assertFalse(detector.check_review_queue_growth())
-        self.assertFalse(detector.check_low_throughput())
-        self.assertFalse(detector.check_error_rate())
-
-
-# ---------------------------------------------------------------------------
-# health_check.py tests
-# ---------------------------------------------------------------------------
-
-class TestHealthStatus(unittest.TestCase):
-    def test_snapshot_contains_required_keys(self):
-        hs = HealthStatus()
-        snap = hs.snapshot()
-        for key in ("status", "gpu_available", "gpu_memory_used_gb",
-                    "queue_depth", "uptime_seconds", "videos_processed_today", "last_error"):
-            self.assertIn(key, snap, f"Missing key: {key}")
-
-    def test_update_changes_values(self):
-        hs = HealthStatus()
-        hs.update(status="healthy", queue_depth=55)
-        snap = hs.snapshot()
-        self.assertEqual(snap["status"], "healthy")
-        self.assertEqual(snap["queue_depth"], 55)
-
-    def test_uptime_increases_over_time(self):
-        hs = HealthStatus()
-        snap1 = hs.snapshot()
-        time.sleep(0.05)
-        snap2 = hs.snapshot()
-        self.assertGreaterEqual(snap2["uptime_seconds"], snap1["uptime_seconds"])
-
-    def test_update_is_thread_safe(self):
-        hs = HealthStatus()
-        errors = []
-
-        def writer(n):
-            try:
-                for _ in range(100):
-                    hs.update(queue_depth=n)
-            except Exception as exc:
-                errors.append(exc)
-
-        threads = [threading.Thread(target=writer, args=(i,)) for i in range(5)]
-        for t in threads:
-            t.start()
-        for t in threads:
-            t.join()
-
-        self.assertEqual(errors, [])
-
-    def test_http_health_endpoint_returns_200(self):
-        """Start a real health server and hit /health with urllib."""
-        import socket
-
-        # Find a free port
-        with socket.socket() as s:
-            s.bind(("127.0.0.1", 0))
-            port = s.getsockname()[1]
-
-        t = start_health_server(port)
-        time.sleep(0.1)  # give the server a moment to bind
-
-        url = f"http://127.0.0.1:{port}/health"
-        with urllib.request.urlopen(url, timeout=2) as resp:
-            self.assertEqual(resp.status, 200)
-            body = json.loads(resp.read())
-            self.assertIn("status", body)
-            self.assertIn("uptime_seconds", body)
-
-    def test_http_404_for_unknown_path(self):
-        """Non /health paths return 404."""
-        import socket
-        from urllib.error import HTTPError
-
-        with socket.socket() as s:
-            s.bind(("127.0.0.1", 0))
-            port = s.getsockname()[1]
-
-        start_health_server(port)
-        time.sleep(0.1)
-
-        with self.assertRaises(HTTPError) as ctx:
-            urllib.request.urlopen(f"http://127.0.0.1:{port}/unknown", timeout=2)
-        self.assertEqual(ctx.exception.code, 404)
-
-
-if __name__ == "__main__":
-    unittest.main()

worker/Dockerfile

@@ -64,5 +64,5 @@ HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
 # Switch to non-root user
 USER appuser
 
-# Default command
-CMD ["python3", "-m", "src.main"]
+# Default command: process tasks from the API until drained
+CMD ["python3", "-m", "src.task_worker"]

worker/docker-compose.yml

@@ -0,0 +1,40 @@
+# VideoDetect — Worker service (standalone compose file)
+# Run: docker compose -f worker/docker-compose.yml up -d
+
+services:
+  worker:
+    build:
+      context: ./
+      dockerfile: Dockerfile
+    container_name: videodetect-worker
+    restart: "no"
+    environment:
+      - API_BASE_URL=${API_BASE_URL:-http://videodetect-api:3000}
+      - TASK_COUNT=${TASK_COUNT:-50}
+      - REVIEW_CONFIDENCE=${REVIEW_CONFIDENCE:-0.75}
+      - LOG_FILE=/logs/videodetect.log
+    volumes:
+      - nas_input:/data/input:ro
+      - scratch_data:/scratch
+      - $PWD/worker-logs:/logs
+    networks:
+      - videodetect_videodetect-network
+    deploy:
+      resources:
+        limits:
+          memory: 4G
+          nvidia-gpus: "1"
+
+volumes:
+  nas_input:
+    driver: local
+    driver_opts:
+      type: nfs
+      o: addr=10.0.0.2,ro,nfsvers=4,hard,intr
+      device: ":/mnt/Bulk/Homes/ryan/Prawns"
+  scratch_data:
+    driver: local
+
+networks:
+  videodetect_videodetect-network:
+    external: true