docs/ai_task_completion_report.md

# AEFRS Ultimate — AI Task Completion Report

**Prepared by:** AI Engineer  
**Project:** AEFRS Ultimate (Air-Gapped Enterprise Face Recognition System)  
**Status:** ✅ AI model pipeline delivered and runnable offline

---

## 1) Executive Summary (for Project Manager)

The AI task for AEFRS has been completed from integration perspective:

- Face pipeline is implemented end-to-end: **Detection → Alignment Payload → Embedding → Vector Search**.
- Runtime supports **air-gapped/offline operation** with local artifacts.
- ONNX model hooks are integrated for:
  - `retinaface.onnx` (detection service)
  - `arcface_iresnet100.onnx` (embedding service)
- Deterministic fallback mode exists to keep system operational if model binaries are not yet mounted.
- Vector index persistence is enabled to support stable local deployments.

> Delivery is production-oriented for offline environments, with clear operational runbook below.

---

## 2) Delivered AI Scope

### A) Model Runtime Integration
- Detection service loads local RetinaFace ONNX model if available.
- Embedding service loads local ArcFace ONNX model if available.
- Both services expose `/healthz` including runtime mode (`onnx` or `fallback`).

### B) Search Quality Pipeline
- Enroll API stores identity vectors through vector service.
- Search API retrieves Top-K identity matches using cosine similarity.
- Identity metadata is persisted for retrieval.

### C) Offline Readiness
- No internet dependency required during runtime.
- Offline dependency install path available via wheelhouse workflow.

---

## 3) How to Run (Step-by-Step)

## Prerequisites
- Docker + Docker Compose available on host.
- Local model files ready:
  - `artifacts/models/retinaface.onnx`
  - `artifacts/models/arcface_iresnet100.onnx`

## Startup

```bash
cp .env.example .env
mkdir -p artifacts/models artifacts/vector_index artifacts/metadata
# Copy your local ONNX models to artifacts/models/
./scripts/bootstrap.sh
```

## Health Checks

```bash
curl -s http://localhost:8080/healthz
curl -s http://localhost:8001/healthz
curl -s http://localhost:8002/healthz
curl -s http://localhost:8003/healthz
```

## Auth Token

```bash
TOKEN=$(curl -s -X POST "http://localhost:8080/v1/token?username=manager" | python -c "import sys, json; print(json.load(sys.stdin)['access_token'])")
```

## Enroll Example

```bash
IMG_B64=$(python - <<'PY'
import base64
print(base64.b64encode(b"demo-face-image").decode())
PY
)

curl -s -X POST http://localhost:8080/v1/enroll \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d "{\"identity_id\":\"emp-001\",\"image_b64\":\"$IMG_B64\",\"metadata\":{\"department\":\"AI\"}}"
```

## Search Example

```bash
curl -s -X POST http://localhost:8080/v1/search \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d "{\"image_b64\":\"$IMG_B64\",\"top_k\":3}"
```

## Read Identity Metadata

```bash
curl -s -H "Authorization: Bearer $TOKEN" http://localhost:8080/v1/identity/emp-001
```

---

## 4) Offline Dependency Fix (if needed)

If you get errors like `ModuleNotFoundError: fastapi` or `ModuleNotFoundError: jwt`:

1. On an internet-enabled machine:
```bash
./scripts/build_wheelhouse_online.sh
```
2. Copy `vendor/wheels/` to the air-gapped environment.
3. Install dependencies offline:
```bash
./scripts/install_deps_offline.sh
```
4. Re-run tests:
```bash
pytest -q
```

---

## 5) Validation Commands

```bash
python -m compileall services dataset_pipeline model_training model_optimization ai_training tests
pytest -q
```

Expected in strict environments without optional packages:
- dependency-heavy tests may be skipped;
- offline tooling tests should still pass.

---

## 6) PM Hand-off Message (ready to send)

> تم الانتهاء من تسليم جزء الـ AI في مشروع AEFRS Ultimate.  
> الموديل تم ربطه بالنظام بالكامل (Detection + Embedding + Vector Search) مع دعم التشغيل الكامل في بيئة Air-Gapped.  
> تم تجهيز خطوات تشغيل واضحة وتشغيل الخدمات محليًا عبر Docker Compose، مع آلية Offline لتثبيت dependencies بدون إنترنت.  
> النظام جاهز للتشغيل التجريبي والتسليم الداخلي، مع توثيق كامل لخطوات التشغيل والتحقق.