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

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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.

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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.

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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

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

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

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

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

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

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

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4) Offline Dependency Fix (if needed)

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

1. On an internet-enabled machine:

./scripts/build_wheelhouse_online.sh

2. Copy vendor/wheels/ to the air-gapped environment.
3. Install dependencies offline:

./scripts/install_deps_offline.sh

4. Re-run tests:

pytest -q

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5) Validation Commands

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.

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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.