# Test Environment

## Overview

**System under test**: Azaion.Detections — FastAPI HTTP service exposing `POST /detect`, `POST /detect/{media_id}`, `GET /detect/stream`, `GET /health`
**Consumer app purpose**: Standalone test runner that exercises the detection service through its public HTTP/SSE interfaces, validating black-box use cases without access to internals.

## Docker Environment

### Services

| Service | Image / Build | Purpose | Ports |
|---------|--------------|---------|-------|
| detections | Build from repo root (setup.py + Cython compile, uvicorn entrypoint) | System under test — the detection microservice | 8000:8000 |
| mock-loader | Custom lightweight HTTP stub (Python/Node) | Mock of the Loader service — serves ONNX model files, accepts TensorRT uploads | 8080:8080 |
| mock-annotations | Custom lightweight HTTP stub (Python/Node) | Mock of the Annotations service — accepts detection results, provides token refresh | 8081:8081 |
| e2e-consumer | Build from `e2e/` directory | Black-box test runner (pytest) | — |

### GPU Configuration

For tests requiring TensorRT (GPU path):
- Deploy `detections` with `runtime: nvidia` and `NVIDIA_VISIBLE_DEVICES=all`
- The test suite has two profiles: `gpu` (TensorRT tests) and `cpu` (ONNX fallback tests)
- CPU-only tests run without GPU runtime, verifying ONNX fallback behavior

### Networks

| Network | Services | Purpose |
|---------|----------|---------|
| e2e-net | all | Isolated test network — all service-to-service communication via hostnames |

### Volumes

| Volume | Mounted to | Purpose |
|--------|-----------|---------|
| test-models | mock-loader:/models | Pre-built ONNX model file for test inference |
| test-media | e2e-consumer:/media | Sample images and video files for detection requests |
| test-classes | detections:/app/classes.json | classes.json with 19 detection classes |
| test-results | e2e-consumer:/results | CSV test report output |

### docker-compose structure

```yaml
services:
  mock-loader:
    build: ./e2e/mocks/loader
    ports: ["8080:8080"]
    volumes:
      - test-models:/models
    networks: [e2e-net]

  mock-annotations:
    build: ./e2e/mocks/annotations
    ports: ["8081:8081"]
    networks: [e2e-net]

  detections:
    build:
      context: .
      dockerfile: Dockerfile
    ports: ["8000:8000"]
    environment:
      - LOADER_URL=http://mock-loader:8080
      - ANNOTATIONS_URL=http://mock-annotations:8081
    volumes:
      - test-classes:/app/classes.json
    depends_on:
      - mock-loader
      - mock-annotations
    networks: [e2e-net]
    # GPU profile adds: runtime: nvidia

  e2e-consumer:
    build: ./e2e
    volumes:
      - test-media:/media
      - test-results:/results
    depends_on:
      - detections
    networks: [e2e-net]
    command: pytest --csv=/results/report.csv

volumes:
  test-models:
  test-media:
  test-classes:
  test-results:

networks:
  e2e-net:
```

## Consumer Application

**Tech stack**: Python 3, pytest, requests, sseclient-py
**Entry point**: `pytest --csv=/results/report.csv`

### Communication with system under test

| Interface | Protocol | Endpoint | Authentication |
|-----------|----------|----------|----------------|
| Health check | HTTP GET | `http://detections:8000/health` | None |
| Single image detect | HTTP POST (multipart) | `http://detections:8000/detect` | None |
| Media detect | HTTP POST (JSON) | `http://detections:8000/detect/{media_id}` | Bearer JWT + x-refresh-token headers |
| SSE stream | HTTP GET (SSE) | `http://detections:8000/detect/stream` | None |

### What the consumer does NOT have access to

- No direct import of Cython modules (inference, annotation, engines)
- No direct access to the detections service filesystem or Logs/ directory
- No shared memory with the detections process
- No direct calls to mock-loader or mock-annotations (except for test setup/teardown verification)

## CI/CD Integration

**When to run**: On PR merge to dev, nightly scheduled run
**Pipeline stage**: After unit tests, before deployment
**Gate behavior**: Block merge if any functional test fails; non-functional failures are warnings
**Timeout**: 15 minutes for CPU profile, 30 minutes for GPU profile

## Reporting

**Format**: CSV
**Columns**: Test ID, Test Name, Execution Time (ms), Result (PASS/FAIL/SKIP), Error Message (if FAIL)
**Output path**: `/results/report.csv` (mounted volume → `./e2e-results/report.csv` on host)