Add detailed file index and enhance skill documentation for autopilot, decompose, deploy, plan, and research skills. Introduce tests-only mode in decompose skill, clarify required files for deploy and plan skills, and improve prerequisite checks across skills for better user guidance and workflow efficiency.

2026-04-22 10:36:32 +00:00 · 2026-03-22 16:15:49 +02:00
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+# Component: API
+
+## Overview
+
+**Purpose**: HTTP API layer exposing object detection capabilities via FastAPI — handles request/response serialization, async task management, SSE streaming, and authentication token forwarding.
+
+**Pattern**: Controller layer — thin API surface that delegates all business logic to the Inference Pipeline.
+
+**Upstream**: Inference Pipeline (Inference class), Domain (constants_inf for labels).
+**Downstream**: None (top-level, client-facing).
+
+## Modules
+
+| Module | Role |
+|--------|------|
+| `main` | FastAPI app definition, endpoints, DTOs, TokenManager, SSE streaming |
+
+## External API Specification
+
+### GET /health
+
+**Response**: `HealthResponse`
+```json
+{
+  "status": "healthy",
+  "aiAvailability": "Enabled",
+  "errorMessage": null
+}
+```
+`aiAvailability` values: None, Downloading, Converting, Uploading, Enabled, Warning, Error.
+
+### POST /detect
+
+**Input**: Multipart form — `file` (image bytes), optional `config` (JSON string).
+**Response**: `list[DetectionDto]`
+```json
+[
+  {
+    "centerX": 0.5,
+    "centerY": 0.5,
+    "width": 0.1,
+    "height": 0.1,
+    "classNum": 0,
+    "label": "ArmorVehicle",
+    "confidence": 0.85
+  }
+]
+```
+**Errors**: 400 (empty image / invalid data), 422 (runtime error), 503 (engine unavailable).
+
+### POST /detect/{media_id}
+
+**Input**: Path param `media_id`, optional JSON body `AIConfigDto`, headers `Authorization: Bearer {token}`, `x-refresh-token: {token}`.
+**Response**: `{"status": "started", "mediaId": "..."}` (202-style).
+**Errors**: 409 (duplicate detection for same media_id).
+**Side effects**: Starts async detection task; results delivered via SSE stream and/or posted to Annotations service.
+
+### GET /detect/stream
+
+**Response**: `text/event-stream` (SSE).
+```
+data: {"annotations": [...], "mediaId": "...", "mediaStatus": "AIProcessing", "mediaPercent": 50}
+```
+`mediaStatus` values: AIProcessing, AIProcessed, Error.
+
+## Data Access Patterns
+
+- In-memory state:
+  - `_active_detections: dict[str, bool]` — guards against duplicate media processing
+  - `_event_queues: list[asyncio.Queue]` — SSE client queues (maxsize=100)
+- No database access
+
+## Implementation Details
+
+- `Inference` is lazy-loaded on first use via `get_inference()` global function
+- `ThreadPoolExecutor(max_workers=2)` runs inference off the async event loop
+- SSE: one `asyncio.Queue` per connected client; events broadcast to all queues; full queues silently drop events
+- `TokenManager` decodes JWT exp from base64 payload (no signature verification), auto-refreshes 60s before expiry
+- `detection_to_dto` maps Detection fields to DetectionDto, looks up label from `constants_inf.annotations_dict`
+- Annotations posted to external service with base64-encoded frame image
+
+## Caveats
+
+- No CORS middleware configured
+- No rate limiting
+- No request body size limits beyond FastAPI defaults
+- `_active_detections` is an in-memory dict — not persistent across restarts, not distributed
+- SSE queue overflow silently drops events (QueueFull caught and ignored)
+- JWT token handling has no signature verification — relies entirely on the Annotations service for auth
+- No graceful shutdown handling for in-progress detections
+
+## Dependency Graph
+
+```mermaid
+graph TD
+    main --> inference
+    main --> constants_inf
+    main --> loader_http_client
+```
+
+## Logging Strategy
+
+No explicit logging in main.py — errors are caught and returned as HTTP responses. Logging happens in downstream components.