---
title: Antai AI Image Detector
emoji: 🔍
colorFrom: red
colorTo: yellow
sdk: docker
pinned: false
app_port: 7860
---

# Antai AI Image Detector – Inference API

FastAPI service implementing a **two-stage multi-model routing architecture** for AI vs human image detection.

## Architecture

```
Image Input
    │
    ▼
Stage 1 — Router (openai/clip-vit-base-patch32)
    │  Zero-shot classification into 5 content buckets
    │
    ▼
Stage 2 — Specialist (per-bucket, all default to Ateeqq/ai-vs-human-image-detector)
    │  AI vs human classification tuned for the detected content type
    │
    ▼
JSON Response
```

## Routing Table

| Bucket | CLIP Candidate Label | Specialist Env Var |
|---|---|---|
| `portrait_face` | `"portrait or face photo"` | `SPECIALIST_FACE_MODEL_ID` |
| `document_ui_screenshot` | `"document screenshot or UI"` | `SPECIALIST_DOCUMENT_MODEL_ID` |
| `art_illustration` | `"artwork illustration or painting"` | `SPECIALIST_ART_MODEL_ID` |
| `nature_landscape` | `"nature landscape or outdoor"` | `SPECIALIST_NATURE_MODEL_ID` |
| `general` | `"general photo or other"` (also fallback) | `SPECIALIST_GENERAL_MODEL_ID` |

## Environment Variables

| Variable | Default | Description |
|---|---|---|
| `ROUTER_MODEL_ID` | `openai/clip-vit-base-patch32` | Router model (zero-shot-image-classification task) |
| `SPECIALIST_GENERAL_MODEL_ID` | `Ateeqq/ai-vs-human-image-detector` | Specialist for the general / fallback bucket |
| `SPECIALIST_FACE_MODEL_ID` | `Ateeqq/ai-vs-human-image-detector` | Specialist for portrait / face imagery |
| `SPECIALIST_DOCUMENT_MODEL_ID` | `Ateeqq/ai-vs-human-image-detector` | Specialist for screenshots and documents |
| `SPECIALIST_ART_MODEL_ID` | `Ateeqq/ai-vs-human-image-detector` | Specialist for art and illustrations |
| `SPECIALIST_NATURE_MODEL_ID` | `Ateeqq/ai-vs-human-image-detector` | Specialist for nature and landscapes |
| `AI_THRESHOLD` | `0.5` | Confidence threshold for `isAI: true` |
| `DEBUG` | `false` | When `true`, adds `rawRouter` and `rawSpecialist` to response |

See `space/.env.example` for a copy-paste template.

## Endpoints

### `POST /detect`
```json
{ "imageUrl": "https://..." }
// or
{ "imageData": "data:image/jpeg;base64,..." }
```

Returns:
```json
{
  "confidence": 0.87,
  "isAI": true,
  "provider": "huggingface",
  "routerLabel": "portrait_face",
  "routerConfidence": 0.73,
  "specialistModel": "Ateeqq/ai-vs-human-image-detector"
}
```

With `DEBUG=true`, also includes:
```json
{
  "rawRouter":     [{ "label": "portrait or face photo", "score": 0.73 }, ...],
  "rawSpecialist": [{ "label": "ai", "score": 0.87 }, { "label": "hum", "score": 0.13 }]
}
```

### `GET /health`
Returns `{ "status": "ok" }` — used by the Node.js backend to check if the Space is awake.

## Docker / Space Build Notes

The Dockerfile uses `huggingface-cli download` (from `huggingface_hub[cli]`) to pre-cache both the router and default specialist at build time.  Each model is a separate `RUN` layer for Docker cache granularity.

**Memory footprint (default config):** ~600 MB (CLIP router) + ~350 MB (one Ateeqq specialist shared across all 5 buckets) ≈ **~950 MB** — well within the HF Spaces free-tier limit.

**Custom specialists:** If you override a specialist via an env var, that model is **not** pre-cached in the Docker image and will download on the first cold start.  For production use, extend the Dockerfile with additional `huggingface-cli download <model-id>` lines or accept the first-request latency.

**ZeroGPU:** If deploying to a HuggingFace ZeroGPU Space, wrap specialist inference with the `@spaces.GPU` decorator and lazy-load pipelines inside it to avoid pre-allocating all VRAM at startup.