# Image Classification Usage

Image classification tasks send one or more captcha images to an OpenAI-compatible vision model and return the indices of matching cells or a boolean answer. No browser automation is involved — these are pure vision model API calls.

## Supported task types

| Task type | Description |
|-----------|-------------|
| `HCaptchaClassification` | hCaptcha 3x3 grid — returns matching cell indices |
| `ReCaptchaV2Classification` | reCAPTCHA v2 3x3 / 4x4 grid — returns matching cell indices |
| `FunCaptchaClassification` | FunCaptcha 2x3 grid — returns the correct cell index |
| `AwsClassification` | AWS CAPTCHA image selection |

## Solution fields

| Task type | Solution field | Example |
|-----------|---------------|---------|
| `HCaptchaClassification` | `objects` or `answer` | `[0, 2, 5]` or `true` |
| `ReCaptchaV2Classification` | `objects` | `[0, 3, 6]` |
| `FunCaptchaClassification` | `objects` | `[4]` |
| `AwsClassification` | `objects` | `[1]` |

## HCaptchaClassification

### Request shape

```json
{
  "clientKey": "your-client-key",
  "task": {
    "type": "HCaptchaClassification",
    "queries": ["<base64-image-1>", "<base64-image-2>", "<base64-image-3>"],
    "question": "Please click each image containing a bicycle"
  }
}
```

The `queries` field accepts a list of base64-encoded images (one per grid cell). The `question` field is the challenge prompt displayed to the user.

### Response

```json
{
  "errorId": 0,
  "status": "ready",
  "solution": {
    "objects": [1, 4]
  }
}
```

## ReCaptchaV2Classification

### Request shape

```json
{
  "clientKey": "your-client-key",
  "task": {
    "type": "ReCaptchaV2Classification",
    "image": "<base64-encoded-grid-image>",
    "question": "Select all images with traffic lights"
  }
}
```

The `image` field is a single base64-encoded image of the full reCAPTCHA grid (3×3 = 9 cells or 4×4 = 16 cells). Cells are numbered 0–8 (or 0–15), left-to-right, top-to-bottom.

### Response

```json
{
  "errorId": 0,
  "status": "ready",
  "solution": {
    "objects": [0, 3, 6]
  }
}
```

## FunCaptchaClassification

### Request shape

```json
{
  "clientKey": "your-client-key",
  "task": {
    "type": "FunCaptchaClassification",
    "image": "<base64-encoded-grid-image>",
    "question": "Pick the image that shows a boat facing left"
  }
}
```

The grid is typically 2×3 (6 cells). Usually one answer is expected.

### Response

```json
{
  "errorId": 0,
  "status": "ready",
  "solution": {
    "objects": [3]
  }
}
```

## AwsClassification

### Request shape

```json
{
  "clientKey": "your-client-key",
  "task": {
    "type": "AwsClassification",
    "image": "<base64-encoded-image>",
    "question": "Select the image that matches"
  }
}
```

### Response

```json
{
  "errorId": 0,
  "status": "ready",
  "solution": {
    "objects": [1]
  }
}
```

## Create and poll (generic example)

```bash
# Step 1: create task
TASK_ID=$(curl -s -X POST http://localhost:8000/createTask \
  -H "Content-Type: application/json" \
  -d '{
    "clientKey": "your-client-key",
    "task": {
      "type": "ReCaptchaV2Classification",
      "image": "'$(base64 -w0 captcha.png)'",
      "question": "Select all images with traffic lights"
    }
  }' | python -c "import sys,json; print(json.load(sys.stdin)['taskId'])")

# Step 2: poll result
curl -s -X POST http://localhost:8000/getTaskResult \
  -H "Content-Type: application/json" \
  -d "{\"clientKey\":\"your-client-key\",\"taskId\":\"$TASK_ID\"}"
```

## Operational notes

- All classification tasks are **synchronous from the model's perspective** — the `asyncio.create_task` wrapper means the HTTP response is immediate, but the actual model call happens in the background.
- Model accuracy depends entirely on the vision model configured via `CAPTCHA_MULTIMODAL_MODEL` (default: `qwen3.5-2b`).
- For best results with classification, the `CAPTCHA_MODEL` (`gpt-5.4`) can be substituted by setting `CAPTCHA_MULTIMODAL_MODEL=gpt-5.4`.
- Images should not be pre-resized — the solver handles normalization internally.