# Image SafeSearch Classifier (`robin.geekydev/image-safesearch-classifier`) Actor

Analyze images with LLM and classify them as NSFW, Safe, Child Safe, Adult, Violence, Racy, Medical or Spoof.

- **URL**: https://apify.com/robin.geekydev/image-safesearch-classifier.md
- **Developed by:** [Robin p](https://apify.com/robin.geekydev) (community)
- **Categories:** AI, Agents, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.00 / 1,000 classified images

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Image SafeSearch Classifier

Classify images with an LLM using SafeSearch-style safety categories. Run it on Apify to moderate image URLs or base64 payloads at scale.

### What it does

For each image URL (or base64 payload), the Actor uses an LLM to analyze visual content and returns:

| Category | Meaning |
|---|---|
| `adult` | Adult / explicit content |
| `racy` | Suggestive but not necessarily explicit |
| `violence` | Violent content |
| `medical` | Medical imagery |
| `spoof` | Spoof / manipulated content |

Likelihood values: `UNKNOWN`, `VERY_UNLIKELY`, `UNLIKELY`, `POSSIBLE`, `LIKELY`, `VERY_LIKELY`.

#### Derived labels

| Label | Rule (defaults) |
|---|---|
| **NSFW** | `adult` or `racy` ≥ threshold (`LIKELY`) |
| **Adult** | `adult` ≥ threshold |
| **Violence** | `violence` ≥ threshold |
| **Racy** | `racy` ≥ threshold |
| **Medical** | `medical` ≥ threshold |
| **Spoof** | `spoof` ≥ threshold |
| **Safe** | adult / violence / racy / medical all below threshold |
| **Child Safe** | adult / racy / violence ≤ `UNLIKELY` (stricter) |

Each dataset item also includes `primaryLabel`, `isNsfw`, `isSafe`, `isChildSafe`, and `reason`.

### Pricing

This Actor uses **pay per result**:

- **$0.01 per successfully classified image** (`$10.00 / 1,000 results`)
- You only pay for successful classifications written to the default dataset
- Failed images are not charged (they are logged separately)

### How to use

1. Open this Actor on Apify
2. Paste one or more public image URLs (or provide base64 in `images`)
3. Optionally adjust thresholds and concurrency
4. Click **Start**
5. Review results in the **Dataset** / **Output** tab

#### Input

| Field | Required | Description |
|---|---|---|
| `imageUrls` | one of urls/images | Public image URLs |
| `images` | one of urls/images | Objects: `{ id?, url?, base64? }` |
| `likelihoodThreshold` | no | `POSSIBLE` | `LIKELY` | `VERY_LIKELY` (default `LIKELY`) |
| `childSafeMaxLikelihood` | no | `VERY_UNLIKELY` | `UNLIKELY` (default `UNLIKELY`) |
| `maxConcurrency` | no | How many images to analyze in parallel (default `5`) |

#### Example input

```json
{
  "imageUrls": [
    "https://example.com/photo-1.jpg",
    "https://example.com/photo-2.jpg"
  ],
  "likelihoodThreshold": "LIKELY",
  "childSafeMaxLikelihood": "UNLIKELY",
  "maxConcurrency": 5
}
```

#### Example dataset item

```json
{
  "imageId": "url-1",
  "imageUrl": "https://example.com/photo-1.jpg",
  "reason": "Ordinary photo with no concerning content.",
  "safeSearch": {
    "adult": "VERY_UNLIKELY",
    "spoof": "UNLIKELY",
    "medical": "UNLIKELY",
    "violence": "VERY_UNLIKELY",
    "racy": "VERY_UNLIKELY"
  },
  "flags": {
    "adult": false,
    "violence": false,
    "racy": false,
    "medical": false,
    "spoof": false
  },
  "labels": ["Safe", "Child Safe"],
  "primaryLabel": "Child Safe",
  "isNsfw": false,
  "isSafe": true,
  "isChildSafe": true,
  "error": null
}
```

### Notes

- Image URLs must be publicly reachable so the Actor can download them for analysis.
- For private images, pass `base64` in the `images` array instead of a URL.
- This Actor moderates content; it does not generate or request illegal material.
- LLM inference cost is covered by the Actor configuration; Apify run compute is billed by Apify.

# Actor input Schema

## `imageUrls` (type: `array`):

List of publicly reachable image URLs to analyze with the LLM.

## `images` (type: `array`):

Optional structured image list. Each item can include url, base64, and an optional id.

## `likelihoodThreshold` (type: `string`):

Minimum likelihood to treat a category as flagged.

## `childSafeMaxLikelihood` (type: `string`):

Image is Child Safe only if adult, racy, and violence are at or below this likelihood.

## `maxConcurrency` (type: `integer`):

How many images to analyze in parallel.

## Actor input object example

```json
{
  "imageUrls": [
    "https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=800"
  ],
  "images": [],
  "likelihoodThreshold": "LIKELY",
  "childSafeMaxLikelihood": "UNLIKELY",
  "maxConcurrency": 5
}
```

# Actor output Schema

## `results` (type: `string`):

Successfully classified images with SafeSearch scores and derived labels.

## `summary` (type: `string`):

Totals for succeeded, failed, NSFW, safe, and child-safe images.

## `errors` (type: `string`):

Images that could not be classified. These are not billed.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "imageUrls": [
        "https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=800"
    ],
    "images": [],
    "likelihoodThreshold": "LIKELY",
    "childSafeMaxLikelihood": "UNLIKELY",
    "maxConcurrency": 5
};

// Run the Actor and wait for it to finish
const run = await client.actor("robin.geekydev/image-safesearch-classifier").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "imageUrls": ["https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=800"],
    "images": [],
    "likelihoodThreshold": "LIKELY",
    "childSafeMaxLikelihood": "UNLIKELY",
    "maxConcurrency": 5,
}

# Run the Actor and wait for it to finish
run = client.actor("robin.geekydev/image-safesearch-classifier").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "imageUrls": [
    "https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=800"
  ],
  "images": [],
  "likelihoodThreshold": "LIKELY",
  "childSafeMaxLikelihood": "UNLIKELY",
  "maxConcurrency": 5
}' |
apify call robin.geekydev/image-safesearch-classifier --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,robin.geekydev/image-safesearch-classifier"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/73u0w101Uk3i0et8L/builds/QwODH0GHdq0CZfSiT/openapi.json