# OpenAI GPT-6 Luna Decisions — Bulk Text & Image Classifier (`seemuapps/gpt-bulk-classifier`) Actor

Label, score and flag texts, images or scraped records in bulk with OpenAI GPT-6 Luna Decisions — your own categories, one typed answer and confidence per row.

- **URL**: https://apify.com/seemuapps/gpt-bulk-classifier.md
- **Developed by:** [Seemu Scraping](https://apify.com/seemuapps) (community)
- **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 evaluated items

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?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## OpenAI GPT-6 Luna Decisions — Bulk Text & Image Classifier

Turn a list of texts, images or scraped records into structured columns. Give it your own labels — ticket queues, lead grades, sentiment, product categories, "is there a person in this photo?" — and every item comes back with one typed answer per question, plus a confidence score you can filter on.

Powered by **OpenAI GPT-6 Luna Decisions** — OpenAI's new Decisions API, which returns typed, probabilistic answers instead of chat text: no prompt engineering, no JSON parsing, no free-form text to clean up. You define the options; the answer is always one of them.

### What you get

For every text, image or record, one dataset row with:

- **Choice questions** — the selected label (`department: "billing"`), a confidence score, and the probability of every option
- **Score questions** — a numeric rating against your own ordered levels (`frustration: 1.96`), the nearest level's label, confidence, and per-level probabilities
- **Yes/no questions** — a plain `true`/`false` plus the underlying probability, so you can set your own threshold
- **Images understood, not just text** — evaluate photos on their own, or a record's caption together with up to 4 of its images
- The original text, image URLs and (when reading from a dataset) the full source record, so results stay joinable
- Export to JSON, CSV, or Google Sheets directly from the Apify console

Ask up to 10 questions at once — they are answered in a single pass per item.

### Use cases

- **Support ticket triage** — route each message to the right team and flag the urgent ones
- **Social media post classification** — label Instagram, TikTok or Facebook posts by caption *and* photo: promo vs. lifestyle, product category, brand safety
- **Product and listing image tagging** — categorise marketplace or e-commerce photos, flag low-quality or off-brand images
- **Review and comment analysis** — sentiment, topic and "is this a complaint?" across thousands of reviews at once
- **Lead qualification** — score scraped profiles or form submissions against your own ICP criteria
- **Content moderation** — flag spam, promotional or unsafe text and images with a tunable confidence threshold

### How to use

1. Paste texts into **Texts** (one per line) and/or image links into **Image URLs** — each is evaluated on its own
2. Or pick a **Dataset ID** from a previous run (e.g. a social media or e-commerce scraper) to label its records in place:
   - **Fields to evaluate** — the text fields to send, e.g. `caption`, `title`
   - **Image fields** — the fields holding image URLs, e.g. `displayUrl`, `images`
3. Define your **Questions**. Each one needs:
   - `key` — the column name in the output
   - `type` — `choice` (pick one option), `score` (rate against ordered levels), or `noul` (yes/no)
   - `instructions` — what you want decided
   - `criteria` — the options for `choice`, the ordered levels for `score`; optional for `noul`
4. Set **Max Items** (default 100; set 0 for no limit) and **Concurrency**
5. Run the actor — results appear in the **Dataset** tab

### Question types

```json
[
  {
    "key": "department",
    "type": "choice",
    "instructions": "Which team should handle this message?",
    "criteria": {
      "billing": "Payment or subscription issues",
      "technical": "Bugs or integration problems",
      "sales": "Pricing or account questions"
    }
  },
  {
    "key": "frustration",
    "type": "score",
    "instructions": "How frustrated the customer appears",
    "criteria": [
      "Calm, just stating facts",
      "Frustrated but civil",
      "Very angry, strong language"
    ]
  },
  {
    "key": "is_urgent",
    "type": "noul",
    "instructions": "The message conveys urgency or time-sensitivity"
  }
]
```

A `choice` question also accepts a plain list of labels (`["positive", "neutral", "negative"]`) when the labels speak for themselves.

### Output format

Each dataset record:

```json
{
  "itemIndex": 0,
  "text": "Hi, I've been trying to connect my Stripe account for 3 days and the integration keeps failing. I'm losing sales. Please help ASAP.",
  "department": "technical",
  "department_confidence": 0.99,
  "department_probabilities": { "billing": 0.01, "technical": 0.99, "sales": 0 },
  "frustration": 0.99,
  "frustration_label": "Frustrated but civil",
  "frustration_confidence": 0.99,
  "frustration_probabilities": { "0": 0.01, "1": 0.99, "2": 0 },
  "is_urgent": true,
  "is_urgent_probability": 0.98
}
```

Image items also carry `imageUrls`. If an image can't be downloaded it is listed in `imageErrors` and the item is evaluated on whatever is left. If none of your options fit an item (say, "which support team?" asked about a photo of a cat), that question is left empty and named in `refusedQuestions` rather than forcing a wrong answer. If an item can't be evaluated at all, its row still appears with an `error` field — the rest of the run continues.

### Pricing

You pay per item evaluated, plus a small fee per image analysed. Items that fail are not charged.

### Notes

- Texts and records are truncated to 8,000 characters before they are evaluated
- Images (JPEG, PNG, WebP, GIF; up to 20 MB) are resized automatically; up to 4 images per item
- Confidence is separate from probability: use `<key>_confidence` to decide whether to act automatically or send a row to a human

# Actor input Schema

## `texts` (type: `array`):

The texts to evaluate — one per line. Leave empty if you are reading from an existing dataset instead.

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

Images to evaluate — one URL per line, each evaluated on its own. JPEG, PNG, WebP and GIF work; large images are resized automatically.

## `datasetId` (type: `string`):

Evaluate the records of an existing dataset instead of the texts above — pick a dataset or paste the dataset ID from any previous run, e.g. the output of a scraper.

## `fields` (type: `array`):

When reading from a dataset, only send these fields to the model, e.g. 'text' and 'title'. Leave empty to send the whole record.

## `imageFields` (type: `array`):

When reading from a dataset, the fields that hold image URLs (a URL or a list of URLs), e.g. 'displayUrl' or 'images'. Those images are evaluated together with the record's text.

## `questions` (type: `array`):

The questions to ask about every text. Each question has a 'key' (the output column name), a 'type' ('choice' picks one option, 'score' rates against ordered levels, 'noul' answers yes/no), 'instructions', and 'criteria' (options for choice, ordered levels for score).

## `maxItems` (type: `integer`):

Maximum number of texts to evaluate in this run. Set 0 for no limit.

## `concurrency` (type: `integer`):

How many texts to evaluate in parallel. Higher is faster; lower it if you hit rate limits.

## `maxImagesPerItem` (type: `integer`):

When a dataset record has several images, evaluate at most this many of them (1–4).

## Actor input object example

```json
{
  "texts": [
    "Hi, I've been trying to connect my Stripe account for 3 days and the integration keeps failing. I'm losing sales. Please help ASAP.",
    "Just wanted to say the new dashboard looks great. No issues here!",
    "How much does the Team plan cost if we add 12 seats?"
  ],
  "questions": [
    {
      "key": "department",
      "type": "choice",
      "instructions": "Which team should handle this message?",
      "criteria": {
        "billing": "Payment or subscription issues",
        "technical": "Bugs or integration problems",
        "sales": "Pricing or account questions"
      }
    },
    {
      "key": "frustration",
      "type": "score",
      "instructions": "How frustrated the customer appears",
      "criteria": [
        "Calm, just stating facts",
        "Frustrated but civil",
        "Very angry, strong language"
      ]
    },
    {
      "key": "is_urgent",
      "type": "noul",
      "instructions": "The message conveys urgency or time-sensitivity"
    }
  ],
  "maxItems": 100,
  "concurrency": 5,
  "maxImagesPerItem": 4
}
```

# Actor output Schema

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

One text per record. Fields: itemIndex, text, imageUrls (when images were evaluated), source (when reading from a dataset), and one column per question key — plus <key>\_confidence and <key>\_probabilities for choice/score questions, <key>\_label for score questions, and <key>\_probability for yes/no questions. A record gets 'imageErrors' if some images could not be read, and an 'error' field if that text could not be evaluated.

# 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 = {
    "texts": [
        "Hi, I've been trying to connect my Stripe account for 3 days and the integration keeps failing. I'm losing sales. Please help ASAP.",
        "Just wanted to say the new dashboard looks great. No issues here!",
        "How much does the Team plan cost if we add 12 seats?"
    ],
    "questions": [
        {
            "key": "department",
            "type": "choice",
            "instructions": "Which team should handle this message?",
            "criteria": {
                "billing": "Payment or subscription issues",
                "technical": "Bugs or integration problems",
                "sales": "Pricing or account questions"
            }
        },
        {
            "key": "frustration",
            "type": "score",
            "instructions": "How frustrated the customer appears",
            "criteria": [
                "Calm, just stating facts",
                "Frustrated but civil",
                "Very angry, strong language"
            ]
        },
        {
            "key": "is_urgent",
            "type": "noul",
            "instructions": "The message conveys urgency or time-sensitivity"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("seemuapps/gpt-bulk-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 = {
    "texts": [
        "Hi, I've been trying to connect my Stripe account for 3 days and the integration keeps failing. I'm losing sales. Please help ASAP.",
        "Just wanted to say the new dashboard looks great. No issues here!",
        "How much does the Team plan cost if we add 12 seats?",
    ],
    "questions": [
        {
            "key": "department",
            "type": "choice",
            "instructions": "Which team should handle this message?",
            "criteria": {
                "billing": "Payment or subscription issues",
                "technical": "Bugs or integration problems",
                "sales": "Pricing or account questions",
            },
        },
        {
            "key": "frustration",
            "type": "score",
            "instructions": "How frustrated the customer appears",
            "criteria": [
                "Calm, just stating facts",
                "Frustrated but civil",
                "Very angry, strong language",
            ],
        },
        {
            "key": "is_urgent",
            "type": "noul",
            "instructions": "The message conveys urgency or time-sensitivity",
        },
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("seemuapps/gpt-bulk-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 '{
  "texts": [
    "Hi, I'\''ve been trying to connect my Stripe account for 3 days and the integration keeps failing. I'\''m losing sales. Please help ASAP.",
    "Just wanted to say the new dashboard looks great. No issues here!",
    "How much does the Team plan cost if we add 12 seats?"
  ],
  "questions": [
    {
      "key": "department",
      "type": "choice",
      "instructions": "Which team should handle this message?",
      "criteria": {
        "billing": "Payment or subscription issues",
        "technical": "Bugs or integration problems",
        "sales": "Pricing or account questions"
      }
    },
    {
      "key": "frustration",
      "type": "score",
      "instructions": "How frustrated the customer appears",
      "criteria": [
        "Calm, just stating facts",
        "Frustrated but civil",
        "Very angry, strong language"
      ]
    },
    {
      "key": "is_urgent",
      "type": "noul",
      "instructions": "The message conveys urgency or time-sensitivity"
    }
  ]
}' |
apify call seemuapps/gpt-bulk-classifier --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,seemuapps/gpt-bulk-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/Rf8QW6nYRCfXnQxhU/builds/WEAEzX0337MUAx4RC/openapi.json