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Ai Detector Text to Text

half-moon-ai/ai-detector/detect-text
AI Detector (Text) is an advanced AI service that analyzes a passage and returns a verdict on whether it was likely written by AI.
Inference
Commercial use
Schema
LLMs
PlaygroundAPI

About

Analyze text to detect if it's AI-generated or human-created.

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("half-moon-ai/ai-detector/detect-text", {
  input: {
    text: "yo guys so i just tried this new coffee place downtown and honestly?? not worth the hype. waited like 30 mins for a latte that tasted burnt lol. maybe i caught them on a bad day idk but wont be going back anytime soon"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.
import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

3. Queue#

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("half-moon-ai/ai-detector/detect-text", {
  input: {
    text: "yo guys so i just tried this new coffee place downtown and honestly?? not worth the hype. waited like 30 mins for a latte that tasted burnt lol. maybe i caught them on a bad day idk but wont be going back anytime soon"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("half-moon-ai/ai-detector/detect-text", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("half-moon-ai/ai-detector/detect-text", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Read more about file handling in our file upload guide.

5. Schema#

Input#

text string* required

Text content to analyze for AI generation.

{
  "text": "yo guys so i just tried this new coffee place downtown and honestly?? not worth the hype. waited like 30 mins for a latte that tasted burnt lol. maybe i caught them on a bad day idk but wont be going back anytime soon"
}

Output#

verdict string* required
confidence float* required
is_ai_generated boolean* required
latency float* required
{
  "verdict": "human",
  "confidence": 0.85,
  "is_ai_generated": false,
  "latency": 13.617770671844482
}

Other types#

Related Models