Tagline

Content Flagger

Safeguard your platform's discussions with tailored moderation.

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Content Moderation

Effectively monitor conversations, ensuring positive and respectful user interaction.

Customized Filtering

Set personalized threshold scores, controlling moderation levels on your platform

User Protection

Promote a safe space by proactively blocking inappropriate or harmful content.

Getting Started

For best results, setting a threshold score for categories like the suggested ones below will allow you to control what content you want to block out and what content you'd like to keep.

API Integration

Want to use this via API? Sign up here to get your API token and Account ID! Try out the following code examples to get this model integrated into your app quickly:

Run via CURL

curl --location --request POST 'https://predict.pyqai.com/' --header 'Authorization: <YOUR API TOKEN HERE>' --header 'Content-Type: application/json' --data-raw '{"model": "136", "version": "451", "account": "<YOUR ACCOUNT ID HERE>", "input_sequence": "Your input goes here", "candidate_labels": ["a class","another class"]}'


import requests
import json

headers = { 'Authorization': "",'Content-Type': 'application/json'}
json_input = { "model": 136, "version": 451, "account": ,"input_sequence": "Your input goes here", "candidate_labels": ["a class","another class"]}
url = "https://predict.pyqai.com"

response = requests.post(headers=headers, json = json_input, url = url )
print(json.loads(response.content))


var myHeaders = new Headers();
myHeaders.append("Authorization", "");
myHeaders.append("Content-Type", "application/json");

var raw = JSON.stringify({
  "model": 136,
  "version": 451,
  "account": ,
  "input_sequence": "Your input here",
  "candidate_labels": [
    "a category",
    "another category"
  ]
});

var requestOptions = {
  method: 'POST',
  headers: myHeaders,
  body: raw,
  redirect: 'follow'
};

fetch("https://predict.pyqai.com", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

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