captchured-ml

Installation

``bash
npm install captchured-api
`

Usage

$3

You can send requests directly to the hosted API:

`
POST https://captchured.shashw1t.in/
`

See the API Documentation below for request/response format.

---

$3

You can run your own instance:

`js
const createApp = require('captchured-api');
const app = createApp();
const port = process.env.PORT || 3000;
app.listen(port, () => {
console.log(Server running on http://localhost:${port});
});
`

Or, from the command line:

`bash
npx captchured-api


or if installed globally


captchured-api
`

---

API Documentation

$3

`
POST /
`

- URL: https://captchured.shashw1t.in/
- Method: POST
- Content-Type: application/json

$3

Send a JSON object with the following structure:

`json
{
"key_count": 3,
"key_sequence": ["a", "b", "c"],
"time_delay": [100, 120],
"mouse_movements": [
{
"x": 100,
"y": 200,
"timeDelay": 50,
"timestamp": 1710000000000
}
],
"mouse_clicks": [
{
"x": 120,
"y": 220,
"timestamp": 1710000000100
}
],
"total_time": 1500,
"environment": {
"timezone": "Asia/Kolkata",
"language": "en-US",
"cpu": 8,
"browser": "Mozilla/5.0",
"os": "Windows",
"deviceType": "Desktop"
}
}
`

#### Field Descriptions

- key_count: Number of keypresses.
- key_sequence: Array of keys pressed.
- time_delay: Array of delays (ms) between keypresses.
- mouse_movements: Array of mouse movement objects.
- mouse_clicks: Array of mouse click objects.
- total_time: Total time spent (ms).
- environment: Object with user environment details.

$3

Returns a JSON object:

`json
{
"message": "Data received and processed successfully",
"cookies": undefined
}
`

When ML model integration is ready, the response will include a prediction result.

$3

`js
fetch('https://captchured.shashw1t.in/', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
key_count: 3,
key_sequence: ['a', 'b', 'c'],
time_delay: [100, 120],
mouse_movements: [],
mouse_clicks: [],
total_time: 1500,
environment: {
timezone: 'Asia/Kolkata',
language: 'en-US',
cpu: 8,
browser: 'Mozilla/5.0',
os: 'Windows',
deviceType: 'Desktop'
}
})
})
.then(res => res.json())
.then(data => console.log(data));
`

Frontend Integration Example

To use the API from your own frontend, send a POST request to the / endpoint with the required data.
You can use fetch, axios, or any HTTP client in your frontend code.

$3

`js
fetch('https://captchured.shashw1t.in/', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
key_count: 3,
key_sequence: ['a', 'b', 'c'],
time_delay: [100, 120],
mouse_movements: [
{ x: 100, y: 200, timeDelay: 50, timestamp: 1710000000000 }
],
mouse_clicks: [
{ x: 120, y: 220, timestamp: 1710000000100 }
],
total_time: 1500,
environment: {
timezone: 'Asia/Kolkata',
language: 'en-US',
cpu: 8,
browser: 'Mozilla/5.0',
os: 'Windows',
deviceType: 'Desktop'
}
})
})
.then(res => res.json())
.then(data => {
// Handle the prediction result or response
console.log(data);
// Example: alert(data.predictionResult);
})
.catch(error => {
console.error('Error:', error);
});
`

$3

1. Collect user behavior data in your frontend (keypresses, mouse movements, etc.).
2. Format the data as shown above.
3. Send a POST request to https://captchured.shashw1t.in/`.
4. Handle the response in your frontend (e.g., display prediction to the user).

---

License

MIT