Images API Reference

GET /api/v1/images?q=cats
X-API-Key: nd_live_xxxxxxxxxxxxxxxx

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://nestdaddy.com/api/v1/images"

response = requests.get(BASE_URL, params={
    "key":   API_KEY,
    "q":     "cats",
    "limit": 20,
    "page":  1,
})

data = response.json()
for img in data["results"]:
    print(img["image_url"], img["width"], "x", img["height"])

const res  = await fetch(
  `https://nestdaddy.com/api/v1/images?key=${API_KEY}&q=cats&limit=20`
);
const data = await res.json();

data.results.forEach(img => {
  console.log(img.image_url, `${img.width}x${img.height}`);
});

$url = "https://nestdaddy.com/api/v1/images?" . http_build_query([
    'key'   => 'YOUR_API_KEY',
    'q'     => 'cats',
    'limit' => 20,
]);
$data = json_decode(file_get_contents($url), true);
foreach ($data['results'] as $img) {
    echo $img['image_url'] . "\n";
}

curl "https://nestdaddy.com/api/v1/images?key=YOUR_KEY&q=nature+landscapes&min_width=1280&min_height=720&limit=50"

const params = new URLSearchParams({
  key: API_KEY, q: "kuala lumpur",
  country: "MY", limit: "30"
});
const data = await (await fetch(`https://nestdaddy.com/api/v1/images?${params}`)).json();

async function collectImages(query, pages = 5) {
  const all = [];
  for (let p = 1; p <= pages; p++) {
    const data = await (await fetch(
      `https://nestdaddy.com/api/v1/images?key=${API_KEY}&q=${encodeURIComponent(query)}&limit=100&page=${p}`
    )).json();
    if (!data.results?.length) break;
    all.push(...data.results);
  }
  return all;
}

import pandas as pd, requests, io

response = requests.get(BASE_URL, params={
    "key": API_KEY, "q": "cats",
    "limit": 100, "format": "jsonl",
})

df = pd.read_json(io.StringIO(response.text), lines=True)
print(df[["image_url", "width", "height", "alt_text"]].head())

from datasets import load_dataset
import requests, tempfile, os

resp = requests.get(BASE_URL, params={
    "key": API_KEY, "q": "cats",
    "limit": 100, "format": "jsonl"
})
with tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as f:
    f.write(resp.text); tmp = f.name

ds = load_dataset("json", data_files=tmp, split="train")
os.unlink(tmp)
print(ds)