Golang Sample	Python Sample	Node.js Sample

We're classifying images into NSFW and SFW categories.

Following are the image categories we classify into NSFW categories.

• Porn
• Explicit Nudity
• Animated Porn
• Suggestive Nudity
• Gore

You can query our pretrained model which should work for most use cases. If you want to build your own custom model tailored to your use case, skip ahead to the next section.

Few integration code samples are provided below.

• Get your API key by signing up on app.nanonets.com

curl --request POST --url 'https://app.nanonets.com/api/v2/ImageCategorization/LabelUrls/' --header 'accept: application/x-www-form-urlencoded' -d 'modelId=7390a500-9fe1-483b-8123-750b96fc660c&urls=https://goo.gl/ICoiHc' -u '-REPLCAE_YOUR_API_KEY:'

#REPLACE YOUR API KEY
 
import requests
 
url = 'https://app.nanonets.com/api/v2/ImageCategorization/LabelUrls/'
 
headers = {
  'accept': 'application/x-www-form-urlencoded'
}
 
data = {'modelId': '7390a500-9fe1-483b-8123-750b96fc660c', 'urls' : ['https://goo.gl/ICoiHc']}
 
response = requests.request('POST', url, headers=headers, auth=requests.auth.HTTPBasicAuth('REPLACE_YOUR_API_KEY', ''), data=data)
 
print(response.text)

var request = require("request");
  
var options = { method: 'POST',
  url: 'http://app.nanonets.com/api/v2/ImageCategorization/LabelUrls/',
  headers:
  { 'cache-control': 'no-cache',
    Authorization: 'Basic ' + new Buffer('REPLACE YOUR API KEY' + ":" + '').toString("base64"),
    'Content-Type': 'application/x-www-form-urlencoded' },
  form:
  { urls: 'https://goo.gl/ICoiHc',
    modelId: '7390a500-9fe1-483b-8123-750b96fc660c' } };

request(options, function (error, response,body) {
  if (error) throw new Error(error);

  console.log(body);
});

git clone https://github.com/NanoNets/nsfw-api
cd nsfw-api

Get your free API Key from http://app.nanonets.com/#/keys

export NANONETS_API_KEY=YOUR_API_KEY_GOES_HERE

python ./code/create_model.py

_Note: This generates a MODEL_ID that you need for the next step

export NANONETS_MODEL_ID=YOUR_MODEL_ID

_Note: you will get YOUR_MODEL_ID from the previous step

The training data is found in data

python ./code/upload_training.py

Once the Images have been uploaded, begin training the Model

python ./code/train_model.py

The model takes ~2 hours to train. You will get an email once the model is trained. In the meanwhile you check the state of the model

python ./code/model_state.py

Once the model is trained. You can make predictions using the model

python ./code/prediction.py PATH_TO_YOUR_IMAGE.jpg

Sample Usage:

python ./code/prediction.py ./data/nsfw/2795.jpg