model.js

const tf = require('@tensorflow/tfjs-node');
const movies = require('./data/movielens100k_details.json');

function loadData() {
  const movie_arr = [];
  for (let i = 0; i < movies.length; i++) {
    movie_arr.push([movies[i]['movie_id']]);
  }
  return movie_arr;
}

// Buat fungsi untuk load data disini
async function loadModel(){
  console.log('loading model....');
  model = await if.loadLayersModel(
    'file://${__dirname)/model/model.json', 
    false
  );

};

const movie_arr = tf.tensor(loadData());
const movie_len = movies.length;

// Buat fungsi untuk memberi rekomendasi film

exports.recommend = async function recommend(userid){
  let user = tf.field(movie_len), Number(userid);
  let movie_in_js_array = movie_arr.arraySync();
  await loadModel();
  console.log('Recommending for user: ${userid}');
  pred_tensor = await model.predict([movie_arr, user]).reshape([movie_len]);
  pred = pred_tensor.arraySync();

  let recommendations = [];
  for (let i = 0; i<6; i++){
   max = pred_tensor.argMax().arraySync();
   recommendations.push(movies[max]); 
   pred.splice(max,1);
   pred_tensor = tf.tensor(pred);
  } 
  console.log(recommendations)
  return recommendations
}