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const prediction = async(args) =>{
  let count = []
  let countt = []
 const users = await Users.find({email: {$ne:`${args.email}`}}).exec();
   users.map((user)=>{
  let name = user.username
      user.likes[0].map((genre)=>{
      let pusht  = 
     { [name]:[`${genre.adventure}`,
         `${genre.action}`,
       `${genre.comedy}`,
       `${genre.drama}`,
       `${genre.fantasy}`,
       `${genre.horror}`,
        `${genre.romance}`,
        `${genre.sciencefiction}`,
       `${genre.thriller}`,
       `${genre.mystery}`,
       `${genre.documentary}`,
       `${genre.western}`,
       `${genre.musical}`,
        `${genre.anime}`,
       `${genre.educational}`,
      ]}
      count.push(pusht)
      console.log(count)
      })
  })
 const userss = await Users.find({email:args.email}).exec()
 userss.map((user)=>{
  user.likes[0].map((genre)=>{
  let pushtt  = 
 { [args.email]:[`${genre.adventure}`,
     `${genre.action}`,
   `${genre.comedy}`,
   `${genre.drama}`,
   `${genre.fantasy}`,
   `${genre.horror}`,
    `${genre.romance}`,
    `${genre.sciencefiction}`,
   `${genre.thriller}`,
   `${genre.mystery}`,
   `${genre.documentary}`,
   `${genre.western}`,
   `${genre.musical}`,
    `${genre.anime}`,
   `${genre.educational}`,
  ]}
  countt.push(pushtt)
  console.log(countt)
  })
})
try{
  count.forEach((feature, i) => {
      const result = knn(tf.tensor(Object.values(feature)), tf.tensor(Object.keys(feature)), tf.tensor(Object.values(countt)), 10);
      console.log('User Prediction', result);     //Logging out User Predictions
      return {peopleyoumaylike: result}
  })
}
catch(e){
  console.log(e)
}
  
  }