EsteveSegura

const download = require('image-downloader')
const detectFood = require('./detectFood');

//download instagram post
async function downloadPost(ig,media_id){
     try {
          let postToDownload = await getMediaIdInfo(ig,media_id);
          let downloadedData = await downloadImageFromUrl(postToDownload.items[0].image_versions2.candidates[0].url,media_id);
          return downloadedData;
     } catch (error) {

EsteveSegura

const spawn = require('child_process').spawn;

//create friendly js prediction
function decodePrediction(str){
     let decode = str.split('-');
     return {
          "label" : decode[0],
          "percentage" : Math.trunc(decode[1])
     }
}

EsteveSegura

import sys
import os
import numpy as np
from PIL import Image as pil_image
from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions


def predictPicture(picturePath):
     mod = VGG16() #Calling VGG16 Model
     img = pil_image.open(picturePath) #loading picture

EsteveSegura

import numpy as np
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Activation
from keras.layers.core import Dense, Flatten
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy, sparse_categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
from keras.layers.normalization import BatchNormalization

EsteveSegura

async function markovChain(){
    let amazonDataSetFile = await utils.readFileLineByLine('../dataset/AmazonDataSet.txt')
    let ourDataSet = await utils.readFileLineByLine('../dataset/sentences.txt')
    let filteredData = amazonDataSetFile.filter((comments) => {
        if(comments.includes("taste") || comments.includes("yummy") || comments.includes("good") || comments.includes("cooking") || comments.includes("delicious") || comments.includes("savory") || comments.includes("devour")){
            return comments
        }
    })
    filteredData = filteredData.slice(0,3500)
    let bothDataSetsTogether = [...filteredData,...ourDataSet]

EsteveSegura

const titlegen = require('titlegen');
const utils = require('./utils.js');


async function generate(path,howManyChain){
    return new Promise(async function(resolve,reject){
        let generator = titlegen.create();
        let finalTextMarkov = []
        let dataset = [];

EsteveSegura

const utils = require('../src/utils.js');
const generateCommentsFromHandMadeData = require('../src/generateCommentsFromHandMadeData.js')

const howManyIterations = 99999;
let allSentences = [];

let sentencesAboutPicture ={
    "mainSentence": [
        "I love photography, it transmits $",
        "The photo talks about $",

EsteveSegura

const utils = require('../src/utils.js');
const generateCommentsFromHandMadeData = require('../src/generateCommentsFromHandMadeData.js')

let sentencesAboutPicture ={
    "mainSentence": [
        "I love photography, it transmits $",
        "The photo talks about $",
        "Just '$'",
        "Beyond photography you can perceive: $",
        "$!",

EsteveSegura

const utils = require('../src/utils.js');
const generateCommentsFromHandMadeData = require('../src/generateCommentsFromHandMadeData.js')

let sentencesAboutFood = {
    "mainSentence":[
        "This picture looks $",
        "Looks $, i love your pictures",
        "I want to try that food, it looks $",
        "It seems $"
    ],

EsteveSegura

const fs = require('fs');
const readline = require('readline');

function randomInt(min,max){
    return Math.round(Math.random() * (max-min)+ min)
}

function saveArrayToTxtLineByLine(arrFinal,pathOutput){
    return new Promise(function(resolve,reject){
        let fileToSave = deleteRepeatedValuesOnArray(arrFinal)