tbass134

let input = coffee_predictionInput(input: mlMultiArray)
guard let prediction = try? model.prediction(input: input) else {
	return
}

let result = prediction
print("classLabel \(result.classLabel)")

if result.classLabel == 1 {
	self.class_image.image = UIImage.init(named: "coffee_hot")

tbass134

func toOneHot(_ string:String) -> [Double] {
		var str = string
		var items = [Double](repeating: 0.0, count: 7)
		let weather_conds:[String] = ["Clear", "Clouds", "Fog", "Haze", "Rain", "Smoke", "Snow"]
		
		if str.lowercased().range(of:"cloud") != nil || str.lowercased().range(of:"overcast") != nil{
			str = "Clouds"
		}
		
		if str.lowercased().range(of:"snow") != nil {

tbass134

function getInfo(contexts) {
  var product = contexts[contexts.length - 1]['parameters']['Product']
  var state = contexts[contexts.length - 1]['parameters']['geo-state-us']

  for (var i = 0; i < contexts.length; i++) {
    var context = contexts[i]['parameters']
    if (typeof context['Product'] != "undefined" && typeof context['geo-state-us'] != "undefined") {
      return context
    }

tbass134

import requests
import json
import pandas as pd
from pandas.io.json import json_normalize #package for flattening json in pandas df
from xml.etree import ElementTree
from pandas.io.json import json_normalize
import sys

YT_API_KEY = '{youtube_api_key}'
playlist_id = '{playlist_id}'

tbass134

import sys
import os
import shutil
import time
import traceback
import requests

import numpy as np

from flask import Flask, request, jsonify

tbass134

from google.cloud import storage
import joblib
import sklearn

def predict(request):
    """Responds to any HTTP request.
    Args:
        request (flask.Request): HTTP request object.
    Returns:
        The response text or any set of values that can be turned into a