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

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

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

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