text: from flask import Flask, render_template, request, json, jsonify
  import os
  import json
  import numpy as np
  import io
  from PIL import Image
  
  app = Flask(__name__)
  app.config.from_object(__name__)
  port = int(os.getenv('PORT', 8080))
  
  @app.route("/", methods=['GET'])
  def hello():
      error=None
      return render_template('index.html', error=error)
  
  @app.route("/iot", methods=['GET'])
  def result():
      print(request)
      
      # Implemente sua lógica aqui e insira as respostas na variável 'resposta'
      
      resposta = {
          "iotData": "data",
          "itu": "data",
          "volumeAgua": "data",
          "fahrenheit": "data"
      }
      response = app.response_class(
          response=json.dumps(resposta),
          status=200,
          mimetype='application/json'
      )
      return response
  
  def prepare_image(image):
      image = image.resize(size=(96,96))
      image = np.array(image, dtype="float") / 255.0
      image = np.expand_dims(image,axis=0)
      image = image.tolist()
      return image
  
  @app.route('/predict', methods=['POST'])
  def predict():
      print(request)
      image = request.files["image"].read()
      image = Image.open(io.BytesIO(image))
      image = prepare_image(image)
  
      # Faça uma requisição para o serviço Watson Machine Learning aqui e retorne a classe detectada na variável 'resposta'
      
      resposta = {
          "class": "data"
      }
      return resposta
  
  if __name__ == '__main__':
      app.run(host='0.0.0.0', port=port)