# Programa de Franco Benassi
# Proyecto #4 y 5 Interfaces Graficas 2020
# Ejercicio 3
from flask import Flask as fk, jsonify, render_template as rt, request as req
aplicacion = fk(__name__)
Material_Particulado = {0: [], 1: [], 2: []}
@aplicacion.route("/datillos", methods=['POST'])
def hola_mundo():
datos = req.json
Material_Particulado[0] = datos['01']
Material_Particulado[1] = datos['25']
Material_Particulado[2] = datos['10']
print(Material_Particulado)
return 'Datos Recibidos - Operación Exitosa'
@aplicacion.route('/')
def inicio():
return rt('mapa.html')
@aplicacion.route('/Get_Data')
def ObtenerDatos():
return Material_Particulado
#Adapted from https://stackoverflow.com/questions/43469281/how-to-predict-input-image-using-trained-model-in-keras
# Adapted from: https://stackoverflow.com/questions/1386352/pil-thumbnail-and-end-up-with-a-square-image/8469920#8469920
# reference https://pillow.readthedocs.io/en/3.0.0/reference/Image.html
from flask import Flask as fk
from flask import render_template
from flask import request
import numpy as np
import base64
import tensorflow as tf
from PIL import ImageOps, Image
app = fk(__name__)
@app.route("/")
def calculator():
return render_template('app/frontend/calculator.html')
@app.route('/uploadImage', methods=['GET', 'POST'])
def uploadImage():
# Get the image request
theImage = fk.request.values.get("theImage", " ")
# Print to console
print(theImage)
decodedimage = base64.b64decode(theImage[22:])
with open("theImage.png", "wb") as f:
f.write(decodedimage)
# Respond numbers