#! /usr/bin/env python
from flask import Flask, render_template, request, Response
import numpy as np
from binascii import a2b_base64
import imageio
from PIL import Image
import io
import time
import ast
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms

global model_states, nb_epoch #to have access later
model_states = ['Not Trained']
nb_epoch=5

app = Flask(name)

model =None
#page to_train
@app.route('/')
def to_train():
return render_template('to_train.html', nb_epoch=nb_epoch)

#train the model
@app.route("/loadmodel/", methods=['GET'])
def load():
global model
class NN(nn.Module):
def init(self):
super(NN, self).init()
self.conv1L = nn.Conv2d(1, 20, 3, 1)
self.conv2L = nn.Conv2d(20, 50, 3, 1)
self.FC1 = nn.Linear(5550, 500)
self.FC2 = nn.Linear(500 ,10)

	def forward(self, x):
		x = F.relu(self.conv1L(x)) #20 x 26
		x = F.max_pool2d(x, (2,2)) #20 x 13
		x = F.relu(self.conv2L(x)) #50 x 11
		x = F.max_pool2d(x, (2,2)) #50 x 5x 5
		x = x.view(-1, 50*5*5) #flatten
		x = self.FC1(x) #500
		x = self.FC2(x) #10
		return F.log_softmax(x, dim=1)
checkpoint = torch.load("Meetup_MNIST.pt")
model = NN()
model.load_state_dict(checkpoint)
print("model loaded")
return "Loading done"

#page where you draw the number
@app.route('/index/', methods=['GET','POST'])
def index():
prediction='?'
if request.method == 'POST':

    dataURL = request.get_data()
    drawURL_clean = dataURL[22:]
    binary_data=a2b_base64(drawURL_clean)
    img = Image.open(io.BytesIO(binary_data))
    img.thumbnail((28,28))
    img.save("data_img/draw.png")

return render_template('index.html', prediction=prediction)

#display prediction
@app.route('/result/')
def result():
time.sleep(0.2)
img = Image.open("data_img/draw.png").convert("1")
transform=transforms.Compose([transforms.ToTensor()])
img = transform(img)
img = torch.unsqueeze(img , 0)
prediction = inference(model , img)
print(prediction)
return render_template("index.html",prediction=prediction)

def inference(model , img):
output = model(img)
output = torch.exp(output)
top_prob,top_class=output.topk(1,dim=1)
return top_class.item()

if name == "main":
app.run(debug=True, threaded=True)

In reply to @aman5319:matrix.org
obviously