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@app.route("/classify")
@cross_origin()
def classify_symbol():
    json_str = request.args.get('points')
    if json_str is None:
        return {"top5": [" ", " ", " ", " ", " "]}

    json_data = json.loads(json_str)
    if 'data' not in json_data:
        return {"top5": [" ", " ", " ", " ", " "]}

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order = sorted([str(i) for i in range(1098)])
chars = sorted(set(np.load("data_processed/dataY.npy")))

def fix_predictions(output):
    outs = [chars[int(order[i.item()])] for i in torch.topk(output, 5, dim = 1).indices[0]]
    return ["\\" + i.split("_")[1] for i in outs]

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model = Model()
model.eval()
state_dict = torch.load("Test.pt", map_location = torch.device("cpu"))
model.load_state_dict(state_dict['model'])
size = 32

J3698

from PIL import Image, ImageDraw
import numpy as np
import torch
from train2 import Model
from torchvision.transforms import ToTensor

J3698

from flask import Flask
from flask_cors import CORS, cross_origin

app = Flask(__name__)
cors = CORS(app)
app.config['CORS_HEADERS'] = 'Content-Type'

@app.route("/classify")
@cross_origin()
def classify_symbol():

J3698

import os
import random
from shutil import copyfile
from tqdm.auto import tqdm

size = 32

os.makedirs(f"images_data{size}/train", exist_ok = True)
os.makedirs(f"images_data{size}/val", exist_ok = True)
os.makedirs(f"images_data{size}/test", exist_ok = True)

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function addReTypesetHandler() {
    var script = document.createElement("script");
    script.innerHTML = ` 
        last = "1";
        setInterval(function() {
            var tag = document.getElementsByClassName("invisible")[0];
            if (tag.innerHTML != last) {
                MathJax.Hub.Queue(["Typeset", MathJax.Hub]);
                last = tag.innerHTML;
            }

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function addClassifyRequestInterval() {
    setInterval(function() {
        if (!shouldUpdate) {
            return
        }
        shouldUpdate = false;

        var xhttp = new XMLHttpRequest();
        xhttp.onreadystatechange = function() {
            if (xhttp.readyState == 4 && xhttp.status == 200) {

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from flask import Flask

app = Flask(__name__)

@app.route("/classify")
def classify_symbol():
    return {"top5": ["A", "B", "C", "D", "E"]}

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def run_pipeline(pipeline):
    with depthai.Device(pipeline) as device:
        q_nn = device.getOutputQueue("nn", maxSize=4, blocking=False)
        while True:
            in_nn = q_nn.tryGet()
            if in_nn is not None:
                # get data
                output = in_nn.getAllLayerNames()[-1]
                data = np.array(in_nn.getLayerFp16(output))