send torch tensor or image via localhost as JSON to another app
I have a basic flask server with a Generator model loaded. I'm sending an input vector via JSON which is hitting the Generator, which spits out a prediction. This works. I want to then send this image (I would settle for sending it as any kind of data I can reconstruct on the other end) to another application running on the same machine. From what I gather, it might be best to encode the image as base64, but all of my attempts have failed. Any guidance is appreciated.
@app.route(&quot;/json&quot;, methods=['GET', 'POST', 'PUT'])
def getjsondata():

if request.method=='POST':
    print(&quot;received POST&quot;)

    data = request.get_json()

    #print(format(data['z']))
    jzf = [float(i) for i in data['z']]
    jzft = torch.FloatTensor(jzf)
    jzftr = jzft.reshape([1, 512])

    z = jzftr.cuda()
    c = None                   # class labels (not used in this example)
    trunc = 1
    img = G(z, c, trunc)
    img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)

    # now what?


I managed a solution. The Python Flask server looks like this and expects to receive a JSON object containing a Z array of 512 floats and truncation of a single float.
from flask import Flask, jsonify, request, send_file # initialize our Flask application
import json
from flask_cors import CORS
import base64
from torchvision import transforms
import dnnlib
import torch
import PIL.Image
from io import BytesIO
from datetime import datetime
import legacy

device = torch.device('cuda')
with dnnlib.util.open_url(&quot;snapshot.pkl&quot;) as f:
    G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore

app = Flask(__name__)
CORS(app)

@app.route(&quot;/test&quot;, methods=['GET', 'POST', 'PUT'])
def test():
    return &quot;OK&quot;

@app.route(&quot;/query&quot;, methods=['GET', 'POST', 'PUT'])
def getjsondata():

    if request.method=='POST':
        # print(&quot;received POST&quot;)

        data = request.get_json()

        #print(format(data['z']))
        jzf = [float(i) for i in data['z']]
        jzft = torch.FloatTensor(jzf)
        jzftr = jzft.reshape([1, 512])

        z = jzftr.cuda()
        c = None                   # class labels (not used in this example)
        trunc = data['truncation']
        img = G(z, c, trunc)

        #img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
        img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)

        # turn into PIL image
        pil_img = transforms.ToPILImage()(img[0]).convert(&quot;RGB&quot;)
        #pil_img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')

        # SAVING...
        #fn = datetime.today().strftime('%Y-%m-%d-%H:%M:%S')
        #pil_img.save('saved_images/' + fn + '.jpg')

        response = serve_pil_image64(pil_img)
        response.headers.add('Access-Control-Allow-Origin', '*')
        # response.headers.add('Content-Transfer-Encoding', 'base64')
        return response


    return 'OK'

def serve_pil_image64(pil_img):
    img_io = BytesIO()
    pil_img.save(img_io, 'JPEG', quality=70)
    img_str = base64.b64encode(img_io.getvalue()).decode(&quot;utf-8&quot;)
    return jsonify({'status': True, 'image': img_str})


if __name__ == '__main__':
    app.run(host='localhost', port=9000, debug=True)

As it stands, I am sending said JSON array from a simple JavaScript/HTML site. It then listens for the response, also JSON.
// construct an HTTP request
var xhr = new XMLHttpRequest();

// upon successful completion of request...
xhr.onreadystatechange = function() {
    if (xhr.readyState == XMLHttpRequest.DONE) {
        var json = JSON.parse(xhr.responseText);
        // console.log(json);
        document.getElementById(&quot;image_output&quot;).src = &quot;data:image/jpeg;base64,&quot; + json.image;

    }
}


xhr.open(&quot;POST&quot;, &quot;http://localhost:9000/json&quot;);
xhr.setRequestHeader('Content-Type', 'application/json; charset=UTF-8');

