Flask with tensorflow object detection demo

@app.route('/api/', methods=["POST"])
def main_interface():
    response = request.get_json()
    data_str = response['image']
    point = data_str.find(',')
    base64_str = data_str[point:]  # remove unused part like this: "data:image/jpeg;base64,"

    image = base64.b64decode(base64_str)       
    img = Image.open(io.BytesIO(image))

    if(img.mode!='RGB'):
        img = img.convert("RGB")
    
    # convert to numpy array.
    img_arr = np.array(img)

    # do object detection in inference function.
    results = inference(sess, detection_graph, img_arr, conf_thresh=0.5)
    print(results)

    return jsonify(results)

// handle image files uploaded by user, send it to server, then draw the result.
function parseFiles(files) {
  const file = files[0];
  const imageType = /image.*/;
  if (file.type.match(imageType)) {
    warning.innerHTML = '';
    const reader = new FileReader();
    reader.readAsDataURL(file);
    reader.onloadend = () => {
      image.src = reader.result;
      // send the img to server
      communicate(reader.result);
    }
  } else {
    setup();
    warning.innerHTML = 'Please drop an image file.';
  }
}

python app.py

// python3
python -m http.server
// python2
python -m SimpleHTTPServer

npm install serve -g // install serve
serve // this will open a mini web serve
// or http-serve
npm install http-server -g
http-server