flask.py

@app.route('/imageclassifier/predict/', methods=['POST'])
def image_classifier():
    # Decoding and pre-processing base64 image
    img = image.img_to_array(
        image.load_img(
            # Request from base 64!
            BytesIO(base64.b64decode(request.form['b64'])),
                    # Need to resize it and normalize to 0-1
                    target_size=(224, 224))) / 255.
    # Type casting!
    img = img.astype('float32')
    
    # Image preprocessing
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    img = normalize(img)

    # perform the inference
    pred = my_fancy_model(img) 

    # Returning JSON response to the frontend
    # Multiple nested wrangling required
    return jsonify(np.array(pred['predictions'])[0])