predict.py

import os

import cv2
import numpy as np
from tensorflow.python.keras.preprocessing.image import load_img, img_to_array
from tensorflow.python.keras.models import load_model
from resize import resize_and_crop

width, height = 224, 224
dimension = f'{ width }px'
model = 'models/model'

models = {}
for i in range(3):
    models[i] = load_model(f'{model}{i}.model')
    #models[i].load_weights(model_weights + i)


def predict(filename, model):

    resize_and_crop(filename, size=(width, height))

    image = cv2.imread('./thumb.jpg')
    image = image.astype("float") / 255.0
    image = image.flatten()
    image = image.reshape((1, width, height, 3))

    array = models[model].predict(image)[0]
    answer = array.argmax(axis=0)

    return answer