import streamlit as st
import numpy as np
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input as keras_preprocess, decode_predictions


@st.cache(allow_output_mutation=True)
def load_image_embedder():
    embedder = VGG16(include_top=False, weights=None)
    embedder.load_weights("path/to/weights.h5")
    return embedder


@st.cache(allow_output_mutation=True)
def predict_(model, input):
    return model.predict(input)


def preprocess_image(image_path):
    img = image.load_img(image_path, target_size=(224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = keras_preprocess(x)  # e.g. normalization
    return x

embedder = load_image_embedder()
image = preprocess_image("path/to/image.png")
prediction = predict_(embedder, image)
st.write(prediction)