app.py

#import libraries
import pandas as pd
impurt numpy as np
import streamlit as st
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow_hub.keras_layer import KerasLayer 

import tensorflow as tf
from tensorflow.keras.models import load_model

#import pickle
import pickle

#load model
def run():
    st.image('https://i.ytimg.com/vi/Y7nGCB3S5Ww/maxresdefault.jpg', use_container_width=True)
    st.title("Skin Type Prediction Model")
    st.write("Upload an image to know your skin type!")
    file = st.file_uploader("Upload an image", type=["jpg", "png"])

    model = load_model('model_aug.keras', custom_objects={'KerasLayer': KerasLayer})
    target_size=(220, 220)

    def import_and_predict(image_data, model):
        image = load_img(image_data, target_size=(220,220))
        img_array = img_to_array(image)
        img_array = tf.expand_dims(img_array, 0)

        #Normalize image
        img_array = img_array/255

        #make prediction
        predictions = model.predict(img_array)

        #Get class with the highest possibility
        idx = np.where(predictions => 0.5, 1, 0).item()

        type = ['oily', 'dry', 'normal']
        result = f'Prediction: {type[idx]}'

        return result
    
    if file is None:
        st.text("Please upload in image file")
    else:
        result = import_and_predict(file, model)
        st.image(file)
        st.write(result)
    
    if __name__ == "__main__"
        run