app.py

import streamlit as st
import requests
import numpy as np
import json
from skimage.transform import resize
from PIL import Image
import tensorflow as tf
from tensorflow.keras.models import Sequential, Model

model = tf.keras.models.load_model('model_a')

# Load page
def run():
    # widget input
    with st.form(key='form_parameters'):
        st.title("Apple or Orange")
        uploaded_file = st.file_uploader("Insert a picture (jpg or jpeg)", type=['jpg','jpeg'])
        
        st.markdown('---')
        submitted = st.form_submit_button('Predict')
    
    if submitted:
        image = Image.open(uploaded_file)
        np_img = np.asarray(image)
        resized = resize(np_img, (256,256),anti_aliasing=True)
        # st.write(resized.shape)
        x = np.expand_dims(resized, axis=0)
        images = np.vstack([x])
        classes = model.predict(images)
        # st.write(res['predictions'][0][0])
        print(classes)
        if classes[0][0] <= 0.1:
            st.write('Apple')
        elif classes[0][0] >= 0.9:
            st.write('Orange')
        else:
            st.write('Unknown')
    
        st.image(resized)
    

if __name__ == '__main__':
    run()