app.py

import streamlit as st 
import tensorflow as tf 
import numpy as np 
from tensorflow.keras.utils import load_img,img_to_array
from tensorflow.keras.preprocessing import image
from PIL import Image,ImageOps

st.title('Image Classification')
upload_file = st.sidebar.file_uploader('upload a radio image',type=['jpg','jpeg','png','PNG'])
generate_pred = st.sidebar.button('predict')
model = tf.keras.models.load_model('best_model.h5')
classes_p = {'COVID19':0,'NORMAL':1}

if upload_file:
    st.image(upload_file,caption='Image Telechargee',use_column_width=True)
    test_image = image.load_img(upload_file,target_size=(64,64))
    image_array = img_to_array(test_image)
    image_array = np.expand_dims(image_array,axis=0)

    if generate_pred:
        prediction = model.predict(image_array)
        classes = np.argmax(prediction[0])
        for key,value in classes_p.items():
            if value == classes:
                st.title('Prediction of image is {}'.format(key))