| 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','png','PNG']) | |
| generate_pred= st.sidebar.button('Predict') | |
| model=tf.keras.models.load_model('best_model.h5') | |
| classes_p={'COVID 19':0,'NORMAL':1} | |
| if upload_file: | |
| st.image(upload_file,caption='Image téléchargé',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)) |