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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)) |