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,ImageChops
st.title('Image Classification')
upload_file = st.sidebar.file_uploader('Upload Ratio Images',type=['jpg','jpeg','png'])
generated_pred = st.sidebar.button('Predict')
model = tf.keras.models.load_model('model.keras')
classes_p = {'Infection_Bacterienne': 0,
 'Infection_Covid': 1,
 'Infection_Virale': 2,
 'Normal': 3}

if upload_file:
    st.image(upload_file,caption='Image telechargee', use_container_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 generated_pred:
    predictions = model.predict(image_array)
    classes = np.argmax(predictions[0])
    for key,value in classes_p.items():
        if value == classes:
            st.title(f'Prediction file is {key}')