Create app.py

app.py

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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))