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 Radio images", 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 téléchargée.", 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:
+        predictions = model.predict(image_array)
+        classes = np.argmax(predictions[0])
+        for key, value in classes_p.items():
+            if value == classes:
+                st.title("prediction of image is {}".format(key))
+
+            
+            
+    
+
+
+
+
+
+