Create app.py

app.py

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+#Import Libraries
+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
+
+#Title
+st.title("Image Classification")
+
+#Loader l image
+upload_file = st.sidebar.file_uploader("Telecharger un fichier",
+type = ['jpg','jpeg','png'])
+generate_pred = st.sidebar.button("Predict")
+
+model = tf.keras.models.load_model("model.h5")
+covid_classes = {'COVID19':0,'NORMAL':1,'PNEUMONIA':2,'TUBERCULOSIS':3}
+
+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 covid_classes.items():
+            if value == classes:
+                st.write("The diagnostic is :",key)
+