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,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}')