Update app.py

app.py

@@ -9,7 +9,11 @@ from tensorflow.keras.preprocessing.image import img_to_array
 st.title('Jacaranda Identification')
 st.markdown('A Deep learning application to identify if a satellite image clip contains Jacaranda trees. The predicting result will be "Jacaranda", or "Others".')
 
-uploaded_file = st.file_uploader("Upload an image file", type="jpg")
+uploaded_file = st.file_uploader("Upload image files", type="jpg", accept_multiple_files=True)
+
+image_iterator = paginator("Select a page", uploaded_file)
+indices_on_page, images_on_page = map(list, zip(*image_iterator))
+st.image(images_on_page, width=100, caption=indices_on_page)
 
 img_height = 224
 img_width = 224
@@ -18,15 +22,22 @@ class_names = ['Jacaranda', 'Others']
 model = tf.keras.models.load_model('model')
 
 if uploaded_file is not None:
-    img = Image.open(uploaded_file)
-    st.image(img)
-
-    img_array = img_to_array(img)
-    img_array = tf.expand_dims(img_array, axis = 0) # Create a batch
-    processed_image = preprocess_input(img_array)
-
+    #n = len(uploaded_file)
+    #row_size = 5
+    #grid = st.columns(row_size)
+    #col = 0
     Generate_pred = st.button("Generate Prediction")
     if Generate_pred:
-        predictions = model.predict(processed_image)
-        score = predictions[0]
-        st.markdown("Predicted class of the image is : {}".format(class_names[np.argmax(score)]))
+        for file in uploaded_file:
+        #    with grid[col]:
+        #        img = Image.open(file)
+        #        st.image(img)
+        #col += 1
+            img = Image.open(file)
+            img_array = img_to_array(img)
+            img_array = tf.expand_dims(img_array, axis = 0) # Create a batch
+            processed_image = preprocess_input(img_array)
+    
+            predictions = model.predict(processed_image)
+            score = predictions[0]
+            st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)]))