Update app.py

app.py

@@ -1,6 +1,4 @@
 import streamlit as st
-from random import randint
-#from .session_state import get_session_state
 
 import cv2
 import pandas
@@ -15,47 +13,40 @@ st.markdown("This is a Deep Learning application to identify if a satellite imag
 st.markdown('The predicting result will be "Palm", or "Others".')
 st.markdown('You can click "Browse files" multiple times until adding all images before generating prediction.\n')
 
-#uploaded_file = st.file_uploader("Upload an image file", type="jpg", accept_multiple_files=True)
-
-#imageContainer = st.empty()
-
-#closeImage = st.button("clear all images")
 img_height = 224
 img_width = 224
 class_names = ['Palm', 'Others']
 model = tf.keras.models.load_model('model')
 
 state = st.session_state
-if not state.widget_key:
-    state.widget_key = str(randint(1000, 100000000))
-
-uploaded_file = st.file_uploader(
-    "Choose a file", accept_multiple_files=True, key=state.widget_key)
+if 'file_uploader_key' not in state:
+    state['file_uploader_key'] = 0
+
+if "uploaded_files" not in state:
+    state["uploaded_files"] = []
+
+uploaded_files = st.file_uploader(
+    "Upload images", 
+    type="jpg" or 'jpeg' or 'bmp' or 'png' or 'tif', 
+    accept_multiple_files=True, 
+    key=state['file_uploader_key'])
+
+if uploaded_files:
+    st.image(uploaded_files, width=100)
+    state["uploaded_files"] = uploaded_files
+    if st.button("Generate prediction"):
+        for file in uploaded_files:
+            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)]))
 
-if st.button('clear uploaded_file'):
-    state.widget_key = str(randint(1000, 100000000))
-state.sync()
 
-#Generate_pred = st.button("Generate Prediction")
-#with st.form("form", clear_on_submit=True):
-#    uploaded_file = st.file_uploader("Upload image files", type="jpg", accept_multiple_files=True)
-#    if uploaded_file is not None:
-#        st.image(uploaded_file, width=100)
-        
-#    submitted = st.form_submit_button("Toggle here to predict or to delete the data")
-            
-    #    if submitted and uploaded_file is not None:
-    
-#            for file in uploaded_file:
-#                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)]))
-    
-#            uploaded_file = None
-        
+if st.button("Clear uploaded files"):
+    state["file_uploader_key"] += 1
+    st.experimental_rerun()