Update app.py

app.py

@@ -1,7 +1,4 @@
 import streamlit as st
-import time
-
-import cv2
 import pandas
 from PIL import Image
 import numpy as np
@@ -9,49 +6,76 @@ import tensorflow as tf
 from tensorflow.keras.applications.resnet50 import preprocess_input 
 from tensorflow.keras.preprocessing.image import img_to_array    
 
-st.title('Jacaranda Identification')
+def main():
+    st.title('Jacaranda Identification')
+    
+    st.markdown("This is a Deep Learning application to identify if a satellite image clip contains Jacaranda trees.\n")
+    st.markdown('The predicting result will be "Jacaranda", or "Others".')
+    st.markdown('You can click "Browse files" multiple times until adding all images before generating prediction.\n')
 
-st.markdown("This is a Deep Learning application to identify if a satellite image clip contains Jacaranda trees.\n")
-st.markdown('The predicting result will be "Jacaranda", or "Others".')
-st.markdown('You can click "Browse files" multiple times until adding all images before generating prediction.\n')
+    run_the_app()
+    
+@st.cache_resource()#(allow_output_mutation=True)
+def load_model():
+    # Load the network. Because this is cached it will only happen once.
+    model = tf.keras.models.load_model('model')
+    return model
 
-img_height = 224
-img_width = 224
-class_names = ['Jacaranda', 'Others']
+@st.cache_data()
+def generate_df():
+    dict = {'Image file name':[],
+            'Class name': []
+            }
+    df = pd.DataFrame(dict)
+    return df
+   
+@st.cache_data()
+def write_df(df, file, cls):
+    rec = {'Image file name': file,
+           'Class name': cls}
+    df.append(rec, ignore_index = True)
 
-model = tf.keras.models.load_model('model')
+@st.cache_data()
+def convert_df(df):
+    return df.to_csv(index=False, encoding='utf-8')
 
-state = st.session_state
-if 'file_uploader_key' not in state:
-    state['file_uploader_key'] = 0
+def run_the_app():
 
-if "uploaded_files" not in state:
-    state["uploaded_files"] = []
+    class_names = ['Jacaranda', 'Others']
+    model = load_model()
+    df = generate_df()
 
-uploaded_file = st.file_uploader(
-    "Upload images", 
-    type="jpg" or 'jpeg' or 'bmp' or 'png' or 'tif', 
-    accept_multiple_files=True, 
-    key=state['file_uploader_key'])
+    uploaded_files = st.file_uploader(
+            "Upload images", 
+            type="jpg" or 'jpeg' or 'bmp' or 'png' or 'tif', 
+            accept_multiple_files=True)
+    
+    if uploaded_files:
+        st.image(uploaded_files, width=100)
 
-if uploaded_file is not None:
-    st.image(uploaded_file, width=100)
-    state["uploaded_files"] = uploaded_file
+        if st.button("Clear uploaded images"):
+            st.empty()
+            st.experimental_rerun()
 
-    if st.button("Clear uploaded images"):
-        state["file_uploader_key"] += 1
-        st.empty()
-        time.sleep(.5)
-        st.experimental_rerun()
-    
-    if st.button("Generate prediction"):
-        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)]))
+        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]
+                cls = class_names[np.argmax(score)]
+                
+                st.markdown("Predicted class of the image {} is : {}".format(file, cls))
+                
+                write_df(df, file, cls)
+
+            csv = convert_df(df)
+            st.download_button("Download the results as CSV", 
+                           data = csv, 
+                           file_name = "jacaranda_identification.csv")
 
+if __name__ == "__main__":
+    main()