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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

deploypalm.py

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+import os
+import streamlit as st
+import cv2
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.applications.resnet50 import preprocess_input 
+from tensorflow.keras.preprocessing.image import img_to_array    
+
+st.title('Palm Identification')
+st.markdown("This is a Deep Learning application to identify if a satellite image clip contains Palm trees.\n")
+st.markdown('The predicting result will be "Palm", or "Others".')
+st.markdown('You can click "Brows files" multiple times until adding all images before generating prediction.\n')
+
+uploaded_file = st.file_uploader("Upload an image file", type="jpg")
+st.image(uploaded_file, width=100)
+
+img_height = 224
+img_width = 224
+class_names = ['Palm', 'Others']
+
+model = tf.keras.models.load_model('model')
+
+if uploaded_file is not None:
+    Generate_pred = st.button("Generate Prediction")
+    if Generate_pred:
+        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)]))
+

model/keras_metadata.pb

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+size 370284

model/saved_model.pb

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+size 3104543

model/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
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+size 119665107

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+opencv-python-headless
+Pillow
+tensorflow
+numpy