Update app.py

app.py

@@ -1,3 +1,32 @@
 import streamlit as st
-x = st.slider('Select a value')
-st.write(x, 'squared is', x*x)
+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('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")
+
+img_height = 224
+img_width = 224
+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)
+
+    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)]))