app.py

import streamlit as st
import cv2
import pandas
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 "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()
imageContainer.image(uploaded_file, width=100)

closeImage = st.button("clear all images")

img_height = 224
img_width = 224
class_names = ['Palm', 'Others']

model = tf.keras.models.load_model('model')

while(1):
    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)]))
    
    if closeImage:
        imageContainer.empty()
        if uploaded_file is not None:
            del uploaded_file