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('Jacaranda Identification')

st.markdown('This is a Deep Learning application to identify if a satellite image clip contains Jacaranda trees.  The predicting result will be "Jacaranda", or "Others".  You can click "Brows files" multiple times until adding all images before generating prediction.')

uploaded_file = st.file_uploader("Upload image files", type="jpg", accept_multiple_files=True)

#image_iterator = paginator("Select a page", uploaded_file)
#indices_on_page, images_on_page = map(list, zip(*image_iterator))
st.image(uploaded_file, width=100)

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

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

if uploaded_file is not None:
    #n = len(uploaded_file)
    #row_size = 5
    #grid = st.columns(row_size)
    #col = 0
    Generate_pred = st.button("Generate Prediction")
    if Generate_pred:
        for file in uploaded_file:
        #    with grid[col]:
        #        img = Image.open(file)
        #        st.image(img)
        #col += 1
            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)]))