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import streamlit as st
import time
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')
img_height = 224
img_width = 224
class_names = ['Palm', 'Others']
model = tf.keras.models.load_model('model')
state = st.session_state
if 'file_uploader_key' not in state:
state['file_uploader_key'] = 0
if "uploaded_files" not in state:
state["uploaded_files"] = []
uploaded_files = st.file_uploader(
"Upload images",
type="jpg" or 'jpeg' or 'bmp' or 'png' or 'tif',
accept_multiple_files=True,
key=state['file_uploader_key'])
if uploaded_files:
state["uploaded_files"] = uploaded_files
if st.button("Clear all"):
state["file_uploader_key"] += 1
time.sleep(.5)
st.experimental_rerun()
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]
st.markdown("Predicted class of the image {} is : {}".format(file, class_names[np.argmax(score)]))
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