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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()
#closeImage = st.button("clear all images")
img_height = 224
img_width = 224
class_names = ['Palm', 'Others']
model = tf.keras.models.load_model('model')
with st.form("form", clear_on_submit=True):
uploaded_file = st.file_uploader("Upload image files", type="jpg", accept_multiple_files=True)
submitted = st.form_submit_button("UPLOAD!")
if submitted and file is not None:
st.write("UPLOADED!")
st.image(uploaded_file, width=100)
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)]))
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