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)]))