#Importer les librairies
import streamlit as st
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.utils import load_img,img_to_array
from tensorflow.keras.preprocessing import image
from PIL import Image,ImageOps


	#Donner un titre
st.title(":red[APPLICATION DE PREDICTION DU COVID] :bar_chart: :chart:")
st.markdown("* NOM: FOSSO TCHATAT SIDOINE ",unsafe_allow_html=True)

#loader l'image
st.image("image/keyce.jpg")

upload_file = st.file_uploader("Telecharger un fichier",type = ['JPEG','jpg','png','PNG'])


model = tf.keras.models.load_model("model.h5")
covid_classes = {'COVID19': 0, 'NORMAL': 1, 'PNEUMONIA': 2, 'TURBERCULOSIS': 3}
tab1, tab2= st.tabs([":bar_chart: Evaluation du model", ":mask: :smile: Prediction"])

with tab1:
	st.image("image/loss.png")
with tab2:
	generate_pred = st.button("Predict")
	if upload_file:
		st.image(upload_file,caption="Image téléchargée",use_column_width=True)
		test_image = image.load_img(upload_file,target_size=(299,299))
		image_array = img_to_array(test_image)
		image_array = np.expand_dims(image_array,axis=0)

		if generate_pred:
			predictions = model.predict(image_array)
			classes = np.argmax(predictions[0])
			for key,value in covid_classes.items():
				if value == classes:
					st.write("The diagostic is :",key)