Create app

app

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+#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)
+	
+	
+