Create app.py

app.py

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+import streamlit as st
+from keras.models import load_model
+from PIL import Image
+import numpy as np
+
+from util import classify, set_background
+
+
+set_background('./bgs/bg5.png')
+
+# set title
+st.title('Pneumonia classification')
+
+# set header
+st.header('Please upload a chest X-ray image')
+
+# upload file
+file = st.file_uploader('', type=['jpeg', 'jpg', 'png'])
+
+# load classifier
+model = load_model('./model/pneumonia_classifier.h5')
+
+# load class names
+with open('./model/labels.txt', 'r') as f:
+    class_names = [a[:-1].split(' ')[1] for a in f.readlines()]
+    f.close()
+
+# display image
+if file is not None:
+    image = Image.open(file).convert('RGB')
+    st.image(image, use_column_width=True)
+
+    # classify image
+    class_name, conf_score = classify(image, model, class_names)
+
+    # write classification
+    st.write("## {}".format(class_name))
+    st.write("### score: {}%".format(int(conf_score * 1000) / 10))