app.py

# Import library
import streamlit as st
import numpy as np
from tensorflow import keras
from PIL import Image


# Load model
model = keras.models.load_model('model_tuned.hdf5')

st.header('Lung X-Ray Prediction')
st.write('Please Upload Your X-Ray Image')

# Upload Image
uploaded_file = st.file_uploader("", type=["jpg", "jpeg", "png"])

# Show Uploaded Image
if uploaded_file is not None:
    image = Image.open(uploaded_file).convert('RGB')
    st.subheader('This Is Your X-Ray Image')
    st.image(image, width=300)

    st.write('Push This Button To Predict')
    if st.button('Predict'):

        # resize uploaded image
        inf = image.resize((224,224))
        # make into array
        inf = np.asarray(inf)
        # expands dims to tensor
        inf = np.expand_dims(inf, axis = 0)

        # define label
        label = ['Covid', 'Normal', 'Pneumonia']

        # Make the prediction
        prediction = model.predict(inf)
        prediction = label[np.argmax(prediction)]
    
        st.subheader(f"This X-Ray Images Has {prediction} Condition")