app.py

import os
import tempfile
import numpy as np
import streamlit as st
import tensorflow as tf
from PIL import Image
from tensorflow.keras.preprocessing.image import load_img, img_to_array


st.title("Nova'23 Classification Model Playground!")
st.write("Upload the x-ray image")

uploaded_file = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
if uploaded_file is not None:
    # Write the uploaded image data to a temporary file
    with tempfile.NamedTemporaryFile(delete=False) as f:
        f.write(uploaded_file.read())

    # Get the path of the temporary file
    img_path = f.name

    # Display the uploaded image
    image = Image.open(uploaded_file)
    st.image(image, caption='Uploaded Image', use_column_width=True)

    # Load and preprocess the image
    height = 180
    width = 180
    channels = 3
    img = load_img(img_path, target_size=(height, width))
    img_array = img_to_array(img)
    img_array = img_array / 255.0
    img_array = tf.reshape(img_array, [1, height, width, channels])

    model = tf.keras.models.load_model('nova.h5')
    # Make a prediction
    prediction = model.predict(img_array)

    if prediction[0][0] > 0.5:
        print("Prediction: Pneumonia")
        st.write("Prediction: Pneumonia")
    else:
        print("Prediction: Normal")
        st.write("Prediction: Normal")

    os.remove(img_path)