app.py

import streamlit as st
import numpy as np
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.efficientnet import preprocess_input
import tensorflow as tf
from PIL import Image

def main():
    st.set_page_config(
         page_title="Alzheimer's Detection",
         layout="centered"
    )

    st.markdown("<h1 style='text-align: center;'>Alzheimer's Detection Tool🧠</h1>", unsafe_allow_html=True)

    # Load the saved model
    loaded_model = tf.keras.models.load_model("model.h5", compile=False)

    def predict_image_class(img_path):
        img = image.load_img(img_path, target_size=(224, 224))
        img_array = image.img_to_array(img)
        img_array = np.expand_dims(img_array, axis=0)
        img_array = preprocess_input(img_array)

        predictions = loaded_model.predict(img_array)
        predicted_class = np.argmax(predictions, axis=1)[0]

        return predicted_class

    uploaded_file = st.file_uploader('Upload an MRI image...', type=['jpg', 'png', 'jpeg'])

    if uploaded_file is not None:
        predicted_class = predict_image_class(uploaded_file)
        
        class_names = ["MILD DEMENTED", "MODERATE DEMENTED", "NON DEMENTED", "VERY MILD DEMENTED"]
        
        st.markdown(f"<div style='text-align: center; font-size: 30px;'>Predicted Alzheimer's stage is: <b>{class_names[predicted_class]}</b></div>", unsafe_allow_html=True)
        st.image(uploaded_file, use_column_width=False, width=300)

if __name__ == '__main__':
    main()