main.py

import streamlit as st
from PIL import Image
import cv2
from YOUR_MODULE_NAME import chexnet, get_array, compute_gradcam, visualize_gradcam, get_predicted_label


def main():
    st.title('DICOM Image Prediction with GradCAM')
    uploaded_file = st.file_uploader("Upload DICOM file", type=['dcm'])

    if uploaded_file is not None:
        dcm_img = get_array(uploaded_file)
        st.subheader('Original X-ray Image:')
        st.image(dcm_img, caption='Original X-ray', use_column_width=True)

        # Load your pre-trained model here
        model = chexnet()  # Modify this according to your implementation

        if st.button('Get Predictions'):
            predictions = model.predict(dcm_img)
            predicted_label = get_predicted_label(predictions, LABELS)
            st.write(f"Predicted Label: {predicted_label}")

            gradcam_labels = [predicted_label]  # Modify this based on your requirement
            gradcams = compute_gradcam(model, dcm_img, LABELS, gradcam_labels)
            visualize_gradcam(dcm_img, gradcams, LABELS, predictions)

if __name__ == '__main__':
    main()