app.py

import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
from astropy.wcs import WCS
from astropy.nddata import Cutout2D
from tensorflow.keras.models import load_model

st.set_option('deprecation.showPyplotGlobalUse', False)

st.title("FITS Image Viewer")

model = load_model("CADET.hdf5")

# Define function to plot the uploaded image
def plot_image(image_array, pred):
    plt.figure(figsize=(10, 5))
    plt.subplot(1, 2, 1)
    plt.imshow(image_array, origin="lower")
    plt.axis('off')

    plt.subplot(1, 2, 2)
    plt.imshow(pred, origin="lower")
    plt.axis('off')
    st.pyplot()

def cut(data0, wcs0, scale=1):
   shape = data0.shape[0]
   x0 = shape / 2
   size = 128 * scale
   cutout = Cutout2D(data0, (x0, x0), (size, size), wcs=wcs0)
   return cutout.data, cutout.wcs

# Create file uploader widget
uploaded_file = st.file_uploader("Choose a FITS file", type=['fits'])

# If file is uploaded, read in the data and plot it
if uploaded_file is not None:
    with fits.open(uploaded_file) as hdul:
        data = hdul[0].data
        wcs = WCS(hdul[0].header)
        data, wcs = cut(data, wcs, scale=1)

        image_data = np.log10(data+1)
        pred = model.predict(image_data.reshape(1, 128, 128, 1)).reshape(128 ,128)

        plot_image(image_data, pred)