app.py

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

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

st.title("Cavity Detection Tool")

st.text("Cavity Detection Tool (CADET) is a machine learning pipeline trained to detect\n X-ray cavities from noisy Chandra images of early-type galaxies.To use this tool upload\n your image, select the scale of interest and make a prediction! If you use output of this\n tool in your research please cite [Plšek et al. 2023](https://arxiv.org/abs/2304.05457)")

model = load_model("CADET.hdf5")

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

col1, col2 = st.columns(2)

# Define function to plot the uploaded image
def plot_image(image_array, scale):
    plt.figure(figsize=(4, 4))

    x0 = image_array.shape[0] // 2 - scale * 128 / 2
    plt.imshow(image_array, origin="lower")
    plt.gca().add_patch(Rectangle((x0, x0), scale*128, scale*128, linewidth=1, edgecolor='w', facecolor='none'))
    
    plt.axis('off')
    with col1: st.pyplot()

# Define function to plot the prediction
def plot_prediction(pred):
    plt.figure(figsize=(4, 4))
    plt.imshow(pred, origin="lower")
    plt.axis('off')
    with col2: 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)
    data, wcs = cutout.data, cutout.wcs

    # REGRID DATA
    factor = size // 128
    data = data.reshape(128, factor, 128, factor).mean(-1).mean(1)
    
    # REGIRD WCS
    ra, dec = wcs.wcs_pix2world(np.array([[63, 63]]),0)[0]
    wcs.wcs.cdelt[0] = wcs.wcs.cdelt[0] * factor
    wcs.wcs.cdelt[1] = wcs.wcs.cdelt[1] * factor
    wcs.wcs.crval[0] = ra
    wcs.wcs.crval[1] = dec
    wcs.wcs.crpix[0] = 64 / factor
    wcs.wcs.crpix[1] = 64 / factor

    return data, wcs


# 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)

        # Add a slider to change the scale
        scale = st.slider("Scale", 1, 4, 1, 1)

        plot_image(np.log10(data+1), scale)

        with col2:
            if st.button('Detect cavities'):                
                data, wcs = cut(data, wcs, scale=scale)
        
                image_data = np.log10(data+1)
    
                y_pred = 0
                for j in [0,1,2,3]:
                    rotated = np.rot90(image_data, j)
                    pred = model.predict(rotated.reshape(1, 128, 128, 1)).reshape(128 ,128)
                    pred = np.rot90(pred, -j)
                    y_pred += pred / 4
    
                # ccd = CCDData(pred, unit="adu", wcs=wcs)
                # ccd.write(f"predicted.fits", overwrite=True)
    
                plot_prediction(y_pred)
    
                # if st.button('Download FITS File'):
                #     with open('predicted.fits', 'rb') as f:
                #         data = f.read()
                #     st.download_button(label="Download", data=data, file_name="predicted.fits", mime="application/octet-stream")