app.py

# Basic libraries
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle

# Astropy
from astropy.io import fits
from astropy.wcs import WCS
from astropy.nddata import Cutout2D, CCDData
from astropy.convolution import Gaussian2DKernel as Gauss
from astropy.convolution import convolve

# HuggingFace
from huggingface_hub import from_pretrained_keras
model = from_pretrained_keras("Plsek/CADET-v1")

# Streamlit
import streamlit as st
st.set_option('deprecation.showPyplotGlobalUse', False)


st.title("Cavity Detection Tool")

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

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

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

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

# Define function to smooth image
def smooth_image(image, scale):
    smoothed = convolve(image, boundary = "wrap", nan_treatment="interpolate",
                    kernel = Gauss(x_stddev = 2, y_stddev = 2))

    return smoothed

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

# Cut input image and rebin it to 128x128 pixels
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)
    
    # Regrid 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)
    
    # Make four columns for buttons
    col1, col2, col3, col4 = st.columns(4)
    col1.subheader("Input image")
    col3.subheader("Prediction")

    # Add a slider to change the scale
    with col1:
        smooth = st.button("Smooth")
    
    with col2:
        st.markdown("""<style>[data-baseweb="select"] {margin-top: 17px;}</style>""", unsafe_allow_html=True)
        max_scale = int(data.shape[0] // 128)
        scale = int(st.selectbox('Scale:',[i+1 for i in range(max_scale)], label_visibility="hidden"))
    
    with col3:
        detect = st.button('Detect cavities')

    # Make two columns for plots
    colA, colB = st.columns(2)

    image = np.log10(data+1)
    if smooth: image = smooth_image(image, scale)
    plot_image(image, scale)
    
    if detect:
        data, wcs = cut(data, wcs, scale=scale)
        image = np.log10(data+1)
        
        y_pred = 0
        for j in [0,1,2,3]:
            rotated = np.rot90(image, j)
            pred = model.predict(rotated.reshape(1, 128, 128, 1)).reshape(128 ,128)
            pred = np.rot90(pred, -j)
            y_pred += pred / 4

        # Thresholding
        y_pred = np.where(y_pred > 0.4, y_pred, 0)

        plot_prediction(y_pred)

        ccd = CCDData(y_pred, unit="adu", wcs=wcs)
        ccd.write("predicted.fits", overwrite=True)
        with open('predicted.fits', 'rb') as f:
            res = f.read()
        
        with col4:
            pass
            # st.markdown("""<style>[data-baseweb="select"] {margin-top: 32px;}</style>""", unsafe_allow_html=True)
        #     # download = st.button('Download')
        #     download = st.download_button(label="Download", data=res, file_name="predicted.fits", mime="application/octet-stream")