app.py

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

# 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

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

st.set_page_config(page_title="Cavity Detection Tool", layout="wide")

# st.title("Cavity Detection Tool")

bordersize = 0.6
_, col, _ = st.columns([bordersize, 3, bordersize])

with col:
    st.markdown("# 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 this tool for your research, please cite [Plšek et al. 2023](https://arxiv.org/abs/2304.05457)")
    
    st.markdown("Input images should be centered at the centre of the galaxy and point sources should be filled with surrounding background ([dmfilth](https://cxc.cfa.harvard.edu/ciao/ahelp/dmfilth.html)).")
    
    # 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 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

def decompose_cavity(pred, th2=0.7, amin=10):
    X, Y = pred.nonzero()
    data = np.array([X,Y]).reshape(2, -1)

    # DBSCAN CLUSTERING ALGORITHM
    try: clusters = DBSCAN(eps=1.5, min_samples=3).fit(data.T).labels_
    except: clusters = []

    N = len(set(clusters))
    cavities = []

    for i in range(N):
        img = np.zeros((128,128))
        b = clusters == i
        xi, yi = X[b], Y[b]
        img[xi, yi] = pred[xi, yi]

        # THRESHOLDING #2
        if not (img > th2).any(): continue

        # MINIMAL AREA
        if np.sum(img) <= amin: continue

        cavities.append(img)

    return cavities
    
# 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, col5, col6, _ = st.columns([bordersize,0.5,0.5,0.5,0.5,0.5,0.5,bordersize])
    col1.subheader("Input image")
    col3.subheader("Prediction")
    col5.subheader("Decomposed")
    
    with col2:
        # st.markdown("""<style>[data-baseweb="select"] {margin-top: -56px;}</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"))
        scale = st.selectbox('Scale:',[f"{(i+1)*128}x{(i+1)*128}" for i in range(max_scale)], label_visibility="hidden")
        scale = int(scale.split("x")[0]) // 128
    
    with col4:
        # st.markdown("""<style>[data-baseweb="select"] {margin-top: 16px;}</style>""", unsafe_allow_html=True)
        detect = st.button('Detect')

    with col6:
        decompose = st.button('Docompose')
        
    # Make two columns for plots
    _, colA, colB, colC, _ = st.columns([bordersize,1,1,1,bordersize])
    
    image = np.log10(data+1)
    plot_image(image, scale)

    # with colB:
    #     threshold = st.slider("", 0.0, 1.0, 0.4, 0.05, label_visibility="hidden")
    
    # 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 > threshold, y_pred, 0)
                
    #     plot_prediction(y_pred)

        # if decompose:
        #     cavs = decompose_cavity(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: 16px;}</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")