# HuggingFace Hub
from huggingface_hub import from_pretrained_keras
model = from_pretrained_keras("Plsek/CADET-v1")
# Basic libraries
import os
import shutil
import numpy as np
from scipy.ndimage import center_of_mass
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
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
# Scikit-learn
from sklearn.cluster import DBSCAN
# Streamlit
import streamlit as st
st.set_option('deprecation.showPyplotGlobalUse', False)
# # 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((x0-0.5, x0-0.5), scale*128, scale*128, linewidth=1, edgecolor='w', facecolor='none'))
# plt.axis('off')
# plt.tight_layout()
# 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()
# # Define function to plot the decomposed prediction
# def plot_decomposed(decomposed):
# plt.figure(figsize=(4, 4))
# plt.imshow(decomposed, origin="lower") #, norm=LogNorm())
# N = int(np.max(decomposed))
# for i in range(N):
# new = np.where(decomposed == i+1, 1, 0)
# x0, y0 = center_of_mass(new)
# color = "white" if i < N//2 else "black"
# plt.text(y0, x0, f"{i+1}", ha="center", va="center", fontsize=15, color=color)
# plt.axis('off')
# with colC: st.pyplot()
# # Define function to 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
# # Define function to apply cutting and produce a prediction
# # @st.cache
# def cut_n_predict(data, wcs, scale):
# 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
# return y_pred, wcs
# # Define function to decompose prediction into individual cavities
# # @st.cache
# def decompose_cavity(pred, th2=0.7, amin=6):
# X, Y = pred.nonzero()
# data = np.array([X,Y]).reshape(2, -1)
# # DBSCAN clustering
# try: clusters = DBSCAN(eps=1.0, 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)
# # Save raw and decomposed predictions to predictions folder
# ccd = CCDData(pred, unit="adu", wcs=wcs)
# ccd.write(f"predictions/predicted.fits", overwrite=True)
# image_decomposed = np.zeros((128,128))
# for i, cav in enumerate(cavities):
# ccd = CCDData(cav, unit="adu", wcs=wcs)
# ccd.write(f"predictions/predicted_{i+1}.fits", overwrite=True)
# image_decomposed += (i+1) * np.where(cav > 0, 1, 0)
# # shutil.make_archive("predictions", 'zip', "predictions")
# return image_decomposed
# # @st.cache
# def load_file(fname):
# with fits.open(fname) as hdul:
# data = hdul[0].data
# wcs = WCS(hdul[0].header)
# return data, wcs
# def change_scale():
# del st.session_state["threshold"]
# # Use wide layout and create columns
# st.set_page_config(page_title="Cavity Detection Tool", layout="wide")
# bordersize = 0.45
# _, col, _ = st.columns([bordersize, 3, bordersize])
# os.system("mkdir -p predictions")
# with col:
# # Create heading and description
# st.markdown("Cavity Detection Tool
", unsafe_allow_html=True)
# 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.")
# st.markdown("To use this tool: upload your image, select the scale of interest, make a prediction, and decompose it into individual cavities!")
# st.markdown("Input images should be in units of counts, centred at the galaxy center, and point sources should be filled with surrounding background ([dmfilth](https://cxc.cfa.harvard.edu/ciao/ahelp/dmfilth.html)).")
# st.markdown("If you use this tool for your research, please cite [Plšek et al. 2023](https://arxiv.org/abs/2304.05457)")
# # _, col_1, col_2, col_3, _ = st.columns([bordersize, 2.0, 0.5, 0.5, bordersize])
# # with col:
# uploaded_file = st.file_uploader("Choose a FITS file", type=['fits'])
# # with col_2:
# # st.markdown("### Examples")
# # NGC4649 = st.button("NGC4649")
# # with col_3:
# # st.markdown("""""", unsafe_allow_html=True)
# # NGC5813 = st.button("NGC5813")
# # if NGC4649:
# # uploaded_file = "NGC4649_example.fits"
# # elif NGC5813:
# # uploaded_file = "NGC5813_example.fits"
# # # If file is uploaded, read in the data and plot it
# # if uploaded_file is not None:
# # data, wcs = load_file(uploaded_file)
# # # if "data" not in locals():
# # # data = np.zeros((128,128))
# # # Make six 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")
# # col6.subheader("")
# # with col1:
# # st.markdown("""""", unsafe_allow_html=True)
# # max_scale = int(data.shape[0] // 128)
# # scale = st.selectbox('Scale:',[f"{(i+1)*128}x{(i+1)*128}" for i in range(max_scale)], label_visibility="hidden", on_change=change_scale)
# # scale = int(scale.split("x")[0]) // 128
# # # Detect button
# # with col3: detect = st.button('Detect', key="detect")
# # # Threshold slider
# # with col4:
# # st.markdown("")
# # # st.markdown("""""", unsafe_allow_html=True)
# # threshold = st.slider("Threshold", 0.0, 1.0, 0.0, 0.05, key="threshold") #, label_visibility="hidden")
# # # Decompose button
# # with col5: decompose = st.button('Decompose', key="decompose")
# # # Make two columns for plots
# # _, colA, colB, colC, _ = st.columns([bordersize,1,1,1,bordersize])
# # image = np.log10(data+1)
# # plot_image(image, scale)
# # if detect or threshold:
# # # if st.session_state.get("detect", True):
# # y_pred, wcs = cut_n_predict(data, wcs, scale)
# # y_pred_th = np.where(y_pred > threshold, y_pred, 0)
# # plot_prediction(y_pred_th)
# # if decompose or st.session_state.get("download", False):
# # image_decomposed = decompose_cavity(y_pred_th)
# # plot_decomposed(image_decomposed)
# # with col6:
# # st.markdown("
", unsafe_allow_html=True)
# # # st.markdown("""""", unsafe_allow_html=True)
# # fname = uploaded_file.name.strip(".fits")
# # # if st.session_state.get("download", False):
# # shutil.make_archive("predictions", 'zip', "predictions")
# # with open('predictions.zip', 'rb') as f:
# # res = f.read()
# # download = st.download_button(label="Download", data=res, key="download",
# # file_name=f'{fname}_{int(scale*128)}.zip',
# # # disabled=st.session_state.get("disabled", True),
# # mime="application/octet-stream")