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	import json, time, os, sys, glob
	import urllib
	import shutil
	import warnings
	import copy
	import random
	import re

	import os.path

	import torch
	import ray
	import jax

	import gradio as gr
	import pandas as pd
	import numpy as np
	import plotly.express as px
	import jax.numpy as jnp
	import tensorflow as tf
	import matplotlib.pyplot as plt
	import colabfold as cf
	import plotly.graph_objects as go

	import torch.nn as nn
	import torch.nn.functional as F


	if "/home/user/app/af_backprop" not in sys.path:
	sys.path.append("/home/user/app/af_backprop")

	# local only
	if "/home/duerr/phd/08_Code/ProteinMPNN/af_backprop" not in sys.path:
	sys.path.append("/home/duerr/phd/08_Code/ProteinMPNN/af_backprop")


	from torch import optim
	from torch.utils.data import DataLoader
	from torch.utils.data.dataset import random_split, Subset
	from moleculekit.molecule import Molecule
	from alphafold.common import protein
	from alphafold.data import pipeline
	from alphafold.model import data, config
	from alphafold.model import model as afmodel
	from alphafold.common import residue_constants

	from utils import *

	sys.path.append("/home/user/app/ProteinMPNN/vanilla_proteinmpnn")
	sys.path.append("/home/duerr/phd/08_Code/ProteinMPNN/ProteinMPNN/vanilla_proteinmpnn")


	# tf.config.set_visible_devices([], "GPU")


	def chain_break(idx_res, Ls, length=200):
	# Minkyung's code
	# add big enough number to residue index to indicate chain breaks
	L_prev = 0
	for L_i in Ls[:-1]:
	idx_res[L_prev + L_i :] += length
	L_prev += L_i
	return idx_res


	def clear_mem():
	backend = jax.lib.xla_bridge.get_backend()
	for buf in backend.live_buffers():
	buf.delete()


	print("Is cuda available", torch.cuda.is_available())
	# stream = os.popen("nvcc --version")
	# output = stream.read()
	# print(output)


	def setup_af(seq, model_name="model_5_ptm"):
	clear_mem()
	# setup model
	cfg = config.model_config("model_5_ptm")
	cfg.model.num_recycle = 0
	cfg.data.common.num_recycle = 0
	cfg.data.eval.max_msa_clusters = 1
	cfg.data.common.max_extra_msa = 1
	cfg.data.eval.masked_msa_replace_fraction = 0
	cfg.model.global_config.subbatch_size = None
	if os.path.exists("/home/duerr"):
	datadir = "/home/duerr/phd/08_Code/ProteinMPNN"
	else:
	datadir = "/home/user/app/"
	model_params = data.get_model_haiku_params(model_name=model_name, data_dir=datadir)
	model_runner = afmodel.RunModel(cfg, model_params, is_training=False)
	Ls = [len(s) for s in seq.split("/")]

	seq = re.sub("[^A-Z]", "", seq.upper())
	length = len(seq)
	feature_dict = {
	**pipeline.make_sequence_features(
	sequence=seq, description="none", num_res=length
	),
	*pipeline.make_msa_features(msas=[[seq]], deletion_matrices=[[[0] length]]),
	}
	feature_dict["residue_index"] = chain_break(feature_dict["residue_index"], Ls)
	inputs = model_runner.process_features(feature_dict, random_seed=0)

	def runner(seq, opt):
	# update sequence
	inputs = opt["inputs"]
	inputs.update(opt["prev"])
	update_seq(seq, inputs)
	update_aatype(inputs["target_feat"][..., 1:], inputs)

	# mask prediction
	mask = seq.sum(-1)
	inputs["seq_mask"] = inputs["seq_mask"].at[:].set(mask)
	inputs["msa_mask"] = inputs["msa_mask"].at[:].set(mask)
	inputs["residue_index"] = jnp.where(mask == 1, inputs["residue_index"], 0)

	# get prediction
	key = jax.random.PRNGKey(0)
	outputs = model_runner.apply(opt["params"], key, inputs)

	prev = {
	"init_msa_first_row": outputs["representations"]["msa_first_row"][None],
	"init_pair": outputs["representations"]["pair"][None],
	"init_pos": outputs["structure_module"]["final_atom_positions"][None],
	}

	aux = {
	"final_atom_positions": outputs["structure_module"]["final_atom_positions"],
	"final_atom_mask": outputs["structure_module"]["final_atom_mask"],
	"plddt": get_plddt(outputs),
	"pae": get_pae(outputs),
	"inputs": inputs,
	"prev": prev,
	}
	return aux

	return jax.jit(runner), {"inputs": inputs, "params": model_params}


	def make_tied_positions_for_homomers(pdb_dict_list):
	my_dict = {}
	for result in pdb_dict_list:
	all_chain_list = sorted(
	[item[-1:] for item in list(result) if item[:9] == "seq_chain"]
	) # A, B, C, ...
	tied_positions_list = []
	chain_length = len(result[f"seq_chain_{all_chain_list[0]}"])
	for i in range(1, chain_length + 1):
	temp_dict = {}
	for j, chain in enumerate(all_chain_list):
	temp_dict[chain] = [i] # needs to be a list
	tied_positions_list.append(temp_dict)
	my_dict[result["name"]] = tied_positions_list
	return my_dict


	def align_structures(pdb1, pdb2, lenRes, index):
	"""Take two structure and superimpose pdb1 on pdb2"""
	import Bio.PDB
	import subprocess

	pdb_parser = Bio.PDB.PDBParser(QUIET=True)
	# Get the structures
	ref_structure = pdb_parser.get_structure("ref", pdb1)
	sample_structure = pdb_parser.get_structure("sample", pdb2)

	aligner = Bio.PDB.CEAligner()
	aligner.set_reference(ref_structure)
	aligner.align(sample_structure)

	io = Bio.PDB.PDBIO()
	io.set_structure(ref_structure)
	io.save(f"outputs/reference.pdb")
	io.set_structure(sample_structure)
	io.save(f"outputs/out_{index}_aligned.pdb")
	# Doing this to get around biopython CEALIGN bug
	# subprocess.call("pymol -c -Q -r cealign.pml", shell=True)

	return aligner.rms, "outputs/reference.pdb", f"outputs/out_{index}_aligned.pdb"


	def save_pdb(outs, filename, LEN):
	"""save pdb coordinates"""
	p = {
	"residue_index": outs["inputs"]["residue_index"][0][:LEN],
	"aatype": outs["inputs"]["aatype"].argmax(-1)[0][:LEN],
	"atom_positions": outs["final_atom_positions"][:LEN],
	"atom_mask": outs["final_atom_mask"][:LEN],
	}
	b_factors = 100.0 * outs["plddt"][:LEN, None] * p["atom_mask"]
	p = protein.Protein(**p, b_factors=b_factors)
	pdb_lines = protein.to_pdb(p)
	with open(filename, "w") as f:
	f.write(pdb_lines)


	@ray.remote(num_gpus=1, max_calls=1)
	def run_alphafold(sequences, num_recycles):
	recycles = num_recycles
	RUNNER, OPT = setup_af(sequences[0])
	plddts = []
	paes = []
	for i, sequence in enumerate(sequences):
	SEQ = re.sub("[^A-Z]", "", sequence.upper())
	MAX_LEN = len(SEQ)
	LEN = len(SEQ)

	x = np.array([residue_constants.restype_order.get(aa, -1) for aa in SEQ])
	x = np.pad(x, [0, MAX_LEN - LEN], constant_values=-1)
	x = jax.nn.one_hot(x, 20)

	OPT["prev"] = {
	"init_msa_first_row": np.zeros([1, MAX_LEN, 256]),
	"init_pair": np.zeros([1, MAX_LEN, MAX_LEN, 128]),
	"init_pos": np.zeros([1, MAX_LEN, 37, 3]),
	}

	positions = []

	for r in range(recycles + 1):
	outs = RUNNER(x, OPT)
	outs = jax.tree_map(lambda x: np.asarray(x), outs)
	positions.append(outs["prev"]["init_pos"][0, :LEN])
	OPT["prev"] = outs["prev"]
	plddts.append(outs["plddt"][:LEN])
	paes.append(outs["pae"])
	if os.path.exists("/home/duerr/phd/08_Code/ProteinMPNN"):
	save_pdb(
	outs, f"/home/duerr/phd/08_Code/ProteinMPNN/outputs/out_{i}.pdb", LEN
	)
	else:
	save_pdb(outs, f"/home/user/app/outputs/out_{i}.pdb", LEN)
	return plddts, paes, LEN


	if os.path.exists("/home/duerr/phd/08_Code/ProteinMPNN"):
	path_to_model_weights = "/home/duerr/phd/08_Code/ProteinMPNN/ProteinMPNN/vanilla_proteinmpnn/vanilla_model_weights"
	else:
	path_to_model_weights = (
	"/home/user/app/ProteinMPNN/vanilla_proteinmpnn/vanilla_model_weights"
	)


	def setup_proteinmpnn(model_name="v_48_020", backbone_noise=0.00):
	from protein_mpnn_utils import (
	loss_nll,
	loss_smoothed,
	gather_edges,
	gather_nodes,
	gather_nodes_t,
	cat_neighbors_nodes,
	_scores,
	_S_to_seq,
	tied_featurize,
	parse_PDB,
	)
	from protein_mpnn_utils import StructureDataset, StructureDatasetPDB, ProteinMPNN

	device = torch.device(
	"cpu"
	) # torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu") #fix for memory issues
	# ProteinMPNN model name: v_48_002, v_48_010, v_48_020, v_48_030, v_32_002, v_32_010; v_32_020, v_32_030; v_48_010=version with 48 edges 0.10A noise
	# Standard deviation of Gaussian noise to add to backbone atoms
	hidden_dim = 128
	num_layers = 3
	model_folder_path = path_to_model_weights
	if model_folder_path[-1] != "/":
	model_folder_path = model_folder_path + "/"
	checkpoint_path = model_folder_path + f"{model_name}.pt"

	checkpoint = torch.load(checkpoint_path, map_location=device)

	noise_level_print = checkpoint["noise_level"]

	model = ProteinMPNN(
	num_letters=21,
	node_features=hidden_dim,
	edge_features=hidden_dim,
	hidden_dim=hidden_dim,
	num_encoder_layers=num_layers,
	num_decoder_layers=num_layers,
	augment_eps=backbone_noise,
	k_neighbors=checkpoint["num_edges"],
	)
	model.to(device)
	model.load_state_dict(checkpoint["model_state_dict"])
	model.eval()
	return model, device


	def get_pdb(pdb_code="", filepath=""):
	if pdb_code is None or pdb_code == "":
	try:
	return filepath.name
	except AttributeError as e:
	return None
	else:
	os.system(f"wget -qnc https://files.rcsb.org/view/{pdb_code}.pdb")
	return f"{pdb_code}.pdb"


	def preprocess_mol(pdb_code="", filepath=""):
	if pdb_code is None or pdb_code == "":
	try:
	mol = Molecule(filepath.name)
	except AttributeError as e:
	return None
	else:
	mol = Molecule(pdb_code)
	mol.write("original.pdb")
	# clean messy files and only include protein itself
	mol.filter("protein")
	# renumber using moleculekit 0...len(protein)
	df = mol.renumberResidues(returnMapping=True)
	# add proteinMPNN index col which used 1..len(chain), 1...len(chain)
	indexes = []
	for chain, g in df.groupby("chain"):
	j = 1
	for i, row in g.iterrows():
	indexes.append(j)
	j += 1
	df["proteinMPNN_index"] = indexes
	mol.write("cleaned.pdb")
	return "cleaned.pdb", df


	def assign_sasa(mol):
	from moleculekit.projections.metricsasa import MetricSasa

	metr = MetricSasa(mode="residue", filtersel="protein")
	sasaR = metr.project(mol)[0]
	is_prot = mol.atomselect("protein")
	resids = pd.DataFrame.from_dict({"resid": mol.resid, "is_prot": is_prot})
	new_masses = []
	i_without_non_prot = 0
	for i, g in resids.groupby((resids["resid"].shift() != resids["resid"]).cumsum()):
	if g["is_prot"].unique()[0] == True:
	g["sasa"] = sasaR[i_without_non_prot]
	i_without_non_prot += 1
	else:
	g["sasa"] = 0
	new_masses.extend(list(g.sasa))
	return np.array(new_masses)


	def process_atomsel(atomsel):
	"""everything lowercase and replace some keywords not relevant for protein design"""
	atomsel = re.sub("sasa", "mass", atomsel, flags=re.I)
	atomsel = re.sub("plddt", "beta", atomsel, flags=re.I)
	return atomsel


	def make_fixed_positions_dict(atomsel, residue_index_df):
	# we use the uploaded file for the selection
	mol = Molecule("original.pdb")
	# use index for selection as resids will change

	# set sasa to 0 for all non protein atoms (all non protein atoms are deleted later)
	mol.masses = assign_sasa(mol)
	print(mol.masses.shape)
	print(assign_sasa(mol).shape)
	atomsel = process_atomsel(atomsel)
	selected_residues = mol.get("index", atomsel)

	# clean up
	mol.filter("protein")
	mol.renumberResidues()
	# based on selected index now get resids
	selected_residues = [str(i) for i in selected_residues]
	if len(selected_residues) == 0:
	return None, []
	selected_residues_str = " ".join(selected_residues)
	selected_residues = set(mol.get("resid", sel=f"index {selected_residues_str}"))

	# use the proteinMPNN index nomenclature to assemble fixed_positions_dict
	fixed_positions_df = residue_index_df[
	residue_index_df["new_resid"].isin(selected_residues)
	]

	chains = set(mol.get("chain", sel="all"))
	fixed_position_dict = {"cleaned": {}}
	# store the selected residues in a list for the visualization later with cleaned.pdb
	selected_residues = list(fixed_positions_df["new_resid"])

	for c in chains:
	fixed_position_dict["cleaned"][c] = []

	for i, row in fixed_positions_df.iterrows():
	fixed_position_dict["cleaned"][row["chain"]].append(row["proteinMPNN_index"])
	return fixed_position_dict, selected_residues


	def update(
	inp,
	file,
	designed_chain,
	fixed_chain,
	homomer,
	num_seqs,
	sampling_temp,
	model_name,
	backbone_noise,
	omit_AAs,
	atomsel,
	):
	from protein_mpnn_utils import (
	loss_nll,
	loss_smoothed,
	gather_edges,
	gather_nodes,
	gather_nodes_t,
	cat_neighbors_nodes,
	_scores,
	_S_to_seq,
	tied_featurize,
	parse_PDB,
	)
	from protein_mpnn_utils import StructureDataset, StructureDatasetPDB, ProteinMPNN

	# pdb_path = get_pdb(pdb_code=inp, filepath=file)

	pdb_path, mol_index = preprocess_mol(pdb_code=inp, filepath=file)

	if pdb_path == None:
	return "Error processing PDB"

	model, device = setup_proteinmpnn(
	model_name=model_name, backbone_noise=backbone_noise
	)

	if designed_chain == "":
	designed_chain_list = []
	else:
	designed_chain_list = re.sub("[^A-Za-z]+", ",", designed_chain).split(",")

	if fixed_chain == "":
	fixed_chain_list = []
	else:
	fixed_chain_list = re.sub("[^A-Za-z]+", ",", fixed_chain).split(",")

	chain_list = list(set(designed_chain_list + fixed_chain_list))
	num_seq_per_target = num_seqs
	save_score = 0 # 0 for False, 1 for True; save score=-log_prob to npy files
	save_probs = (
	0 # 0 for False, 1 for True; save MPNN predicted probabilites per position
	)
	score_only = 0 # 0 for False, 1 for True; score input backbone-sequence pairs
	conditional_probs_only = 0 # 0 for False, 1 for True; output conditional probabilities p(s_i given the rest of the sequence and backbone)
	conditional_probs_only_backbone = 0 # 0 for False, 1 for True; if true output conditional probabilities p(s_i given backbone)

	batch_size = 1 # Batch size; can set higher for titan, quadro GPUs, reduce this if running out of GPU memory
	max_length = 20000 # Max sequence length

	out_folder = "." # Path to a folder to output sequences, e.g. /home/out/
	jsonl_path = "" # Path to a folder with parsed pdb into jsonl

	if omit_AAs == "":
	omit_AAs = "X" # Specify which amino acids should be omitted in the generated sequence, e.g. 'AC' would omit alanine and cystine.

	pssm_multi = 0.0 # A value between [0.0, 1.0], 0.0 means do not use pssm, 1.0 ignore MPNN predictions
	pssm_threshold = 0.0 # A value between -inf + inf to restric per position AAs
	pssm_log_odds_flag = 0 # 0 for False, 1 for True
	pssm_bias_flag = 0 # 0 for False, 1 for True

	folder_for_outputs = out_folder

	NUM_BATCHES = num_seq_per_target // batch_size
	BATCH_COPIES = batch_size
	temperatures = [sampling_temp]
	omit_AAs_list = omit_AAs
	alphabet = "ACDEFGHIKLMNPQRSTVWYX"

	omit_AAs_np = np.array([AA in omit_AAs_list for AA in alphabet]).astype(np.float32)

	chain_id_dict = None
	if atomsel == "":
	fixed_positions_dict, selected_residues = None, []
	else:
	fixed_positions_dict, selected_residues = make_fixed_positions_dict(
	atomsel, mol_index
	)

	pssm_dict = None
	omit_AA_dict = None
	bias_AA_dict = None

	bias_by_res_dict = None
	bias_AAs_np = np.zeros(len(alphabet))

	###############################################################
	pdb_dict_list = parse_PDB(pdb_path, input_chain_list=chain_list)
	dataset_valid = StructureDatasetPDB(
	pdb_dict_list, truncate=None, max_length=max_length
	)
	if homomer:
	tied_positions_dict = make_tied_positions_for_homomers(pdb_dict_list)
	else:
	tied_positions_dict = None

	chain_id_dict = {}
	chain_id_dict[pdb_dict_list[0]["name"]] = (designed_chain_list, fixed_chain_list)
	with torch.no_grad():
	for ix, prot in enumerate(dataset_valid):
	score_list = []
	all_probs_list = []
	all_log_probs_list = []
	S_sample_list = []
	batch_clones = [copy.deepcopy(prot) for i in range(BATCH_COPIES)]
	(
	X,
	S,
	mask,
	lengths,
	chain_M,
	chain_encoding_all,
	chain_list_list,
	visible_list_list,
	masked_list_list,
	masked_chain_length_list_list,
	chain_M_pos,
	omit_AA_mask,
	residue_idx,
	dihedral_mask,
	tied_pos_list_of_lists_list,
	pssm_coef,
	pssm_bias,
	pssm_log_odds_all,
	bias_by_res_all,
	tied_beta,
	) = tied_featurize(
	batch_clones,
	device,
	chain_id_dict,
	fixed_positions_dict,
	omit_AA_dict,
	tied_positions_dict,
	pssm_dict,
	bias_by_res_dict,
	)
	pssm_log_odds_mask = (
	pssm_log_odds_all > pssm_threshold
	).float() # 1.0 for true, 0.0 for false
	name_ = batch_clones[0]["name"]

	randn_1 = torch.randn(chain_M.shape, device=X.device)
	log_probs = model(
	X,
	S,
	mask,
	chain_M * chain_M_pos,
	residue_idx,
	chain_encoding_all,
	randn_1,
	)
	mask_for_loss = mask * chain_M * chain_M_pos
	scores = _scores(S, log_probs, mask_for_loss)
	native_score = scores.cpu().data.numpy()
	message = ""
	seq_list = []
	seq_recovery = []
	seq_score = []
	for temp in temperatures:
	for j in range(NUM_BATCHES):
	randn_2 = torch.randn(chain_M.shape, device=X.device)
	if tied_positions_dict == None:
	sample_dict = model.sample(
	X,
	randn_2,
	S,
	chain_M,
	chain_encoding_all,
	residue_idx,
	mask=mask,
	temperature=temp,
	omit_AAs_np=omit_AAs_np,
	bias_AAs_np=bias_AAs_np,
	chain_M_pos=chain_M_pos,
	omit_AA_mask=omit_AA_mask,
	pssm_coef=pssm_coef,
	pssm_bias=pssm_bias,
	pssm_multi=pssm_multi,
	pssm_log_odds_flag=bool(pssm_log_odds_flag),
	pssm_log_odds_mask=pssm_log_odds_mask,
	pssm_bias_flag=bool(pssm_bias_flag),
	bias_by_res=bias_by_res_all,
	)
	S_sample = sample_dict["S"]
	else:
	sample_dict = model.tied_sample(
	X,
	randn_2,
	S,
	chain_M,
	chain_encoding_all,
	residue_idx,
	mask=mask,
	temperature=temp,
	omit_AAs_np=omit_AAs_np,
	bias_AAs_np=bias_AAs_np,
	chain_M_pos=chain_M_pos,
	omit_AA_mask=omit_AA_mask,
	pssm_coef=pssm_coef,
	pssm_bias=pssm_bias,
	pssm_multi=pssm_multi,
	pssm_log_odds_flag=bool(pssm_log_odds_flag),
	pssm_log_odds_mask=pssm_log_odds_mask,
	pssm_bias_flag=bool(pssm_bias_flag),
	tied_pos=tied_pos_list_of_lists_list[0],
	tied_beta=tied_beta,
	bias_by_res=bias_by_res_all,
	)
	# Compute scores
	S_sample = sample_dict["S"]
	log_probs = model(
	X,
	S_sample,
	mask,
	chain_M * chain_M_pos,
	residue_idx,
	chain_encoding_all,
	randn_2,
	use_input_decoding_order=True,
	decoding_order=sample_dict["decoding_order"],
	)
	mask_for_loss = mask * chain_M * chain_M_pos
	scores = _scores(S_sample, log_probs, mask_for_loss)
	scores = scores.cpu().data.numpy()
	all_probs_list.append(sample_dict["probs"].cpu().data.numpy())
	all_log_probs_list.append(log_probs.cpu().data.numpy())
	S_sample_list.append(S_sample.cpu().data.numpy())
	for b_ix in range(BATCH_COPIES):
	masked_chain_length_list = masked_chain_length_list_list[b_ix]
	masked_list = masked_list_list[b_ix]
	seq_recovery_rate = torch.sum(
	torch.sum(
	torch.nn.functional.one_hot(S[b_ix], 21)
	* torch.nn.functional.one_hot(S_sample[b_ix], 21),
	axis=-1,
	)
	* mask_for_loss[b_ix]
	) / torch.sum(mask_for_loss[b_ix])
	seq = _S_to_seq(S_sample[b_ix], chain_M[b_ix])
	score = scores[b_ix]
	score_list.append(score)
	native_seq = _S_to_seq(S[b_ix], chain_M[b_ix])
	if b_ix == 0 and j == 0 and temp == temperatures[0]:
	start = 0
	end = 0
	list_of_AAs = []
	for mask_l in masked_chain_length_list:
	end += mask_l
	list_of_AAs.append(native_seq[start:end])
	start = end
	native_seq = "".join(
	list(np.array(list_of_AAs)[np.argsort(masked_list)])
	)
	l0 = 0
	for mc_length in list(
	np.array(masked_chain_length_list)[
	np.argsort(masked_list)
	]
	)[:-1]:
	l0 += mc_length
	native_seq = native_seq[:l0] + "/" + native_seq[l0:]
	l0 += 1
	sorted_masked_chain_letters = np.argsort(
	masked_list_list[0]
	)
	print_masked_chains = [
	masked_list_list[0][i]
	for i in sorted_masked_chain_letters
	]
	sorted_visible_chain_letters = np.argsort(
	visible_list_list[0]
	)
	print_visible_chains = [
	visible_list_list[0][i]
	for i in sorted_visible_chain_letters
	]
	native_score_print = np.format_float_positional(
	np.float32(native_score.mean()),
	unique=False,
	precision=4,
	)
	line = ">{}, score={}, fixed_chains={}, designed_chains={}, model_name={}\n{}\n".format(
	name_,
	native_score_print,
	print_visible_chains,
	print_masked_chains,
	model_name,
	native_seq,
	)
	message += f"{line}\n"
	start = 0
	end = 0
	list_of_AAs = []
	for mask_l in masked_chain_length_list:
	end += mask_l
	list_of_AAs.append(seq[start:end])
	start = end

	seq = "".join(
	list(np.array(list_of_AAs)[np.argsort(masked_list)])
	)
	# add non designed chains to predicted sequence
	l0 = 0
	for mc_length in list(
	np.array(masked_chain_length_list)[np.argsort(masked_list)]
	)[:-1]:
	l0 += mc_length
	seq = seq[:l0] + "/" + seq[l0:]
	l0 += 1
	score_print = np.format_float_positional(
	np.float32(score), unique=False, precision=4
	)
	seq_rec_print = np.format_float_positional(
	np.float32(seq_recovery_rate.detach().cpu().numpy()),
	unique=False,
	precision=4,
	)
	chain_s = ""
	if len(visible_list_list[0]) > 0:
	chain_M_bool = chain_M.bool()
	not_designed = _S_to_seq(S[b_ix], ~chain_M_bool[b_ix])

	labels = (
	chain_encoding_all[b_ix][~chain_M_bool[b_ix]]
	.detach()
	.cpu()
	.numpy()
	)

	for c in set(labels):
	chain_s += "/"
	nd_mask = labels == c
	for i, x in enumerate(not_designed):
	if nd_mask[i]:
	chain_s += x
	seq_recovery.append(seq_rec_print)
	seq_score.append(score_print)
	line = (
	">T={}, sample={}, score={}, seq_recovery={}\n{}\n".format(
	temp, b_ix, score_print, seq_rec_print, seq
	)
	)
	seq_list.append(seq + chain_s)
	message += f"{line}\n"
	if fixed_positions_dict != None:
	message += f"\nfixed positions:* {fixed_positions_dict['cleaned']} \n\n*uses CHAIN:[1..len(chain)] residue numbering"
	# somehow sequences still contain X, remove again
	for i, x in enumerate(seq_list):
	for aa in omit_AAs:
	seq_list[i] = x.replace(aa, "")
	all_probs_concat = np.concatenate(all_probs_list)
	all_log_probs_concat = np.concatenate(all_log_probs_list)
	np.savetxt("all_probs_concat.csv", all_probs_concat.mean(0).T, delimiter=",")
	np.savetxt(
	"all_log_probs_concat.csv",
	np.exp(all_log_probs_concat).mean(0).T,
	delimiter=",",
	)
	S_sample_concat = np.concatenate(S_sample_list)
	fig = px.imshow(
	np.exp(all_log_probs_concat).mean(0).T,
	labels=dict(x="positions", y="amino acids", color="probability"),
	y=list(alphabet),
	template="simple_white",
	)
	fig.update_xaxes(side="top")

	fig_tadjusted = px.imshow(
	all_probs_concat.mean(0).T,
	labels=dict(x="positions", y="amino acids", color="probability"),
	y=list(alphabet),
	template="simple_white",
	)

	fig_tadjusted.update_xaxes(side="top")
	seq_dict = {"seq_list": seq_list, "recovery": seq_recovery, "seq_score": seq_score}
	return (
	message,
	fig,
	fig_tadjusted,
	gr.File.update(value="all_log_probs_concat.csv", visible=True),
	gr.File.update(value="all_probs_concat.csv", visible=True),
	pdb_path,
	gr.Dropdown.update(choices=seq_list),
	selected_residues,
	seq_dict,
	)


	def update_AF(seq_dict, pdb, num_recycles, selectedResidues):

	# # run alphafold using ray
	# plddts, pae, num_res = run_alphafold(
	# startsequence, num_recycles
	# )
	allSeqs = seq_dict["seq_list"]
	lenSeqs = len(allSeqs)
	if len(allSeqs[0]) > 700:
	return (
	"""
	<div class="p-4 mb-4 text-sm text-yellow-700 bg-orange-50 rounded-lg" role="alert">
	<span class="font-medium">Sorry!</span> Currently only small proteins can be run in the server in order to reduce wait time. Try a protein <700 aa. Bigger proteins you can run on <a href="https://github.com/sokrypton/colabfold">ColabFold</a>
	</div>
	""",
	plt.figure(),
	plt.figure(),
	)

	plddts, paes, num_res = ray.get(run_alphafold.remote(allSeqs, num_recycles))

	sequences = {}
	for i in range(lenSeqs):
	rms, input_pdb, aligned_pdb = align_structures(
	pdb, f"outputs/out_{i}.pdb", num_res, i
	)
	sequences[i] = {
	"Seq": i,
	"RMSD": f"{rms:.2f}",
	"Score": seq_dict["seq_score"][i],
	"Recovery": seq_dict["recovery"][i],
	"Mean pLDDT": f"{np.mean(plddts[i]):.4f}",
	}
	results = pd.DataFrame.from_dict(sequences, orient="index")
	print(results)
	plots = []
	for index, plddts_val in enumerate(plddts):
	# if recycle == 0 or recycle == len(plddts) - 1:
	# visible = True
	# else:
	# visible = "legendonly"
	visible = True
	plots.append(
	go.Scatter(
	x=np.arange(len(plddts_val)),
	y=plddts_val,
	hovertemplate="<i>pLDDT</i>: %{y:.2f} <br><i>Residue index:</i> %{x}<br>Sequence "
	+ str(index),
	name=f"seq {index}",
	visible=visible,
	)
	)
	plotAF_plddt = go.Figure(data=plots)
	plotAF_plddt.update_layout(
	title="pLDDT",
	xaxis_title="Residue index",
	yaxis_title="pLDDT",
	height=500,
	template="simple_white",
	legend=dict(yanchor="bottom", y=0.01, xanchor="left", x=0.99),
	)
	pae_plots = []
	for i, pae in enumerate(paes):
	plt.figure()
	plt.title(f"Predicted Aligned Error sequence {i}")
	Ln = pae.shape[0]
	plt.imshow(pae, cmap="bwr", vmin=0, vmax=30, extent=(0, Ln, Ln, 0))
	plt.colorbar()
	plt.xlabel("Scored residue")
	plt.ylabel("Aligned residue")
	plt.savefig(f"outputs/pae_plot_{i}.png", dpi=300)
	plt.close()
	pae_plots.append(f"outputs/pae_plot_{i}.png")
	# doesnt work (likely because too large)
	# plotAF_pae = px.imshow(
	# pae,
	# labels=dict(x="Scored residue", y="Aligned residue", color=""),
	# template="simple_white",
	# y=np.arange(len(plddts_val)),
	# )
	# plotAF_pae.write_html("test.html")
	# plotAF_pae.update_layout(title="Predicted Aligned Error", template="simple_white")

	return (
	molecule(
	input_pdb,
	aligned_pdb,
	lenSeqs,
	num_res,
	selectedResidues,
	allSeqs,
	sequences,
	),
	plotAF_plddt,
	pae_plots,
	results,
	)


	def read_mol(molpath):
	with open(molpath, "r") as fp:
	lines = fp.readlines()
	mol = ""
	for l in lines:
	mol += l
	return mol


	def molecule(
	input_pdb, aligned_pdb, lenSeqs, num_res, selectedResidues, allSeqs, sequences
	):

	mol = read_mol("outputs/reference.pdb")
	options = ""
	pred_mol = "["
	seqdata = "{"
	selected = "selected"
	for i in range(lenSeqs):
	seqdata += (
	str(i)
	+ ': { "score": '
	+ sequences[i]["Score"]
	+ ', "rmsd": '
	+ sequences[i]["RMSD"]
	+ ', "recovery": '
	+ sequences[i]["Recovery"]
	+ ', "plddt": '
	+ sequences[i]["Mean pLDDT"]
	+ ', "seq":"'
	+ allSeqs[i]
	+ '"}'
	)
	options += f'<option {selected} value="{i}">sequence {i} </option>' # RMSD {sequences[i]["RMSD"]}, score {sequences[i]["Score"]}, recovery {sequences[i]["Recovery"]} pLDDT {sequences[i]["Mean pLDDT"]}
	p = f"outputs/out_{i}_aligned.pdb"
	pred_mol += f"`{read_mol(p)}`"
	selected = ""
	if i != lenSeqs - 1:
	pred_mol += ","
	seqdata += ","
	pred_mol += "]"
	seqdata += "}"

	x = (
	"""<!DOCTYPE html>
	<html>
	<head>
	<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
	<link rel="stylesheet" href="https://unpkg.com/flowbite@1.4.5/dist/flowbite.min.css" />
	<style>
	body{
	font-family:sans-serif
	}
	.mol-container {
	width: 100%;
	height: 700px;
	position: relative;
	}
	.space-x-2 > * + *{
	margin-left: 0.5rem;
	}
	.p-1{
	padding:0.5rem;
	}
	.w-4{
	width:1rem;
	}
	.h-4{
	height:1rem;
	}
	.mt-4{
	margin-top:1rem;
	}
	.mol-container select{
	background-image:None;
	}
	</style>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.3/jquery.min.js" integrity="sha512-STof4xm1wgkfm7heWqFJVn58Hm3EtS31XFaagaa8VMReCXAkQnJZ+jEy8PCC/iT18dFy95WcExNHFTqLyp72eQ==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
	<script src="https://3Dmol.csb.pitt.edu/build/3Dmol-min.js"></script>
	</head>
	<body>
	<div class="max-w-2xl flex items-center space-x-2 py-3">
	<label for="seq"
	class=" text-right whitespace-nowrap block text-base font-medium text-gray-900 dark:text-gray-400">Select
	a sequence</label>
	<select id="seq"
	class="bg-gray-50 border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full p-2.5 dark:bg-gray-700 dark:border-gray-600 dark:placeholder-gray-400 dark:text-white dark:focus:ring-blue-500 dark:focus:border-blue-500">
	"""
	+ options
	+ """
	</select>
	</div>
	<div class="font-mono bg-gray-100 py-3 px-2 font-sm rounded">
	<code>> seq <span id="id"></span>, score <span id="score"></span>, RMSD <span id="seqrmsd"></span>, Recovery
	<span id="recovery"></span>, pLDDT <span id="plddt"></span></code><br>
	<p id="seqText" class="max-w-4xl font-xs block" style="word-break: break-all;">

	</p>
	</div>
	<div id="container" class="mol-container"></div>
	<div class="flex items-center">
	<div class="px-4 pt-2">
	<label for="sidechain" class="relative inline-flex items-center mb-4 cursor-pointer ">
	<input id="sidechain" type="checkbox" class="sr-only peer">
	<div class="w-11 h-6 bg-gray-200 rounded-full peer peer-focus:ring-4 peer-focus:ring-blue-300 dark:peer-focus:ring-blue-800 dark:bg-gray-700 peer-checked:after:translate-x-full peer-checked:after:border-white after:absolute after:top-0.5 after:left-[2px] after:bg-white after:border-gray-300 after:border after:rounded-full after:h-5 after:w-5 after:transition-all dark:border-gray-600 peer-checked:bg-blue-600"></div>
	<span class="ml-3 text-sm font-medium text-gray-900 dark:text-gray-300">Show side chains</span>
	</label>
	</div>
	<div class="px-4 pt-2">
	<label for="startstructure" class="relative inline-flex items-center mb-4 cursor-pointer ">
	<input id="startstructure" type="checkbox" class="sr-only peer" checked>
	<div class="w-11 h-6 bg-gray-200 rounded-full peer peer-focus:ring-4 peer-focus:ring-blue-300 dark:peer-focus:ring-blue-800 dark:bg-gray-700 peer-checked:after:translate-x-full peer-checked:after:border-white after:absolute after:top-0.5 after:left-[2px] after:bg-white after:border-gray-300 after:border after:rounded-full after:h-5 after:w-5 after:transition-all dark:border-gray-600 peer-checked:bg-blue-600"></div>
	<span class="ml-3 text-sm font-medium text-gray-900 dark:text-gray-300">Show input structure</span>
	</label>
	</div>
	<button type="button" class="text-gray-900 bg-white hover:bg-gray-100 border border-gray-200 focus:ring-4 focus:outline-none focus:ring-gray-100 font-medium rounded-lg text-sm px-5 py-2.5 text-center inline-flex items-center dark:focus:ring-gray-600 dark:bg-gray-800 dark:border-gray-700 dark:text-white dark:hover:bg-gray-700 mr-2 mb-2" id="download">
	<svg class="w-6 h-6 mr-2 -ml-1" fill="none" stroke="currentColor" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M4 16v1a3 3 0 003 3h10a3 3 0 003-3v-1m-4-4l-4 4m0 0l-4-4m4 4V4"></path></svg>
	Download predicted structure
	</button>
	</div>
	<div class="text-sm">
	<div> RMSD AlphaFold vs. native: <span id="rmsd"></span> Å computed using CEAlign on the aligned fragment</div>
	</div>
	<div class="text-sm flex items-start">
	<div class="w-1/2">

	<div class="font-medium mt-4 flex items-center space-x-2"><b>AF2 model of redesigned sequence</b></div>
	<div>AlphaFold model confidence:</div>
	<div class="flex space-x-2 py-1"><span class="w-4 h-4" style="background-color: rgb(0, 83, 214);"> </span><span class="legendlabel">Very high
	(pLDDT > 90)</span></div>
	<div class="flex space-x-2 py-1"><span class="w-4 h-4" style="background-color: rgb(101, 203, 243);"> </span><span class="legendlabel">Confident
	(90 > pLDDT > 70)</span></div>
	<div class="flex space-x-2 py-1"><span class="w-4 h-4" style="background-color: rgb(255, 219, 19);"> </span><span class="legendlabel">Low (70 >
	pLDDT > 50)</span></div>
	<div class="flex space-x-2 py-1"><span class="w-4 h-4" style="background-color: rgb(255, 125, 69);"> </span><span class="legendlabel">Very low
	(pLDDT < 50)</span></div>
	<div class="row column legendDesc"> AlphaFold produces a per-residue confidence
	score (pLDDT) between 0 and 100. Some regions below 50 pLDDT may be unstructured in isolation.
	</div>
	</div>
	<div class="w-1/2">
	<div class="font-medium mt-4 flex items-center space-x-2"><b>Input structure </b><span class="w-4 h-4 bg-gray-300 inline-flex" ></span></div>

	<div class="flex space-x-2 py-1"><span class="w-4 h-4" style="background-color:hotpink" > </span><span class="legendlabel">Fixed positions</span></div>

	</div>
	</div>
	<script>

	function drawStructures(i, selectedResidues) {
	$("#rmsd").text(seqs[i]["rmsd"])
	$("#seqText").text(seqs[i]["seq"])
	$("#seqrmsd").text(seqs[i]["rmsd"])
	$("#id").text(i)
	$("#score").text(seqs[i]["score"])
	$("#recovery").text(seqs[i]["recovery"])
	$("#plddt").text(seqs[i]["plddt"])

	viewer = $3Dmol.createViewer(element, config);
	viewer.addModel(data[i], "pdb");
	viewer.addModel(pdb, "pdb");



	viewer.getModel(1).setStyle({}, { cartoon: { colorscheme: { prop: "resi", map: colors } } })
	viewer.getModel(0).setStyle({}, { cartoon: { colorfunc: colorAlpha } });
	viewer.zoomTo();
	viewer.render();
	viewer.zoom(0.8, 2000);
	viewer.getModel(0).setHoverable({}, true,
	function (atom, viewer, event, container) {
	if (!atom.label) {
	atom.label = viewer.addLabel(atom.resn + atom.resi + " pLDDT=" + atom.b, { position: atom, backgroundColor: "mintcream", fontColor: "black" });
	}
	},
	function (atom, viewer) {
	if (atom.label) {
	viewer.removeLabel(atom.label);
	delete atom.label;
	}
	}
	);
	}
	let viewer = null;
	let voldata = null;
	let element = null;
	let config = null;
	let currentIndex = 0;
	let seqs = """
	+ seqdata
	+ """
	let data = """
	+ pred_mol
	+ """
	let pdb = `"""
	+ mol
	+ """`
	var selectedResidues = """
	+ f"{selectedResidues}"
	+ """
	//AlphaFold code from https://gist.github.com/piroyon/30d1c1099ad488a7952c3b21a5bebc96
	let colorAlpha = function (atom) {
	if (atom.b < 50) {
	return "OrangeRed";
	} else if (atom.b < 70) {
	return "Gold";
	} else if (atom.b < 90) {
	return "MediumTurquoise";
	} else {
	return "Blue";
	}
	};

	let colors = {}
	for (let i=0; i<"""
	+ str(num_res)
	+ """;i++){
	if (selectedResidues.includes(i)){
	colors[i]="hotpink"
	}else{
	colors[i]="lightgray"
	}}

	let colorFixedSidechain = function(atom){
	if (selectedResidues.includes(atom.resi)){
	return "hotpink"
	}else if (atom.elem == "O"){
	return "red"
	}else if (atom.elem == "N"){
	return "blue"
	}else if (atom.elem == "S"){
	return "yellow"
	}else{
	return "lightgray"
	}
	}

	$(document).ready(function () {
	element = $("#container");
	config = { backgroundColor: "white" };
	//viewer.ui.initiateUI();

	drawStructures(currentIndex, selectedResidues)
	$("#sidechain").change(function () {
	if (this.checked) {
	BB = ["C", "O", "N"]

	if ($("#startstructure").prop("checked")) {
	viewer.getModel(0).setStyle( {"and": [{resn: ["GLY", "PRO"], invert: true},{atom: BB, invert: true},]},{stick: {colorscheme: "WhiteCarbon", radius: 0.3}, cartoon: { colorfunc: colorAlpha }});
	viewer.getModel(1).setStyle( {"and": [{resn: ["GLY", "PRO"], invert: true},{atom: BB, invert: true},]},{stick: {colorfunc:colorFixedSidechain, radius: 0.3}, cartoon: {colorscheme:{prop:"resi",map:colors} }});
	}else{
	viewer.getModel(0).setStyle( {"and": [{resn: ["GLY", "PRO"], invert: true},{atom: BB, invert: true},]},{stick: {colorscheme: "WhiteCarbon", radius: 0.3}, cartoon: { colorfunc: colorAlpha }});
	viewer.getModel(1).setStyle();
	}

	viewer.render()
	} else {
	if ($("#startstructure").prop("checked")) {
	viewer.getModel(0).setStyle({cartoon: { colorfunc: colorAlpha }});
	viewer.getModel(1).setStyle({cartoon: {colorscheme:{prop:"resi",map:colors} }});
	}else{
	viewer.getModel(0).setStyle({cartoon: { colorfunc: colorAlpha }});
	viewer.getModel(1).setStyle();
	}
	viewer.render()
	}
	});
	$("#seq").change(function () {
	drawStructures(this.value, selectedResidues)
	currentIndex = this.value
	$("#sidechain").prop( "checked", false );
	$("#startstructure").prop( "checked", true );
	});
	$("#startstructure").change(function () {
	if (this.checked) {
	$("#sidechain").prop( "checked", false );
	viewer.getModel(1).setStyle({},{cartoon: {colorscheme:{prop:"resi",map:colors} } })
	viewer.getModel(0).setStyle({}, { cartoon: { colorfunc: colorAlpha } });
	viewer.render()
	} else {
	$("#sidechain").prop( "checked", false );
	viewer.getModel(1).setStyle({},{})
	viewer.getModel(0).setStyle({}, { cartoon: { colorfunc: colorAlpha } });
	viewer.render()
	}
	});
	$("#download").click(function () {
	download("outputs/out_" + currentIndex + "_aligned.pdb", data[currentIndex]);
	})
	});
	function download(filename, text) {
	var element = document.createElement("a");
	element.setAttribute("href", "data:text/plain;charset=utf-8," + encodeURIComponent(text));
	element.setAttribute("download", filename);
	element.style.display = "none";
	document.body.appendChild(element);
	element.click();
	document.body.removeChild(element);
	}
	</script>
	</body></html>"""
	)

	return f"""<iframe style="width: 800px; height: 1300px" name="result" allow="midi; geolocation; microphone; camera;
	display-capture; encrypted-media;" sandbox="allow-modals allow-forms
	allow-scripts allow-same-origin allow-popups
	allow-top-navigation-by-user-activation allow-downloads" allowfullscreen=""
	allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>"""


	def set_examples(example):
	(
	label,
	inp,
	designed_chain,
	fixed_chain,
	homomer,
	num_seqs,
	sampling_temp,
	atomsel,
	) = example
	return [
	label,
	inp,
	designed_chain,
	fixed_chain,
	homomer,
	gr.Slider.update(value=num_seqs),
	gr.Radio.update(value=sampling_temp),
	atomsel,
	]


	proteinMPNN = gr.Blocks()

	with proteinMPNN:
	gr.Markdown("# ProteinMPNN")
	gr.Markdown(
	"""This model takes as input a protein structure and based on its backbone predicts new sequences that will fold into that backbone.
	Optionally, we can run AlphaFold2 on the predicted sequence to check whether the predicted sequences adopt the same backbone (WIP).
	"""
	)
	gr.Markdown("![](https://simonduerr.eu/ProteinMPNN.png)")

	with gr.Tabs():
	with gr.TabItem("Input"):
	inp = gr.Textbox(
	placeholder="PDB Code or upload file below", label="Input structure"
	)
	file = gr.File(file_count="single")

	with gr.TabItem("Settings"):
	with gr.Row():
	designed_chain = gr.Textbox(value="A", label="Designed chain")
	fixed_chain = gr.Textbox(
	placeholder="Use commas to fix multiple chains", label="Fixed chain"
	)
	with gr.Row():
	num_seqs = gr.Slider(
	minimum=1, maximum=15, value=1, step=1, label="Number of sequences"
	)
	sampling_temp = gr.Radio(
	choices=[0.1, 0.15, 0.2, 0.25, 0.3],
	value=0.1,
	label="Sampling temperature",
	)
	gr.Markdown(
	""" Sampling temperature for amino acids, `T=0.0` means taking argmax, `T>>1.0` means sample randomly. Suggested values `0.1, 0.15, 0.2, 0.25, 0.3`. Higher values will lead to more diversity.
	"""
	)
	with gr.Row():
	model_name = gr.Dropdown(
	choices=[
	"v_48_002",
	"v_48_010",
	"v_48_020",
	"v_48_030",
	],
	label="Model",
	value="v_48_020",
	)
	backbone_noise = gr.Dropdown(
	choices=[0, 0.02, 0.10, 0.20, 0.30], label="Backbone noise", value=0
	)
	with gr.Row():
	homomer = gr.Checkbox(value=False, label="Homomer?")
	gr.Markdown(
	"for correct symmetric tying lenghts of homomer chains should be the same"
	)
	with gr.Row():
	omit_AAs = gr.Textbox(
	placeholder="Specify omitted amino acids ", label="Omitted amino acids"
	)
	gr.Markdown("## Fixed positions")
	gr.Markdown(
	"""You can fix important positions in the protein. Resid should be specified with the same numbering as in the input pdb file. The fixed residues will be highlighted in the output.
	The [VMD selection](http://www.ks.uiuc.edu/Research/vmd/vmd-1.9.2/ug/node89.html) synthax is used. You can also select based on ligands or chains in the input structure to specify interfaces to be fixed.

	- <code>within 5 of resid 94</code> All residues that have >1 atom closer than 5 Å to any atom of residue 94
	- <code>name CA and within 5 of resid 94</code> All residues that have CA atom closer than 5 Å to any atom of residue 94
	- <code>resid 94 96 119</code> Residues 94, 94 and 119
	- <code>within 5 of resname ZN</code> All residues with any atom <5 Å of zinc ion
	- <code>chain A and within 5 of chain B </code> All residues of chain A that are part of the interface with chain B
	- <code>protein and within 5 of nucleic </code> All residues that bind to DNA (if present in structure)
	- <code>not (chain A and within 5 of chain B) </code> only modify residues that are in the interface with the fixed chain, not further away
	- <code>chain A or (chain B and sasa < 20) </code> Keep chain A and all core residues fixeds
	- <code>pLDDT >70 </code> Redesign all residues with low pLDDT

	Note that <code>sasa</code> and <code>pLDDT</code> selectors modify default VMD behavior. SASA is calculated using moleculekit and written to the mass attribute. Selections based on mass do not work.
	pLDDT is an alias for beta, it only works correctly with structures that contain the appropriate values in the beta column of the PDB file. """
	)
	atomsel = gr.Textbox(
	placeholder="Specify atom selection ", label="Fixed positions"
	)

	btn = gr.Button("Run")
	label = gr.Textbox(label="Label", visible=False)
	examples = gr.Dataset(
	components=[
	label,
	inp,
	designed_chain,
	fixed_chain,
	homomer,
	num_seqs,
	sampling_temp,
	atomsel,
	],
	samples=[
	["Homomer design", "1O91", "A,B,C", "", True, 2, 0.1, ""],
	["Monomer design", "6MRR", "A", "", False, 2, 0.1, ""],
	["Redesign of Homomer to Heteromer", "3HTN", "A,B", "C", False, 2, 0.1, ""],
	[
	"Redesign of MID1 scaffold keeping binding site fixed",
	"3V1C",
	"A,B",
	"",
	False,
	2,
	0.1,
	"within 5 of resname ZN",
	],
	[
	"Redesign of DNA binding protein",
	"3JRD",
	"A,B",
	"",
	False,
	2,
	0.1,
	"within 8 of nucleic",
	],
	[
	"Surface Redesign of miniprotein",
	"7JZM",
	"A,B",
	"",
	False,
	2,
	0.1,
	"chain B or (chain A and sasa < 20)",
	],
	],
	)

	gr.Markdown("# Output")

	with gr.Tabs():
	with gr.TabItem("Designed sequences"):
	out = gr.Textbox(label="Status")

	with gr.TabItem("Amino acid probabilities"):
	plot = gr.Plot()
	all_log_probs = gr.File(visible=False)
	with gr.TabItem("T adjusted probabilities"):
	gr.Markdown("Sampling temperature adjusted amino acid probabilties")
	plot_tadjusted = gr.Plot()
	all_probs = gr.File(visible=False)
	with gr.TabItem("Structure validation w/ AF2"):
	gr.HTML(
	"""
	<div class="flex items-center p-2 bg-gradient-to-r from-yellow-400 via-red-500 to-pink-500 rounded-lg shadow-sm">
	<div class="p-3 mr-4">
	<svg class="w-10 h-10 px-1 text-400" viewBox="0 0 16 16">
	<path fill-rule="evenodd" d="M1.75 1.5a.25.25 0 00-.25.25v9.5c0 .138.112.25.25.25h2a.75.75 0 01.75.75v2.19l2.72-2.72a.75.75 0 01.53-.22h6.5a.25.25 0 00.25-.25v-9.5a.25.25 0 00-.25-.25H1.75zM0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0114.25 13H8.06l-2.573 2.573A1.457 1.457 0 013 14.543V13H1.75A1.75 1.75 0 010 11.25v-9.5zM9 9a1 1 0 11-2 0 1 1 0 012 0zm-.25-5.25a.75.75 0 00-1.5 0v2.5a.75.75 0 001.5 0v-2.5z"></path>
	</svg>
	</div>
	<div>
	<p class="text-base text-gray-700 dark:text-gray-200">
	AF2 code is experimental and relies on @sokrypton's trick to speed up compile/module runtime. Results might differ from DeepMind's published results.
	Predictions are made using <code>model_5_ptm</code> and without MSA based on the selected single sequence (<code>designed_chain</code> + <code>fixed_chain</code>).
	</p>
	</div>
	</div>
	"""
	)
	with gr.Row():
	with gr.Row():
	chosen_seq = gr.Dropdown(
	choices=[],
	label="Select a sequence for validation",
	visible=False,
	)
	num_recycles = gr.Dropdown(
	choices=[0, 1, 3, 5], value=3, label="num Recycles"
	)
	btnAF = gr.Button("Run AlphaFold on all sequences")
	with gr.Row():
	mol = gr.HTML()
	with gr.Column():
	gr.Markdown("## Metrics")
	p = {
	0: {
	"Seq": "NA",
	"RMSD": "NA",
	"Score": "NA",
	"Recovery": "NA",
	"Mean pLDDT": "NA",
	}
	}
	placeholder = pd.DataFrame.from_dict(p, orient="index")
	results = gr.Dataframe(
	placeholder,
	interactive=False,
	row_count=(1, "dynamic"),
	headers=["Seq", "RMSD", "Score", "Recovery", "Mean pLDDT"],
	)
	plotAF_plddt = gr.Plot(label="pLDDT")
	# remove maxh80 class from css
	plotAF_pae = gr.Gallery(label="PAE plots") # gr.Plot(label="PAE")
	tempFile = gr.Variable()
	selectedResidues = gr.Variable()
	seq_dict = gr.Variable()
	btn.click(
	fn=update,
	inputs=[
	inp,
	file,
	designed_chain,
	fixed_chain,
	homomer,
	num_seqs,
	sampling_temp,
	model_name,
	backbone_noise,
	omit_AAs,
	atomsel,
	],
	outputs=[
	out,
	plot,
	plot_tadjusted,
	all_log_probs,
	all_probs,
	tempFile,
	chosen_seq,
	selectedResidues,
	seq_dict,
	],
	)
	btnAF.click(
	fn=update_AF,
	inputs=[seq_dict, tempFile, num_recycles, selectedResidues],
	outputs=[mol, plotAF_plddt, plotAF_pae, results],
	)
	examples.click(fn=set_examples, inputs=examples, outputs=examples.components)
	gr.Markdown(
	"""Citation: Robust deep learning based protein sequence design using ProteinMPNN <br>
	Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Alexis Courbet, Robbert J. de Haas, Neville Bethel, Philip J. Y. Leung, Timothy F. Huddy, Sam Pellock, Doug Tischer, Frederick Chan, Brian Koepnick, Hannah Nguyen, Alex Kang, Banumathi Sankaran, Asim Bera, Neil P. King, David Baker <br>
	bioRxiv 2022.06.03.494563; doi: [10.1101/2022.06.03.494563](https://doi.org/10.1101/2022.06.03.494563) <br><br> Server built by [@simonduerr](https://twitter.com/simonduerr) and hosted by Huggingface"""
	)


	ray.init(runtime_env={"working_dir": "./af_backprop"})

	proteinMPNN.launch()