Create app-backup.py

app-backup.py

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+import os,sys
+
+# install environment goods
+#os.system("pip -q install dgl -f https://data.dgl.ai/wheels/cu113/repo.html")
+os.system('pip install dgl==1.0.2+cu116 -f https://data.dgl.ai/wheels/cu116/repo.html')
+#os.system('pip install gradio')
+os.environ["DGLBACKEND"] = "pytorch"
+#os.system(f'pip install -r ./PROTEIN_GENERATOR/requirements.txt')
+print('Modules installed')
+
+#os.system('pip install --force gradio==3.36.1')
+#os.system('pip install gradio_client==0.2.7')
+#os.system('pip install \"numpy<2\"')
+#os.system('pip install numpy --upgrade')
+#os.system('pip install --force numpy==1.24.1')
+
+
+if not os.path.exists('./SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt'):
+    print('Downloading model weights 1')
+    os.system('wget http://files.ipd.uw.edu/pub/sequence_diffusion/checkpoints/SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt')
+    print('Successfully Downloaded')
+
+if not os.path.exists('./SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt'):
+    print('Downloading model weights 2')
+    os.system('wget http://files.ipd.uw.edu/pub/sequence_diffusion/checkpoints/SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt')
+    print('Successfully Downloaded')
+
+import numpy as np
+import gradio as gr
+import py3Dmol
+from io import StringIO
+import json
+import secrets
+import copy
+import matplotlib.pyplot as plt
+from utils.sampler import HuggingFace_sampler
+from utils.parsers_inference import parse_pdb
+from model.util import writepdb
+from utils.inpainting_util import *
+
+
+plt.rcParams.update({'font.size': 13})
+
+with open('./tmp/args.json','r') as f:
+    args = json.load(f)
+
+# manually set checkpoint to load
+args['checkpoint'] = None
+args['dump_trb'] = False
+args['dump_args'] = True
+args['save_best_plddt'] = True
+args['T'] = 25
+args['strand_bias'] = 0.0
+args['loop_bias'] = 0.0
+args['helix_bias'] = 0.0
+
+
+
+def protein_diffusion_model(sequence, seq_len, helix_bias, strand_bias, loop_bias, 
+                    secondary_structure, aa_bias, aa_bias_potential, 
+                    #target_charge, target_ph, charge_potential, 
+                    num_steps, noise, hydrophobic_target_score, hydrophobic_potential,
+                    contigs, pssm, seq_mask, str_mask, rewrite_pdb):
+    
+    dssp_checkpoint = './SEQDIFF_230205_dssp_hotspots_25mask_EQtasks_mod30.pt'
+    og_checkpoint = './SEQDIFF_221219_equalTASKS_nostrSELFCOND_mod30.pt'
+
+    model_args = copy.deepcopy(args)
+
+    # make sampler
+    S = HuggingFace_sampler(args=model_args)
+
+    # get random prefix 
+    S.out_prefix = './tmp/'+secrets.token_hex(nbytes=10).upper()
+
+    # set args
+    S.args['checkpoint'] = None
+    S.args['dump_trb'] = False
+    S.args['dump_args'] = True
+    S.args['save_best_plddt'] = True
+    S.args['T'] = 20
+    S.args['strand_bias'] = 0.0
+    S.args['loop_bias'] = 0.0
+    S.args['helix_bias'] = 0.0
+    S.args['potentials'] = None
+    S.args['potential_scale'] = None
+    S.args['aa_composition'] = None
+
+
+    # get sequence if entered and make sure all chars are valid
+    alt_aa_dict = {'B':['D','N'],'J':['I','L'],'U':['C'],'Z':['E','Q'],'O':['K']}
+    if sequence not in ['',None]:
+        L = len(sequence)
+        aa_seq = []
+        for aa in sequence.upper():
+            if aa in alt_aa_dict.keys():
+                aa_seq.append(np.random.choice(alt_aa_dict[aa]))
+            else:
+                aa_seq.append(aa)
+
+        S.args['sequence'] = aa_seq
+    elif contigs not in ['',None]:
+        S.args['contigs'] = [contigs]
+    else:
+        S.args['contigs'] = [f'{seq_len}']
+        L = int(seq_len)
+    
+    print('DEBUG: ',rewrite_pdb)
+    if rewrite_pdb not in ['',None]:
+        S.args['pdb'] = rewrite_pdb.name
+
+    if seq_mask not in ['',None]:
+        S.args['inpaint_seq'] = [seq_mask]
+    if str_mask not in ['',None]:
+        S.args['inpaint_str'] = [str_mask]
+
+    if secondary_structure in ['',None]:
+        secondary_structure = None
+    else:
+        secondary_structure = ''.join(['E' if x == 'S' else x for x in secondary_structure])
+        if L < len(secondary_structure):
+            secondary_structure = secondary_structure[:len(sequence)]
+        elif L == len(secondary_structure):
+            pass
+        else:
+            dseq = L - len(secondary_structure)
+            secondary_structure += secondary_structure[-1]*dseq
+    
+
+    # potentials
+    potential_list = []
+    potential_bias_list = []
+
+    if aa_bias not in ['',None]:
+        potential_list.append('aa_bias')
+        S.args['aa_composition'] = aa_bias
+        if aa_bias_potential in ['',None]:
+            aa_bias_potential = 3
+        potential_bias_list.append(str(aa_bias_potential))
+    '''
+    if target_charge not in ['',None]:
+        potential_list.append('charge')
+        if charge_potential in ['',None]:
+            charge_potential = 1
+        potential_bias_list.append(str(charge_potential))
+        S.args['target_charge'] = float(target_charge)
+        if target_ph in ['',None]:
+            target_ph = 7.4
+        S.args['target_pH'] = float(target_ph)
+    '''
+    
+    if hydrophobic_target_score not in ['',None]:
+        potential_list.append('hydrophobic')
+        S.args['hydrophobic_score'] = float(hydrophobic_target_score)
+        if hydrophobic_potential in ['',None]:
+            hydrophobic_potential = 3
+        potential_bias_list.append(str(hydrophobic_potential))
+    
+    if pssm not in ['',None]:
+        potential_list.append('PSSM')
+        potential_bias_list.append('5')
+        S.args['PSSM'] = pssm.name
+        
+
+    if len(potential_list) > 0:
+        S.args['potentials'] = ','.join(potential_list)
+        S.args['potential_scale'] = ','.join(potential_bias_list)
+
+
+    # normalise secondary_structure bias from range 0-0.3
+    S.args['secondary_structure'] = secondary_structure
+    S.args['helix_bias'] = helix_bias
+    S.args['strand_bias'] = strand_bias
+    S.args['loop_bias'] = loop_bias
+    
+    # set T
+    if num_steps in ['',None]:
+        S.args['T'] = 20
+    else:
+        S.args['T'] = int(num_steps)
+
+    # noise
+    if 'normal' in noise:
+        S.args['sample_distribution'] = noise
+        S.args['sample_distribution_gmm_means'] = [0]
+        S.args['sample_distribution_gmm_variances'] = [1]
+    elif 'gmm2' in noise:
+        S.args['sample_distribution'] = noise
+        S.args['sample_distribution_gmm_means'] = [-1,1]
+        S.args['sample_distribution_gmm_variances'] = [1,1]
+    elif 'gmm3' in noise:
+        S.args['sample_distribution'] = noise
+        S.args['sample_distribution_gmm_means'] = [-1,0,1]
+        S.args['sample_distribution_gmm_variances'] = [1,1,1]
+
+
+
+    if secondary_structure not in ['',None] or helix_bias+strand_bias+loop_bias > 0:
+        S.args['checkpoint'] = dssp_checkpoint
+        S.args['d_t1d'] = 29
+        print('using dssp checkpoint')
+    else:
+        S.args['checkpoint'] = og_checkpoint
+        S.args['d_t1d'] = 24
+        print('using og checkpoint')
+    
+
+    for k,v in S.args.items():
+        print(f"{k} --> {v}")
+    
+    # init S
+    S.model_init()
+    S.diffuser_init()
+    S.setup()
+
+    # sampling loop
+    plddt_data = []
+    for j in range(S.max_t):
+        print(f'on step {j}')
+        output_seq, output_pdb, plddt = S.take_step_get_outputs(j)
+        plddt_data.append(plddt)
+        yield output_seq, output_pdb, display_pdb(output_pdb), get_plddt_plot(plddt_data, S.max_t)
+    
+    output_seq, output_pdb, plddt = S.get_outputs()
+    yield output_seq, output_pdb, display_pdb(output_pdb), get_plddt_plot(plddt_data, S.max_t)
+
+def get_plddt_plot(plddt_data, max_t):
+    x = [i+1 for i in range(len(plddt_data))]
+    fig, ax = plt.subplots(figsize=(15,6))
+    ax.plot(x,plddt_data,color='#661dbf', linewidth=3,marker='o')
+    ax.set_xticks([i+1 for i in range(max_t)])
+    ax.set_yticks([(i+1)/10 for i in range(10)])
+    ax.set_ylim([0,1])
+    ax.set_ylabel('model confidence (plddt)')
+    ax.set_xlabel('diffusion steps (t)')
+    return fig
+
+def display_pdb(path_to_pdb):
+    '''
+        #function to display pdb in py3dmol
+    '''
+    pdb = open(path_to_pdb, "r").read()
+    
+    view = py3Dmol.view(width=500, height=500)
+    view.addModel(pdb, "pdb")
+    view.setStyle({'model': -1}, {"cartoon": {'colorscheme':{'prop':'b','gradient':'roygb','min':0,'max':1}}})#'linear', 'min': 0, 'max': 1, 'colors': ["#ff9ef0","#a903fc",]}}}) 
+    view.zoomTo()
+    output = view._make_html().replace("'", '"')
+    print(view._make_html())
+    x = f"""<!DOCTYPE html><html></center> {output} </center></html>"""  # do not use ' in this input
+    
+    return f"""<iframe height="500px" width="100%"  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>"""
+
+'''
+    return f"""<iframe  style="width: 100%; height:700px" 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>"""
+'''
+
+
+
+# MOTIF SCAFFOLDING
+def get_motif_preview(pdb_id, contigs):
+    '''
+        #function to display selected motif in py3dmol
+    '''
+    input_pdb = fetch_pdb(pdb_id=pdb_id.lower())
+
+    # rewrite pdb
+    parse = parse_pdb(input_pdb)
+    #output_name = './rewrite_'+input_pdb.split('/')[-1]
+    #writepdb(output_name, torch.tensor(parse_og['xyz']),torch.tensor(parse_og['seq']))
+    #parse = parse_pdb(output_name)
+    output_name = input_pdb
+
+    pdb = open(output_name, "r").read()
+    view = py3Dmol.view(width=500, height=500)
+    view.addModel(pdb, "pdb")
+
+    if contigs in ['',0]:
+        contigs = ['0']
+    else:
+        contigs = [contigs]
+
+    print('DEBUG: ',contigs)
+    
+    pdb_map = get_mappings(ContigMap(parse,contigs))
+    print('DEBUG: ',pdb_map)
+    print('DEBUG: ',pdb_map['con_ref_idx0'])
+    roi = [x[1]-1 for x in pdb_map['con_ref_pdb_idx']]
+
+    colormap = {0:'#D3D3D3', 1:'#F74CFF'}
+    colors = {i+1: colormap[1] if i in roi else colormap[0] for i in range(parse['xyz'].shape[0])}
+    view.setStyle({"cartoon": {"colorscheme": {"prop": "resi", "map": colors}}})
+    view.zoomTo()
+    output = view._make_html().replace("'", '"')
+    print(view._make_html())
+    x = f"""<!DOCTYPE html><html></center> {output} </center></html>"""  # do not use ' in this input
+    
+    return f"""<iframe height="500px" width="100%"  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>""", output_name
+
+def fetch_pdb(pdb_id=None):
+    if pdb_id is None or pdb_id == "":
+        return None
+    else:
+        os.system(f"wget -qnc https://files.rcsb.org/view/{pdb_id}.pdb")
+        return f"{pdb_id}.pdb"
+
+# MSA AND PSSM GUIDANCE
+def save_pssm(file_upload):
+    filename = file_upload.name
+    orig_name = file_upload.orig_name
+    if filename.split('.')[-1] in ['fasta', 'a3m']:
+        return msa_to_pssm(file_upload)
+    return filename
+
+def msa_to_pssm(msa_file):
+    # Define the lookup table for converting amino acids to indices
+    aa_to_index = {'A': 0, 'R': 1, 'N': 2, 'D': 3, 'C': 4, 'Q': 5, 'E': 6, 'G': 7, 'H': 8, 'I': 9, 'L': 10,
+                'K': 11, 'M': 12, 'F': 13, 'P': 14, 'S': 15, 'T': 16, 'W': 17, 'Y': 18, 'V': 19, 'X': 20, '-': 21}
+    # Open the FASTA file and read the sequences
+    records = list(SeqIO.parse(msa_file.name, "fasta"))
+
+    assert len(records) >= 1, "MSA must contain more than one protein sequecne."
+
+    first_seq = str(records[0].seq)
+    aligned_seqs = [first_seq]
+    # print(aligned_seqs)
+    # Perform sequence alignment using the Needleman-Wunsch algorithm
+    aligner = Align.PairwiseAligner()
+    aligner.open_gap_score = -0.7
+    aligner.extend_gap_score = -0.3
+    for record in records[1:]:
+        alignment = aligner.align(first_seq, str(record.seq))[0]
+        alignment = alignment.format().split("\n")
+        al1 = alignment[0]
+        al2 = alignment[2]
+        al1_fin = ""
+        al2_fin = ""
+        percent_gap = al2.count('-')/ len(al2)
+        if percent_gap > 0.4:
+            continue
+        for i in range(len(al1)):
+            if al1[i] != '-':
+                al1_fin += al1[i]
+                al2_fin += al2[i]
+        aligned_seqs.append(str(al2_fin))
+    # Get the length of the aligned sequences
+    aligned_seq_length = len(first_seq)
+    # Initialize the position scoring matrix
+    matrix = np.zeros((22, aligned_seq_length))
+    # Iterate through the aligned sequences and count the amino acids at each position
+    for seq in aligned_seqs:
+        #print(seq)
+        for i in range(aligned_seq_length):
+            if i == len(seq):
+                break
+            amino_acid = seq[i]
+            if amino_acid.upper() not in aa_to_index.keys():
+                continue
+            else:
+                aa_index = aa_to_index[amino_acid.upper()]
+            matrix[aa_index, i] += 1
+    # Normalize the counts to get the frequency of each amino acid at each position
+    matrix /= len(aligned_seqs)
+    print(len(aligned_seqs))
+    matrix[20:,]=0
+
+    outdir = ".".join(msa_file.name.split('.')[:-1]) + ".csv"
+    np.savetxt(outdir, matrix[:21,:].T, delimiter=",")
+    return outdir
+
+def get_pssm(fasta_msa, input_pssm):
+
+    if input_pssm not in ['',None]:
+        outdir = input_pssm.name
+    else:
+        outdir = save_pssm(fasta_msa)
+
+    pssm = np.loadtxt(outdir, delimiter=",", dtype=float)
+    fig, ax = plt.subplots(figsize=(15,6))
+    plt.imshow(torch.permute(torch.tensor(pssm),(1,0)))
+
+    return fig, outdir
+
+
+#toggle options
+def toggle_seq_input(choice):
+    if choice == "protein length":
+        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
+    elif choice == "custom sequence":
+        return gr.update(visible=False, value=None), gr.update(visible=True, value=None)
+
+def toggle_secondary_structure(choice):
+    if choice == "sliders":
+        return gr.update(visible=True, value=None),gr.update(visible=True, value=None),gr.update(visible=True, value=None),gr.update(visible=False, value=None)
+    elif choice == "explicit":
+        return gr.update(visible=False, value=None),gr.update(visible=False, value=None),gr.update(visible=False, value=None),gr.update(visible=True, value=None)
+
+
+# Define the Gradio interface
+with gr.Blocks(theme='ParityError/Interstellar') as demo:
+    
+ 
+
+    #with gr.Row().style(equal_height=False):
+    with gr.Row():
+        with gr.Column():
+            with gr.Tabs():
+                with gr.TabItem("Inputs"):
+                    gr.Markdown("""## INPUTS""")
+                    gr.Markdown("""#### Start Sequence
+                                Specify the protein length for complete unconditional generation, or scaffold a motif (or your name) using the custom sequence input""")
+                    seq_opt = gr.Radio(["protein length","custom sequence"], label="How would you like to specify the starting sequence?", value='protein length')
+
+                    sequence = gr.Textbox(label="custom sequence", lines=1, placeholder='AMINO ACIDS: A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y\n  MASK TOKEN: X', visible=False)
+                    seq_len = gr.Slider(minimum=5.0, maximum=250.0, label="protein length", value=100, visible=True)
+                    
+                    seq_opt.change(fn=toggle_seq_input,
+                                            inputs=[seq_opt],
+                                            outputs=[seq_len, sequence],
+                                            queue=False)
+
+                    gr.Markdown("""### Optional Parameters""")
+                    with gr.Accordion(label='Secondary Structure',open=True):
+                        gr.Markdown("""Try changing the sliders or inputing explicit secondary structure conditioning for each residue""")
+                        sec_str_opt = gr.Radio(["sliders","explicit"], label="How would you like to specify secondary structure?", value='sliders')
+
+                        secondary_structure = gr.Textbox(label="secondary structure", lines=1, placeholder='HELIX = H  STRAND = S  LOOP = L  MASK = X(must be the same length as input sequence)', visible=False)
+                        
+                        with gr.Column():
+                            helix_bias = gr.Slider(minimum=0.0, maximum=0.05, label="helix bias", visible=True)
+                            strand_bias = gr.Slider(minimum=0.0, maximum=0.05, label="strand bias", visible=True)
+                            loop_bias = gr.Slider(minimum=0.0, maximum=0.20, label="loop bias", visible=True)
+                    
+                        sec_str_opt.change(fn=toggle_secondary_structure,
+                                                inputs=[sec_str_opt],
+                                                outputs=[helix_bias,strand_bias,loop_bias,secondary_structure],
+                                                queue=False)
+                        
+                    with gr.Accordion(label='Amino Acid Compositional Bias',open=False):
+                        gr.Markdown("""Bias sequence composition for particular amino acids by specifying the one letter code followed by the fraction to bias. This can be input as a list for example: W0.2,E0.1""")
+                        with gr.Row():
+                            aa_bias = gr.Textbox(label="aa bias", lines=1, placeholder='specify one letter AA and fraction to bias, for example W0.1 or M0.1,K0.1' )
+                            aa_bias_potential = gr.Textbox(label="aa bias scale", lines=1, placeholder='AA Bias potential scale (recomended range 1.0-5.0)')
+                    
+                    '''
+                    with gr.Accordion(label='Charge Bias',open=False):
+                        gr.Markdown("""Bias for a specified net charge at a particular pH using the boxes below""")
+                        with gr.Row():
+                            target_charge = gr.Textbox(label="net charge", lines=1, placeholder='net charge to target')
+                            target_ph = gr.Textbox(label="pH", lines=1, placeholder='pH at which net charge is desired')
+                            charge_potential = gr.Textbox(label="charge potential scale", lines=1, placeholder='charge potential scale (recomended range 1.0-5.0)')
+                    '''
+
+                    with gr.Accordion(label='Hydrophobic Bias',open=False):
+                        gr.Markdown("""Bias for or against hydrophobic composition, to get more soluble proteins, bias away with a negative target score (ex. -5)""")
+                        with gr.Row():
+                            hydrophobic_target_score = gr.Textbox(label="hydrophobic score", lines=1, placeholder='hydrophobic score to target (negative score is good for solublility)')
+                            hydrophobic_potential = gr.Textbox(label="hydrophobic potential scale", lines=1, placeholder='hydrophobic potential scale (recomended range 1.0-2.0)')
+                    
+                    with gr.Accordion(label='Diffusion Params',open=False):
+                        gr.Markdown("""Increasing T to more steps can be helpful for harder design challenges, sampling from different distributions can change the sequence and structural composition""")
+                        with gr.Row():
+                            num_steps = gr.Textbox(label="T", lines=1, placeholder='number of diffusion steps (25 or less will speed things up)')
+                            noise = gr.Dropdown(['normal','gmm2 [-1,1]','gmm3 [-1,0,1]'], label='noise type', value='normal') 
+                
+                with gr.TabItem("Motif Selection"):
+
+                    gr.Markdown("""### Motif Selection Preview""")
+                    gr.Markdown('Contigs explained: to grab residues (seq and str) on a pdb chain you will provide the chain letter followed by a range of residues as indexed in the pdb file for example (A3-10) is the syntax to select residues 3-10 on chain A (the chain always needs to be specified). To add diffused residues to either side of this motif you can specify a range or discrete value without a chain letter infront. To add 15 residues before the motif and 20-30 residues (randomly sampled) after use the following syntax: 15,A3-10,20-30 commas are used to separate regions selected from the pdb and designed (diffused) resiudes which will be added. ')
+                    pdb_id_code = gr.Textbox(label="PDB ID", lines=1, placeholder='INPUT PDB ID TO FETCH (ex. 1DPX)', visible=True)
+                    contigs = gr.Textbox(label="contigs", lines=1, placeholder='specify contigs to grab particular residues from pdb ()', visible=True)
+                    gr.Markdown('Using the same contig syntax, seq or str of input motif residues can be masked, allowing the model to hold strucutre fixed and design sequence or vice-versa')
+                    with gr.Row():
+                        seq_mask = gr.Textbox(label='seq mask',lines=1,placeholder='input residues to mask sequence')
+                        str_mask = gr.Textbox(label='str mask',lines=1,placeholder='input residues to mask structure')
+                    preview_viewer = gr.HTML()
+                    rewrite_pdb = gr.File(label='PDB file')
+                    preview_btn = gr.Button("Preview Motif")
+
+                with gr.TabItem("MSA to PSSM"):
+                    gr.Markdown("""### MSA to PSSM Generation""")
+                    gr.Markdown('input either an MSA or PSSM to guide the model toward generating samples within your family of interest')
+                    with gr.Row():
+                        fasta_msa = gr.File(label='MSA')
+                        input_pssm = gr.File(label='PSSM (.csv)')
+                    pssm = gr.File(label='Generated PSSM')
+                    pssm_view = gr.Plot(label='PSSM Viewer')
+                    pssm_gen_btn = gr.Button("Generate PSSM")
+
+
+            btn = gr.Button("GENERATE")
+
+        #with gr.Row():
+        with gr.Column():
+            gr.Markdown("""## OUTPUTS""")
+            gr.Markdown("""#### Confidence score for generated structure at each timestep""")
+            plddt_plot = gr.Plot(label='plddt at step t')
+            gr.Markdown("""#### Output protein sequnece""")
+            output_seq = gr.Textbox(label="sequence")
+            gr.Markdown("""#### Download PDB file""")
+            output_pdb = gr.File(label="PDB file")
+            gr.Markdown("""#### Structure viewer""")
+            output_viewer = gr.HTML()
+    '''
+    gr.Markdown("""### Don't know where to get started? Click on an example below to try it out!""")
+    gr.Examples(
+        [["","125",0.0,0.0,0.2,"","","","20","normal",'','','',None,'','',None],
+         ["","100",0.0,0.0,0.0,"","W0.2","2","20","normal",'','','',None,'','',None],
+#         ["","100",0.0,0.0,0.0,
+#        "XXHHHHHHHHHXXXXXXXHHHHHHHHHXXXXXXXHHHHHHHHXXXXSSSSSSSSSSSXXXXXXXXSSSSSSSSSSSSXXXXXXXSSSSSSSSSXXXXXXX",
+#                "","","25","normal",'','','',None,'','',None],
+#         ["XXXXXXXXXXXXXXXXXXXXXXXXXIPDXXXXXXXXXXXXXXXXXXXXXXPEPSEQXXXXXXXXXXXXXXXXXXXXXXXXXXIPDXXXXXXXXXXXXXXXXXXX",
+#          "",0.0,0.0,0.0,"","","","25","normal",'','','',None,'','',None],
+#         ["","",0.0,0.0,0.0,"","","","25","normal",'','',
+#          '9,D10-11,8,D20-20,4,D25-35,65,D101-101,2,D104-105,8,D114-116,15,D132-138,6,D145-145,2,D148-148,12,D161-161,3',
+#          './tmp/PSSM_lysozyme.csv',
+#          'D25-25,D27-31,D33-35,D132-137',
+#          'D26-26','./tmp/150l.pdb']
+         ],
+        inputs=[sequence, 
+                seq_len, 
+                helix_bias, 
+                strand_bias, 
+                loop_bias, 
+                secondary_structure, 
+                aa_bias, 
+                aa_bias_potential,
+                #target_charge, 
+                #target_ph, 
+                #charge_potential, 
+                num_steps, 
+                noise, 
+                hydrophobic_target_score, 
+                hydrophobic_potential,
+                contigs,
+                pssm,
+                seq_mask,
+                str_mask,
+                rewrite_pdb],
+        outputs=[output_seq,
+                 output_pdb,
+                 output_viewer,
+                 plddt_plot],
+        fn=protein_diffusion_model,
+        ) 
+    '''
+    preview_btn.click(get_motif_preview,[pdb_id_code, contigs],[preview_viewer, rewrite_pdb])
+
+    pssm_gen_btn.click(get_pssm,[fasta_msa,input_pssm],[pssm_view, pssm])
+
+    btn.click(protein_diffusion_model, 
+                [sequence, 
+                 seq_len, 
+                 helix_bias, 
+                 strand_bias, 
+                 loop_bias, 
+                 secondary_structure, 
+                 aa_bias, 
+                 aa_bias_potential, 
+                 #target_charge, 
+                 #target_ph, 
+                 #charge_potential, 
+                 num_steps, 
+                 noise, 
+                 hydrophobic_target_score, 
+                 hydrophobic_potential,
+                 contigs,
+                 pssm,
+                 seq_mask, 
+                 str_mask,
+                 rewrite_pdb], 
+                [output_seq,
+                 output_pdb,
+                 output_viewer,
+                 plddt_plot])
+
+demo.queue()
+demo.launch(debug=True)
+