app.py

import gradio as gr

import re 
import urllib

import tempfile

from output_helpers import viewer_html, output_html, load_js, get_js



import json
import os
import shlex
import subprocess
from datetime import datetime

from einops import repeat
import torch

from core import data
from core import utils
import models
import sampling

# from draw_samples import draw_and_save_samples, parse_resample_idx_string


print("working directory", os.getcwd())

def draw_and_save_samples(
    model,
    samples_per_len=8,
    lengths=range(50, 512),
    save_dir="./",
    mode="backbone",
    **sampling_kwargs,
):
    device = model.device
    sample_files = []
    if mode == "backbone":
        total_sampling_time = 0
        for l in lengths:
            prot_lens = torch.ones(samples_per_len).long() * l
            seq_mask = model.make_seq_mask_for_sampling(prot_lens=prot_lens)
            aux = sampling.draw_backbone_samples(
                model,
                seq_mask=seq_mask,
                pdb_save_path=f"{save_dir}/len{format(l, '03d')}_samp",
                return_aux=True,
                return_sampling_runtime=True,
                **sampling_kwargs,
            )
            total_sampling_time += aux["runtime"]
            sample_files+= [f"{save_dir}/len{format(l, '03d')}_samp{i}.pdb" for i in range(samples_per_len)]
        return sample_files
    elif mode == "allatom":
        total_sampling_time = 0
        for l in lengths:
            prot_lens = torch.ones(samples_per_len).long() * l
            seq_mask = model.make_seq_mask_for_sampling(prot_lens=prot_lens)
            aux = sampling.draw_allatom_samples(
                model,
                seq_mask=seq_mask,
                pdb_save_path=f"{save_dir}/len{format(l, '03d')}",
                return_aux=True,
                **sampling_kwargs,
            )
            total_sampling_time += aux["runtime"]
            sample_files+= [f"{save_dir}/len{format(l, '03d')}_samp{i}.pdb" for i in range(samples_per_len)]
        return sample_files


def parse_idx_string(idx_str):
    spans = idx_str.split(",")
    idxs = []
    for s in spans:
        if "-" in s:
            start, stop = s.split("-")
            idxs.extend(list(range(int(start), int(stop))))
        else:
            idxs.append(int(s))
    return idxs

def changemode(m):
    if (m == "unconditional"):
        return gr.update(visible=True), gr.update(visible=False),gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)
    else:
        return gr.update(visible=False), gr.update(visible=True),gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)

def fileselection(val):
    if (val == "upload"):
        return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)
    else:
        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)

def update_structuresel(pdb, radio_val):
    pdb_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdb")


    representations = [{
        "model": 0,
        "chain": "",
        "resname": "",
        "style": "cartoon",
        "color": "whiteCarbon",
        "residue_range": "",
        "around": 0,
        "byres": False,
        "visible": False,
      }]
    

    if (radio_val == "PDB"):
        if (len(pdb) != 4):
            return gr.update(open=True),gr.update(), gr.update(value="",visible=False)
        else:
            urllib.request.urlretrieve(
                    f"http://files.rcsb.org/download/{pdb.lower()}.pdb1",
                    pdb_file.name,
                )
            return gr.update(open=False),gr.update(value=pdb_file.name), gr.update(value=f"""<iframe style="width: 100%; height: 930px" 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='{viewer_html(pdb_file.name, representations=representations)}'></iframe>""",visible=True)
    elif (radio_val == "AFDB2"):
        if (re.match("[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}",pdb) != None):
            urllib.request.urlretrieve(
                    f"https://alphafold.ebi.ac.uk/files/AF-{pdb}-F1-model_v2.pdb",
                    pdb_file.name
                )
            return gr.update(open=False),gr.update(value=pdb_file.name), gr.update(value=f"""<iframe style="width: 100%; height: 930px" 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='{viewer_html(pdb_file.name, representations=representations)}'></iframe>""",visible=True)
        else:
            return gr.update(open=True), gr.update(value="regex not matched",visible=True)
    else:
        return gr.update(open=False),gr.update(value=f"{pdb.name}"), gr.update(value=f"""<iframe style="width: 100%; height: 930px" 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='{viewer_html(pdb.name, representations=representations)}'></iframe>""",visible=True)

from Bio.PDB import PDBParser, cealign
from Bio.PDB.PDBIO import PDBIO

class dotdict(dict):
    """dot.notation access to dictionary attributes"""
    __getattr__ = dict.get
    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

def protpardelle(path_to_file, m, resample_idx,  modeltype, minlen, maxlen, steplen, perlen):
        # Set up params, arguments, sampling config
    ####################
    
    args = {}
    args["model_checkpoint"] = "checkpoints" #Path to denoiser model weights and config",
    
    args["mpnnpath"] = "checkpoints/minimpnn_state_dict.pth" #"Path to minimpnn model weights",
        
    args["modeldir"] = None #"Model base directory, ex 'training_logs/other/lemon-shape-51'",
    
    args["modelepoch"] = None #"Model epoch, ex 1000")


    args["type"]=modeltype # "Type of model"
    if m == "conditional":
        args["param"] = None #"Which sampling param to vary"
        args["paramval"]=None #"Which param val to use"
        args["parampath"]= None # Path to json file with params, either use param/paramval or parampath, not both",
        args["perlen"] = int(perlen) #How many samples per sequence length"
        args["minlen"] = None #"Minimum sequence length"
        args["maxlen"] = None #Maximum sequence length, not inclusive",
        args["steplen"] = int(steplen) #"How frequently to select sequence length, for steplen 2, would be 50, 52, 54, etc",
        args["num_lens"] = None #"If steplen not provided, how many random lengths to sample at",
        args["targetdir"] = "." #"Directory to save results"
        args["input_pdb"] = path_to_file # "PDB file to condition on"
        args["resample_idxs"] = resample_idx[1:-1] # "Indices from PDB file to resample. Zero-indexed, comma-delimited, can use dashes, eg 0,2-5,7"
    else:
        args["param"] = "n_steps" #"Which sampling param to vary"
        args["paramval"]="100" #"Which param val to use"
        args["parampath"]= None # Path to json file with params, either use param/paramval or parampath, not both",
        args["perlen"] = int(perlen) #How many samples per sequence length"
        args["minlen"] = int(minlen) #"Minimum sequence length"
        args["maxlen"] = int(maxlen)+1 #Maximum sequence length
        args["steplen"] = int(steplen) #"How frequently to select sequence length, for steplen 2, would be 50, 52, 54, etc",
        args["num_lens"] = None #"If steplen not provided, how many random lengths to sample at",
        args["targetdir"] = "." #"Directory to save results"
        args["resample_idxs"] = None

    args = dotdict(args)
    is_test_run = False
    seed = 0
    samples_per_len = args.perlen
    min_len = args.minlen
    max_len = args.maxlen
    len_step_size = args.steplen
    device = "cuda:0"

    # setting default sampling config
    if args.type == "backbone":
        sampling_config = sampling.default_backbone_sampling_config()
    elif args.type == "allatom":
        sampling_config = sampling.default_allatom_sampling_config()

    sampling_kwargs = vars(sampling_config)

    # Parse conditioning inputs
    input_pdb_len = None
    if args.input_pdb:
        input_feats = utils.load_feats_from_pdb(args.input_pdb, protein_only=True)
        input_pdb_len = input_feats["aatype"].shape[0]
        if args.resample_idxs:
            print(
                f"Warning: when sampling conditionally, the input pdb length ({input_pdb_len} residues) is used automatically for the sampling lengths."
            )
            resample_idxs = parse_idx_string(args.resample_idxs)
        else:
            resample_idxs = list(range(input_pdb_len))
        cond_idxs = [i for i in range(input_pdb_len) if i not in resample_idxs]
        to_batch_size = lambda x: repeat(x, "... -> b ...", b=samples_per_len).to(
            device
        )

        # For unconditional model, center coords on whole structure
        centered_coords = data.apply_random_se3(
            input_feats["atom_positions"],
            atom_mask=input_feats["atom_mask"],
            translation_scale=0.0,
        )
        cond_kwargs = {}
        cond_kwargs["gt_coords"] = to_batch_size(centered_coords)
        cond_kwargs["gt_cond_atom_mask"] = to_batch_size(input_feats["atom_mask"])
        cond_kwargs["gt_cond_atom_mask"][:, resample_idxs] = 0
        cond_kwargs["gt_aatype"] = to_batch_size(input_feats["aatype"])
        cond_kwargs["gt_cond_seq_mask"] = torch.zeros_like(cond_kwargs["gt_aatype"])
        cond_kwargs["gt_cond_seq_mask"][:, cond_idxs] = 1
        sampling_kwargs.update(cond_kwargs)

    print("input_pdb_len", input_pdb_len)

    # Determine lengths to sample at
    if min_len is not None and max_len is not None:
        if len_step_size is not None:
            sampling_lengths = range(min_len, max_len, len_step_size)
        else:
            sampling_lengths = list(
                torch.randint(min_len, max_len, size=(args.num_lens,))
            )
    elif input_pdb_len is not None:
        sampling_lengths = [input_pdb_len]
    else:
        raise Exception("Need to provide a set of protein lengths or an input pdb.")

    total_num_samples = len(list(sampling_lengths)) * samples_per_len

    model_directory = args.modeldir
    epoch = args.modelepoch
    base_dir = args.targetdir

    date_string = datetime.now().strftime("%y-%m-%d-%H-%M-%S")
    if is_test_run:
        date_string = f"test-{date_string}"

    # Update sampling config with arguments
    if args.param:
        var_param = args.param
        var_value = args.paramval
        sampling_kwargs[var_param] = (
            None
            if var_value == "None"
            else int(var_value)
            if var_param == "n_steps"
            else float(var_value)
        )
    elif args.parampath:
        with open(args.parampath) as f:
            var_params = json.loads(f.read())
            sampling_kwargs.update(var_params)

    # this is only used for the readme, keep s_min and s_max as params instead of struct_noise_schedule
    sampling_kwargs_readme = list(sampling_kwargs.items())

    print("Base directory:", base_dir)
    save_dir = f"{base_dir}/samples/{date_string}"
    save_init_dir = f"{base_dir}/samples_inits/{date_string}"

    # make dirs if do not exist
    if not os.path.exists(save_dir):
        subprocess.run(shlex.split(f"mkdir -p {save_dir}"))
    
    if not os.path.exists(save_init_dir):
        subprocess.run(shlex.split(f"mkdir -p {save_init_dir}"))

    print("Samples saved to:", save_dir)
    torch.manual_seed(seed)

    # Load model
    if args.type == "backbone":
        if args.model_checkpoint:
            checkpoint = f"{args.model_checkpoint}/backbone_state_dict.pth"
            cfg_path = f"{args.model_checkpoint}/backbone.yml"
        else:
            checkpoint = (
                f"{model_directory}/checkpoints/epoch{epoch}_training_state.pth"
            )
            cfg_path = f"{model_directory}/configs/backbone.yml"
        cfg = utils.load_config(cfg_path)
        weights = torch.load(checkpoint, map_location=device)["model_state_dict"]
        model = models.Protpardelle(cfg, device=device)
        model.load_state_dict(weights)
        model.to(device)
        model.eval()
        model.device = device
    elif args.type == "allatom":
        if args.model_checkpoint:
            checkpoint = f"{args.model_checkpoint}/allatom_state_dict.pth"
            cfg_path = f"{args.model_checkpoint}/allatom.yml"
        else:
            checkpoint = (
                f"{model_directory}/checkpoints/epoch{epoch}_training_state.pth"
            )
            cfg_path = f"{model_directory}/configs/allatom.yml"
        config = utils.load_config(cfg_path)
        weights = torch.load(checkpoint, map_location=device)["model_state_dict"]
        model = models.Protpardelle(config, device=device)
        model.load_state_dict(weights)
        model.load_minimpnn(args.mpnnpath)
        model.to(device)
        model.eval()
        model.device = device

    with open(save_dir + "/run_parameters.txt", "w") as f:
        f.write(f"Sampling run for {date_string}\n")
        f.write(f"Random seed {seed}\n")
        f.write(f"Model checkpoint: {checkpoint}\n")
        f.write(
            f"{samples_per_len} samples per length from {min_len}:{max_len}:{len_step_size}\n"
        )
        f.write("Sampling params:\n")
        for k, v in sampling_kwargs_readme:
            f.write(f"{k}\t{v}\n")

    # Draw samples
    output_files = draw_and_save_samples(
        model,
        samples_per_len=samples_per_len,
        lengths=sampling_lengths,
        save_dir=save_dir,
        mode=args.type,
        **sampling_kwargs,
    )

    return output_files


def api_predict(pdb_content,m, resample_idx,  modeltype, minlen, maxlen, steplen, perlen):
    
    if (m == "conditional"):
        tempPDB = tempfile.NamedTemporaryFile(delete=False, suffix=".pdb")
        tempPDB.write(pdb_content.encode())
        tempPDB.close()

        path_to_file = tempPDB.name
    else:
        path_to_file = None

    try:
        designs = protpardelle(path_to_file, m, resample_idx, modeltype, minlen, maxlen, steplen, perlen)
    except Exception as e:
        print(e)
        
        raise gr.Error(e)
    
    # load each design as string
    design_str = []
    for d in designs:
        with open(d, "r") as f:
            design_str.append(f.read())

    results = list(zip(designs, design_str))
    return json.dumps(results)
    
def predict(pdb_radio, path_to_file,m, resample_idx,  modeltype, minlen, maxlen, steplen, perlen):
    print("running predict")
    try:
        designs = protpardelle(path_to_file, m, resample_idx, modeltype, minlen, maxlen, steplen, perlen)
    except Exception as e:
        print(e)
        
        raise gr.Error(e)

        return gr.update(open=True), gr.update(value="something went wrong")

    parser = PDBParser()
    aligner = cealign.CEAligner()
    io=PDBIO()
    aligned_designs = []
    metrics = []
    if (m == "conditional"):
        ref = parser.get_structure("ref", path_to_file)
        aligner.set_reference(ref)
       
        for d in designs:
            design = parser.get_structure("design", d)
            aligner.align(design)
            metrics.append({"rms": f"{aligner.rms:.1f}", "len": len(list(design[0].get_residues()))})
            io.set_structure(design)
            io.save(d.replace(".pdb", f"_al.pdb"))
            aligned_designs.append(d.replace(".pdb", f"_al.pdb"))
    else:
        for d in designs:
            design = parser.get_structure("design", d)
            metrics.append({"len": len(list(design[0].get_residues()))})
        aligned_designs = designs

    output_view = f"""<iframe style="width: 100%; height: 900px" 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='{output_html(path_to_file, aligned_designs, metrics, resample_idx=resample_idx, mode=m)}'></iframe>"""
    
    return gr.update(open=False), gr.update(value=output_view,visible=True)


protpardelleDemo = gr.Blocks()

with protpardelleDemo:
    gr.Markdown("# Protpardelle")
    gr.Markdown(""" An all-atom protein generative model 
                Alexander E. Chu, Lucy Cheng, Gina El Nesr, Minkai Xu,  Po-Ssu Huang
doi: https://doi.org/10.1101/2023.05.24.542194""")
    
    with gr.Accordion(label="Input options", open=True) as input_accordion:
        model = gr.Dropdown(["backbone", "allatom"], value="allatom", label="What to sample?")
        
        m = gr.Radio(['unconditional','conditional'],value="unconditional", label="Choose a Mode")
        

        #unconditional
        with gr.Group(visible=True) as uncond:
            gr.Markdown("Unconditional Sampling")
            # length = gr.Slider(minimum=0, maximum=200, step=1, value=50, label="length")
            # param = gr.Dropdown(["length", "param"], value="length", label="Which sampling param to vary?")
            # paramval = gr.Dropdown(["nsteps"], label="paramval", info="Which param val to use?")
        
        #conditional
        with gr.Group(visible=False) as cond:
            with gr.Accordion(label="Structure to condition on", open=True) as input_accordion:
                pdb_radio = gr.Radio(['PDB','AF2 EBI DB', 'upload'],value="PDB", label="source of the structure")
                pdbcode = gr.Textbox(label="Uniprot code to be retrieved Alphafold2 Database", visible=True)
                pdbfile = gr.File(label="PDB File", visible=False)
                btn_load = gr.Button("Load PDB")
                pdb_radio.change(fileselection, inputs=pdb_radio, outputs=[pdbcode, pdbfile, btn_load])
                
                     
                
            pdb_html = gr.HTML("", visible=False)   


            path_to_file = gr.Textbox(label="Path to file", visible=False)
            resample_idxs = gr.Textbox(label="Cond Idxs", interactive=False, info="Zero indexed list of indices to condition on, select in sequence viewer above")
            btn_load.click(update_structuresel, inputs=[pdbcode, pdb_radio], outputs=[input_accordion,path_to_file,pdb_html])
            pdbfile.change(update_structuresel, inputs=[pdbfile,pdb_radio], outputs=[input_accordion,path_to_file,pdb_html])
        
        with gr.Accordion(label="Sizes", open=True) as size_uncond:
            with gr.Row():
                minlen = gr.Slider(minimum=2, maximum=200,value=50, step=1, label="minlen", info="Minimum sequence length")
                maxlen = gr.Slider(minimum=3, maximum=200,value=60, step=1, label="maxlen", info="Maximum sequence length")
                steplen = gr.Slider(minimum=1, maximum=50, step=1, value=1, label="steplen", info="How frequently to select sequence length?" )
        perlen = gr.Slider(minimum=1, maximum=200, step=1, value=2, label="perlen", info="How many samples per sequence length?")

    
    btn_conditional = gr.Button("Run conditional",visible=False)
    btn_unconditional = gr.Button("Run unconditional")
    m.change(changemode, inputs=m, outputs=[uncond, cond, btn_unconditional, btn_conditional, size_uncond])
    out = gr.HTML("", visible=True)

    btn_unconditional.click(predict, inputs=[pdb_radio, path_to_file,m, resample_idxs, model, minlen, maxlen, steplen, perlen], outputs=[input_accordion, out])

    btn_conditional.click(fn=None,
                     inputs=[resample_idxs],
                     outputs=[resample_idxs],
                     _js=get_js
                     ) #
    out_text = gr.Textbox(label="Output", visible=False)
    #hidden button for named api route
    pdb_content = gr.Textbox(label="PDB Content", visible=False)
    btn_api = gr.Button("Run API",visible=False)
    btn_api.click(api_predict, inputs=[pdb_content,m, resample_idxs, model, minlen, maxlen, steplen, perlen], outputs=[out_text], api_name="protpardelle")

    resample_idxs.change(predict, inputs=[pdb_radio, path_to_file,m, resample_idxs, model, minlen, maxlen, steplen, perlen], outputs=[input_accordion, out])
    protpardelleDemo.load(None, None, None, _js=load_js)
protpardelleDemo.queue()
protpardelleDemo.launch(allowed_paths=['samples'])