app.py

import gradio as gr
import sys
import random
import os
import pandas as pd
import torch
import itertools
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
import shap

sys.path.append("scripts/")
from foldseek_util import get_struc_seq
from utils import seed_everything, save_pickle
from models import PLTNUM_PreTrainedModel
from datasets_ import PLTNUMDataset


class Config:
    def __init__(self):
        self.batch_size = 2
        self.use_amp = False
        self.num_workers = 1
        self.max_length = 512
        self.used_sequence = "left"
        self.padding_side = "right"
        self.task = "classification"
        self.sequence_col = "sequence"
        self.seed = 42
        self.max_evals = 10



def predict_stability_with_pdb(model_choice, organism_choice, pdb_files, cfg=Config()):
    results = {"file_name": [],
               "raw prediction value": [],
               "binary prediction value": []
               }
    file_names = []
    input_sequences = []

    for pdb_file in pdb_files:
        pdb_path = pdb_file.name
        os.system("chmod 777 bin/foldseek")
        sequences = get_foldseek_seq(pdb_path)
        if not sequences:
            results["file_name"].append(pdb_file.name.split("/")[-1])
            results["raw prediction value"].append(None)
            results["binary prediction value"].append(None)
            continue

        sequence = sequences[2] if model_choice == "SaProt" else sequences[0]
        file_names.append(pdb_file.name.split("/")[-1])
        input_sequences.append(sequence)

    raw_prediction, binary_prediction = predict_stability_core(model_choice, organism_choice, input_sequences, cfg)
    results["file_name"] = results["file_name"] + file_names
    results["raw prediction value"] = results["raw prediction value"] + raw_prediction
    results["binary prediction value"] = results["binary prediction value"] + binary_prediction
            
    df = pd.DataFrame(results)
    output_csv = "/tmp/predictions.csv"
    df.to_csv(output_csv, index=False)

    return output_csv

def predict_stability_with_sequence(model_choice, organism_choice, sequence, cfg=Config()):
    try:
        if not sequence:
            return "No valid sequence provided."
        raw_prediction, binary_prediction = predict_stability_core(model_choice, organism_choice, [sequence], cfg)
        df = pd.DataFrame({"sequence": sequence, "raw prediction value": raw_prediction, "binary prediction value": binary_prediction})
        output_csv = "/tmp/predictions.csv"
        df.to_csv(output_csv, index=False)

        return output_csv 
    except Exception as e:
        return f"An error occurred: {str(e)}"


def predict_stability_core(model_choice, organism_choice, sequences, cfg=Config()):
    cell_line = "HeLa" if organism_choice == "Human" else "NIH3T3"
    cfg.model = f"sagawa/PLTNUM-{model_choice}-{cell_line}"
    cfg.architecture = model_choice
    cfg.model_path = f"sagawa/PLTNUM-{model_choice}-{cell_line}"

    output = predict(cfg, sequences)
    return output


def get_foldseek_seq(pdb_path):
    parsed_seqs = get_struc_seq(
        "bin/foldseek",
        pdb_path,
        ["A"],
        process_id=random.randint(0, 10000000),
    )["A"]
    return parsed_seqs


def predict(cfg, sequences):
    cfg.token_length = 2 if cfg.architecture == "SaProt" else 1
    cfg.device = "cuda" if torch.cuda.is_available() else "cpu"

    if cfg.used_sequence == "both":
        cfg.max_length += 1

    seed_everything(cfg.seed)
    df = pd.DataFrame({cfg.sequence_col: sequences})

    tokenizer = AutoTokenizer.from_pretrained(
        cfg.model_path, padding_side=cfg.padding_side
    )
    cfg.tokenizer = tokenizer
    dataset = PLTNUMDataset(cfg, df, train=False)
    dataloader = DataLoader(
        dataset,
        batch_size=cfg.batch_size,
        shuffle=False,
        num_workers=cfg.num_workers,
        pin_memory=True,
        drop_last=False,
    )

    model = PLTNUM_PreTrainedModel.from_pretrained(cfg.model_path, cfg=cfg)
    model.to(cfg.device)

    model.eval()
    predictions = []

    for inputs, _ in dataloader:
        inputs = inputs.to(cfg.device)
        with torch.no_grad():
            with torch.amp.autocast(cfg.device, enabled=cfg.use_amp):
                preds = (
                    torch.sigmoid(model(inputs))
                    if cfg.task == "classification"
                    else model(inputs)
                )
        predictions += preds.cpu().tolist()

    predictions = list(itertools.chain.from_iterable(predictions))
    
    return predictions, [1 if x > 0.5 else 0 for x in predictions]



def calculate_shap_values_with_pdb(model_choice, organism_choice, pdb_files, cfg=Config()):
    input_sequences = []

    for pdb_file in pdb_files:
        pdb_path = pdb_file.name
        os.system("chmod 777 bin/foldseek")
        sequences = get_foldseek_seq(pdb_path)
        sequence = sequences[2] if model_choice == "SaProt" else sequences[0]
        input_sequences.append(sequence)

    shap_values = calculate_shap_values_core(model_choice, organism_choice, input_sequences, cfg)

    output_path = "/tmp/shap_values.pkl"
    save_pickle(
        output_path, shap_values
    )

    return output_path


def calculate_shap_fn(texts, model, cfg):
    if len(texts) == 1:
        texts = texts[0]
    else:
        texts = texts.tolist()

    inputs = cfg.tokenizer(
        texts,
        return_tensors="pt",
        padding=True,
        truncation=True,
        max_length=cfg.max_length,
    )
    inputs = {k: v.to(cfg.device) for k, v in inputs.items()}
    with torch.no_grad():
        outputs = model(inputs)
        outputs = torch.sigmoid(outputs).detach().cpu().numpy()
    return outputs


def calculate_shap_values_core(model_choice, organism_choice, sequences, cfg=Config()):
    cell_line = "HeLa" if organism_choice == "Human" else "NIH3T3"
    cfg.model = f"sagawa/PLTNUM-{model_choice}-{cell_line}"
    cfg.architecture = model_choice
    cfg.model_path = f"sagawa/PLTNUM-{model_choice}-{cell_line}"
    cfg.device = "cuda" if torch.cuda.is_available() else "cpu"

    seed_everything(cfg.seed)
    tokenizer = AutoTokenizer.from_pretrained(
        cfg.model_path, padding_side=cfg.padding_side
    )
    cfg.tokenizer = tokenizer

    model = PLTNUM_PreTrainedModel.from_pretrained(cfg.model_path, cfg=cfg).to(cfg.device)
    model.eval()

    # build an explainer using a token masker
    explainer = shap.Explainer(lambda x: calculate_shap_fn(x, model, cfg), cfg.tokenizer)

    shap_values = explainer(
        sequences,
        batch_size=cfg.batch_size,
        max_evals=cfg.max_evals,
    )

    return shap_values


# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown(
        """
        # PLTNUM: Protein LifeTime Neural Model
        **Predict the protein half-life from its sequence or PDB file.**
        """
    )

    gr.Image(
        "https://raw.githubusercontent.com/sagawatatsuya/PLTNUM/main/model-image.png",
        label="Model Image",
    )

    # Model and Organism selection in the same row to avoid layout issues
    with gr.Row():
        model_choice = gr.Radio(
            choices=["SaProt", "ESM2"],
            label="Select PLTNUM's base model.",
            value="SaProt",
        )
        organism_choice = gr.Radio(
            choices=["Mouse", "Human"],
            label="Select the target organism.",
            value="Mouse",
        )

    with gr.Tabs():
        with gr.TabItem("Upload PDB File"):
            gr.Markdown("### Upload your PDB files:")
            pdb_files = gr.File(label="Upload PDB Files", file_count="multiple")
            predict_button = gr.Button("Predict Stability")
            prediction_output = gr.File(
                label="Download Predictions"
            )

            predict_button.click(
                fn=predict_stability_with_pdb,
                inputs=[model_choice, organism_choice, pdb_files],
                outputs=prediction_output,
            )

            calculate_shap_values_button = gr.Button("Calculate SHAP Values")
            shap_values_output = gr.File(
                label="Download SHAP Values"
            )
            calculate_shap_values_button.click(
                fn=calculate_shap_values_with_pdb,
                inputs=[model_choice, organism_choice, pdb_files],
                outputs=shap_values_output,
            )


        with gr.TabItem("Enter Protein Sequence"):
            gr.Markdown("### Enter the protein sequence:")
            sequence = gr.Textbox(
                label="Protein Sequence",
                placeholder="Enter your protein sequence here...",
                lines=8,
            )
            predict_button = gr.Button("Predict Stability")
            prediction_output = gr.File(
                label="Download Predictions"
            )

            predict_button.click(
                fn=predict_stability_with_sequence,
                inputs=[model_choice, organism_choice, sequence],
                outputs=prediction_output,
            )

    gr.Markdown(
        """
        ### How to Use:
        - **Select Model**: Choose between 'SaProt' or 'ESM2' for your prediction.
        - **Select Organism**: Choose between 'Mouse' or 'Human'.
        - **Upload PDB File**: Choose the 'Upload PDB File' tab and upload your file.
        - **Enter Sequence**: Alternatively, switch to the 'Enter Protein Sequence' tab and input your sequence.
        - **Predict**: Click 'Predict Stability' to receive the prediction.
        """
    )

    gr.Markdown(
        """
        ### About the Tool
        This tool allows researchers and scientists to predict the stability of proteins using advanced algorithms. It supports both PDB file uploads and direct sequence input.
        """
    )

demo.launch()