app.py

import os
import sys
import argparse
import torch
from torch.utils.data import DataLoader
from transformers import EsmForMaskedLM, AutoModel, EsmTokenizer
from utils.drug_tokenizer import DrugTokenizer
from utils.metric_learning_models_att_maps import Pre_encoded, FusionDTI
from bertviz import head_view
import tempfile
from flask import Flask, request, render_template_string

os.environ["TOKENIZERS_PARALLELISM"] = "false"
sys.path.append("../")

app = Flask(__name__)

def parse_config():
    parser = argparse.ArgumentParser()
    parser.add_argument('-f')
    parser.add_argument("--prot_encoder_path", type=str, default="westlake-repl/SaProt_650M_AF2", help="path/name of protein encoder model located")
    parser.add_argument("--drug_encoder_path", type=str, default="HUBioDataLab/SELFormer", help="path/name of SMILE pre-trained language model")
    parser.add_argument("--agg_mode", default="mean_all_tok", type=str, help="{cls|mean|mean_all_tok}")
    parser.add_argument("--fusion", default="CAN", type=str, help="{CAN|BAN}")
    parser.add_argument("--batch_size", type=int, default=64)
    parser.add_argument("--group_size", type=int, default=1)
    parser.add_argument("--lr", type=float, default=1e-4)
    parser.add_argument("--dropout", type=float, default=0.1)
    parser.add_argument("--test", type=int, default=0)
    parser.add_argument("--use_pooled", action="store_true", default=True)
    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
    parser.add_argument("--save_path_prefix", type=str, default="save_model_ckp/", help="save the result in which directory")
    parser.add_argument("--save_name", default="fine_tune", type=str, help="the name of the saved file")
    parser.add_argument("--dataset", type=str, default="Human", help="Name of the dataset to use (e.g., 'BindingDB', 'Human', 'Biosnap')")
    return parser.parse_args()

args = parse_config()
device = args.device

prot_tokenizer = EsmTokenizer.from_pretrained(args.prot_encoder_path)
drug_tokenizer = DrugTokenizer()

prot_model = EsmForMaskedLM.from_pretrained(args.prot_encoder_path)
drug_model = AutoModel.from_pretrained(args.drug_encoder_path)

encoding = Pre_encoded(prot_model, drug_model, args).to(device)

def get_case_feature(model, dataloader, device):
    with torch.no_grad():
        for step, batch in enumerate(dataloader):
            prot_input_ids, prot_attention_mask, drug_input_ids, drug_attention_mask, label = batch
            prot_input_ids, prot_attention_mask, drug_input_ids, drug_attention_mask = \
                prot_input_ids.to(device), prot_attention_mask.to(device), drug_input_ids.to(device), drug_attention_mask.to(device)

            prot_embed, drug_embed = model.encoding(prot_input_ids, prot_attention_mask, drug_input_ids, drug_attention_mask)
            prot_embed, drug_embed = prot_embed.cpu(), drug_embed.cpu()
            prot_input_ids, drug_input_ids = prot_input_ids.cpu(), drug_input_ids.cpu()
            prot_attention_mask, drug_attention_mask = prot_attention_mask.cpu(), drug_attention_mask.cpu()
            label = label.cpu()

            return [(prot_embed, drug_embed, prot_input_ids, drug_input_ids, prot_attention_mask, drug_attention_mask, label)]

def visualize_attention(model, case_features, device, prot_tokenizer, drug_tokenizer):
    model.eval()
    with torch.no_grad():
        for batch in case_features:
            prot, drug, prot_ids, drug_ids, prot_mask, drug_mask, label = batch
            prot, drug = prot.to(device), drug.to(device)
            prot_mask, drug_mask = prot_mask.to(device), drug_mask.to(device)

            output, attention_weights = model(prot, drug, prot_mask, drug_mask)
            prot_tokens = [prot_tokenizer.decode([pid.item()], skip_special_tokens=True) for pid in prot_ids.squeeze()]
            drug_tokens = [drug_tokenizer.decode([did.item()], skip_special_tokens=True) for did in drug_ids.squeeze()]
            tokens = prot_tokens + drug_tokens

            attention_weights = attention_weights.unsqueeze(1)

            # Generate HTML content using head_view with html_action='return'
            html_head_view = head_view(attention_weights, tokens, sentence_b_start=512, html_action='return')

            # Parse the HTML and modify it to replace sentence labels
            html_content = html_head_view.data
            html_content = html_content.replace("Sentence A -> Sentence A", "Protein -> Protein")
            html_content = html_content.replace("Sentence B -> Sentence B", "Drug -> Drug")
            html_content = html_content.replace("Sentence A -> Sentence B", "Protein -> Drug")
            html_content = html_content.replace("Sentence B -> Sentence A", "Drug -> Protein")

            # Save the modified HTML content to a temporary file
            with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as f:
                f.write(html_content.encode('utf-8'))
                temp_file_path = f.name
            
            return temp_file_path

@app.route('/', methods=['GET', 'POST'])
def index():
    protein_sequence = ""
    drug_sequence = ""
    result = None

    if request.method == 'POST':
        if 'clear' in request.form:
            protein_sequence = ""
            drug_sequence = ""
        else:
            protein_sequence = request.form['protein_sequence']
            drug_sequence = request.form['drug_sequence']
            
            dataset = [(protein_sequence, drug_sequence, 1)]
            dataloader = DataLoader(dataset, batch_size=1, collate_fn=collate_fn_batch_encoding)
            
            case_features = get_case_feature(encoding, dataloader, device)
            model = FusionDTI(446, 768, args).to(device)
            
            best_model_dir = f"{args.save_path_prefix}{args.dataset}_{args.fusion}"  
            checkpoint_path = os.path.join(best_model_dir, 'best_model.ckpt')
            
            if os.path.exists(checkpoint_path):
                model.load_state_dict(torch.load(checkpoint_path, map_location=device))
            
            html_file_path = visualize_attention(model, case_features, device, prot_tokenizer, drug_tokenizer)
            
            with open(html_file_path, 'r') as f:
                result = f.read()
    
    return render_template_string('''
        <html>
            <head>
                <title>Drug Target Interaction Visualization</title>
                <style>
                    body { font-family: 'Times New Roman', Times, serif; margin: 40px; }
                    h2 { color: #333; }
                    .container { display: flex; }
                    .left { flex: 1; padding-right: 20px; }
                    .right { flex: 1; }
                    textarea {
                        width: 100%;
                        padding: 12px 20px;
                        margin: 8px 0;
                        display: inline-block;
                        border: 1px solid #ccc;
                        border-radius: 4px;
                        box-sizing: border-box;
                        font-size: 16px;
                        font-family: 'Times New Roman', Times, serif;
                    }
                    .button-container {
                        display: flex;
                        justify-content: space-between;
                    }
                    input[type="submit"], .button {
                        width: 48%;
                        color: white;
                        padding: 14px 20px;
                        margin: 8px 0;
                        border: none;
                        border-radius: 4px;
                        cursor: pointer;
                        font-size: 16px;
                        font-family: 'Times New Roman', Times, serif;
                    }
                    .submit {
                        background-color: #FFA500;
                    }
                    .submit:hover {
                        background-color: #FF8C00;
                    }
                    .clear {
                        background-color: #D3D3D3;
                    }
                    .clear:hover {
                        background-color: #A9A9A9;
                    }
                    .result {
                        font-size: 18px;
                    }
                </style>
            </head>
            <body>
                <h2 style="text-align: center;">Drug Target Interaction Visualization</h2>
                <div class="container">
                    <div class="left">
                        <form method="post">
                            <label for="protein_sequence">Protein Sequence:</label>
                            <textarea id="protein_sequence" name="protein_sequence" rows="4" placeholder="Enter protein sequence here..." required>{{ protein_sequence }}</textarea><br>
                            <label for="drug_sequence">Drug Sequence:</label>
                            <textarea id="drug_sequence" name="drug_sequence" rows="4" placeholder="Enter drug sequence here..." required>{{ drug_sequence }}</textarea><br>
                            <div class="button-container">
                                <input type="submit" name="submit" class="button submit" value="Submit">
                                <input type="submit" name="clear" class="button clear" value="Clear">
                            </div>
                        </form>
                    </div>
                    <div class="right" style="display: flex; justify-content: center; align-items: center;">
                        {% if result %}
                            <div class="result">
                                {{ result|safe }}
                            </div>
                        {% endif %}
                    </div>
                </div>
            </body>
        </html>
    ''', protein_sequence=protein_sequence, drug_sequence=drug_sequence, result=result)

def collate_fn_batch_encoding(batch):
    query1, query2, scores = zip(*batch)
    
    query_encodings1 = prot_tokenizer.batch_encode_plus(
        list(query1),
        max_length=512,
        padding="max_length",
        truncation=True,
        add_special_tokens=True,
        return_tensors="pt",
    )
    query_encodings2 = drug_tokenizer.batch_encode_plus(
        list(query2),
        max_length=512,
        padding="max_length",
        truncation=True,
        add_special_tokens=True,
        return_tensors="pt",
    )
    scores = torch.tensor(list(scores))

    attention_mask1 = query_encodings1["attention_mask"].bool()
    attention_mask2 = query_encodings2["attention_mask"].bool()

    return query_encodings1["input_ids"], attention_mask1, query_encodings2["input_ids"], attention_mask2, scores

if __name__ == '__main__':
    app.run(debug=True, host="0.0.0.0", port=7860)