gui.py

from api_prediction import AptaBLE_Pipeline
import gradio as gr
import pandas as pd
import torch
import tempfile
from tabulate import tabulate
import itertools
import os
import random
# Visualization

os.environ['GRADIO_SERVER_NAME'] = '0.0.0.0'
title='AptaBLE Model Inference'
desc='AptaBLE, trained to predict the likelihood an aptamer will form a complex with a target protein!\n\nPass in a FASTA-formatted file of all aptamers and input your protein target amino acid sequence. Your output scores are available for download via an Excel file. At the moment, our demo only supports inference with DNA aptamers.'

global pipeline

pipeline = AptaBLE_Pipeline(
    lr=1e-6,
    weight_decay=None,
    epochs=None,
    model_type=None,
    model_version=None,
    model_save_path=None,
    accelerate_save_path=None,
    tensorboard_logdir=None,
    d_model=128,
    d_ff=512,
    n_layers=6,
    n_heads=8,
    dropout=0.1,
    load_best_pt=True, # already loads the pretrained model using the datasets included in repo -- no need to run the bottom two cells
    device='cuda',
    seed=1004)

def comparison(protein, aptamer_file, analysis):
    print('analysis: ', analysis)
    display = []
    table_data = pd.DataFrame()
    r_names, aptamers = read_fasta(aptamer_file)
    proteins = [protein for i in range(len(aptamers))]
    df = pd.DataFrame(columns=['Protein', 'Protein Seq', 'Aptamer', 'Aptamer Seq', 'Score'])
    # print('Number of aptamers: ', len(aptamers))
    scores = get_scores(aptamers, proteins)
    df['Protein'] = ['protein_prov.']*len(aptamers)
    df['Aptamer'] = r_names
    df['Protein Seq'] = proteins
    df['Aptamer Seq'] = aptamers
    df['Score'] = scores

    with tempfile.NamedTemporaryFile(delete=False, suffix=".xlsx") as temp_file:
        with pd.ExcelWriter(temp_file.name, engine='openpyxl') as writer:
            df.to_excel(writer, index=False)
        temp_file_path = temp_file.name

    print('Saving to excel!')
    df.to_excel(f'{aptamer_file}.xlsx')

    torch.cuda.empty_cache()

    return '\n'.join(display), temp_file_path

def read_fasta(file_path):
    headers = []
    sequences = []
    with open(file_path, 'r') as file:
        content = file.readlines()
    for i in range(0, len(content), 2):
        header = content[i].strip()
        if header.startswith('>'):
            headers.append(header)
            sequences.append(content[i+1].strip())
    return headers, sequences

def get_scores(aptamers, proteins):
    pipeline.model.to('cuda')
    scores = pipeline.inference(aptamers, proteins, [0]*len(aptamers))
    pipeline.model.to('cpu')
    return scores


iface = gr.Interface(
    fn=comparison,
    inputs=[
        gr.Textbox(lines=2, placeholder="Protein"),
        gr.File(type="filepath"),
    ],
    outputs=[
        gr.Textbox(placeholder="Scores"),
        gr.File(label="Download Excel")
    ],
    description=desc
)

iface.launch()