inference.py

from api_prediction import AptaTransPipeline_Dist
import gradio as gr

def infer(prot, apta):
    pipeline = AptaTransPipeline_Dist(
        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)
    
    scores = pipeline.inference([apta], [prot], [0])
    return scores[0]

def comparison(protein, aptamers):
    aptamers = aptamers.split('\n')
    pairs = [[protein, aptamer] for aptamer in aptamers]
    print(pairs)
    scores = []
    
    for pair in pairs:
        score = infer(pair[0], pair[1])
        scores.append(score)

    return scores

iface = gr.Interface(
    fn=comparison,
    inputs=[
        gr.Textbox(lines=2, placeholder="Protein"),
        gr.Textbox(lines=10,placeholder="Aptamers (1 per line)")
    ],
    outputs=gr.Textbox()
)

iface.launch()