| from api_prediction import AptaTransPipeline_Dist | |
| import argparse | |
| 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() | |