plinder_inference_template / inference_app.py
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Update inference_app.py
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import time
import gradio as gr
from gradio_molecule3d import Molecule3D
def predict (input_sequence, input_ligand,input_msa, input_protein):
start_time = time.time()
# Do inference here
# return an output pdb file with the protein and ligand with resname LIG or UNK.
# also return any metrics you want to log, metrics will not be used for evaluation but might be useful for users
metrics = {"mean_plddt": 80, "binding_affinity": -2}
end_time = time.time()
run_time = end_time - start_time
return ["test_out.pdb", "test_docking_pose.sdf"], metrics, run_time
with gr.Blocks() as app:
gr.Markdown("# Template for inference")
gr.Markdown("Title, description, and other information about the model")
with gr.Row():
input_sequence = gr.Textbox(lines=3, label="Input Protein sequence (FASTA)")
input_ligand = gr.Textbox(lines=3, label="Input ligand SMILES")
with gr.Row():
input_msa = gr.File(label="Input Protein MSA (A3M)")
input_protein = gr.File(label="Input protein monomer")
# define any options here
# for automated inference the default options are used
# slider_option = gr.Slider(0,10, label="Slider Option")
# checkbox_option = gr.Checkbox(label="Checkbox Option")
# dropdown_option = gr.Dropdown(["Option 1", "Option 2", "Option 3"], label="Radio Option")
btn = gr.Button("Run Inference")
gr.Examples(
[
[
"SVKSEYAEAAAVGQEAVAVFNTMKAAFQNGDKEAVAQYLARLASLYTRHEELLNRILEKARREGNKEAVTLMNEFTATFQTGKSIFNAMVAAFKNGDDDSFESYLQALEKVTAKGETLADQIAKAL:SVKSEYAEAAAVGQEAVAVFNTMKAAFQNGDKEAVAQYLARLASLYTRHEELLNRILEKARREGNKEAVTLMNEFTATFQTGKSIFNAMVAAFKNGDDDSFESYLQALEKVTAKGETLADQIAKAL",
"COc1ccc(cc1)n2c3c(c(n2)C(=O)N)CCN(C3=O)c4ccc(cc4)N5CCCCC5=O",
"test_out.pdb"
],
],
[input_sequence, input_ligand, input_protein],
)
reps = [
{
"model": 0,
"style": "cartoon",
"color": "whiteCarbon",
},
{
"model": 1,
"style": "stick",
"color": "greenCarbon",
}
]
out = Molecule3D(reps=reps)
metrics = gr.JSON(label="Metrics")
run_time = gr.Textbox(label="Runtime")
btn.click(predict, inputs=[input_sequence, input_ligand, input_msa, input_protein], outputs=[out,metrics, run_time])
app.launch()