Spaces:
Running
Running
File size: 2,635 Bytes
bebad14 dffaf30 bebad14 bca3a49 bebad14 bca3a49 bebad14 bca3a49 dadbe2e bca3a49 bebad14 bca3a49 bebad14 bca3a49 bebad14 bca3a49 bebad14 f354223 bebad14 c0df2f3 43105fe f354223 bca3a49 bebad14 28cb117 bebad14 44470f9 28cb117 5122f94 28cb117 f354223 28cb117 130e4f7 28cb117 bca3a49 4853a01 bca3a49 28cb117 dadbe2e bebad14 bca3a49 dffaf30 bebad14 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import time
import gradio as gr
from gradio_molecule3d import Molecule3D
from run_on_seq import run_on_sample_seqs
from env_consts import RUN_CONFIG_PATH, OUTPUT_PROT_PATH, OUTPUT_LIG_PATH
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 = run_on_sample_seqs(input_sequence, input_protein, input_ligand, OUTPUT_PROT_PATH, OUTPUT_LIG_PATH,
RUN_CONFIG_PATH)
end_time = time.time()
run_time = end_time - start_time
return [OUTPUT_PROT_PATH, OUTPUT_LIG_PATH], metrics, run_time
with gr.Blocks() as app:
gr.Markdown("DockFormer")
# 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()
|