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import time | |
import gradio as gr | |
from gradio_molecule3d import Molecule3D | |
def predict (input_sequence, input_ligand): | |
start_time = time.time() | |
# Do inference here | |
# return an output directory | |
end_time = time.time() | |
run_time = end_time - start_time | |
return "test_out.pdb", 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 sequence") | |
input_ligand = gr.Textbox(lines=3, label="Input ligand SMILES") | |
# 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", | |
], | |
], | |
[input_sequence, input_ligand], | |
) | |
reps = [ | |
{ | |
"model": 0, | |
"chain": "", | |
"resname": "", | |
"style": "cartoon", | |
"color": "whiteCarbon", | |
"residue_range": "", | |
"around": 0, | |
"byres": False, | |
"visible": False | |
}, | |
{ | |
"model": 1, | |
"chain": "", | |
"resname": "", | |
"style": "stick", | |
"color": "greenCarbon", | |
"residue_range": "", | |
"around": 0, | |
"byres": False, | |
"visible": False | |
} | |
] | |
out = Molecule3D(reps=reps) | |
run_time = gr.Textbox(label="Runtime") | |
btn.click(predict, inputs=[input_sequence, input_ligand], outputs=[out, run_time]) | |
app.launch() | |