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import logging
import pathlib
import gradio as gr
import pandas as pd
from gt4sd.algorithms.generation.moler import MoLeR, MoLeRDefaultGenerator
from gt4sd.algorithms.registry import ApplicationsRegistry
from utils import draw_grid_generate
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
TITLE = "MoLeR"
def run_inference(
algorithm_version: str,
scaffolds: str,
beam_size: int,
number_of_samples: int,
seed: int,
):
config = MoLeRDefaultGenerator(
algorithm_version=algorithm_version,
scaffolds=scaffolds,
beam_size=beam_size,
num_samples=4,
seed=seed,
num_workers=1,
)
model = MoLeR(configuration=config)
samples = list(model.sample(number_of_samples))
seed_mols = [] if scaffolds == "" else scaffolds.split(".")
return draw_grid_generate(seed_mols, samples)
if __name__ == "__main__":
# Preparation (retrieve all available algorithms)
all_algos = ApplicationsRegistry.list_available()
algos = [
x["algorithm_version"]
for x in list(filter(lambda x: TITLE in x["algorithm_name"], all_algos))
]
# Load metadata
metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")
examples = pd.read_csv(metadata_root.joinpath("examples.csv"), header=None).fillna(
""
)
with open(metadata_root.joinpath("article.md"), "r") as f:
article = f.read()
with open(metadata_root.joinpath("description.md"), "r") as f:
description = f.read()
demo = gr.Interface(
fn=run_inference,
title="MoLeR (MOlecule-LEvel Representation)",
inputs=[
gr.Dropdown(algos, label="Algorithm version", value="v0"),
gr.Textbox(
label="Scaffolds",
placeholder="CC(C#C)N(C)C(=O)NC1=CC=C(Cl)C=C1",
lines=1,
),
gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Beam_size"),
gr.Slider(
minimum=1, maximum=50, value=10, label="Number of samples", step=1
),
gr.Number(value=42, label="Seed", precision=0),
],
outputs=gr.HTML(label="Output"),
article=article,
description=description,
examples=examples.values.tolist(),
)
demo.launch(debug=True, show_error=True)
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