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import logging
import pathlib
import gradio as gr
import pandas as pd
from gt4sd.algorithms.generation.torchdrug import (
TorchDrugGenerator,
TorchDrugGCPN,
TorchDrugGraphAF,
)
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: str, algorithm_version: str, number_of_samples: int):
if algorithm == "GCPN":
config = TorchDrugGCPN(algorithm_version=algorithm_version)
elif algorithm == "GraphAF":
config = TorchDrugGraphAF(algorithm_version=algorithm_version)
else:
raise ValueError(f"Unsupported model {algorithm}.")
model = TorchDrugGenerator(configuration=config)
samples = list(model.sample(number_of_samples))
return draw_grid_generate(samples=samples, n_cols=5)
if __name__ == "__main__":
# Preparation (retrieve all available algorithms)
all_algos = ApplicationsRegistry.list_available()
algos = [
x["algorithm_version"]
for x in list(filter(lambda x: "TorchDrug" 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="TorchDrug (GCPN and GraphAF)",
inputs=[
gr.Dropdown(["GCPN", "GraphAF"], label="Algorithm", value="GCPN"),
gr.Dropdown(
list(set(algos)), label="Algorithm version", value="zinc250k_v0"
),
gr.Slider(
minimum=1, maximum=50, value=10, label="Number of samples", step=1
),
],
outputs=gr.HTML(label="Output"),
article=article,
description=description,
examples=examples.values.tolist(),
)
demo.launch(debug=True, show_error=True)
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