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import logging |
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import pathlib |
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import gradio as gr |
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import pandas as pd |
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from gt4sd.algorithms.generation.torchdrug import ( |
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TorchDrugGenerator, |
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TorchDrugGCPN, |
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TorchDrugGraphAF, |
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) |
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from gt4sd.algorithms.registry import ApplicationsRegistry |
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from utils import draw_grid_generate |
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logger = logging.getLogger(__name__) |
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logger.addHandler(logging.NullHandler()) |
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TITLE = "MoLeR" |
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def run_inference(algorithm: str, algorithm_version: str, number_of_samples: int): |
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if algorithm == "GCPN": |
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config = TorchDrugGCPN(algorithm_version=algorithm_version) |
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elif algorithm == "GraphAF": |
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config = TorchDrugGraphAF(algorithm_version=algorithm_version) |
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else: |
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raise ValueError(f"Unsupported model {algorithm}.") |
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model = TorchDrugGenerator(configuration=config) |
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samples = list(model.sample(number_of_samples)) |
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return draw_grid_generate(samples=samples, n_cols=5) |
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if __name__ == "__main__": |
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all_algos = ApplicationsRegistry.list_available() |
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algos = [ |
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x["algorithm_version"] |
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for x in list(filter(lambda x: "TorchDrug" in x["algorithm_name"], all_algos)) |
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] |
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metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards") |
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examples = pd.read_csv(metadata_root.joinpath("examples.csv"), header=None).fillna( |
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"" |
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) |
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with open(metadata_root.joinpath("article.md"), "r") as f: |
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article = f.read() |
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with open(metadata_root.joinpath("description.md"), "r") as f: |
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description = f.read() |
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demo = gr.Interface( |
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fn=run_inference, |
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title="TorchDrug (GCPN and GraphAF)", |
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inputs=[ |
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gr.Dropdown(["GCPN", "GraphAF"], label="Algorithm", value="GCPN"), |
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gr.Dropdown( |
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list(set(algos)), label="Algorithm version", value="zinc250k_v0" |
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), |
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gr.Slider( |
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minimum=1, maximum=50, value=10, label="Number of samples", step=1 |
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), |
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], |
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outputs=gr.HTML(label="Output"), |
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article=article, |
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description=description, |
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examples=examples.values.tolist(), |
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) |
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demo.launch(debug=True, show_error=True) |
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