app.py

import logging
import pathlib
import gradio as gr
import pandas as pd
from gt4sd.algorithms.controlled_sampling.advanced_manufacturing import (
    CatalystGenerator,
    AdvancedManufacturing,
)
from gt4sd.algorithms.registry import ApplicationsRegistry

from utils import draw_grid_generate

logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())


def run_inference(
    algorithm_version: str,
    target_binding_energy: float,
    primer_smiles: str,
    length: float,
    number_of_points: int,
    number_of_steps: int,
    number_of_samples: int,
):

    config = CatalystGenerator(
        algorithm_version=algorithm_version,
        number_of_points=number_of_points,
        number_of_steps=number_of_steps,
        generated_length=length,
        primer_smiles=primer_smiles,
    )
    model = AdvancedManufacturing(config, target=target_binding_energy)
    samples = list(model.sample(number_of_samples))
    seeds = [] if primer_smiles == "" else [primer_smiles]

    return draw_grid_generate(samples=samples, n_cols=5, seeds=seeds)


if __name__ == "__main__":

    # Preparation (retrieve all available algorithms)
    all_algos = ApplicationsRegistry.list_available()
    algos = [
        x["algorithm_version"]
        for x in list(
            filter(lambda x: "AdvancedManufact" 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(
        ""
    )
    print("Examples: ", examples.values.tolist())

    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="Advanced Manufacturing",
        inputs=[
            gr.Dropdown(
                algos,
                label="Algorithm version",
                value="v0",
            ),
            gr.Slider(minimum=1, maximum=100, value=10, label="Target binding energy"),
            gr.Textbox(
                label="Primer SMILES",
                placeholder="FP(F)F.CP(C)c1ccccc1.[Au]",
                lines=1,
            ),
            gr.Slider(
                minimum=5,
                maximum=400,
                value=100,
                label="Maximal sequence length",
                step=1,
            ),
            gr.Slider(
                minimum=16, maximum=128, value=32, label="Number of points", step=1
            ),
            gr.Slider(
                minimum=16, maximum=128, value=50, label="Number of steps", step=1
            ),
            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)