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import logging |
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import pathlib |
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import pickle |
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import gradio as gr |
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from typing import Dict, Any |
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import pandas as pd |
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from gt4sd.algorithms.generation.diffusion import ( |
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DiffusersGenerationAlgorithm, |
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GeoDiffGenerator, |
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) |
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from utils import draw_grid_generate |
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from rdkit import Chem |
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logger = logging.getLogger(__name__) |
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logger.addHandler(logging.NullHandler()) |
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def run_inference(prompt_file: str, prompt_id: int, number_of_samples: int): |
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with open(prompt_file.name, "rb") as f: |
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prompts = pickle.load(f) |
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if all(isinstance(x, int) for x in prompts.keys()): |
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prompt = prompts[prompt_id] |
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else: |
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prompt = prompts |
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config = GeoDiffGenerator(prompt=prompt) |
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model = DiffusersGenerationAlgorithm(config) |
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results = list(model.sample(number_of_samples)) |
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smiles = [Chem.MolToSmiles(m) for m in results] |
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return draw_grid_generate(samples=smiles, n_cols=5) |
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if __name__ == "__main__": |
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metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards") |
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examples = [ |
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[str(metadata_root.joinpath("mol_dct.pkl")), 0, 2], |
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[str(metadata_root.joinpath("mol_dct.pkl")), 1, 2], |
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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="GeoDiff", |
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inputs=[ |
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gr.File(file_types=[".pkl"], label="GeoDiff prompt"), |
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gr.Number(value=0, label="Prompt ID", precision=0), |
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gr.Slider(minimum=1, maximum=5, value=2, label="Number of samples", step=1), |
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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, |
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) |
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demo.launch(debug=True, show_error=True) |
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