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| import logging | |
| import pathlib | |
| from typing import List | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| from gt4sd.algorithms.conditional_generation.paccmann_rl import ( | |
| PaccMannRL, | |
| PaccMannRLOmicBasedGenerator, | |
| PaccMannRLProteinBasedGenerator, | |
| ) | |
| from gt4sd.algorithms.generation.paccmann_vae import PaccMannVAE, PaccMannVAEGenerator | |
| 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, | |
| inference_type: str, | |
| protein_target: str, | |
| omics_target: str, | |
| temperature: float, | |
| length: float, | |
| number_of_samples: int, | |
| ): | |
| if inference_type == "Unbiased": | |
| algorithm_class = PaccMannVAEGenerator | |
| model_class = PaccMannVAE | |
| target = None | |
| elif inference_type == "Conditional": | |
| if "Protein" in algorithm_version: | |
| algorithm_class = PaccMannRLProteinBasedGenerator | |
| target = protein_target | |
| elif "Omic" in algorithm_version: | |
| algorithm_class = PaccMannRLOmicBasedGenerator | |
| try: | |
| test_target = [float(x) for x in omics_target.split(" ")] | |
| except Exception: | |
| raise ValueError( | |
| f"Expected 2128 space-separated omics values, got {omics_target}" | |
| ) | |
| if len(test_target) != 2128: | |
| raise ValueError( | |
| f"Expected 2128 omics values, got {len(target)}: {target}" | |
| ) | |
| target = omics_target | |
| else: | |
| raise ValueError(f"Unknown algorithm version {algorithm_version}") | |
| model_class = PaccMannRL | |
| else: | |
| raise ValueError(f"Unknown inference type {inference_type}") | |
| config = algorithm_class( | |
| algorithm_version.split("_")[-1], | |
| temperature=temperature, | |
| generated_length=length, | |
| ) | |
| model = model_class(config, target=target) | |
| 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_application"].split("Based")[0].split("PaccMannRL")[-1] | |
| + "_" | |
| + x["algorithm_version"] | |
| for x in list(filter(lambda x: "PaccMannRL" 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="PaccMannRL", | |
| inputs=[ | |
| gr.Dropdown(algos, label="Algorithm version", value="Protein_v0"), | |
| gr.Radio( | |
| choices=["Conditional", "Unbiased"], | |
| label="Inference type", | |
| value="Conditional", | |
| ), | |
| gr.Textbox( | |
| label="Protein target", | |
| placeholder="MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTT", | |
| lines=1, | |
| ), | |
| gr.Textbox( | |
| label="Gene expression target", | |
| placeholder=f"{' '.join(map(str, np.round(np.random.rand(2128), 2)))}", | |
| lines=1, | |
| ), | |
| gr.Slider(minimum=0.5, maximum=2, value=1, label="Decoding temperature"), | |
| gr.Slider( | |
| minimum=5, | |
| maximum=400, | |
| value=100, | |
| label="Maximal sequence length", | |
| 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) | |