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import logging
import os
import pathlib
import shutil
import tempfile
from pathlib import Path
import gradio as gr
import pandas as pd
from gt4sd.properties.crystals import CRYSTALS_PROPERTY_PREDICTOR_FACTORY
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
suffix_dict = {"metal_nonmetal_classifier": ".csv"}
def create_temp_file(path: str) -> str:
temp_dir = tempfile.gettempdir()
temp_folder = os.path.join(temp_dir, "gt4sd_crystal")
os.makedirs(temp_folder, exist_ok=True)
# Clean up directory
for i in os.listdir(temp_folder):
print("Removing", i)
os.remove(os.path.join(temp_folder, i))
temp_path = os.path.join(temp_folder, path.split("/")[-1])
shutil.copy2(path, temp_path)
return temp_path
def main(property: str, data_file: str):
print(data_file, data_file.orig_name, data_file.name)
if data_file is None:
raise TypeError("You have to pass either an input file for the crystal model")
# Copy file into a UNIQUE temporary directory
file_path = Path(create_temp_file(data_file.name))
folder = file_path.parent
print(file_path)
print(folder)
if file_path.suffix == ".cif":
input_path = folder
elif file_path.suffix == ".csv":
input_path = file_path
elif file_path.suffix == ".zip":
# Unzip zip
shutil.unpack_archive(file_path, file_path.parent)
if len(list(filter(lambda x: x.endswith(".cif"), os.listdir(folder)))) == 0:
raise ValueError("No `.cif` files were found inside the `.zip`.")
input_path = folder
else:
raise TypeError(
"You have to pass a `.csv` (for `metal_nonmetal_classifier`),"
" a `.cif` (for all other properties) or a `.zip` with multiple"
f" `.cif` files. Not {type(data_file)}."
)
prop_name = property.replace(" ", "_").lower()
algo, config = CRYSTALS_PROPERTY_PREDICTOR_FACTORY[prop_name]
# Pass hyperparameters if applicable
kwargs = {"algorithm_version": "v0"}
model = algo(config(**kwargs))
result = model(input=input_path)
return pd.DataFrame(result)
if __name__ == "__main__":
# Preparation (retrieve all available algorithms)
properties = list(CRYSTALS_PROPERTY_PREDICTOR_FACTORY.keys())[::-1]
properties = list(map(lambda x: x.replace("_", " ").title(), properties))
# Load metadata
metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")
examples = [
["Formation Energy", metadata_root.joinpath("7206075.cif")],
["Bulk moduli", metadata_root.joinpath("crystals.zip")],
["Metal Nonmetal Classifier", metadata_root.joinpath("metal.csv")],
["Bulk moduli", metadata_root.joinpath("9000046.cif")],
]
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=main,
title="Crystal properties",
inputs=[
gr.Dropdown(properties, label="Property", value="Instability"),
gr.File(
file_types=[".cif", ".csv", ".zip"],
label="Input file for crystal model",
),
],
outputs=gr.DataFrame(label="Output"),
article=article,
description=description,
examples=examples,
)
demo.launch(debug=True, show_error=True)
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