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import evaluate | |
from evaluate.utils import launch_gradio_widget | |
import gradio as gr | |
# from pathlib import Path | |
# import sys | |
# import os | |
# from .logging import get_logger | |
# logger = get_logger(__name__) | |
# ### | |
# def launch_gradio_widget(metric): | |
# """Launches `metric` widget with Gradio.""" | |
# try: | |
# import gradio as gr | |
# except ImportError as error: | |
# logger.error("To create a metric widget with Gradio make sure gradio is installed.") | |
# raise error | |
# local_path = Path(sys.path[0]) | |
# # if there are several input types, use first as default. | |
# if isinstance(metric.features, list): | |
# (feature_names, feature_types) = zip(*metric.features[0].items()) | |
# else: | |
# (feature_names, feature_types) = zip(*metric.features.items()) | |
# gradio_input_types = infer_gradio_input_types(feature_types) | |
# def compute(data): | |
# return metric.compute(**parse_gradio_data(data, gradio_input_types)) | |
# iface = gr.Interface( | |
# fn=compute, | |
# inputs=gr.Dataframe( | |
# headers=feature_names, | |
# col_count=len(feature_names), | |
# row_count=1, | |
# datatype=json_to_string_type(gradio_input_types), | |
# ), | |
# outputs=gr.Textbox(label=metric.name), | |
# description=( | |
# metric.info.description + "\nIf this is a text-based metric, make sure to wrap you input in double quotes." | |
# " Alternatively you can use a JSON-formatted list as input." | |
# ), | |
# title=f"Metric: {metric.name}", | |
# article=parse_readme(local_path / "README.md"), | |
# # TODO: load test cases and use them to populate examples | |
# # examples=[parse_test_cases(test_cases, feature_names, gradio_input_types)] | |
# ) | |
# iface.launch() | |
# ### | |
module = evaluate.load("saicharan2804/molgen_metric") | |
# launch_gradio_widget(module) | |
iface = gr.Interface( | |
fn = module, | |
inputs=[ | |
gr.File(label="Generated SMILES"), | |
gr.File(label="Training Data", value=None), | |
], | |
outputs="text" | |
) | |
iface.launch() |