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File size: 2,069 Bytes
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import evaluate
from evaluate.utils import launch_gradio_widget
# 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/my_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() |