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)