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# from evaluate.utils import launch_gradio_widget
from tests import test_cases
from evaluate.utils.logging import get_logger
from evaluate.utils import (
infer_gradio_input_types,
parse_gradio_data,
json_to_string_type,
parse_readme,
parse_test_cases,
)
from pathlib import Path
import evaluate
import sys
import evaluate
logger = get_logger(__name__)
def launch_gradio_widget(metric, test_cases):
"""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.inputs.Dataframe(
headers=feature_names,
col_count=len(feature_names),
row_count=5,
datatype=json_to_string_type(gradio_input_types),
),
outputs=gr.outputs.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("danieldux/metric_template_1")
launch_gradio_widget(module, test_cases)
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