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import evaluate
from evaluate.utils import infer_gradio_input_types, parse_gradio_data, json_to_string_type, parse_readme
from pathlib import Path
import sys
def launch_gradio_widget(metric):
"""Launches metric widget with Gradio."""
try:
import gradio as gr
except ImportError as error:
raise ImportError("To create a metric widget with Gradio, make sure gradio is installed.") from error
local_path = Path(sys.path[0])
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(
model='gpt2',
tasks='wikitext',
**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 + "\nThis metric is computed using the 'gpt2' model on the 'wikitext' task.\n"
"Ensure your input is appropriate for the selected task. "
"If this is a text-based metric, wrap your 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"),
)
iface.launch()
module = evaluate.load("d-matrix/dmxMetric")
launch_gradio_widget(module) |