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import evaluate
import sys
from pathlib import Path
from evaluate.utils import infer_gradio_input_types, json_to_string_type, parse_readme, parse_gradio_data, parse_test_cases
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))
header_html = '''<div style="max-width:800px; margin:auto; float:center; margin-top:0; margin-bottom:0; padding:0;">
<img src="https://huggingface.co/spaces/xu1998hz/sescore/resolve/main/img/logo_sescore.png" style="margin:0; padding:0; margin-top:-10px; margin-bottom:-50px;">
</div>
<h2 style='margin-top: 5pt; padding-top:10pt;'>About <i>SEScore</i></h2>
<p><b>SEScore</b> is a reference-based text-generation evaluation metric that requires no pre-human-annotated error data,
described in our paper <a href="https://arxiv.org/abs/2210.05035"><b>"Not All Errors are Equal: Learning Text Generation Metrics using
Stratified Error Synthesis"</b></a> from EMNLP 2022.</p>
<p>Its effectiveness over prior methods like BLEU, BERTScore, BARTScore, PRISM, COMET and BLEURT has been demonstrated on a diverse set of language generation tasks, including
translation, captioning, and web text generation. <a href="https://twitter.com/LChoshen/status/1580136005654700033">Readers have even described SEScore as "one unsupervised evaluation to rule them all"</a>
and we are very excited to share it with you!</p>
<h2 style='margin-top: 10pt; padding-top:0;'>Try it yourself!</h2>
<p>Provide sample (gold) reference text and (model output) predicted text below and see how SEScore rates them! It is most performant
in a relative ranking setting, so in general <b>it will rank better predictions higher than worse ones.</b> Providing useful
absolute numbers based on SEScore is an ongoing direction of investigation.</p>
'''.replace('\n',' ')
tail_markdown = parse_readme(local_path / "description.md")
iface = gr.Interface(
fn=compute,
inputs=gr.inputs.Dataframe(
headers=feature_names,
col_count=len(feature_names),
row_count=2,
datatype=json_to_string_type(gradio_input_types),
),
outputs=gr.outputs.Textbox(label=metric.name),
description=header_html,
#title=f"SEScore Metric Usage Example",
article=tail_markdown,
# TODO: load test cases and use them to populate examples
# examples=[parse_test_cases(test_cases, feature_names, gradio_input_types)]
)
print(dir(iface))
iface.launch()
module = evaluate.load("xu1998hz/sescore")
launch_gradio_widget(module)
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