absa_evaluator / gradio_tst.py
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import json
import os
import re
import sys
from pathlib import Path
import numpy as np
from datasets import Value
import logging
REGEX_YAML_BLOCK = re.compile(r"---[\n\r]+([\S\s]*?)[\n\r]+---[\n\r]")
def infer_gradio_input_types(feature_types):
"""
Maps metric feature types to input types for gradio Dataframes:
- float/int -> numbers
- string -> strings
- any other -> json
Note that json is not a native gradio type but will be treated as string that
is then parsed as a json.
"""
input_types = []
for feature_type in feature_types:
input_type = "json"
if isinstance(feature_type, Value):
if feature_type.dtype.startswith("int") or feature_type.dtype.startswith("float"):
input_type = "number"
elif feature_type.dtype == "string":
input_type = "str"
input_types.append(input_type)
return input_types
def json_to_string_type(input_types):
"""Maps json input type to str."""
return ["str" if i == "json" else i for i in input_types]
def parse_readme(filepath):
"""Parses a repositories README and removes"""
if not os.path.exists(filepath):
return "No README.md found."
with open(filepath, "r") as f:
text = f.read()
match = REGEX_YAML_BLOCK.search(text)
if match:
text = text[match.end() :]
return text
def parse_gradio_data(data, input_types):
"""Parses data from gradio Dataframe for use in metric."""
metric_inputs = {}
data.replace("", np.nan, inplace=True)
data.dropna(inplace=True)
for feature_name, input_type in zip(data, input_types):
if input_type == "json":
metric_inputs[feature_name] = [json.loads(d) for d in data[feature_name].to_list()]
elif input_type == "str":
metric_inputs[feature_name] = [d.strip('"') for d in data[feature_name].to_list()]
else:
metric_inputs[feature_name] = data[feature_name]
return metric_inputs
def parse_test_cases(test_cases, feature_names, input_types):
"""
Parses test cases to be used in gradio Dataframe. Note that an apostrophe is added
to strings to follow the format in json.
"""
if len(test_cases) == 0:
return None
examples = []
for test_case in test_cases:
parsed_cases = []
for feat, input_type in zip(feature_names, input_types):
if input_type == "json":
parsed_cases.append([str(element) for element in test_case[feat]])
elif input_type == "str":
parsed_cases.append(['"' + element + '"' for element in test_case[feat]])
else:
parsed_cases.append(test_case[feat])
examples.append([list(i) for i in zip(*parsed_cases)])
return examples
def launch_gradio_widget2(metric):
"""Launches `metric` widget with Gradio."""
try:
import gradio as gr
except ImportError as error:
logging.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(share=True)