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"""
Come back to this file and previous versions in this repo to configure a fix for the broken launch_gradio_widget command
"""
import sys
import gradio as gr
import pandas as pd
import evaluate
from evaluate.utils import infer_gradio_input_types, json_to_string_type, parse_readme, parse_test_cases
# from evaluate.utils import launch_gradio_widget # using this directly is erroneous - lets fix this
from fixed_f1 import FixedF1
from pathlib import Path
metric = FixedF1()
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)
local_path = Path(sys.path[0])
test_cases = [ {"predictions":[1,2,3,4,5], "references":[1,2,5,4,3]} ] # configure this randomly using randint generator and feature names?
# configure this based on the input type, etc. for launch_gradio_widget
def compute(input_df: pd.DataFrame, method: str):
metric = FixedF1(average=method)
cols = [col for col in input_df.columns]
predicted = [int(num) for num in input_df[cols[0]].to_list()]
references = [int(num) for num in input_df[cols[1]].to_list()]
metric.add_batch(predictions=predicted, references=references)
outputs = metric.compute()
f"Your metrics are as follows: \n {outputs}"
space = gr.Interface(
fn=compute,
inputs=[
gr.Dataframe(
headers=feature_names,
col_count=len(feature_names),
row_count=5,
datatype=json_to_string_type(gradio_input_types),
),
gr.Radio(
["weighted", "micro", "macro", "binary", "None"],
label="Averaging Method",
info="Method for averaging the F1 score across labels."
)
],
outputs=gr.Textbox(label=metric.name),
description=(
metric.info.description + "\nIf this is a text-based metric, make sure to 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"),
# TODO: load test cases and use them to populate examples
examples=[
# correct depth?
pd.DataFrame(parse_test_cases(test_cases, feature_names, gradio_input_types)[0]),
pd.DataFrame(columns=["Metric", "Averaging Method"], data=[["f1", "weighted"]])
],
cache_examples=False
)
space.launch() |