app.py

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 if method != "None" else None)

    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()

    return 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", "None", "binary"], 
            label="Averaging Method", 
            info="Method for averaging the F1 score across labels. \n `binary` only works if you are evaluating a binary classification model."
        )
    ],
    outputs=gr.Textbox(label=metric.name),
    description=metric.info.description,
    title=f"Metric: {metric.name}",
    article=parse_readme(local_path / "README.md"),
    examples=[
        # [pd.DataFrame(parse_test_cases(test_cases, feature_names, gradio_input_types)[0]), "weighted"],
    ],
    cache_examples=False
    )

space.launch()