import evaluate
from evaluate.utils import infer_gradio_input_types, json_to_string_type, parse_readme
from fixed_f1 import FixedF1
# from evaluate.utils import launch_gradio_widget # using this directly is erroneous - lets fix this

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)

gradio_input_types = infer_gradio_input_types(feature_types)

def compute():
    metric._compute()

import gradio as gr

space = 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 your input in double quotes."
        " Alternatively you can use a JSON-formatted list as input."
    ),
    title=f"Metric: {metric.name}",
    article=parse_readme("./README.md"),
    # TODO: load test cases and use them to populate examples
    # examples=[parse_test_cases(test_cases, feature_names, gradio_input_types)]
    )

space.launch()