app.py

import sys
import gradio as gr
import pandas as pd
import numpy as np
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_precision import FixedPrecision
from pathlib import Path

added_description = """
See the 🤗 Space showing off how to combine various metrics here: 
    [MarioBarbeque/CombinedEvaluationMetrics](https://huggingface.co/spaces/MarioBarbeque/CombinedEvaluationMetrics)

In the specific use case of the `FixedPrecision` metric, one writes the following:\n

```python
precision = FixedPrecision(average=..., zero_division=...)

precision.add_batch(predictions=..., references=...)
precision.compute()
```\n

where the `average` parameter can be chosen to configure the way precision scores across labels are averaged. Acceptable values include `[None, 'micro', 'macro', 'weighted']` (
or `binary` if there exist only two labels). Similarly, the `zero_division` parameter "Sets the value to return when there is a zero division". Options include:
{`“warn”`, `0.0`, `1.0`, `np.nan`}. Since "warn" can still result in an error, we fix to it NaN in this demo.\n
"""

metric = FixedPrecision()

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])
# configure these randomly using randint generator and feature names?
test_case_1 = [ {"predictions":[1,2,3,4,5], "references":[1,2,5,4,3]} ] 
test_case_2 = [ {"predictions":[9,8,7,6,5], "references":[7,8,9,6,5]} ]

# configure this based on the input type, etc. for launch_gradio_widget
def compute(input_df: pd.DataFrame, method: str):
    
    metric = FixedPrecision(average=method if method != "None" else None, zero_division=np.nan)

    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"The precision score for these predictions is: \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 precision 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 + added_description,
    title="FixedPrecision Metric", # think about how to generalize this with the launch_gradio_widget - it seems fine as is really
    article=parse_readme(local_path / "README.md"),
    examples=[
        [
            parse_test_cases(test_case_1, feature_names, gradio_input_types)[0], # notice how we unpack this for when we fix launch_gradio_widget
            "weighted"
        ],
        [
            parse_test_cases(test_case_2, feature_names, gradio_input_types)[0],
            "micro"
        ],
    ],
    cache_examples=False
    )

space.launch()