add code reminder for fixing widget fn

issue_fix.py

@@ -0,0 +1,72 @@
+"""
+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()