update inputs passed to compute and examples

app.py

@@ -19,10 +19,12 @@ 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, feature_names: list[str]):
+def compute(input_df: pd.DataFrame):
 
-    predicted = [int(num) for num in input_df[feature_names[0]].to_list()]
-    references = [int(num) for num in input_df[feature_names[1]].to_list()]
+    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)
     
@@ -32,15 +34,12 @@ def compute(input_df: pd.DataFrame, feature_names: list[str]):
 
 space = gr.Interface(
     fn=compute,
-    inputs=[
-        gr.Dataframe(
+    inputs=gr.Dataframe(
         headers=feature_names,
         col_count=len(feature_names),
         row_count=5,
         datatype=json_to_string_type(gradio_input_types),
         ),
-        list(feature_names)
-    ],
     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."
@@ -50,11 +49,8 @@ space = gr.Interface(
     article=parse_readme(local_path / "README.md"),
     # TODO: load test cases and use them to populate examples
     examples=[
-        [
-            # consider how to generalize this
-            parse_test_cases(test_cases, feature_names, gradio_input_types)[0], 
-            feature_names
-        ]
+        # correct depth?
+        parse_test_cases(test_cases, feature_names, gradio_input_types)[0]
     ],
     cache_examples=False
     )