add test cases and try out readme parser as defined in the lib

app.py

@@ -1,8 +1,15 @@
+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
+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
-import gradio as gr
+from pathlib import Path
+
+def compute(input: pd.DataFrame):
+
+    metric._compute()
 
 metric = FixedF1()
 
@@ -10,19 +17,18 @@ 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()
+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?
 
 space = gr.Interface(
     fn=compute,
     inputs=gr.Dataframe(
         headers=feature_names,
         col_count=len(feature_names),
-        row_count=1,
+        row_count=5,
         datatype=json_to_string_type(gradio_input_types),
         ),
     outputs=gr.Textbox(label=metric.name),
@@ -31,9 +37,9 @@ space = gr.Interface(
         " Alternatively you can use a JSON-formatted list as input."
     ),
     title=f"Metric: {metric.name}",
-    article=parse_readme("./README.md"),
+    article=parse_readme(local_path / "README.md"),
     # TODO: load test cases and use them to populate examples
-    # examples=[parse_test_cases(test_cases, feature_names, gradio_input_types)]
+    examples=[parse_test_cases(test_cases, feature_names, gradio_input_types)]
     )
 
 space.launch()