add examples

app.py

@@ -2,4 +2,4 @@ import evaluate
 from gradio_tst import launch_gradio_widget2
 
 module = evaluate.load("clustering_evaluator.py")
-launch_gradio_widget2(module)
+launch_gradio_widget2(module)

clustering_evaluator.py

@@ -38,8 +38,8 @@ However, it allows to compute additional metrics when truth labels are passed to
 _KWARGS_DESCRIPTION = """
 Computes the quality of clustering results.
 Args:
-    samples' vector representations
-    predictions: computed cluster labels
+    samples: vector representations
+    predictions: predicted cluster labels
     truth_labels (optional): truth labels to compute additional metrics
 Returns:
     silhouete_score

gradio_tst.py

@@ -117,6 +117,12 @@ def launch_gradio_widget2(metric):
     def compute(data):
         return metric.compute(**parse_gradio_data(data, gradio_input_types))
 
+    test_cases = [
+        {
+            "samples": [[0.1, 0.2, 0.3], [0.7, 0.6, 0.9], [0.8, 0.95, 0.85]],
+            "predictions": [0, 1, 1],
+        }
+    ]
     iface = gr.Interface(
         fn=compute,
         inputs=gr.Dataframe(
@@ -133,8 +139,9 @@ def launch_gradio_widget2(metric):
         ),
         title=f"Metric: {metric.name}",
         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)
+        ],
     )
 
-    iface.launch(share=True)
+    iface.launch(share=True)