Update app.py

app.py

@@ -1,37 +1,37 @@
 try:
         import gradio as gr
-    except ImportError as error:
-        logger.error("To create a metric widget with Gradio make sure gradio is installed.")
-        raise error
+except ImportError as error:
+    logger.error("To create a metric widget with Gradio make sure gradio is installed.")
+    raise error
 
-    local_path = Path(sys.path[0])
-    # if there are several input types, use first as default.
-    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])
+# if there are several input types, use first as default.
+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)
 
-    def compute(data):
-        return metric.compute(**parse_gradio_data(data, gradio_input_types))
+def compute(data):
+    return metric.compute(**parse_gradio_data(data, gradio_input_types))
 
-    iface = gr.Interface(
-        fn=compute,
-        inputs=gr.inputs.Dataframe(
-            headers=feature_names,
-            col_count=len(feature_names),
-            row_count=1,
-            datatype=json_to_string_type(gradio_input_types),
-        ),
-        outputs=gr.outputs.Textbox(label=metric.name),
-        description=(
-            metric.info.description + "\nIf this is a text-based metric, make sure to wrap you 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=[parse_test_cases(test_cases, feature_names, gradio_input_types)]
-    )
+iface = gr.Interface(
+    fn=compute,
+    inputs=gr.inputs.Dataframe(
+        headers=feature_names,
+        col_count=len(feature_names),
+        row_count=1,
+        datatype=json_to_string_type(gradio_input_types),
+    ),
+    outputs=gr.outputs.Textbox(label=metric.name),
+    description=(
+        metric.info.description + "\nIf this is a text-based metric, make sure to wrap you 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=[parse_test_cases(test_cases, feature_names, gradio_input_types)]
+)
 
-    iface.launch()
+iface.launch()