Refactor Gradio widget and import statements

app.py

@@ -1,41 +1,68 @@
-# import evaluate
 # from evaluate.utils import launch_gradio_widget
+from tests import test_cases
+from evaluate.utils.logging import get_logger
+from evaluate.utils import (
+    infer_gradio_input_types,
+    parse_gradio_data,
+    json_to_string_type,
+    parse_readme,
+    parse_test_cases,
+)
+from pathlib import Path
 
+import evaluate
+import sys
+import evaluate
+import datasets
 
-# module = evaluate.load("danieldux/metric_template_1")
-# launch_gradio_widget(module)
+logger = get_logger(__name__)
 
-import evaluate
-import gradio as gr
 
-# Load your metric module
-module = evaluate.load("danieldux/metric_template_1")
+def launch_gradio_widget(metric, test_cases):
+    """Launches `metric` widget with Gradio."""
 
-# Define pre-populated examples
-examples = [
-    {
-        "references": ["1111", "1112", "1113", "1114"],
-        "predictions": ["1111", "1113", "1120", "1211"],
-    }
-    # {"references": ["It is a sunny day."], "predictions": ["The day is sunny."]}
-]
-
-
-# Use the modules "compute" function that takes references and predictions
-def compute_metric(references, predictions):
-    return module.compute(references=[references], predictions=[predictions])
-
-
-# Create a Gradio interface
-iface = gr.Interface(
-    fn=compute_metric,
-    inputs=[
-        gr.inputs.Textbox(lines=2, label="References"),
-        gr.inputs.Textbox(lines=2, label="Predictions"),
-    ],
-    outputs="text",
-    examples=examples,  # Pass the pre-populated examples here
-)
+    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
+
+    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))
+
+    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()
+
+
+module = evaluate.load("danieldux/metric_template_1")
 
-# Launch the interface
-iface.launch()
+launch_gradio_widget(module, test_cases)