changed app.py

app.py

@@ -1,34 +1,30 @@
-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
+import gradio as gr
+import pandas as pd
+from jiwer import wer
 
-# 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 1
+def compute(input):
+    preds = input['prediction'].tolist()
+    truths = input['truth'].tolist()
+    print(truths, preds, type(truths))
+    err = wer(truths, preds)
+    print(err)
+    return err
 
-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"),
-)
 
-iface.launch()
+demo = gr.Interface(
+        fn=compute,
+        inputs=gr.components.Dataframe(
+            headers=["truth", "prediction"],
+            col_count=2,
+            row_count=1,
+            label="Input"
+        ),
+        outputs=gr.components.Textbox(label="WER"),
+        description=(
+            "This is a calculator that you can use to find WER"
+        ),
+        title="WER Calculator",
+    )
+
+demo.launch()