use gr.Dataframe

app.py

@@ -12,7 +12,7 @@ data = {"Dataset": datasets}
 def mean(data, framework):
     try:
         return f"{round(mase.loc[data, framework].metric_error.mean(),3)} +/- {round(mase.loc[data, framework].metric_error.std(),3)}"
-    except KeyError as e:
+    except KeyError:
         return "n/a"
 
 
@@ -20,22 +20,20 @@ for framework in frameworks:
     data.update({framework: [mean(dataset, framework) for dataset in datasets]})
 
 df = pd.DataFrame(data=data)
-table = df.to_markdown(index=False)
-
 
 with gr.Blocks() as demo:
     gr.Markdown(
-        f"""
+        """
     # Time Series Forecasting Leaderboard
     
-    This is a leaderboard of the MASE metric for time series forecasting problem on the different open datasets and models.
+    This is a leaderboard of the [MASE](https://huggingface.co/spaces/evaluate-metric/mase) metric for time series forecasting problem on the different open datasets and models.
 
     The table is generated from the paper [AutoGluon–TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://github.com/autogluon/autogluon) by Oleksandr Shchur, Caner Turkmen, Nick Erickson, Huibin Shen, Alexander Shirkov, Tony Hu, and Bernie Wang.
 
     ## MASE Metric
     """
     )
-    gr.Markdown(table)
+    gr.Dataframe(df)
 
 if __name__ == "__main__":
     demo.launch()