Add model's URL to leaderboard

app.py

@@ -57,7 +57,10 @@ with tab1:
 
         evaluation_metrics.append(
             {
-                "Model Name": row["model_name"] + " (" + row["inference_function"] + ")",
+                "Model Name": row["model_name"]
+                + "\n("
+                + row["inference_function"]
+                + ")",
                 "Accuracy": macro_avg_accuracy,
                 "Recall": macro_avg_recall,
                 "Precision": macro_avg_precision,
@@ -68,8 +71,20 @@ with tab1:
         "F1 score", ascending=False
     )
     results_df["Rank"] = range(1, len(results_df) + 1)
-    results_df = results_df[["Rank", "Model Name", "Accuracy", "Recall", "Precision", "F1 score"]]
-    st.dataframe(results_df, hide_index=True)
+    results_df["URL"] = [
+        f"https://huggingface.co/{row['model_name']}" for row in model_predictions_rows
+    ]
+
+    results_df = results_df[
+        ["Rank", "Model Name", "URL", "Accuracy", "Recall", "Precision", "F1 score"]
+    ]
+    st.data_editor(
+        results_df,
+        column_config={
+            "URL": st.column_config.LinkColumn("URL", display_text="URL"),
+        },
+        hide_index=True,
+    )
     st.write("Note: The metrics are macro-averaged across all dialects.")
 
 with tab2:
@@ -109,7 +124,10 @@ with tab2:
 
         # Store the predictions in a private dataset
         utils.upload_predictions(
-            os.environ["PREDICTIONS_DATASET_NAME"], predictions, model_name, inference_function
+            os.environ["PREDICTIONS_DATASET_NAME"],
+            predictions,
+            model_name,
+            inference_function,
         )
 
         st.toast(f"Inference completed!")