removed requirement to be from autoeval org

app.py

@@ -158,7 +158,8 @@ st.experimental_set_query_params(**{"dataset": [dataset]})
 dataset_df = dataframe[dataframe.dataset == dataset]
 dataset_df = dataset_df.dropna(axis="columns", how="all")
 
-if "config" in dataset_df.columns:
+if len(dataset_df) > 0:
+
     selectable_configs = list(set(dataset_df["config"]))
     config = st.sidebar.selectbox(
         "Config",
@@ -166,7 +167,6 @@ if "config" in dataset_df.columns:
     )
     dataset_df = dataset_df[dataset_df.config == config]
 
-if "split" in dataset_df.columns:
     selectable_splits = list(set(dataset_df["split"]))
     split = st.sidebar.selectbox(
         "Split",
@@ -174,33 +174,33 @@ if "split" in dataset_df.columns:
     )
     dataset_df = dataset_df[dataset_df.split == split]
 
-selectable_metrics = list(filter(lambda column: column not in ("model_id", "dataset", "split", "config"), dataset_df.columns))
+    selectable_metrics = list(filter(lambda column: column not in ("model_id", "dataset", "split", "config"), dataset_df.columns))
 
-dataset_df = dataset_df.filter(["model_id"] + selectable_metrics)
-dataset_df = dataset_df.dropna(thresh=2)  # Want at least two non-na values (one for model_id and one for a metric).
+    dataset_df = dataset_df.filter(["model_id"] + selectable_metrics)
+    dataset_df = dataset_df.dropna(thresh=2)  # Want at least two non-na values (one for model_id and one for a metric).
 
-sorting_metric = st.sidebar.radio(
-    "Sorting Metric",
-    selectable_metrics,
-)
+    sorting_metric = st.sidebar.radio(
+        "Sorting Metric",
+        selectable_metrics,
+    )
 
-st.markdown(
-    "Please click on the model's name to be redirected to its model card."
-)
+    st.markdown(
+        "Please click on the model's name to be redirected to its model card."
+    )
 
-st.markdown(
-    "Want to beat the leaderboard? Don't see your model here? Simply request an automatic evaluation [here](https://huggingface.co/spaces/autoevaluate/autoevaluate)."
-)
+    st.markdown(
+        "Want to beat the leaderboard? Don't see your model here? Simply request an automatic evaluation [here](https://huggingface.co/spaces/autoevaluate/autoevaluate)."
+    )
 
-# Make the default metric appear right after model names
-cols = dataset_df.columns.tolist()
-cols.remove(sorting_metric)
-cols = cols[:1] + [sorting_metric] + cols[1:]
-dataset_df = dataset_df[cols]
+    # Make the default metric appear right after model names
+    cols = dataset_df.columns.tolist()
+    cols.remove(sorting_metric)
+    cols = cols[:1] + [sorting_metric] + cols[1:]
+    dataset_df = dataset_df[cols]
 
-# Sort the leaderboard, giving the sorting metric highest priority and then ordering by other metrics in the case of equal values.
-dataset_df = dataset_df.sort_values(by=cols[1:], ascending=[metric in ascending_metrics for metric in cols[1:]])
-dataset_df = dataset_df.replace(np.nan, '-')
+    # Sort the leaderboard, giving the sorting metric highest priority and then ordering by other metrics in the case of equal values.
+    dataset_df = dataset_df.sort_values(by=cols[1:], ascending=[metric in ascending_metrics for metric in cols[1:]])
+    dataset_df = dataset_df.replace(np.nan, '-')
 
 # Make the leaderboard
 gb = GridOptionsBuilder.from_dataframe(dataset_df)