added metric sort orders, added feature to display all metrics at the same time

app.py

@@ -4,16 +4,20 @@ from tqdm.auto import tqdm
 import streamlit as st
 from huggingface_hub import HfApi, hf_hub_download
 from huggingface_hub.repocard import metadata_load
+from ascending_metrics import ascending_metrics
+import numpy as np
 
 
 def make_clickable(model_name):
     link = "https://huggingface.co/" + model_name
     return f'<a target="_blank" href="{link}">{model_name}</a>'
 
+def make_bold(value):
+    return f'<b>{value}</b>'
+
 
 def get_model_ids():
     api = HfApi()
-    # TODO: switch to hf-leaderboards for the final version.
     models = api.list_models(filter="model-index")
     model_ids = [x.modelId for x in models]
     return model_ids
@@ -101,14 +105,16 @@ dataset = st.sidebar.selectbox(
 dataset_df = dataframe[dataframe.dataset == dataset]
 dataset_df = dataset_df.dropna(axis="columns", how="all")
 
-metric = st.sidebar.selectbox(
-    "Metric",
-    list(filter(lambda column: column not in ("model_id", "dataset"), dataset_df.columns)),
+selectable_metrics = list(filter(lambda column: column not in ("model_id", "dataset"), dataset_df.columns))
+metric = st.sidebar.radio(
+    "Sorting Metric",
+    selectable_metrics,
 )
 
-dataset_df = dataset_df.filter(["model_id", metric])
-dataset_df = dataset_df.dropna()
-dataset_df = dataset_df.sort_values(by=metric, ascending=False)
+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.sort_values(by=metric, ascending=metric in ascending_metrics)
+dataset_df = dataset_df.replace(np.nan, '-')
 
 st.markdown(
     "Please click on the model's name to be redirected to its model card which includes documentation and examples on how to use it."
@@ -120,7 +126,22 @@ dataset_df.index += 1
 
 # turn the model ids into clickable links
 dataset_df["model_id"] = dataset_df["model_id"].apply(make_clickable)
+dataset_df[metric] = dataset_df[metric].apply(make_bold)
+
+# Make the selected metric appear right after model names
+cols = dataset_df.columns.tolist()
+cols.remove(metric)
+cols = cols[:1] + [metric] + cols[1:]
+dataset_df = dataset_df[cols]
+
+# Highlight selected metric
+def highlight_cols(s):
+    huggingface_yellow = "#FFD21E"
+    return "background-color: %s" % huggingface_yellow
+
+dataset_df = dataset_df.style.applymap(highlight_cols, subset=pd.IndexSlice[:, [metric]])
 
+# Turn table into html
 table_html = dataset_df.to_html(escape=False)
 table_html = table_html.replace("<th>", '<th align="left">')  # left-align the headers
 st.write(table_html, unsafe_allow_html=True)

ascending_metrics.py

@@ -0,0 +1,10 @@
+ascending_metrics = {
+    "wer",
+    "cer",
+    "loss",
+    "mae",
+    "mahalanobis",
+    "mse",
+    "perplexity",
+    "ter",
+}

requirements.txt

@@ -1,4 +1,5 @@
 pandas
 tqdm
 streamlit
-huggingface_hub
+huggingface_hub
+numpy