Update app.py

app.py

@@ -1,10 +1,10 @@
-import gradio as gr
+import json
+import os
 
+import gradio as gr
 import pandas as pd
+from huggingface_hub import HfApi, hf_hub_download
 from huggingface_hub.repocard import metadata_load
-from huggingface_hub import hf_hub_download, HfApi
-import os
-import json
 
 
 def make_clickable_model(model_name, link=None):
@@ -14,15 +14,22 @@ def make_clickable_model(model_name, link=None):
     # return (
     #     f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name.split("/")[-1]}</a>'
     # )
-    return (
-            f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name}</a>'
-        )
+    return f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name}</a>'
 
 
 def add_rank(df):
-    cols_to_rank = [col for col in df.columns if
-                    col not in ["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)",
-                                "Embedding Dimensions", "Max Tokens"]]
+    cols_to_rank = [
+        col
+        for col in df.columns
+        if col
+        not in [
+            "Model",
+            "Model Size (Million Parameters)",
+            "Memory Usage (GB, fp32)",
+            "Embedding Dimensions",
+            "Max Tokens",
+        ]
+    ]
     if len(cols_to_rank) == 1:
         df.sort_values(cols_to_rank[0], ascending=False, inplace=True)
     else:
@@ -60,10 +67,7 @@ def get_vidore_data():
                 for dataset in results:
                     results[dataset] = {key: value for key, value in results[dataset].items() if "ndcg_at_5" in key}
 
-                MODEL_INFOS[model.modelId] = {
-                    "metadata": meta,
-                    "results": results
-                }
+                MODEL_INFOS[model.modelId] = {"metadata": meta, "results": results}
             except:
                 continue
 
@@ -96,6 +100,7 @@ def add_rank_and_format(df):
     df["Model"] = df["Model"].apply(make_clickable_model)
     return df
 
+
 # 1. Force headers to wrap
 # 2. Force model column (maximum) width
 # 3. Prevent model column from overflowing, scroll instead
@@ -124,7 +129,7 @@ table > tbody > tr > td:nth-child(2) > div {
 def get_refresh_function():
     def _refresh():
         data_task_category = get_vidore_data()
-        return data_task_category
+        return add_rank_and_format(data_task_category)
 
     return _refresh
 
@@ -144,10 +149,11 @@ with gr.Blocks(css=css) as block:
     gr.Markdown("# ViDoRe: The Visual Document Retrieval Benchmark 📚🔍")
     gr.Markdown("## From the paper - ColPali: Efficient Document Retrieval with Vision Language Models 👀")
 
-
-    gr.Markdown(f"""
+    gr.Markdown(
+        f"""
     Visual Document Retrieval Benchmark leaderboard. To submit, refer to the <a href="https://github.com/tonywu71/vidore-benchmark/" target="_blank" style="text-decoration: underline">ViDoRe GitHub repository</a>.  Refer to the [ColPali paper](https://arxiv.org/abs/XXXX.XXXXX) for details on metrics, tasks and models.
-    """)
+    """
+    )
 
     with gr.Row():
         datatype = ["number", "markdown"] + ["number"] * (NUM_DATASETS + 1)
@@ -155,22 +161,23 @@ with gr.Blocks(css=css) as block:
 
     with gr.Row():
         refresh_button = gr.Button("Refresh")
-        refresh_button.click(get_refresh_function(), inputs=None, outputs=dataframe,
-                             concurrency_limit=20)
+        refresh_button.click(get_refresh_function(), inputs=None, outputs=dataframe, concurrency_limit=20)
 
-    gr.Markdown(f"""
+    gr.Markdown(
+        f"""
     - **Total Datasets**: {NUM_DATASETS}
     - **Total Scores**: {NUM_SCORES}
     - **Total Models**: {NUM_MODELS}
-    """ + r"""
+    """
+        + r"""
     Please consider citing:
 
     ```bibtex
-    @article{}
+    INSERT LATER
     ```
-    """)
+    """
+    )
 
 
 if __name__ == "__main__":
     block.queue(max_size=10).launch(debug=True)
-