reformat

app.py

@@ -8,9 +8,11 @@ import pandas as pd
 from datasets import load_dataset
 from gradio_leaderboard import Leaderboard, SelectColumns, SearchColumns, ColumnFilter
 
-df = pd.read_json("https://raw.githubusercontent.com/huggingface/lighteval/main/src/lighteval/tasks/tasks_table.jsonl", lines=True).explode("suite").reset_index(drop=True)
+df = pd.read_json("https://raw.githubusercontent.com/huggingface/lighteval/main/src/lighteval/tasks/tasks_table.jsonl",
+                  lines=True).explode("suite").reset_index(drop=True)
 
-with urllib.request.urlopen("https://raw.githubusercontent.com/huggingface/lighteval/main/src/lighteval/tasks/tasks_prompt_formatting.py") as f:
+with urllib.request.urlopen(
+        "https://raw.githubusercontent.com/huggingface/lighteval/main/src/lighteval/tasks/tasks_prompt_formatting.py") as f:
     tasks_prompt_functions_raw = f.read().decode('utf-8')
     tree = ast.parse(tasks_prompt_functions_raw)
     tasks_prompt_functions = {}
@@ -22,11 +24,14 @@ with urllib.request.urlopen("https://raw.githubusercontent.com/huggingface/light
             function_code = ast.get_source_segment(tasks_prompt_functions_raw, node)
             tasks_prompt_functions[function_name] = function_code
 
+
 def load_task_metadata(task_id):
     task_row = df.iloc[task_id]
-    return (task_row.to_dict(), f"""Examples from the HF repository ([{task_row['hf_repo']}](https://huggingface.co/datasets/{task_row['hf_repo']}))""",
+    return (task_row.to_dict(),
+            f"""Examples from the HF repository ([{task_row['hf_repo']}](https://huggingface.co/datasets/{task_row['hf_repo']}))""",
             tasks_prompt_functions.get(task_row["prompt_function"]), "unknown")
 
+
 def load_task_examples(task_id):
     task_row = df.iloc[task_id]
     dataset = load_dataset(task_row["hf_repo"], task_row["hf_subset"], split="+".join(task_row["evaluation_splits"]),
@@ -34,9 +39,9 @@ def load_task_examples(task_id):
 
     sample_data = next(dataset.iter(20))
     # dictionary of lists to list of dictionaries
-    return pd.DataFrame(dict(zip(sample_data, t if not isinstance(t, dict) and not isinstance(t, list) else json.dumps(t)))
-                        for t in zip(*sample_data.values()))
-
+    return pd.DataFrame(
+        dict(zip(sample_data, t if not isinstance(t, dict) and not isinstance(t, list) else json.dumps(t)))
+        for t in zip(*sample_data.values()))
 
 
 with gr.Blocks() as demo:
@@ -47,10 +52,13 @@ with gr.Blocks() as demo:
         with gr.TabItem("🗃️ Tasks List"):
             Leaderboard(
                 value=df,
-                select_columns=SelectColumns(default_selection=["name", "suite", "prompt_function", "hf_repo", "hf_subset", "evaluation_splits", "metric"],
-                                            cant_deselect=["name", "suite"],
-                                            label="Columns to display"),
-                search_columns=SearchColumns(primary_column="name", secondary_columns=["suite", "prompt_function", "hf_repo", "metric"],
+                select_columns=SelectColumns(
+                    default_selection=["name", "suite", "prompt_function", "hf_repo", "hf_subset", "evaluation_splits",
+                                       "metric"],
+                    cant_deselect=["name", "suite"],
+                    label="Columns to display"),
+                search_columns=SearchColumns(primary_column="name",
+                                             secondary_columns=["suite", "prompt_function", "hf_repo", "metric"],
                                              placeholder="Search for a task by name, suite, prompt_function, hf_repo or "
                                                          "metric. To search by suite, for example, type 'suite:<query>'. Separate queries by \";\"",
                                              label="Search"),
@@ -75,9 +83,10 @@ with gr.Blocks() as demo:
             task_dataset_header = gr.Markdown("Examples from the HF repository")
             task_dataset = gr.Dataframe(wrap=True)
 
-            gr.on(triggers=[task_inspector_selector.change], inputs=[task_inspector_selector], outputs=[task_metadata, task_dataset_header, task_prompt_function], fn=load_task_metadata)
-            gr.on(triggers=[task_inspector_selector.change], inputs=[task_inspector_selector], outputs=[task_dataset], fn=load_task_examples)
-
+            gr.on(triggers=[task_inspector_selector.change], inputs=[task_inspector_selector],
+                  outputs=[task_metadata, task_dataset_header, task_prompt_function], fn=load_task_metadata)
+            gr.on(triggers=[task_inspector_selector.change], inputs=[task_inspector_selector], outputs=[task_dataset],
+                  fn=load_task_examples)
 
 if __name__ == "__main__":
     demo.launch()