still trying to make the leaderboard
Browse files
app.py
CHANGED
@@ -119,17 +119,16 @@ def get_user(profile: gr.OAuthProfile | None) -> str:
|
|
119 |
return profile.name
|
120 |
|
121 |
def get_leaderboard(problem_type: str):
|
122 |
-
|
123 |
-
|
124 |
-
|
125 |
-
|
126 |
-
|
127 |
|
128 |
-
|
129 |
score_field = "score" if "score" in df.columns else "objective" # fallback
|
130 |
|
131 |
df = df.sort_values(by=score_field, ascending=True)
|
132 |
-
# leaderboard = df[["submission_time", "problem_type", score_field]].reset_index(drop=True)
|
133 |
return df
|
134 |
|
135 |
def gradio_interface() -> gr.Blocks:
|
@@ -142,7 +141,7 @@ def gradio_interface() -> gr.Blocks:
|
|
142 |
|
143 |
Leaderboard(
|
144 |
value=leaderboard_df,
|
145 |
-
select_columns=["submission_time", "feasibility", "score", "objective"],
|
146 |
search_columns=["submission_time", "score"],
|
147 |
hide_columns=["result_filename", "submission_filename", "minimize_objective", "boundary_json", "evaluated"],
|
148 |
# filter_columns=["T", "Precision", "Model Size"],
|
|
|
119 |
return profile.name
|
120 |
|
121 |
def get_leaderboard(problem_type: str):
|
122 |
+
try:
|
123 |
+
ds = load_dataset(results_repo, split='train')
|
124 |
+
df = pd.DataFrame(ds)
|
125 |
+
except:
|
126 |
+
df = pd.DataFrame(columns=["submission_time", "feasibility", "score", "objective", "result_filename", "submission_filename", "minimize_objective", "boundary_json", "evaluated", "user"])
|
127 |
|
128 |
+
|
129 |
score_field = "score" if "score" in df.columns else "objective" # fallback
|
130 |
|
131 |
df = df.sort_values(by=score_field, ascending=True)
|
|
|
132 |
return df
|
133 |
|
134 |
def gradio_interface() -> gr.Blocks:
|
|
|
141 |
|
142 |
Leaderboard(
|
143 |
value=leaderboard_df,
|
144 |
+
select_columns=["submission_time", "feasibility", "score", "objective", "user"],
|
145 |
search_columns=["submission_time", "score"],
|
146 |
hide_columns=["result_filename", "submission_filename", "minimize_objective", "boundary_json", "evaluated"],
|
147 |
# filter_columns=["T", "Precision", "Model Size"],
|