fix portuguese itens in collections

app.py

@@ -106,7 +106,7 @@ def init_space(full_init: bool = True):
         benchmark_cols=BENCHMARK_COLS,
         show_incomplete=SHOW_INCOMPLETE_EVALS
     )
-    update_collections(original_df.copy())
+    #update_collections(original_df.copy())
     leaderboard_df = original_df.copy()
     
     plot_df = create_plot_df(create_scores_df(raw_data))
@@ -556,7 +556,7 @@ def update_dynamic_files_wrapper():
 scheduler = BackgroundScheduler(daemon=True)
 scheduler.add_job(restart_space, "interval", seconds=10800, next_run_time=datetime.now() + timedelta(hours=3)) # restarted every 3h
 scheduler.add_job(update_dynamic_files_wrapper, "interval", seconds=1800, next_run_time=datetime.now() + timedelta(minutes=5)) # launched every 30 minutes
-#scheduler.add_job(update_collections, "interval", args=(original_df.copy(),), seconds=3600, next_run_time=datetime.now() + timedelta(minutes=1))
+scheduler.add_job(update_collections, "interval", args=(original_df.copy(),), seconds=3600, next_run_time=datetime.now() + timedelta(minutes=1))
 scheduler.start()
 
 demo.queue(default_concurrency_limit=40).launch()

src/tools/collections.py

@@ -33,6 +33,7 @@ def update_collections(df: DataFrame):
 
     cur_best_models = []
     cur_best_scores = []
+    cur_itens = []
     scores_per_type = {'pretrained': 0, 'other': 0, 'language': 0}
 
     types_to_consider = [('pretrained', [ModelType.PT]), ('other', [ModelType.LA, ModelType.FT, ModelType.chat])]
@@ -50,10 +51,12 @@ def update_collections(df: DataFrame):
     #df = df[df[AutoEvalColumn.precision.name].isin(['bfloat16', 'float16', "?"])]
     
     ix = 0
-    interval_scores = []
-    interval_itens_languages = []
-    interval_itens = []
+    
     for size in intervals:
+        interval_scores = []
+        interval_itens_languages = []
+        interval_itens = []
+        
         numeric_interval = pd.IntervalIndex([intervals[size]])
         mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
         size_df = df.loc[mask]
@@ -95,8 +98,10 @@ def update_collections(df: DataFrame):
                     ix += 1
                     item_object_id = collection.items[-1].item_object_id
                     cur_best_models.append(hf_path)
+                    cur_best_scores.append(float(score))
                     interval_scores.append(float(score))
                     interval_itens_languages.append(language)
+                    cur_itens.append(item_object_id)
                     interval_itens.append(item_object_id)
                     scores_per_type[model_type] = float(score)
                     break
@@ -137,8 +142,10 @@ def update_collections(df: DataFrame):
                     ix += 1
                     item_object_id = collection.items[-1].item_object_id
                     cur_best_models.append(hf_path)
+                    cur_best_scores.append(float(score))
                     interval_scores.append(float(score))
                     interval_itens_languages.append(language)
+                    cur_itens.append(item_object_id)
                     interval_itens.append(item_object_id)
                     scores_per_type[model_type] = float(score)
                     break
@@ -148,14 +155,14 @@ def update_collections(df: DataFrame):
     # fix order:
     starting_idx = len(cur_best_models)
     k = 0
-    for i in np.argsort(interval_scores):
+    for i in np.argsort(cur_best_scores):
         if i == k:
             continue
         else:
             try:
                 #print(cur_best_models[i], interval_itens[i], starting_idx+k, interval_scores[i])
                 update_collection_item(
-                    collection_slug=PATH_TO_COLLECTION, item_object_id=interval_itens[i], position=starting_idx+k
+                    collection_slug=PATH_TO_COLLECTION, item_object_id=cur_itens[i], position=starting_idx+k
                 )
             except:
                 traceback.print_exc()