Fix Python compatibility

app.py

@@ -94,7 +94,6 @@ def init_leaderboard(dataframe):
                 type="slider",
                 min=1,
                 max=500,
-                step=1,
                 label="Number of parameters (billions)",
             ),
         ],
@@ -223,5 +222,6 @@ scheduler = BackgroundScheduler()
 scheduler.add_job(restart_space, "interval", seconds=1800)
 scheduler.start()
 demo.queue(default_concurrency_limit=40).launch(
+    server_name="0.0.0.0",
     allowed_paths=["images/solbench.svg"],
 )

src/display/utils.py

@@ -2,8 +2,9 @@
 # -*- coding: utf-8 -*-
 # flake8: noqa E501
 
-from dataclasses import dataclass, make_dataclass
+from dataclasses import dataclass, field, make_dataclass
 from enum import Enum
+from functools import partial
 
 import pandas as pd
 
@@ -13,44 +14,51 @@ from src.about import Tasks
 def fields(raw_class):
     return [v for k, v in raw_class.__dict__.items() if not k.startswith("__") and not k.endswith("__")]
 
-
 # These classes are for user facing column names,
 # to avoid having to change them all around the code
 # when a modif is needed
 @dataclass
 class ColumnContent:
     name: str
-    type: str
-    displayed_by_default: bool
-    hidden: bool = False
-    never_hidden: bool = False
+    type: str = field(default='str')
+    displayed_by_default: bool = field(default=False)
+    hidden: bool = field(default=False)
+    never_hidden: bool = field(default=False)
 
-## Leaderboard columns
-auto_eval_column_dict = []
 
-# Init
-auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
-auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
+# Helper function to create a ColumnContent with default_factory
+def create_column_content(name, type='str', displayed_by_default=False, hidden=False, never_hidden=False):
+    return field(default_factory=lambda: ColumnContent(name, type, displayed_by_default, hidden, never_hidden))
 
-# Scores
-auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
-for task in Tasks:
-    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
-
-# Model information
-auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
-auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
-auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
-auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
-auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("License", "str", False)])
-auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("Parameters ⚙️", "number", False)])
-auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Likes ❤️", "number", False)])
-auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
-auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
-
-# We use make dataclass to dynamically fill the scores from Tasks
-AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
+auto_eval_column_dict = [
+    ("model_type_symbol", ColumnContent, create_column_content("", "str", True, never_hidden=True)),
+    ("model", ColumnContent, create_column_content("Model", "markdown", True, never_hidden=True)),
+    ("average", ColumnContent, create_column_content("Average", "number", True)),
+]
 
+# Add task-specific columns
+for task in Tasks:
+    auto_eval_column_dict.append((task.name, ColumnContent, create_column_content(task.value.col_name, "number", True)))
+
+# Add model information columns
+model_info_columns = [
+    ("model_type", "Type", "str", False),
+    ("architecture", "Architecture", "str", False),
+    ("weight_type", "Weight type", "str", False, True),
+    ("precision", "Precision", "str", False),
+    ("license", "License", "str", False),
+    ("params", "Parameters ⚙️", "number", False),
+    ("likes", "Likes ❤️", "number", False),
+    ("still_on_hub", "Available on the hub", "bool", False),
+    ("revision", "Model sha", "str", False, False),
+]
+
+for col_name, display_name, col_type, displayed_by_default, *args in model_info_columns:
+    hidden = args[0] if args else False
+    auto_eval_column_dict.append((col_name, ColumnContent, create_column_content(display_name, col_type, displayed_by_default, hidden)))
+
+# Create the AutoEvalColumn dataclass
+AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)()
 
 # For the queue columns in the submission tab
 @dataclass(frozen=True)