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from dataclasses import dataclass, make_dataclass | |
from enum import Enum | |
import pandas as pd | |
from src.about import Tasks | |
def fields(raw_class): | |
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"] | |
# These classes are for user facing column names, | |
# to avoid having to change them all around the code | |
# when a modif is needed | |
class ColumnContent: | |
name: str | |
type: str | |
displayed_by_default: bool | |
hidden: bool = False | |
never_hidden: bool = False | |
## Leaderboard columns | |
auto_eval_column_dict = [] | |
# Init | |
auto_eval_column_dict.append(["library_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)]) | |
auto_eval_column_dict.append(["library", ColumnContent, ColumnContent("Library", "markdown", True, never_hidden=True)]) | |
#Scores | |
auto_eval_column_dict.append(["overall_risk", ColumnContent, ColumnContent("Overall Risk β¬οΈ", "number", True)]) | |
for task in Tasks: | |
auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)]) | |
# Library information | |
auto_eval_column_dict.append(["library_type", ColumnContent, ColumnContent("Type", "str", False)]) | |
auto_eval_column_dict.append(["framework", ColumnContent, ColumnContent("Framework", "str", False)]) | |
auto_eval_column_dict.append(["version", ColumnContent, ColumnContent("Version", "str", False, False)]) | |
auto_eval_column_dict.append(["language", ColumnContent, ColumnContent("Language", "str", False)]) | |
auto_eval_column_dict.append(["license_name", ColumnContent, ColumnContent("License", "str", True)]) | |
auto_eval_column_dict.append(["stars", ColumnContent, ColumnContent("GitHub β", "number", False)]) | |
auto_eval_column_dict.append(["last_update", ColumnContent, ColumnContent("Last Updated", "str", False)]) | |
auto_eval_column_dict.append(["verified", ColumnContent, ColumnContent("Independently Verified", "bool", False)]) | |
auto_eval_column_dict.append(["availability", ColumnContent, ColumnContent("Active Maintenance", "bool", True)]) | |
# We use make dataclass to dynamically fill the scores from Tasks | |
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True) | |
## For the queue columns in the submission tab | |
class EvalQueueColumn: # Queue column | |
library = ColumnContent("library", "markdown", True) | |
version = ColumnContent("version", "str", True) | |
language = ColumnContent("language", "str", True) | |
framework = ColumnContent("framework", "str", True) | |
library_type = ColumnContent("library_type", "str", True) | |
status = ColumnContent("status", "str", True) | |
## All the library information that we might need | |
class LibraryDetails: | |
name: str | |
display_name: str = "" | |
symbol: str = "" # emoji | |
class LibraryType(Enum): | |
ML = LibraryDetails(name="machine learning", symbol="π’") | |
LLM = LibraryDetails(name="llm framework", symbol="πΆ") | |
AGENT = LibraryDetails(name="agent framework", symbol="β") | |
VIS = LibraryDetails(name="visualization", symbol="π¦") | |
GENERAL = LibraryDetails(name="general ai", symbol="π£") | |
Unknown = LibraryDetails(name="", symbol="?") | |
def to_str(self, separator=" "): | |
return f"{self.value.symbol}{separator}{self.value.name}" | |
def from_str(type): | |
if "machine learning" in type or "π’" in type: | |
return LibraryType.ML | |
if "llm framework" in type or "πΆ" in type: | |
return LibraryType.LLM | |
if "agent framework" in type or "β" in type: | |
return LibraryType.AGENT | |
if "visualization" in type or "π¦" in type: | |
return LibraryType.VIS | |
if "general ai" in type or "π£" in type: | |
return LibraryType.GENERAL | |
return LibraryType.Unknown | |
class Language(Enum): | |
Python = LibraryDetails("Python") | |
JavaScript = LibraryDetails("JavaScript") | |
TypeScript = LibraryDetails("TypeScript") | |
Java = LibraryDetails("Java") | |
CPP = LibraryDetails("C++") | |
Other = LibraryDetails("Other") | |
class AssessmentStatus(Enum): | |
Verified = LibraryDetails("Verified") | |
Unverified = LibraryDetails("Unverified") | |
Disputed = LibraryDetails("Disputed") | |
# Column selection | |
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden] | |
EVAL_COLS = [c.name for c in fields(EvalQueueColumn)] | |
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)] | |
# Task columns for benchmarking - use the display column names from the Tasks enum | |
BENCHMARK_COLS = [task.value.col_name for task in Tasks] | |