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import dash
from dash import html, dcc, Input, Output, State
import dash_ag_grid as dag
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import base64
import os
# Define the columns
MAIN_COLS = ['#P', 'Model', 'UGI π', 'W/10 π', 'NatInt π‘', 'Coding π»', 'Unruly', 'Internet', 'Societal/Political', 'Political Lean π', 'Ideology Name']
AXES_COLS_1 = ['govt', 'dipl', 'econ', 'scty']
AXES_COLS_2 = ['Federal-Unitary', 'Democratic-Autocratic', 'Security-Freedom', 'Nationalism-Internationalism',
'Militarist-Pacifist', 'Assimilationist-Multiculturalist', 'Collectivize-Privatize',
'Planned-LaissezFaire', 'Isolationism-Globalism', 'Irreligious-Religious',
'Progressive-Traditional', 'Acceleration-Bioconservative']
UGI_CATEGORY_COLS = ['Unruly', 'Internet', 'Societal/Political']
def load_leaderboard_data(csv_file_path):
try:
df = pd.read_csv(csv_file_path, na_values=['NA'])
# Add type sort value
def get_type_sort_value(row):
if pd.isna(row['Total Parameters']):
return 3 # P
if row['Is Foundation'] and not row['Is Merged']:
return 0 # B
if row['Is Merged']:
return 2 # M
if row['Is Finetuned'] and not row['Is Merged']:
return 1 # F
return 4
df['model_type_sort'] = df.apply(get_type_sort_value, axis=1)
# Convert date columns to datetime and then to ISO format strings
for col in ['Release Date', 'Test Date']:
df[col] = pd.to_datetime(df[col], format='%m/%d/%Y', errors='coerce')
df[col] = df[col].dt.strftime('%Y-%m-%d') # Store as YYYY-MM-DD
# Calculate the date two weeks ago from today
two_weeks_ago = (datetime.now() - timedelta(days=6)).strftime('%Y-%m-%d') #temp 6
# Store model name and link separately
df['Model_Link'] = df['Model Link'].fillna('')
df['Model_Display'] = df['author/model_name']
# Check for new models based on Test Date
df['is_new'] = df.apply(
lambda row: 'π' if pd.notna(row["Test Date"]) and row["Test Date"] >= two_weeks_ago else '',
axis=1
)
# Add pinned and selected columns
df['pinned'] = False
df['selected'] = False
# Convert percentage strings to floats for all relevant columns
percentage_columns = ['Political Lean π'] + AXES_COLS_1 + AXES_COLS_2
for col in percentage_columns:
df[col] = pd.to_numeric(df[col].astype(str).str.rstrip('%'), errors='coerce')
# Round numeric columns and handle NA values
numeric_columns = df.select_dtypes(include=[np.number]).columns
for col in numeric_columns:
df[col] = df[col].apply(lambda x: None if pd.isna(x) else round(x, 3))
df = df.sort_values('UGI π', ascending=False)
return df
except Exception as e:
print(f"Error loading CSV file: {e}")
return pd.DataFrame()
def load_ideology_descriptions():
try:
with open('ideologies.js', 'r', encoding='utf-8') as file:
content = file.read()
# Extract the array content between brackets
start_idx = content.find('[')
end_idx = content.rfind(']') + 1
if start_idx == -1 or end_idx == 0:
return {}
ideology_data = content[start_idx:end_idx]
# Convert JavaScript object syntax to Python
ideology_data = ideology_data.replace('true', 'True').replace('false', 'False')
ideology_data = eval(ideology_data)
# Create a dictionary mapping ideology names to their descriptions
return {item['name']: item['desc'] for item in ideology_data}
except Exception as e:
print(f"Error loading ideologies.js: {e}")
return {}
# Load descriptions once at startup
IDEOLOGY_DESCRIPTIONS = load_ideology_descriptions()
def get_kofi_button_base64():
current_dir = os.path.dirname(os.path.realpath(__file__))
# Return both light and dark theme images as a dictionary
images = {}
for theme in ['light', 'dark']:
filename = 'support_me_on_kofi_white.png' if theme == 'light' else 'support_me_on_kofi_dark.png'
with open(os.path.join(current_dir, f"Images/{filename}"), "rb") as image_file:
images[theme] = base64.b64encode(image_file.read()).decode('utf-8')
return images
# Initialize the Dash app
app = dash.Dash(__name__)
server = app.server
# Custom CSS
app.index_string = '''
<!DOCTYPE html>
<html>
<head>
{%metas%}
<title>UGI Leaderboard</title>
{%favicon%}
{%css%}
<style>
:root {
--bg-color: #ffffff;
--text-color: #000000;
--grid-bg: #ffffff;
--grid-border: #ddd;
--link-color: #007bff;
--secondary-text: #666;
--pinned-bg: #f5f5f5;
--border-color: #ccc;
}
@media (prefers-color-scheme: dark) {
:root {
--bg-color: #0d1117;
--text-color: #e6e6e6;
--grid-bg: #161b22;
--grid-border: #30363d;
--link-color: #58a6ff;
--secondary-text: #8b949e;
--pinned-bg: #1c2128;
--border-color: #30363d;
}
}
body {
font-family: 'Segoe UI', Arial, sans-serif;
margin: 0;
padding: 20px;
background-color: var(--bg-color);
color: var(--text-color);
}
/* Header and Title Styles */
.page-title {
text-align: center;
margin: 0;
font-size: 38px;
color: var(--text-color) !important;
}
.page-subtitle {
text-align: center;
margin: 0;
font-size: 20px;
font-weight: 600;
color: var(--text-color) !important;
}
/* Filter Styles */
.model-type-filter {
color: var(--text-color) !important;
margin-right: 10px;
font-weight: bold;
},
#model-type-filter label,
#na-model-filter label {
color: var(--text-color) !important;
margin-right: 10px;
font-weight: bold;
}
/* Grid Styles */
.ag-theme-alpine {
--ag-font-family: 'Segoe UI', Arial, sans-serif;
--ag-font-size: 14px;
--ag-background-color: var(--grid-bg);
--ag-border-color: var(--grid-border);
--ag-header-background-color: var(--grid-bg);
--ag-odd-row-background-color: var(--grid-bg);
--ag-header-foreground-color: var(--text-color);
--ag-foreground-color: var(--text-color);
--ag-row-border-color: var(--grid-border);
}
.ag-floating-top {
border-bottom: 3px solid var(--border-color) !important;
}
.ag-floating-top:empty {
border-bottom: none !important;
}
.pinned-row {
background-color: var(--pinned-bg) !important;
font-weight: 500;
}
/* Text Alignment Classes */
.ag-left-aligned-header {
text-align: left !important;
}
.ag-left-aligned-cell {
text-align: left !important;
}
.ag-header-cell-text {
white-space: normal !important;
line-height: 1.2em;
overflow: visible;
padding-bottom: 4px;
}
.ag-header-cell {
height: auto !important;
min-height: 48px;
}
.wrap-text {
white-space: normal !important;
line-height: 1.2em;
}
.no-break {
white-space: nowrap !important;
}
/* Border Classes */
.border-left {
border-left: 2px solid var(--grid-border) !important;
}
.border-right {
border-right: 2px solid var(--grid-border) !important;
}
/* Link Styles */
.model-link {
color: var(--link-color) !important;
text-decoration: none;
}
.model-link:hover {
text-decoration: underline;
}
.source-link {
color: var(--link-color) !important;
text-decoration: none;
}
/* Details/Summary Styles */
.details-summary {
cursor: pointer;
font-weight: bold;
font-size: 1.2em;
margin-top: 20px;
color: var(--text-color) !important;
}
.ideology-note {
color: var(--secondary-text) !important;
font-size: 0.9em;
}
/* Markdown Content */
.markdown-content {
color: var(--text-color) !important;
}
.markdown-content a {
color: var(--link-color) !important;
}
/* Ko-fi Button Visibility */
.kofi-light {
display: none;
}
.kofi-dark {
display: none;
}
@media (prefers-color-scheme: light) {
.kofi-light {
display: block;
}
}
@media (prefers-color-scheme: dark) {
.kofi-dark {
display: block;
}
/* Dark Theme Specific Overrides */
.ag-theme-alpine {
--ag-background-color: #161b22 !important;
--ag-header-background-color: #161b22 !important;
--ag-odd-row-background-color: #161b22 !important;
--ag-row-background-color: #161b22 !important;
--ag-header-foreground-color: #e6e6e6 !important;
--ag-foreground-color: #e6e6e6 !important;
--ag-row-border-color: #30363d !important;
--ag-border-color: #30363d !important;
--ag-secondary-border-color: #30363d !important;
--ag-alpine-active-color: #58a6ff !important;
--ag-selected-row-background-color: #1c2128 !important;
--ag-row-hover-color: #1c2128 !important;
}
.ag-header-cell-filtered {
background-color: rgba(88, 166, 255, 0.1) !important;
}
input[type="checkbox"] {
accent-color: var(--link-color);
}
/* Ensure text colors in dark mode */
.page-title,
.page-subtitle,
.model-type-filter label,
#model-type-filter label,
#na-model-filter label {
color: #e6e6e6 !important;
}
.filter-description,
.ideology-note {
color: #8b949e !important;
}
}
a:visited {
color: var(--link-color) !important;
}
.markdown-content a:visited {
color: var(--link-color) !important;
}
.split-header-container {
display: flex;
flex-direction: column;
line-height: 1.2em;
}
.split-header-top, .split-header-bottom {
white-space: nowrap;
}
.ag-theme-alpine .new-emoji-cell.ag-cell {
font-size: 18px !important;
display: flex !important;
align-items: center !important;
justify-content: flex-start !important;
padding-left: 12px !important;
}
</style>
</head>
<body>
{%app_entry%}
<footer>
{%config%}
{%scripts%}
{%renderer%}
</footer>
</body>
</html>
'''
# Load data
df = load_leaderboard_data("ugi-leaderboard-data.csv")
# Define helper functions
def create_numeric_column(field, width=125, sort=None, sortIndex=None, **kwargs):
column = {
"field": field,
"width": width,
"filter": "agNumberColumnFilter",
"filterParams": {
"defaultOption": "inRange",
"filterOptions": ['equals', 'notEqual', 'greaterThan', 'greaterThanOrEqual', 'lessThan', 'lessThanOrEqual', 'inRange']
},
"headerClass": "ag-left-aligned-header wrap-text",
"cellClass": "ag-left-aligned-cell",
"wrapHeaderText": True,
"autoHeaderHeight": True,
"suppressSizeToFit": True,
"sortingOrder": ['desc', 'asc'],
"comparator": {
"function": """
function(valueA, valueB, nodeA, nodeB, isInverted) {
const a = nodeA.data.__sortValue;
const b = nodeB.data.__sortValue;
return a - b;
}
"""
}
}
# Update filterParams if provided in kwargs
if 'filterParams' in kwargs:
column['filterParams'].update(kwargs['filterParams'])
if sort:
column["sort"] = sort
if sortIndex is not None:
column["sortIndex"] = sortIndex
return column
def create_text_column(field, width=120):
return {
"field": field,
"width": width,
"filter": "agTextColumnFilter",
"filterParams": {
"defaultOption": "contains",
"filterOptions": ['contains', 'notContains', 'startsWith', 'endsWith']
},
"headerClass": "ag-left-aligned-header wrap-text",
"cellClass": "ag-left-aligned-cell",
"wrapHeaderText": True,
"autoHeaderHeight": True
}
# Define column configurations
columnDefs = [
{
"headerName": "π",
"field": "pinned",
"width": 55,
"filter": False,
"suppressMenu": True,
"cellRenderer": "PinRenderer",
"pinned": "left"
},
{
"headerName": "",
"field": "is_new",
"width": 55,
"filter": False,
"suppressMenu": True,
"cellClass": "new-emoji-cell",
"pinned": "left"
},
{
"field": "#P",
"width": 115,
"filter": "agNumberColumnFilter",
"filterParams": {
"defaultOption": "equals",
"filterOptions": ['equals', 'notEqual', 'greaterThan', 'greaterThanOrEqual', 'lessThan', 'lessThanOrEqual', 'inRange']
},
"headerClass": "ag-left-aligned-header wrap-text",
"cellClass": "ag-right-aligned-cell",
"wrapHeaderText": True,
"autoHeaderHeight": True,
"suppressSizeToFit": True,
"sortingOrder": ['desc', 'asc'],
"pinned": "left"
},
{
"field": "model_type_sort",
"hide": True
},
{
"headerName": "T",
"field": "model_type_sort", # Changed to use the sort field directly
"width": 45,
"filter": False,
"suppressMenu": True,
"cellRenderer": "TypeRenderer",
"pinned": "left",
"sortable": True,
"sortingOrder": ['asc', 'desc']
},
{
"field": "Model_Display",
"headerName": "Model",
"cellRenderer": "ModelLink",
"filter": "agTextColumnFilter",
"filterParams": {
"defaultOption": "contains",
"filterOptions": ['contains', 'notContains', 'startsWith', 'endsWith']
},
"width": 380,
"suppressMenu": False,
"pinned": "left",
"headerClass": "ag-left-aligned-header wrap-text",
"wrapHeaderText": True,
"autoHeaderHeight": True
},
{
"field": "UGI π",
"width": 120,
"filter": "agNumberColumnFilter",
"filterParams": {
"defaultOption": "greaterThanOrEqual"
},
"headerClass": "ag-left-aligned-header wrap-text",
"cellClass": "ag-left-aligned-cell",
"wrapHeaderText": True,
"autoHeaderHeight": True,
"suppressSizeToFit": True,
"sortingOrder": ['desc', 'asc']
},
create_numeric_column("W/10 π", width=130, filterParams={
"defaultOption": "greaterThanOrEqual",
"filterOptions": ['equals', 'notEqual', 'greaterThan', 'greaterThanOrEqual', 'lessThan', 'lessThanOrEqual', 'inRange']
}),
{
"field": "NatInt π‘",
"headerName": "NatInt π‘",
"width": 140,
"filter": "agNumberColumnFilter",
"filterParams": {
"defaultOption": "greaterThanOrEqual",
"filterOptions": ['equals', 'notEqual', 'greaterThan', 'greaterThanOrEqual', 'lessThan', 'lessThanOrEqual', 'inRange']
},
"headerClass": "ag-left-aligned-header wrap-text",
"cellClass": "ag-left-aligned-cell",
"wrapHeaderText": True,
"autoHeaderHeight": True,
"suppressSizeToFit": True,
"sortingOrder": ['desc', 'asc']
},
create_numeric_column("Coding π»", width=140, filterParams={
"defaultOption": "greaterThanOrEqual"
}),
{
"field": "Political Lean π",
"width": 175,
"filter": "agNumberColumnFilter",
"filterParams": {
"defaultOption": "inRange",
"filterOptions": ['equals', 'notEqual', 'greaterThan', 'greaterThanOrEqual', 'lessThan', 'lessThanOrEqual', 'inRange']
},
"valueFormatter": {
"function": "d3.format('.1f')(params.value) + '%'"
},
"sortingOrder": ['desc', 'asc'],
"headerClass": "ag-left-aligned-header wrap-text",
"cellClass": "ag-left-aligned-cell",
"wrapHeaderText": True,
"autoHeaderHeight": True
}
]
ugi_category_columns = [
create_numeric_column(col, width=120) for col in UGI_CATEGORY_COLS
]
political_columns = [
{
"headerName": "Ideology",
"field": "Ideology Name",
"width": 160,
"filter": "agTextColumnFilter",
"filterParams": {
"defaultOption": "contains",
"filterOptions": ['contains', 'notContains', 'startsWith', 'endsWith']
},
"headerClass": "ag-left-aligned-header wrap-text",
"cellClass": "ag-left-aligned-cell",
"wrapHeaderText": True,
"autoHeaderHeight": True
}
]
# Add axes columns with different widths
for i, col in enumerate(AXES_COLS_1):
col_def = {
"field": col,
"width": 105,
"filter": "agNumberColumnFilter",
"filterParams": {
"defaultOption": "inRange",
"filterOptions": ['equals', 'notEqual', 'greaterThan', 'greaterThanOrEqual', 'lessThan', 'lessThanOrEqual', 'inRange']
},
"valueFormatter": {
"function": "d3.format('.1f')(params.value) + '%'"
},
"headerClass": "ag-left-aligned-header wrap-text",
"cellClass": ["ag-left-aligned-cell"],
"sortingOrder": ['desc', 'asc']
}
if i == 0: # First column (govt)
col_def["cellClass"].append("border-left")
elif i == len(AXES_COLS_1) - 1: # Last column (scty)
col_def["cellClass"].append("border-right")
columnDefs.append(col_def)
template_with_split_header = """
<div class="ag-cell-label-container" role="presentation">
<span ref="eMenu" class="ag-header-icon ag-header-cell-menu-button"></span>
<div ref="eLabel" class="ag-header-cell-label" role="presentation">
<div class="split-header-container">
<div class="split-header-top">β {high}</div>
<div class="split-header-bottom">β {low}</div>
</div>
<span ref="eText" class="ag-header-cell-text" style="display: none"></span>
<span ref="eSortOrder" class="ag-header-icon ag-sort-order"></span>
<span ref="eSortAsc" class="ag-header-icon ag-sort-ascending-icon"></span>
<span ref="eSortDesc" class="ag-header-icon ag-sort-descending-icon"></span>
<span ref="eSortNone" class="ag-header-icon ag-sort-none-icon"></span>
<span ref="eFilter" class="ag-header-icon ag-filter-icon"></span>
</div>
</div>
"""
for col in AXES_COLS_2:
high, low = col.split('-')
columnDefs.append({
"field": col,
"headerComponentParams": {
"template": template_with_split_header.format(high=high, low=low)
},
"width": 175,
"filter": "agNumberColumnFilter",
"filterParams": {
"defaultOption": "inRange",
"filterOptions": ['equals', 'notEqual', 'greaterThan', 'greaterThanOrEqual', 'lessThan', 'lessThanOrEqual', 'inRange']
},
"valueFormatter": {
"function": "d3.format('.1f')(params.value) + '%'"
},
"sortingOrder": ['desc', 'asc']
})
# Date Columns
columnDefs.extend([
{
"field": "Release Date",
"width": 130,
"filter": "agDateColumnFilter",
"valueFormatter": {
"function": """
function(params) {
if (!params.value) return '';
const [year, month, day] = params.value.split('-');
return `${month}/${day}/${year}`;
}
"""
},
"comparator": {
"function": """
function(valueA, valueB) {
if (!valueA && !valueB) return 0;
if (!valueA) return 1;
if (!valueB) return -1;
return valueA.localeCompare(valueB);
}
"""
},
"cellClass": ["ag-left-aligned-cell", "border-left"],
"headerClass": "ag-left-aligned-header wrap-text",
"wrapHeaderText": True,
"autoHeaderHeight": True,
"sortable": True
},
{
"field": "Test Date",
"width": 130,
"filter": "agDateColumnFilter",
"valueFormatter": {
"function": """
function(params) {
if (!params.value) return '';
const [year, month, day] = params.value.split('-');
return `${month}/${day}/${year}`;
}
"""
},
"comparator": {
"function": """
function(valueA, valueB) {
if (!valueA && !valueB) return 0;
if (!valueA) return 1;
if (!valueB) return -1;
return valueA.localeCompare(valueB);
}
"""
},
"cellClass": "ag-left-aligned-cell",
"headerClass": "ag-left-aligned-header wrap-text",
"wrapHeaderText": True,
"autoHeaderHeight": True,
"sortable": True
}
])
# Define the grid options with postSort
dashGridOptions = {
"animateRows": True,
"pagination": False,
"enableCellTextSelection": True,
"ensureDomOrder": True,
"suppressRowClickSelection": True,
"suppressCellFocus": True,
"getRowId": "params => params.data.Model_Display",
"pinnedTopRowData": [],
"suppressMaintainUnsortedOrder": True,
"suppressMultiSort": True,
"rowBuffer": 10,
"maxBlocksInCache": 2,
"onGridReady": {
"function": """
function(params) {
console.log('Grid ready');
window.gridApi = params.api;
}
"""
},
"onRowDataChanged": {
"function": """
function(params) {
console.log('Row data changed event');
console.log('Current pinned rows:', params.api.getGridOption('pinnedTopRowData'));
console.log('Current main rows:', []);
params.api.forEachNode(node => console.log(node.data.Model_Display));
}
"""
},
"theme": "ag-theme-alpine-dark" if "prefers-color-scheme: dark" else "ag-theme-alpine",
"columnState": {
"function": """
function() {
return {
columnVisibility: {}
};
}
"""
}
}
# Define the layout
app.layout = html.Div([
dcc.Store(id='pinned-rows-store', data=[]),
dcc.Store(id='pinned-ids-store', data=[]),
dcc.Store(id='pinned-models-store', data=[]),
dcc.Store(id='filter-change-trigger', data=0),
# Header
html.Div([
html.Div([
html.A("Contact/Model Requests", href="mailto:[email protected]", className="model-link"),
html.Span(" (or create a HF discussion)")
], style={'float': 'left'}),
html.Div([
html.A(
html.Img(
src=f"data:image/png;base64,{get_kofi_button_base64()['light']}",
style={'width': '165px'},
className='kofi-light'
),
href="https://ko-fi.com/dontplantoend",
target="_blank"
),
html.A(
html.Img(
src=f"data:image/png;base64,{get_kofi_button_base64()['dark']}",
style={'width': '165px'},
className='kofi-dark'
),
href="https://ko-fi.com/dontplantoend",
target="_blank"
)
], style={'float': 'right'})
], style={'overflow': 'hidden', 'marginBottom': '20px', 'padding': '0 20px'}),
# Title
html.Div([
html.H1("π’ UGI Leaderboard",
className="page-title",
style={'fontSize': '38px'}),
html.H2("Uncensored General Intelligence",
className="page-subtitle"),
], style={'marginBottom': '30px'}),
html.Div([
html.Div("To filter columns, click the β‘ next to a column's name. On mobile, hold the column name for the menu to appear.",
style={'marginBottom': '20px', 'color': 'var(--text-color)'}), # Use text-color variable
], style={'padding': '0 20px'}),
# Model Type Filter
html.Div([
html.Div([
html.Label("Display Models:",
className="model-type-filter"),
dcc.Checklist(
id='model-type-filter',
options=[
{'label': html.Span('Base', style={'color': '#71de5f', 'fontSize': '16.5px'}), 'value': 'Is Foundation'},
{'label': html.Span('Finetune', style={'color': '#f6b10b', 'fontSize': '16.5px'}), 'value': 'Is Finetuned'},
{'label': html.Span('Merge', style={'color': '#f08aff', 'fontSize': '16.5px'}), 'value': 'Is Merged'},
{'label': html.Span('Proprietary', style={'color': '#19cdce', 'fontSize': '16.5px'}), 'value': 'proprietary'}
],
value=['Is Foundation', 'Is Finetuned', 'Is Merged', 'proprietary'],
inline=True,
style={'display': 'inline-block'},
labelStyle={'fontWeight': 'normal', 'marginRight': '15px'}
)
], style={'float': 'left'}),
html.Div([
dcc.Checklist(
id='na-model-filter',
options=[{'label': 'NA Models', 'value': 'show_na'}],
value=[],
inline=True,
style={'display': 'inline-block'},
labelStyle={'fontWeight': 'normal'} # Make sure NA Models isn't bold
)
], style={'float': 'right'})
], style={'marginBottom': '10px', 'padding': '0 20px', 'overflow': 'hidden'}),
# Additional Columns Filter
html.Div([
html.Label("Show Additional Columns:",
className="model-type-filter"), # Use same class for consistent styling
dcc.Checklist(
id='additional-columns-filter',
options=[
{'label': 'UGI Categories', 'value': 'ugi_categories'},
{'label': 'Political Test Axes', 'value': 'political_axes'}
],
value=[],
inline=True,
style={'display': 'inline-block'},
labelStyle={'fontWeight': 'normal', 'marginRight': '15px'} # Add consistent spacing
)
], style={'marginBottom': '13px', 'padding': '0 20px', 'overflow': 'hidden'}),
# Grid
html.Div([
dag.AgGrid(
id='leaderboard-grid',
columnDefs=columnDefs,
rowData=df.to_dict('records'),
defaultColDef={
"sortable": True,
"resizable": True,
"filter": "agNumberColumnFilter",
"floatingFilter": False,
"sortingOrder": ['desc', 'asc'],
"filterParams": {
"defaultOption": "between"
},
"comparator": {
"function": """
function(valueA, valueB, nodeA, nodeB, isInverted) {
const isEmptyA = valueA === null || valueA === undefined || valueA === '' || isNaN(valueA);
const isEmptyB = valueB === null || valueB === undefined || valueB === '' || isNaN(valueB);
// Force empty values to bottom
if (isEmptyA && !isEmptyB) return 1;
if (!isEmptyA && isEmptyB) return -1;
if (isEmptyA && isEmptyB) return 0;
// Normal comparison for non-empty values
if (typeof valueA === 'number' && typeof valueB === 'number') {
return valueA - valueB;
}
return String(valueA).localeCompare(String(valueB));
}
"""
}
},
dashGridOptions=dashGridOptions,
dangerously_allow_code=True,
className="ag-theme-alpine",
style={"height": "600px", "width": "100%"},
enableEnterpriseModules=False,
getRowId="params.data.Model_Display"
)
], style={'marginBottom': '30px'}),
# Description
html.Div([
html.H3("About"),
html.P([html.Strong("UGI:"), " Uncensored General Intelligence. A benchmark measuring both willingness to answer and accuracy in fact-based contentious questions. The test set is made of roughly 100 questions/tasks, covering topics that are commonly difficult to get LLMs to answer. The leaderboard's questions are kept private in order to avoid the common problem of not knowing if a model is intelligent or if it was just trained on the test questions."]),
html.P([html.Strong("W/10:"), " Willingness/10. A more narrow subset of the UGI questions, solely focused on measuring how far a model can be pushed before going against its instructions or refusing to answer."]),
html.P("A high UGI but low W/10 could mean for example that the model can provide a lot of accurate sensitive information, but will refuse to form the information into something it sees as offensive or against its rules."),
html.P([html.Strong("NatInt:"), " Natural Intelligence. A general knowledge quiz covering real-world subjects that llms are not commonly benchmarked on, such as pop culture trivia. This measures if the model understands a diverse range of topics, as opposed to over-training on textbook information and the types of questions commonly tested on benchmarks."]),
html.P([html.Strong("Coding:"), " A simple 50 question quiz measuring how vast a model's programming knowledge is. Each question is worth 2 points."]),
html.P([
html.Strong("Political Lean:"),
" Measures a model's tendency to hold left wing vs right wing political beliefs. Ranges between -100% and 100%, where left wing is left of zero (negative) and right wing is right of zero (positive). Uses the axes of the ",
html.A("12axes",
href="https://politicaltests.github.io/12axes/",
target="_blank",
style={'color': 'var(--link-color)'}
),
" test most aligned with modern left vs right issues. Excludes Federal vs Unitary, Democratic vs. Autocratic, and Militarist vs. Pacifist from the score since they don't line up as well with left vs. right wing."
], style={'marginBottom': '4px'}),
html.Ul([
html.Li("NA if model wasn't capable of answering a sufficient number of questions.")
], style={'marginTop': '0px', 'marginBottom': '16px'}),
html.P("Aggregate Political Scores", style={'marginBottom': '4px'}),
html.Ul([
html.Li("Govt: Higher = State authority, Lower = Individual liberty"),
html.Li("Dipl: Higher = Global outlook, Lower = National interests"),
html.Li("Econ: Higher = Economic equality, Lower = Market freedom"),
html.Li("Scty: Higher = Progressive values, Lower = Traditional values")
], style={'marginTop': '0px', 'marginBottom': '16px'}),
html.Br(),
html.P("All local models are tested using Q6_K.gguf quants.")
], style={
'maxWidth': '1200px',
'margin': '0 auto',
'padding': '0 20px',
'color': 'var(--text-color)'
}),
# Add 12axes Ideology Descriptions here
html.Details([
html.Summary("12axes Ideology Descriptions",
className="details-summary"),
html.Div([
html.I("Only showing ideologies at least one model has.",
className='ideology-note',
style={'fontSize': '0.9em'}),
dcc.Markdown("\n\n".join([
f"**{ideology}**: {IDEOLOGY_DESCRIPTIONS.get(ideology, 'No description available.')}"
for ideology in sorted(set(df['Ideology Name'].dropna()))
if ideology # Skip empty values
]), className='markdown-content'),
html.Div([
html.A("Source",
href="https://github.com/politicaltests/politicaltests.github.io/blob/main/12axes/ideologies.js",
target="_blank",
className="source-link")
], style={'marginTop': '20px'})
], style={'paddingTop': '10px'})
], style={'marginTop': '30px', 'marginBottom': '50px', 'maxWidth': '1200px', 'margin': '30px auto 80px'})
], style={'maxWidth': '100%', 'margin': '0 auto'})
def debug_callback(value):
print("Model filter value:", value)
return value
@app.callback(
[Output('leaderboard-grid', 'rowData'),
Output('model-type-filter', 'value'),
Output('pinned-models-store', 'data')],
[Input('model-type-filter', 'value'),
Input('na-model-filter', 'value'),
Input('leaderboard-grid', 'pinnedTopRowData')],
prevent_initial_call=False
)
def update_grid(selected_types, show_na, pinned_rows):
if selected_types is None:
selected_types = []
if not selected_types:
return [], selected_types, []
filtered_df = df.copy()
# Sort by UGI initially
filtered_df = filtered_df.sort_values('UGI π', ascending=False)
# Get pinned model IDs
pinned_models = []
if pinned_rows:
pinned_models = [row['Model_Display'] for row in pinned_rows]
# Remove pinned models from the dataframe
filtered_df = filtered_df[~filtered_df['Model_Display'].isin(pinned_models)]
mask = pd.Series(False, index=filtered_df.index)
# Model type filtering
if 'Is Finetuned' in selected_types:
if 'Is Merged' in selected_types:
mask |= filtered_df['Is Finetuned']
else:
mask |= (filtered_df['Is Finetuned'] & ~filtered_df['Is Merged'])
elif 'Is Merged' in selected_types:
mask |= filtered_df['Is Merged']
if 'Is Foundation' in selected_types:
mask |= (filtered_df['Is Foundation'] & ~filtered_df['Total Parameters'].isna())
if 'proprietary' in selected_types:
mask |= filtered_df['Total Parameters'].isna()
filtered_df = filtered_df[mask]
# NA filtering
political_columns = ['Political Lean π', 'govt', 'dipl', 'econ', 'scty'] + AXES_COLS_2
has_na = filtered_df[political_columns].isna().any(axis=1)
if show_na is None or not show_na:
filtered_df = filtered_df[~has_na]
# Always sort by UGI descending
filtered_df = filtered_df.sort_values('UGI π', ascending=False)
records = filtered_df.to_dict('records')
return records, selected_types, pinned_models
@app.callback(
Output('leaderboard-grid', 'columnDefs'),
[Input('additional-columns-filter', 'value')]
)
def update_columns(additional_columns):
# Start with base columns up to UGI column
current_columns = columnDefs[:7] # Include up to UGI column
# Add UGI category columns if selected
if 'ugi_categories' in additional_columns:
current_columns.extend(ugi_category_columns) # Use the pre-defined ugi_category_columns
# Add remaining base columns (W/10, NatInt, Coding, Political Lean)
current_columns.extend(columnDefs[7:11])
# Add political columns if selected
if 'political_axes' in additional_columns:
current_columns.extend(political_columns)
current_columns.extend([col for col in columnDefs if col['field'] in AXES_COLS_1])
current_columns.extend([col for col in columnDefs if col['field'] in AXES_COLS_2])
# Always add date columns at the end
current_columns.extend([col for col in columnDefs if col['field'] in ['Release Date', 'Test Date']])
return current_columns
@app.callback(
Output('ideology-descriptions', 'children'),
[Input('leaderboard-grid', 'rowData')]
)
def update_ideology_descriptions(row_data):
if not row_data:
return []
# Load ideology descriptions
ideology_descriptions = load_ideology_descriptions()
# Get unique ideologies from current grid data
unique_ideologies = sorted(set(row['Ideology Name'] for row in row_data if row.get('Ideology Name')))
# Create markdown content
markdown_content = []
for ideology in unique_ideologies:
if ideology in ideology_descriptions:
markdown_content.append(f"**{ideology}**: {ideology_descriptions[ideology]}")
return dcc.Markdown("\n\n".join(markdown_content), className='markdown-content')
if __name__ == '__main__':
app.run_server(host='0.0.0.0', port=8050)
app.clientside_callback(
"""
function(n_clicks, current_data) {
if (!n_clicks) return current_data;
const pinnedRows = current_data.filter(row => row.pinned);
const unpinnedRows = current_data.filter(row => !row.pinned);
return [...pinnedRows, ...unpinnedRows];
}
""",
Output('leaderboard-grid', 'rowData'),
Input('leaderboard-grid', 'cellRendererData'),
State('leaderboard-grid', 'rowData')
)
app.clientside_callback(
"""
function(n_clicks) {
if (!window.gridApi) return;
console.log('Filter changed');
const pinnedRows = window.gridApi.getGridOption('pinnedTopRowData') || [];
console.log('Current pinned rows:', pinnedRows.map(r => r.Model_Display));
if (pinnedRows.length > 0) {
const pinnedIds = new Set(pinnedRows.map(row => row.Model_Display));
const currentRows = [];
window.gridApi.forEachNode(node => {
if (!pinnedIds.has(node.data.Model_Display)) {
currentRows.push(node.data);
}
});
console.log('Filtering out pinned rows');
window.gridApi.setGridOption('rowData', currentRows);
}
return window.dash_clientside.no_update;
}
""",
Output('leaderboard-grid', 'rowData'),
Input('model-type-filter', 'value')
) |