Delete display

display/css_html_js.py

@@ -1,282 +0,0 @@
-custom_css = """
-
-.markdown-text {
-    font-size: 16px !important;
-    line-height: 1.6 !important;
-}
-
-.gradio-dataframe table {
-    table-layout: auto;   /* 根据内容自动分配列宽 */
-    width: 100%;          /* 占满容器宽度 */
-}
-
-.gradio-dataframe th,
-.gradio-dataframe td {
-    white-space: nowrap;          /* 不换行 */
-    padding: 6px 10px;            /* 内边距更美观 */
-}
-
-/* Enhanced Leaderboard table styling */
-.dataframe {
-    background: white !important;
-    border-radius: 12px !important;
-    box-shadow: 0 4px 16px rgba(0, 0, 0, 0.08) !important;
-    overflow: hidden !important;
-    border: 1px solid #e8ecef !important;
-    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', system-ui, sans-serif !important;
-}
-
-.dataframe th {
-    background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%) !important;
-    color: #2c3e50 !important;
-    font-weight: 600 !important;
-    font-size: 11px !important;
-    padding: 16px 12px !important;
-    text-align: center !important;
-    border-bottom: 2px solid #dee2e6 !important;
-    letter-spacing: 0.025em !important;
-    text-transform: uppercase !important;
-}
-
-/* Override any conflicting styles */
-.dataframe thead th {
-    font-size: 11px !important;
-}
-
-.dataframe th span {
-    font-size: 11px !important;
-}
-
-.dataframe td {
-    padding: 14px 12px !important;
-    border-bottom: 1px solid #f0f2f5 !important;
-    text-align: center !important;
-    vertical-align: middle !important;
-    font-size: 15px !important;
-    color: #2c3e50 !important;
-    line-height: 1.4 !important;
-}
-
-.dataframe tr:hover td {
-    background-color: #f8f9fa !important;
-    transition: background-color 0.2s ease !important;
-}
-
-.dataframe tr:nth-child(even) td {
-    background-color: #fdfdfd !important;
-}
-
-/* Enhanced hyperlinks in table */
-.dataframe a {
-    color: #0066cc !important;
-    text-decoration: underline !important;
-    font-weight: 500 !important;
-    transition: all 0.2s ease !important;
-    border-radius: 4px !important;
-    padding: 2px 6px !important;
-    display: inline-block !important;
-}
-
-.dataframe a:hover {
-    color: #004499 !important;
-    background-color: rgba(0, 102, 204, 0.1) !important;
-    text-decoration: underline !important;
-    transform: translateY(-1px) !important;
-}
-
-.dataframe a:visited {
-    color: #5a6c7d !important;
-}
-
-/* Model name styling (assuming first column contains model names) */
-.dataframe td:first-child {
-    font-weight: 600 !important;
-    color: #1a202c !important;
-    text-align: left !important;
-    padding-left: 16px !important;
-}
-
-/* Score highlighting */
-.dataframe td:last-child {
-    font-weight: 600 !important;
-    font-size: 16px !important;
-}
-
-#models-to-add-text {
-    font-size: 18px !important;
-}
-
-#citation-button span {
-    font-size: 16px !important;
-}
-
-#citation-button textarea {
-    font-size: 16px !important;
-}
-
-#citation-button > label > button {
-    margin: 6px;
-    transform: scale(1.3);
-}
-
-/* Citation section styling */
-#citation-textbox textarea {
-    background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%) !important;
-    border: 2px solid #dee2e6 !important;
-    border-radius: 12px !important;
-    font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace !important;
-    font-size: 12px !important;
-    padding: 20px !important;
-    line-height: 1.6 !important;
-    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1) !important;
-}
-
-#citation-textbox label > button {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
-    border: none !important;
-    border-radius: 8px !important;
-    color: white !important;
-    padding: 8px 16px !important;
-    margin: 8px !important;
-    transform: scale(1.1);
-    transition: transform 0.2s ease;
-}
-
-#citation-textbox label > button:hover {
-    transform: scale(1.15) !important;
-}
-
-#leaderboard-table {
-    margin-top: 25px !important;
-}
-
-#leaderboard-table-lite {
-    margin-top: 25px !important;
-}
-
-#search-bar-table-box > div:first-child {
-    background: none;
-    border: none;
-}
- 
-#search-bar {
-    padding: 0px;
-}
-
-/* Enhanced table column widths and responsiveness */
-#leaderboard-table td:nth-child(2),
-#leaderboard-table th:nth-child(2) {
-    max-width: 400px;
-    overflow: hidden;
-    text-overflow: ellipsis;
-    white-space: nowrap;
-}
-
-/* Responsive table improvements */
-.dataframe {
-    width: 100% !important;
-    margin: 16px 0 !important;
-}
-
-/* Better mobile responsiveness */
-@media (max-width: 768px) {
-    .dataframe th,
-    .dataframe td {
-        padding: 8px 6px !important;
-        font-size: 13px !important;
-    }
-    
-    .dataframe th {
-        font-size: 12px !important;
-    }
-    
-    #leaderboard-table td:nth-child(2),
-    #leaderboard-table th:nth-child(2) {
-        max-width: 200px;
-    }
-}
-
-/* Rank column special styling */
-.dataframe td:nth-child(1) {
-    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
-    color: white !important;
-    font-weight: 700 !important;
-    font-size: 16px !important;
-    border-radius: 8px !important;
-    margin: 4px !important;
-    min-width: 40px !important;
-}
-
-.dataframe tr:nth-child(1) td:nth-child(1) {
-    background: linear-gradient(135deg, #ffd700 0%, #ffed4e 100%) !important;
-    color: #8b4513 !important;
-}
-
-.dataframe tr:nth-child(2) td:nth-child(1) {
-    background: linear-gradient(135deg, #c0c0c0 0%, #e8e8e8 100%) !important;
-    color: #4a4a4a !important;
-}
-
-.dataframe tr:nth-child(3) td:nth-child(1) {
-    background: linear-gradient(135deg, #cd7f32 0%, #daa520 100%) !important;
-    color: white !important;
-}
-
-.tab-buttons button {
-    font-size: 20px;
-}
-
-#scale-logo {
-    border-style: none !important;
-    box-shadow: none;
-    display: block;
-    margin-left: auto;
-    margin-right: auto;
-    max-width: 600px;
-}
-
-#scale-logo .download {
-    display: none;
-}
-#filter_type{
-    border: 0;
-    padding-left: 0;
-    padding-top: 0;
-}
-#filter_type label {
-    display: flex;
-}
-#filter_type label > span{
-    margin-top: var(--spacing-lg);
-    margin-right: 0.5em;
-}
-#filter_type label > .wrap{
-    width: 103px;
-}
-#filter_type label > .wrap .wrap-inner{  
-    padding: 2px;
-}
-#filter_type label > .wrap .wrap-inner input{
-    width: 1px
-}
-#filter-columns-type{
-    border:0;
-    padding:0.5;
-}
-#filter-columns-size{
-    border:0;
-    padding:0.5;
-}
-#box-filter > .form{
-    border: 0
-}
-"""
-
-
-get_window_url_params = """
-    function(url_params) {
-        const params = new URLSearchParams(window.location.search);
-        url_params = Object.fromEntries(params);
-        return url_params;
-    }
-    """

display/formatting.py

@@ -1,27 +0,0 @@
-def model_hyperlink(link, model_name):
-    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
-
-
-def make_clickable_model(model_name):
-    link = f"https://huggingface.co/{model_name}"
-    return model_hyperlink(link, model_name)
-
-
-def styled_error(error):
-    return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
-
-
-def styled_warning(warn):
-    return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
-
-
-def styled_message(message):
-    return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
-
-
-def has_no_nan_values(df, columns):
-    return df[columns].notna().all(axis=1)
-
-
-def has_nan_values(df, columns):
-    return df[columns].isna().any(axis=1)

display/utils.py

@@ -1,110 +0,0 @@
-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
-@dataclass
-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(["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)])
-#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("Hub License", "str", False)])
-auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
-auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "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)
-
-## For the queue columns in the submission tab
-@dataclass(frozen=True)
-class EvalQueueColumn:  # Queue column
-    model = ColumnContent("model", "markdown", True)
-    revision = ColumnContent("revision", "str", True)
-    private = ColumnContent("private", "bool", True)
-    precision = ColumnContent("precision", "str", True)
-    weight_type = ColumnContent("weight_type", "str", "Original")
-    status = ColumnContent("status", "str", True)
-
-## All the model information that we might need
-@dataclass
-class ModelDetails:
-    name: str
-    display_name: str = ""
-    symbol: str = "" # emoji
-
-
-class ModelType(Enum):
-    PT = ModelDetails(name="pretrained", symbol="🟢")
-    FT = ModelDetails(name="fine-tuned", symbol="🔶")
-    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
-    RL = ModelDetails(name="RL-tuned", symbol="🟦")
-    Unknown = ModelDetails(name="", symbol="?")
-
-    def to_str(self, separator=" "):
-        return f"{self.value.symbol}{separator}{self.value.name}"
-
-    @staticmethod
-    def from_str(type):
-        if "fine-tuned" in type or "🔶" in type:
-            return ModelType.FT
-        if "pretrained" in type or "🟢" in type:
-            return ModelType.PT
-        if "RL-tuned" in type or "🟦" in type:
-            return ModelType.RL
-        if "instruction-tuned" in type or "⭕" in type:
-            return ModelType.IFT
-        return ModelType.Unknown
-
-class WeightType(Enum):
-    Adapter = ModelDetails("Adapter")
-    Original = ModelDetails("Original")
-    Delta = ModelDetails("Delta")
-
-class Precision(Enum):
-    float16 = ModelDetails("float16")
-    bfloat16 = ModelDetails("bfloat16")
-    Unknown = ModelDetails("?")
-
-    def from_str(precision):
-        if precision in ["torch.float16", "float16"]:
-            return Precision.float16
-        if precision in ["torch.bfloat16", "bfloat16"]:
-            return Precision.bfloat16
-        return Precision.Unknown
-
-# 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)]
-
-BENCHMARK_COLS = [t.value.col_name for t in Tasks]
-