Update app.py

app.py

@@ -1,122 +1,305 @@
 import gradio as gr
 import pandas as pd
 from apscheduler.schedulers.background import BackgroundScheduler
+# Removed Hugging Face Hub imports as they are not needed for the simplified leaderboard
 
-"""
-MLE‑Dojo Leaderboard — polished Gradio app (v2.4)
--------------------------------------------------
-Displays Elo scores of mainstream LLMs across MLE‑Dojo benchmark categories
-with a clean, modern aesthetic.
-"""
-
-# ─────────────────────────────────────────────────────────────────────────────
-# 0️⃣  Safe imports (fallback to placeholders when src.* unavailable)
-# ─────────────────────────────────────────────────────────────────────────────
+# --- Make sure these imports work relative to your file structure ---
+# Option 1: If src is a directory in the same folder as your script:
 try:
-    from src.about import (
-        CITATION_BUTTON_LABEL,
-        CITATION_BUTTON_TEXT,
-        INTRODUCTION_TEXT,
-        LLM_BENCHMARKS_TEXT,
-        TITLE as RAW_TITLE,
-    )
-    from src.display.css_html_js import custom_css as EXTRA_CSS
-    from src.envs import REPO_ID
-    from src.submission.submit import add_new_eval
-    print("✓ Imported project‑local definitions from src.*")
+    from src.about import (
+        CITATION_BUTTON_LABEL,
+        CITATION_BUTTON_TEXT,
+        EVALUATION_QUEUE_TEXT, # Keep if used by commented-out submit tab
+        INTRODUCTION_TEXT,
+        LLM_BENCHMARKS_TEXT,
+        TITLE,
+    )
+    from src.display.css_html_js import custom_css # Assuming this exists but might be empty
+    from src.envs import REPO_ID # Keep if needed for restart_space or other functions
+    from src.submission.submit import add_new_eval # Keep if using the submit tab
+    print("Successfully imported from src module.")
+# Option 2: If you don't have these files, define placeholders (REMOVE THIS if using Option 1)
 except ImportError:
-    print("⚠  src.* imports failed — using internal placeholders.")
-    CITATION_BUTTON_LABEL = "Citation"
-    CITATION_BUTTON_TEXT  = "Please cite us if you use this benchmark …"
-    INTRODUCTION_TEXT     = "Welcome to the **MLE‑Dojo** benchmark leaderboard."
-    LLM_BENCHMARKS_TEXT   = "Further details about our evaluation suite."
-    RAW_TITLE             = None
-    EXTRA_CSS             = ""
-    REPO_ID               = ""
-    def add_new_eval(*_):
-        return "Submission placeholder."
-
-# ─────────────────────────────────────────────────────────────────────────────
-# 1️⃣  Static data (replace with live data source if desired)
-# ─────────────────────────────────────────────────────────────────────────────
-MODELS = [
-    {"model_name": "gpt-4o-mini",   "url": "https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/", "organizer": "OpenAI", "license": "Proprietary", "MLE-Lite_Elo": 753,  "Tabular_Elo": 839,  "NLP_Elo": 758,  "CV_Elo": 754,  "Overall": 778},
-    {"model_name": "gpt-4o",        "url": "https://openai.com/index/hello-gpt-4o/",                                         "organizer": "OpenAI", "license": "Proprietary", "MLE-Lite_Elo": 830,  "Tabular_Elo": 861,  "NLP_Elo": 903,  "CV_Elo": 761,  "Overall": 841},
-    {"model_name": "o3-mini",       "url": "https://openai.com/index/openai-o3-mini/",                                       "organizer": "OpenAI", "license": "Proprietary", "MLE-Lite_Elo": 1108, "Tabular_Elo": 1019, "NLP_Elo": 1056, "CV_Elo": 1207, "Overall": 1096},
-    {"model_name": "deepseek-v3",   "url": "https://api-docs.deepseek.com/news/news1226",                                   "organizer": "DeepSeek", "license": "DeepSeek",     "MLE-Lite_Elo": 1004, "Tabular_Elo": 1015, "NLP_Elo": 1028, "CV_Elo": 1067, "Overall": 1023},
-    {"model_name": "deepseek-r1",   "url": "https://api-docs.deepseek.com/news/news250120",                                 "organizer": "DeepSeek", "license": "DeepSeek",     "MLE-Lite_Elo": 1137, "Tabular_Elo": 1053, "NLP_Elo": 1103, "CV_Elo": 1083, "Overall": 1100},
-    {"model_name": "gemini-2.0-flash", "url": "https://ai.google.dev/gemini-api/docs/models#gemini-2.0-flash",               "organizer": "Google",   "license": "Proprietary", "MLE-Lite_Elo": 847,  "Tabular_Elo": 923,  "NLP_Elo": 860,  "CV_Elo": 978,  "Overall": 895},
-    {"model_name": "gemini-2.0-pro",   "url": "https://blog.google/technology/google-deepmind/gemini-model-updates-february-2025/", "organizer": "Google",   "license": "Proprietary", "MLE-Lite_Elo": 1064, "Tabular_Elo": 1139, "NLP_Elo": 1028, "CV_Elo": 973,  "Overall": 1054},
-    {"model_name": "gemini-2.5-pro",   "url": "https://deepmind.google/technologies/gemini/pro/",                           "organizer": "Google",   "license": "Proprietary", "MLE-Lite_Elo": 1257, "Tabular_Elo": 1150, "NLP_Elo": 1266, "CV_Elo": 1177, "Overall": 1214},
+    print("Warning: Using placeholder values because src module imports failed.")
+    CITATION_BUTTON_LABEL="Citation"
+    CITATION_BUTTON_TEXT="Please cite us if you use this benchmark..."
+    EVALUATION_QUEUE_TEXT="Current evaluation queue:"
+    INTRODUCTION_TEXT="Welcome to the MLE-Dojo Benchmark Leaderboard."
+    LLM_BENCHMARKS_TEXT="Information about the benchmarks..."
+    TITLE="<h1>🏆 MLE-Dojo Benchmark Leaderboard</h1>"
+    custom_css="" # Start with empty CSS if not imported
+    REPO_ID="your/space-id" # Replace with actual ID if needed
+    def add_new_eval(*args): return "Submission placeholder."
+# --- End Placeholder Definitions ---
+
+
+# --- Elo Leaderboard Configuration ---
+# Enhanced data with Rank (placeholder), Organizer, License, and URL
+# !!! IMPORTANT: Replace placeholder URLs with actual model/project pages. !!!
+# Verify organizer and license information for accuracy.
+data = [
+{'model_name': 'gpt-4o-mini', 'url': 'https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/', 'organizer': 'OpenAI', 'license': 'Proprietary', 'MLE-Lite_Elo': 753, 'Tabular_Elo': 839, 'NLP_Elo': 758, 'CV_Elo': 754, 'Overall': 778},
+{'model_name': 'gpt-4o', 'url': 'https://openai.com/index/hello-gpt-4o/', 'organizer': 'OpenAI', 'license': 'Proprietary', 'MLE-Lite_Elo': 830, 'Tabular_Elo': 861, 'NLP_Elo': 903, 'CV_Elo': 761, 'Overall': 841},
+{'model_name': 'o3-mini', 'url': 'https://openai.com/index/openai-o3-mini/', 'organizer': 'OpenAI', 'license': 'Proprietary', 'MLE-Lite_Elo': 1108, 'Tabular_Elo': 1019, 'NLP_Elo': 1056, 'CV_Elo': 1207, 'Overall': 1096}, # Fill details later
+{'model_name': 'deepseek-v3', 'url': 'https://api-docs.deepseek.com/news/news1226', 'organizer': 'DeepSeek', 'license': 'DeepSeek', 'MLE-Lite_Elo': 1004, 'Tabular_Elo': 1015, 'NLP_Elo': 1028, 'CV_Elo': 1067, 'Overall': 1023},
+{'model_name': 'deepseek-r1', 'url': 'https://api-docs.deepseek.com/news/news250120', 'organizer': 'DeepSeek', 'license': 'DeepSeek', 'MLE-Lite_Elo': 1137, 'Tabular_Elo': 1053, 'NLP_Elo': 1103, 'CV_Elo': 1083, 'Overall': 1100},
+{'model_name': 'gemini-2.0-flash', 'url': 'https://ai.google.dev/gemini-api/docs/models#gemini-2.0-flash', 'organizer': 'Google', 'license': 'Proprietary', 'MLE-Lite_Elo': 847, 'Tabular_Elo': 923, 'NLP_Elo': 860, 'CV_Elo': 978, 'Overall': 895},
+{'model_name': 'gemini-2.0-pro', 'url': 'https://blog.google/technology/google-deepmind/gemini-model-updates-february-2025/', 'organizer': 'Google', 'license': 'Proprietary', 'MLE-Lite_Elo': 1064, 'Tabular_Elo': 1139, 'NLP_Elo': 1028, 'CV_Elo': 973, 'Overall': 1054},
+{'model_name': 'gemini-2.5-pro', 'url': 'https://deepmind.google/technologies/gemini/pro/', 'organizer': 'Google', 'license': 'Proprietary', 'MLE-Lite_Elo': 1257, 'Tabular_Elo': 1150, 'NLP_Elo': 1266, 'CV_Elo': 1177, 'Overall': 1214},
 ]
-master_df = pd.DataFrame(MODELS)
-
-CATEGORIES = ["Overall", "MLE-Lite", "Tabular", "NLP", "CV"]
-COL_MAP = {
-    "Overall": "Overall",
-    "MLE-Lite": "MLE-Lite_Elo",
-    "Tabular": "Tabular_Elo",
-    "NLP": "NLP_Elo",
-    "CV": "CV_Elo",
+
+# Create a master DataFrame
+# Note: Columns 'organizer' and 'license' are created in lowercase here.
+master_df = pd.DataFrame(data)
+
+# Define categories for selection (user-facing)
+CATEGORIES = ["Overall", "MLE-Lite", "Tabular", "NLP", "CV"] # Overall first
+DEFAULT_CATEGORY = "Overall" # Set a default category
+
+# Map user-facing categories to DataFrame column names
+category_to_column = {
+    "MLE-Lite": "MLE-Lite_Elo",
+    "Tabular": "Tabular_Elo",
+    "NLP": "NLP_Elo",
+    "CV": "CV_Elo",
+    "Overall": "Overall"
 }
 
-# ─────────────────────────────────────────────────────────────────────────────
-# 2️⃣  Helper to produce leaderboard DataFrame
-# ─────────────────────────────────────────────────────────────────────────────
-
-def make_leaderboard(category: str = "Overall") -> pd.DataFrame:
-    col = COL_MAP.get(category, "Overall")
-    df = (
-        master_df.loc[:, ["model_name", "url", "organizer", "license", col]]
-        .rename(columns={"model_name": "Model", col: "Elo", "organizer": "Organizer", "license": "License"})
-        .sort_values("Elo", ascending=False)
-        .assign(Rank=lambda d: range(1, len(d) + 1))
-        .loc[:, ["Rank", "Model", "Organizer", "License", "Elo", "url"]]
-    )
-    df["Model"] = df.apply(lambda r: f"<a href='{r.url}' target='_blank'>{r.Model}</a>", axis=1)
-    return df.drop(columns=["url"]).reset_index(drop=True)
-
-# ─────────────────────────────────────────────────────────────────────────────
-# 3️⃣  Theme + CSS
-# ─────────────────────────────────────────────────────────────────────────────
-THEME = gr.themes.Soft(
-    primary_hue="cyan",
-    neutral_hue="slate",
-    font=["Inter", "Helvetica", "Arial", "sans-serif"],
-)
-
-BASE_CSS = r"""
-@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');
-html { scroll-behavior:smooth; }
-body  { font-family:'Inter',system-ui,Arial,sans-serif; background:#f9fafb; color:#1f2937; }
-.hero-title { font-size:3rem; font-weight:700; line-height:1.1; margin:.2em 0 .1em; background:linear-gradient(90deg,#06b6d4,#6366f1); -webkit-background-clip:text; color:transparent; }
-.subtitle   { font-size:1.25rem; color:#4b5563; margin-bottom:1.5rem; }
-.gr-radio { flex-direction:row !important; gap:.5rem; }
-.gr-radio label { padding:.45em 1.2em; border:1px solid #e5e7eb; border-radius:9999px; cursor:pointer; transition:all .15s ease; font-size:.95rem; }
-.gr-radio input[type=radio]:checked+span { background:#06b6d4; color:white; border-color:#06b6d4; }
-.gr-radio label:hover { box-shadow:0 0 0 2px #bae6fd inset; }
-#leaderboard-table { background:white; border-radius:.75rem; box-shadow:0 4px 14px rgba(0,0,0,.05); overflow:hidden; }
-#leaderboard-table table { width:100%; border-collapse:collapse; }
-#leaderboard-table th, #leaderboard-table td { padding:.75em 1em; text-align:left; border-bottom:1px solid #f1f5f9; }
-#leaderboard-table th { background:#f8fafc; font-weight:600; color:#475569; }
-#leaderboard-table tr:nth-child(even) { background:#f9fafb; }
-#leaderboard-table tr:hover { background:#ecfeff; }
-#leaderboard-table td a { color:#2563eb; text-decoration:none; }
-#leaderboard-table td a:hover { text-decoration:underline; }
-.gr-accordion label { font-weight:600; }
-.gr-button { font-weight:500; padding:.55em 1.4em; }
-@media (max-width:480px){ .hero-title{font-size:2.4rem;} .subtitle{font-size:1.05rem;} }
+# --- Helper function to update leaderboard ---
+def update_leaderboard(category):
+    """
+    Selects relevant columns, sorts by the chosen category's Elo score,
+    adds Rank, formats model name as a link, and returns the DataFrame.
+    """
+    score_column = category_to_column.get(category)
+    if score_column is None or score_column not in master_df.columns:
+        print(f"Warning: Invalid category '{category}' or column '{score_column}'. Falling back to default.")
+        score_column = category_to_column[DEFAULT_CATEGORY]
+        # Check fallback column too
+        if score_column not in master_df.columns:
+            # Return empty df with correct columns if still invalid
+            # Use lowercase keys here consistent with master_df for the empty case
+            print(f"Error: Default column '{score_column}' also not found.")
+            return pd.DataFrame({
+                "Rank": [],
+                "Model": [],
+                "Elo Score": [],
+                "Organizer": [], # Changed 'organizer' -> 'Organizer' for consistency in empty case
+                "License": []   # Changed 'license' -> 'License' for consistency in empty case
+            })
+
+    # Select base columns + the score column for sorting
+    # Ensure 'organizer' and 'license' are selected correctly (lowercase)
+    cols_to_select = ['model_name', 'url', 'organizer', 'license', score_column]
+    df = master_df[cols_to_select].copy()
+
+    # Sort by the selected 'Elo Score' descending
+    df.sort_values(by=score_column, ascending=False, inplace=True)
+
+    # Add Rank based on the sorted order
+    df.reset_index(drop=True, inplace=True)
+    df.insert(0, 'Rank', df.index + 1)
+
+    # Format Model Name as HTML Hyperlink
+    # The resulting column name will be 'Model' (capitalized)
+    df['Model'] = df.apply(
+        lambda row: f"<a href='{row['url'] if pd.notna(row['url']) else '#'}' target='_blank' style='color: #007bff; text-decoration: none;'>{row['model_name']}</a>",
+        axis=1
+    )
+
+    # Rename the score column to 'Elo Score' for consistent display
+    df.rename(columns={score_column: 'Elo Score'}, inplace=True)
+
+    # Rename 'organizer' and 'license' to match desired display headers
+    df.rename(columns={'organizer': 'Organizer', 'license': 'License'}, inplace=True)
+
+    # Select and reorder columns for final display using the ACTUAL column names in df
+    # Use capitalized 'Organizer' and 'License' here because they have been renamed.
+    final_columns = ["Rank", "Model", "Organizer", "License", "Elo Score"]
+    df = df[final_columns]
+
+    # Note: The DataFrame returned now has columns:
+    # 'Rank', 'Model', 'Organizer', 'License', 'Elo Score'
+    return df
+
+# --- Mock/Placeholder functions/data for other tabs ---
+# (If the Submit tab is used, ensure these variables are appropriately populated or handled)
+print("Warning: Evaluation queue data fetching is disabled/mocked due to leaderboard changes.")
+finished_eval_queue_df = pd.DataFrame(columns=["Model", "Status", "Requested", "Started"])
+running_eval_queue_df = pd.DataFrame(columns=["Model", "Status", "Requested", "Started"])
+pending_eval_queue_df = pd.DataFrame(columns=["Model", "Status", "Requested", "Started"])
+EVAL_COLS = ["Model", "Status", "Requested", "Started"] # Define for the dataframe headers
+EVAL_TYPES = ["str", "str", "str", "str"] # Define for the dataframe types
+
+# --- Keep restart function if relevant ---
+def restart_space():
+    # Make sure REPO_ID is correctly defined/imported if this function is used
+    print(f"Attempting to restart space: {REPO_ID}")
+    # Replace with your actual space restart mechanism if needed (e.g., HfApi().restart_space(REPO_ID))
+
+
+# --- Gradio App Definition ---
+
+# ***** FONT SIZE INCREASED HERE *****
+# Add CSS rules to make the base font size larger.
+# Adjust the '1.2em' value (e.g., to '1.4em', '16px') to change the size.
+# The !important flag helps override theme defaults.
+# If the imported custom_css already has content, append to it.
+font_size_css = """
+body {
+    font-size: 1.5em !important; /* Increase base font size */
+}
+/* Optional: Target specific elements if needed */
+/*
+#leaderboard-table th, #leaderboard-table td {
+    font-size: 1em !important; /* Adjust table font size relative to new body size */
+    padding: 5px 7px !important; /* Increase padding for better spacing */
+}
+h1, .markdown-text h1 { font-size: 2.2em !important; } /* Make main title larger */
+h2, .markdown-text h2 { font-size: 1.8em !important; } /* Make section titles larger */
+button { font-size: 1.1em !important; padding: 8px 16px !important; } /* Slightly larger buttons */
+.gr-input, .gr-dropdown, .gr-textbox textarea { font-size: 1em !important; } /* Ensure inputs scale too */
+*/
 """
+# Append the new CSS to any existing custom_css
+custom_css += font_size_css
 
-custom_css = EXTRA_CSS + BASE_CSS
+# Add basic table styling if not already present
+if "table {" not in custom_css:
+    custom_css += """
+table { width: 100%; border-collapse: collapse; margin-top: 10px; margin-bottom: 10px; }
+th, td { padding: 8px 12px; border: 1px solid #ddd; text-align: left; white-space: normal; vertical-align: top; } /* Allow wrapping, top align */
+th { background-color: #f2f2f2; font-weight: bold; }
+tr:nth-child(even) { background-color: #f9f9f9; }
+tr:hover { background-color: #e9e9e9; }
+td a { color: #007bff; text-decoration: none; }
+td a:hover { text-decoration: underline; }
+"""
 
-# ─────────────────────────────────────────────────────────────────────────────
-# 4️⃣  Build Gradio UI
-# ─────────────────────────────────────────────────────────────────────────────
-demo = gr.Blocks(css=custom_css, theme=THEME)
+# Use a theme for better default styling
+demo = gr.Blocks(css=custom_css, theme=gr.themes.Soft())
 
 with demo:
-    # Hero section
-    gr.HTML(RAW_TITLE or (
-        """
-        <h1 class='hero
+    # Use the TITLE variable imported or defined above
+    gr.HTML(TITLE)
+
+    # Use the INTRODUCTION_TEXT variable imported or defined above
+    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("🏅 MLE-Dojo Benchmark", elem_id="llm-benchmark-tab-table", id=0):
+            with gr.Column():
+                gr.Markdown("## Model Elo Rankings by Category")
+                category_selector = gr.Radio(
+                    choices=CATEGORIES,
+                    label="Select Category:",
+                    value=DEFAULT_CATEGORY,
+                    interactive=True,
+                )
+                leaderboard_df_component = gr.Dataframe(
+                    # Initialize with sorted data for the default category
+                    value=update_leaderboard(DEFAULT_CATEGORY),
+                    # Headers for DISPLAY should match the *renamed* columns from update_leaderboard
+                    headers=["Rank", "Model", "Organizer", "License", "Elo Score"],
+                    # Datatype maps to the final df columns: Rank, Model, Organizer, License, Elo Score
+                    datatype=["number", "html", "str", "str", "number"],
+                    interactive=False,
+                    # --- FIX APPLIED: Removed unsupported 'height' argument ---
+                    # row_count determines the number of rows to display
+                    row_count=(len(master_df), "fixed"), # Display all rows
+                    col_count=(5, "fixed"),
+                    wrap=True, # Allow text wrapping in cells
+                    elem_id="leaderboard-table" # CSS hook for custom styling
+                )
+                # Link the radio button change to the update function
+                category_selector.change(
+                    fn=update_leaderboard,
+                    inputs=category_selector,
+                    outputs=leaderboard_df_component
+                )
+
+        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-about", id=1):
+            # Use the LLM_BENCHMARKS_TEXT variable imported or defined above
+            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+
+        # --- Submit Tab (Commented out as in original request) ---
+        # Make sure EVALUATION_QUEUE_TEXT and add_new_eval are imported/defined if uncommented
+        # with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-submit", id=2):
+        #     with gr.Column():
+        #          with gr.Row():
+        #              gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") # Requires import/definition
+        #          with gr.Column():
+        #              with gr.Accordion(f"✅ Finished Evaluations ({len(finished_eval_queue_df)})", open=False):
+        #                   finished_eval_table = gr.components.Dataframe(
+        #                       value=finished_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5,
+        #                  )
+        #              with gr.Accordion(f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})", open=False):
+        #                   running_eval_table = gr.components.Dataframe(
+        #                       value=running_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5,
+        #                  )
+        #              with gr.Accordion(f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})", open=False):
+        #                  pending_eval_table = gr.components.Dataframe(
+        #                      value=pending_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5,
+        #                  )
+        #     with gr.Row():
+        #          gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+        #     with gr.Row():
+        #          with gr.Column():
+        #              model_name_textbox = gr.Textbox(label="Model name (on Hugging Face Hub)")
+        #              revision_name_textbox = gr.Textbox(label="Revision / Commit Hash", placeholder="main")
+        #              model_type = gr.Dropdown(choices=["Type A", "Type B", "Type C"], label="Model type", multiselect=False, value=None, interactive=True) # Example choices
+        #          with gr.Column():
+        #              precision = gr.Dropdown(choices=["float16", "bfloat16", "float32", "int8", "auto"], label="Precision", multiselect=False, value="auto", interactive=True)
+        #              weight_type = gr.Dropdown(choices=["Original", "Adapter", "Delta"], label="Weights type", multiselect=False, value="Original", interactive=True)
+        #              base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
+        #     submit_button = gr.Button("Submit Eval")
+        #     submission_result = gr.Markdown()
+        #     # Ensure add_new_eval is correctly imported/defined and handles these inputs
+        #     submit_button.click(
+        #          add_new_eval, # Requires import/definition
+        #          [ model_name_textbox, base_model_name_textbox, revision_name_textbox, precision, weight_type, model_type, ],
+        #          submission_result,
+        #      )
+
+    # --- Citation Row (at the bottom, outside Tabs) ---
+    with gr.Accordion("📙 Citation", open=False):
+        # Use the CITATION_BUTTON_TEXT and CITATION_BUTTON_LABEL variables imported or defined above
+        citation_button = gr.Textbox(
+            value=CITATION_BUTTON_TEXT,
+            label=CITATION_BUTTON_LABEL,
+            lines=10, # Adjust lines if needed for new font size
+            elem_id="citation-button",
+            show_copy_button=True,
+        )
+
+# IGNORE_WHEN_COPYING_START
+# content_copy  download
+# Use code with caution.
+# IGNORE_WHEN_COPYING_END
+
+# --- Keep scheduler if relevant ---
+# Only start scheduler if the script is run directly
+if __name__ == "__main__":
+    try:
+        scheduler = BackgroundScheduler()
+        # Add job only if restart_space is callable (i.e., not a placeholder or failed import)
+        if callable(restart_space):
+             # Check if REPO_ID seems valid before scheduling
+             if REPO_ID and REPO_ID != "your/space-id":
+                 scheduler.add_job(restart_space, "interval", seconds=1800) # Restart every 30 mins
+                 scheduler.start()
+             else:
+                 print("Warning: REPO_ID not set or is placeholder; space restart job not scheduled.")
+        else:
+             print("Warning: restart_space function not available; space restart job not scheduled.")
+    except Exception as e:
+        print(f"Failed to initialize or start scheduler: {e}")
+
+
+# --- Launch the app ---
+# Ensures the app launches only when the script is run directly
+if __name__ == "__main__":
+    # Ensure you have installed necessary libraries: pip install gradio pandas apscheduler
+    # Make sure your src module files (about.py etc.) are accessible OR use the placeholder definitions above.
+    print("Launching Gradio App...")
+    demo.launch()