import gradio as gr from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns import pandas as pd from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download import os import shutil from src.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE, Tasks ) from src.display.css_html_js import custom_css from src.display.utils import ( BENCHMARK_COLS, COLS, EVAL_COLS, EVAL_TYPES, AutoEvalColumn, LibraryType, fields, Language, AssessmentStatus ) from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN, LOCAL_MODE from src.populate import get_evaluation_queue_df, get_leaderboard_df from src.submission.submit import add_new_eval def restart_space(): """Restart the Hugging Face space""" if LOCAL_MODE: print("Running in local mode, skipping space restart") return try: API.restart_space(repo_id=REPO_ID) except Exception as e: print(f"Failed to restart space: {e}") print("Continuing without restart") ### Space initialisation def initialize_data_directories(): """Initialize directories for assessment data""" # Create local directories if they don't exist os.makedirs(EVAL_REQUESTS_PATH, exist_ok=True) os.makedirs(EVAL_RESULTS_PATH, exist_ok=True) if LOCAL_MODE: print("Running in local mode, using local directories only") return # Try to download from HF if not in local mode try: print(f"Downloading request data from {QUEUE_REPO} to {EVAL_REQUESTS_PATH}") snapshot_download( repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN ) except Exception as e: print(f"Failed to download request data: {e}") print("Using local data only") try: print(f"Downloading result data from {RESULTS_REPO} to {EVAL_RESULTS_PATH}") snapshot_download( repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN ) except Exception as e: print(f"Failed to download result data: {e}") print("Using local data only") # Initialize data initialize_data_directories() # Load data for leaderboard LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) # Load queue data ( finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df, ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) def init_leaderboard(dataframe): """Initialize the leaderboard component""" if dataframe is None or dataframe.empty: # Create an empty dataframe with the expected columns all_columns = COLS + [task.value.col_name for task in Tasks] empty_df = pd.DataFrame(columns=all_columns) print("Warning: Leaderboard DataFrame is empty. Using empty dataframe.") dataframe = empty_df return Leaderboard( value=dataframe, datatype=[c.type for c in fields(AutoEvalColumn)], select_columns=SelectColumns( default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default], cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden], label="Select Columns to Display:", ), search_columns=[AutoEvalColumn.library.name, AutoEvalColumn.license_name.name], hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden], filter_columns=[ ColumnFilter(AutoEvalColumn.library_type.name, type="checkboxgroup", label="Library types"), ColumnFilter(AutoEvalColumn.language.name, type="checkboxgroup", label="Programming Language"), ColumnFilter( AutoEvalColumn.stars.name, type="slider", min=0, max=50000, label="GitHub Stars", ), ColumnFilter( AutoEvalColumn.availability.name, type="boolean", label="Show only active libraries", default=True ), ], bool_checkboxgroup_label="Filter libraries", interactive=False, ) demo = gr.Blocks(css=custom_css) with demo: gr.HTML(TITLE) gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.TabItem("🏅 Vulnerability Leaderboard", elem_id="vulnerability-leaderboard-tab", id=0): leaderboard = init_leaderboard(LEADERBOARD_DF) with gr.TabItem("📝 About", elem_id="about-tab", id=2): gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text") with gr.TabItem("🚀 Submit Library", elem_id="submit-library-tab", id=3): with gr.Column(): with gr.Row(): gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text") with gr.Column(): with gr.Accordion( f"✅ Completed Assessments ({len(finished_eval_queue_df)})", open=False, ): with gr.Row(): finished_eval_table = gr.components.Dataframe( value=finished_eval_queue_df, headers=EVAL_COLS, datatype=EVAL_TYPES, row_count=5, ) with gr.Accordion( f"🔄 In Progress Assessments ({len(running_eval_queue_df)})", open=False, ): with gr.Row(): 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 Assessment Queue ({len(pending_eval_queue_df)})", open=False, ): with gr.Row(): 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 a library for vulnerability assessment", elem_classes="markdown-text") with gr.Row(): with gr.Column(): library_name_textbox = gr.Textbox(label="Library name (org/repo format)") library_version_textbox = gr.Textbox(label="Version", placeholder="v1.0.0") library_type = gr.Dropdown( choices=[t.to_str(" : ") for t in LibraryType if t != LibraryType.Unknown], label="Library type", multiselect=False, value=None, interactive=True, ) with gr.Column(): language = gr.Dropdown( choices=[i.value.name for i in Language if i != Language.Other], label="Programming Language", multiselect=False, value="Python", interactive=True, ) framework = gr.Textbox(label="Framework/Ecosystem (e.g., PyTorch, React)") repository_url = gr.Textbox(label="Repository URL") submit_button = gr.Button("Submit for Assessment") submission_result = gr.Markdown() submit_button.click( add_new_eval, [ library_name_textbox, library_version_textbox, repository_url, language, framework, library_type, ], submission_result, ) with gr.Row(): with gr.Accordion("📙 Citation", open=False): citation_button = gr.Textbox( value=CITATION_BUTTON_TEXT, label=CITATION_BUTTON_LABEL, lines=20, elem_id="citation-button", show_copy_button=True, ) # Only schedule space restarts if not in local mode if not LOCAL_MODE: scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=1800) scheduler.start() # Launch the app demo.queue(default_concurrency_limit=40).launch(show_error=True)