import gradio as gr from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns from apscheduler.schedulers.background import BackgroundScheduler from huggingface_hub import snapshot_download import pandas as pd import os import logging from datetime import datetime from src.core.evaluation import EvaluationManager, EvaluationRequest from src.core.queue_manager import QueueManager from src.logging_config import setup_logging from src.about import ( CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, EVALUATION_QUEUE_TEXT, INTRODUCTION_TEXT, LLM_BENCHMARKS_TEXT, TITLE, ) from src.display.css_html_js import custom_css from src.display.utils import ( BENCHMARK_COLS, COLS, EVAL_COLS, EVAL_TYPES, AutoEvalColumn, ModelType, fields, WeightType, Precision ) from src.envs import ( API, CACHE_PATH, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN ) from src.populate import get_evaluation_queue_df, get_leaderboard_df from src.submission.submit import add_new_eval # Setup logging setup_logging(log_dir="logs") logger = logging.getLogger('web') # Initialize managers evaluation_manager = EvaluationManager( results_dir=EVAL_RESULTS_PATH, backup_dir=os.path.join(CACHE_PATH, "eval-backups") ) queue_manager = QueueManager( queue_dir=os.path.join(CACHE_PATH, "eval-queue") ) def restart_space(): """Restart the Hugging Face space.""" logger.info("Restarting space") API.restart_space(repo_id=REPO_ID) def initialize_space(): """Initialize the space by downloading required data.""" logger.info("Initializing space") try: logger.info(f"Downloading queue data from {QUEUE_REPO}") 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: logger.error(f"Failed to download queue data: {str(e)}") restart_space() try: logger.info(f"Downloading results data from {RESULTS_REPO}") 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: logger.error(f"Failed to download results data: {str(e)}") restart_space() # Initialize space initialize_space() LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS) ( finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df, ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS) def process_evaluation_queue(): """Process pending evaluation requests.""" logger.info("Processing evaluation queue") while True: request = queue_manager.get_next_request() if not request: break try: # Convert queue request to evaluation request eval_request = EvaluationRequest( model_id=request.model_id, revision=request.revision, precision="float16", # Default precision weight_type="Safetensors", submitted_time=request.timestamp ) # Run evaluation results = evaluation_manager.run_evaluation(eval_request) logger.info(f"Evaluation complete for {request.model_id}") # Mark request as complete queue_manager.mark_complete(request.request_id) except Exception as e: logger.error(f"Evaluation failed for {request.model_id}: {str(e)}") # Keep request in active queue for retry def init_leaderboard(df): """Initialize the leaderboard with the given DataFrame.""" if df is None or df.empty: df = pd.DataFrame(columns=COLS) logger.info("Creating empty leaderboard - no evaluations completed yet") # Create the leaderboard return gr.Dataframe( headers=COLS, datatype=["str"] * len(COLS), row_count=10, col_count=(len(COLS), "fixed"), value=df, wrap=True, column_widths=[50] + [None] * (len(COLS) - 1), type="pandas", ) 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("🔒 Security Leaderboard", elem_id="security-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 Model", elem_id="submit-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"✅ Finished Evaluations ({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"🔄 Running Evaluation Queue ({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 Evaluation 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 Your Model for Security Evaluation", elem_classes="markdown-text") with gr.Row(): with gr.Column(): model_name_textbox = gr.Textbox( label="Model name (organization/model-name)", placeholder="huggingface/model-name" ) revision_name_textbox = gr.Textbox( label="Revision commit", placeholder="main" ) model_type = gr.Dropdown( choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown], label="Model type", multiselect=False, value=None, interactive=True, ) with gr.Column(): precision = gr.Dropdown( choices=[i.value.name for i in Precision if i != Precision.Unknown], label="Precision", multiselect=False, value="float16", interactive=True, ) weight_type = gr.Dropdown( choices=[i.value.name for i in WeightType], label="Weight Format", multiselect=False, value="Safetensors", interactive=True, ) base_model_name_textbox = gr.Textbox( label="Base model (for delta or adapter weights)", placeholder="Optional: base model path" ) with gr.Row(): gr.Markdown( """ ### Security Requirements: 1. Model weights must be in safetensors format 2. Model card must include security considerations 3. Model will be evaluated on secure coding capabilities """, elem_classes="markdown-text" ) submit_button = gr.Button("Submit for Security Evaluation") submission_result = gr.Markdown() def handle_submission(model, base_model, revision, precision, weight_type, model_type): """Handle new model submission.""" try: logger.info(f"New submission received for {model}") # Add to queue request_id = queue_manager.add_request( model_id=model, revision=revision if revision else "main" ) # Process queue process_evaluation_queue() return gr.Markdown("Submission successful! Your model has been added to the evaluation queue.") except Exception as e: logger.error(f"Submission failed: {str(e)}") return gr.Markdown(f"Error: {str(e)}") submit_button.click( handle_submission, [ model_name_textbox, base_model_name_textbox, revision_name_textbox, precision, weight_type, model_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, ) # Setup schedulers scheduler = BackgroundScheduler() scheduler.add_job(restart_space, "interval", seconds=1800) scheduler.add_job(process_evaluation_queue, "interval", seconds=300) # Process queue every 5 minutes scheduler.start() logger.info("Application startup complete") demo.queue(default_concurrency_limit=40).launch()