import gradio as gr import pandas as pd import os import re from datetime import datetime from huggingface_hub import hf_hub_download from huggingface_hub import HfApi, HfFolder LEADERBOARD_FILE = "leaderboard.csv" GROUND_TRUTH_FILE = "ground_truth.csv" LAST_UPDATED = datetime.now().strftime("%B %d, %Y") # Ensure authentication and suppress warnings os.environ["HF_HUB_DISABLE_SYMLINKS_WARNING"] = "1" HF_TOKEN = os.getenv("HF_TOKEN") if not HF_TOKEN: raise ValueError("HF_TOKEN environment variable is not set or invalid.") def initialize_leaderboard_file(): """ Ensure the leaderboard file exists and has the correct headers. """ if not os.path.exists(LEADERBOARD_FILE): pd.DataFrame(columns=[ "Model Name", "Overall Accuracy", "Valid Accuracy", "Correct Predictions", "Total Questions", "Timestamp" ]).to_csv(LEADERBOARD_FILE, index=False) elif os.stat(LEADERBOARD_FILE).st_size == 0: pd.DataFrame(columns=[ "Model Name", "Overall Accuracy", "Valid Accuracy", "Correct Predictions", "Total Questions", "Timestamp" ]).to_csv(LEADERBOARD_FILE, index=False) def clean_answer(answer): if pd.isna(answer): return None answer = str(answer) clean = re.sub(r'[^A-Da-d]', '', answer) return clean[0].upper() if clean else None def update_leaderboard(results): """ Append new submission results to the leaderboard file and push updates to the Hugging Face repository. """ new_entry = { "Model Name": results['model_name'], "Overall Accuracy": round(results['overall_accuracy'] * 100, 2), "Valid Accuracy": round(results['valid_accuracy'] * 100, 2), "Correct Predictions": results['correct_predictions'], "Total Questions": results['total_questions'], "Timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), } try: # Update the local leaderboard file new_entry_df = pd.DataFrame([new_entry]) file_exists = os.path.exists(LEADERBOARD_FILE) new_entry_df.to_csv( LEADERBOARD_FILE, mode='a', # Append mode index=False, header=not file_exists # Write header only if the file is new ) print(f"Leaderboard updated successfully at {LEADERBOARD_FILE}") # Push the updated file to the Hugging Face repository using HTTP API api = HfApi() token = HfFolder.get_token() api.upload_file( path_or_fileobj=LEADERBOARD_FILE, path_in_repo="leaderboard.csv", repo_id="SondosMB/ss", # Your Space repository repo_type="space", token=token ) print("Leaderboard changes pushed to Hugging Face repository.") except Exception as e: print(f"Error updating leaderboard file: {e}") def load_leaderboard(): if not os.path.exists(LEADERBOARD_FILE) or os.stat(LEADERBOARD_FILE).st_size == 0: return pd.DataFrame({ "Model Name": [], "Overall Accuracy": [], "Valid Accuracy": [], "Correct Predictions": [], "Total Questions": [], "Timestamp": [], }) return pd.read_csv(LEADERBOARD_FILE) def evaluate_predictions(prediction_file, model_name, add_to_leaderboard): try: ground_truth_path = hf_hub_download( repo_id="SondosMB/ground-truth-dataset", filename="ground_truth.csv", repo_type="dataset", use_auth_token=True ) ground_truth_df = pd.read_csv(ground_truth_path) except FileNotFoundError: return "Ground truth file not found in the dataset repository.", load_leaderboard() except Exception as e: return f"Error loading ground truth: {e}", load_leaderboard() if not prediction_file: return "Prediction file not uploaded.", load_leaderboard() try: predictions_df = pd.read_csv(prediction_file.name) merged_df = pd.merge(predictions_df, ground_truth_df, on='question_id', how='inner') merged_df['pred_answer'] = merged_df['predicted_answer'].apply(clean_answer) valid_predictions = merged_df.dropna(subset=['pred_answer']) correct_predictions = (valid_predictions['pred_answer'] == valid_predictions['Answer']).sum() total_predictions = len(merged_df) total_valid_predictions = len(valid_predictions) overall_accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0 valid_accuracy = correct_predictions / total_valid_predictions if total_valid_predictions > 0 else 0 results = { 'model_name': model_name if model_name else "Unknown Model", 'overall_accuracy': overall_accuracy, 'valid_accuracy': valid_accuracy, 'correct_predictions': correct_predictions, 'total_questions': total_predictions, } if add_to_leaderboard: update_leaderboard(results) return "Evaluation completed and added to leaderboard.", load_leaderboard() else: return "Evaluation completed but not added to leaderboard.", load_leaderboard() except Exception as e: return f"Error during evaluation: {str(e)}", load_leaderboard() initialize_leaderboard_file() # Function to set default mode # Function to set default mode import gradio as gr # Ensure CSS is correctly defined css_tech_theme = """ body { background-color: #f4f6fa; color: #333333; font-family: 'Roboto', sans-serif; line-height: 1.8; } .center-content { display: flex; flex-direction: column; align-items: center; justify-content: center; text-align: center; margin: 30px 0; padding: 20px; } h1, h3 { color: #5e35b1; margin: 15px 0; text-align: center; } """ # Ensure all required functions and variables are defined def evaluate_predictions(file, model_name, add_to_leaderboard): # Add logic for evaluating predictions return "Evaluation completed", 90.0 # Example return def load_leaderboard(): # Add logic for loading leaderboard return [{"Model Name": "Example", "Accuracy": 90}] LAST_UPDATED = "December 21, 2024" # Create the Gradio Interface with gr.Blocks(css=css_tech_theme) as demo: gr.Markdown("""

🏆 Mobile-MMLU Benchmark Competition

🌟 Welcome to the Competition Overview

Competition Logo

Welcome to the Mobile-MMLU Benchmark Competition. Here you can submit your predictions, view the leaderboard, and track your performance!

""") with gr.Tabs(elem_id="tabs"): with gr.TabItem("📖 Overview"): gr.Markdown(""" ## Overview Welcome to the **Mobile-MMLU Benchmark Competition**! Evaluate mobile-compatible Large Language Models (LLMs) on **16,186 scenario-based and factual questions** across **80 fields**. --- ### What is Mobile-MMLU? Mobile-MMLU is a benchmark designed to test the capabilities of LLMs optimized for mobile use. Contribute to advancing mobile AI systems by competing to achieve the highest accuracy. --- ### How It Works 1. **Download the Dataset** Access the dataset and instructions on our [GitHub page](https://github.com/your-github-repo). 2. **Generate Predictions** Use your LLM to answer the dataset questions. Format your predictions as a CSV file. 3. **Submit Predictions** Upload your predictions on this platform. 4. **Evaluation** Submissions are scored on accuracy. 5. **Leaderboard** View real-time rankings on the leaderboard. --- """) with gr.TabItem("📤 Submission"): with gr.Row(): file_input = gr.File(label="📂 Upload Prediction CSV", file_types=[".csv"], interactive=True) model_name_input = gr.Textbox(label="🖋️ Model Name", placeholder="Enter your model name") with gr.Row(): overall_accuracy_display = gr.Number(label="🏅 Overall Accuracy", interactive=False) add_to_leaderboard_checkbox = gr.Checkbox(label="📊 Add to Leaderboard?", value=True) eval_button = gr.Button("Evaluate", elem_id="evaluate-button") eval_status = gr.Textbox(label="📢 Evaluation Status", interactive=False) eval_button.click( evaluate_predictions, inputs=[file_input, model_name_input, add_to_leaderboard_checkbox], outputs=[eval_status, overall_accuracy_display], ) with gr.TabItem("🏅 Leaderboard"): leaderboard_table = gr.Dataframe( value=load_leaderboard(), label="Leaderboard", interactive=False, wrap=True, ) refresh_button = gr.Button("Refresh Leaderboard") refresh_button.click( lambda: load_leaderboard(), inputs=[], outputs=[leaderboard_table], ) gr.Markdown(f"**Last updated:** {LAST_UPDATED}") demo.launch()