app.py

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("""
    <div class="center-content">
        <h1>🏆 Mobile-MMLU Benchmark Competition</h1>
        <h3>🌟 Welcome to the Competition Overview</h3>
        <img src="https://via.placeholder.com/200" alt="Competition Logo">
        <p>
            Welcome to the Mobile-MMLU Benchmark Competition. Here you can submit your predictions, 
            view the leaderboard, and track your performance!
        </p>
        <hr>
    </div>
    """)

    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()