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, h2 {
#     color: #5e35b1;
#     margin: 15px 0;
#     text-align: center;
# }
# img {
#     width: 100px;
#     height: 100px;
# }
# """

# # 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>
#         <h2>🌟 Welcome to the Competition</h2>
#         <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("""
#             **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")
#             eval_status = gr.Textbox(label="Evaluation Status", interactive=False)

#             def handle_evaluation(file, model_name, add_to_leaderboard):
#                 status, leaderboard = evaluate_predictions(file, model_name, add_to_leaderboard)
#                 if leaderboard.empty:
#                     overall_accuracy = 0
#                 else:
#                     overall_accuracy = leaderboard.iloc[-1]["Overall Accuracy"]
#                 return status, overall_accuracy

#             eval_button.click(
#                 handle_evaluation,
#                 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 on **{LAST_UPDATED}**")

# demo.launch()

import gradio as gr

# Custom CSS to match website style
# Define CSS to match a modern, professional design
# Define enhanced CSS for the entire layout
css_tech_theme = """
body {
    font-family: 'Roboto', sans-serif;
    background-color: #f4f6fa;
    color: #333333;
    margin: 0;
    padding: 0;
}

/* Header Styling */
header {
    text-align: center;
    padding: 60px 20px;
    background: linear-gradient(135deg, #6a1b9a, #64b5f6);
    color: #ffffff;
    border-radius: 12px;
    margin-bottom: 30px;
    box-shadow: 0 6px 20px rgba(0, 0, 0, 0.2);
}

header h1 {
    font-size: 3.5em;
    font-weight: bold;
    margin-bottom: 10px;
}

header h2 {
    font-size: 2em;
    margin-bottom: 15px;
}

header p {
    font-size: 1.2em;
    line-height: 1.8;
}

.header-buttons {
    display: flex;
    justify-content: center;
    gap: 15px;
    margin-top: 20px;
}

.header-buttons a {
    text-decoration: none;
    font-size: 1.1em;
    padding: 15px 30px;
    border-radius: 30px;
    font-weight: bold;
    background: #ffffff;
    color: #6a1b9a;
    transition: transform 0.3s, background 0.3s;
    box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
}

.header-buttons a:hover {
    background: #64b5f6;
    color: #ffffff;
    transform: scale(1.05);
}

/* Pre-Tabs Section */
.pre-tabs {
    text-align: center;
    padding: 40px 20px;
    background: linear-gradient(135deg, #ffffff, #f9fafb);
    border-top: 5px solid #64b5f6;
    border-bottom: 5px solid #6a1b9a;
}

.pre-tabs h2 {
    font-size: 2.5em;
    color: #333333;
    margin-bottom: 15px;
}

.pre-tabs p {
    font-size: 1.2em;
    color: #555555;
    line-height: 1.8;
}

/* Tabs Section */
.tabs {
    margin: 0 auto;
    padding: 20px;
    background: #ffffff;
    border-radius: 12px;
    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
    max-width: 1200px;
}

/* Post-Tabs Section */
.post-tabs {
    text-align: center;
    padding: 40px 20px;
    background: linear-gradient(135deg, #64b5f6, #6a1b9a);
    color: #ffffff;
    border-radius: 12px;
    margin-top: 30px;
}

.post-tabs h2 {
    font-size: 2.5em;
    margin-bottom: 15px;
}

.post-tabs p {
    font-size: 1.2em;
    line-height: 1.8;
    margin-bottom: 20px;
}

.post-tabs a {
    text-decoration: none;
    font-size: 1.1em;
    padding: 15px 30px;
    border-radius: 30px;
    font-weight: bold;
    background: #ffffff;
    color: #6a1b9a;
    transition: transform 0.3s, background 0.3s;
    box-shadow: 0 4px 10px rgba(0, 0, 0, 0.1);
}

.post-tabs a:hover {
    background: #6a1b9a;
    color: #ffffff;
    transform: scale(1.05);
}

/* Footer */
footer {
    background: linear-gradient(135deg, #6a1b9a, #8e44ad);
    color: #ffffff;
    text-align: center;
    padding: 40px 20px;
    margin-top: 30px;
    border-radius: 12px;
    box-shadow: 0 4px 10px rgba(0, 0, 0, 0.2);
}

footer h2 {
    font-size: 1.8em;
    margin-bottom: 15px;
}

footer p {
    font-size: 1.1em;
    line-height: 1.6;
    margin-bottom: 20px;
}

footer .social-links {
    display: flex;
    justify-content: center;
    gap: 15px;
    margin-top: 20px;
}

footer .social-links a {
    text-decoration: none;
    font-size: 1.1em;
    padding: 10px 20px;
    border-radius: 8px;
    font-weight: bold;
    background: #ffffff;
    color: #6a1b9a;
    transition: transform 0.3s, background 0.3s;
}

footer .social-links a:hover {
    background: #64b5f6;
    color: #ffffff;
    transform: scale(1.1);
}
"""

# Gradio Interface
with gr.Blocks(css=css_tech_theme) as demo:
    # Header Section
    gr.Markdown("""
    <header>
        <h1>🏆 Mobile-MMLU Benchmark Competition</h1>
        <h2>🚀 Push the Boundaries of Mobile AI</h2>
        <p>
            Test and optimize mobile-compatible Large Language Models (LLMs) with cutting-edge benchmarks 
            across 80 fields and over 16,000 questions.
        </p>
        <div class="header-buttons">
            <a href="#overview">Learn More</a>
            <a href="#submission">Submit Predictions</a>
            <a href="#leaderboard">View Leaderboard</a>
        </div>
    </header>
    """)

    # Pre-Tabs Section
    gr.Markdown("""
    <section class="pre-tabs">
        <h2>Why Participate?</h2>
        <p>
            The Mobile-MMLU Benchmark Competition is a unique opportunity to test your LLMs against 
            real-world scenarios. Compete to drive innovation and make your mark in mobile AI.
        </p>
    </section>
    """)

    # Tabs Section
    with gr.Tabs(elem_id="tabs"):
        # Overview Tab
        with gr.TabItem("📖 Overview"):
            gr.Markdown("""
            <div class="tabs">
                <h2>About the Competition</h2>
                <p>
                    The **Mobile-MMLU Benchmark Competition** is an exciting challenge for mobile-optimized 
                    LLMs. Compete to achieve the highest accuracy and contribute to advancements in mobile AI.
                </p>
                <h3>How It Works</h3>
                <ul>
                    <li>1️⃣ <strong>Download the Dataset:</strong> Access the dataset and instructions on our 
                    <a href="https://github.com/your-github-repo" target="_blank">GitHub page</a>.</li>
                    <li>2️⃣ <strong>Generate Predictions:</strong> Use your LLM to answer the dataset questions. 
                    Format your predictions as a CSV file.</li>
                    <li>3️⃣ <strong>Submit Predictions:</strong> Upload your predictions on this platform.</li>
                    <li>4️⃣ <strong>Evaluation:</strong> Submissions are scored based on accuracy.</li>
                    <li>5️⃣ <strong>Leaderboard:</strong> View real-time rankings on the leaderboard.</li>
                </ul>
            </div>
            """)

        # Submission Tab
        with gr.TabItem("📤 Submission"):
            gr.Markdown("<div class='tabs'><h2>Submit Your Predictions</h2></div>")
            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")
            eval_status = gr.Textbox(label="Evaluation Status", interactive=False)

            def handle_evaluation(file, model_name, add_to_leaderboard):
                return "Evaluation complete. Model added to leaderboard.", 85.0

            eval_button.click(
                handle_evaluation,
                inputs=[file_input, model_name_input, add_to_leaderboard_checkbox],
                outputs=[eval_status, overall_accuracy_display],
            )

        # Leaderboard Tab
        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],
            )
    # Post-Tabs Section
    gr.Markdown("""
    <section class="post-tabs">
        <h2>Ready to Compete?</h2>
        <p>
            Submit your predictions today and make your mark in advancing mobile AI technologies. 
            Show the world what your model can achieve!
        </p>
        <a href="#submission">Start Submitting</a>
    </section>
    """)

    # Footer Section
    gr.Markdown("""
    <footer>
        <h2>Stay Connected</h2>
        <p>
            Follow us on social media or contact us for any queries. Let's shape the future of AI together!
        </p>
        <div class="social-links">
            <a href="https://twitter.com" target="_blank">Twitter</a>
            <a href="https://linkedin.com" target="_blank">LinkedIn</a>
            <a href="https://github.com" target="_blank">GitHub</a>
        </div>
    </footer>
    """)

# Launch the interface
demo.launch()