app.py

import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from datasets import load_dataset, get_dataset_config_names
import torch
import re
import json
import pandas as pd
import matplotlib.pyplot as plt
import traceback # Import traceback for detailed error logging

# Cache to avoid reloading the model
model_cache = {}

HF_TOKEN = os.environ.get("HF_TOKEN")

# --- Constants for Benchmarks ---
MMLU_DATASET = "cais/mmlu"
MMLU_PRO_DATASET = "cais/mmlu_pro"
# Humanity's Last Exam is a composite benchmark, not a single dataset readily available like MMLU/MMLU-Pro.
# For this implementation, we will focus on MMLU and MMLU-Pro, which are direct datasets.
# Integrating HLE would require evaluating across multiple specific datasets.

def get_all_benchmark_options():
    """
    Dynamically fetches all available subjects for MMLU and MMLU-Pro.
    Returns a dictionary mapping benchmark dataset IDs to their subjects,
    and a flattened list suitable for a Gradio dropdown.
    """
    all_options = {}
    gr_dropdown_options = []

    # Get subjects for MMLU
    try:
        mmlu_subjects = get_dataset_config_names(MMLU_DATASET, token=HF_TOKEN)
        all_options[MMLU_DATASET] = ["ALL"] + mmlu_subjects
        gr_dropdown_options.extend([f"MMLU - {s}" for s in all_options[MMLU_DATASET]])
    except Exception as e:
        print(f"Warning: Could not load MMLU dataset configs. Error: {e}")
        all_options[MMLU_DATASET] = []

    # Get subjects for MMLU-Pro
    try:
        mmlu_pro_subjects = get_dataset_config_names(MMLU_PRO_DATASET, token=HF_TOKEN)
        all_options[MMLU_PRO_DATASET] = ["ALL"] + mmlu_pro_subjects
        gr_dropdown_options.extend([f"MMLU-Pro - {s}" for s in all_options[MMLU_PRO_DATASET]])
    except Exception as e:
        print(f"Warning: Could not load MMLU-Pro dataset configs. It might not be accessible or available. Error: {e}")
        all_options[MMLU_PRO_DATASET] = []

    return all_options, gr_dropdown_options

# Initialize these once globally when the app starts
ALL_BENCHMARK_SUBJECTS, GRADIO_DROPDOWN_OPTIONS = get_all_benchmark_options()


def load_model(model_id):
    """
    Loads a Hugging Face model and its tokenizer, then creates a text-generation pipeline.
    Uses a cache to avoid re-loading if the model is already in memory.
    Provides Gradio Info/Error messages for user feedback.
    Raises an exception if model loading fails.
    """
    gr.Info(f"Attempting to load model: {model_id}...")
    if model_id in model_cache:
        gr.Info(f"Model '{model_id}' already loaded from cache.")
        return model_cache[model_id]
    try:
        # Load tokenizer and model, using bfloat16 if CUDA is available for efficiency
        tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            token=HF_TOKEN,
            torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
        ).to("cuda" if torch.cuda.is_available() else "cpu")
        
        # Create a text-generation pipeline
        generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
        
        # Cache the loaded generator
        model_cache[model_id] = generator
        gr.Info(f"Model '{model_id}' loaded successfully.")
        return generator
    except Exception as e:
        # Re-raise the exception to be caught by the outer run_evaluation try-except
        raise ValueError(f"Failed to load model '{model_id}'. Please verify the model ID and your Hugging Face token. Error: {e}")


def format_prompt(item):
    """
    Formats a single MMLU/MMLU-Pro question item into a clear prompt for the LLM.
    The prompt is designed for the model to output a single letter answer (A, B, C, D).
    """
    prompt = f"""{item['question']}
A. {item['choices'][0]}
B. {item['choices'][1]}
C. {item['choices'][2]}
D. {item['choices'][3]}
Answer:"""
    return prompt, item['answer'] # Returns the prompt string and the correct choice index (0-3)

def extract_choice_letter(output):
    """
    Extracts the most likely choice letter (A, B, C, D) from the model's generated output.
    It prioritizes an exact match after "Answer:", then looks for any single capital letter.
    """
    # Look for "Answer: X" pattern first (e.g., "Answer: A" or "Answer: B")
    match = re.search(r"Answer:\s*([ABCD])", output, re.IGNORECASE) # Added IGNORECASE for robustness
    if match:
        return match.group(1).upper() # Ensure it's uppercase

    # Fallback: look for a single capital letter A-D anywhere in the output
    match = re.search(r"\b([ABCD])\b", output.strip())
    if match:
        return match.group(1)
    
    return None # Return None if no valid choice letter is found

def get_choice_letter(index):
    """Converts a numerical choice index (0-3) to a capital letter (A-D)."""
    if 0 <= index <= 3:
        return chr(ord('A') + index)
    return None # Return None for invalid indices

def evaluate_single_subject(generator, dataset_id, subject, sample_count, progress):
    """
    Evaluates a given model generator on a specific subject from a specified dataset.
    
    Args:
        generator: The Hugging Face pipeline for text generation.
        dataset_id (str): The ID of the dataset (e.g., "cais/mmlu", "cais/mmlu_pro").
        subject (str): The specific subject/config name within the dataset.
        sample_count (int): The maximum number of samples to evaluate.
        progress (gr.Progress): Gradio progress tracker.

    Returns:
        tuple: (accuracy, list_of_detailed_results)
    Raises:
        Exception: If dataset loading fails.
    """
    gr.Info(f"Loading dataset: {dataset_id} - {subject}...")
    try:
        # Load the "test" split of the dataset
        dataset = load_dataset(dataset_id, subject, token=HF_TOKEN)["test"]
    except Exception as e:
        # Re-raise the exception to be caught by the outer run_evaluation try-except
        raise RuntimeError(f"Failed to load dataset '{dataset_id}' for subject '{subject}'. Error: {e}")

    # Limit the number of samples and shuffle for consistent evaluation across runs
    num_samples_to_evaluate = min(sample_count, len(dataset))
    dataset = dataset.shuffle(seed=42).select(range(num_samples_to_evaluate))

    correct_count = 0
    subject_results = []

    # Iterate through the selected samples with a progress bar
    for i, item in enumerate(progress.tqdm(dataset, desc=f"Processing {subject} samples")):
        prompt, answer_idx = format_prompt(item)
        expected_letter = get_choice_letter(answer_idx)

        # Generate only 1 new token for the answer (A, B, C, D)
        # do_sample=False ensures deterministic output for a given prompt (greedy decoding)
        output_raw = generator(prompt, max_new_tokens=1, do_sample=False)[0]["generated_text"]
        
        # Check for potential reasoning model output
        is_reasoning_model_output = '<' in output_raw or re.search(r"\b(because|therefore|thus|reasoning)\b", output_raw, re.IGNORECASE) is not None
        
        # Extract the predicted letter from the model's raw output
        predicted_letter = extract_choice_letter(output_raw)

        is_correct = (predicted_letter == expected_letter)
        correct_count += is_correct
        
        # Store detailed results for logging and display
        subject_results.append({
            "question": item['question'],
            "choices": item['choices'],
            "model_raw_output": output_raw.strip(),
            "expected_answer_letter": expected_letter,
            "predicted_answer_letter": predicted_letter,
            "is_correct": is_correct,
            "is_reasoning_model_output": is_reasoning_model_output # Store the flag
        })
    
    # Calculate accuracy for the current subject
    accuracy = (correct_count / len(dataset)) * 100 if len(dataset) > 0 else 0
    return accuracy, subject_results


def run_evaluation(model_id, selected_benchmark_subject, sample_count, progress=gr.Progress()):
    """
    Main function to orchestrate the evaluation process.
    Handles single subject or 'ALL' subjects evaluation for MMLU/MMLU-Pro.
    Returns Gradio.update objects to control UI component visibility and content.
    """
    gr.Info("Starting evaluation...")
    if not model_id:
        gr.Warning("Please enter a Hugging Face Model ID before running the evaluation.")
        # Return updates to hide logs/debug and show empty results
        return "", gr.update(value="", visible=False), gr.update(visible=False), \
               gr.update(visible=False), gr.update(visible=False), gr.update(value="", visible=False)

    # Parse the selected benchmark and subject from the dropdown string
    parts = selected_benchmark_subject.split(" - ")
    if len(parts) != 2:
        gr.Error("Invalid benchmark selection format. Please select from the dropdown.")
        return "", gr.update(value="", visible=False), gr.update(visible=False), \
               gr.update(visible=False), gr.update(visible=False), gr.update(value="", visible=False)
    
    benchmark_name = parts[0]
    subject_name = parts[1]

    dataset_id_map = {
        "MMLU": MMLU_DATASET,
        "MMLU-Pro": MMLU_PRO_DATASET
    }
    current_dataset_id = dataset_id_map.get(benchmark_name)

    if not current_dataset_id:
        gr.Error(f"Unknown benchmark selected: {benchmark_name}. This should not happen.")
        return "", gr.update(value="", visible=False), gr.update(visible=False), \
               gr.update(visible=False), gr.update(visible=False), gr.update(value="", visible=False)

    try:
        generator = load_model(model_id) # This function will raise an exception on failure
        
        all_evaluation_results = []
        total_correct_overall = 0
        total_samples_overall = 0
        eval_summary_lines = []

        if subject_name == "ALL":
            subjects_to_evaluate = ALL_BENCHMARK_SUBJECTS.get(current_dataset_id, [])
            if "ALL" in subjects_to_evaluate:
                subjects_to_evaluate.remove("ALL")

            if not subjects_to_evaluate:
                gr.Warning(f"No subjects found to evaluate for '{benchmark_name}'.")
                return "", gr.update(value="", visible=False), gr.update(visible=False), \
                       gr.update(visible=False), gr.update(visible=False), gr.update(value="", visible=False)

            for i, sub in enumerate(progress.tqdm(subjects_to_evaluate, desc=f"Evaluating ALL {benchmark_name} subjects")):
                gr.Info(f"Evaluating {benchmark_name} - {sub} ({i+1}/{len(subjects_to_evaluate)})...")
                try:
                    accuracy, subject_details = evaluate_single_subject(generator, current_dataset_id, sub, sample_count, progress)
                    all_evaluation_results.extend(subject_details)

                    num_evaluated_samples = len(subject_details)
                    num_correct_in_subject = sum(d['is_correct'] for d in subject_details)

                    total_correct_overall += num_correct_in_subject
                    total_samples_overall += num_evaluated_samples
                    eval_summary_lines.append(f"- {benchmark_name} - {sub}: {accuracy:.2f}% ({num_correct_in_subject}/{num_evaluated_samples} samples)")
                except Exception as e:
                    gr.Error(f"Skipping {benchmark_name} - {sub} due to an error: {e}")
                    eval_summary_lines.append(f"- {benchmark_name} - {sub}: Error during evaluation.")
                    continue

            overall_accuracy = (total_correct_overall / total_samples_overall) * 100 if total_samples_overall > 0 else 0
            score_string = f"Overall Average Accuracy for {benchmark_name}: {overall_accuracy:.2f}% across {total_samples_overall} total samples.\n\n"
            score_string += "Detailed breakdown:\n" + "\n".join(eval_summary_lines)

        else:
            accuracy, subject_details = evaluate_single_subject(generator, current_dataset_id, subject_name, sample_count, progress)
            all_evaluation_results.extend(subject_details)
            overall_accuracy = accuracy
            num_evaluated_samples = len(subject_details)
            score_string = f"Accuracy for {benchmark_name} - {subject_name}: {accuracy:.2f}% out of {num_evaluated_samples} samples."

        # Format detailed results for display in the text box
        # The key change here is to wrap the entire multi-line string construction for each item
        # within parentheses to ensure it's treated as a single element in the list comprehension.
        formatted_details = "\n\n".join([
            (
                f"### Question:\n{item['question']}\n\n"
                + f"**Choices:**\n" + "\n".join([f"{get_choice_letter(i)}. {c}" for i, c in enumerate(item['choices'])]) + "\n\n"
                + (f"**Note:** Reasoning models are currently not fully supported for single-letter extraction. The original model output followed:\n" if item.get('is_reasoning_model_output') else "")
                + f"**Model Raw Output:** {item['model_raw_output']}\n"
                + f"**Expected Answer:** {item['expected_answer_letter']}\n"
                + f"**Predicted Answer:** {item['predicted_answer_letter']}\n"
                + f"**Correct:** {'Yes' if item['is_correct'] else 'No'}"
            )
            for item in all_evaluation_results
        ])

        # Record the evaluation result to a JSONL file for the leaderboard
        record = {
            "model_id": model_id,
            "benchmark": benchmark_name,
            "subject": subject_name,
            "accuracy": overall_accuracy,
            "sample_count": total_samples_overall if subject_name == "ALL" else len(all_evaluation_results),
            "timestamp": pd.Timestamp.now().isoformat()
        }
        with open("eval.jsonl", "a") as f:
            f.write(json.dumps(record) + "\n")

        gr.Info("Evaluation completed successfully!")
        return score_string, \
               gr.update(value="", visible=False), gr.update(visible=False), \
               gr.update(visible=True), gr.update(visible=True), gr.update(value=formatted_details, visible=False)

    except Exception as e:
        error_message = str(e)
        detailed_error_traceback = traceback.format_exc()
        gr.Error("An error occurred during evaluation.")
        
        # Return updates for error state
        return "Error occurred during evaluation. We'll evaluate for you! If this persists, please open a community support tab for assistance.", \
               gr.update(value=detailed_error_traceback, visible=True), gr.update(visible=True), \
               gr.update(visible=False), gr.update(visible=False), gr.update(value="", visible=False)

def save_text(text_content):
    """Saves the provided text content to a file and returns the file path for download."""
    if not text_content:
        gr.Warning("No evaluation results to download.")
        return None
    file_path = "evaluation_results.txt"
    try:
        with open(file_path, "w") as f:
            f.write(text_content)
        return file_path
    except Exception as e:
        gr.Error(f"Error saving file: {e}")
        return None

def load_leaderboard():
    """
    Loads evaluation data from 'eval.jsonl', computes average accuracy per model,
    and prepares data for the leaderboard plot and table.
    """
    try:
        # Read the JSONL file into a pandas DataFrame
        df = pd.read_json("eval.jsonl", lines=True)
        
        # Calculate average accuracy per model across all recorded evaluations
        df_avg = df.groupby("model_id")["accuracy"].mean().reset_index()
        df_avg.columns = ["Model ID", "Average Accuracy (%)"]
        
        # Sort models by average accuracy in descending order
        df_sorted = df_avg.sort_values(by="Average Accuracy (%)", ascending=False)
        
        # Select top 10 models for the bar chart
        top_models = df_sorted.head(10)

        # Create the matplotlib plot
        fig, ax = plt.subplots(figsize=(10, 6)) # Adjust figure size for better readability
        # For horizontal bars, it's often better to plot data sorted in ascending order
        # so the highest bar appears at the top of the chart.
        top_models_plot = top_models.sort_values(by="Average Accuracy (%)", ascending=True)

        ax.barh(top_models_plot['Model ID'], top_models_plot['Average Accuracy (%)'], color='#007bff') # Use a nice blue color
        ax.set_xlabel("Average Accuracy (%)", fontsize=12)
        ax.set_ylabel("Model ID", fontsize=12)
        ax.set_title("Top 10 Models by Average MMLU/MMLU-Pro Accuracy", fontsize=14)
        ax.set_xlim(0, 100) # Ensure accuracy scale is 0-100%
        ax.tick_params(axis='x', labelsize=10)
        ax.tick_params(axis='y', labelsize=10)
        ax.grid(axis='x', linestyle='--', alpha=0.7) # Add grid lines
        plt.tight_layout() # Adjust layout to prevent labels overlapping

        # Return the figure and the sorted dataframe as a list of dictionaries for Gradio Dataframe
        return fig, df_sorted.to_dict('records')
    except FileNotFoundError:
        gr.Warning("No evaluation data found yet. Run an evaluation to populate the leaderboard!")
        return plt.figure(), pd.DataFrame(columns=["Model ID", "Average Accuracy (%)"]).to_dict('records')
    except Exception as e:
        gr.Error(f"Error loading leaderboard: {e}")
        # Return an empty plot and dataframe in case of any other error
        return plt.figure(), pd.DataFrame(columns=["Model ID", "Average Accuracy (%)"]).to_dict('records')


# --- Gradio Interface Definition ---
with gr.Blocks(css="""
    /* General body and container styling */
    body { font-family: 'Inter', sans-serif; background-color: #f0f2f5; margin: 0; padding: 20px; }
    .gradio-container { 
        max-width: 1200px; 
        margin: 20px auto; 
        padding: 30px; 
        box-shadow: 0 8px 16px rgba(0,0,0,0.15); 
        border-radius: 12px; 
        background-color: #ffffff; 
        border: 1px solid #e0e0e0;
    }
    
    /* Headings */
    h1 { 
        color: #2c3e50; 
        text-align: center; 
        margin-bottom: 30px; 
        font-size: 2.5em; 
        font-weight: 700; 
        letter-spacing: -0.02em; 
    }
    h3 { color: #34495e; font-size: 1.2em; margin-bottom: 10px; }

    /* Markdown text */
    .markdown-text { text-align: center; color: #555; line-height: 1.6; }
    .markdown-text div { font-size: 1.1em; }

    /* Buttons */
    .gr-button { 
        background-color: #007bff; /* Primary blue */
        color: white; 
        border: none; 
        padding: 12px 25px; 
        border-radius: 8px; 
        cursor: pointer; 
        transition: background-color 0.3s ease, transform 0.2s ease; 
        font-size: 1.1em;
        font-weight: 600;
        box-shadow: 0 4px 8px rgba(0,0,0,0.1);
    }
    .gr-button:hover { 
        background-color: #0056b3; /* Darker blue on hover */
        transform: translateY(-2px); /* Slight lift effect */
    }
    .gr-button:active {
        transform: translateY(0);
        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    }
    /* Specific button styling for debug/show details */
    #debug-button, #show-details-button {
        background-color: #6c757d; /* Grey for secondary actions */
    }
    #debug-button:hover, #show-details-button:hover {
        background-color: #5a6268;
    }
    #download-button {
        background-color: #28a745; /* Green for download */
    }
    #download-button:hover {
        background-color: #218838;
    }


    /* Input/Output Boxes */
    .gr-box { 
        border: 1px solid #dee2e6; 
        border-radius: 10px; 
        padding: 20px; 
        margin-bottom: 20px; 
        background-color: #fdfdfd; 
        box-shadow: inset 0 1px 3px rgba(0,0,0,0.05);
    }
    .gr-output-text { 
        white-space: pre-wrap; 
        word-wrap: break-word; 
        background-color: #f9f9fb; 
        border: 1px solid #e9ecef; 
        border-radius: 8px; 
        padding: 15px; 
        min-height: 100px; /* Ensure a minimum height */
    }
    /* Specific error output style */
    #error-message-output {
        background-color: #ffe0e0;
        border-color: #ff9999;
        color: #cc0000;
    }


    /* Labels for inputs */
    .gr-textbox label, .gr-dropdown label, .gr-slider label { 
        font-weight: 600; 
        color: #495057; 
        margin-bottom: 8px; 
        display: block; 
        font-size: 1em;
    }

    /* Tab styling */
    .gr-tab-item { padding: 25px; } /* More padding inside tabs */
    .gr-tabs-nav button {
        font-weight: 600;
        font-size: 1.1em;
        padding: 10px 20px;
        border-top-left-radius: 8px;
        border-top-right-radius: 8px;
    }
""") as demo:
    gr.Markdown("""
    # 🤖 LLM Benchmark Evaluator
    """)

    with gr.Tabs():
        with gr.TabItem("🚀 Run Evaluation"):
            gr.Markdown("""
            <div style="text-align: center; margin-bottom: 20px; color: #666; font-size: 1.1em;">
                Enter your Hugging Face Model ID, choose a benchmark (MMLU or MMLU-Pro),
                select a subject (or 'ALL' for a comprehensive evaluation),
                and specify the number of samples per subject.
            </div>
            """)
            
            with gr.Column(elem_classes="gr-box"):
                model_id_input = gr.Textbox(
                    label="Your Hugging Face Model ID", 
                    placeholder="e.g., mistralai/Mistral-7B-Instruct-v0.2", 
                    interactive=True
                )
                with gr.Row():
                    benchmark_subject_dropdown = gr.Dropdown(
                        label="Choose Benchmark and Subject",
                        choices=GRADIO_DROPDOWN_OPTIONS,
                        value="MMLU - ALL", # Default to MMLU ALL for initial load
                        interactive=True,
                        min_width=400 # Ensure sufficient width
                    )
                    sample_count_slider = gr.Slider(
                        label="Number of Samples per Subject (1-100)",
                        minimum=1,
                        maximum=100, 
                        value=10, # Default to 10 samples
                        step=1,
                        interactive=True,
                        min_width=200
                    )
                run_button = gr.Button("🚀 Run Evaluation", elem_classes="gr-button")

            with gr.Column(elem_classes="gr-box"):
                acc_output = gr.Textbox(
                    label="Benchmark Accuracy Results", 
                    interactive=False, 
                    elem_classes="gr-output-text", 
                    lines=5,
                    placeholder="Evaluation results will appear here."
                )
                
                # Container for debug info, initially hidden
                with gr.Column(visible=False, elem_id="debug-error-column") as debug_error_column:
                    error_message_output = gr.Textbox(
                        label="Debug Information (Error Details)", 
                        lines=10, interactive=False, elem_classes="gr-output-text", elem_id="error-message-output",
                        placeholder="Error details will appear here if an error occurs."
                    )
                    debug_button = gr.Button("🐛 Hide Debug Info", visible=True, elem_id="debug-button", elem_classes="gr-button")

                with gr.Row():
                    show_details_button = gr.Button("🔍 Show Detailed Logs", visible=False, elem_id="show-details-button", elem_classes="gr-button")
                    download_button = gr.Button("📥 Download Full Evaluation Logs", visible=False, elem_id="download-button", elem_classes="gr-button")

                # Detailed output, initially hidden
                detail_output = gr.Textbox(
                    label="Detailed Evaluation Logs", 
                    lines=20, 
                    interactive=False, 
                    elem_classes="gr-output-text",
                    placeholder="Detailed logs for each question will appear here upon successful evaluation.",
                    visible=False # Initially hidden
                )
                
            # Define button click actions
            run_button.click(
                run_evaluation,
                inputs=[model_id_input, benchmark_subject_dropdown, sample_count_slider],
                outputs=[
                    acc_output, 
                    error_message_output, debug_error_column, # For error state
                    show_details_button, download_button, detail_output # For success state
                ]
            )

            # Toggle visibility of detail_output
            show_details_button.click(
                lambda s: gr.update(visible=not s), # Toggle visibility
                inputs=[detail_output], # Pass the component itself as input
                outputs=[detail_output] # The component to update
            )
            # Change button text based on visibility
            show_details_button.click(
                lambda s: "🙈 Hide Detailed Logs" if not s else "🔍 Show Detailed Logs",
                inputs=[detail_output],
                outputs=[show_details_button]
            )

            # Toggle visibility of debug error column
            debug_button.click(
                lambda s: gr.update(visible=not s), # Toggle visibility
                inputs=[debug_error_column], # Pass the component itself as input
                outputs=[debug_error_column] # The component to update
            )
            # Change debug button text based on visibility
            debug_button.click(
                lambda s: "🐛 Show Debug Info" if not s else "🐛 Hide Debug Info",
                inputs=[debug_error_column],
                outputs=[debug_button]
            )

            download_button.click(
                save_text,
                inputs=[detail_output],
                outputs=gr.File(label="Download Evaluation Results", file_count="single", type="filepath")
            )

        with gr.TabItem("📊 Leaderboard"):
            gr.Markdown("""
            <div style="text-align: center; margin-bottom: 20px; color: #666; font-size: 1.1em;">
                See how different models perform on average across all evaluated benchmarks.
                This leaderboard updates with every new evaluation.
            </div>
            """)
            with gr.Row():
                leaderboard_plot_output = gr.Plot(label="Top 10 Models by Average Accuracy", scale=2) # Scale for better visibility
                leaderboard_table_output = gr.Dataframe(
                    headers=["Model ID", "Average Accuracy (%)"],
                    interactive=False,
                    datatype=["str", "number"],
                    row_count=10, # Display top 10 rows initially, but can scroll
                    col_count=2,
                    label="Full Leaderboard Data"
                )
            
            # Load leaderboard when the tab is selected or when the app loads
            demo.load(load_leaderboard, inputs=[], outputs=[leaderboard_plot_output, leaderboard_table_output])

# Launch the Gradio app
demo.launch()