app.py

import gradio as gr
import pandas as pd
import os
import shutil
import json

# Function to load leaderboard data from a CSV file
def load_leaderboard_data(csv_file_path):
    try:
        df = pd.read_csv(csv_file_path)
        return df
    except Exception as e:
        print(f"Error loading CSV file: {e}")
        return pd.DataFrame()  # Return an empty DataFrame in case of error

# Function to process uploaded JSON file
def process_json_file(file):
    if file is None:
        return None, "Please upload a JSON file."
    try:
        with open(file.name, 'r') as f:
            data = json.load(f)
        return data, None
    except Exception as e:
        return None, f"Error reading JSON file: {str(e)}"

# Function to save the uploaded JSON file
def save_json_file(file):
    if file is None:
        return "No file uploaded."

    # Define the directory to save uploaded files
    save_dir = "uploaded_jsons"
    os.makedirs(save_dir, exist_ok=True)

    # Get the original filename
    original_filename = os.path.basename(file.name)

    # Define the path to save the file
    save_path = os.path.join(save_dir, original_filename)

    # Copy the uploaded file to the save directory
    shutil.copy2(file.name, save_path)

    return f"File saved to {save_path}"

# Load the leaderboard data
leaderboard1 = load_leaderboard_data("leaderboard_swe.csv")
leaderboard2 = load_leaderboard_data("leaderboard_gaia.csv")

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# 🥇 TRAIL: Trace Reasoning and Agentic Issue Localization Leaderboard")
    with gr.Row():
        with gr.Column():
            gr.Markdown("## TRAIL-SWE Leaderboard")
            gr.Dataframe(leaderboard1)
        with gr.Column():
            gr.Markdown("## TRAIL-GAIA Leaderboard")
            gr.Dataframe(leaderboard2)
    
    with gr.Blocks() as submit_page:
        gr.Markdown("## Submit Your JSON File Here")
        file_input = gr.File(label="Upload JSON File", file_types=['.json'])
        json_preview = gr.JSON(label="JSON Preview")
        submit_button = gr.Button("Submit", interactive=True)
        output = gr.Textbox(label="Status")
        
        def handle_submission(file):
            if file is None:
                return None, "Please upload a JSON file."
            try:
                # Process and preview the JSON
                with open(file.name, 'r') as f:
                    data = json.load(f)
                # Save the file
                save_result = save_json_file(file)
                return data, save_result
            except Exception as e:
                return None, f"Error: {str(e)}"
        
        submit_button.click(
            fn=handle_submission,
            inputs=[file_input],
            outputs=[json_preview, output]
        )

if __name__ == "__main__":
    demo.launch()



"""
import gradio as gr
import pandas as pd
import os
import json
import uuid
import hashlib
from datetime import datetime
from huggingface_hub import HfApi, login, HfFolder

# Configuration
LEADERBOARD_CSV = "leaderboard.csv"
SUBMISSIONS_FOLDER = "submissions"
CONFIG_FILE = "config.json"
DEFAULT_COLUMNS = ["rank", "submission_name", "score", "user", "timestamp"]
VERIFY_USERS = False  # Set to True to enable HF authentication

# Default configuration
DEFAULT_CONFIG = {
    "title": "Hugging Face Competition Leaderboard",
    "description": "Submit your results for the competition",
    "metric_name": "Score",
    "higher_is_better": True,
    "max_submissions_per_user": 5,
    "allow_submission_edits": True
}

# Ensure submissions folder exists
os.makedirs(SUBMISSIONS_FOLDER, exist_ok=True)

# Load or create config
if os.path.exists(CONFIG_FILE):
    with open(CONFIG_FILE, "r") as f:
        config = json.load(f)
else:
    config = DEFAULT_CONFIG
    with open(CONFIG_FILE, "w") as f:
        json.dump(config, f, indent=2)

# Initialize leaderboard if it doesn't exist
if not os.path.exists(LEADERBOARD_CSV):
    pd.DataFrame(columns=DEFAULT_COLUMNS).to_csv(LEADERBOARD_CSV, index=False)

def read_leaderboard():
    #Read the current leaderboard
    if os.path.exists(LEADERBOARD_CSV):
        df = pd.read_csv(LEADERBOARD_CSV)
        return df
    return pd.DataFrame(columns=DEFAULT_COLUMNS)

def verify_user(username, token):
    #Verify a user with their Hugging Face token
    if not VERIFY_USERS:
        return True
        
    try:
        api = HfApi(token=token)
        user_info = api.whoami()
        return user_info["name"] == username
    except:
        return False

def count_user_submissions(username):
    #Count how many submissions a user already has
    df = read_leaderboard()
    return len(df[df["user"] == username])

def update_leaderboard():
    #Update the leaderboard based on submissions
    # Read all submissions
    submissions = []
    for filename in os.listdir(SUBMISSIONS_FOLDER):
        if filename.endswith(".json"):
            with open(os.path.join(SUBMISSIONS_FOLDER, filename), "r") as f:
                try:
                    data = json.load(f)
                    submissions.append(data)
                except json.JSONDecodeError:
                    print(f"Error decoding {filename}")
    
    if not submissions:
        return pd.DataFrame(columns=DEFAULT_COLUMNS)
    
    # Create dataframe and sort by score
    df = pd.DataFrame(submissions)
    
    # Sort based on configuration (higher or lower is better)
    ascending = not config.get("higher_is_better", True)
    df = df.sort_values("score", ascending=ascending)
    
    # Add rank
    df["rank"] = range(1, len(df) + 1)
    
    # Save updated leaderboard
    df.to_csv(LEADERBOARD_CSV, index=False)
    return df

def submit(submission_name, score, username, hf_token="", submission_details=None):
    #Add a new submission to the leaderboard
    if not submission_name or not username:
        return "Submission name and username are required", None
    
    try:
        score = float(score)
    except ValueError:
        return "Score must be a valid number", None
    
    # Verify user if enabled
    if VERIFY_USERS and not verify_user(username, hf_token):
        return "Invalid Hugging Face credentials", None
    
    # Check submission limit
    max_submissions = config.get("max_submissions_per_user", 5)
    if count_user_submissions(username) >= max_submissions:
        return f"You've reached the maximum of {max_submissions} submissions", None
    
    # Create submission entry
    submission_id = str(uuid.uuid4())[:8]
    submission = {
        "submission_id": submission_id,
        "submission_name": submission_name,
        "score": score,
        "user": username,
        "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    }
    
    # Add optional details
    if submission_details:
        submission["details"] = submission_details
    
    # Save submission to file
    filename = f"{username}_{submission_name.replace(' ', '_')}_{submission_id}.json"
    with open(os.path.join(SUBMISSIONS_FOLDER, filename), "w") as f:
        json.dump(submission, f)
    
    # Update leaderboard
    leaderboard = update_leaderboard()
    return f"Submission '{submission_name}' added successfully!", leaderboard

def render_leaderboard():
    #Display the current leaderboard
    df = update_leaderboard()
    if len(df) == 0:
        return "No submissions yet."
    
    # Format the dataframe for display
    display_df = df[DEFAULT_COLUMNS].copy()
    return display_df

# Create the Gradio interface
with gr.Blocks(title=config["title"]) as demo:
    gr.Markdown(f"# {config['title']}")
    gr.Markdown(f"{config['description']}")
    
    with gr.Tab("Leaderboard"):
        gr.Markdown("## Current Rankings")
        metric_name = config.get("metric_name", "Score")
        higher_better = "higher is better" if config.get("higher_is_better", True) else "lower is better"
        gr.Markdown(f"*Ranked by {metric_name} ({higher_better})*")
        
        leaderboard_output = gr.Dataframe(
            headers=["Rank", "Submission", metric_name, "User", "Timestamp"],
            datatype=["number", "str", "number", "str", "str"],
            interactive=False
        )
        refresh_btn = gr.Button("Refresh Leaderboard")
        refresh_btn.click(render_leaderboard, inputs=[], outputs=[leaderboard_output])
    
    with gr.Tab("Submit"):
        gr.Markdown("## Submit Your Results")
        with gr.Row():
            with gr.Column():
                submission_name = gr.Textbox(label="Submission Name", placeholder="MyAwesomeModel v1.0")
                score = gr.Number(label=metric_name, precision=4)
                username = gr.Textbox(label="Username", placeholder="Your Hugging Face username")
                
                # Only show token field if verification is enabled
                if VERIFY_USERS:
                    hf_token = gr.Textbox(
                        label="Hugging Face Token", 
                        placeholder="hf_...",
                        type="password"
                    )
                else:
                    hf_token = gr.Textbox(visible=False)
                
                submission_details = gr.Textbox(
                    label="Additional Details (optional)", 
                    placeholder="Model details, training info, etc.",
                    lines=5
                )
                submit_btn = gr.Button("Submit to Leaderboard")
        
        submit_output = gr.Markdown()
        submission_leaderboard = gr.Dataframe(
            headers=["Rank", "Submission", metric_name, "User", "Timestamp"],
            datatype=["number", "str", "number", "str", "str"],
            interactive=False
        )
        
        submit_btn.click(
            submit, 
            inputs=[submission_name, score, username, hf_token, submission_details], 
            outputs=[submit_output, submission_leaderboard]
        )
    
    # Add admin tab if desired
    with gr.Tab("About"):
        gr.Markdown("## About This Leaderboard")
    
    # Initialize the leaderboard on load
    demo.load(render_leaderboard, inputs=[], outputs=[leaderboard_output])

if __name__ == "__main__":
    demo.launch()
"""