import gradio as gr
import pandas as pd
import numpy as np
import os
import re
from datetime import datetime
import json
import torch
from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor, as_completed
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from huggingface_hub import HfApi
import shutil
import tempfile
from sklearn.metrics import cohen_kappa_score
import krippendorff

from utils.hub_storage import HubStorage
from utils.token_handler import TokenHandler

# Initialize storage once at startup
try:
    REPO_ID = "rahmanidashti/llm-as-a-rel"  # Replace with your space name
    hub_storage = HubStorage(REPO_ID)
except Exception as e:
    raise RuntimeError(f"Failed to initialize HuggingFace Hub storage: {e}")


def process_single_instance(args):
    idx, eval_csv, qa_dataset, evaluator, eval_metrics = args
    query, query_id, answer_ids, meta_info = qa_dataset[idx]

    try:
        pred_rank = eval_csv[eval_csv['query_id'] == query_id]['pred_rank'].item()
    except IndexError:
        raise IndexError(f'Error when processing query_id={query_id}, please make sure the predicted results exist for this query.')
    except Exception as e:
        raise RuntimeError(f'Unexpected error occurred while fetching prediction rank for query_id={query_id}: {e}')

    if isinstance(pred_rank, str):
        try:
            pred_rank = eval(pred_rank)
        except SyntaxError as e:
            raise ValueError(f'Failed to parse pred_rank as a list for query_id={query_id}: {e}')
    
    if not isinstance(pred_rank, list):
        raise TypeError(f'Error when processing query_id={query_id}, expected pred_rank to be a list but got {type(pred_rank)}.')

    pred_dict = {pred_rank[i]: -i for i in range(min(100, len(pred_rank)))}
    answer_ids = torch.LongTensor(answer_ids)
    result = evaluator.evaluate(pred_dict, answer_ids, metrics=eval_metrics)

    result["idx"], result["query_id"] = idx, query_id
    return result

def compute_metrics(csv_path: str, dataset: str, split: str, num_workers: int = 4):
    """
    computing the metrics for the evaluation.

    Parameters:
    csv_path (str): The path to the submission file for evaluation.
    """
    candidate_ids_dict = {
        'amazon': [i for i in range(957192)],
        'mag': [i for i in range(1172724, 1872968)],
        'prime': [i for i in range(129375)]
    }
    try:
        # eval_csv = pd.read_csv(csv_path)
        eval_csv = pd.read_csv(csv_path, sep=" ", header=None, names=['qid', 'Q0', 'docid', 'score'])
        eval_csv['score'] = [0 if x < 0 else 3 if x > 3 else x for x in eval_csv['score']]
        test_eval_df = pd.merge(test_data, eval_csv, on=['qid', 'docid'], how='outer')
        cohen_kappa = cohen_kappa_score(test_eval_df['score_x'], test_eval_df['score_y'])
        krippendorff_alpha = krippendorff.alpha(reliability_data=[test_eval_df['score_x'], test_eval_df['score_y']], value_domain=[0,1,2,3], level_of_measurement='ordinal')

        # if 'query_id' not in eval_csv.columns:
            # raise ValueError('No `query_id` column found in the submitted csv.')
        # if 'pred_rank' not in eval_csv.columns:
            # raise ValueError('No `pred_rank` column found in the submitted csv.')

        # eval_csv = eval_csv[['query_id', 'pred_rank']]

        # if dataset not in candidate_ids_dict:
        #     raise ValueError(f"Invalid dataset '{dataset}', expected one of {list(candidate_ids_dict.keys())}.")
        # if split not in ['test', 'test-0.1', 'human_generated_eval']:
        #     raise ValueError(f"Invalid split '{split}', expected one of ['test', 'test-0.1', 'human_generated_eval'].")

        # evaluator = Evaluator(candidate_ids_dict[dataset])
        # eval_metrics = ['hit@1', 'hit@5', 'recall@20', 'mrr']
        # qa_dataset = load_qa(dataset, human_generated_eval=split == 'human_generated_eval')
        # split_idx = qa_dataset.get_idx_split()
        # all_indices = split_idx[split].tolist()

        # results_list = []
        # query_ids = []

        # Prepare args for each worker
        # args = [(idx, eval_csv, qa_dataset, evaluator, eval_metrics) for idx in all_indices]

        # with ProcessPoolExecutor(max_workers=num_workers) as executor:
        #     futures = [executor.submit(process_single_instance, arg) for arg in args]
        #     for future in tqdm(as_completed(futures), total=len(futures)):
        #         result = future.result()  # This will raise an error if the worker encountered one
        #         results_list.append(result)
        #         query_ids.append(result['query_id'])

        # Concatenate results and compute final metrics
        # eval_csv = pd.concat([eval_csv, pd.DataFrame(results_list)], ignore_index=True)
        final_results = {
            # metric: np.mean(eval_csv[eval_csv['query_id'].isin(query_ids)][metric]) for metric in eval_metrics
            'Kappa': round(cohen_kappa, 4),
            'Alpha': round(krippendorff_alpha, 4) 
        }
        return final_results

    except pd.errors.EmptyDataError:
        return "Error: The CSV file is empty or could not be read. Please check the file and try again."
    except FileNotFoundError:
        return f"Error: The file {csv_path} could not be found. Please check the file path and try again."
    except Exception as error:
        return f"{error}"

# Data dictionaries for leaderboard
data_synthesized_full = {
    'Method': ['BM25', 'DPR (roberta)', 'ANCE (roberta)'],
    'LLMJudge-DL2023_Kappa': [44.94, 15.29, 30.96],
    'LLMJudge-DL2023_Alpha': [67.42, 47.93, 51.06],
}

# data_synthesized_full = {
#     'Method': ['BM25', 'DPR (roberta)', 'ANCE (roberta)', 'QAGNN (roberta)', 'ada-002', 'voyage-l2-instruct', 'LLM2Vec', 'GritLM-7b', 'multi-ada-002', 'ColBERTv2'],
#     'STARK-AMAZON_Hit@1': [44.94, 15.29, 30.96, 26.56, 39.16, 40.93, 21.74, 42.08, 40.07, 46.10],
#     'STARK-AMAZON_Hit@5': [67.42, 47.93, 51.06, 50.01, 62.73, 64.37, 41.65, 66.87, 64.98, 66.02],
#     'STARK-AMAZON_R@20': [53.77, 44.49, 41.95, 52.05, 53.29, 54.28, 33.22, 56.52, 55.12, 53.44],
#     'STARK-AMAZON_MRR': [55.30, 30.20, 40.66, 37.75, 50.35, 51.60, 31.47, 53.46, 51.55, 55.51],
#     'STARK-MAG_Hit@1': [25.85, 10.51, 21.96, 12.88, 29.08, 30.06, 18.01, 37.90, 25.92, 31.18],
#     'STARK-MAG_Hit@5': [45.25, 35.23, 36.50, 39.01, 49.61, 50.58, 34.85, 56.74, 50.43, 46.42],
#     'STARK-MAG_R@20': [45.69, 42.11, 35.32, 46.97, 48.36, 50.49, 35.46, 46.40, 50.80, 43.94],
#     'STARK-MAG_MRR': [34.91, 21.34, 29.14, 29.12, 38.62, 39.66, 26.10, 47.25, 36.94, 38.39],
#     'STARK-PRIME_Hit@1': [12.75, 4.46, 6.53, 8.85, 12.63, 10.85, 10.10, 15.57, 15.10, 11.75],
#     'STARK-PRIME_Hit@5': [27.92, 21.85, 15.67, 21.35, 31.49, 30.23, 22.49, 33.42, 33.56, 23.85],
#     'STARK-PRIME_R@20': [31.25, 30.13, 16.52, 29.63, 36.00, 37.83, 26.34, 39.09, 38.05, 25.04],
#     'STARK-PRIME_MRR': [19.84, 12.38, 11.05, 14.73, 21.41, 19.99, 16.12, 24.11, 23.49, 17.39]
# }

# data_synthesized_10 = {
#     'Method': ['BM25', 'DPR (roberta)', 'ANCE (roberta)', 'QAGNN (roberta)', 'ada-002', 'voyage-l2-instruct', 'LLM2Vec', 'GritLM-7b', 'multi-ada-002', 'ColBERTv2', 'Claude3 Reranker', 'GPT4 Reranker'],
#     'STARK-AMAZON_Hit@1': [42.68, 16.46, 30.09, 25.00, 39.02, 43.29, 18.90, 43.29, 40.85, 44.31, 45.49, 44.79],
#     'STARK-AMAZON_Hit@5': [67.07, 50.00, 49.27, 48.17, 64.02, 67.68, 37.80, 71.34, 62.80, 65.24, 71.13, 71.17],
#     'STARK-AMAZON_R@20': [54.48, 42.15, 41.91, 51.65, 49.30, 56.04, 34.73, 56.14, 52.47, 51.00, 53.77, 55.35],
#     'STARK-AMAZON_MRR': [54.02, 30.20, 39.30, 36.87, 50.32, 54.20, 28.76, 55.07, 51.54, 55.07, 55.91, 55.69],
#     'STARK-MAG_Hit@1': [27.81, 11.65, 22.89, 12.03, 28.20, 34.59, 19.17, 38.35, 25.56, 31.58, 36.54, 40.90],
#     'STARK-MAG_Hit@5': [45.48, 36.84, 37.26, 37.97, 52.63, 50.75, 33.46, 58.64, 50.37, 47.36, 53.17, 58.18],
#     'STARK-MAG_R@20': [44.59, 42.30, 44.16, 47.98, 49.25, 50.75, 29.85, 46.38, 53.03, 45.72, 48.36, 48.60],
#     'STARK-MAG_MRR': [35.97, 21.82, 30.00, 28.70, 38.55, 42.90, 26.06, 48.25, 36.82, 38.98, 44.15, 49.00],
#     'STARK-PRIME_Hit@1': [13.93, 5.00, 6.78, 7.14, 15.36, 12.14, 9.29, 16.79, 15.36, 15.00, 17.79, 18.28],
#     'STARK-PRIME_Hit@5': [31.07, 23.57, 16.15, 17.14, 31.07, 31.42, 20.7, 34.29, 32.86, 26.07, 36.90, 37.28],
#     'STARK-PRIME_R@20': [32.84, 30.50, 17.07, 32.95, 37.88, 37.34, 25.54, 41.11, 40.99, 27.78, 35.57, 34.05],
#     'STARK-PRIME_MRR': [21.68, 13.50, 11.42, 16.27, 23.50, 21.23, 15.00, 24.99, 23.70, 19.98, 26.27, 26.55]
# }

# data_human_generated = {
#     'Method': ['BM25', 'DPR (roberta)', 'ANCE (roberta)', 'QAGNN (roberta)', 'ada-002', 'voyage-l2-instruct', 'LLM2Vec', 'GritLM-7b', 'multi-ada-002', 'ColBERTv2', 'Claude3 Reranker', 'GPT4 Reranker'],
#     'STARK-AMAZON_Hit@1': [27.16, 16.05, 25.93, 22.22, 39.50, 35.80, 29.63, 40.74, 46.91, 33.33, 53.09, 50.62],
#     'STARK-AMAZON_Hit@5': [51.85, 39.51, 54.32, 49.38, 64.19, 62.96, 46.91, 71.60, 72.84, 55.56, 74.07, 75.31],
#     'STARK-AMAZON_R@20': [29.23, 15.23, 23.69, 21.54, 35.46, 33.01, 21.21, 36.30, 40.22, 29.03, 35.46, 35.46],
#     'STARK-AMAZON_MRR': [18.79, 27.21, 37.12, 31.33, 52.65, 47.84, 38.61, 53.21, 58.74, 43.77, 62.11, 61.06],
#     'STARK-MAG_Hit@1': [32.14, 4.72, 25.00, 20.24, 28.57, 22.62, 16.67, 34.52, 23.81, 33.33, 38.10, 36.90],
#     'STARK-MAG_Hit@5': [41.67, 9.52, 30.95, 26.19, 41.67, 36.90, 28.57, 44.04, 41.67, 36.90, 45.24, 46.43],
#     'STARK-MAG_R@20': [32.46, 25.00, 27.24, 28.76, 35.95, 32.44, 21.74, 34.57, 39.85, 30.50, 35.95, 35.95],
#     'STARK-MAG_MRR': [37.42, 7.90, 27.98, 25.53, 35.81, 29.68, 21.59, 38.72, 31.43, 35.97, 42.00, 40.65],
#     'STARK-PRIME_Hit@1': [22.45, 2.04, 7.14, 6.12, 17.35, 16.33, 9.18, 25.51, 24.49, 15.31, 28.57, 28.57],
#     'STARK-PRIME_Hit@5': [41.84, 9.18, 13.27, 13.27, 34.69, 32.65, 21.43, 41.84, 39.80, 26.53, 46.94, 44.90],
#     'STARK-PRIME_R@20': [42.32, 10.69, 11.72, 17.62, 41.09, 39.01, 26.77, 48.10, 47.21, 25.56, 41.61, 41.61],
#     'STARK-PRIME_MRR': [30.37, 7.05, 10.07, 9.39, 26.35, 24.33, 15.24, 34.28, 32.98, 19.67, 36.32, 34.82]
# }

# Initialize DataFrames
df_synthesized_full = pd.DataFrame(data_synthesized_full)
# df_synthesized_10 = pd.DataFrame(data_synthesized_10)
# df_human_generated = pd.DataFrame(data_human_generated)

# Model type definitions
model_types = {
    'Sparse Retriever': ['BM25'],
    'Small Dense Retrievers': ['DPR (roberta)', 'ANCE (roberta)', 'QAGNN (roberta)'],
    'LLM-based Dense Retrievers': ['ada-002', 'voyage-l2-instruct', 'LLM2Vec', 'GritLM-7b'],
    'Multivector Retrievers': ['multi-ada-002', 'ColBERTv2'],
    'LLM Rerankers': ['Claude3 Reranker', 'GPT4 Reranker'],
    'Others': []  # Will be populated dynamically with submitted models
}

# Submission form validation functions
def validate_email(email_str):
    """Validate email format(s)"""
    emails = [e.strip() for e in email_str.split(';')]
    email_pattern = re.compile(r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$')
    return all(email_pattern.match(email) for email in emails)

def validate_github_url(url):
    """Validate GitHub URL format"""
    github_pattern = re.compile(
        r'^https?:\/\/(?:www\.)?github\.com\/[\w-]+\/[\w.-]+\/?$'
    )
    return bool(github_pattern.match(url))

def validate_csv(file_obj):
    """Validate CSV file format and content"""
    try:
        df = pd.read_csv(file_obj.name)
        required_cols = ['query_id', 'pred_rank']
        
        if not all(col in df.columns for col in required_cols):
            return False, "CSV must contain 'query_id' and 'pred_rank' columns"
            
        try:
            first_rank = eval(df['pred_rank'].iloc[0]) if isinstance(df['pred_rank'].iloc[0], str) else df['pred_rank'].iloc[0]
            if not isinstance(first_rank, list) or len(first_rank) < 20:
                return False, "pred_rank must be a list with at least 20 candidates"
        except:
            return False, "Invalid pred_rank format"
            
        return True, "Valid CSV file"
    except Exception as e:
        return False, f"Error processing CSV: {str(e)}"

def sanitize_name(name):
    """Sanitize name for file system use"""
    return re.sub(r'[^a-zA-Z0-9]', '_', name)

def read_json_from_hub(api: HfApi, repo_id: str, file_path: str) -> dict:
    """
    Read and parse JSON file from HuggingFace Hub.
    
    Args:
        api: HuggingFace API instance
        repo_id: Repository ID
        file_path: Path to file in repository
    
    Returns:
        dict: Parsed JSON content
    """
    try:
        # Download the file content as bytes
        content = api.hf_hub_download(
            repo_id=repo_id,
            filename=file_path,
            repo_type="space"
        )
        
        # Read and parse JSON
        with open(content, 'r') as f:
            return json.load(f)
    except Exception as e:
        print(f"Error reading JSON file {file_path}: {str(e)}")
        return None

def scan_submissions_directory():
    """
    Scans the submissions directory and updates the model types dictionary
    with submitted models.
    """
    try:
        # Initialize HuggingFace API
        api = HfApi()
        
        # Track submissions for each split
        submissions_by_split = {
            'test': [],
            'test-0.1': [],
            'human_generated_eval': []
        }
        
        # Get all files from repository
        try:
            all_files = api.list_repo_files(
                repo_id=REPO_ID,
                repo_type="space"
            )
            # Filter for files in submissions directory
            repo_files = [f for f in all_files if f.startswith('submissions/')]
        except Exception as e:
            print(f"Error listing repository contents: {str(e)}")
            return submissions_by_split
            
        # Group files by team folders
        folder_files = {}
        for filepath in repo_files:
            parts = filepath.split('/')
            if len(parts) < 3:  # Need at least submissions/team_folder/file
                continue
                
            folder_name = parts[1]  # team_folder name
            if folder_name not in folder_files:
                folder_files[folder_name] = []
            folder_files[folder_name].append(filepath)
        
        # Process each team folder
        for folder_name, files in folder_files.items():
            try:
                # Find latest.json in this folder
                latest_file = next((f for f in files if f.endswith('latest.json')), None)
                if not latest_file:
                    print(f"No latest.json found in {folder_name}")
                    continue
                
                # Read latest.json
                latest_info = read_json_from_hub(api, REPO_ID, latest_file)
                if not latest_info:
                    print(f"Failed to read latest.json for {folder_name}")
                    continue
                
                timestamp = latest_info.get('latest_submission')
                if not timestamp:
                    print(f"No timestamp found in latest.json for {folder_name}")
                    continue
                
                # Find metadata file for latest submission
                metadata_file = next(
                    (f for f in files if f.endswith(f'metadata_{timestamp}.json')), 
                    None
                )
                if not metadata_file:
                    print(f"No matching metadata file found for {folder_name} timestamp {timestamp}")
                    continue
                
                # Read metadata file
                submission_data = read_json_from_hub(api, REPO_ID, metadata_file)
                if not submission_data:
                    print(f"Failed to read metadata for {folder_name}")
                    continue
                
                if latest_info.get('status') != 'approved':
                    print(f"Skipping unapproved submission in {folder_name}")
                    continue
                
                # Add to submissions by split
                split = submission_data.get('Split')
                if split in submissions_by_split:
                    submissions_by_split[split].append(submission_data)
                    
                    # Update model types if necessary
                    method_name = submission_data.get('Method Name')
                    model_type = submission_data.get('Model Type', 'Others')
                    
                    # Add to model type if it's a new method
                    method_exists = any(method_name in methods for methods in model_types.values())
                    if not method_exists and model_type in model_types:
                        model_types[model_type].append(method_name)
                
            except Exception as e:
                print(f"Error processing folder {folder_name}: {str(e)}")
                continue
                
        return submissions_by_split
        
    except Exception as e:
        print(f"Error scanning submissions directory: {str(e)}")
        return None

def initialize_leaderboard():
    """
    Initialize the leaderboard with baseline results and submitted results.
    """
    # global df_synthesized_full, df_synthesized_10, df_human_generated
    global df_synthesized_full
    
    try:
        # First, initialize with baseline results
        df_synthesized_full = pd.DataFrame(data_synthesized_full)
        # df_synthesized_10 = pd.DataFrame(data_synthesized_10)
        # df_human_generated = pd.DataFrame(data_human_generated)
        
        print("Initialized with baseline results")
        
        # Then scan and add submitted results
        submissions = scan_submissions_directory()
        # print(submissions)
        if submissions:
            for split, split_submissions in submissions.items():
                for submission in split_submissions:
                    if submission.get('results'):  # Make sure we have results
                        # Update appropriate DataFrame based on split
                        if split == 'test':
                            df_to_update = df_synthesized_full
                        elif split == 'test-0.1':
                            df_to_update = df_synthesized_10
                        else:  # human_generated_eval
                            df_to_update = df_human_generated
                            
                        # Prepare new row data
                        new_row = {
                            'Method': submission['Method Name'],
                            f'STARK-{submission["Dataset"].upper()}_Hit@1': submission['results']['hit@1'],
                            f'STARK-{submission["Dataset"].upper()}_Hit@5': submission['results']['hit@5'],
                            f'STARK-{submission["Dataset"].upper()}_R@20': submission['results']['recall@20'],
                            f'STARK-{submission["Dataset"].upper()}_MRR': submission['results']['mrr']
                        }
                        
                        # Update existing row or add new one
                        method_mask = df_to_update['Method'] == submission['Method Name']
                        if method_mask.any():
                            for col in new_row:
                                df_to_update.loc[method_mask, col] = new_row[col]
                        else:
                            df_to_update.loc[len(df_to_update)] = new_row
        
        print("Leaderboard initialization complete")
        
    except Exception as e:
        print(f"Error initializing leaderboard: {str(e)}")

def get_file_content(file_path):
    """
    Helper function to safely read file content from HuggingFace repository
    """
    try:
        api = HfApi()
        content_path = api.hf_hub_download(
            repo_id=REPO_ID,
            filename=file_path,
            repo_type="space"
        )
        with open(content_path, 'r') as f:
            return f.read()
    except Exception as e:
        print(f"Error reading file {file_path}: {str(e)}")
        return None

def save_submission(submission_data, csv_file):
    """
    Save submission data and CSV file using model_name_team_name format
    
    Args:
        submission_data (dict): Metadata and results for the submission
        csv_file: The uploaded CSV file object
    """
    # Create folder name from model name and team name
    model_name_clean = sanitize_name(submission_data['Method Name'])
    team_name_clean = sanitize_name(submission_data['Team Name'])
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    
    # Create folder name: model_name_team_name
    folder_name = f"{model_name_clean}_{team_name_clean}"
    submission_id = f"{folder_name}_{timestamp}"
    
    # Create submission directory structure
    base_dir = "submissions"
    submission_dir = os.path.join(base_dir, folder_name)
    os.makedirs(submission_dir, exist_ok=True)
    
    # Save CSV file with timestamp to allow multiple submissions
    csv_filename = f"predictions_{timestamp}.csv"
    csv_path = os.path.join(submission_dir, csv_filename)
    if hasattr(csv_file, 'name'):
        with open(csv_file.name, 'rb') as source, open(csv_path, 'wb') as target:
            target.write(source.read())
    
    # Add file paths to submission data
    submission_data.update({
        "csv_path": csv_path,
        "submission_id": submission_id,
        "folder_name": folder_name
    })
    
    # Save metadata as JSON with timestamp
    metadata_path = os.path.join(submission_dir, f"metadata_{timestamp}.json")
    with open(metadata_path, 'w') as f:
        json.dump(submission_data, f, indent=4)
    
    # Update latest.json to track most recent submission
    latest_path = os.path.join(submission_dir, "latest.json")
    with open(latest_path, 'w') as f:
        json.dump({
            "latest_submission": timestamp,
            "status": "pending_review",
            "method_name": submission_data['Method Name']
        }, f, indent=4)
    
    return submission_id

def update_leaderboard_data(submission_data):
    """
    Update leaderboard data with new submission results
    Only uses model name in the displayed table
    """
    global df_synthesized_full, df_synthesized_10, df_human_generated
    
    # Determine which DataFrame to update based on split
    split_to_df = {
        'test': df_synthesized_full,
        'test-0.1': df_synthesized_10,
        'human_generated_eval': df_human_generated
    }
    
    df_to_update = split_to_df[submission_data['Split']]
    
    # Prepare new row data
    new_row = {
        'Method': submission_data['Method Name'],  # Only use method name in table
        f'STARK-{submission_data["Dataset"].upper()}_Hit@1': submission_data['results']['hit@1'],
        f'STARK-{submission_data["Dataset"].upper()}_Hit@5': submission_data['results']['hit@5'],
        f'STARK-{submission_data["Dataset"].upper()}_R@20': submission_data['results']['recall@20'],
        f'STARK-{submission_data["Dataset"].upper()}_MRR': submission_data['results']['mrr']
    }
    
    # Check if method already exists
    method_mask = df_to_update['Method'] == submission_data['Method Name']
    if method_mask.any():
        # Update existing row
        for col in new_row:
            df_to_update.loc[method_mask, col] = new_row[col]
    else:
        # Add new row
        df_to_update.loc[len(df_to_update)] = new_row

# Function to get emails from meta_data
def get_emails_from_metadata(meta_data):
    """
    Extracts emails from the meta_data dictionary.
    
    Args:
        meta_data (dict): The metadata dictionary that contains the 'Contact Email(s)' field.
    
    Returns:
        list: A list of email addresses.
    """
    return [email.strip() for email in meta_data.get("Contact Email(s)", "").split(";")]

# Function to format meta_data as an HTML table (without Prediction CSV)
def format_metadata_as_table(meta_data):
    """
    Formats metadata dictionary into an HTML table for the email.
    Handles multiple contact emails separated by a semicolon.

    Args:
        meta_data (dict): Dictionary containing submission metadata.

    Returns:
        str: HTML string representing the metadata table.
    """
    table_rows = ""
    
    for key, value in meta_data.items():
        if key == "Contact Email(s)":
            # Ensure that contact emails are split by semicolon
            emails = value.split(';')
            formatted_emails = "; ".join([email.strip() for email in emails])
            table_rows += f"<tr><td><b>{key}</b></td><td>{formatted_emails}</td></tr>"
        elif key != "Prediction CSV":  # Exclude the Prediction CSV field
            table_rows += f"<tr><td><b>{key}</b></td><td>{value}</td></tr>"

    table_html = f"""
    <table border="1" cellpadding="5" cellspacing="0">
        {table_rows}
    </table>
    """
    return table_html

# Function to get emails from meta_data
def get_emails_from_metadata(meta_data):
    """
    Extracts emails from the meta_data dictionary.
    
    Args:
        meta_data (dict): The metadata dictionary that contains the 'Contact Email(s)' field.
    
    Returns:
        list: A list of email addresses.
    """
    return [email.strip() for email in meta_data.get("Contact Email(s)", "").split(";")]
            
def format_evaluation_results(results):
    """
    Formats the evaluation results dictionary into a readable string.

    Args:
        results (dict): Dictionary containing evaluation metrics and their values.

    Returns:
        str: Formatted string of evaluation results.
    """
    result_lines = [f"{metric}: {value}" for metric, value in results.items()]
    return "\n".join(result_lines)

def get_model_type_for_method(method_name):
    """
    Find the model type category for a given method name.
    Returns 'Others' if not found in predefined categories.
    """
    for type_name, methods in model_types.items():
        if method_name in methods:
            return type_name
    return 'Others'

def validate_model_type(method_name, selected_type):
    """
    Validate if the selected model type is appropriate for the method name.
    Returns (is_valid, message).
    """
    # Check if method exists in any category
    existing_type = None
    for type_name, methods in model_types.items():
        if method_name in methods:
            existing_type = type_name
            break
    
    # If method exists, it must be submitted under its predefined category
    if existing_type:
        if existing_type != selected_type:
            return False, f"This method name is already registered under '{existing_type}'. Please use the correct category."
        return True, "Valid model type"
    
    # For new methods, any category is valid
    return True, "Valid model type"

def process_submission(method_name, team_name, dataset, split, contact_email, code_repo, csv_file, model_description, hardware, paper_link, model_type):
    """Process and validate submission"""
    temp_files = []
    try:
        # Input validation
        if not all([method_name, team_name, dataset, split, contact_email, code_repo, csv_file, model_type]):
            return "Error: Please fill in all required fields"
        
        # Validate model type
        is_valid, message = validate_model_type(method_name, model_type)
        if not is_valid:
            return f"Error: {message}"

        # Create metadata
        meta_data = {
            "Method Name": method_name,
            "Team Name": team_name,
            "Dataset": dataset,
            "Split": split,
            "Contact Email(s)": contact_email,
            "Code Repository": code_repo,
            "Model Description": model_description,
            "Hardware": hardware,
            "(Optional) Paper link": paper_link,
            "Model Type": model_type
        }
        
        # Generate folder name and timestamp
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        folder_name = f"{sanitize_name(method_name)}_{sanitize_name(team_name)}"
        
        # Process CSV file
        temp_csv_path = None
        if isinstance(csv_file, str):
            temp_csv_path = csv_file
        else:
            temp_fd, temp_csv_path = tempfile.mkstemp(suffix='.csv')
            temp_files.append(temp_csv_path)
            os.close(temp_fd)
            
            if hasattr(csv_file, 'name'):
                shutil.copy2(csv_file.name, temp_csv_path)
            else:
                with open(temp_csv_path, 'wb') as temp_file:
                    if hasattr(csv_file, 'seek'):
                        csv_file.seek(0)
                    if hasattr(csv_file, 'read'):
                        shutil.copyfileobj(csv_file, temp_file)
                    else:
                        temp_file.write(csv_file)

        if not os.path.exists(temp_csv_path):
            raise FileNotFoundError(f"Failed to create temporary CSV file at {temp_csv_path}")

        # Compute metrics
        results = compute_metrics(
            csv_path=temp_csv_path,
            dataset=dataset.lower(),
            split=split,
            num_workers=4
        )
        
        if isinstance(results, str):
            # send_error_notification(meta_data, results)
            return f"Evaluation error: {results}"

        # Process results
        processed_results = {
            "hit@1": round(results['hit@1'] * 100, 2),
            "hit@5": round(results['hit@5'] * 100, 2),
            "recall@20": round(results['recall@20'] * 100, 2),
            "mrr": round(results['mrr'] * 100, 2)
        }
        
        # Save files to HuggingFace Hub
        try:
            # 1. Save CSV file
            csv_filename = f"predictions_{timestamp}.csv"
            csv_path_in_repo = f"submissions/{folder_name}/{csv_filename}"
            hub_storage.save_to_hub(
                file_content=temp_csv_path,
                path_in_repo=csv_path_in_repo,
                commit_message=f"Add submission: {method_name} by {team_name}"
            )

            # 2. Save metadata
            submission_data = {
                **meta_data,
                "results": processed_results,
                "status": "approved",  # or "pending_review"
                "submission_date": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
                "csv_path": csv_path_in_repo
            }
            
            metadata_fd, temp_metadata_path = tempfile.mkstemp(suffix='.json')
            temp_files.append(temp_metadata_path)
            os.close(metadata_fd)
            
            with open(temp_metadata_path, 'w') as f:
                json.dump(submission_data, f, indent=4)
            
            metadata_path = f"submissions/{folder_name}/metadata_{timestamp}.json"
            hub_storage.save_to_hub(
                file_content=temp_metadata_path,
                path_in_repo=metadata_path,
                commit_message=f"Add metadata: {method_name} by {team_name}"
            )

            # 3. Create or update latest.json
            latest_info = {
                "latest_submission": timestamp,
                "status": "approved",  # or "pending_review"
                "method_name": method_name,
                "team_name": team_name
            }
            
            latest_fd, temp_latest_path = tempfile.mkstemp(suffix='.json')
            temp_files.append(temp_latest_path)
            os.close(latest_fd)
            
            with open(temp_latest_path, 'w') as f:
                json.dump(latest_info, f, indent=4)
            
            latest_path = f"submissions/{folder_name}/latest.json"
            hub_storage.save_to_hub(
                file_content=temp_latest_path,
                path_in_repo=latest_path,
                commit_message=f"Update latest submission info for {method_name}"
            )

        except Exception as e:
            raise RuntimeError(f"Failed to save files to HuggingFace Hub: {str(e)}")
        
        # Send confirmation email and update leaderboard data
        # send_submission_confirmation(meta_data, processed_results)
        update_leaderboard_data(submission_data)
        
        # Return success message
        return f"""
        Submission successful! 
        
        Evaluation Results:
        Hit@1: {processed_results['hit@1']:.2f}%
        Hit@5: {processed_results['hit@5']:.2f}%
        Recall@20: {processed_results['recall@20']:.2f}%
        MRR: {processed_results['mrr']:.2f}%
        
        Your submission has been saved and a confirmation email has been sent to {contact_email}.
        Once approved, your results will appear in the leaderboard under: {method_name}
        
        You can find your submission at:
        https://huggingface.co/spaces/{REPO_ID}/tree/main/submissions/{folder_name}
        
        Please refresh the page to see your submission in the leaderboard.
        """
        
    except Exception as e:
        error_message = f"Error processing submission: {str(e)}"
        # send_error_notification(meta_data, error_message)
        return error_message
    finally:
        # Clean up temporary files
        for temp_file in temp_files:
            try:
                if os.path.exists(temp_file):
                    os.unlink(temp_file)
            except Exception as e:
                print(f"Warning: Failed to delete temporary file {temp_file}: {str(e)}")

def filter_by_model_type(df, selected_types):
    """
    Filter DataFrame by selected model types, including submitted models.
    """
    if not selected_types:
        return df.head(0)
        
    # Get all models from selected types
    selected_models = []
    for type_name in selected_types:
        selected_models.extend(model_types[type_name])
    
    # Filter DataFrame to include only selected models
    return df[df['Method'].isin(selected_models)]

def format_dataframe(df, dataset):
    columns = ['Method'] + [col for col in df.columns if dataset in col]
    filtered_df = df[columns].copy()
    filtered_df.columns = [col.split('_')[-1] if '_' in col else col for col in filtered_df.columns]
    # filtered_df = filtered_df.sort_values('MRR', ascending=False)
    filtered_df = filtered_df.sort_values('Kappa', ascending=False)
    return filtered_df

def update_tables(selected_types):
    """
    Update tables based on selected model types.
    Include all models from selected categories.
    """
    if not selected_types:
        # return [df.head(0) for df in [df_synthesized_full, df_synthesized_10, df_human_generated]]
        return [df.head(0) for df in [df_synthesized_full]]
    
    filtered_df_full = filter_by_model_type(df_synthesized_full, selected_types)
    # filtered_df_10 = filter_by_model_type(df_synthesized_10, selected_types)
    # filtered_df_human = filter_by_model_type(df_human_generated, selected_types)
    
    outputs = []
    # for df in [filtered_df_full, filtered_df_10, filtered_df_human]:
    for df in [filtered_df_full]:
        # for dataset in ['DL2023', 'MAG', 'PRIME']:
        for dataset in ['DL2023']:
            outputs.append(format_dataframe(df, f"LLMJudge-{dataset}"))

    print(outputs)
    print(type(outputs))
    print(type(outputs[0]))
    return outputs

def load_test_data():
    # Split the text into a list
    test_data = os.getenv('LLMJudgeTest').split()
    # Reshape the list into a 2D array where each row contains 4 elements
    test_data = [test_data[i:i+4] for i in range(0, len(test_data), 4)]
    # Create a DataFrame
    test_data = pd.DataFrame(test_data, columns=['qid', 'Q0', 'pid', 'score'])
    
    return test_data

css = """
table > thead {
    white-space: normal
}

table {
    --cell-width-1: 250px
}

table > tbody > tr > td:nth-child(2) > div {
    overflow-x: auto
}

.tab-nav {
    border-bottom: 1px solid rgba(255, 255, 255, 0.1);
    margin-bottom: 1rem;
}
"""

# Main application
with gr.Blocks(css=css) as demo:
    gr.Markdown("# LLM-as-a-Rel: Automatic Relevance Judgment Leaderboard")
    gr.Markdown("Refer to the [LLMJudge overview paper](https://arxiv.org/pdf/2408.08896) for details on metrics, tasks and models.")
    
    # Initialize leaderboard at startup
    print("Starting leaderboard initialization...")
    initialize_leaderboard()
    print("Leaderboard initialization finished")

    # Model type filter
    model_type_filter = gr.CheckboxGroup(
        choices=list(model_types.keys()),
        value=list(model_types.keys()),
        label="Model types",
        interactive=True
    )
    
    # Initialize dataframes list
    all_dfs = []
    
    # Create nested tabs structure
    with gr.Tabs() as outer_tabs:
        with gr.TabItem("Synthesized (full)"):
            with gr.Tabs() as inner_tabs1:
                # for dataset in ['AMAZON', 'MAG', 'PRIME']:
                for dataset in ['DL2023']:
                    with gr.TabItem(dataset):
                        all_dfs.append(gr.DataFrame(interactive=False))
                        
        # with gr.TabItem("Synthesized (10%)"):
        #     with gr.Tabs() as inner_tabs2:
        #         for dataset in ['AMAZON', 'MAG', 'PRIME']:
        #             with gr.TabItem(dataset):
        #                 all_dfs.append(gr.DataFrame(interactive=False))
                        
        # with gr.TabItem("Human-Generated"):
        #     with gr.Tabs() as inner_tabs3:
        #         for dataset in ['AMAZON', 'MAG', 'PRIME']:
        #             with gr.TabItem(dataset):
        #                 all_dfs.append(gr.DataFrame(interactive=False))
    
    # Submission section

    # load test data
    test_data = load_test_data()
    print("Test data is loaded.")

    gr.Markdown("---")
    gr.Markdown("## Submit Your Results:")
    gr.Markdown("""
    Submit your results to be included in the leaderboard. Please ensure your submission meets all requirements.
    For questions, contact hossein.rahmani.22@ucl.ac.uk. Detailed instructions can be referred at [submission instructions](https://docs.google.com/document/d/11coGjTmOEi9p9-PUq1oy0eTOj8f_8CVQhDl5_0FKT14/edit?usp=sharing).
    """)
    
    with gr.Row():
        with gr.Column():
            method_name = gr.Textbox(
                label="Method Name (max 25 chars)*",
                placeholder="e.g., MyRetrievalModel-v1"
            )
            dataset = gr.Dropdown(
                choices=["amazon", "mag", "prime"],
                label="Dataset*",
                value="amazon"
            )
            split = gr.Dropdown(
                choices=["test", "test-0.1", "human_generated_eval"],
                label="Split*",
                value="test"
            )
            team_name = gr.Textbox(
                label="Team Name (max 25 chars)*",
                placeholder="e.g., Stanford NLP"
            )
            contact_email = gr.Textbox(
                label="Contact Email(s)*",
                placeholder="email@example.com; another@example.com"
            )
            model_type = gr.Dropdown(
                choices=list(model_types.keys()),
                label="Model Type*",
                value="Others",
                info="Select the appropriate category for your model"
            )
            
        with gr.Column():
            model_description = gr.Textbox(
                label="Model Description*",
                lines=3,
                placeholder="Briefly describe how your retriever model works..."
            )
            code_repo = gr.Textbox(
                label="Code Repository*",
                placeholder="https://github.com/snap-stanford/stark-leaderboard"
            )
            hardware = gr.Textbox(
                label="Hardware Specifications*",
                placeholder="e.g., 4x NVIDIA A100 80GB"
            )
            csv_file = gr.File(
                label="Prediction TXT*",
                file_types=[".txt"],
                type="filepath"  
            )
            paper_link = gr.Textbox(
                label="Paper Link (Optional)",
                placeholder="https://arxiv.org/abs/..."
            )
    
    submit_btn = gr.Button("Submit", variant="primary")
    result = gr.Textbox(label="Submission Status", interactive=False)

    # Set up event handlers
    model_type_filter.change(update_tables, inputs=[model_type_filter], outputs=all_dfs)
    
    # Event handler for submission button
    submit_btn.click(
        fn=process_submission,
        inputs=[
            method_name, team_name, dataset, split, contact_email,
            code_repo, csv_file, model_description, hardware, paper_link, model_type
        ],
        outputs=result
    ).success(  # Add a success handler to update tables after successful submission
        fn=update_tables,
        inputs=[model_type_filter],
        outputs=all_dfs
    )
    
    # Initial table update
    demo.load(update_tables, inputs=[model_type_filter], outputs=all_dfs)

# Launch the application
demo.launch()