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 stark_qa import load_qa
from stark_qa.evaluator import Evaluator
from utils.hub_storage import HubStorage
from utils.token_handler import TokenHandler
# Initialize storage once at startup
try:
REPO_ID = "snap-stanford/stark-leaderboard" # 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):
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)
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
}
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)', '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']
}
# 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 scan_submissions_directory():
"""
Scans the submissions directory and updates the leaderboard tables with all submitted results.
Returns a dictionary mapping split names to lists of submissions.
"""
global df_synthesized_full, df_synthesized_10, df_human_generated
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
if not repo_files:
print("No submissions directory found or empty")
return
# 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
try:
latest_content = api.hf_hub_download(
repo_id=REPO_ID,
repo_type="space",
filename=latest_file,
text=True
)
latest_info = json.loads(latest_content)
except Exception as e:
print(f"Error reading latest.json for {folder_name}: {str(e)}")
continue
# Check submission status
if latest_info.get('status') != 'approved':
print(f"Skipping unapproved submission in {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
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
try:
metadata_content = api.hf_hub_download(
repo_id=REPO_ID,
repo_type="space",
filename=metadata_file,
text=True
)
submission_data = json.loads(metadata_content)
except Exception as e:
print(f"Error reading metadata for {folder_name}: {str(e)}")
continue
# Update leaderboard
split = submission_data.get('Split')
if split in submissions_by_split:
submissions_by_split[split].append(submission_data)
update_leaderboard_data(submission_data)
print(f"Successfully added submission from {folder_name} to {split} leaderboard")
else:
print(f"Invalid split '{split}' found in {folder_name}")
except Exception as e:
print(f"Error processing folder {folder_name}: {str(e)}")
continue
# Print summary
print("\nLeaderboard initialization summary:")
for split, submissions in submissions_by_split.items():
print(f"{split}: {len(submissions)} submissions")
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
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
scan_submissions_directory()
print("Leaderboard initialization complete")
except Exception as e:
print(f"Error initializing leaderboard: {str(e)}")
# Utility function to get file content
def get_file_content(file_path):
"""
Helper function to safely read file content from HuggingFace repository
"""
try:
api = HfApi()
content = api.file_download(
repo_id=REPO_ID,
repo_type="space",
filename=file_path
)
return content.read().decode('utf-8')
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"| {key} | {formatted_emails} |
"
elif key != "Prediction CSV": # Exclude the Prediction CSV field
table_rows += f"| {key} | {value} |
"
table_html = f"""
"""
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 process_submission(
method_name, team_name, dataset, split, contact_email,
code_repo, csv_file, model_description, hardware, paper_link
):
"""Process and validate submission"""
temp_files = []
try:
# Input validation
if not all([method_name, team_name, dataset, split, contact_email, code_repo, csv_file]):
return "Error: Please fill in all required fields"
# Length validation
if len(method_name) > 25:
return "Error: Method name must be 25 characters or less"
if len(team_name) > 25:
return "Error: Team name must be 25 characters or less"
if not validate_email(contact_email):
return "Error: Invalid email format"
if not validate_github_url(code_repo):
return "Error: Invalid GitHub repository URL"
# Create metadata at the beginning to ensure it's available for error handling
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
}
# 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):
if not selected_types:
return df.head(0)
selected_models = [model for type in selected_types for model in model_types[type]]
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)
return filtered_df
def update_tables(selected_types):
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 dataset in ['AMAZON', 'MAG', 'PRIME']:
outputs.append(format_dataframe(df, f"STARK-{dataset}"))
return outputs
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("# Semi-structured Retrieval Benchmark (STaRK) Leaderboard")
gr.Markdown("Refer to the [STaRK paper](https://arxiv.org/pdf/2404.13207) 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']:
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
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 stark-qa@cs.stanford.edu
""")
with gr.Row():
with gr.Column():
method_name = gr.Textbox(
label="Method Name (max 25 chars)*",
placeholder="e.g., MyRetrievalModel-v1"
)
team_name = gr.Textbox(
label="Team Name (max 25 chars)*",
placeholder="e.g., Stanford NLP"
)
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"
)
contact_email = gr.Textbox(
label="Contact Email(s)*",
placeholder="email@example.com; another@example.com"
)
with gr.Column():
code_repo = gr.Textbox(
label="Code Repository*",
placeholder="https://github.com/snap-stanford/stark-leaderboard"
)
csv_file = gr.File(
label="Prediction CSV*",
file_types=[".csv"],
type="filepath" # Important: specify type as filepath
)
model_description = gr.Textbox(
label="Model Description*",
lines=3,
placeholder="Briefly describe how your retriever model works..."
)
hardware = gr.Textbox(
label="Hardware Specifications*",
placeholder="e.g., 4x NVIDIA A100 80GB"
)
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
],
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()