app.py

import os
import json
import datetime
import requests
from email.utils import parseaddr

import gradio as gr
import pandas as pd
import numpy as np

from datasets import load_dataset, VerificationMode
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import HfApi

# InfoStrings
from scorer import question_scorer
from content import format_error, format_warning, format_log, TITLE, INTRODUCTION_TEXT, SUBMISSION_TEXT, CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, model_hyperlink

TOKEN = os.environ.get("TOKEN", None)

OWNER="financebench"
DATA_DATASET = f"{OWNER}/finance-events-latest"
INTERNAL_DATA_DATASET = f"{OWNER}/finance-events-latest"
SUBMISSION_DATASET = f"{OWNER}/submissions_internal"
SUBMISSION_DATASET_PUBLIC = f"{OWNER}/submissions_public"
CONTACT_DATASET = f"{OWNER}/contact_info"
RESULTS_DATASET = f"{OWNER}/results"
LEADERBOARD_PATH = f"{OWNER}/leaderboard"
api = HfApi()

YEAR_VERSION = ""
ref_scores_len = {"valid": 165, "test": 301}
ref_level_len = {"valid": {1: 53, 2: 86, 3: 26}, "test": {1: 93, 2: 159, 3: 49}}

os.makedirs("scored", exist_ok=True)

# Should be False on spaces and True outside
LOCAL_DEBUG = False #not (os.environ.get("system") == "spaces")

# Display the results
eval_results = load_dataset(RESULTS_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", verification_mode=VerificationMode.NO_CHECKS, trust_remote_code=True)
contact_infos = load_dataset(CONTACT_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", verification_mode=VerificationMode.NO_CHECKS, trust_remote_code=True)
def get_dataframe_from_results(eval_results, split):
    local_df = eval_results[split]
    local_df = local_df.map(lambda row: {"model": model_hyperlink(row["url"], row["model"])})
    local_df = local_df.remove_columns(["system_prompt", "url", "organisation", "username"])
    local_df = local_df.rename_column("model", "Agent name")
    local_df = local_df.rename_column("model_family", "Model family")
    local_df = local_df.rename_column("score", "Return (%)")
    local_df = local_df.rename_column("date", "Submission date")
    df = pd.DataFrame(local_df)
    df = df.sort_values(by=["Return (%)"], ascending=False)

    numeric_cols = [c for c in local_df.column_names if "return" in c.lower()]
    df[numeric_cols] = df[numeric_cols].multiply(100).round(decimals=2)
    #df = df.style.format("{:.2%}", subset=numeric_cols)

    return df

eval_dataframe_val = get_dataframe_from_results(eval_results=eval_results, split="valid")
eval_dataframe_test = get_dataframe_from_results(eval_results=eval_results, split="test")

# Gold answers
gold_results = {}
gold_dataset = load_dataset(INTERNAL_DATA_DATASET, "", token=TOKEN, trust_remote_code=True)
gold_results = {split: {row["task_id"]: row for row in gold_dataset[split]} for split in ["test", "valid"]}


def restart_space():
    api.restart_space(repo_id=LEADERBOARD_PATH, token=TOKEN)

TYPES = ["markdown", "str", "number", "str"]

def add_new_eval(
    val_or_test: str,
    model: str,
    model_family: str,
    system_prompt: str,
    url: str,
    path_to_file: str,
    organisation: str,
    mail: str,
    profile: gr.OAuthProfile, 
):
    # Was the profile created less than 2 month ago?
    user_data = requests.get(f"https://huggingface.co/api/users/{profile.username}/overview")
    creation_date = json.loads(user_data.content)["createdAt"]
    if datetime.datetime.now() - datetime.datetime.strptime(creation_date, '%Y-%m-%dT%H:%M:%S.%fZ') < datetime.timedelta(days=60):
        return format_error("This account is not authorized to submit on FinanceBench.")
        

    contact_infos = load_dataset(CONTACT_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", verification_mode=VerificationMode.NO_CHECKS, trust_remote_code=True)
    user_submission_dates = sorted(row["date"] for row in contact_infos[val_or_test] if row["username"] == profile.username)
    if len(user_submission_dates) > 0 and user_submission_dates[-1] == datetime.datetime.today().strftime('%Y-%m-%d'):
        return format_error("You already submitted once today, please try again tomorrow.")


    is_valid = val_or_test == "valid"
    # Very basic email parsing
    _, parsed_mail = parseaddr(mail)
    if not "@" in parsed_mail:
        return format_warning("Please provide a valid email adress.")

    print("Adding new eval")

    # Check if the combination model/org already exists and prints a warning message if yes
    if model.lower() in set([m.lower() for m in eval_results[val_or_test]["model"]]) and organisation.lower() in set([o.lower() for o in eval_results[val_or_test]["organisation"]]):
        return format_warning("This model has been already submitted.")
    
    if path_to_file is None:
        return format_warning("Please attach a file.")

    # SAVE UNSCORED SUBMISSION
    if LOCAL_DEBUG:
        print("mock uploaded submission")
    else:
        api.upload_file(
            repo_id=SUBMISSION_DATASET, 
            path_or_fileobj=path_to_file.name, 
            path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_raw_{datetime.datetime.today()}.jsonl",
            repo_type="dataset", 
            token=TOKEN
        )

    # SAVE CONTACT
    contact_info = {
        "model": model,
        "model_family": model_family,
        "url": url,
        "organisation": organisation,
        "username": profile.username,
        "mail": mail,
        "date": datetime.datetime.today().strftime('%Y-%m-%d')
    }
    contact_infos[val_or_test]= contact_infos[val_or_test].add_item(contact_info)
    if LOCAL_DEBUG:
        print("mock uploaded contact info")
    else:
        contact_infos.push_to_hub(CONTACT_DATASET, config_name = YEAR_VERSION, token=TOKEN)

    # SCORE SUBMISSION
    file_path = path_to_file.name        
    scores = {"all": 0, 1: 0, 2: 0, 3: 0}
    num_questions = {"all": 0, 1: 0, 2: 0, 3: 0}
    task_ids = []
    with open(f"scored/{organisation}_{model}.jsonl", "w") as scored_file:
        with open(file_path, 'r') as f:
            for ix, line in enumerate(f):
                try:
                    task = json.loads(line)
                except Exception:
                    return format_error(f"Line {ix} is incorrectly formatted. Please fix it and resubmit your file.")

                if "model_answer" not in task:
                    return format_error(f"Line {ix} contains no model_answer key. Please fix it and resubmit your file.")
                answer = task["model_answer"]
                task_id = task["task_id"]
                try:
                    level = int(gold_results[val_or_test][task_id]["Level"])
                except KeyError:
                    return format_error(f"{task_id} not found in split {val_or_test}. Are you sure you submitted the correct file?")

                score = question_scorer(task['model_answer'], gold_results[val_or_test][task_id]["Final answer"])
                
                scored_file.write(
                    json.dumps({
                        "id": task_id,
                        "model_answer": answer,
                        "score": score,
                        "level": level
                    }) + "\n"
                )
                task_ids.append(task_id)

                scores["all"] += score
                scores[level] += score
                num_questions["all"] += 1
                num_questions[level] += 1

    # Check if there's any duplicate in the submission
    if len(task_ids) != len(set(task_ids)):
        return format_error("There are duplicates in your submission. Please check your file and resubmit it.")

    if any([num_questions[level] != ref_level_len[val_or_test][level] for level in [1, 2, 3]]):
        return format_error(f"Your submission has {num_questions[1]} questions for level 1, {num_questions[2]} for level 2, and {num_questions[3]} for level 3, but it should have {ref_level_len[val_or_test][1]}, {ref_level_len[val_or_test][2]}, and {ref_level_len[val_or_test][3]} respectively. Please check your submission.")

    # SAVE SCORED SUBMISSION
    if LOCAL_DEBUG:
        print("mock uploaded scored submission")
    else:
        api.upload_file(
            repo_id=SUBMISSION_DATASET, 
            path_or_fileobj=f"scored/{organisation}_{model}.jsonl",
            path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_scored_{datetime.datetime.today()}.jsonl", 
            repo_type="dataset", 
            token=TOKEN
        )

        # Save scored file
        if is_valid:
            api.upload_file(
                repo_id=SUBMISSION_DATASET_PUBLIC, 
                path_or_fileobj=f"scored/{organisation}_{model}.jsonl",
                path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_scored_{datetime.datetime.today()}.jsonl", 
                repo_type="dataset", 
                token=TOKEN
            )

    # SAVE TO LEADERBOARD DATA
    eval_entry = {
        "model": model,
        "model_family": model_family,
        "system_prompt": system_prompt,
        "url": url,
        "organisation": organisation,
        "score": scores["all"]/ref_scores_len[val_or_test],
        "date": datetime.datetime.today().strftime('%Y-%m-%d')
    }
    if num_questions[1] + num_questions[2] + num_questions[3] != ref_scores_len[val_or_test]:
        return format_error(f"Your submission has {len(scores['all'])} questions for the {val_or_test} set, but it should have {ref_scores_len[val_or_test]}. Please check your submission.")
    # Catching spam submissions of 100%

    # Testing for duplicates - to see if we want to add something like it as it would allow people to try to see the content of other submissions
    #eval_entry_no_date = {k: v for k, v in eval_entry if k != "date"}
    #columns_no_date = [c for c in eval_results[val_or_test].column_names if c != "date"]
    #if eval_entry_no_date in eval_results[val_or_test].select_columns(columns_no_date):
    #    return format_error(f"Your submission is an exact duplicate from an existing submission.")

    eval_results[val_or_test] = eval_results[val_or_test].add_item(eval_entry)
    print(eval_results)
    if LOCAL_DEBUG:
        print("mock uploaded results to lb")
    else:
        eval_results.push_to_hub(RESULTS_DATASET, config_name = YEAR_VERSION, token=TOKEN)


    return format_log(f"Model {model} submitted by {organisation} successfully.\nPlease wait a few hours and refresh the leaderboard to see your score displayed.")


def refresh():
    eval_results = load_dataset(RESULTS_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", verification_mode=VerificationMode.NO_CHECKS,trust_remote_code=True)
    eval_dataframe_val = get_dataframe_from_results(eval_results=eval_results, split="valid")
    eval_dataframe_test = get_dataframe_from_results(eval_results=eval_results, split="test")
    return eval_dataframe_val, eval_dataframe_test

def upload_file(files):
    file_paths = [file.name for file in files]
    return file_paths


demo = gr.Blocks()
with demo:
    with gr.Row():
        gr.HTML("""
        <div style="display: flex; justify-content: flex-start; align-items: center; padding: 15px 0; border-bottom: 1px solid 
#eaeaea; width: 100%;">
            <div>
                <a href="https://financebench.ai/hello-world/" target="_self" style="text-decoration: none; color: inherit; font-weight: bold; font-size: 24px; margin-right: 40px;">
                    FinanceBench.ai
                </a>
            </div>
            <div style="display: flex; gap: 25px;">
                <a href="https://financebench.ai/hello-world/" target="_self" style="text-decoration: none; color: inherit; font-weight: 500;">HOME</a>
                <a href="https://huggingface.co/spaces/financebench/leaderboard" target="_self" style="text-decoration: none; color: inherit; font-weight: 500;">LEADERBOARD</a>
                <a href="https://financebench.ai/get-started/" target="_self" style="text-decoration: none; color: inherit; font-weight: 500;">GET STARTED</a>
            </div>
        </div>
        """)
    #gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            citation_button = gr.Textbox(
                value=CITATION_BUTTON_TEXT,
                label=CITATION_BUTTON_LABEL,
                elem_id="citation-button",
            ) #.style(show_copy_button=True)

    with gr.Tab("Results: Test"):
        leaderboard_table_test = gr.components.Dataframe(
            value=eval_dataframe_test, datatype=TYPES, interactive=False,
            column_widths=["20%"] 
        )
    with gr.Tab("Results: valid"):
        leaderboard_table_val = gr.components.Dataframe(
            value=eval_dataframe_val, datatype=TYPES, interactive=False,
            column_widths=["20%"] 
        )

    refresh_button = gr.Button("Refresh")
    refresh_button.click(
        refresh,
        inputs=[],
        outputs=[
            leaderboard_table_val,
            leaderboard_table_test,
        ],
    )
    with gr.Accordion("Submit a new model for evaluation"):
        with gr.Row():
            gr.Markdown(SUBMISSION_TEXT, elem_classes="markdown-text")
        with gr.Row():
            with gr.Column():
                level_of_test = gr.Radio(["valid", "test"], value="valid", label="Split")
                model_name_textbox = gr.Textbox(label="Agent name")
                model_family_textbox = gr.Textbox(label="Model family")
                system_prompt_textbox = gr.Textbox(label="System prompt example")
                url_textbox = gr.Textbox(label="Url to model information")
            with gr.Column():
                organisation = gr.Textbox(label="Organisation")
                mail = gr.Textbox(label="Contact email (will be stored privately, & used if there is an issue with your submission)")
                file_output = gr.File()


        with gr.Row():
            gr.LoginButton()
            submit_button = gr.Button("Submit Eval")
        submission_result = gr.Markdown()
        submit_button.click(
            add_new_eval,
            [
                level_of_test,
                model_name_textbox,
                model_family_textbox,
                system_prompt_textbox,
                url_textbox,
                file_output,
                organisation,
                mail
            ],
            submission_result,
        )

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=3600)
scheduler.start()
demo.launch(debug=True)