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="Blanca"
DATA_DATASET = f"{OWNER}/CQs-Gen_test"
INTERNAL_DATA_DATASET = f"{OWNER}/CQs-Gen_test"
SUBMISSION_DATASET = f"{OWNER}/submissions_internal"
SUBMISSION_DATASET_PUBLIC = f"{OWNER}/submissions_public"
#CONTACT_DATASET = f"{OWNER}/contact_info"
RESULTS_DATASET = f"{OWNER}/results_public"
LEADERBOARD_PATH = f"HiTZ/Critical_Questions_Leaderboard"
api = HfApi()

YEAR_VERSION = "2025"
ref_scores_len = {"test": 34}
#ref_level_len = {"validation": {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
test_results = load_dataset(
    RESULTS_DATASET,
    YEAR_VERSION,
    split="test",
    token=TOKEN,
    download_mode="force_redownload",
    verification_mode=VerificationMode.NO_CHECKS,
    trust_remote_code=True,
)
eval_results = {"test": test_results}
#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"])
    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", "Score (%)")
    local_df = local_df.rename_column("date", "Submission date")
    df = pd.DataFrame(local_df)
    df = df.sort_values(by=["Score (%)"], ascending=False)

    #df["Score (%)"] = df["Score (%)"].multiply(100).round(2)

    return df


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

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


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

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

def add_new_eval(
    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 this leaderboard.")
        

    #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.")

    val_or_test = "test"
    is_validation = False
    # 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 = 0
    num_questions = 0
    task_ids = []

    with open(f"scored/{organisation}_{model}.jsonl", "w") as scored_file: 
        with open(file_path, 'r') as f:
            data = json.load(f)
            for ix, line in data.items():
                return format_error(os.getcwd())

                # TODO: look at each question, compare it to the references, output a label between 0 and 1
                
                #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} missing 'model_answer'.")
                #answer = task["model_answer"]
                #task_id = task["task_id"]

                #if task_id not in gold_results[val_or_test]:
                #    return format_error(f"{task_id} not found in gold set.")

                #score = question_scorer(answer, gold_results[val_or_test][task_id]["Final answer"])
                score = 1

                scored_file.write(
                    json.dumps({
                        "id": task_id,
                        #"model_answer": answer,
                        "score": score
                    }) + "\n"
                )

                task_ids.append(task_id)
                scores += score
                num_questions += 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_validation:
            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 / ref_scores_len,#[val_or_test],
        #"score_level1": scores[1]/num_questions[1],
        #"score_level2": scores[2]/num_questions[2],
        #"score_level3": scores[3]/num_questions[3],
        "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%
    #if all((eval_entry[k] == 1 for k in ["score_level1", "score_level2", "score_level3"])):
    #    return format_error(f"There was a problem with your submission. Please open a discussion.")

    # 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():
    test_results = load_dataset(
        RESULTS_DATASET,
        YEAR_VERSION,
        split="test",
        token=TOKEN,
        download_mode="force_redownload",
        verification_mode=VerificationMode.NO_CHECKS,
        trust_remote_code=True,
    )
    eval_dataframe_test = get_dataframe_from_results(eval_results={"test": test_results}, split="test")
    return eval_dataframe_test



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


demo = gr.Blocks()
with demo:
    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: Validation"):
    #    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_test])

    with gr.Accordion(""):
        with gr.Row():
            gr.Markdown(SUBMISSION_TEXT, elem_classes="markdown-text")
        with gr.Row():
            with gr.Column():
                level_of_test = gr.Radio(["test"], value="test", 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,
            [
                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)