app.py

import os
from datetime import datetime
import random

import gradio as gr
from datasets import load_dataset, Dataset
from huggingface_hub import whoami


EXAM_DATASET_ID = os.getenv("EXAM_DATASET_ID") or "agents-course/unit_1_quiz"
EXAM_MAX_QUESTIONS = os.getenv("EXAM_MAX_QUESTIONS") or 10
EXAM_PASSING_SCORE = os.getenv("EXAM_PASSING_SCORE") or 0.7

ds = load_dataset(EXAM_DATASET_ID, split="train")

# Convert dataset to a list of dicts and randomly sort
quiz_data = ds.to_pandas().to_dict("records")
random.shuffle(quiz_data)

# Limit to max questions if specified
if EXAM_MAX_QUESTIONS:
    quiz_data = quiz_data[: int(EXAM_MAX_QUESTIONS)]


def on_user_logged_in(token: gr.OAuthToken | None):
    """
    If the user has a valid token, hide the login button and show the Start button.
    Otherwise, keep the login button visible, hide Start.
    """
    if token is not None:
        return gr.update(visible=False), gr.update(visible=False)
    else:
        # Not logged in, keep the login visible, hide Start
        return gr.update(visible=True), gr.update(visible=False)


def push_results_to_hub(user_answers, token: gr.OAuthToken | None):
    """
    Create a new dataset from user_answers and push it to the Hub.
    Calculates grade and checks against passing threshold.
    """
    if token is None:
        gr.Warning("Please log in to Hugging Face before pushing!")
        return

    # Calculate grade
    correct_count = sum(1 for answer in user_answers if answer["is_correct"])
    total_questions = len(user_answers)
    grade = correct_count / total_questions if total_questions > 0 else 0

    if grade < float(EXAM_PASSING_SCORE):
        gr.Warning(
            f"Score {grade:.1%} below passing threshold of {float(EXAM_PASSING_SCORE):.1%}"
        )
        return f"You scored {grade:.1%}. Please try again to achieve at least {float(EXAM_PASSING_SCORE):.1%}"

    gr.Info("Submitting answers to the Hub. Please wait...", duration=2)

    user_info = whoami(token=token.token)
    repo_id = f"{EXAM_DATASET_ID}_student_responses"
    submission_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

    new_ds = Dataset.from_list(user_answers)
    new_ds = new_ds.map(
        lambda x: {
            "username": user_info["name"],
            "datetime": submission_time,
            "grade": grade,
        }
    )
    new_ds.push_to_hub(repo_id)
    return f"Your responses have been submitted to the Hub! Final grade: {grade:.1%}"


def handle_quiz(question_idx, user_answers, selected_answer, is_start):
    """
    A single function that handles both 'Start' and 'Next' logic:
      - If is_start=True, skip storing an answer and show the first question.
      - Otherwise, store the last answer and move on.
      - If we've reached the end, display results.
    """
    # Hide the start button once the first question is shown
    start_btn_update = gr.update(visible=False) if is_start else None

    # If this is the first time (start=True), begin at question_idx=0
    if is_start:
        question_idx = 0
    else:
        # If not the very first question, store the user's last selection
        if question_idx < len(quiz_data):
            current_q = quiz_data[question_idx]
            correct_reference = current_q["correct_answer"]
            correct_reference = f"answer_{correct_reference}".lower()
            is_correct = selected_answer == current_q[correct_reference]
            user_answers.append(
                {
                    "question": current_q["question"],
                    "selected_answer": selected_answer,
                    "correct_answer": current_q[correct_reference],
                    "is_correct": is_correct,
                    "correct_reference": correct_reference,
                }
            )
        question_idx += 1

    # If we've reached the end, show final results
    if question_idx >= len(quiz_data):
        correct_count = sum(1 for answer in user_answers if answer["is_correct"])
        grade = correct_count / len(user_answers)
        results_text = (
            f"**Quiz Complete!**\n\n"
            f"Your score: {grade:.1%}\n"
            f"Passing score: {float(EXAM_PASSING_SCORE):.1%}\n\n"
        )
        return (
            "",  # question_text becomes blank
            gr.update(choices=[], visible=False),
            f"{'✅ Passed!' if grade >= float(EXAM_PASSING_SCORE) else '❌ Did not pass'}",
            question_idx,
            user_answers,
            start_btn_update,
            gr.update(value=results_text, visible=True),  # show final_markdown
        )
    else:
        # Otherwise, show the next question
        q = quiz_data[question_idx]
        updated_question = f"## Question {question_idx + 1} \n### {q['question']}"
        return (
            updated_question,
            gr.update(
                choices=[
                    q["answer_a"],
                    q["answer_b"],
                    q["answer_c"],
                    q["answer_d"],
                ],
                value=None,
                visible=True,
            ),
            "Select an answer and click 'Next' to continue.",
            question_idx,
            user_answers,
            start_btn_update,
            gr.update(visible=False),  # Hide final_markdown for now
        )


def success_message(response):
    # response is whatever push_results_to_hub returned
    return f"{response}\n\n**Success!**"


with gr.Blocks() as demo:
    demo.title = f"Dataset Quiz for {EXAM_DATASET_ID}"
    # State variables
    question_idx = gr.State(value=0)
    user_answers = gr.State(value=[])

    with gr.Row(variant="compact"):
        gr.Markdown(f"## Welcome to the {EXAM_DATASET_ID} Quiz")
    with gr.Row(variant="compact"):
        gr.Markdown(
            "Log in first, then click 'Start' to begin. Answer each question, click 'Next', and finally click 'Submit' to publish your results to the Hugging Face Hub."
        )
    # We display question text with Markdown
    with gr.Row(
        variant="panel",
    ):
        question_text = gr.Markdown("")
        radio_choices = gr.Radio(
            choices=["None"], value="None", visible=False, label="Your Answer", scale=1.5
        )

    with gr.Row(variant="compact"):
        status_text = gr.Markdown("")

    with gr.Row(variant="compact"):
        # Final results after all questions are done
        final_markdown = gr.Markdown("", visible=False)

        next_btn = gr.Button("Next ⏭️")
        submit_btn = gr.Button("Submit ✅")

    with gr.Row(variant="compact"):
        login_btn = gr.LoginButton()
        # We'll hide the Start button until user logs in
        start_btn = gr.Button("Start", visible=False)

    # Use click() instead of login()
    login_btn.click(fn=on_user_logged_in, inputs=None, outputs=[login_btn, start_btn]).then(
        fn=handle_quiz,
        inputs=[question_idx, user_answers, radio_choices, gr.State(True)],
        outputs=[
            question_text,
            radio_choices,
            status_text,
            question_idx,
            user_answers,
            start_btn,
            final_markdown,
        ],
    )

    # Click "Start" => show first question, hide Start button
    start_btn.click(
        fn=handle_quiz,
        inputs=[question_idx, user_answers, radio_choices, gr.State(True)],
        outputs=[
            question_text,
            radio_choices,
            status_text,
            question_idx,
            user_answers,
            start_btn,
            final_markdown,
        ],
    ).then(lambda x:x, user_answers)

    # Click "Next" => store selection, move on
    next_btn.click(
        fn=handle_quiz,
        inputs=[question_idx, user_answers, radio_choices, gr.State(False)],
        outputs=[
            question_text,
            radio_choices,
            status_text,
            question_idx,
            user_answers,
            start_btn,
            final_markdown,
        ],
    ).then(lambda x:x, user_answers)

    submit_btn.click(fn=push_results_to_hub, inputs=[user_answers])


if __name__ == "__main__":
    # Note: If testing locally, you'll need to run `huggingface-cli login` or set HF_TOKEN
    # environment variable for the login to work locally.
    demo.launch()