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burtenshaw
commited on
Commit
·
18fb6c3
1
Parent(s):
6d5550e
update logic flow of app to include start button
Browse files
app.py
CHANGED
@@ -6,7 +6,6 @@ import gradio as gr
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from datasets import load_dataset, Dataset
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from huggingface_hub import whoami
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EXAM_DATASET_ID = os.getenv("EXAM_DATASET_ID") or "agents-course/unit_1_quiz"
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EXAM_MAX_QUESTIONS = os.getenv("EXAM_MAX_QUESTIONS") or 10
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EXAM_PASSING_SCORE = os.getenv("EXAM_PASSING_SCORE") or 0.7
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@@ -24,14 +23,37 @@ if EXAM_MAX_QUESTIONS:
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def on_user_logged_in(token: gr.OAuthToken | None):
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"""
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If the user has a valid token,
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Otherwise, keep the login button visible
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"""
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if token is not None:
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return
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else:
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def push_results_to_hub(user_answers, token: gr.OAuthToken | None):
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@@ -74,36 +96,24 @@ def push_results_to_hub(user_answers, token: gr.OAuthToken | None):
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def handle_quiz(question_idx, user_answers, selected_answer, is_start):
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"""
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-
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- If is_start=True, skip storing an answer and show the first question.
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- Otherwise, store the last answer and move on.
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- If we've reached the end, display results.
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"""
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{
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"question": current_q["question"],
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"selected_answer": selected_answer,
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"correct_answer": current_q[correct_reference],
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"is_correct": is_correct,
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"correct_reference": correct_reference,
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}
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)
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question_idx += 1
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# If we've reached the end, show final results
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if question_idx >= len(quiz_data):
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correct_count = sum(1 for answer in user_answers if answer["is_correct"])
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grade = correct_count / len(user_answers)
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@@ -112,37 +122,35 @@ def handle_quiz(question_idx, user_answers, selected_answer, is_start):
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f"Your score: {grade:.1%}\n"
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f"Passing score: {float(EXAM_PASSING_SCORE):.1%}\n\n"
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)
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return
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"", # question_text
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gr.update(choices=[], visible=False),
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f"{'✅ Passed!' if grade >= float(EXAM_PASSING_SCORE) else '❌ Did not pass'}",
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question_idx,
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user_answers,
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gr.update(
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gr.update(visible=False), # Hide final_markdown for now
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)
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def success_message(response):
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@@ -152,47 +160,58 @@ def success_message(response):
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with gr.Blocks() as demo:
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demo.title = f"Dataset Quiz for {EXAM_DATASET_ID}"
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# State variables
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question_idx = gr.State(value=0)
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user_answers = gr.State(value=[])
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with gr.Row(variant="compact"):
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gr.Markdown(f"## Welcome to the {EXAM_DATASET_ID} Quiz")
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with gr.Row(variant="compact"):
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gr.Markdown(
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"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."
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)
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-
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with gr.Row(
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variant="panel",
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):
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question_text = gr.Markdown("")
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radio_choices = gr.Radio(
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choices=[],
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)
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with gr.Row(variant="compact"):
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status_text = gr.Markdown("")
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with gr.Row(variant="compact"):
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# Click "Start" => show first question, hide Start button
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start_btn.click(
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fn=handle_quiz,
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inputs=[question_idx, user_answers,
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outputs=[
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question_text,
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radio_choices,
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@@ -200,11 +219,12 @@ with gr.Blocks() as demo:
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question_idx,
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user_answers,
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start_btn,
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final_markdown,
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],
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)
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# Click "Next" => store selection, move on
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next_btn.click(
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fn=handle_quiz,
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inputs=[question_idx, user_answers, radio_choices, gr.State(False)],
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@@ -215,13 +235,14 @@ with gr.Blocks() as demo:
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question_idx,
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user_answers,
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start_btn,
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final_markdown,
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],
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)
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submit_btn.click(fn=push_results_to_hub, inputs=[user_answers])
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if __name__ == "__main__":
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# Note: If testing locally, you'll need to run `huggingface-cli login` or set HF_TOKEN
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# environment variable for the login to work locally.
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from datasets import load_dataset, Dataset
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from huggingface_hub import whoami
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EXAM_DATASET_ID = os.getenv("EXAM_DATASET_ID") or "agents-course/unit_1_quiz"
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EXAM_MAX_QUESTIONS = os.getenv("EXAM_MAX_QUESTIONS") or 10
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EXAM_PASSING_SCORE = os.getenv("EXAM_PASSING_SCORE") or 0.7
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def on_user_logged_in(token: gr.OAuthToken | None):
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"""
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If the user has a valid token, show Start button.
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Otherwise, keep the login button visible.
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"""
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if token is not None:
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return [
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gr.update(visible=False), # login button visibility
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gr.update(visible=True), # start button visibility
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gr.update(visible=False), # next button visibility
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gr.update(visible=False), # submit button visibility
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"", # question text
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[], # radio choices (empty list = no choices)
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"Click 'Start' to begin the quiz", # status message
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0, # question_idx
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[], # user_answers
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"", # final_markdown content
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token, # user token
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]
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else:
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return [
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gr.update(visible=True), # login button visibility
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gr.update(visible=False), # start button visibility
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gr.update(visible=False), # next button visibility
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gr.update(visible=False), # submit button visibility
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"", # question text
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[], # radio choices
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"", # status message
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0, # question_idx
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[], # user_answers
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"", # final_markdown content
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None, # no token
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]
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def push_results_to_hub(user_answers, token: gr.OAuthToken | None):
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def handle_quiz(question_idx, user_answers, selected_answer, is_start):
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"""
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Handle quiz state transitions and store answers
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"""
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if not is_start and question_idx < len(quiz_data):
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current_q = quiz_data[question_idx]
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correct_reference = current_q["correct_answer"]
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correct_reference = f"answer_{correct_reference}".lower()
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is_correct = selected_answer == current_q[correct_reference]
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user_answers.append(
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{
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"question": current_q["question"],
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"selected_answer": selected_answer,
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"correct_answer": current_q[correct_reference],
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"is_correct": is_correct,
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"correct_reference": correct_reference,
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}
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)
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question_idx += 1
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if question_idx >= len(quiz_data):
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correct_count = sum(1 for answer in user_answers if answer["is_correct"])
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grade = correct_count / len(user_answers)
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f"Your score: {grade:.1%}\n"
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f"Passing score: {float(EXAM_PASSING_SCORE):.1%}\n\n"
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)
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return [
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"", # question_text
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gr.update(choices=[], visible=False), # hide radio choices
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f"{'✅ Passed!' if grade >= float(EXAM_PASSING_SCORE) else '❌ Did not pass'}",
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question_idx,
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user_answers,
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gr.update(visible=False), # start button visibility
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gr.update(visible=False), # next button visibility
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gr.update(visible=True), # submit button visibility
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results_text, # final results text
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]
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# Show next question
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q = quiz_data[question_idx]
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return [
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f"## Question {question_idx + 1} \n### {q['question']}", # question text
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gr.update( # properly update radio choices
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choices=[q["answer_a"], q["answer_b"], q["answer_c"], q["answer_d"]],
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value=None,
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visible=True,
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),
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"Select an answer and click 'Next' to continue.",
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question_idx,
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user_answers,
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gr.update(visible=False), # start button visibility
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gr.update(visible=True), # next button visibility
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gr.update(visible=False), # submit button visibility
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"", # clear final markdown
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]
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def success_message(response):
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with gr.Blocks() as demo:
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demo.title = f"Dataset Quiz for {EXAM_DATASET_ID}"
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# State variables
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question_idx = gr.State(value=0)
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user_answers = gr.State(value=[])
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user_token = gr.State(value=None)
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with gr.Row(variant="compact"):
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gr.Markdown(f"## Welcome to the {EXAM_DATASET_ID} Quiz")
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with gr.Row(variant="compact"):
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gr.Markdown(
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"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."
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)
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with gr.Row(variant="panel"):
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question_text = gr.Markdown("")
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radio_choices = gr.Radio(
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choices=[], label="Your Answer", scale=1.5, visible=False
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)
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with gr.Row(variant="compact"):
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status_text = gr.Markdown("")
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final_markdown = gr.Markdown("")
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with gr.Row(variant="compact"):
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login_btn = gr.LoginButton(visible=True)
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start_btn = gr.Button("Start ⏭️", visible=True)
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next_btn = gr.Button("Next ⏭️", visible=False)
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submit_btn = gr.Button("Submit ✅", visible=False)
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# Wire up the event handlers
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login_btn.click(
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fn=on_user_logged_in,
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inputs=None,
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outputs=[
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login_btn,
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start_btn,
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next_btn,
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submit_btn,
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question_text,
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radio_choices,
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status_text,
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question_idx,
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user_answers,
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final_markdown,
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user_token,
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],
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start_btn.click(
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fn=handle_quiz,
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inputs=[question_idx, user_answers, gr.State(""), gr.State(True)],
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outputs=[
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question_text,
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radio_choices,
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question_idx,
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user_answers,
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start_btn,
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next_btn,
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submit_btn,
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final_markdown,
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],
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)
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next_btn.click(
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fn=handle_quiz,
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inputs=[question_idx, user_answers, radio_choices, gr.State(False)],
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question_idx,
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user_answers,
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start_btn,
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next_btn,
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submit_btn,
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final_markdown,
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],
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)
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submit_btn.click(fn=push_results_to_hub, inputs=[user_answers])
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if __name__ == "__main__":
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# Note: If testing locally, you'll need to run `huggingface-cli login` or set HF_TOKEN
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# environment variable for the login to work locally.
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