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Commit
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359570c
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Parent(s):
768c800
implement certificate in app
Browse files- Quattrocento-Bold.ttf +0 -0
- Quattrocento-Regular.ttf +0 -0
- app.py +205 -96
- certificate.pdf +0 -0
- templates/certificate.png +0 -0
Quattrocento-Bold.ttf
ADDED
Binary file (154 kB). View file
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Quattrocento-Regular.ttf
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Binary file (148 kB). View file
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app.py
CHANGED
@@ -1,6 +1,12 @@
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import os
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from datetime import datetime
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import random
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import pandas as pd
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from huggingface_hub import HfApi, hf_hub_download, Repository
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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
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EXAM_PASSING_SCORE = os.getenv("EXAM_PASSING_SCORE") or 0.8
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ds = load_dataset(EXAM_DATASET_ID, split="train")
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DATASET_REPO_URL = "https://huggingface.co/datasets/agents-course/certificates"
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CERTIFIED_USERS_FILENAME = "certified_students.csv"
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CERTIFIED_USERS_DIR = "certificates"
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repo = Repository(
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local_dir=CERTIFIED_USERS_DIR,
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clone_from=DATASET_REPO_URL,
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use_auth_token=os.getenv("HF_TOKEN"),
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)
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# Convert dataset to a list of dicts and randomly sort
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quiz_data = ds.to_pandas().to_dict("records")
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random.shuffle(quiz_data)
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# Limit to max questions if specified
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if EXAM_MAX_QUESTIONS:
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]
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def
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"""
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print("ADD CERTIFIED USER")
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repo.git_pull()
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history = pd.read_csv(os.path.join(CERTIFIED_USERS_DIR, CERTIFIED_USERS_FILENAME))
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# Check if this hf_username is already in our dataset:
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check = history.loc[history["hf_username"] == hf_username]
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if not check.empty:
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history = history.drop(labels=check.index[0], axis=0)
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new_row = pd.DataFrame(
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{
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"hf_username": hf_username,
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"pass_percentage": pass_percentage,
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"datetime": submission_time,
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},
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index=[0],
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)
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)
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repo.push_to_hub(commit_message="Update certified users list")
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"""
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# Calculate grade
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correct_count = sum(1 for answer in user_answers if answer["is_correct"])
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grade = correct_count / total_questions if total_questions > 0 else 0
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if grade < float(EXAM_PASSING_SCORE):
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-
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)
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return f"You scored {grade:.1%}. Please try again to achieve at least {float(EXAM_PASSING_SCORE):.1%}"
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gr.Info("Submitting answers to the Hub. Please wait...", duration=2)
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user_info = whoami(token=token.token)
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repo_id = f"{EXAM_DATASET_ID}_student_responses"
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submission_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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# filter down user answers to only include first character of the question and the answer
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new_ds = Dataset.from_list(user_answers)
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new_ds = new_ds.map(
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lambda x: {
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"username": user_info["name"],
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"datetime": submission_time,
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"grade": grade,
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}
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)
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sanitized_name = user_info["name"].replace("-", "000")
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new_ds.push_to_hub(repo_id=repo_id, split=sanitized_name)
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def handle_quiz(
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token: gr.OAuthToken | None,
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profile: gr.OAuthProfile | None,
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):
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"""
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Handle quiz state transitions and store answers
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"""
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if token is None or profile is None:
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gr.Warning("Please log in to Hugging Face before starting the quiz!")
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return
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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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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"{'π Passed! Click now on
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question_idx,
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user_answers,
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gr.update(visible=False), # start button
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gr.update(visible=False), # next button
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gr.update(visible=True), # submit button
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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']}", #
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gr.update( #
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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
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gr.update(visible=True), # next button
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gr.update(visible=False), # submit button
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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
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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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login_btn = gr.LoginButton(visible=True)
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start_btn = gr.Button("Start βοΈ", visible=True)
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next_btn = gr.Button("Next βοΈ", visible=False)
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submit_btn = gr.Button("
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# Wire up the event handlers
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login_btn.click(
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status_text,
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question_idx,
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user_answers,
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user_token,
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],
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)
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start_btn,
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next_btn,
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submit_btn,
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],
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)
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start_btn,
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next_btn,
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submit_btn,
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],
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)
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submit_btn.click(
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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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demo.launch()
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import os
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from datetime import datetime
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import random
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import requests
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from io import BytesIO
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from datetime import date
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import tempfile
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from PIL import Image, ImageDraw, ImageFont
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from huggingface_hub import upload_file
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import pandas as pd
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from huggingface_hub import HfApi, hf_hub_download, Repository
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from datasets import load_dataset, Dataset
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from huggingface_hub import whoami
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import asyncio
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from functools import partial
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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 1
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EXAM_PASSING_SCORE = os.getenv("EXAM_PASSING_SCORE") or 0.8
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CERTIFYING_ORG_LINKEDIN_ID = os.getenv("CERTIFYING_ORG_LINKEDIN_ID", "000000")
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COURSE_TITLE = os.getenv("COURSE_TITLE", "AI Agents Fundamentals")
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ds = load_dataset(EXAM_DATASET_ID, split="train")
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DATASET_REPO_URL = "https://huggingface.co/datasets/agents-course/certificates"
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# Convert dataset to a list of dicts and randomly sort
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quiz_data = ds.to_pandas().to_dict("records")
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# random.shuffle(quiz_data)
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# Limit to max questions if specified
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if EXAM_MAX_QUESTIONS:
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]
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def generate_certificate(name: str, profile_url: str):
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"""Generate certificate image and PDF."""
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certificate_path = os.path.join(
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os.path.dirname(__file__), "templates", "certificate.png"
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)
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im = Image.open(certificate_path)
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d = ImageDraw.Draw(im)
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name_font = ImageFont.truetype("Quattrocento-Regular.ttf", 100)
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date_font = ImageFont.truetype("Quattrocento-Regular.ttf", 48)
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name = name.title()
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d.text((1000, 740), name, fill="black", anchor="mm", font=name_font)
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d.text((1480, 1170), str(date.today()), fill="black", anchor="mm", font=date_font)
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pdf = im.convert("RGB")
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pdf.save("certificate.pdf")
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return im, "certificate.pdf"
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def create_linkedin_button(username: str, cert_url: str | None) -> str:
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"""Create LinkedIn 'Add to Profile' button HTML."""
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current_year = date.today().year
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current_month = date.today().month
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# Use the dataset certificate URL if available, otherwise fallback to default
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certificate_url = cert_url or "https://huggingface.co/agents-course-finishers"
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linkedin_params = {
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"startTask": "CERTIFICATION_NAME",
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"name": COURSE_TITLE,
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"organizationName": "Hugging Face",
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"organizationId": CERTIFYING_ORG_LINKEDIN_ID,
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"organizationIdissueYear": str(current_year),
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"issueMonth": str(current_month),
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"certUrl": certificate_url,
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"certId": username, # Using username as cert ID
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}
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# Build the LinkedIn button URL
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base_url = "https://www.linkedin.com/profile/add?"
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params = "&".join(
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f"{k}={requests.utils.quote(v)}" for k, v in linkedin_params.items()
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)
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button_url = base_url + params
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message = f"""
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<a href="{button_url}" target="_blank" style="display: block; margin-top: 20px; text-align: center;">
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<img src="https://download.linkedin.com/desktop/add2profile/buttons/en_US.png"
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alt="LinkedIn Add to Profile button">
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</a>
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"""
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return message
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+
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async def upload_certificate_to_hub(username: str, certificate_img) -> str:
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"""Upload certificate to the dataset hub and return the URL asynchronously."""
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# Save image to temporary file
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with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
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certificate_img.save(tmp.name)
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try:
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# Run upload in a thread pool since upload_file is blocking
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loop = asyncio.get_event_loop()
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upload_func = partial(
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upload_file,
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path_or_fileobj=tmp.name,
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path_in_repo=f"certificates/{username}/{date.today()}.png",
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repo_id="agents-course/certificates",
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repo_type="dataset",
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token=os.getenv("HF_TOKEN"),
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)
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await loop.run_in_executor(None, upload_func)
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# Construct the URL to the image
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cert_url = (
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f"https://huggingface.co/datasets/agents-course/certificates/"
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f"resolve/main/certificates/{username}/{date.today()}.png"
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)
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# Clean up temp file
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os.unlink(tmp.name)
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return cert_url
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except Exception as e:
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print(f"Error uploading certificate: {e}")
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os.unlink(tmp.name)
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return None
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async def push_results_to_hub(
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user_answers, token: gr.OAuthToken | None, profile: gr.OAuthProfile | None
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):
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"""Handle quiz completion and certificate generation."""
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if token is None or profile is None:
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gr.Warning("Please log in to Hugging Face before submitting!")
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return (
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gr.update(visible=True, value="Please login first"),
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gr.update(visible=False),
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gr.update(visible=False),
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)
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# Calculate grade
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correct_count = sum(1 for answer in user_answers if answer["is_correct"])
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grade = correct_count / total_questions if total_questions > 0 else 0
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if grade < float(EXAM_PASSING_SCORE):
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return (
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gr.update(
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visible=True,
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value=f"You scored {grade:.1%}. Please try again to achieve at least "
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f"{float(EXAM_PASSING_SCORE):.1%}",
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),
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gr.update(visible=False),
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gr.update(visible=False),
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)
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try:
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# Generate certificate
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198 |
+
certificate_img, _ = generate_certificate(
|
199 |
+
name=profile.name, profile_url=profile.picture
|
200 |
+
)
|
201 |
+
|
202 |
+
# Start certificate upload asynchronously
|
203 |
+
gr.Info("Uploading your certificate...")
|
204 |
+
cert_url = await upload_certificate_to_hub(profile.username, certificate_img)
|
205 |
+
|
206 |
+
if cert_url is None:
|
207 |
+
gr.Warning("Certificate upload failed, but you still passed!")
|
208 |
+
cert_url = "https://huggingface.co/agents-course"
|
209 |
|
210 |
+
# Create LinkedIn button
|
211 |
+
linkedin_button = create_linkedin_button(profile.username, cert_url)
|
212 |
+
|
213 |
+
result_message = f"""
|
214 |
+
π Congratulations! You passed with a score of {grade:.1%}!
|
215 |
+
|
216 |
+
{linkedin_button}
|
217 |
+
"""
|
218 |
+
|
219 |
+
return (
|
220 |
+
gr.update(visible=True, value=result_message),
|
221 |
+
gr.update(visible=True, value=certificate_img),
|
222 |
+
gr.update(visible=True),
|
223 |
+
)
|
224 |
+
|
225 |
+
except Exception as e:
|
226 |
+
print(f"Error generating certificate: {e}")
|
227 |
+
return (
|
228 |
+
gr.update(
|
229 |
+
visible=True, value=f"π Congratulations! You passed with {grade:.1%}!"
|
230 |
+
),
|
231 |
+
gr.update(visible=False),
|
232 |
+
gr.update(visible=False),
|
233 |
+
)
|
234 |
|
235 |
|
236 |
def handle_quiz(
|
|
|
241 |
token: gr.OAuthToken | None,
|
242 |
profile: gr.OAuthProfile | None,
|
243 |
):
|
244 |
+
"""Handle quiz state transitions and store answers"""
|
|
|
|
|
245 |
if token is None or profile is None:
|
246 |
gr.Warning("Please log in to Hugging Face before starting the quiz!")
|
247 |
+
return [
|
248 |
+
"", # question_text
|
249 |
+
gr.update(choices=[], visible=False), # radio choices
|
250 |
+
"Please login first", # status_text
|
251 |
+
question_idx, # question_idx
|
252 |
+
user_answers, # user_answers
|
253 |
+
gr.update(visible=True), # start button
|
254 |
+
gr.update(visible=False), # next button
|
255 |
+
gr.update(visible=False), # submit button
|
256 |
+
gr.update(visible=False), # certificate image
|
257 |
+
gr.update(visible=False), # linkedin button
|
258 |
+
]
|
259 |
|
260 |
if not is_start and question_idx < len(quiz_data):
|
261 |
current_q = quiz_data[question_idx]
|
|
|
281 |
f"Your score: {grade:.1%}\n"
|
282 |
f"Passing score: {float(EXAM_PASSING_SCORE):.1%}\n\n"
|
283 |
)
|
284 |
+
has_passed = grade >= float(EXAM_PASSING_SCORE)
|
285 |
return [
|
286 |
"", # question_text
|
287 |
+
gr.update(choices=[], visible=False), # radio choices
|
288 |
+
f"{'π Passed! Click now on π Get your certificate!' if has_passed else 'β Did not pass'}", # status_text
|
289 |
+
question_idx, # question_idx
|
290 |
+
user_answers, # user_answers
|
291 |
+
gr.update(visible=False), # start button
|
292 |
+
gr.update(visible=False), # next button
|
293 |
+
gr.update(visible=True, value=f"π Get your certificate" if has_passed else "β Did not pass", interactive=has_passed), # submit button
|
294 |
+
gr.update(visible=False), # certificate image
|
295 |
+
gr.update(visible=False), # linkedin button
|
296 |
]
|
297 |
|
298 |
# Show next question
|
299 |
q = quiz_data[question_idx]
|
300 |
return [
|
301 |
+
f"## Question {question_idx + 1} \n### {q['question']}", # question_text
|
302 |
+
gr.update( # radio choices
|
303 |
choices=[q["answer_a"], q["answer_b"], q["answer_c"], q["answer_d"]],
|
304 |
value=None,
|
305 |
visible=True,
|
306 |
),
|
307 |
+
"Select an answer and click 'Next' to continue.", # status_text
|
308 |
+
question_idx, # question_idx
|
309 |
+
user_answers, # user_answers
|
310 |
+
gr.update(visible=False), # start button
|
311 |
+
gr.update(visible=True), # next button
|
312 |
+
gr.update(visible=False), # submit button
|
313 |
+
gr.update(visible=False), # certificate image
|
314 |
+
gr.update(visible=False), # linkedin button
|
315 |
]
|
316 |
|
317 |
|
|
|
339 |
with gr.Row(variant="panel"):
|
340 |
question_text = gr.Markdown("")
|
341 |
radio_choices = gr.Radio(
|
342 |
+
choices=[], label="Your Answer", scale=1, visible=False
|
343 |
)
|
344 |
|
345 |
with gr.Row(variant="compact"):
|
346 |
status_text = gr.Markdown("")
|
347 |
+
certificate_img = gr.Image(type="pil", visible=False)
|
348 |
+
linkedin_btn = gr.HTML(visible=False)
|
349 |
|
350 |
with gr.Row(variant="compact"):
|
351 |
login_btn = gr.LoginButton(visible=True)
|
352 |
start_btn = gr.Button("Start βοΈ", visible=True)
|
353 |
next_btn = gr.Button("Next βοΈ", visible=False)
|
354 |
+
submit_btn = gr.Button("π Get your certificate", visible=False)
|
355 |
|
356 |
# Wire up the event handlers
|
357 |
login_btn.click(
|
|
|
367 |
status_text,
|
368 |
question_idx,
|
369 |
user_answers,
|
370 |
+
certificate_img,
|
371 |
+
linkedin_btn,
|
372 |
user_token,
|
373 |
],
|
374 |
)
|
|
|
385 |
start_btn,
|
386 |
next_btn,
|
387 |
submit_btn,
|
388 |
+
certificate_img,
|
389 |
+
linkedin_btn,
|
390 |
],
|
391 |
)
|
392 |
|
|
|
402 |
start_btn,
|
403 |
next_btn,
|
404 |
submit_btn,
|
405 |
+
certificate_img,
|
406 |
+
linkedin_btn,
|
407 |
],
|
408 |
)
|
409 |
|
410 |
+
submit_btn.click(
|
411 |
+
fn=push_results_to_hub,
|
412 |
+
inputs=[user_answers],
|
413 |
+
outputs=[status_text, certificate_img, linkedin_btn],
|
414 |
+
)
|
415 |
|
416 |
if __name__ == "__main__":
|
417 |
# Note: If testing locally, you'll need to run `huggingface-cli login` or set HF_TOKEN
|
418 |
# environment variable for the login to work locally.
|
419 |
+
demo.queue() # Enable queuing for async operations
|
420 |
demo.launch()
|
certificate.pdf
ADDED
Binary file (209 kB). View file
|
|
templates/certificate.png
ADDED
![]() |