README.md

metadata

title: Smart Quiz Maker
emoji: 🧠
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 5.43.1
app_file: app.py
pinned: false

Smart Quiz Maker

Smart Quiz Maker is a modular Hugging Face and Gradio demo that generates interactive multiple-choice quizzes based on a user's topic. The system uses multi-source retrieval (Wikipedia + Wikidata + web snippets), semantic keyword extraction, and a question-generation model to produce quizzes you can try in the browser.

Data sources used

The app gathers context from multiple sources to improve coverage and reduce missing information:

• Wikipedia REST summary — concise topic summary.
• Wikidata search descriptions — fallback short facts / labels.
• Web snippets from Wikipedia search results — additional paragraphs and context when the summary is sparse.

These sources are combined and chunked to produce the context used by keyword extraction and question generation.

Configurable options (UI)

• Number of questions: choose 3, 5, or 10 questions per quiz. Default is 3.
• Difficulty: choose among easy, medium, and hard. Difficulty affects question templates and phrasing.

Key features

• Multi-source retrieval: combines Wikipedia, Wikidata, and web snippets for richer context.
• Robust keyword extraction: spaCy NER preferred, fallback frequency extraction if spaCy unavailable.
• Semantic selection: sentence-transformers (all-MiniLM-L6-v2) for semantic ranking / MMR-style selection of candidate answers.
• Question generation: uses a T5-based QG model (if available) with templated fallbacks for robustness.
• Better distractors: semantic-neighbor selection using embeddings to create plausible wrong options; heuristic fallback when embeddings are not available.
• Stable Gradio UI: prevents feedback before selection and enforces 3 options per question (1 correct + 2 distractors).
• Deterministic option de-duplication (normalization to avoid repeated options like python vs python:).

How it works (pipeline)

1. User enters a topic and selects n_questions and difficulty.
2. Backend fetches context from Wikipedia, Wikidata, and page snippets.
3. Keywords/candidate answers are extracted (spaCy NER → token frequency fallback).
4. Candidates are ranked by semantic relevance (sentence-transformers) and a top-N set is chosen.
5. For each chosen answer:
   • A question is generated (T5 QG model if available; otherwise, randomized templates by difficulty).
   • Two distractors are generated using semantic similarity among candidates or a heuristic fallback.
6. UI presents each question with exactly three options. User selections show immediate feedback and scoring.

Run locally

Install dependencies and run the app:

pip install -r requirements.txt
python app.py