Build a Web App That Generates MCQs from Public Domain Legal Code

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I am looking for a skilled full-stack developer to build a working prototype of a web application that automatically generates multiple-choice questions (MCQs) from legal code that is in the public domain. The application should scrape legal text from a specified source, store it in a local database, use OpenAI’s GPT-4 to generate MCQs based on each code section, and then allow users to interact with the questions via a clean and responsive web interface. The application will offer three modes of question delivery: random mode, sequential mode, and a Law Student Mode that filters only those sections of the code which are likely to appear on a bar examination. I will provide a list of the relevant sections to be used in Law Student Mode. The application should also support monetization features, including ad placement (Google AdSense or placeholders) and a simple paid upgrade system that unlocks certain features like answer explanations, score tracking, or performance dashboards. Payment integration can be basic at this stage and should use Stripe or PayPal. Each generated question must be stored in the database and linked to the section it was generated from. Additionally, each question should include a dynamic hyperlink back to the original legal code section hosted online. The goal is to produce a clean, fast, reliable MVP that demonstrates this concept in action. The project will be paid in fixed-price milestones, with payment released at each successful stage. I prefer developers who can handle the full stack, but I am also open to partnerships between frontend and backend specialists if you apply together or suggest someone. Clear communication and clean code are essential. Below is a more detailed breakdown of the application’s required features and deliverables. 1. Legal Code Scraper • Scrape a specific legal code division (for example, a division of a public state code) from a well-structured public website • Parse and extract the following data for each section: section number, section heading/title, full section text • Store all the above in a local database (use SQLite for prototype, upgradeable later) • Ensure robust handling of structured content (multi-part sections, footnotes, etc.) 2. Database • SQLite schema to support: • Legal code sections (with division, part, chapter, section number, title, full body text) • MCQs generated from those sections • Link between each MCQ and the section it was generated from • Optionally include tables for user responses, scores, and session history • Each MCQ should include: • The question text • Four answer choices (labeled A through D) • Correct answer identified • A field for explanation (optional but helpful for premium feature gating) • A dynamic URL that links directly to the original online version of the legal code section the question was based on 3. GPT-4 Integration for MCQ Generation • For each legal code section, generate 1 to 3 MCQs using OpenAI’s GPT-4 API • Questions should test key facts or reasoning based on the text • Ensure consistent structure and clarity in MCQ formatting • Automatically store generated questions in the database and link them to their source section • Handle rate-limiting or batching if needed for bulk generation • Allow for future regeneration or manual review/editing of MCQs if desired 4. Web Application • Responsive web interface using React or a simple HTML plus Bootstrap solution • Quiz interface that displays MCQs one at a time • Allow users to choose: • Random mode: draw questions randomly from the database • Sequential mode: deliver questions in order by section number • Law Student Mode: filter questions based on a predefined list of bar-relevant section numbers (list provided by me) • User can select the number of questions and which Division or topic to practice • Display four answer choices per question with click-to-select • Indicate whether the user got the answer right or wrong after selection • Optionally display an explanation (this feature can be gated for premium users) 5. Law Student Mode • This mode filters the questions to only those generated from sections that appear on the bar exam • I will provide the list of section numbers or identifiers to be used in this mode • The filter should be toggleable via the UI and persist across sessions if the user is logged in 6. Monetization • Placeholder ad areas or Google AdSense integration for free users • Support for Stripe or PayPal to allow users to upgrade to a premium tier • Premium users should be able to: • View answer explanations • Track their scores over time • Export question sets or quiz results • No complex billing logic needed in this phase, just basic payment access gating 7. User System (Optional for MVP but ideal) • User login with email/password or OAuth (e.g., Google login) • Track correct and incorrect answers by session or user • Simple dashboard or history log (optional if time allows) 8. Deployment and Documentation • App should be hosted on a developer-friendly platform like Render, Vercel, or Heroku • Include a README with deployment instructions and environment variable setup • OpenAI API key and any payment keys should be securely managed • Developer should provide basic post-delivery support for at least one week Payment Structure I prefer to divide this project into fixed-price milestones. Each milestone will be paid upon successful delivery and acceptance of the related features. Below is a proposed milestone breakdown. I am open to discussion about timing or combining tasks depending on your preferences. Milestone 1: Scraper and Database Schema • Deliver a working scraper that collects a full Division of legal code and stores it in a clean database • $400 Milestone 2: MCQ Generator with GPT-4 • Generate 1 to 3 MCQs per section and store them in the database • Ensure each question links back to its source section dynamically • $600 Milestone 3: Frontend UI with Random and Sequential Quiz Modes • Build a quiz interface to deliver MCQs randomly or sequentially • Allow users to select a Division and number of questions • $800 Milestone 4: Law Student Mode • Implement filter based on a list of sections I provide • Toggle view for Law Student Mode vs. Full Mode • $400 Milestone 5: Monetization Features • Add ad slots or Google AdSense integration • Integrate Stripe or PayPal for premium features • Lock explanations and performance tracking behind paywall • $500 Milestone 6: Deployment and Documentation • Deploy working version to a cloud platform • Provide README and walkthrough for deployment • $500 Total: $3200 Skills Required Python, Web Scraping, OpenAI GPT, Flask, React.js, SQLite, Stripe API, Google AdSense Who Should Apply This project is ideal for a full-stack developer who enjoys building education or legal tools using modern APIs and clean design principles. If you have experience with OpenAI’s GPT-4 API, web scraping of structured data, building user-friendly quiz apps, and implementing monetization tools, I’d love to hear from you. Strong communication and the ability to deliver in milestones are important. Please respond with examples of similar work you’ve done and your suggested improvements or stack choices for this project. I’m happy to discuss approach and flexibility in scope depending on your strengths.

Keyword: Python

Price: $3.0