Description
PrediQ – AI-Powered Exam Preparation Web Application
PrediQ is a fully functional exam preparation web application built with Flask and Python. It is designed for 10th and 2nd PU students and gives them access to previous year question papers, AI-generated practice papers, and semantic difficulty analysis — all inside one clean web interface. If you are a BCA, MCA, or B.Tech CS/IT student looking for a project that covers web development, artificial intelligence, and database management together, this is the project for you.
The project uses NLTK and TF-IDF vectorization to analyze question difficulty, ReportLab and FPDF2 to generate downloadable PDF practice papers, and Flask-Login with SHA-256 hashing for secure authentication. It also includes a full admin dashboard and a mock payment system that simulates real-world freemium access control. This is not a basic CRUD project — it is a complete, multi-feature system that will impress during your viva and stand out on your resume.
What You Get
- Complete source code with all files and folders
- app.py, config.py, db_manager.py, semantic_analyzer.py, pdf_manager.py
- All Jinja2 HTML templates for student and admin panels
- Custom CSS and JavaScript files
- SQLite database schema and setup
- requirements.txt for one-command dependency installation
- Step-by-step setup and run instructions
Key Features
- AI Practice Paper Generation — Select difficulty level (Easy, Medium, Hard) and generate a custom practice paper instantly as a PDF
- Semantic NLP Analysis — NLTK and scikit-learn TF-IDF pipeline automatically scores each question by difficulty
- Previous Year Papers — Filter by year, board, course, and subject and download in one click
- Freemium Access Model — 2 free downloads per student, then a mock payment of Rs.40 for additional access
- Secure Authentication — SHA-256 hashed passwords and Flask-Login session management
- Admin Dashboard — Upload papers, track payments, monitor downloads, and view platform statistics
- PDF Generation — Practice papers are exported as formatted PDFs using ReportLab and FPDF2
- Download History — Students can track every paper they have downloaded
Technology Stack
| Layer | Technology | Purpose |
|---|---|---|
| Backend | Flask 3.0.0, Python 3.8+ | Web framework and core language |
| Frontend | Bootstrap 5.3.0, Jinja2 | Responsive UI and dynamic templating |
| Database | SQLite3 | Lightweight relational database |
| NLP / AI | NLTK, scikit-learn | Semantic analysis and TF-IDF vectorization |
| PyPDF2, ReportLab, FPDF2 | PDF reading and custom paper generation | |
| Security | SHA-256, Flask-Login | Password hashing and session management |
Who Should Buy This Project
This project is ideal for BCA, MCA, B.Tech CS/IT, and BTE students who need a strong final year project that demonstrates practical skills in Python web development, artificial intelligence, and database design. It is also useful for students who want to learn how to build a real-world SaaS-style application with a freemium model and admin control panel.
Complete Project — More Details:
Watch Full Tutorial on YouTube: https://youtube.com/decodeit2
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