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InterPrep-AI Next Generation: Project Structure

Overview

InterPrep-AI Next Generation is a comprehensive interview preparation platform that combines AI-powered question generation, company-specific practice, personalized learning paths, and a professional user interface. This document outlines the project structure and architecture.

Technology Stack

  • Backend: Flask (Python)
  • Frontend: React with Material UI
  • Database: PostgreSQL
  • AI Integration: Gemini API, SentenceTransformers, FAISS

Project Structure

interprep-ai-nextgen/
├── backend/                      # Flask backend
│   ├── app/
│   │   ├── __init__.py           # Flask app initialization
│   │   ├── config.py             # Configuration settings
│   │   ├── extensions.py         # Flask extensions (SQLAlchemy, etc.)
│   │   ├── models/               # Database models
│   │   │   ├── __init__.py
│   │   │   ├── user.py           # User and profile models
│   │   │   ├── question.py       # Question and submission models
│   │   │   ├── learning.py       # Learning resources models
│   │   │   └── favorites.py      # User favorites and history
│   │   ├── api/                  # API endpoints
│   │   │   ├── __init__.py
│   │   │   ├── auth.py           # Authentication endpoints
│   │   │   ├── questions.py      # Question endpoints
│   │   │   ├── submissions.py    # Code submission endpoints
│   │   │   ├── learning.py       # Learning resources endpoints
│   │   │   └── users.py          # User management endpoints
│   │   ├── services/             # Business logic services
│   │   │   ├── __init__.py
│   │   │   ├── auth_service.py   # Authentication logic
│   │   │   ├── question_service.py # Question generation/retrieval
│   │   │   ├── evaluation_service.py # Code evaluation
│   │   │   └── learning_service.py # Learning content management
│   │   ├── ai/                   # AI integration
│   │   │   ├── __init__.py
│   │   │   ├── gemini.py         # Gemini API integration
│   │   │   ├── embeddings.py     # SentenceTransformer integration
│   │   │   └── search.py         # FAISS search integration
│   │   └── utils/                # Utility functions
│   │       ├── __init__.py
│   │       ├── security.py       # Security utilities
│   │       └── helpers.py        # Helper functions
│   ├── migrations/               # Database migrations
│   ├── tests/                    # Backend tests
│   ├── requirements.txt          # Python dependencies
│   └── run.py                    # Application entry point
│
├── frontend/                     # React frontend
│   ├── public/                   # Static files
│   ├── src/
│   │   ├── components/           # Reusable UI components
│   │   │   ├── common/           # Common UI elements
│   │   │   ├── auth/             # Authentication components
│   │   │   ├── dashboard/        # Dashboard components
│   │   │   ├── practice/         # Practice interface components
│   │   │   ├── learning/         # Learning resources components
│   │   │   └── profile/          # User profile components
│   │   ├── pages/                # Page components
│   │   │   ├── Home.jsx          # Landing page
│   │   │   ├── Login.jsx         # Login page
│   │   │   ├── Register.jsx      # Registration page
│   │   │   ├── Dashboard.jsx     # User dashboard
│   │   │   ├── Practice.jsx      # Practice interface
│   │   │   ├── Learning.jsx      # Learning resources
│   │   │   └── Profile.jsx       # User profile
│   │   ├── services/             # API client services
│   │   │   ├── api.js            # API client setup
│   │   │   ├── auth.js           # Authentication service
│   │   │   ├── questions.js      # Questions service
│   │   │   └── learning.js       # Learning resources service
│   │   ├── context/              # React context providers
│   │   │   ├── AuthContext.jsx   # Authentication context
│   │   │   └── UserContext.jsx   # User data context
│   │   ├── utils/                # Utility functions
│   │   ├── App.jsx               # Main application component
│   │   ├── index.jsx             # Entry point
│   │   └── theme.js              # Material UI theme
│   ├── package.json              # Node.js dependencies
│   └── README.md                 # Frontend documentation
│
└── README.md                     # Project documentation

Database Schema

User Management

users
├── id (PK)
├── username
├── email
├── password_hash
├── created_at
├── last_login
└── is_active

user_profiles
├── id (PK)
├── user_id (FK -> users.id)
├── full_name
├── bio
├── preferences (JSON)
└── settings (JSON)

Questions and Submissions

questions
├── id (PK)
├── title
├── content
├── difficulty
├── topic
├── company
├── source_type (generated/book/user)
├── created_at
└── embedding (vector)

submissions
├── id (PK)
├── user_id (FK -> users.id)
├── question_id (FK -> questions.id)
├── code
├── language
├── status
├── feedback
└── submitted_at

favorites
├── id (PK)
├── user_id (FK -> users.id)
├── question_id (FK -> questions.id)
└── added_at

practice_history
├── id (PK)
├── user_id (FK -> users.id)
├── question_id (FK -> questions.id)
├── viewed_at
└── completed (boolean)

Learning Resources

learning_categories
├── id (PK)
├── name
├── description
└── order

learning_topics
├── id (PK)
├── category_id (FK -> learning_categories.id)
├── name
├── description
├── order
└── icon

learning_content
├── id (PK)
├── topic_id (FK -> learning_topics.id)
├── title
├── content_type (text/code/image/video)
├── content
├── order
└── metadata (JSON)

learning_progress
├── id (PK)
├── user_id (FK -> users.id)
├── content_id (FK -> learning_content.id)
├── completed
└── last_accessed

API Endpoints

Authentication

  • POST /api/auth/register - Register a new user
  • POST /api/auth/login - Login and get JWT token
  • POST /api/auth/refresh - Refresh JWT token
  • POST /api/auth/logout - Logout and invalidate token

Users

  • GET /api/users/me - Get current user profile
  • PUT /api/users/me - Update user profile
  • GET /api/users/me/stats - Get user statistics
  • GET /api/users/me/history - Get practice history
  • GET /api/users/me/favorites - Get favorite questions

Questions

  • GET /api/questions - List questions with filters
  • GET /api/questions/{id} - Get question details
  • POST /api/questions/generate - Generate new question
  • POST /api/questions/{id}/similar - Generate similar question
  • POST /api/questions/{id}/favorite - Add to favorites
  • DELETE /api/questions/{id}/favorite - Remove from favorites

Submissions

  • POST /api/submissions - Submit code solution
  • GET /api/submissions - List user submissions
  • GET /api/submissions/{id} - Get submission details

Learning

  • GET /api/learning/categories - List learning categories
  • GET /api/learning/topics - List learning topics
  • GET /api/learning/topics/{id}/content - Get topic content
  • GET /api/learning/content/{id} - Get specific content
  • POST /api/learning/progress - Update learning progress

Frontend Routes

  • / - Landing page
  • /login - Login page
  • /register - Registration page
  • /dashboard - User dashboard
  • /practice - Question practice interface
  • /practice/:id - Specific question practice
  • /companies - Company-specific questions
  • /companies/:company - Questions for specific company
  • /learning - Learning resources
  • /learning/:topic - Topic-specific learning
  • /profile - User profile and settings

Integration with Existing AI Components

The existing AI components from the original InterPrep-AI will be integrated as services in the backend:

  1. Question Generation Service

    • Moved to backend/app/ai/gemini.py
    • Exposed via /api/questions/generate endpoint

  2. Solution Evaluation Service

    • Moved to backend/app/services/evaluation_service.py
    • Exposed via /api/submissions endpoint

  3. RAG Pipeline

    • Embeddings generation moved to backend/app/ai/embeddings.py
    • FAISS search moved to backend/app/ai/search.py
    • Exposed via /api/questions endpoint with search parameters

Learning Resources Structure

Based on "Cracking the Coding Interview" book, the learning resources will be structured as follows:

Categories

  1. Interview Preparation

    • The Interview Process
    • Behind the Scenes
    • Special Situations
    • Before the Interview
    • Behavioral Questions

  2. Technical Fundamentals

    • Big O
    • Technical Questions
    • The Offer and Beyond

  3. Data Structures

    • Arrays and Strings
    • Linked Lists
    • Stacks and Queues
    • Trees and Graphs

  4. Concepts and Algorithms

    • Bit Manipulation
    • Math and Logic Puzzles
    • Object-Oriented Design
    • Recursion and Dynamic Programming
    • System Design and Scalability
    • Sorting and Searching
    • Testing

  5. Language-Specific Knowledge

    • C and C++
    • Java
    • Databases
    • Threads and Locks

  6. Advanced Topics

    • Useful Math
    • Topological Sort
    • Dijkstra's Algorithm
    • Hash Table Collision Resolution
    • And more...

Development Workflow

  1. Phase 1: Core Backend Foundation

    • Set up Flask project structure
    • Implement database models and migrations
    • Create basic API endpoints
    • Integrate existing AI components

  2. Phase 2: Authentication & User Management

    • Implement user registration and login
    • Set up JWT authentication
    • Create user profiles and settings
    • Add favorites and history tracking

  3. Phase 3: Frontend Foundation & Practice Interface

    • Set up React project with routing and state management
    • Implement authentication UI
    • Create the split-screen practice interface
    • Connect to backend APIs

  4. Phase 4: Learning Resources Integration

    • Create database schema for learning content
    • Develop API endpoints for accessing learning materials
    • Build frontend components for displaying learning content
    • Import structured content from "Cracking the Coding Interview"

  5. Phase 5: UI Polish & Personalization

    • Enhance dashboard with user statistics
    • Implement responsive design
    • Add animations and transitions
    • Optimize performance and user experience