SkillWise-AI 🚀

Master your interview skills with AI-powered practice sessions

SkillWise-AI is a comprehensive web application that helps engineering students and professionals prepare for technical interviews using advanced AI technology. The platform provides personalized interview practice, daily quiz challenges, study assistance, and resume analysis with ATS optimization.

🌐 Live Demo: https://my-project.tech

🎯 Project Objective

The primary objective of SkillWise-AI is to bridge the gap between academic knowledge and interview readiness for engineering students. By leveraging AI technology, the platform provides:

Personalized Interview Practice: AI-powered mock interviews tailored to specific domains and difficulty levels
Skill Assessment: Comprehensive evaluation and feedback on technical and soft skills
Knowledge Reinforcement: Daily quiz challenges covering core engineering subjects
Resume Optimization: AI-driven resume analysis with ATS compatibility scoring
Study Assistance: Subject-specific AI tutoring based on standard textbooks

✨ Key Features

🎤 AI-Powered Mock Interviews

Real-time voice-to-text transcription using Whisper API
Dynamic question generation based on domain (Technical, Behavioral, System Design)
Difficulty levels: Beginner, Intermediate, Advanced
Comprehensive feedback with scoring (0-100)
Session history and progress tracking

📚 Daily Quiz Challenges

Auto-generated questions for 6 core engineering subjects:
- Data Structures & Algorithms
- Operating Systems
- Computer Networks
- Database Management Systems
- Software Engineering
- Algorithm Design & Analysis
Multiple choice questions with instant feedback
Performance analytics and streak tracking

🤖 AI Study Assistant

Subject-specific tutoring using standard textbooks
Conversational AI based on Llama-4-Scout model
Context-aware responses with chat history
Supports ADA, CN, DBMS, OS, SE, and DS subjects

📄 Resume Analysis

AI-powered grammatical error detection
ATS (Applicant Tracking System) compatibility scoring
Personalized improvement suggestions
Support for PDF, DOCX, and TXT formats

👤 User Management

Secure authentication with JWT tokens
Profile management with photo and resume upload
Password reset functionality via email
Session tracking and performance analytics

🛠 Tech Stack

Frontend (React 19.1.0)

React 19.1.0 - Latest React with concurrent features and modern hooks
Vite 6.3.5 - Ultra-fast build tool and development server
Tailwind CSS 4.1.5 - Utility-first CSS framework with custom configuration
Redux Toolkit 2.8.2 - Modern Redux state management
React Router DOM 7.6.1 - Declarative routing for React applications
Framer Motion 12.19.1 - Production-ready motion library for React
Axios 1.9.0 - Promise-based HTTP client
React Hot Toast 2.5.2 - Beautiful toast notifications
React Icons 5.5.0 - Popular icon library
React Markdown 10.1.0 - Markdown component for React
Recharts 3.0.2 - Composable charting library
@lottiefiles/dotlottie-react 0.14.2 - Lottie animations for React

Backend (Node.js/Express)

Express.js 5.1.0 - Fast, unopinionated web framework
MongoDB - NoSQL database with Mongoose 8.14.1 ODM
JWT - JSON Web Token authentication with jsonwebtoken 9.0.2
Cloudinary 1.41.3 - Cloud-based image and file storage
Bcrypt.js 3.0.2 - Password hashing library
Nodemailer 7.0.3 - Email sending functionality
Multer 2.0.1 - Middleware for handling multipart/form-data
Morgan 1.10.0 - HTTP request logger middleware
CORS 2.8.5 - Cross-Origin Resource Sharing middleware
Cookie Parser 1.4.7 - Cookie parsing middleware
HTTP Proxy Middleware 3.0.5 - Proxy middleware for API routing

AI/ML Services (Python/FastAPI)

FastAPI - Modern Python web framework for building APIs
Groq API - High-performance LLM inference for AI responses
Whisper API - Speech-to-text transcription service
LangChain - Framework for developing LLM applications
LangChain Groq - Groq integration for LangChain
LangChain Community - Community-driven LangChain components
LangChain HuggingFace - HuggingFace integration
Motor 3.0.0 - Async MongoDB driver for Python
PyMongo 4.0.0 - MongoDB Python driver
Pydantic - Data validation using Python type annotations
PyPDF2 - PDF text extraction library
Python-DOCX - Word document processing
PyJWT - JSON Web Token implementation
FAISS-CPU - Facebook AI Similarity Search library
Uvicorn - ASGI server implementation

📁 Project Structure

Mini-Project/
│
├── 📱 client/                          # React Frontend Application
│   ├── src/
│   │   ├── components/                 # Reusable UI components
│   │   │   ├── AuthLayout.jsx         # Authentication wrapper layout
│   │   │   ├── Navbar.jsx             # Navigation component
│   │   │   ├── ProtectedRoute.jsx     # Route protection wrapper
│   │   │   ├── Loading.jsx            # Loading state components
│   │   │   ├── VoiceRecorder.jsx      # Voice recording component
│   │   │   ├── Gamification.jsx       # Gamification features
│   │   │   ├── StreakVisualizer.jsx   # Streak tracking visualization
│   │   │   ├── ErrorBoundary.jsx      # Error boundary component
│   │   │   ├── DotLottieLoader.jsx    # Lottie animation loader
│   │   │   ├── ResumeATS.jsx          # Resume ATS analysis
│   │   │   └── shared/                # Shared components
│   │   ├── pages/                     # Main application pages
│   │   │   ├── Home.jsx               # Landing page
│   │   │   ├── Dashboard.jsx          # User dashboard
│   │   │   ├── Profile.jsx            # User profile management
│   │   │   ├── Login.jsx              # User authentication
│   │   │   ├── Register.jsx           # User registration
│   │   │   ├── ForgotPassword.jsx     # Password reset request
│   │   │   ├── ResetPassword.jsx      # Password reset form
│   │   │   ├── Quiz.jsx               # Quiz interface
│   │   │   ├── QuizSelection.jsx      # Subject selection for quiz
│   │   │   ├── QuizResults.jsx        # Quiz results display
│   │   │   ├── MockInterview.jsx      # Mock interview interface
│   │   │   ├── MockInterviewSelection.jsx # Interview type selection
│   │   │   ├── MockInterviewResults.jsx   # Interview results
│   │   │   ├── InterviewHistory.jsx   # Interview session history
│   │   │   ├── StudyAssistant.jsx     # AI study assistant
│   │   │   └── ResumeATSPage.jsx      # Resume analysis page
│   │   ├── redux/                     # State management
│   │   │   ├── store.js               # Redux store configuration
│   │   │   └── slices/                # Redux slices for different features
│   │   ├── utils/                     # Utility functions
│   │   │   ├── api.js                 # API configuration and utilities
│   │   │   └── dqApi.js               # Daily questions API
│   │   ├── hooks/                     # Custom React hooks
│   │   ├── contexts/                  # React context providers
│   │   └── assets/                    # Static assets and images
│   ├── public/                        # Public static files
│   │   ├── ada.jpg                    # Subject images
│   │   ├── cn.jpg
│   │   ├── dbms.jpg
│   │   ├── ds.jpg
│   │   ├── os.jpg
│   │   └── se.jpg
│   ├── package.json                   # Frontend dependencies
│   ├── vite.config.js                 # Vite configuration
│   ├── tailwind.config.js             # Tailwind CSS configuration
│   ├── eslint.config.js               # ESLint configuration
│   └── vercel.json                    # Vercel deployment config
│
├── 🖥️ server/                          # Node.js Backend Server
│   ├── controllers/                   # Business logic controllers
│   │   ├── authController.js          # Authentication logic
│   │   ├── userController.js          # User management
│   │   ├── dqController.js            # Daily questions management
│   │   ├── interviewController.js     # Interview session management
│   │   └── assistantController.js     # Study assistant proxy
│   ├── models/                        # Database schemas (Mongoose)
│   │   ├── userModel.js               # User data schema
│   │   ├── dqModel.js                 # Daily questions schema
│   │   ├── interviewModel.js          # Interview session schema
│   │   ├── assistantModel.js          # Study assistant session schema
│   │   ├── resumeModel.js             # Resume analysis schema
│   │   └── quizHistoryModel.js        # Quiz performance history
│   ├── routes/                        # API route definitions
│   │   ├── authRoutes.js              # Authentication endpoints
│   │   ├── userRoutes.js              # User management endpoints
│   │   ├── dqRoutes.js                # Daily questions endpoints
│   │   ├── interviewRoutes.js         # Interview endpoints
│   │   └── assistantRoutes.js         # Study assistant endpoints
│   ├── middlewares/                   # Custom middleware
│   │   └── isLogin.js                 # JWT authentication middleware
│   ├── config/                        # Configuration files
│   │   ├── cloudinaryConfig.js        # Cloudinary setup
│   │   └── cloudinaryStorage.js       # File storage configuration
│   ├── utils/                         # Utility functions
│   │   ├── email.js                   # Email service utilities
│   │   ├── catchAsync.js              # Async error handling
│   │   └── pythonAPI.js               # Python service integration
│   ├── jwt/                           # JWT utilities
│   │   └── jsonWebToken.js            # Token generation and validation
│   ├── package.json                   # Backend dependencies
│   └── server.js                      # Express server entry point
│
├── 🐍 services/                        # Python AI/ML Services
│   ├── controllers/                   # AI service controllers
│   │   ├── assistant_controller.py    # Study assistant AI logic
│   │   └── interview_controller.py    # Interview AI logic and feedback
│   ├── models/                        # Data models and AI prompts
│   │   ├── database_models.py         # Pydantic models for database
│   │   ├── request_models.py          # API request validation models
│   │   ├── response_model.py          # API response models
│   │   └── prompts.py                 # AI prompts and templates
│   ├── routes/                        # FastAPI route definitions
│   │   ├── assistant_routes.py        # Study assistant API endpoints
│   │   └── interview_routes.py        # Interview AI API endpoints
│   ├── database/                      # Database configuration
│   │   └── db_config.py               # MongoDB async connection
│   ├── utils/                         # Utility functions
│   │   ├── helper.py                  # Text processing utilities
│   │   └── auth_middleware.py         # JWT authentication for FastAPI
│   ├── speech_output/                 # Generated speech files
│   ├── minenv/                        # Python virtual environment
│   ├── api.py                         # FastAPI main application
│   ├── auth.py                        # Authentication logic
│   ├── requirements.txt               # Python dependencies
│   └── test_auth.py                   # Authentication testing
│
├── � Documentation Files
│   ├── README.md                      # Main project documentation
│   ├── ASSISTANT_PAGINATION_IMPLEMENTATION.md
│   ├── DOTLOTTIE_LOADER_FIX.md
│   ├── INTERVIEW_FEEDBACK_DISPLAY_ENHANCEMENT.md
│   ├── TEXT_TO_SPEECH_INTEGRATION.md
│   ├── VOICE_TO_TEXT_INTEGRATION.md
│   └── AUTHENTICATION_GUIDE.md
│
└── 🔧 Configuration Files
    ├── package.json                   # Root package.json for workspace
    └── debug.txt                      # Debug information

🚀 Getting Started

Prerequisites

Node.js (v18 or higher)
Python (v3.8 or higher)
MongoDB (local or cloud instance)
Git

Installation

Clone the repository

git clone https://github.com/yourusername/SkillWise-AI.git
cd Mini-Project

Setup Backend Server (Node.js)

cd server
npm install

Setup Frontend Client (React)

cd ../client
npm install

Setup AI Services (Python)

cd ../services
python -m venv minenv
# On Windows
minenv\Scripts\activate
# On macOS/Linux
source minenv/bin/activate
pip install -r requirements.txt

Environment Configuration

Create .env files in each directory:

Server/.env

PORT=5000
MONGODB_URI=mongodb://localhost:27017/SkillWise-AI
JWT_SECRET=your_jwt_secret_key_here
JWT_EXPIRES_IN=7d
CLOUDINARY_CLOUD_NAME=your_cloudinary_name
CLOUDINARY_API_KEY=your_cloudinary_key
CLOUDINARY_API_SECRET=your_cloudinary_secret
EMAIL_USERNAME=your_email@gmail.com
EMAIL_PASSWORD=your_app_password
FRONTEND_URL=http://localhost:5173

Services/.env

GROQ_API_KEY_DQ=your_groq_api_key_for_daily_questions
GROQ_API_AUDIO=your_groq_audio_api_key
MONGODB_URI=mongodb://localhost:27017/SkillWise-AI

Client/.env

VITE_APP_BACKEND_URL=http://localhost:5000
VITE_APP_SERVICES_URL=http://localhost:8000

Running the Application

You can use VS Code tasks or run manually:

Using VS Code Tasks (Recommended)

Open VS Code in the project root
Use Ctrl+Shift+P (Cmd+Shift+P on macOS) and type "Tasks: Run Task"
Select one of the available tasks:
- Start Client Development Server - Runs the React frontend
- Start Server Development Server - Runs the Node.js backend
- Start Python API Service - Runs the FastAPI Python services

Manual Startup

Start MongoDB (if running locally)
Start the Backend Server

cd server
npm run dev

Start the AI Services

cd services
# Activate virtual environment first
python api.py

Start the Frontend Client

cd client
npm run dev

The application will be available at:

Frontend: http://localhost:5173
Backend API: http://localhost:5000
AI Services: http://localhost:8000
API Documentation: http://localhost:8000/docs

🔄 Development Workflow

Development Scripts

Frontend (client/)

npm run dev      # Start development server
npm run build    # Build for production
npm run preview  # Preview production build
npm run lint     # Run ESLint

Backend (server/)

npm run dev      # Start with nodemon (auto-reload)
npm start        # Start production server

AI Services (services/)

python api.py    # Start FastAPI server
uvicorn api:api --reload --host 0.0.0.0 --port 8000  # Start with auto-reload

Architecture Overview

The application follows a microservices architecture:

Frontend (React): Handles user interface and client-side state management
Backend (Node.js/Express): Manages user authentication, database operations, and file uploads
AI Services (Python/FastAPI): Provides AI-powered features like interview feedback, study assistance, and resume analysis

Key Development Features

Hot Reload: All services support hot reload during development
Error Boundaries: React error boundaries catch and handle client-side errors gracefully
Async Operations: Comprehensive async/await patterns with proper error handling
Type Safety: Pydantic models ensure type safety in Python services
Authentication: JWT-based authentication across all services
File Upload: Cloudinary integration for file storage and management

📚 API Documentation

Authentication Endpoints (Node.js Backend)

Base URL: http://localhost:5000/api/v1/auth

Method	Endpoint	Description	Auth Required
`POST`	`/register`	User registration	❌
`POST`	`/login`	User login	❌
`GET`	`/logout`	User logout	❌
`POST`	`/forgot-password`	Password reset request	❌
`POST`	`/reset-password/:token`	Reset password with token	❌
`PATCH`	`/update-password`	Update current password	✅
`PATCH`	`/update-profile`	Update user profile	✅
`GET`	`/me`	Get current user details	✅

User Management Endpoints

Base URL: http://localhost:5000/api/v1/users

Method	Endpoint	Description	Auth Required
`GET`	`/profile`	Get user profile	✅
`PATCH`	`/profile`	Update user profile	✅
`POST`	`/upload-avatar`	Upload profile picture	✅
`POST`	`/upload-resume`	Upload resume file	✅

Daily Questions Endpoints

Base URL: http://localhost:5000/api/v1/dq

Method	Endpoint	Description	Auth Required
`GET`	`/daily-questions`	Get all daily questions	✅
`GET`	`/subject/:subject`	Get questions by subject	✅
`POST`	`/submit-answers`	Submit quiz answers	✅
`GET`	`/history`	Get quiz history	✅
`GET`	`/streak`	Get current streak	✅

Interview Endpoints

Base URL: http://localhost:5000/api/v1/interview

Method	Endpoint	Description	Auth Required
`POST`	`/create-session`	Create interview session	✅
`GET`	`/sessions`	Get user interview sessions	✅
`GET`	`/session/:id`	Get specific session	✅
`PATCH`	`/session/:id`	Update session	✅
`DELETE`	`/session/:id`	Delete session	✅

AI Services Endpoints (FastAPI Python)

Base URL: http://localhost:8000/services/api/v1

Interview AI Endpoints

Method	Endpoint	Description	Auth Required
`POST`	`/interview/transcript`	Convert voice to text	✅
`POST`	`/interview/get-interview`	Get AI interview questions	✅
`POST`	`/interview/feedback`	Generate interview feedback	✅
`POST`	`/interview/text-to-speech`	Convert text to speech	✅
`POST`	`/interview/resume-based-interview`	Resume-based interview	✅

Study Assistant Endpoints

Method	Endpoint	Description	Auth Required
`POST`	`/assistant/chat`	Chat with study assistant	✅
`POST`	`/assistant/resume`	Analyze resume with ATS	✅
`POST`	`/assistant/daily-questions`	Generate daily questions	✅
`GET`	`/assistant/subjects`	Get available subjects	✅

Request/Response Examples

Authentication

// POST /api/v1/auth/login
{
  "email": "user@example.com",
  "password": "securePassword123"
}

// Response
{
  "status": "success",
  "token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
  "user": {
    "id": "60f7b3b3b3b3b3b3b3b3b3b3",
    "name": "John Doe",
    "email": "user@example.com"
  }
}

Voice Transcription

// POST /services/api/v1/interview/transcript
// Form Data: file (audio file)

// Response
{
  "text": "Hello, my name is John and I am a software developer..."
}

Interview Feedback

// POST /services/api/v1/interview/feedback
{
  "questions": ["Tell me about yourself"],
  "answers": ["I am a software developer..."],
  "domain": "technical",
  "difficulty": "intermediate"
}

// Response
{
  "overall_score": 85,
  "feedback": {
    "technical_knowledge": 90,
    "communication": 80,
    "confidence": 85
  },
  "detailed_feedback": "Your technical knowledge is excellent...",
  "suggestions": ["Practice explaining complex concepts more clearly"]
}

Interactive API Documentation

FastAPI Swagger UI: http://localhost:8000/docs
FastAPI ReDoc: http://localhost:8000/redoc

Authentication

All protected endpoints require a JWT token in the Authorization header:

Authorization: Bearer <your_jwt_token>

🎨 Features in Detail

🎤 AI-Powered Mock Interviews

Voice Recognition: Real-time speech-to-text using Groq Whisper API
Dynamic Question Generation: AI generates contextual follow-up questions based on responses
Multiple Interview Types: Technical coding, behavioral, system design, and resume-based interviews
Difficulty Scaling: Beginner, Intermediate, and Advanced difficulty levels
Comprehensive Feedback: Detailed scoring on technical knowledge, communication skills, and confidence
Session History: Track progress across multiple interview sessions
Resume-Based Questions: AI analyzes uploaded resume to generate relevant questions

📚 Daily Quiz Challenges

6 Core Engineering Subjects:
- Data Structures & Algorithms (DS)
- Operating Systems (OS)
- Computer Networks (CN)
- Database Management Systems (DBMS)
- Software Engineering (SE)
- Algorithm Design & Analysis (ADA)
Intelligent Question Generation: AI-powered question creation based on curriculum
Performance Analytics: Detailed tracking of performance trends and weak areas
Streak System: Gamified daily practice with streak counters and achievements
Instant Feedback: Immediate results with detailed explanations for each answer
Adaptive Difficulty: Questions adapt based on user performance and learning curve

🤖 AI Study Assistant

Subject-Specific Expertise: Specialized AI tutors for each engineering domain
Textbook Integration: Responses based on standard engineering textbooks and curriculum
Conversational Interface: Natural language interaction with context-aware responses
Chat History: Persistent conversation history for continued learning
Multi-Modal Support: Text-based queries with comprehensive explanations
Personalized Learning: Adapts explanations based on user's knowledge level

📄 Resume Analysis & ATS Optimization

AI-Powered Grammar Check: Advanced grammatical error detection and correction
ATS Compatibility Scoring: Detailed analysis for Applicant Tracking System optimization
Format Analysis: Support for PDF, DOCX, and TXT file formats
Personalized Suggestions: Tailored recommendations for improvement
Industry Standards: Analysis based on current hiring trends and requirements
Skills Gap Analysis: Identification of missing keywords and skills

👤 Advanced User Management

Secure Authentication: JWT-based authentication with refresh token support
Profile Customization: Comprehensive profile management with photo upload
Password Security: Bcrypt hashing with password reset functionality via email
Session Tracking: Detailed analytics on user performance and progress
Cloud Storage: Secure file storage using Cloudinary integration
Data Privacy: GDPR-compliant user data handling and privacy controls

🎮 Gamification Features

Streak Tracking: Visual streak counters with calendar integration
Performance Graphs: Interactive charts showing progress over time
Achievement System: Badges and milestones for consistent practice
Leaderboards: Competitive elements to encourage regular engagement
Progress Visualization: GitHub-style contribution graphs for quiz activities

🚀 Deployment

Frontend Deployment (Vercel)

The frontend is configured for deployment on Vercel with the included vercel.json:

# Install Vercel CLI
npm i -g vercel

# Deploy from client directory
cd client
vercel --prod

Backend Deployment Options

Option 1: Railway/Render

# Add to package.json in server/
{
  "scripts": {
    "start": "node server.js",
    "build": "npm install"
  }
}

Option 2: Docker Deployment

# Dockerfile for backend
FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm install
COPY . .
EXPOSE 5000
CMD ["npm", "start"]

Python Services Deployment

FastAPI with Docker

# Dockerfile for services
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "api:api", "--host", "0.0.0.0", "--port", "8000"]

Environment Variables for Production

Update your production environment variables:

# Production URLs
VITE_APP_BACKEND_URL=https://your-backend-domain.com
VITE_APP_SERVICES_URL=https://your-python-services-domain.com
FRONTEND_URL=https://your-frontend-domain.com

# Database
MONGODB_URI=mongodb+srv://username:password@cluster.mongodb.net/SkillWise-AI

# Security
JWT_SECRET=your_super_secure_jwt_secret_for_production

Monitoring and Analytics

Error Tracking: Implement Sentry for production error monitoring
Performance: Use React DevTools and browser performance monitoring
API Monitoring: FastAPI built-in metrics and logging
Database Monitoring: MongoDB Atlas monitoring for production

Development Setup

Fork the repository and clone your fork
Create a feature branch: git checkout -b feature/amazing-feature
Install dependencies for all services (see Getting Started)
Make your changes following the coding standards
Test thoroughly across all services
Commit your changes: git commit -m 'Add amazing feature'
Push to your branch: git push origin feature/amazing-feature
Open a Pull Request with a clear description

Coding Standards

Frontend (React)

Use functional components with hooks
Follow ESLint configuration
Use Tailwind CSS for styling
Implement proper error boundaries
Write meaningful component names

Backend (Node.js)

Use async/await for asynchronous operations
Implement proper error handling with try-catch
Follow RESTful API conventions
Use meaningful variable and function names
Add JSDoc comments for complex functions

AI Services (Python)

Follow PEP 8 style guidelines
Use type hints with Pydantic models
Implement proper async patterns
Add docstrings for all functions
Handle exceptions gracefully

Third-Party Licenses

This project uses several open-source libraries and services:

React: MIT License
FastAPI: MIT License
Express.js: MIT License
Tailwind CSS: MIT License
MongoDB: Server Side Public License (SSPL)
Groq API: Commercial API service
Cloudinary: Commercial service with free tier

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README.md

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README.md

File metadata and controls

SkillWise-AI 🚀

📋 Table of Contents

🎯 Project Objective

✨ Key Features

🎤 AI-Powered Mock Interviews

📚 Daily Quiz Challenges

🤖 AI Study Assistant

📄 Resume Analysis

👤 User Management

🛠 Tech Stack

Frontend (React 19.1.0)

Backend (Node.js/Express)

AI/ML Services (Python/FastAPI)

📁 Project Structure

🚀 Getting Started

Prerequisites

Installation

Environment Configuration

Server/.env

Services/.env

Client/.env

Running the Application

Using VS Code Tasks (Recommended)

Manual Startup

🔄 Development Workflow

Development Scripts

Frontend (client/)

Backend (server/)

AI Services (services/)

Architecture Overview

Key Development Features

📚 API Documentation

Authentication Endpoints (Node.js Backend)

User Management Endpoints

Daily Questions Endpoints

Interview Endpoints

AI Services Endpoints (FastAPI Python)

Interview AI Endpoints

Study Assistant Endpoints

Request/Response Examples

Authentication

Voice Transcription

Interview Feedback

Interactive API Documentation

Authentication

🎨 Features in Detail

🎤 AI-Powered Mock Interviews

📚 Daily Quiz Challenges

🤖 AI Study Assistant

📄 Resume Analysis & ATS Optimization

👤 Advanced User Management

🎮 Gamification Features

🚀 Deployment

Frontend Deployment (Vercel)

Backend Deployment Options

Option 1: Railway/Render

Option 2: Docker Deployment

Python Services Deployment

FastAPI with Docker

Environment Variables for Production

Monitoring and Analytics

Development Setup

Coding Standards

Frontend (React)

Backend (Node.js)

AI Services (Python)

Third-Party Licenses