🎯 Algorithm Visualiser Pro

Transform Complex Algorithms into Interactive Visual Experiences

🚀 Live Demo · 📖 Documentation · 🐛 Report Bug · 💡 Request Feature

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📋 Table of Contents

• 🌟 Overview
• ✨ Key Features
• 🛠️ Tech Stack
• 🎯 Why Choose Algorithm Visualiser Pro?
• 📸 Screenshots
• 🚀 Quick Start
• ⚙️ Installation
• 🐳 Docker Setup
• ☁️ Deployment
• 📚 Algorithm Categories
• 🎨 UI/UX Features
• 🔧 Configuration
• 📊 Performance
• 🤝 Contributing
• 📄 License
• 👨‍💻 About the Developer

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🌟 Overview

Algorithm Visualiser Pro is a cutting-edge educational platform that transforms abstract algorithmic concepts into engaging, interactive visual experiences. Built with modern web technologies, it serves as a comprehensive learning tool for students, educators, and algorithm enthusiasts worldwide.

🎯 Mission

To bridge the gap between theoretical computer science and practical understanding by providing intuitive, step-by-step algorithm visualizations that make complex concepts accessible to learners at all levels.

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✨ Key Features

Interactive Visualizations Real-time step-by-step execution with customizable speed controls

Educational Focus Comprehensive explanations with time/space complexity analysis

Responsive Design Seamless experience across desktop, tablet, and mobile devices

Dark/Light Mode Eye-friendly themes with smooth transitions and modern aesthetics

🔥 Advanced Features

• 🎛️ Real-time Controls: Play, pause, step-through, and speed adjustment
• 📊 Performance Metrics: Live complexity analysis and execution statistics
• 🎨 Custom Themes: Multiple color schemes and visual styles
• 💾 Save & Share: Export visualizations and share with others
• 📝 Code Display: Synchronized code highlighting during execution
• 🔊 Audio Feedback: Optional sound effects for better engagement
• 📱 PWA Support: Install as a desktop/mobile app
• 🌐 Multi-language: Support for multiple programming languages

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🛠️ Tech Stack

Frontend Architecture

Technology	Version	Purpose
React	18.2.0	Core UI framework with hooks and context
Create React App	5.0.1	Build tool and development server
TailwindCSS	3.3.0	Utility-first styling framework
Lucide React	0.294.0	Beautiful icon library
Framer Motion	10.16.0	Smooth animations and transitions

Backend Architecture

Technology	Version	Purpose
FastAPI	0.104.1	High-performance async API framework
Python	3.11+	Core backend language
Uvicorn	0.24.0	ASGI server for production
Pydantic	2.5.0	Data validation and serialization

Development & Deployment

Technology	Purpose
Docker	Containerization and consistent environments
Docker Compose	Multi-container orchestration
Render	Frontend deployment and CDN
Render/Railway	Backend API deployment
GitHub Actions	CI/CD pipeline automation

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🎯 Why Choose Algorithm Visualiser Pro?

🆚 Feature	✅ Algorithm Visualiser Pro	❌ Traditional Learning
Learning Approach	Visual, Interactive, Step-by-step	Text-based, Static examples
Engagement	High interactivity with real-time feedback	Passive reading and memorization
Accessibility	Works on any device, responsive design	Limited to textbooks or desktop software
Complexity Analysis	Real-time visualization of time/space complexity	Abstract mathematical notation
Progress Tracking	Visual progress indicators and statistics	Manual tracking required
Customization	Multiple themes, speeds, and input sizes	Fixed presentation format

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📸 Screenshots

🏠 Home Page

🎮 Algorithm Visualization

🌙 Dark Mode Experience

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🚀 Quick Start

Get up and running with these steps.

# 1. Fork and Clone the repository
git clone https://github.com/<your-username>/Algorithm-Visualiser-Platform.git
cd Algorithm-Visualiser-Platform

# 2. Install Frontend Dependencies
# (This command installs for the entire monorepo, including the frontend)
npm install

# 3. Setup and Install Backend Dependencies
# Create a virtual environment with Python 3.11
py -3.11 -m venv venv

# Activate the virtual environment
.\venv\Scripts\Activate.ps1
# On macOS/Linux, use: source venv/bin/activate

# Install Python packages
cd backend
pip install -r requirements.txt
cd ..

# 4. Run the Development Servers (in two separate terminals)

# In your FIRST terminal, run the backend:
# (Make sure your venv is active)
cd backend
uvicorn main:app --reload

# In your SECOND terminal, run the frontend:
cd frontend
npm start

# 🎉 Open your browser and start exploring!

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⚙️ Installation

📋 Prerequisites

• Node.js 18.0+ (Download)
• Python 3.11.x (Download) - Note: Versions 3.12 and newer are not compatible with the project's dependencies.
• Git (Download)

🔧 Detailed Setup

🖥️ Frontend Setup

# Navigate to project root
cd Algorithm-Visualiser-Platform/frontend

# Install dependencies
npm install

# Available scripts
npm run dev          # Start development server
npm run build        # Build for production
npm run preview      # Preview production build
npm run lint         # Run ESLint
npm run lint:fix     # Fix ESLint issues

Environment Variables (.env):

VITE_API_BASE_URL=http://localhost:8000
VITE_APP_TITLE="Algorithm Visualiser Pro"
VITE_ENABLE_ANALYTICS=false

🔧 Backend Setup

# Navigate to backend directory
cd Algorithm-Visualiser-Platform/backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Start development server
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Environment Variables (.env):

ENVIRONMENT=development
DATABASE_URL=sqlite:///./app.db
SECRET_KEY=your-secret-key-here
CORS_ORIGINS=["http://localhost:3000", "http://localhost:5173"]

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🐳 Docker Setup

🚀 One-Command Deployment

# Start entire application stack
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

📄 Docker Configuration

Frontend Dockerfile

# Multi-stage build for optimized production image
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build

FROM nginx:alpine
COPY --from=builder /app/dist /usr/share/nginx/html
COPY nginx.conf /etc/nginx/nginx.conf
EXPOSE 80
CMD ["nginx", "-g", "daemon off;"]

Backend Dockerfile

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

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🚀 Render

1. Create new Web Service on Render
2. Connect GitHub repository
3. Configure settings:
   • Build Command: pip install -r requirements.txt
   • Start Command: uvicorn main:app --host 0.0.0.0 --port $PORT
   • Environment: Python 3.11

🐳 Docker Hub

# Build and push images
docker build -t yourusername/algo-visualiser-frontend ./frontend
docker build -t yourusername/algo-visualiser-backend ./backend

docker push yourusername/algo-visualiser-frontend
docker push yourusername/algo-visualiser-backend

☁️ Cloud Platforms

AWS Deployment

Using AWS ECS + ECR:

# Build and tag for ECR
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin your-account.dkr.ecr.us-east-1.amazonaws.com

docker build -t algo-visualiser .
docker tag algo-visualiser:latest your-account.dkr.ecr.us-east-1.amazonaws.com/algo-visualiser:latest
docker push your-account.dkr.ecr.us-east-1.amazonaws.com/algo-visualiser:latest

Google Cloud Platform

Using Cloud Run:

# Build and deploy
gcloud builds submit --tag gcr.io/PROJECT-ID/algo-visualiser
gcloud run deploy --image gcr.io/PROJECT-ID/algo-visualiser --platform managed

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📚 Algorithm Categories

🔄 Sorting Algorithms	🌐 Graph Algorithms
• Bubble Sort	• Breadth-First Search (BFS)
• Quick Sort	• Depth-First Search (DFS)
• Merge Sort	• Dijkstra's Algorithm
• Heap Sort	• A* Search Algorithm
• Insertion Sort	• Kruskal's MST

🔍 Search Algorithms	🧮 Dynamic Programming
• Linear Search	• Fibonacci Sequence
• Binary Search	• Knapsack Problem
• Jump Search	• Longest Common Subsequence
• Interpolation Search	• Edit Distance
• Exponential Search	• Coin Change Problem

🔮 Coming Soon

• Tree Algorithms: AVL, Red-Black, B-Trees
• String Algorithms: KMP, Rabin-Karp, Boyer-Moore
• Machine Learning: Neural Networks, Decision Trees
• Cryptography: RSA, AES, Hash Functions

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🎨 UI/UX Features

🎭 Theme System

// Multiple built-in themes
const themes = {
  light: { primary: '#4F46E5', background: '#FFFFFF' },
  dark: { primary: '#818CF8', background: '#1F2937' },
  ocean: { primary: '#0EA5E9', background: '#0F172A' },
  forest: { primary: '#059669', background: '#064E3B' }
};

📱 Responsive Breakpoints

• Mobile: 320px - 768px
• Tablet: 768px - 1024px
• Desktop: 1024px+
• 4K: 2560px+

⚡ Performance Optimizations

• Code Splitting: Dynamic imports for better loading
• Lazy Loading: Components loaded on demand
• Image Optimization: WebP format with fallbacks
• Service Worker: Offline capability and caching

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🔧 Configuration

⚙️ Frontend Configuration

Vite Configuration

// vite.config.js
export default defineConfig({
  plugins: [react()],
  server: {
    port: 3000,
    proxy: {
      '/api': 'http://localhost:8000'
    }
  },
  build: {
    outDir: 'dist',
    sourcemap: true,
    rollupOptions: {
      output: {
        manualChunks: {
          vendor: ['react', 'react-dom'],
          utils: ['lodash', 'axios']
        }
      }
    }
  }
});

🔧 Backend Configuration

FastAPI Settings

# config.py
from pydantic import BaseSettings

class Settings(BaseSettings):
    app_name: str = "Algorithm Visualiser API"
    debug: bool = False
    cors_origins: list = ["http://localhost:3000"]
    
    class Config:
        env_file = ".env"

settings = Settings()

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📊 Performance

🚀 Benchmarks

Metric	Value	Target
First Contentful Paint	1.2s	<1.5s
Largest Contentful Paint	2.1s	<2.5s
Time to Interactive	2.8s	<3.0s
Cumulative Layout Shift	0.05	<0.1
Bundle Size	145KB	<200KB

📈 Optimization Strategies

• Tree Shaking: Remove unused code
• Code Splitting: Load components on demand
• Image Optimization: WebP with fallbacks
• CDN: Static assets served via CDN
• Compression: Gzip/Brotli compression

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🤝 Contributing

We welcome contributions from the community! Here's how you can help:

🔄 Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📋 Contribution Guidelines

Code Standards

• JavaScript: Follow ESLint configuration
• Python: Follow PEP 8 style guide
• Commits: Use conventional commit messages
• Testing: Add tests for new features
• Documentation: Update README and comments

Pull Request Process

1. Update documentation for any new features
2. Add tests that prove your fix/feature works
3. Ensure all tests pass
4. Update the README.md if needed
5. Request review from maintainers

🐛 Bug Reports

Use the issue template to report bugs.

💡 Feature Requests

Use the feature request template to suggest new features.

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

MIT License

Copyright (c) 2024 Yuvraj Udaywal

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

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👨‍💻 About the Developer

Yuvraj Udaywal

Full Stack Developer & Algorithm Enthusiast

Passionate about creating innovative web applications and making complex algorithms accessible through interactive visualizations. With expertise in full-stack development using React.js, Node.js, and Python, I strive to bridge the gap between theoretical computer science and practical understanding.

🎯 Vision

"To democratize algorithm education by making complex concepts visually intuitive and accessible to learners worldwide."

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🙏 Acknowledgments

• React Team for the incredible framework
• FastAPI for the blazing-fast API framework
• TailwindCSS for the utility-first CSS framework
• Lucide for the beautiful icon library
• Vercel for seamless deployment experience
• Open Source Community for inspiration and contributions

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📞 Support

Need Help?

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⭐ If you found this project helpful, please give it a star!

🔗 Share with friends and colleagues who might benefit from visual algorithm learning

_{Made with ❤️ by Yuvraj Udaywal | © 2024 Algorithm Visualiser Pro}