🚀 Learning Path Advisor Pro

AI-Powered Career Development Platform

Transform your career journey with cutting-edge AI that creates personalized learning paths tailored to your goals!

Features • Quick Start • Documentation • Contributing

📊 At a Glance

Feature	Description
🎓 Course Database	165+ curated courses across 18 categories
🤖 AI Algorithms	Dijkstra, A*, BFS pathfinding + MEU, Minimax strategies
⚡ Performance	Response caching, rate limiting, performance monitoring
🔒 Security	CORS protection, input validation, rate limiting
📱 UI/UX	Responsive design, interactive visualizations
🧪 Testing	18 tests, 95%+ coverage
📚 Documentation	Complete API docs, contribution guidelines

🎯 Why Choose Learning Path Advisor Pro?

Our platform uses advanced AI algorithms to revolutionize how professionals plan and execute their career development. Whether you're looking to switch careers, advance in your current field, or acquire new skills, our intelligent system guides you every step of the way.

📈 Real-World Use Cases

🔄 Career Changers

From Marketing to Data Science? We'll show you exactly which courses to take and in what order, considering your existing skills.

Example Path:

Marketing Basics → Statistics → Python Basics → Data Analysis → Machine Learning
Estimated time: 6-8 months

🚀 Career Advancers

Already a developer? Advance to senior roles with targeted skill development.

Example Path:

Web Development → Cloud Computing → DevOps → System Architecture
Estimated time: 4-6 months

🎯 Skill Seekers

Exploring new domains? Get personalized recommendations based on your learning style.

Example Path:

Design Basics → UI/UX Design → Figma Mastery → Design Systems
Estimated time: 3-4 months

🌟 Key Features

🤖 Advanced AI Decision Making

Goal-Based Agents: Find the most efficient routes to your learning objectives using Dijkstra's, A*, and BFS algorithms
Utility-Based Agents: Optimize for your personal learning style and risk tolerance
Hybrid Intelligence: Combines multiple AI approaches for superior recommendations
Risk Analysis: Understand the complexity and challenges of each learning path

📚 Comprehensive Learning Ecosystem

165+ Courses: Extensive database covering Programming, Marketing, Business, Design, and more
Prerequisite Mapping: Smart course sequencing based on dependencies
18 Learning Categories: From AI/ML to Creative Arts to Business Development
Skill Gap Analysis: Identify what you need to reach your career goals

🎨 Modern User Experience

Professional UI: Clean, easy-on-the-eyes interface designed for long learning sessions
Interactive Visualizations: See your learning path mapped out visually
Progress Dashboard: Track your advancement with detailed statistics
Mobile Responsive: Learn anywhere, on any device
RESTful API: Complete API with rate limiting and caching

🎯 Personalized Learning Paths

Multiple Learning Styles: Fastest, Easiest, Balanced, or Challenging approaches
Career-Specific Planning: Direct paths to target careers
Risk Tolerance Settings: Match learning intensity to your comfort level
Customizable Filters: Find courses by difficulty, time, or keyword

🚀 Quick Start

🪟 Windows

# One-click start
start.bat

What it does:

✅ Creates virtual environment
✅ Installs dependencies
✅ Sets up configuration
✅ Starts server automatically

🐧 Linux/macOS

# One-click start
chmod +x start.sh
./start.sh

What it does:

✅ Creates virtual environment
✅ Installs dependencies
✅ Sets up configuration
✅ Starts server automatically

🌐 Access the Application

Once started, access:

🖥️ Frontend UI: Open learning-path-advisor/frontend/index.html in browser
🔌 API Endpoint: http://localhost:5000
📖 API Docs: http://localhost:5000/api/docs
📊 Health Check: http://localhost:5000/api/system/health

📋 Prerequisites

python --version  # Should be 3.8 or higher

That's it! No database setup, no complex configuration needed.

📖 Usage Guide

🎬 Getting Your First Learning Path (5 Simple Steps)

Step-by-Step Tutorial

Step 1️⃣: Add Your Current Skills

// Example: You've completed these courses
Completed Courses: ["Python Basics", "HTML & CSS"]

Step 2️⃣: Choose Your Goal

// Example: You want to become a Full Stack Developer
Goal Course: "Full Stack Development"

Step 3️⃣: Select Learning Style

Style	Best For	Example
⚡ Fastest	Quick learners, time-pressed	Skip optional courses
🎯 Easiest	Beginners, confidence builders	Gentle difficulty curve
⚖️ Balanced	Most users	Optimal time/difficulty mix
🚀 Challenging	Advanced learners	Maximum skill development

Step 4️⃣: Get AI-Generated Path

Your Personalized Learning Path:
├─ JavaScript Fundamentals (4 weeks)
├─ Database Basics (3 weeks)
├─ Backend Development (6 weeks)
├─ Frontend Frameworks (5 weeks)
└─ Full Stack Development (8 weeks)

Total: ~6 months | Difficulty: ⭐⭐⭐⭐

Step 5️⃣: Track Your Progress

✅ Mark courses as completed
📊 View progress dashboard
🎯 Adjust path as needed

🎨 Interactive Features

🔍 Course Discovery & Search

Advanced Filtering:

// Filter by category
GET /api/courses-by-category
{ "category": "Programming" }

// Filter by difficulty (1-10)
GET /api/courses-by-difficulty
{ "min_difficulty": 3, "max_difficulty": 7 }

// Search by keywords
Search: "machine learning python"
Results: All ML courses with Python prerequisites

📊 Progress Dashboard

Track your learning journey:

📈 Completion Rate: See % of path completed
⏱️ Time Invested: Track hours spent learning
🏆 Achievements: Unlock milestones
📅 Learning Streak: Daily consistency tracking

🎓 Career Planning

Skill Gap Analysis:

POST /api/skill-gap-analysis
{
  "current_skills": ["Python", "HTML", "CSS"],
  "target_skills": ["Full Stack", "Cloud", "DevOps"]
}

Response:
{
  "missing_skills": ["JavaScript", "Docker", "AWS"],
  "recommended_path": [...],
  "estimated_time": "8 months"
}

🔌 API Documentation

🌟 Popular Endpoints

🎯 Get Learning Path

Request:

POST /api/path
Content-Type: application/json

{
  "start_courses": ["Python Basics"],
  "goal_course": "Machine Learning",
  "learning_style": "balanced",
  "algorithm": "dijkstra"
}

Response:

{
  "success": true,
  "path": ["Python Basics", "Data Structures", "Algorithms", "Machine Learning"],
  "total_time": 240,
  "total_difficulty": 28,
  "estimated_weeks": 16,
  "path_details": [
    {
      "course": "Data Structures",
      "difficulty": 7,
      "time_hours": 60,
      "prerequisites_met": true
    }
  ]
}

💡 Get Course Recommendation

Request:

POST /api/recommend
Content-Type: application/json

{
  "completed_courses": ["Python Basics", "Data Structures"],
  "strategy": "meu",
  "risk_tolerance": 0.5
}

Response:

{
  "success": true,
  "recommendation": {
    "course": "Algorithms",
    "expected_utility": 8.5,
    "confidence": 0.85,
    "reasoning": "High utility, prerequisites met, matches risk profile"
  },
  "alternatives": [
    {"course": "Web Development", "utility": 7.8},
    {"course": "Database Systems", "utility": 7.5}
  ]
}

📚 Get All Courses

Request:

GET /api/all-courses

Response:

{
  "success": true,
  "count": 165,
  "courses": {
    "Python Basics": {
      "difficulty": 3,
      "time_hours": 40,
      "utility": 9,
      "category": "Programming",
      "prerequisites": []
    },
    "Machine Learning": {
      "difficulty": 8,
      "time_hours": 120,
      "utility": 10,
      "category": "AI/ML",
      "prerequisites": ["Data Structures", "Algorithms"]
    }
  }
}

🏥 System Health & Monitoring

Health Check:

GET /api/system/health

Performance Stats:

GET /api/system/performance/summary

Cache Statistics:

GET /api/system/cache/stats

📖 Complete API Reference

Endpoint	Method	Description
`/api/path`	POST	Calculate optimal learning path
`/api/recommend`	POST	Get next course recommendation
`/api/course-details`	POST	Get detailed course information
`/api/path-with-alternatives`	POST	Get path with alternative routes
`/api/course-sequence`	POST	Get recommended course sequence
`/api/available-courses`	POST	Get available courses based on completion
`/api/all-courses`	GET	Get all courses in database
`/api/all-categories`	GET	Get all course categories
`/api/career-path`	POST	Plan career transition path
`/api/skill-gap-analysis`	POST	Analyze skill gaps
`/api/system/health`	GET	System health status
`/api/docs`	GET	Interactive API documentation

📚 Full Documentation: http://localhost:5000/api/docs (when server running)

🛠️ Technology Stack

Backend

Core:

Flask 2.3.3
Python 3.8+
Werkzeug 2.3.7

Features:

Flask-CORS
Flask-Limiter
python-dotenv

Algorithms

Pathfinding:

Dijkstra's Algorithm
A* Search
Breadth-First Search

Decision Making:

Maximum Expected Utility
Minimax Strategy
Hybrid Intelligence

Frontend

Technologies:

Vanilla JavaScript (ES6+)
Responsive CSS
Interactive UI/UX

Features:

State Management
API Client
Real-time Updates

🔒 Security Features

✅ Rate Limiting: Prevents API abuse (100 req/hour default)
✅ CORS Protection: Configurable origin restrictions
✅ Input Validation: Sanitizes and validates all inputs
✅ Error Handling: Graceful error responses
✅ Environment Config: Separate dev/prod configurations

⚡ Performance Features

🚀 Response Caching: Reduces redundant computations
📊 Performance Monitoring: Tracks endpoint response times
🔄 Algorithm Optimization: Efficient pathfinding implementations
💾 Memory Management: Cached results with TTL

🧪 Testing & Quality

✅ 18 Unit Tests: Comprehensive test coverage
✅ 95%+ Coverage: High code coverage
✅ Pytest Framework: Modern testing tools
✅ CI/CD Ready: Automated testing pipeline

📁 Project Structure

goal-planning-aiAgent/
├── learning-path-advisor/
│   ├── backend/
│   │   ├── server.py                 # Flask API server
│   │   ├── enhanced_learning_path_advisor.py
│   │   ├── advanced_goal_based_agent.py
│   │   ├── advanced_utility_based_agent.py
│   │   ├── hybrid_agent.py
│   │   ├── database.py               # Course database
│   │   ├── cache.py                  # Response caching
│   │   ├── performance_monitor.py    # Performance tracking
│   │   └── config.py                 # Configuration
│   └── frontend/
│       ├── index.html                # Main UI
│       ├── script.js                 # Frontend logic
│       ├── styles.css                # Styling
│       ├── api-client.js             # API communication
│       └── state-manager.js          # State management
├── tests/
│   └── test_api.py                   # API tests
├── docs/                             # Documentation
├── setup.py                          # Setup script
├── run.py                            # Run script
├── start.bat                         # Windows launcher
├── start.sh                          # Unix launcher
├── requirements.txt                  # Python dependencies
├── .env.example                      # Environment template
└── README.md                         # This file

⚙️ Configuration

Environment Variables

Create .env file (auto-created by setup script):

# Server Configuration
FLASK_ENV=development           # Options: development, production, testing
API_HOST=0.0.0.0               # Server host
API_PORT=5000                  # Server port

# CORS Configuration
CORS_ORIGINS=*                 # Comma-separated allowed origins
                              # Production example: http://yourdomain.com,https://yourdomain.com

# Rate Limiting
RATE_LIMIT_ENABLED=true        # Enable/disable rate limiting
RATE_LIMIT_DEFAULT=100 per hour # Requests per time window
RATE_LIMIT_STORAGE_URL=memory:// # Storage backend (memory:// or redis://localhost:6379)

# Logging
LOG_LEVEL=INFO                 # Options: DEBUG, INFO, WARNING, ERROR, CRITICAL

# Algorithm Defaults
DEFAULT_PATHFINDING_ALGORITHM=dijkstra  # Options: dijkstra, astar, bfs
DEFAULT_DECISION_STRATEGY=meu          # Options: meu, minimax, evk
DEFAULT_LEARNING_STYLE=balanced        # Options: fastest, easiest, balanced, challenging

Configuration Profiles

🔧 Development (default)

FLASK_ENV=development
LOG_LEVEL=DEBUG
RATE_LIMIT_ENABLED=false

Features:

Debug mode enabled
Detailed logging
No rate limiting
Auto-reload on code changes

🚀 Production

FLASK_ENV=production
LOG_LEVEL=WARNING
RATE_LIMIT_ENABLED=true
CORS_ORIGINS=https://yourdomain.com

Features:

Debug mode disabled
Minimal logging
Rate limiting enabled
Restricted CORS

🧪 Testing

FLASK_ENV=testing
LOG_LEVEL=DEBUG
RATE_LIMIT_ENABLED=false

Features:

Test mode enabled
Full logging
No rate limiting
Isolated test environment

Customizing Algorithms

# In your API request
{
  "algorithm": "astar",           # Use A* instead of Dijkstra
  "strategy": "minimax",          # Use Minimax decision strategy
  "learning_style": "challenging", # Maximize learning value
  "risk_tolerance": 0.7           # Higher risk tolerance
}

🧪 Testing

Running Tests

Basic Test Run:

pytest tests/ -v

Expected Output:

18 passed in 1.23s ✓

With Coverage Report:

pytest tests/ --cov=learning-path-advisor/backend --cov-report=html

View Report:

open htmlcov/index.html

Test Categories

Category	Tests	Description
🔒 Validation	6 tests	Input validation, course existence checks
🌐 API Endpoints	10 tests	All API endpoint functionality
⚠️ Error Handling	2 tests	Exception handling, error responses

Writing Tests

def test_custom_learning_path():
    """Test custom learning path generation"""
    payload = {
        'start_courses': ['Python Basics'],
        'goal_course': 'AI Engineer',
        'learning_style': 'challenging'
    }
    response = client.post('/api/path', json=payload)
    
    assert response.status_code == 200
    assert 'path' in response.json
    assert len(response.json['path']) > 0

Continuous Integration

# Example GitHub Actions workflow
name: Tests
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Run tests
        run: pytest tests/ -v

🤝 Contributing

We welcome contributions! Here's how to get started:

Quick Contribution Guide

🍴 Fork the Repository

git clone https://github.com/YOUR_USERNAME/goal-planning-aiAgent.git
cd goal-planning-aiAgent

🌿 Create a Branch
```
git checkout -b feature/amazing-feature
```
✨ Make Your Changes
- Write clean, documented code
- Follow existing code style
- Add tests for new features
✅ Test Your Changes
```
pytest tests/ -v
```
📤 Submit Pull Request
- Clear description of changes
- Link to related issues
- Screenshots for UI changes

Contribution Ideas

🎯 For Beginners

📝 Improve documentation
🐛 Fix typos
✍️ Write tutorials
🌐 Add translations
🎨 Improve UI/UX

🚀 For Experienced Developers

⚡ Performance optimizations
🤖 New AI algorithms
🔒 Security enhancements
📊 Advanced visualizations
🧪 Increase test coverage

📖 Full Guidelines: See CONTRIBUTING.md for detailed instructions.

Development Setup

# Setup development environment
python setup.py

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

# Install dev dependencies
pip install -r requirements.txt

# Run in development mode
python run.py

Code of Conduct

Be respectful and inclusive
Welcome newcomers
Provide constructive feedback
Follow the Contributor Covenant

📝 License

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

What This Means

✅ You CAN:

✓ Use commercially
✓ Modify the code
✓ Distribute
✓ Use privately

❌ You CANNOT:

✗ Hold authors liable
✗ Use trademark

📋 You MUST:

Include copyright notice
Include license text

🌟 Showcase

Built With Learning Path Advisor

Add your project here! Submit a PR to showcase how you're using this platform.

📊 Project Stats

📈 Development Activity

Total Courses: 165+
AI Algorithms: 6 (3 pathfinding + 3 decision)
Test Coverage: 95%+
API Endpoints: 15+
Code Lines: ~5,000
Documentation Pages: 3

🙏 Acknowledgments

Algorithms & Research

Dijkstra's Algorithm: Edsger W. Dijkstra (1956)
A Search*: Peter Hart, Nils Nilsson, Bertram Raphael (1968)
Decision Theory: Based on classical AI and game theory principles

Inspiration

Course databases inspired by industry-standard learning paths
UI/UX influenced by modern educational platforms
API design following RESTful best practices

Contributors

Thanks to all contributors who help improve this project! 🎉
See CONTRIBUTORS.md for full list

📞 Support & Community

📖 Documentation

View Docs

Interactive API documentation with examples

💬 Discussions

Join Discussion

Ask questions, share ideas

🐛 Issues

Report Bug

Found a bug? Let us know!

✨ Features

Request Feature

Have an idea? We'd love to hear it!

Getting Help

Check Documentation: Most questions are answered in docs/
Search Issues: Someone might have had the same question
Ask the Community: Open a discussion
Report Bugs: Use issue templates

🗺️ Roadmap

Current Version: 1.0.0

✅ Completed Features

Multiple pathfinding algorithms (Dijkstra, A*, BFS)
Decision strategies (MEU, Minimax, EVK)
165+ course database
RESTful API with documentation
Rate limiting and caching
Performance monitoring
Comprehensive testing
Interactive frontend UI

🚧 In Progress

User authentication system
Progress persistence (database)
Mobile app (React Native)
Course recommendation ML model

🔮 Future Plans

Integration with learning platforms (Coursera, Udemy, etc.)
Social features (share paths, compare progress)
Gamification enhancements
Multi-language support
Advanced analytics dashboard
AI-powered course difficulty prediction
Personalized learning speed adjustments

Version History

Version	Date	Highlights
1.0.0	2024	Initial release with core features

💡 FAQ

How accurate are the learning paths?

Learning paths are generated using industry-standard algorithms and curated course data. The accuracy depends on:

Correct prerequisite mapping
Accurate course difficulty ratings
Your learning style selection

Paths are optimized but can be adjusted based on your personal experience.

Can I add my own courses?

Currently, the course database is pre-loaded. Custom course addition is planned for future releases. You can modify database.py for custom deployments.

Is this suitable for corporate training?

Yes! The platform can be customized for:

Corporate training programs
Bootcamp curricula
University course planning
Personal skill development

Contact for enterprise licensing.

How do I deploy to production?

See DEPLOYMENT_CHECKLIST.md for:

Production configuration
Server setup (Gunicorn, Nginx)
Security hardening
Monitoring setup

Can I use this offline?

Yes! Once set up:

Frontend works without internet
Backend runs locally
No external API calls required

All algorithms and data are self-contained.

⭐ Star History

If you find this project helpful, please consider giving it a star!

Made with ❤️ by the Learning Path Advisor Team

Learning Path Advisor Pro - Where AI meets education for your career success ✨

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Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
docs		docs
learning-path-advisor		learning-path-advisor
tests		tests
.env.example		.env.example
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt
run.py		run.py
setup.py		setup.py
start.bat		start.bat
start.sh		start.sh

License

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🚀 Learning Path Advisor Pro

AI-Powered Career Development Platform

📊 At a Glance

🎯 Why Choose Learning Path Advisor Pro?

🔄 Career Changers

🚀 Career Advancers

🎯 Skill Seekers

🌟 Key Features

🤖 Advanced AI Decision Making

📚 Comprehensive Learning Ecosystem

🎨 Modern User Experience

🎯 Personalized Learning Paths

🚀 Quick Start

🪟 Windows

🐧 Linux/macOS

🌐 Access the Application

📋 Prerequisites

📖 Usage Guide

🎬 Getting Your First Learning Path (5 Simple Steps)

Step 1️⃣: Add Your Current Skills

Step 2️⃣: Choose Your Goal

Step 3️⃣: Select Learning Style

Step 4️⃣: Get AI-Generated Path

Step 5️⃣: Track Your Progress

🎨 Interactive Features

🔌 API Documentation

🌟 Popular Endpoints

📖 Complete API Reference

🛠️ Technology Stack

Backend

Algorithms

Frontend

🔒 Security Features

⚡ Performance Features

🧪 Testing & Quality

📁 Project Structure

⚙️ Configuration

Environment Variables

Configuration Profiles

Customizing Algorithms

🧪 Testing

Running Tests

Test Categories

Writing Tests

Continuous Integration

🤝 Contributing

Quick Contribution Guide

Contribution Ideas

Development Setup

Code of Conduct

📝 License

What This Means

🌟 Showcase

Built With Learning Path Advisor

📊 Project Stats

🙏 Acknowledgments

Algorithms & Research

Inspiration

Contributors

📞 Support & Community

📖 Documentation

💬 Discussions

🐛 Issues

✨ Features

Getting Help

🗺️ Roadmap

Current Version: 1.0.0

Version History

💡 FAQ

⭐ Star History

Made with ❤️ by the Learning Path Advisor Team

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