Building intelligent solutions with Python, Django, React, and cutting-edge AI technologies
Welcome to my comprehensive Python portfolio showcasing progressive mastery from fundamental programming concepts to production-ready full-stack applications and AI-powered enterprise solutions.
This repository demonstrates my journey through 14 structured learning modules and 2 production-scale projects, encompassing:
- 🎯 50+ Python programs ranging from basic algorithms to complex systems
- 🤖 AI-Powered HR Management System with Llama 3 integration
- ⚡ Full-Stack Django-React Application with JWT authentication
- 🔧 Enterprise-grade architecture and best practices
- 🧪 Comprehensive testing with pytest and modern testing frameworks
A cutting-edge HR platform leveraging Meta's Llama 3 70B for intelligent candidate screening
- AI Integration: Real-time candidate shortlisting using Groq API + Llama 3 70B
- Job Management: Complete CRUD operations for HR personnel
- Application Tracking: Comprehensive applicant workflow system
- File Management: Secure CV upload and processing with PDF validation
- Responsive Design: Mobile-optimized Bootstrap 5 interface
# AI-powered candidate evaluation using Llama 3
def evaluate_candidate(applicant, job_requirements):
prompt = f"Evaluate this candidate for {job_requirements}"
response = groq_client.chat.completions.create(
model="llama3-70b-8192",
messages=[{"role": "user", "content": prompt}]
)
return parse_ai_response(response)- Backend: Django 4.2, Django ORM
- AI/ML: Groq API, Meta Llama 3 70B
- Frontend: Bootstrap 5, Purple Admin Template
- Database: SQLite (dev), PostgreSQL ready (prod)
- Testing: pytest, pytest-django
- Environment-based configuration
- Secure file upload handling
- Database migrations and model relationships
- Admin interface for HR operations
- RESTful API design patterns
Modern SPA with secure JWT authentication and scalable architecture
- JWT Token System: Access/refresh token rotation
- Password Security: Django's built-in hashing
- CORS Configuration: Secure cross-origin requests
- Input Validation: Comprehensive data sanitization
// React frontend with secure API integration
const api = axios.create({
baseURL: 'http://localhost:8000/api',
headers: {
'Authorization': `Bearer ${getAccessToken()}`
}
});
// Automatic token refresh
api.interceptors.response.use(
response => response,
async error => {
if (error.response?.status === 401) {
await refreshAccessToken();
return api.request(error.config);
}
return Promise.reject(error);
}
);- Backend: Django REST Framework, Simple JWT
- Frontend: React 18, Vite, React Router
- UI: Tailwind CSS, Shadcn/ui, Lucide icons
- Testing: Vitest, React Testing Library
- Database: PostgreSQL production-ready
/api/user/register/- Secure user registration/api/token/- JWT token authentication/api/token/refresh/- Token rotation system- Protected routes with middleware authentication
| Project | Skills Demonstrated | Complexity |
|---|---|---|
| Printing & Variables | String formatting, type conversion, f-strings | ⭐ |
| Data Types & Type System | Primitive types, operators, type casting | ⭐⭐ |
| Tip Calculator | Mathematical operations, user input, business logic | ⭐⭐ |
| Treasure Island Game | Conditional logic, control flow, game development | ⭐⭐⭐ |
| Rock-Paper-Scissors | Randomization, arrays, game logic, ASCII art | ⭐⭐⭐ |
| Password Generator | Loops, string manipulation, security concepts | ⭐⭐⭐ |
| Project | Technical Skills | Applications |
|---|---|---|
| Hangman Game | ASCII art, word lists, game state management | Game Development |
| Caesar Cipher | Encryption/decryption, modular arithmetic, text processing | Cryptography |
| Dictionary Mastery | Data structures, JSON-like operations, complex data handling | Data Management |
| Function Design | Return values, scope management, modular programming | Software Architecture |
| Testing & Debugging | pytest, error handling, code validation | Quality Assurance |
| Scope Management | Local/global variables, memory management, best practices | System Design |
- Clean Code Principles: PEP 8 compliance, descriptive naming, modular design
- Testing Methodology: Unit tests, integration tests, TDD concepts
- Version Control: Git workflow, branching strategies, collaborative development
- Documentation: Comprehensive READMEs, code comments, API documentation
- MVC/MVT Patterns: Django's Model-View-Template architecture
- RESTful Design: Proper HTTP methods, status codes, resource modeling
- Database Design: Relational modeling, migrations, ORM optimization
- Security Implementation: Authentication, authorization, data validation
- Environment Management: Virtual environments, dependency isolation
- Configuration Management: Environment variables, settings separation
- API Development: REST standards, serialization, error handling
- Frontend Integration: CORS, JWT, modern JavaScript/React patterns
- LLM Integration: Groq API, prompt engineering, response parsing
- Real-time Processing: Asynchronous operations, error handling
- Data Processing: File handling, PDF processing, text analysis
- Scalable AI Architecture: Modular design for AI features
Python: 3.8+
Django: 4.2
Django REST Framework: Latest
PostgreSQL: Production Ready
SQLite: Development
JWT: Simple JWT
Testing: pytest, pytest-djangoReact: 18.2
Vite: Latest
Tailwind CSS: 3.4+
Shadcn/ui: Modern component library
Axios: HTTP client
React Router: Client-side routing
Testing: Vitest, React Testing LibraryLLM: Meta Llama 3 70B
API: Groq Cloud Platform
Prompt Engineering: Custom implementations
Text Processing: NLP fundamentalsVersion Control: Git
Environment: Python venv
Package Management: pip, npm
Code Quality: ESLint, PEP 8
Debugging: Django Debug Toolbar- 50+ Python programs with increasing complexity
- 2 production-ready applications with enterprise features
- Comprehensive test coverage across all major projects
- Documentation-first approach with detailed READMEs
- Multi-tier architecture (Frontend, Backend, AI services)
- Real-time AI integration with production APIs
- Security-first implementation with JWT and data validation
- Scalable design patterns for enterprise applications
- Systematic skill building from basics to advanced concepts
- Project-based learning with real-world applications
- Modern tech stack aligned with industry standards
- Continuous improvement with refactoring and optimization
# Python Environment
Python 3.8+
pip (latest)
virtualenv
# Node.js Environment (for React projects)
Node.js 16+
npm or yarn# Clone the repository
git clone https://github.com/DanielGeek/python_projects.git
cd python_projects
# Explore daily exercises
ls 0*-day-*/
# Run flagship projects
cd 13-AIHR # AI-Powered HR System
# or
cd 14-Django-React-Full-Stack-App # Full-Stack Appcd 13-AIHR
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver# Backend
cd 14-Django-React-Full-Stack-App/backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver
# Frontend (new terminal)
cd ../frontend
npm install
npm run dev# Django Projects
pytest # AIHR Project
python manage.py test # Django-React Backend
# React Frontend
npm test # Frontend unit tests
npm run test:coverage # Coverage reports- PEP 8 Compliance: All Python code follows style guidelines
- ESLint Configuration: JavaScript/React code quality
- Type Safety: PropTypes and TypeScript considerations
- Documentation: Comprehensive inline and external documentation
- Full-Stack Proficiency: End-to-end application development
- Modern Architecture: Microservices, REST APIs, SPAs
- AI Integration: Practical LLM implementation in production
- Security-First: Authentication, authorization, data protection
- AI-Powered Solutions: Real-world AI integration beyond demos
- Scalable Design: Enterprise-ready architecture patterns
- User-Centric Approach: Responsive design, UX considerations
- Performance Optimization: Efficient database queries, frontend optimization
- Continuous Learning: Progressive skill development
- Adaptability: Multiple tech stacks and paradigms
- Best Practices: Industry-standard development workflows
- Collaboration Ready: Clean code, documentation, version control
🔗 Live Demonstrations Available Upon Request
📧 Contact for recruitment opportunities and technical discussions
🌟 Open to challenging roles in Full-Stack Development, AI Engineering, and Backend Systems
All projects are open source and available under the MIT License - see individual project licenses for details.
💡 Note: This portfolio represents a journey of continuous learning and technical growth. Each project builds upon previous knowledge, demonstrating the ability to master new technologies and apply them to solve real-world problems.
🚀 Ready to bring this expertise to your team!