Professional Background Generator

A web application that generates comprehensive Professional Background sections for CVs using AI. This tool helps professionals create compelling career narratives that showcase their experience, achievements, and competencies.

Features

• PDF & Markdown Support: Upload CVs in PDF or Markdown format
• AI-Powered Generation: Uses OpenAI GPT-3.5 to create professional background sections
• Smart Formatting: Automatically converts PDF text to clean Markdown
• Copy to Clipboard: Easy copying of generated content
• Real-time Processing: Fast generation with progress feedback
• Clean Interface: Simple, user-friendly web interface

What is a Professional Background?

A Professional Background section is a comprehensive narrative (300-400 words) that tells the story of your career journey. It includes:

• Career Overview: Years of experience, industry specialization, and career level
• Career Progression: Key roles, companies, promotions, and responsibility evolution
• Achievements & Impact: Major accomplishments with quantifiable results
• Professional Competencies: Technical skills, methodologies, and leadership qualities

This section is perfect for senior professionals, executives, or anyone wanting to showcase their career narrative in detail.

Architecture

Backend (Python/Flask)

backend/
├── app_backend.py      # Flask application and API routes
├── cv_processor.py     # CV processing and AI operations
├── openai_client.py    # OpenAI API client wrapper
├── utils.py            # Utility functions (text processing)
├── requirements.txt    # Python dependencies
└── Dockerfile          # Backend containerization

Frontend (HTML/CSS/JavaScript)

frontend/
├── index.html          # Main application page
├── background.js       # Application logic and API calls
├── styles.css          # Styling and layout
├── nginx.conf          # Nginx configuration
└── Dockerfile          # Frontend containerization

Technology Stack

Backend

• Python 3.11+
• Flask - Web framework
• Flask-CORS - Cross-origin resource sharing
• OpenAI API - GPT-3.5 Turbo for text generation
• PyPDF2 - PDF text extraction
• python-dotenv - Environment variable management

Frontend

• HTML5/CSS3 - Structure and styling
• JavaScript (ES6+) - Application logic
• Marked.js - Markdown rendering
• Nginx - Web server (in Docker)

Installation

Prerequisites

• Python 3.11 or higher
• Node.js (optional, for development)
• OpenAI API key
• Docker & Docker Compose (for containarerized deployment)

Development and run

docker-compose up -d

Services:

• Backend: http://localhost:5000
• Frontend: http://localhost:8181

API Endpoints

Health Check

GET /api/health

Returns service health status.

Upload CV

POST /api/upload-cv
Content-Type: multipart/form-data

file: <PDF or Markdown file>

Response:

{
  "cv_text": "extracted CV content...",
  "file_type": "pdf",
  "filename": "example.pdf",
  "character_count": 5420,
  "converted_to_markdown": true,
  "message": "CV successfully uploaded from PDF file and converted to Markdown"
}

Generate Professional Background

POST /api/generate-professional-background
Content-Type: application/json

{
  "user_cv": "Complete CV content as text..."
}

Response:

{
  "professional_background": "Generated markdown content...",
  "processing_time": 3.45,
  "message": "Professional background generated successfully"
}

Usage

1. Upload or Paste CV

   • Click "Upload CV File" to upload PDF or Markdown
   • Or paste CV content directly in the text area

2. Generate Background

   • Click "Generate Professional Background"
   • Wait for AI processing (typically 3-5 seconds)

3. Copy & Use

   • Review the generated content
   • Click "Copy to Clipboard" or copy the raw markdown
   • Add to your CV, typically near the beginning

Project Structure

Code Organization

• app_backend.py: Flask routes, request handling, and validation
• cv_processor.py: AI prompt engineering and CV processing logic
• openai_client.py: OpenAI API communication wrapper
• utils.py: Reusable utility functions (text processing, formatting)

Key Classes

• CVProcessor: Manages CV processing and AI operations
• OpenAIClient: Handles OpenAI API calls with retry logic
• TextUtils: Provides text processing utilities

Configuration

Backend Configuration

Edit backend/app_backend.py to customize:

• Server host and port
• API rate limits
• Maximum CV length (default: 25,000 characters)
• Minimum CV length (default: 100 characters)

AI Configuration

Edit backend/openai_client.py to customize:

• Model selection (default: gpt-3.5-turbo)
• Temperature settings (creativity level)
• Token limits (response length)

Development

Running Tests

cd backend
pytest

Code Style

The project follows PEP 8 style guidelines. Use formatters:

black backend/
flake8 backend/

Adding New Features

1. Add utility functions to utils.py
2. Add AI operations to cv_processor.py
3. Add API endpoints to app_backend.py
4. Update frontend in background.js

Troubleshooting

Common Issues

"OPENAI_API_KEY not found"

• Ensure .env file exists with valid API key
• Check environment variable is loaded: echo $OPENAI_API_KEY

"CV content is too short"

• Provide at least 100 characters of CV content
• Include detailed experience and education information

"Failed to process uploaded file"

• Ensure PDF is not encrypted or password-protected
• Try converting PDF to text manually first

CORS errors

• Ensure backend is running on port 5000
• Check Flask-CORS is properly configured

Performance

• Average Generation Time: 3-5 seconds
• Maximum CV Length: 25,000 characters
• Concurrent Requests: Supports multiple simultaneous requests
• Rate Limiting: Respects OpenAI API rate limits

Security

• API keys stored in environment variables (never committed)
• Input validation and sanitization
• File upload restrictions (PDF and Markdown only)
• Content length limits to prevent abuse
• CORS configured for specific origins

License

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

Contributing

Contributions are welcome! Please:

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

Support

For issues, questions, or suggestions:

• Open an issue on GitHub
• Contact the development team

Acknowledgments

• OpenAI for GPT-3.5 API
• Flask community for excellent documentation
• All contributors to this project

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Built with ❤️ for professionals looking to enhance their CVs