alphajob/
├── src/                          # Source code
│   ├── config/                   # Configuration management
│   ├── models/                   # Data models
│   ├── services/                 # Business logic services
│   ├── handlers/                 # Form and field handlers
│   ├── utils/                    # Utility functions
├── data/                         # Runtime data (gitignored)
├── scripts/                      # Utility scripts
├── tests/                        # Test suite

AI Job Bidder - Automated Job Application System

An intelligent job application automation system that uses AI to tailor resumes and fill out application forms automatically.

🏗️ Architecture

Project Structure

alphajob/
├── src/                          # Source code
│   ├── config/                   # Configuration management
│   ├── models/                   # Data models
│   ├── services/                 # Business logic services
│   ├── handlers/                 # Form and field handlers
│   ├── utils/                    # Utility functions
├── data/                         # Runtime data (gitignored)
├── scripts/                      # Utility scripts
├── tests/                        # Test suite

🚀 Features

• AI-Powered Resume Tailoring: Automatically customizes resumes for each job using Ollama LLM
• Intelligent Form Filling: Smart form field detection and filling with context-aware responses
• Browser Automation: Automated application submission using Playwright
• Multi-Format Support: Handles DOCX and TXT resume formats
• Configuration Management: Centralized configuration with environment variable support
• Modular Architecture: Clean separation of concerns for easy maintenance and testing

📋 Prerequisites

• Python 3.9+
• Ollama installed and running locally
• Playwright browsers installed

🔧 Installation

Clone the repository

git clone https://github.com/cloudhighfive/alphajob.git
cd alphajob

Create virtual environment

python -m venv venv
source venv/bin/activate  # On macOS/Linux
# OR
venv\\Scripts\\activate    # On Windows

Install dependencies

pip install -r requirements.txt

Install Playwright browsers

playwright install

Setup configuration

cp config.json.example config.json
cp .env.example .env

Edit config.json with your information:

• Personal details
• Work experience
• Skills
• Preferences

🎯 Usage

Basic Usage

python main.py

Using the Refactored Architecture

from src.config import get_settings
from src.services import AIService, BrowserService, ResumeService

# Load configuration
settings = get_settings()

# Initialize services
ai_service = AIService(settings)
browser_service = BrowserService(settings)
resume_service = ResumeService(settings)

# Use services
tailored_resume = resume_service.tailor_for_job(job_description, job_title, company)

📝 Configuration

config.json

The main configuration file contains:

{
"user_info": {
"personal_info": {...},
"links": {...},
"work_authorization": {...},
"demographics": {...},
"background": {...},
"preferences": {...},
"files": {...}
},
"ai_settings": {
"model": "llama3.1",
"temperature": 0.7,
"tone": "professional and enthusiastic"
},
"prompts": {...}
}

Environment Variables (.env)

DEBUG=false
LOG_LEVEL=INFO
MAX_RETRIES=3
TIMEOUT=30

🏛️ Architecture Principles

Separation of Concerns

• Config Layer: Centralized configuration management
• Model Layer: Data structures and validation
• Service Layer: Business logic and external integrations
• Handler Layer: Request/response handling
• Utility Layer: Shared helper functions

Design Patterns

• Dependency Injection: Services receive dependencies via constructor
• Factory Pattern: Configuration factory for creating settings
• Repository Pattern: Data access abstraction
• Strategy Pattern: Multiple strategies for form field handling

Best Practices

1. Type Hints: All functions have type annotations
2. Pydantic Models: Strong validation for configuration and data
3. Logging: Centralized logging with proper levels
4. Error Handling: Comprehensive error handling with meaningful messages
5. Documentation: Docstrings for all public functions and classes
6. Testing: Unit tests for critical components

🧪 Testing

# Run all tests
pytest tests/

# Run with coverage
pytest --cov=src tests/

# Run specific test file
pytest tests/test_ai_service.py

🔄 Migration from Legacy Code

The project has been refactored from a monolithic 2359-line script to a modular architecture. Key changes:

1. Configuration: Moved from manual dict loading to Pydantic models with validation
2. AI Integration: Extracted into AIService with clean interface
3. Browser Automation: Isolated in BrowserService with proper encapsulation
4. Resume Handling: Centralized in ResumeService
5. Form Handling: Modularized into handler classes

Backwards Compatibility

The legacy ai_job_bidder.py is still available but deprecated. New code should use the modular src/ structure.

📦 Dependencies

Key dependencies:

• pydantic - Data validation and settings management
• pydantic-settings - Settings from environment variables
• ollama - AI/LLM integration
• playwright - Browser automation
• python-docx - DOCX file handling
• beautifulsoup4 - HTML parsing
• python-dotenv - Environment variable management

🤝 Contributing

1. Follow the established architecture patterns
2. Add tests for new features
3. Update documentation
4. Use type hints
5. Follow PEP 8 style guide

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Note: This README was generated by AI to provide a quick overview of the project.