OpenAI Document Analyzer

A powerful Python application for analyzing text and PDF files using OpenAI's latest chat completion models. This tool allows you to ask questions about documents using customizable prompts and examples.

Features

• 📄 Support for both text (.txt) and PDF (.pdf) files
• 🤖 Compatible with all OpenAI chat models (GPT-4o, GPT-4o-mini, GPT-4 Turbo, GPT-3.5 Turbo)
• 🔄 Dynamic model switching during runtime
• 📝 Customizable prompts and examples for context
• 🛡️ Robust error handling and input validation
• 🎯 PEP8 compliant code with type hints

Requirements

• Python 3.8+
• OpenAI API key

Installation

Quick Setup

1. Clone the repository:

git clone https://github.com/ddtdanilo/OpenAI-Document-Analyzer.git
cd OpenAI-Document-Analyzer

2. Run the setup script:

python3 setup.py

This will:

• Create a virtual environment (optional)
• Install all dependencies
• Create a .env file from the template

3. Add your OpenAI API key to the .env file:

OPENAI_API_KEY=your_api_key_here

Manual Setup

1. Create a virtual environment:

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Configure environment:

cp env.example .env
# Edit .env and add your OpenAI API key

Usage

Basic Usage

python scripts/text_analysis.py examples/example_prompt.txt examples/example_response.txt examples/example_text_to_analyze.txt

Using Different Models

You can specify the default model in your .env file:

OPENAI_MODEL=gpt-4o-mini  # or gpt-4-turbo, gpt-3.5-turbo, etc.

Or switch models interactively during runtime by typing model at the prompt.

Available Commands

• Type your question to analyze the text
• Type model to change the AI model
• Type exit to quit

Available Models

Model	Description
gpt-4o	Latest and most capable model (default)
gpt-4o-mini	Smaller, faster, and more affordable GPT-4o
gpt-4-turbo	High performance model with vision capabilities
gpt-4-turbo-preview	Preview version of GPT-4 Turbo
gpt-3.5-turbo	Fast and efficient for most tasks
gpt-3.5-turbo-16k	Extended context window version

Project Structure

OpenAI-Document-Analyzer/
├── scripts/
│   ├── __init__.py             # Package initialization
│   ├── text_analysis.py       # Main application script
│   └── document_analyzer.py   # DocumentAnalyzer class
├── tests/
│   ├── __init__.py             # Tests package initialization
│   ├── conftest.py             # Pytest configuration and fixtures
│   ├── test_document_analyzer.py  # Unit tests
│   └── test_data/              # Test data directory
├── examples/
│   ├── example_prompt.txt      # Sample prompt
│   ├── example_response.txt    # Sample response
│   └── example_text_to_analyze.txt  # Sample text to analyze
├── .github/
│   └── workflows/
│       ├── tests.yml           # Main CI/CD pipeline
│       ├── coverage-badge.yml  # Auto-generated coverage badge
│       └── release.yml         # Automated releases
├── requirements.txt            # Python dependencies
├── package.json               # Semantic release configuration
├── setup.py                   # Setup script
├── test_setup.py              # Installation verification script
├── run_tests.py               # Test runner script
├── env.example                # Environment template
├── CHANGELOG.md               # Auto-generated changelog
└── README.md                  # This file

Dependencies

• openai>=1.0.0 - OpenAI Python client library
• pypdf>=3.17.0 - PDF processing library
• python-dotenv>=1.0.0 - Environment variable management

API Reference

load_text(filepath: str) -> str

Load text content from a file (supports .txt and .pdf formats).

ask_questions(prompt: str, example_prompt: str, example_response: str, text_to_analyze: str, model: Optional[str] = None) -> str

Generate AI responses based on the provided context and prompt.

Error Handling

The application includes comprehensive error handling for:

• Missing or invalid files
• Unsupported file formats
• API errors and rate limits
• Invalid model selections

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Guidelines

1. Follow PEP8 style guide
2. Add type hints to all functions
3. Include docstrings for all modules and functions
4. Write tests for new features
5. Update documentation as needed

License

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

Acknowledgments

• OpenAI for providing the powerful language models
• Contributors and users of this project

Support

For issues, questions, or contributions, please visit the GitHub repository.

Running Tests

The project includes a comprehensive test suite using pytest. Multiple options available:

Quick Test Run

If you have virtual environment activated:

./venv/bin/python3 run_tests.py

Or using pytest directly:

pytest tests/ -v

Manual Test Commands

1. Install test dependencies:

pip install -r requirements.txt

2. Run basic tests:

pytest tests/

3. Run tests with detailed output:

pytest tests/ -v

4. Run tests with coverage report:

pytest tests/ --cov=scripts/ --cov-report=term-missing

5. Run specific test file:

pytest tests/test_document_analyzer.py -v

Continuous Integration

🚀 Automated Workflows:

• ✅ Tests - Run automatically on push/PR across Python 3.8, 3.9, 3.10
• 📊 Coverage - Auto-generated badge + Coveralls integration
• 🏷️ Releases - Semantic versioning with automatic changelog generation

Coverage Tracking:

• 🏷️ Auto-generated badge - Updates automatically on each push
• 📊 Coveralls integration - Detailed coverage reports and trends

Test Coverage

Current test coverage includes:

• DocumentAnalyzer class initialization
• Text analysis functionality (mocked)
• File loading (both .txt and .pdf)
• Error handling for invalid files
• PDF text extraction (mocked)
• Complete document analysis workflow

Releases

This project uses semantic versioning with automated releases:

Commit Convention

Use conventional commits for automatic version bumping:

feat: add new document analysis feature    # → Minor version bump (1.1.0)
fix: resolve PDF parsing issue            # → Patch version bump (1.0.1)  
docs: update README                       # → No version bump
chore: update dependencies                # → No version bump

BREAKING CHANGE: remove deprecated API    # → Major version bump (2.0.0)

Automatic Release Process

1. Push to main → Triggers release workflow
2. Analyze commits → Determines version bump type
3. Generate changelog → Based on commit messages
4. Create release → Automatic GitHub release with notes
5. Update badges → Coverage and release status

Release Outputs

• 📋 CHANGELOG.md - Automatically generated and maintained
• 🏷️ Git tags - Semantic version tags (v1.0.0, v1.1.0, etc.)
• 📦 GitHub Releases - With auto-generated release notes