# FireSense
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
FireSense is an AI-powered fire detection system that uses the Gemma 3N E4B vision model to analyze video content for fire and smoke detection. It provides real-time analysis, comprehensive fire characteristics assessment, and emergency response recommendations.
## Features
- 🚀 **Fast Development**: Leverages uv for 10-100x faster dependency installation
- 📦 **Modern Packaging**: PEP 621 compliant with pyproject.toml
- 🔍 **Type Safety**: Full mypy strict mode support
- ✅ **Testing**: Comprehensive pytest setup with coverage
- 🎨 **Code Quality**: Pre-configured with ruff, black, and pre-commit
- 📚 **Documentation**: Ready for MkDocs with Material theme
- 🔄 **CI/CD**: GitHub Actions workflow included
## Quick Start
### Prerequisites
- Python 3.11 or higher
- [uv](https://github.com/astral-sh/uv) package manager
### Installation
#### From PyPI (Recommended)
```bash
pip install firesense
```
#### From Source
1. Clone the repository:
```bash
git clone https://github.com/gregorymulla/firesense_ai.git
cd firesense_ai
```
2. Install with pip:
```bash
pip install -e ".[dev]"
```
#### Using uv (Fastest)
1. Install uv:
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
2. Install firesense:
```bash
uv pip install firesense
```
## Usage
### Running the Application
```bash
# Analyze a video file
firesense analyze video.mp4
# Analyze with custom settings
firesense analyze video.mp4 --interval 1.0 --confidence 0.8
# Preview frame extraction
firesense preview video.mp4 --frames 10
# Launch demo UI
firesense demo wildfire_example_01
# Process multiple videos
firesense batch /path/to/videos --pattern "*.mp4"
```
### Development Commands
```bash
# Run tests
make test
# Run linting
make lint
# Format code
make format
# Type check
make type-check
# Run all checks
make check
# Build documentation
make docs
# Clean build artifacts
make clean
```
## Project Structure
```
firesense/
├── src/gemma_3n/ # Source code
│ └── fire_detection/ # Fire detection system
│ ├── models/ # Data models and AI interface
│ ├── processing/ # Video and frame processing
│ └── vision/ # Computer vision utilities
├── tests/ # Test suite
│ ├── unit/ # Unit tests
│ └── integration/ # Integration tests
├── docs/ # Documentation
├── scripts/ # Utility scripts
└── .github/ # GitHub Actions
```
## Configuration
The application can be configured using environment variables with the `GEMMA_` prefix:
```bash
export GEMMA_DEBUG=true
export GEMMA_API_PORT=9000
export GEMMA_LOG_LEVEL=DEBUG
```
Or using a `.env` file:
```env
GEMMA_DEBUG=true
GEMMA_API_PORT=9000
GEMMA_LOG_LEVEL=DEBUG
```
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Run tests and checks (`make check`)
5. Commit your changes (`git commit -m 'Add amazing feature'`)
6. Push to the branch (`git push origin feature/amazing-feature`)
7. Open a Pull Request
## Releasing
To publish a new release to PyPI, simply push a commit to the main branch with a message starting with "new release" followed by the version number:
```bash
git commit -m "new release 0.3.0"
git push origin main
```
The GitHub Actions workflow will automatically:
1. Extract the version from the commit message
2. Update the version in `pyproject.toml` and `__init__.py`
3. Build and publish the package to PyPI
4. Create a git tag
5. Create a GitHub release
**Note**: Make sure you have set up the `PYPI_API_TOKEN` secret in your GitHub repository settings.
## License
This project is licensed under the MIT License - see the LICENSE file for details.
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"description": "# FireSense\n\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/MIT)\n\nFireSense is an AI-powered fire detection system that uses the Gemma 3N E4B vision model to analyze video content for fire and smoke detection. It provides real-time analysis, comprehensive fire characteristics assessment, and emergency response recommendations.\n\n## Features\n\n- \ud83d\ude80 **Fast Development**: Leverages uv for 10-100x faster dependency installation\n- \ud83d\udce6 **Modern Packaging**: PEP 621 compliant with pyproject.toml\n- \ud83d\udd0d **Type Safety**: Full mypy strict mode support\n- \u2705 **Testing**: Comprehensive pytest setup with coverage\n- \ud83c\udfa8 **Code Quality**: Pre-configured with ruff, black, and pre-commit\n- \ud83d\udcda **Documentation**: Ready for MkDocs with Material theme\n- \ud83d\udd04 **CI/CD**: GitHub Actions workflow included\n\n## Quick Start\n\n### Prerequisites\n\n- Python 3.11 or higher\n- [uv](https://github.com/astral-sh/uv) package manager\n\n### Installation\n\n#### From PyPI (Recommended)\n\n```bash\npip install firesense\n```\n\n#### From Source\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/gregorymulla/firesense_ai.git\ncd firesense_ai\n```\n\n2. Install with pip:\n```bash\npip install -e \".[dev]\"\n```\n\n#### Using uv (Fastest)\n\n1. Install uv:\n```bash\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n```\n\n2. Install firesense:\n```bash\nuv pip install firesense\n```\n\n## Usage\n\n### Running the Application\n\n```bash\n# Analyze a video file\nfiresense analyze video.mp4\n\n# Analyze with custom settings\nfiresense analyze video.mp4 --interval 1.0 --confidence 0.8\n\n# Preview frame extraction\nfiresense preview video.mp4 --frames 10\n\n# Launch demo UI\nfiresense demo wildfire_example_01\n\n# Process multiple videos\nfiresense batch /path/to/videos --pattern \"*.mp4\"\n```\n\n### Development Commands\n\n```bash\n# Run tests\nmake test\n\n# Run linting\nmake lint\n\n# Format code\nmake format\n\n# Type check\nmake type-check\n\n# Run all checks\nmake check\n\n# Build documentation\nmake docs\n\n# Clean build artifacts\nmake clean\n```\n\n## Project Structure\n\n```\nfiresense/\n\u251c\u2500\u2500 src/gemma_3n/ # Source code\n\u2502 \u2514\u2500\u2500 fire_detection/ # Fire detection system\n\u2502 \u251c\u2500\u2500 models/ # Data models and AI interface\n\u2502 \u251c\u2500\u2500 processing/ # Video and frame processing\n\u2502 \u2514\u2500\u2500 vision/ # Computer vision utilities\n\u251c\u2500\u2500 tests/ # Test suite\n\u2502 \u251c\u2500\u2500 unit/ # Unit tests\n\u2502 \u2514\u2500\u2500 integration/ # Integration tests\n\u251c\u2500\u2500 docs/ # Documentation\n\u251c\u2500\u2500 scripts/ # Utility scripts\n\u2514\u2500\u2500 .github/ # GitHub Actions\n```\n\n## Configuration\n\nThe application can be configured using environment variables with the `GEMMA_` prefix:\n\n```bash\nexport GEMMA_DEBUG=true\nexport GEMMA_API_PORT=9000\nexport GEMMA_LOG_LEVEL=DEBUG\n```\n\nOr using a `.env` file:\n\n```env\nGEMMA_DEBUG=true\nGEMMA_API_PORT=9000\nGEMMA_LOG_LEVEL=DEBUG\n```\n\n## Contributing\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Make your changes\n4. Run tests and checks (`make check`)\n5. Commit your changes (`git commit -m 'Add amazing feature'`)\n6. Push to the branch (`git push origin feature/amazing-feature`)\n7. Open a Pull Request\n\n## Releasing\n\nTo publish a new release to PyPI, simply push a commit to the main branch with a message starting with \"new release\" followed by the version number:\n\n```bash\ngit commit -m \"new release 0.3.0\"\ngit push origin main\n```\n\nThe GitHub Actions workflow will automatically:\n1. Extract the version from the commit message\n2. Update the version in `pyproject.toml` and `__init__.py`\n3. Build and publish the package to PyPI\n4. Create a git tag\n5. Create a GitHub release\n\n**Note**: Make sure you have set up the `PYPI_API_TOKEN` secret in your GitHub repository settings.\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.",
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