firesense 0.8.2

AI-powered fire detection system using Gemma 3N E4B vision model

FireSense

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 package manager

Installation

From PyPI (Recommended)

pip install firesense

From Source

1. Clone the repository:

git clone https://github.com/gregorymulla/firesense_ai.git
cd firesense_ai

2. Install with pip:

pip install -e ".[dev]"

Using uv (Fastest)

1. Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install firesense:

uv pip install firesense

Usage

Running the Application

# 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

# 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:

export GEMMA_DEBUG=true
export GEMMA_API_PORT=9000
export GEMMA_LOG_LEVEL=DEBUG

Or using a .env file:

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:

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.