firesense 0.10.2

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

FireSense

FireSense is a video feed analyzer that detects fires, powered by the Gemma 3N vision model.

Pipeline

Quick Start

Installation

# Install pypi package
pip install firesense

or

# Install from source code:
uv pip install -e .

Command Line Usage

# Analyze a YouTube video
firesense analyze <youtube_video_id>

# Launch demo with local server
firesense demo <youtube_video_id> --local

https://github.com/user-attachments/assets/b8fdba5b-bd2d-44c6-be4e-8fb6499b62a8

Ngrok Integration

# setup ngrok
ngrok config add-authtoken <your_ngrok_auth_key>

# Launch demo with local server and ngrok tunnel
firesense demo <youtube_video_id> --local --ngrok

Python Usage

from firesense import setup_model, infer

# Setup model
model, tokenizer = setup_model()

# Run inference
inference_result = infer(model, tokenizer, system_prompt, user_prompt, image_path)

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 (uv run mypy src)
• ✅ Testing: Comprehensive pytest setup with coverage

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

Testing

# Install the project with development dependencies using:
uv pip install -e ".[dev]"

# Run the tests with:
uv run pytest

Type Safety

# Full mypy strict mode support 
uv run mypy src

Releasing

New releases are manually pushed to pypi:

make publish

License

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