Metadata-Version: 2.4
Name: srilankan-food-trainer
Version: 0.1.0
Summary: Easy-to-use library for extending Sri Lankan food recognition models
Home-page: https://github.com/ranasinghehashini/srilankan-food-trainer
Author: Hashini Ranasinghe
Author-email: your.email@example.com
Keywords: food-recognition,deep-learning,pytorch,few-shot-learning,sri-lankan-food
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: torch>=2.0.0
Requires-Dist: torchvision>=0.15.0
Requires-Dist: Pillow>=9.0.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: tqdm>=4.62.0
Requires-Dist: requests>=2.28.0
Requires-Dist: huggingface_hub>=0.19.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license-file
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# 🍛 Sri Lankan Food Trainer

Easy-to-use Python library for extending Sri Lankan food recognition models with your own classes!

## Installation
```bash
pip install srilankan-food-trainer
```

## Quick Start
```python
from srilankan_food_trainer import FoodModelExtender

# 1. Create extender
extender = FoodModelExtender()

# 2. Add your class
extender.add_class("potato_curry", "/path/to/images.zip")

# 3. Train
extender.train()

# 4. Save
extender.save("my_model.pth")
```

## Features

- ✅ Download pre-trained model automatically
- ✅ Add custom food classes with minimal code
- ✅ Few-shot learning support
- ✅ Easy model export

## Requirements

- Python 3.8+
- PyTorch 2.0+
- GPU recommended (but works on CPU)

## License

MIT License - see LICENSE file for details.
