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https://github.com/wkambale/keras-gemini
A Python package that combines the power of Keras with Gemini for natural language-driven neural network building.
https://github.com/wkambale/keras-gemini
gemini keras package python3
Last synced: 18 days ago
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A Python package that combines the power of Keras with Gemini for natural language-driven neural network building.
- Host: GitHub
- URL: https://github.com/wkambale/keras-gemini
- Owner: wkambale
- License: mit
- Created: 2024-10-07T19:59:18.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-07T22:10:35.000Z (3 months ago)
- Last Synced: 2024-12-24T20:04:02.938Z (18 days ago)
- Topics: gemini, keras, package, python3
- Language: Python
- Homepage: https://pypi.org/project/keras-gemini/
- Size: 17.6 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# keras-gemini
A Python package that combines the power of Keras with Gemini for natural language-driven neural network building.
### Built With
- Python
- Keras
- Gemini API
- NLTK
## Getting Started
To get a local copy up and running follow these simple steps.
### Prerequisites
- Python 3.x
### Installation
```sh
pip install keras-gemini
```
## Usage
```python
from keras_gemini import prompt_to_keras
model = prompt_to_keras("Build a 3-layer sequential model")
if model:
model.summary()
```
## Run the Examples
To run these examples, users simply need to navigate to the examples/ directory and run any of the scripts. For example:
```bash
python examples/build_basic_model.py
```
## Features
- Natural Language Model Building: Build Keras sequential models by simply describing the desired architecture in natural language. For example:
`Build a 3-layer sequential model`
- Automatic Model Compilation: The package automatically compiles the generated Keras model with default settings (optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']).
- Seamless Integration with Gemini: The `KerasGemini()` integrates directly into your Gemini chatbot flow, allowing for natural conversational model building.
## Upcoming Features (Roadmap)
- [ ] Support for More Layer Types: Add support for a wider range of Keras layers (Convolutional, Recurrent, etc.) to enable building diverse network architectures.
- [ ] Customizable Layer Parameters: Allow users to specify layer parameters (activation functions, number of units, etc.) through natural language prompts.
- [ ] Advanced NLP for Model Understanding: Implement more robust natural language processing techniques to better extract user intent and complex model specifications.
- [ ] Model Training and Evaluation: Provide functionality to train and evaluate the generated Keras models directly within the Gemini conversation.
- [ ] Model Persistence: Allow users to save and load their custom-built models for later use.
- [ ] Interactive Model Building: Enable users to iteratively refine their models by adding or removing layers, modifying parameters, and getting feedback in real-time.
## Contributing
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## License
Distributed under the MIT License. See [License](https://github.com/wkambale/keras_gemini/blob/main/LICENSE) for more information.