Welcome to the NextWord-Predictor-ML-MLOps-Django repository! This project combines Machine Learning (ML), MLOps, and the Django framework to create an advanced next word prediction system.
The NextWord-Predictor-ML-MLOps-Django project aims to accurately predict the next word based on a given input text. It utilizes state-of-the-art ML techniques trained on a corpus to analyze patterns and make accurate predictions. The project integrates MLOps practices to streamline the ML pipeline, ensuring efficient deployment and monitoring of the ML model. The Django framework is used to build a user-friendly web interface for easy interaction with the prediction system.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
├── web_app
│ ├──manage.py
│
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
- ML-based next word prediction using advanced algorithms
- Seamless integration with MLOps for efficient ML model deployment and monitoring
- Web interface built with Django for user-friendly interaction
- Easily customizable and extendable codebase
- Clone the repository:
git clone https://github.com/your-username/NextWord-Predictor-ML-MLOps-Django.git - Install the required dependencies:
pip install -r requirements.txt - Configure the settings and parameters in
config.py. - Run the application:
python manage.py runserver
- Access the web interface by visiting
http://localhost:8000in your browser. - Enter the input text and click "Predict" to get the next word prediction.
Contributions are welcome! If you want to contribute to this project, please follow these steps:
- Fork the repository.
- Create a new branch:
git checkout -b feature/your-feature. - Make your changes and commit them:
git commit -m 'Add your feature'. - Push to the branch:
git push origin feature/your-feature. - Open a pull request.
This project is licensed under the MIT License.
For any questions or inquiries, please reach out to [email protected].
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