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| title: NLPToolkit | |
| emoji: 💬 | |
| colorFrom: yellow | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 5.11.0 | |
| app_file: app.py | |
| pinned: false | |
| NLP Model Deployment with FastAPI | |
| [](https://github.com/Chemically-Motivated-Solutions/NLPToolkit/actions) | |
| [](https://github.com/Chemically-Motivated-Solutions/NLPToolkit/actions) | |
| [](https://github.com/Chemically-Motivated-Solutions/NLPToolkit/network/updates) | |
| [](https://www.python.org/) | |
| [](https://github.com/Chemically-Motivated-Solutions/NLPToolkit/blob/main/LICENSE) | |
| [](https://codecov.io/gh/Chemically-Motivated-Solutions/NLPToolkit) | |
| [](https://github.com/Chemically-Motivated-Solutions/NLPToolkit/releases) | |
| [](https://github.com/Chemically-Motivated-Solutions/NLPToolkit/issues) | |
| Overview | |
| This project demonstrates how to deploy Natural Language Processing (NLP) models using FastAPI, a modern web framework for building APIs with Python. The application integrates two pre-trained models from the Hugging Face Transformers library: | |
| Sequence Classification Model: Utilized for tasks like sentiment analysis. | |
| Question Answering Model: Designed to provide answers based on a given context. | |
| Features | |
| RESTful API Endpoints: | |
| /predict: Accepts user input and returns model predictions. | |
| /health: Provides health status of the API. | |
| Model Integration: | |
| Incorporates Hugging Face's AutoModelForSequenceClassification and AutoModelForQuestionAnswering for NLP tasks. | |
| Installation | |
| Clone the Repository: | |
| bash | |
| Copy code | |
| git clone https://github.com/yourusername/nlp-fastapi-deployment.git | |
| cd nlp-fastapi-deployment | |
| Set Up a Virtual Environment: | |
| bash | |
| Copy code | |
| python -m venv venv | |
| source venv/bin/activate # On Windows: venv\Scripts\activate | |
| Install Dependencies: | |
| bash | |
| Copy code | |
| pip install -r requirements.txt | |
| Usage | |
| Start the FastAPI Server: | |
| bash | |
| Copy code | |
| uvicorn main:app --reload | |
| The API will be accessible at http://127.0.0.1:8000. | |
| Interact with the API: | |
| Navigate to http://127.0.0.1:8000/docs to access the interactive API documentation provided by Swagger UI. | |
| **Example Request:** | |
| ```bash | |
| curl -X POST "http://127.0.0.1:8000/predict" -H "Content-Type: application/json" -d '{"text": "Your input text here"}' | |
| ``` | |
| ## Project Structure | |
| ```plaintext | |
| nlp-fastapi-deployment/ | |
| ├── app/ | |
| │ ├── __init__.py | |
| │ ├── main.py # Main application file | |
| │ ├── models.py # Pydantic models for request and response | |
| │ ├── nlp_models.py # Functions for loading and utilizing NLP models | |
| │ └── utils.py # Utility functions | |
| ├── requirements.txt # Project dependencies | |
| ├── README.md # Project documentation | |
| └── .gitignore # Git ignore file | |
| ``` | |
| ## Dependencies | |
| - FastAPI: Web framework for building APIs with Python. | |
| - Transformers: Library for state-of-the-art NLP models. | |
| - Torch: Deep learning framework used by Transformers. | |
| - Uvicorn: ASGI server for running FastAPI applications. | |
| **Ensure all dependencies are listed in requirements.txt for easy installation.** | |
| ## Contributing | |
| Contributions are welcome! Please fork the repository and submit a pull request with your changes. | |
| ## License | |
| This project is licensed under the MIT License. See the LICENSE file for details. | |
| ## Acknowledgements | |
| - Hugging Face for providing accessible NLP models. | |
| - FastAPI for the high-performance API framework. | |
| **For a visual guide on creating a deep learning API with FastAPI, you might find the following resource helpful:** | |
| https://youtu.be/NrarIs9n24I | |
| An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index). | |