---
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

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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
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git clone https://github.com/yourusername/nlp-fastapi-deployment.git
cd nlp-fastapi-deployment
Set Up a Virtual Environment:

bash
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python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
Install Dependencies:

bash
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pip install -r requirements.txt
Usage
Start the FastAPI Server:

bash
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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).