Update README.md

README.md

@@ -4,9 +4,101 @@ emoji: 💬
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 sdk: gradio
-sdk_version: 5.9.1
+sdk_version: 5.11.0
 app_file: app.py
 pinned: false
 ---
 
-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).
+NLP Model Deployment with FastAPI
+
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+[![Python Version](https://img.shields.io/badge/python-3.x-blue.svg)](https://www.python.org/)
+[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/Chemically-Motivated-Solutions/NLPToolkit/blob/main/LICENSE)
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+[![Issues](https://img.shields.io/github/issues/Chemically-Motivated-Solutions/NLPToolkit)](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).