README.md

---
title: SurgerySort
emoji: 📊
colorFrom: purple
colorTo: yellow
sdk: streamlit
sdk_version: 1.26.0
app_file: app.py
pinned: false
---

# Clinical Text Classification App

![App Screenshot](app_screenshot.png)

## Introduction

This is a clinical text classification app that uses a pre-trained language model to classify clinical text into two categories: "surgery" or "non-surgery". The app is designed to help healthcare professionals quickly sort through large volumes of clinical text and identify posts related to surgical interventions. The app is built using Streamlit and leverages the Hugging Face Transformers library for fine-tuning the language model.

## Installation

1. Clone this repository to your local machine.
2. Install the required dependencies using the following command:
   ```bash
   pip install -r requirements.txt
   ```

## Usage

1. Run the app using the following command:
   ```bash
   streamlit run app.py
   ```
2. The app will open in your default web browser.
3. Enter clinical text data in the provided text input or upload a CSV file containing clinical text and labels.
4. Click the "Submit" button to initiate the text classification process.
5. The app will display the predicted labels for each input text and provide visualizations of the training process.

## Features

- Text classification: The app uses a pre-trained language model to classify clinical text into "surgery" or "non-surgery" categories.
- User-friendly interface: The app provides an intuitive interface for entering text data and visualizing classification results.
- Efficient training: The app leverages the Hugging Face Transformers library for efficient model training and fine-tuning.

## Known Issues

- **Performance**: Processing large volumes of text data may slow down the app. Consider breaking down large text data into smaller chunks.
- **Data Storage**: The app may encounter performance issues if data access and storage methods are inefficient. Optimize data storage and retrieval mechanisms for better performance.

## Future Enhancements

- **Model Selection**: Allow users to choose from a variety of pre-trained models for text classification.
- **Data Preprocessing**: Implement advanced data preprocessing techniques to handle noisy or unstructured clinical text data.
- **Real-time Inference**: Enable real-time text classification for dynamic data streams.
- **Model Interpretability**: Provide insights into the model's decision-making process for better transparency.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

- This app was built using the Streamlit framework and the Hugging Face Transformers library.

---
*Note: This readme provides a high-level overview of the app. For detailed instructions and information, refer to the app's documentation and comments in the source code.*

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference