Toxic-Prediction: Genotoxicity Prediction Model

This is a machine learning model trained on Massbank data to predict the genotoxicity of chemical substances. The repository includes the pre-trained model and a web frontend for interaction.

Overview

The core of this project is a machine learning/deep learning model trained on mass spectrometry data (from Massbank) to assess the potential genotoxicity of chemical compounds. This repository provides not only the pre-trained model files but also a complete web application, including a Python-based backend (likely Flask or FastAPI) and a user interface.

✨ Features

• Genotoxicity Prediction: Predicts substance genotoxicity based on mass spectrometry data.
• Pre-trained Model: Includes a ready-to-use model trained on the Massbank dataset.
• Web Interface: Provides a simple and user-friendly frontend for making predictions.
• Containerized: Includes a Dockerfile for quick and easy deployment using Docker.

🚀 Tech Stack

• Backend: Python (Inferred from app.py and requirements.txt, likely Flask / FastAPI)
• Frontend: HTML, CSS, JavaScript (Located in static and templates)
• Model: (Located in models, e.g., Scikit-learn, TensorFlow, PyTorch)
• Deployment: Docker

📂 Project Structure

.
├── models/            \# Stores pre-trained model files
├── static/            \# Stores static assets (CSS, JS, images)
├── templates/         \# Stores HTML templates
├── .idea/             \# IDE configuration (can be ignored)
├── app.py             \# Main application backend script
├── Dockerfile         \# Docker configuration file
├── README.md          \# This README file
└── requirements.txt   \# Python dependency list

🛠️ Getting Started

1. Local Setup (Virtual Environment Recommended)

a. Clone the repository

git clone [https://github.com/Molyleaf/toxic-prediction.git](https://github.com/Molyleaf/toxic-prediction.git)
cd toxic-prediction

b. (Optional) Create and activate a virtual environment

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

c. Install dependencies

pip install -r requirements.txt

d. Run the application

python app.py

After launching, open your browser and navigate to http://127.0.0.1:5000 (or the port specified in the console) to use the application.

---

2. Running with Docker

If you have Docker installed, you can run the project with these commands.

a. Build the Docker image

docker build -t toxic-prediction .

b. Run the Docker container

# This maps port 5000 on your host to port 5000 in the container
docker run -p 5000:5000 toxic-prediction

The application will be available at http://localhost:5000.

🤝 Contributing

Contributions are welcome! If you have suggestions or want to contribute code, please follow these steps:

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License.