Eyesense

An API for ocular disease detection from fundus images using a deep learning model.

---

� Project Overview

Eyesense is a machine learning project that leverages a pre-trained Xception model to analyze eye fundus images and predict the likelihood of various ocular diseases. The project is exposed as a RESTful API built with FastAPI.

The primary goal is to provide a tool that can serve as a preliminary diagnostic aid, capable of identifying conditions such as cataracts, glaucoma, diabetes-related retinopathy, and more.

✨ Features

• AI-Powered Predictions: Utilizes a deep learning model for accurate disease classification.
• RESTful API: A clean, fast, and documented API for easy integration.
• Dockerized: Containerized for consistent deployment and scalability.
• Cloud-Integrated: Downloads the trained model from Google Cloud Storage on startup.

---

�️ Tech Stack

• Backend: FastAPI, Uvicorn
• ML/DL: TensorFlow, Keras, Scikit-learn, Numpy
• Image Processing: OpenCV, Pillow
• Deployment: Docker, Google Cloud Storage
• Testing: Pytest, HTTPX
• Linting: Flake8

---

🚀 Getting Started

These instructions will help you set up a local copy of the project for development and testing.

📋 Prerequisites

• Python 3.10
• Docker
• Google Cloud SDK (for authentication to GCS)

---

🔧 Local Installation (API)

Follow these steps to set up the development environment:

1️⃣ Clone the repository:

git clone https://github.com/caazzi/eyesense.git
cd eyesense

2️⃣ (Optional) Create a virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate     # Windows

3️⃣ Install dependencies:

pip install -r requirements.txt

4️⃣ Run the Streamlit app:

streamlit run app/app.py

The application will open in your default browser.

---

⚙️ Running the Tests

To ensure the project is working correctly, run the test scripts using:

pytest tests/

---

🔩 End-to-End Tests

These tests verify the entire workflow, from uploading an image to receiving a prediction.

Example:

def test_prediction():
    img = load_test_image("data/example_images/eye.jpg")
    result = model.predict(img)
    assert result is not None

---

⌨️ Code Style Tests

To maintain clean and consistent code formatting, use Flake8:

flake8 app/ --max-line-length=120

---

📦 Deployment

To deploy the application on a live server, follow these steps:

1. Ensure all dependencies are installed in the production environment.
2. Run streamlit run app/app.py inside a cloud server or container.
3. Configure Streamlit sharing or deploy using services like Heroku, AWS, or Google Cloud.

---

🛠️ Built With

• TensorFlow/Keras - Machine learning framework
• Streamlit - Web application framework
• OpenCV - Image processing library

---

🖇️ Contributing

Please read COLLABORATION.md for details on our code of conduct and submission guidelines.

---

✒️ Authors

Claudio Azzi - @caazzi

Erika Chang - @erika-chang

George Silva - @gbs1234

Joao Sales - @masalesvic

---

📄 License

This project is licensed under the MIT License - see the LICENSE.md file for details.

---

🎁 Acknowledgments

• Thanks to the open-source community for their amazing tools 🔧
• Shoutout to researchers advancing AI in healthcare 📢
• Special thanks to contributors & testers 🫂