uniccongroup/african-deep-tech-hackathon

African Deep Tech Hackathon – Malaria Detection App Features - Upload cell images and detect malaria infection (Parasitized or Uninfected) - Medical recommendation and risk level output - API access for integration - Docker support for easy deployment Getting Started 1. Clone the Repository git clone github.com cd african-deep-tech-hackathon 3. Install Requirements (Python 3.8+) python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt Place your trained YOLOv8 classification model at: runs/classify/train4/weights/best.pt You can change the path in the MODEL_PATH variable in app.py if needed. 4. Run the App python app.py Visit localhost to use the web interface. Using Docker 1. Build Docker Image docker build -t malaria-hackathon:latest . 2. Run the Container docker run -d -p 5000:5000 --name malaria-app malaria-hackathon:latest Visit localhost API Endpoint POST /api/predict Form Data: - file: image file (.jpg, .png, etc.) Response Example: { "prediction": "Parasitized", "confidence": 0.98, "all_probabilities": { "Parasitized": 0.98, "Uninfected": 0.02 }, "recommendation": "...", "risk_level": "danger" } Sample Test Image You can use test cell images from the Cell Images for Detecting Malaria dataset: