SurfSafe AI is a Flask-based web application that uses a YOLOv8 custom-trained model to detect sharks in uploaded videos. The system stores the videos and detection results in a local SQLite database.

├── .vscode/ # VS Code settings (optional) ├── static/ # Folder to store uploaded video files ├── templates/ # HTML templates for rendering in Flask ├── app.py # Main Flask application file ├── requirements.txt # Python dependencies ├── sharkVideoStore.db # SQLite database for video's metadata storage ├── yolov8_custom_shark_detection_V1.pt # YOLOv8 trained shark detection model

Follow these steps to set up and run the project locally.

git clone https://github.com/rashmin29/surfsafe-ai.git
cd surfsafe-ai

### 2. Create the virtual environment and install the dependencies 

python -m venv virtualenv

Windows:
.\virtualenv\Scripts\activate

Mac:
source virtualenv/bin/activate

Install the depedencies:
pip install -r requirements.txt


### 3. Run the flask app

python app.py