Project Sea Lanka

AI-Powered Prediction System for Identifying Optimal Fishing Zones and Species in the Coastal and Deep Waters of Sri Lanka. Utilizing environmental factors such as sea surface temperature and chlorophyll levels. The system will also incorporate past fish catch history data. The primary audience for this project includes fishermen and fish researchers, with the main objective being the enhancement of fish catch efficiency.

Prerequisites

Before you begin, ensure you have the following installed:

• Node.js and npm (for the backend)
• Python 3.10 and pip (for the Flask microservice)
• Flutter (for the frontend app)

Cloning the Repository

Start by cloning the repository to your local machine:

git clone https://github.com/yourusername/Project-Sea-Lanka.git
cd Project-Sea-Lanka

Setting Up the Environment for Python

1. Create a Virtual Environment: After cloning the repository, you should navigate to the python project directory and create a new virtual environment. This can be done using:

   cd model_service
   python -m venv venv

   This command creates a new virtual environment named venv within the project directory.

2. Activate the Virtual Environment: Before installing any packages, you need to activate the virtual environment. The activation command varies depending on the operating system:

   • On Windows:

     .\venv\Scripts\activate
     

   • On macOS and Linux:

     source venv/bin/activate
     

3. Install Dependencies: With the virtual environment activated, you can install the project's dependencies using the requirements.txt file provided:

   pip install -r requirements.txt

Setting Up Node Environment

1. Navigate to the node_backend Directory: Change into the node_backend directory from the root of the project:

   cd node_backend/

2. Install Dependencies: Run the following command to install the necessary npm packages:

   npm install
   

3. Environment Variables: Copy the .env.example file to a new file named .env and update the variables to match your local environment settings.

   cp .env.example .env

Setting up ML model

Download the machine learning model from the provided link and place it in the model_service/model directory. If you happened to use a different model, make sure to edit the model name in app.py file. https://drive.google.com/file/d/1fXGqDmMSRz1KkR0DIYTLAFTlLMoFSRlT/view?usp=sharing

Running the Application

1. Manually Running Each Component: If you prefer to run each component manually, follow these steps:

   a. Start the Express Backend Server:

   cd node_backend
   npm start

   b. Start the Flask Microservice:

   cd model_service
   python app.py

   c. Run the Flutter Frontend: Navigate to the Flutter directory and run the app on your device or emulator.

Accessing the Application

• The backend should be accessible at http://localhost:YOUR_BACKEND_PORT.
• The Flask microservice should be accessible at http://localhost:YOUR_FLASK_PORT.
• The Flutter app should be set up to communicate with these services based on the configuration you provided.