Server Setup

This guide will help you set up and run the project locally. Before you begin, ensure you have the following prerequisites:

A database set up for culinary data.
A Distance Matrix AI API key.
Model from Keras Tensorflow

Prerequisites

1. Database Setup

Ensure you have a database configured for storing culinary data. The database details should be available and accessible. You can use any relational database (e.g., MySQL, PostgreSQL, SQLite).

2. Distance Matrix AI API Key

Obtain a Distance Matrix AI API key. Follow the instructions on the Distance Matrix AI Platform (opens in a new tab) to generate your API key.

Steps to Run the Project Locally

1. Clone the Repository

First, clone the project repository to your local machine:

git clone -b cloud-computing https://github.com/Xeismonium/Kukuliner.git
cd your-repository

2. Create a Virtual Environment

Create a virtual environment to manage your project dependencies:

python -m venv venv

Activate the virtual environment:

On Windows:
```
venv\Scripts\activate
```
On macOS/Linux:
```
source venv/bin/activate
```

3. Install Dependencies

Install the required dependencies using pip:

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in the root of your project directory and add the following environment variables:

DB_HOST=xxx
DB_USER=xxx
DB_PASSWORD=xxx
DB_DATABASE=xxx
MODEL_URL=xxx
DISTANCEMATRIX_API_KEY=xxx

FLASK_DEBUG=1
PORT=8080

Replace all of these with with your actual data.

5. Run the Flask Application

Start the Flask development server:

flask run

By default, the Flask application will run on http://127.0.0.1:5000/.

[!NOTE] PORT 8080 in ENV is for use in the production stage.

Accessing the Application

Open your web browser and navigate to http://127.0.0.1:5000/ to access the application.

Troubleshooting

Ensure your virtual environment is activated before running commands.
Double-check your environment variables in the .env file.
Verify that your database is running and accessible.
Check the console output for any error messages and stack traces.

For further assistance, refer to the project documentation or raise an issue on the project's repository.

This documentation should help you set up and run the project locally. If you have any questions or need additional support, feel free to reach out to the project maintainers.

Client Setup