ExpiryPal

ExpiryPal is a system designed to help users manage their stored groceries and reduce food waste by tracking expiration dates. The platform combines a user-friendly interface, backend services, machine learning and FCM to deliver timely notifications

Technologies Used

• MySQL
• Python with Flask
• SQLAlchemy
• JavaScript with Vite + React.js
• TailwindCSS
• Material UI
• YOLO
• Cloudinary
• Firebase

Backend Configuration (Flask)

Follow these steps and commands when cloning the project:

Inside the /be folder:

• Run the following command to navigate to the backend directory:

  cd .\be\

• Create a virtual environment in the root directory with the command:

  python -m venv name_of_venv

• Navigate to the virtual environment directory with the command:

  .\name_of_venv\Scripts\activate

• Install the Flask BE project dependencies with the command:

  pip install -r .\requirements.txt

• Create a project-name-firebase-adminsdk.json file in the be/secret directory with the following structure:

  {
  "type": "service_account",
  "project_id": "firebase_project_id",
  "private_key_id": "private_key",
  "private_key": "-----BEGIN PRIVATE KEY-----",
  "client_email": "firebase-adminsdk-*********.com",
  "client_id": "*******",
  "auth_uri": "*******",
  "token_uri": "*******",
  "auth_provider_x509_cert_url": "*******",
  "client_x509_cert_url": "*******",
  "universe_domain": "*******"
  }

• Steps to download project-name-firebase-adminsdk.json

• Create a .env file in the root BE directory with the following structure:

  DB_CONN = "mysql+pymysql://user:password@host:port/expirypal"
  # Replace user, password, host, and port according to your MySQL configuration.
  ML_URL = "your_MachineLearning_url_here"
  JWT_SECRET_KEY = "JWT_SECRET_KEY"
  HASS_API_KEY = 'HOME_ASSISTANT_API_KEY'
  HASS_BASE_URL = 'https://hass.mdu-smartroom.se'
  CLOUDINARY_CLOUD_NAME = "CLOUDINARY_CLOUD_NAME"
  CLOUDINARY_API_KEY = "CLOUDINARY_API_KEY"
  CLOUDINARY_API_SECRET = "CLOUDINARY_API_SECRET"
  FIREBASE_CRED_PATH = ../secret/project-name-firebase-adminsdk.json

If necessary, delete the migrations folder (only if the migration doesn't run properly).

• Run the following command to prepare the migration:

  flask db init

• Run the following command to perform the migration:

  flask db migrate

• Run the following command to update the migrated data:

  flask db upgrade

• Run the following command to start the Flask BE project:

  py app.py

• The backend should now be running.

Frontend Configuration (Vite + React.js)

Follow these steps and commands when cloning the project:

Inside the /fe folder:

• Run the following command to navigate to the frontend directory:

  cd .\fe\

• Create a .env file in the root FE directory with the following structure:

  VITE_BE_URL=your_backend_url_here
  # Replace the URL with the one generated when running the backend.

• Run the following command to install all Node.js dependencies:

  npm i

• Run the following command to start the Vite + React.js project:

  npm run dev

• The frontend should now be running.

Machine Learning Configuration (Flask)

Follow these steps and commands when cloning the project:

Inside the /ML folder:

• Run the following command to navigate to the backend directory:

  cd .\ML\

• Create a virtual environment in the root directory with the command:

  python -m venv name_of_venv

• Navigate to the virtual environment directory with the command:

  .\name_of_venv\Scripts\activate

• Enter the following link and download the 2 files (yolov8x-seg.pt and clip-vit-base-patch32):

  https://drive.google.com/drive/folders/1JH9ruRaYMBEmpRF97fKQDsGQayKEujhI

• Put both files in ML folder.
• Install the Flask ML project dependencies with the command:

  pip install -r .\requirements.txt

• Create a .env file in the root ML directory with the following structure:

  DB_CONN = "mysql+pymysql://user:password@host:port/expirypal"
  # Replace user, password, host, and port according to your MySQL configuration.
  BE_URL = "your_backend_url_here"
  CLOUDINARY_CLOUD_NAME = "CLOUDINARY_CLOUD_NAME"
  CLOUDINARY_API_KEY = "CLOUDINARY_API_KEY"
  CLOUDINARY_API_SECRET = "CLOUDINARY_API_SECRET"

• Run the following command to start the Flask ML project:

  py app.py

• The machine learning should now be running.

Common Issues

If you cannot activate the Python environment and encounter an ExecutionPolicy error:

• Run one of the following commands in PowerShell:

  # Choose one of the following
  Set-ExecutionPolicy Unrestricted -Scope CurrentUser
  Set-ExecutionPolicy Unrestricted -Scope Process

• You can later reset it with the following command:

  set-executionpolicy remotesigned

• Then, simply activate the environment in the Visual Studio Code terminal by running:

  ./activate

steps-to-download-project-name-firebase-adminsdk-json

1. Access Firebase Console

2. Go to Firebase Console: https://console.firebase.google.com. --Log in with your Google account. --Select Your Project

3. Click on your project (e.g., ExpiryPal). --Go to Project Settings

   --In the left-hand menu, select Project Settings. --Navigate to "Service Accounts" Tab

   --At the top of the settings page, click the Service Accounts tab. --Generate a New Private Key

   --Click on Generate new private key. --A confirmation popup will appear. Click Generate Key. --Download the JSON File

   --The file project-name-firebase-adminsdk.json will be downloaded automatically.

4. Move this file to your project directory be/secret.