SmartInventoryReader 📸📦

A camera-enabled scanner that prioritizes barcodes, QR codes, and general image searches to determine an item's price before giving the user the choice to add it to their manageable inventory.

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Technologies Used 🛠️

• NET 8.0: Backend API
• React + TypeScript + Vite: Frontend application
• Serilog: Logging
• Entity Framework Core: Database ORM
• PostgreSQL: Database
• Docker: Containerization
• FastAPI: LLM Service
• OpenCV, EasyOCR, Transformers: Image processing and recognition

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Prerequisites 📋

• Docker: Ensure Docker is installed and running.
• NET SDK 8.0: Install from here.
• Node.js: Install from here.

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Running the Project 🚀

1. Clone the Repository 🧑‍💻

       git clone https://github.com/Mason-Household/SmartInventoryReader.git
       cd SmartInventoryReader

2. Set Up Environment Variables 🌍

   • Create a .env file in the root directory and add the following:

       JWT_SECRET_KEY=your_secret_key
       JWT_ISSUER=your_issuer
       JWT_AUDIENCE=your_audience
       JWT_EXPIRATION=your_expiration_time

3. Build and Run with Docker 🐳

       docker-compose up --build -d

4. Access the Services 🌐

• Backend API: http://localhost:8080
• Frontend: http://localhost:3000
• LLM Service: http://localhost:8000

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Authentication 🔒

• The application uses Hugging Face tokens for authentication.
• To obtain a Hugging Face token, visit Hugging Face Token and generate a new token.
• Ensure you have set the environment variables correctly in the .env file with your Hugging Face token.
• The application also supports logging in with Google, as well as using your email and password.
• For Google, follow the respective OAuth2 flow and set the required environment variables for client IDs and secrets.
• The tokens are used to secure the API endpoints and manage user sessions.

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Running Individual Parts 🧩

• Backend API 🖥️

• Navigate to the Backend directory:

      cd backend

• Restore dependencies, build, and run:

      dotnet restore; dotnet build; dotnet run

Frontend Application 🌐

• Navigate to the PrettyScanner directory:

      cd PrettyScanner

• Instqall dependencies and start the development server:

      npm install; npm run dev

LLM Service 🤖

• Navigate to the LLM directory:

      cd LLM

• Install dependencies and run the service:

      pip install -r requirements.txt; uvicorn main:app --reload

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Troubleshooting 🛠️

• Common Issues and Solutions:

• Docker Build Failures:

  • Ensure Docker is running.
  • Check for any syntax errors in Dockerfile or docker-compose.yml.

• Database Connection Issues:

  • Verify PostgreSQL is running.
  • Check connection strings in the environment variables.

• Frontend Not Loading:

  • Ensure all dependencies are installed.
  • Check the console for any errors.

• Backend API Errors:

  • Check the logs for detailed error messages.
  • Ensure all migrations are applied.

• Logs and Monitoring 📊

  • Backend Logs: Check the logs in the Backend directory.
  • Frontend Logs: Use the browser console for any frontend errors.

• LLM Service Logs: Check the terminal where the service is running.

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Why These Technologies? 🤔

• .NET 8.0: Provides a robust and scalable backend framework.
• React + TypeScript + Vite: Ensures a fast and modern frontend development experience.
• Serilog: Offers flexible and structured logging.
• Entity Framework Core: Simplifies database interactions.
• PostgreSQL: A powerful, open-source relational database.
• Docker: Ensures consistent environments across different stages of development.
• FastAPI: Provides a high-performance framework for the LLM service.
• OpenCV, EasyOCR, Transformers: Enables advanced image processing and recognition capabilities.

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Contributing 🤝

I welcome a contribution! If you can do it better, especially the front end, please submit a PR and I'll review it with appreciation!!!

• License 📜

      This project is licensed under the MIT License.