API Backend for Waste-to-Energy Processing

This repository contains the backend for a Waste-to-Energy processing application. The backend is built using Flask and provides multiple API endpoints for different waste processing methods such as Fermentation, HTL (Hydrothermal Liquefaction), Combustion, and Anaerobic Digestion.

Table of Contents

• Project Structure
• Installation
• Running the Application
• API Endpoints
• Environment Variables
• Docker Usage
• Contributing
• License

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Project Structure

/api-backend
│
├── /app
│   ├── /blueprints               # Contains Blueprints for each functional module
│   │   ├── fermentation.py        # Fermentation-related API routes
│   │   ├── htl.py                 # HTL-related API routes
│   │   ├── combustion.py          # Combustion-related API routes
│   │   └── digestion.py           # Digestion-related API routes
│   ├── /services                 # Business logic and data processing scripts
│   │   ├── fermentation_service.py
│   │   ├── htl_service.py
│   │   ├── combustion_service.py
│   │   └── digestion_service.py
│   ├── /data                     # Data files (CSV, Excel, etc.)
│   │   └── sludge_data_dmt.csv    # Sludge data for HTL functions   
│   ├── __init__.py               # App factory and Blueprint registration
│   └── config.py                 # Configuration for environment variables
│
├── Dockerfile                    # Dockerfile for containerizing the API
├── requirements.txt              # Python dependencies
├── .env                          # Environment variables for local development
└── wsgi.py                       # Entry point for WSGI server

Key Components:

• Blueprints: Separate each API route into its module (e.g., fermentation, HTL, combustion, digestion).
• Services: Contains the business logic and data handling for each processing method.
• Data: Stores relevant CSV and other data files used in processing the requests.
• Configuration: Manages environment-specific settings through config.py and environment variables.

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Installation

1. Clone the Repository:

   git clone https://github.com/your-username/api-backend.git
   cd api-backend

2. Create and Activate a Virtual Environment:

   python -m venv venv
   source venv/bin/activate   # On macOS/Linux
   venv\Scripts\activate      # On Windows

3. Install the Dependencies:

   pip install -r requirements.txt

4. Set Up Environment Variables: Create a .env file in the root directory and add your environment variables (e.g., FLASK_ENV, SECRET_KEY). You can use the .env.example as a template:

   FLASK_ENV=development
   SECRET_KEY=your-secret-key

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Running the Application

1. Run the Flask App Locally:

   flask run

   The app will be running on http://127.0.0.1:5000/.

2. WSGI Server: For production environments, use a WSGI server (e.g., Gunicorn) to run the app via wsgi.py.

   Example:

   gunicorn --bind 0.0.0.0:5000 wsgi:app

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API Endpoints

1. HTL (Hydrothermal Liquefaction)

• GET /api/v1/htl/county/<countyname>: Fetch HTL data for a given county.
• GET /api/v1/htl/sludge?sludge=<value>&unit=<unit>: Calculate diesel price and global warming potential based on sludge mass.

2. Fermentation

• GET /api/v1/fermentation/county/<countyname>: Fetch fermentation data for a given county.
• GET /api/v1/fermentation/biomass?mass=<value>: Calculate ethanol production based on biomass mass.

3. Combustion

• GET /api/v1/combustion/county/<countyname>: Fetch combustion data for a given county.
• GET /api/v1/combustion/mass?mass=<value>: Calculate electricity and emissions from feedstock.

4. Anaerobic Digestion

• GET /api/v1/digestion/county/<countyname>: Fetch anaerobic digestion data for a given county.
• GET /api/v1/digestion/mass?mass=<value>: Calculate biogas production based on feedstock mass.

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Environment Variables

• FLASK_ENV: Environment the app is running in (development, testing, production).
• SECRET_KEY: Secret key for Flask app.
• DATABASE_URL: If you add a database, configure the URL here.

You can add more environment-specific variables in your .env file.

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Docker Usage

1. Build the Docker Image:

   docker build -t api-backend .

2. Run the Container:

   docker run -p 5000:5000 api-backend

   This will run the app inside a Docker container and expose it on port 5000.

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Contributing

If you want to contribute to this project:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m 'Add a new feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Notes

• The app is configured to handle multiple routes and dynamically process data based on requests.
• Always ensure that the environment variables are set correctly, especially for production environments.