This application predicts energy prices using machine learning models (XGBoost and LSTM) based on various factors like weather conditions, historical trends, and more.

• frontend: React TypeScript application with Tailwind CSS
• backend: FastAPI Python application with ML models
• dataset: Contains the dataset used for training models
• scripts: Utility scripts for data preprocessing and model training

This project is configured to run in GitHub Codespaces using Docker Compose. Follow these steps:

1. Open in Codespaces: Click the "Code" button on the GitHub repository and select "Open with Codespaces"

2. Start the Application: From the terminal, run:

   docker-compose up --build

   Alternatively, you can use:

   docker-compose up --build

   Or run the commands separately:

   docker-compose build && docker-compose up

• / - Home endpoint
• /predict - ML prediction endpoint
• /weather/uk - UK weather data
• /solar - Solar generation data
• /stats - Current statistics
• /api/get-api-keys - Get configured API keys
• /api/save-api-keys - Save API keys
• /api/test-api-key - Test API key validity

• Frontend: React, TypeScript, Vite, Tailwind CSS
• Backend: FastAPI, Python 3.10, XGBoost, TensorFlow (LSTM)
• Containerization: Docker, Docker Compose

To run this project locally without Docker:

1. Backend:

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

2. Frontend:

   cd frontend
   npm install
   npm run dev

This is a demonstration project. In production, you would need to set up proper environment variables, security measures, and more robust error handling.

Muhammad Usman Mateen