Quick-Start Guide

Follow these steps to build, populate, and run the Melt-o-meter application. Prerequisites:

• Docker
• Docker Compose
• Git (or download the project files)

Step 1: Clone & Configure

1. Clone the project repository:

git clone https://github.com/tcbitt/meltometer.git

2. Create a .env file in the project root. The docker-compose.yml and docker-entrypoint.sh show it needs these variables. Copy the entire block below into the .env and populate it with your desired credentials:

POSTGRES_DB=melt_o_meter_db
POSTGRES_USER=melt_o_meter_user
POSTGRES_PASSWORD=your_secret_password

# Django settings (must match the .env variables above)
DATABASE_URL=postgres://melt_o_meter_user:your_secret_password@db:5432/melt_o_meter_db
SECRET_KEY=your_django_secret_key
DEBUG=True

Step 2: Build and Run Containers

Build and start the web, nginx, and db services in detached mode.

docker-compose up -d --build

The web container will automatically wait for the database, collect static files, and apply migrations, as defined in docker-entrypoint.sh.

Step 3: Load Data

Execute the load_chemicals management command inside the running web container to populate the database. This can be done by navigating to the project folder that contains the docker-compose.yml and running this from the CLI.

docker-compose exec web python manage.py load_chemical

Step 4: Train Model

Execute the train_model command. This will query your database, train the RandomForestRegressor, and create model.joblib and chart_data.json.

docker-compose exec web python manage.py train_model

Step 5: Run the Application

Go to http://localhost:8000/