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Docker Compose Medical Text Extraction Service

This project demonstrates how to deploy a multi-container application using Docker Compose. In this project, an ML service is created where users can input medical prescription text, and the service processes the input using the OpenAI API, extracts structured data, and stores it in a PostgreSQL database. Here's the project overview:

  1. Frontend: A Streamlit app for users to input prescription text.
  2. Backend: A FastAPI app that handles the API endpoints and data processing.
  3. Database: A PostgreSQL database that stores the structured data.

Project Structure

docker-tutorial/3-text-extractor/
├── backend/
│   ├── config.py
│   ├── database.py
│   ├── models.py
│   ├── extraction.py
│   ├── main.py
│   ├── Dockerfile
│   └── requirements.txt
├── frontend/
│   ├── app.py
│   ├── Dockerfile
│   └── requirements.txt
├── .env
├── docker-compose.yml
└── README.md

Running with Docker

  1. Set Environment Variables:

    Create a .env file in the project directory with the following environment variables:

    OPENAI_API_KEY=YOUR_OPENAI_API_KEY
POSTGRES_USER=YOUR_POSTGRES_USER_NAME
POSTGRES_PASSWORD=YOUR_POSTGRES_PASSWORD
POSTGRES_DB=YOUR_POSTGRES_DATABASE_NAME

  2. Build and run the Docker Containers:

    Navigate to the project directory and run:

    cd 3-text-extractor
docker compose up --build -d

    Note: You must be in the same directory as the docker-compose.yml file.

    This single command will build and start up all the containers: the frontend app, backend APIs, and PostgreSQL database

Accessing the Application

  1. Frontend:

    • Open your browser and go to http://localhost:8501 to access the Streamlit frontend.
    • Enter natural language text and click Process Text to extract structured data.

  2. Backend:

    • The backend API will be running on http://localhost:8000 and is responsible for handling requests and processing data using OpenAI.

  3. PostgreSQL Database:

    • The database is running in the postgres container.
    • You can connect to it with your preferred Postgres client using localhost:5432 and the credentials defined in the .env file.

Checking Container Status

To check if your containers are running, use the following command:

docker ps

To check docker compose logs in real-time:

docker compose logs -f

To access the shell of a specific container, use:

docker exec -it <container_id_or_name> bash

For example, to enter the backend container, you can run:

docker exec -it 3-text-extraction-backend_api-1 bash

Cleaning Up

To stop and remove the containers:

docker-compose down

To remove all images associated with this project:

docker-compose down --rmi all