Walrus Community API

Project Overview

This project is a Walrus Community CRUD RESTful API developed using FastAPI and MongoDB, with a streamlit service for user interaction. The API allows for creating, reading, updating, and deleting Walrus data, as well as predicting via a trained model.

Project Structure

The project has two main services: streamlit_service and walrus_service, each containing several Python files responsible for different aspects of the application.

Streamlit Service

This service provides an interface for user interaction, implemented with Streamlit and located in the streamlit_service directory.

Files

• config.py
  • Loads environment variables.
• entrypoint.py
  • Currently empty, used only as entrypoint for streamlit
• status_codes.py
  • Holds HTTP status codes.

Pages

The following files in the pages directory are responsible for different pages in the Streamlit interface:

• 1_add.py: Page for adding Walrus.
• 2_get.py: Page for retrieving a specific Walrus by name.
• 3_get_all.py: Page for retrieving all Walruses.
• 4_modify.py: Page for modifying the favorite food of a Walrus.
• 5_delete.py: Page for deleting a Walrus by name.
• 6_prediction.py: Page for predicting via the trained model.

Walrus Service

This service is responsible for the CRUD operations for Walrus and is located in the walrus_service directory.

Files

• api.py
  • Defines the API routes for CRUD operations and predictions.
• config.py
  • Loads environment variables.
• database.py
  • Defines abstract classes for database operations and custom exceptions.
• memory_controller.py
  • Implements in-memory database operations.
• models.py
  • Defines Pydantic models for Walrus data.
• mongo_controller.py
  • Implements MongoDB database operations.
• training_run.py
  • Contains (commented-out) code for training runs.
• walrus_classifier.py
  • Implements a linear classifier and an inference model with PyTorch.

Running the Project

Prerequisites

• Python 3.9
• Docker
• MongoDB
• Postman (For API Testing)

Steps

1. Navigate to the project directory.
2. Run docker-compose up to start the services.
3. Interact with the Streamlit interface or test the API using Postman.

Testing

The project can be tested manually using Postman or programmatically using Python's requests or httpx libraries.

Code Quality and Formatting

• Flake8 for style guide enforcement.
• Mypy for type checking.
• Black and isort for code formatting.

Notes

• Ensure to have the required environment variables set up, as specified in the config.py files of each service.
• Review the docker-compose.yml and dev.yml files for service configuration and port setup.