Weather Automation Study Case

This repository contains a small study case designed to explore FastAPI, asynchronous APIs, background tasks, validation, and service-oriented application structure.

The goal is to simulate how a real backend service would behave when dealing with APIs, background processing, validation layers, and basic authentication mechanisms.

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Features

• FastAPI REST API with two endpoints:
  • /healht: Service status check.
  • /weather: Returns mocked weather data by city.
• Query parameter support (city, unit) to simulate dynamic response.
• Mocked weather data instead of external API integration.
• Basic request validation using Pydantic models.
• JWT authentication via request headers (Authorization: Bearer <token>)
• Background task executing using Celery workers.
• Scheduled tasks using Celery Beat (hourly simulation of data fetching)
• Redis integration as Celery broker.
• Structured logging across modules.
• Basic business rule validation before returning responses.
• Error handling for invalid requests and authentication failures.

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Commands

uv sync # Install dependencies
uv run src/main.py # Start the app (localhost:8000)
uv run celery -A src.tasks.celery_app worker --beat --pool=solo --loglevel=info # Start Celery

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Limitations

• No real external weather API integration.
• No persistent database layer.
• Authentication is simplified (no refresh tokens or user system)
• Celery setup is minimal and intended only for study purposes.

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Notes about .env

This project uses a .env file to manage sensitive configuration values, such as the JWT secret used for token encoding and decoding.

Since this is a study case, the .env file is not included in the repository.

To run the project locally, create a .env file in the root directory with the following content:

KEY=your_encoded_jwt_secret_key_here
SECRET=the_same_secret_used_to_encode_jwt_token_here

These values are required for JWT generation and validation to work correctly.

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Final Notes

This project was developed as a personal study case to better understand FastAPI and common backend patterns in Python applications. It is intentionally simplified and focuses on learning core concepts rather than production readiness.