Flask RESTful API for Weather Data Management

A minimal Flask-based RESTful API service designed to ingest and update weather data. This project provides an endpoint to receive weather parameters and store or process them, with supporting scripts for data streaming and Docker containerization.

Features

• REST API endpoint /weather accepting GET requests with weather data parameters.
• Data parsing and type conversion using pandas.
• Dockerfile for containerized deployment using Gunicorn as the WSGI server.
• A separate Python script for streaming weather data from an external API.

Tech Stack

• Python 3.9
• Flask
• pandas
• Gunicorn
• Docker
• Google Cloud BigQuery (implied by imports, though usage details are partial)

Getting Started

Prerequisites

• Docker (optional, for containerized deployment)
• Python 3.9 environment

Installation

Clone the repository:

git clone https://github.com/justin-napolitano/flask_test.git
cd flask_test

Install dependencies:

pip install -r requirements.txt

Running Locally

Run the Flask app directly:

python main.py

The API will be available at http://localhost:5000/weather.

Running with Docker

Build the Docker image:

docker build -t flask_test .

Run the container:

docker run -p 8080:8080 flask_test

The API will be available at http://localhost:8080/weather.

Project Structure

• Dockerfile: Defines the container image setup using Python 3.9 slim and Gunicorn.
• main.py: Flask app with /weather GET endpoint to receive and process weather data.
• requirements.txt: Python dependencies.
• restful_api.ipynb: (Not described, assumed to be a Jupyter notebook related to the API.)
• weather_data_stream.py: Script to fetch weather data from an external API, process it, and presumably send updates to the Flask service.

Future Work / Roadmap

• Complete and document integration with Google Cloud BigQuery for data storage.
• Add POST method support for more secure and flexible data submission.
• Implement authentication and validation on API endpoints.
• Expand weather_data_stream.py to automate periodic data fetching and updating.
• Add error handling and logging for robustness.
• Document restful_api.ipynb usage and purpose.
• Container orchestration and deployment automation.

Note: Some functionality and integration details are inferred based on partial code snippets and imports.