Building a Weather Dashboard using SPython and OpenWeather API

This Python application fetches and displays real-time weather data using the OpenWeather API and stores it in AWS S3. Let's explore its features, setup, and potential improvements.

Key Features:

• Real-time Weather Data: Retrieves current weather conditions for specified locations.
• Detailed Information: Displays temperature, humidity, wind speed, and weather descriptions.
• AWS S3 Integration: Automatically saves weather data to an AWS S3 bucket.
• Multiple City Support: Tracks weather information for several cities simultaneously.
• Historical Tracking: Includes timestamps with each data entry.
• Robust Error Handling: Manages issues like invalid API keys, network problems, and unsupported locations.

Prerequisites:

• An AWS account with appropriate permissions.
• Python 3.8.10 or later.
• A valid OpenWeather API key.
• Necessary Python packages (installed via requirements.txt).

Project Structure:

The project is organized clearly:

<code>Open-Weather-API-Project/
├── src/
│   ├── __init__.py
│   └── weather_dashboard.py
├── .gitignore
├── README.md
└── requirements.txt</code>

Setup and Execution:

1. Clone the Repository: Use Git to clone the project: git clone https://github.com/ameh0429/Open-Weather-API-Project.git and navigate to the project directory: cd Open-Weather-API-Project.

2. Install Dependencies: Install required Python packages using pip: pip install -r requirements.txt. Note that you might need to resolve dependency conflicts; the instructions mention upgrading requests to requests>=2.31 if needed.

3. Configure Environment Variables: Create a .env file (if one doesn't exist) and add your OpenWeather API key and AWS bucket name:

<code>OPENWEATHER_API_KEY=your_api_key
AWS_BUCKET_NAME=your_bucket_name</code>

4. Configure AWS Credentials: Configure your AWS credentials using the AWS CLI: aws configure.

5. Run the Application: Execute the main script: python src/weather_dashboard.py.

6. Verify S3 Data: Check your designated S3 bucket to confirm that the weather data has been successfully uploaded.

Architecture Diagram:

Screenshots:

The provided screenshots illustrate various stages of the setup process, including dependency installation, environment variable configuration, AWS credential setup, the Python script, and the successful upload of data to S3.

Future Enhancements:

• Extended Forecasts: Integrate support for longer-range weather predictions (e.g., 7-day forecasts).
• Unit Testing: Implement comprehensive unit tests to improve code reliability and maintainability.
• Geolocation: Add the capability to fetch weather data based on the user's current location.

This detailed explanation provides a comprehensive overview of the project, making it easier for users to understand and implement it.

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