Weather Worldwide

Video Walkthrough

https://cmu.box.com/s/tq285hmakq6ujcshtpwfcj8a7zelqu5w

Peer

Chad Merrill

Introduction

Weather Worldwide is an innovative weather forecasting application built using Streamlit, providing users with real-time weather data across a broad selection of global locations. It integrates OpenWeatherMap for weather data, Vertex AI for advanced data processing, and DeepL for multilingual support, offering a user-friendly interface for accessing accurate weather forecasts.

Features

• Global Weather Forecasts: Access real-time weather data for cities across various countries.
• Multilingual Support: Translate weather information into multiple languages using DeepL.
• Custom Weather Details: Users can select specific weather details they are interested in, such as temperature, humidity, cloud cover, precipitation, and wind speed/direction.
• Interactive UI: Streamlit-powered interface for an engaging user experience.
• Model Selection: Choose between different models (Text Bison, Gemini Pro) for processing weather data.
• Error Handling: Graceful error handling for issues like API connectivity problems, providing a smooth user experience.

Set Up API Keys and Credentials

• Obtain and set up API keys for OpenWeatherMap and DeepL.
• Set the Google Cloud credentials by placing the key.json file in your project directory and referencing it in the environment variable GOOGLE_APPLICATION_CREDENTIALS.
• Update PROJECT_ID, REGION, and BUCKET_URI with your Google Cloud configurations for Vertex AI.

Usage

• Use the sidebar to select a country and city, choose the language for the weather forecast, and select the weather details you wish to view.
• Click the "Give me the Weather details!" button to retrieve and display the weather forecast.
• Optionally, change the underlying model used for processing the weather data via the sidebar.

Technologies Used

• Streamlit: For creating the interactive web application.
• OpenWeatherMap API: For fetching real-time weather data.
• Vertex AI: For advanced data processing using machine learning models.
• DeepL API: For translating the weather data into different languages.

Acknowledgments

• OpenWeatherMap for providing the weather data API.
• DeepL for the translation services.