Weather Forecast Web

We aim to empower developers with AI, enabling quick implementation of GMP features. This project uses AI Coding to build a Weather API demo page, helping developers understand and use the API.

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Inspiration

Our goal is to empower the developer community through AI technology, helping developers quickly implement the powerful capabilities of the Google Maps Platform. To this end, this project leverages AI Coding capabilities to rapidly build a page that can demonstrate the functionality of the Weather API, enabling developers to better understand and utilize the API, thereby driving efficient development and innovative practices in related applications.

What it does

  • Obtain user's current location and display weather data for that area.
  • Mark and display current weather forecast locations on a dynamic map.
  • Show historical weather data.
  • Display future weather data.
  • Show current weather data.

How we built it

  1. AI-Assisted Planning and Design
    We input our initial product ideas into Gemini, which helps us quickly organize and output a core list of features, priorities, and draft layouts for application pages, laying a clear blueprint for the project.

  2. Automated Documentation Generation
    Based on the planning results, we have Gemini generate detailed product requirement documents (PRDs). Subsequently, Gemini recommends the optimal tech stack based on the PRD and automatically generates the project's technical architecture documentation, ensuring high alignment within the team.

  3. Smart API Documentation Processing
    We directly feed the third-party Weather API documentation to Gemini for parsing. It quickly produces a unified and focused internal interface document, significantly simplifying the integration work for developers.

  4. One-Click Project Framework Generation
    By integrating all documents, we use the Gemini CLI tool to execute a single command, automatically generating the entire project's base framework, directory structure, and core template code. This efficiently completes the project's initial setup.

  5. Manual Refinement and Testing
    Developers take over the complex business logic development, interaction optimization, and code reviews based on the code generated by Gemini. We are responsible for rigorous testing and bug fixing to ensure the quality and robustness of the software.

  6. Automated Deployment (CI/CD)
    We deploy the application on Google Cloud Run and configure an automated workflow (CI/CD) linked to GitHub. Any code pushed to the main branch automatically triggers a build and deployment, enabling seamless and rapid updates.

Challenges we ran into

  • Since Weather API is a relatively new product, AI cannot effectively integrate with it directly. The Weather API documentation needs to be provided to AI as context, which will then organize it into API documentation to properly implement the functionality.
  • Weather API requires latitude and longitude information, which is not very convenient for user interaction, especially when querying weather by entering an address. We need to first call the Geocoding API to obtain the location's latitude and longitude, and then use this information to retrieve the weather.
  • Weather API is not available in some regions and needs to be handled separately.

Accomplishments that we're proud of

The entire project was completed through full vibe coding, with all files and code created by AI. This validated the path from idea to implementation.

What we learned

How to Quickly Build a Google Maps Platform Demo Page Using AI

What's next for Weather Forecast Web

Integrate Other APIs to Create More Google Maps Platform Demos

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