RecipeFinder

Inspiration

I'll provide a detailed implementation plan for building this AI-enabled application using the RecipeNLG dataset, Google Cloud integrations, and GitLab’s CI/CD capabilities

What it does

The Recipe Finder application is a web-based tool designed to help users discover and manage recipes. I deployed on Google Cloud Run, it provides a scalable platform for accessing recipes anytime, anywhere

How i built it

I build frontend with GitLab OAuth, so i build with Google Cloud Vision API for image analysis for fastAPI Backend, and i make like dating app so its fun and engage

Challenges i ran into

These challenges highlight the complexity of coordinating frontend, backend, and CI/CD pipelines, especially with a new toolset like Google Cloud Build and Gitlab. Cloud Build and GitLab Integration: Setting up cloudbuild.yaml and ensuring environment variables were correctly passed from GitLab to Google Cloud Build was challenging, especially aligning the Vite VITE_ prefixes with the CI/CD process.

Accomplishments that iam proud of

Despite the hurdles, you’ve achieved significant milestones: Successful Deployment: The completed deployment revision on Cloud Run (with all steps—Updating service, Creating revision, Routing traffic—marked as Completed) is a major accomplishment, proving the end-to-end pipeline from code to production works. Learning Gitlab: Successfully pushing changes to a remote repository, even with initial difficulties, is a proud moment, enabling continuous deployment.

What i learned

Cloud Build Basics: I mastered creating and updating Cloud Build triggers, configuring cloudbuild.yaml, and monitoring builds with gcloud builds log, enhancing my CI/CD knowledge. Scalability Awareness: Deploying to Cloud Run introduced i to cloud scaling concepts, setting the stage for future optimizations.