Within your course repository, create a directory "final" and create initial versions of the files required for the project. Commit and push to your remote repository.

cd <path_to_repo>
mkdir final
touch final/screencast_url.txt
touch final/project_url.txt
git add final
git commit -m "initial commit for final"
git push

CustomGPT

In this project, you will utilize coding agents to specify, implement, test, and deploy an agent-based web application of your choosing that synthesizes the knowledge and skills that you have gained throughout the course to implement an agent with custom skills and tools at its disposal. The web application must be deployed as a container running on Cloud Run.

Project ideas include agentic access for

• Shop recommendations and reviews
• Social media feeds such as Facebook, Instagram, TikTok, Twitter, Discord
• Song and movie information such as Spotify
• Google Cloud APIs (Translate, Knowledge Graph, BigQuery public data)
• Image analysis and information (Animals, plants)
• Sports statistics analysis
• Place summaries and reviews such as Yelp and Google Places
• E-mail summaries
• News summaries from Google News or Reddit
• Recipe instructions
• Hiking trail reviews
• Services published on Rapid APIs and public-apis repository

While the use of coding agents is assumed, you must be able to demonstrate understanding of all code that is submitted via a complete source-code walkthrough of the application itself. In addition, you must also ensure any API keys do not show up in your source files and that any credentials that are required by your application are done as environment variables either via Google Cloud's Secrets Manager or in the deployment step using the --set-env-vars flag.

gcloud run deploy final --image gcr.io/... --set-env-vars APP_ID=e5c9382,API_KEY=cae2635...343

Upon completing your project, via a narrated screencast of no longer than 15 minutes, you will show the following in the exact order specified.

• Checking out the application source code from your course repository.
• Describing steps one would need to take to run your project (e.g. setting up the Google Cloud services, service accounts, and credentials required, setting up the APIs you have integrated, and setting up any software packages needed).
• Building and deploying the container within your Google Cloud project. Pause the screencast to wait for deployment in order to shorten the length of the screencast.
• Demo the web application by stepping through all of the functionality it supports
• Perform a source code walk-through using your repository via the MCECS Gitlab website that explains how the source code of each feature has been implemented.

To ensure you are the one completing the screencast, your screencast MUST initially include a video of you narrating the walkthrough. It is recommended for you to record your screencast as a recorded screen share on Zoom. Note that, when logged into Zoom, recordings are automatically captured and uploaded to PSU's Media Space and can be accessed after a short time by visiting "My Media" within Media Space.

Application

All code including the Dockerfile used to build the container implementing your Cloud Run application should be pushed to your repository in the final directory. A valid URL to the running service on Cloud Run must also be provided in final/project_url.txt.

Screencast

Upload your completed screencast on MediaSpace. Ensure that it is published as "Unlisted". To do so, visit MediaSpace and click on "My Media".

Click on the screencast video that has been uploaded. Then, in the tabs below, select the "Publish" tab, click on "Unlisted", and then "Save".

Then, update the file final/screencast_url.txt in your repository to contain the URL that your unlisted screencast on MediaSpace is located. Push the changes to your repository before the due date.

We will be using your screencast and git repository to evaluate your project.