This agent recontextualizes product images into new scenes based on a user's prompt. It can take a product image from a Google Cloud Storage (GCS) URI or a local file artifact, and then generate a new image with the product in a different setting.

• Recontextualize Product Images: The core functionality of this agent is to take a product image and place it into a new scene described by a prompt.
• GCS and Local Image Support: The agent can accept product images from either a GCS URI or a local file upload (artifact).
• Person Image Support: The agent can optionally take a person's image and include it in the generated scene.
• Multiple Image Generation: The agent can generate multiple images in a single request.

Setup:

gsutil cp -r img/*.png $BUCKET/products/

The agent uses the imagen-product-recontext-preview-06-30 model on Google Cloud's AI Platform to perform the image generation. It has two main tools:

• generate_recontextualized_images_from_gcs: This tool is used when the product image is provided as a GCS URI.
• generate_recontextualized_images_from_artifact: This tool is used when the product image is provided as a local file artifact.

The agent will automatically select the appropriate tool based on the user's input. After generating the images, they are saved as artifacts and uploaded to a GCS bucket.

• Google Cloud Project: You need a Google Cloud project with the AI Platform API enabled.
• Authentication: You need to be authenticated with Google Cloud.
• Environment Variables: The following environment variables need to be set:
  • GOOGLE_CLOUD_PROJECT: Your Google Cloud project ID.
  • GOOGLE_CLOUD_LOCATION: The Google Cloud region to use (e.g., us-central1).
  • BUCKET: The GCS bucket to upload the generated images to.

Make sure images are less than 30 mb

use gs://prism-research-25/products/cup.png and put it on top of mt. everest. the product caption is: orange stanley cup 32 oz with straw

Generate a picture of me in a desert running with these shoes: gs://prism-research-25/products/shoe.png. These are black running shoes. The picture of me is here: gs://prism-research-25/products/a_guy.png

Make sure to add the Vertex AI Admin and Service Agent roles to the Service Agent for Reasoning Engine: service-YOURPROJECTNUMBER@gcp-sa-aiplatform-re.iam.gserviceaccount.com

Also make sure the above service account has access to read/write from the BUCKET location

Create an Agent Engine using the deployment_guide.ipynb notebook

Then note the Agent Engine ID (last numeric portion of the Resource Name). e.g.:

agent_engine = vertexai.agent_engines.get('projects/679926387543/locations/us-central1/reasoningEngines/1093257605637210112')

Update the agent_config_example.json, then run:

./publish_to_agentspace_v2.sh --action create --config agent_config.json

Usage: ./publish_to_agentspace_v2.sh [OPTIONS]

Options:
  -a, --action <create|update|list|delete>  Action to perform (required)
  -c, --config <file>              JSON configuration file
  -p, --project-id <id>            Google Cloud project ID
  -n, --project-number <number>    Google Cloud project number
  -e, --app-id <id>                Agent Space application ID
  -r, --reasoning-engine <id>      Reasoning Engine ID (required for create/update)
  -d, --display-name <name>        Agent display name (required for create/update)
  -s, --description <desc>         Agent description (required for create)
  -i, --agent-id <id>              Agent ID (required for update/delete)
  -t, --instructions <text>        Agent instructions, use the root agent instructions here (required for create)
  -u, --icon-uri <uri>             Icon URI (optional)
  -l, --location <location>        Location (default: us)
  -h, --help                       Display this help message

./publish_to_agentspace_v2.sh --action create --config agent_config.json
./publish_to_agentspace_v2.sh --action update --config agent_config.json
./publish_to_agentspace_v2.sh --action list --config agent_config.json
./publish_to_agentspace_v2.sh --action delete --config agent_config.json

Create agent:

./publish_to_agentspace_v2.sh --action create --project-id my-project --project-number 12345 \
--app-id my-app --reasoning-engine 67890 --display-name 'My Agent' \
--description 'Agent description' --instructions 'Agent instructions here'

Update agent:

./publish_to_agentspace_v2.sh --action update --project-id my-project --project-number 12345 \
--app-id my-app --reasoning-engine 67890 --display-name 'My Agent' \
--agent-id 123456789 --description 'Updated description'

List agents:

./publish_to_agentspace_v2.sh --action list --project-id my-project --project-number 12345 \
--app-id my-app

Delete agent:

./publish_to_agentspace_v2.sh --action delete --project-id my-project --project-number 12345 \
--app-id my-app --agent-id 123456789