Enable real classifiers in local dev by default

[mihow]

TODO

• Consider adding global moth model after the zero shot classifier (Add general detector + global moth model to example pipeline #992)
• Update the constant classifier to return the 4 corners of the image (something static for testing, but more useful than random. but is this needed in the example at all?)
• Update env or django settings variables that set the default processing pipelines for new projects

If you test this:

docker compose build ml_backend
docker compose up -d

Then click Register Pipelines in the project processing services config screen

Note: the minimal pipeline is still used in the CI stack for testing (docker-compose.ci.yml)

Summary by CodeRabbit

• Chores
  • Updated ML backend Docker configuration for the service setup.

[netlify]

✅ Deploy Preview for antenna-preview ready!

Name	Link
🔨 Latest commit	0959796
🔍 Latest deploy log	https://app.netlify.com/projects/antenna-preview/deploys/691cf1063ffbe10008561147
😎 Deploy Preview	https://deploy-preview-952--antenna-preview.netlify.app
📱 Preview on mobile	Toggle QR Code... Use your smartphone camera to open QR code link.
Lighthouse	1 paths audited Performance: 29 (🔴 down 9 from production) Accessibility: 80 (no change from production) Best Practices: 100 (no change from production) SEO: 92 (no change from production) PWA: 80 (no change from production) View the detailed breakdown and full score reports

---

To edit notification comments on pull requests, go to your Netlify project configuration.

[coderabbitai]

Walkthrough

The docker-compose.yml file was updated to change the ml_backend service's build context and volume mount paths from ./processing_services/minimal to ./processing_services/example.

Changes

Cohort / File(s)	Summary
Docker Compose Configuration docker-compose.yml	Updated ml_backend service build context and mounted volume path from minimal to example subdirectory

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~2 minutes

• Straightforward path substitution with no logic changes
• Single file affected with repetitive pattern (same update applied twice)

Poem

🐰 From minimal to example, the paths now shift,
A simple docker update, a swift little gift!
The services now build where examples reside,
Configuration tweaked with nothing to hide.

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	The description is largely incomplete and does not follow the required template structure. It lacks a proper Summary, List of Changes, Related Issues, Detailed Description, How to Test, and Checklist sections.	Reorganize the description to match the template: add a clear Summary section, list changes systematically, reference issue #992, provide proper testing instructions, and complete the checklist.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and specifically describes the main change: enabling real classifiers in the local development environment by default, which aligns with the docker-compose.yml changes updating the ML backend configuration.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

✨ Finishing touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch feat/default-processing-service

---

📜 Recent review details

Configuration used: CodeRabbit UI

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 562f734 and 0959796.

📒 Files selected for processing (1)

• docker-compose.yml (1 hunks)

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (5)

• GitHub Check: Agent
• GitHub Check: Redirect rules
• GitHub Check: Header rules
• GitHub Check: Pages changed
• GitHub Check: test

🔇 Additional comments (2)

docker-compose.yml (2)

158-169: Verify Django settings and environment variables were updated to match.

Per the PR objectives, the default processing pipelines for new projects should be configured via Django settings or environment variables. Verify that complementary changes were made to:

1. Django settings files (e.g., to set default processing pipeline to use the example service)
2. Environment variable defaults (.envs/.local/.django or similar)
3. Any initialization scripts that bootstrap the processing pipeline configuration

The docker-compose change alone redirects the build, but the application logic may still need configuration updates to actually use the real classifiers by default.

---

160-162: Change verified as correct.

Both the ./processing_services/example and ./processing_services/minimal directories exist with proper configurations. The example service includes full ML dependencies (transformers, torch, torchvision, scipy) and cache volumes, while minimal contains only base dependencies. The CI configuration correctly maintains ./processing_services/minimal in docker-compose.ci.yml, preserving the separate pipelines for local development and CI testing as intended. The docker-compose.yml changes on lines 160-162 are accurate and properly redirect to the example service with real classifiers.

---

Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out.

❤️ Share

• X
• Mastodon
• Reddit
• LinkedIn

_{Comment @coderabbitai help to get the list of available commands and usage tips.}

[Copilot]

Pull Request Overview

This PR switches the default ML backend for local development from the minimal processing service to the example processing service, enabling real ML classifiers (transformers, PyTorch) instead of the basic mock implementations.

Key changes:

• Updates the ml_backend service in docker-compose.yml to use the example processing service with real ML models
• The CI environment continues to use the minimal service for faster testing

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[Copilot]

The example service uses ML models (transformers, PyTorch) that download models on first use. Consider adding cache volumes to avoid re-downloading models on container restarts:

volumes:
  - ./processing_services/example/:/app
  - ./processing_services/example/huggingface_cache:/root/.cache/huggingface
  - ./processing_services/example/pytorch_cache:/root/.cache/torch

This is similar to the configuration in processing_services/example/docker-compose.yml and would improve startup performance after the initial model download.