«LLM-Assisted Interpretation of Graphene Image Analysis in Serverless Workflows» Code and data artefact to WoAIS/DEBS 2026 paper Preparation ----------- - run local Ollama with the model qwen2.5:0.5b-instruct - place microscopy images into the directory data/cv_annotations/ of the following format: PNG image data, 907 x 907, 8-bit grayscale, non-interlaced Running ------- - follow the instructions from th ecompose file, with slight modification as follows: docker compose -f docker-compose.eval.yml build - to build the container image separately, use: docker build -f Dockerfile.eval -t tdf --progress=plain . The build process might take around 10 minutes with the optimisation of bypassing pip by installing system packages. It would otherwise take around 25 minutes due to the behaviour of PyPI. - run the experiments, e.g.: docker compose -f docker-compose.eval.yml run --rm eval-handler python scripts/handler.py --strategy A The output should be something like: ... === DESCRIPTOR DATASET SUMMARY === Total images: 3 ... ✓ Results saved to /app/results/handler_output.json (which is mapped to results/handler_output.json on the host)