Structuring a reproducible notebook for reviewing AI-generated image drafts?

General
1

I’ve been generating image drafts using an external browser tool (Muse Image) for a poster layout project, and now I’m trying to build a Jupyter notebook that lets my team review the outputs in a structured, reproducible way. The tool itself isn’t integrated with Jupyter at all - I just download the finished images locally and want the notebook to handle organization and review.

What I’m trying to solve

Right now I have a folder of PNGs with inconsistent naming, and a spreadsheet of prompts that doesn’t line up cleanly with the files. I’d like the notebook to:

  • Read a CSV or JSON manifest linking each local image path to its prompt text, reference images used, and generation parameters (aspect ratio, iteration number, etc.)
  • Display thumbnails inline with prompt metadata for quick scanning
  • Let reviewers add labels (approved/revise/reject) and free-text comments, ideally through notebook widgets or a simple form cell
  • Persist those labels back to the manifest so re-running the notebook doesn’t lose prior review data

Parameterization and reporting

Has anyone parameterized a notebook (via papermill or similar) to swap in different experiment batches without editing cells directly? I also want a clean way to export the final reviewed notebook to HTML for stakeholders who don’t use Jupyter, including thumbnails and comments, but excluding any raw code cells.

I’m not sharing a finished project - just trying to figure out a sane folder structure, manifest schema, and widget setup before I write more one-off code. Any patterns, extension recommendations, or example repos for this kind of visual experiment review workflow would be genuinely helpful.

2

Yes, I’ve used papermill, nbformat, and Jupytext to go from a templated Python to a notebook. Lately, mostly tended towards the latter two as I find papermill seems to introduce unnecessary cruft.
Example:

  • Go here and launch a session with ‘launch binder’. Then in the session that comes up, pick the second notebook listed under ‘Available Notebooks’ and look at that and the underlying code. I wrapped it in snakemake; of course, you don’t have to do that.

You can do this a couple of ways. The simplest is jupyter nbconvert --to html --no-input pointed at a notebook file. However, I found better was to build a script that made a nbconvert preprocessor that respects the source_hidden=true metadata in a cell so I could collapse all cells and have them come out absent in the HTML or I could just collapse selected ones and still have some code cells present in the output HTML, if I wanted. As part of this script, I made custom CSS and JavaScript to style the collapsed cells in the resulting HTML. I made a repo with a my binder-launchable session demonstrating this pipeline in a generic way here. Basically, it is a very customized version of the advice in the last paragraph here. It takes a demonstration notebook that has both collapsed and uncollapsed code cells and makes a report out of it in HTML format. The HTML will respect the collapsed and uncollapsed cells. The idea being that HTML can then be printed from your favorite browser.

3

The papermill / Jupytext + nbconvert --no-input path fomightez sketched is the right reporting half. For the review half, keep the notebook dumb and put state on disk.

Folder layout that has worked for batch image review:

experiments/<batch_id>/
images/ # originals, never renamed in place
manifest.csv # source of truth
thumbs/ # optional, generated

manifest.csv columns: id, relpath, prompt, refs, aspect, iteration, status, comment, reviewer, reviewed_at

On first run, build the CSV from the folder (hash or mtime + original filename as id) and left-join the prompt spreadsheet on a stable key. Don’t try to recover alignment from “almost matching” PNG names.

Display with IPython.display.Image(filename=path, width=240) next to the prompt text. For widgets: Dropdown (approved / revise / reject) + Textarea + a Save button that writes only that row back to manifest.csv (pandas, or csv module). Re-run then reads the CSV first, so labels survive a kernel restart. Avoid storing review state only in widget traitlets.

Papermill: one parameters cell with BATCH_DIR = “experiments/2026-07-poster”. Same notebook, different batch folders.

When an approved still later leaves Jupyter — download, HTML export, Instagram, a print vendor — remember that displaying or nbconvert-ing the PNG does not rewrite C2PA / XMP the generator wrote into the bytes. Keep the archive master in images/, and check the exact file you will post. A notebook-friendly pre-upload stills cleaner is enough for that last-mile copy; it does not replace the manifest, widgets, or your disclosure rules.