upload_tracker.py

upload_tracker.py is a ledger tool of about forty lines of code that uses only the Python standard library

Resubmitting already-uploaded material makes reviewers lose trust in the entire directory, so the author uses a plain-text ledger (upload_tracker.py) to track published files and automatically skip completed items on reruns.

The author wrote about 200 lines of Python standard-library scripts (make_metadata.py and upload_tracker.py) to generate the CSVs required by the three platforms and track upload status.

The author packaged the scripts as the ai sticker factory playbook, containing four scripts: make_metadata.py, upload_tracker.py, md2pdf.py, and pack_product.py, of which the latter two depend on Pillow and reportlab.

upload_tracker.py uses only the standard library and prevents duplicate submissions by recording submitted items.

Duplicate uploads are the fastest way to get a stock account flagged and banned, and the author once sent 30 duplicates at once by re-running a partially failed script.

The author uses a text-file ledger script (upload_tracker.py) that relies only on the Python standard library to track uploaded files and avoid duplicate uploads.

Using a local ledger script (such as upload_tracker.py) to record submitted entries makes reruns idempotent and avoids the risk of duplicate submissions.

Of the four scripts the author ultimately used—make_metadata.py, upload_tracker.py, md2pdf.py and pack_product.py—two depend only on the Python standard library, and the other two need only Pillow and reportlab.

Do not manually edit the metadata CSV; instead, write the header once in code; automate the structure rather than the judgment; track every submission.

Submitting the same batch twice is one of the fastest ways to get an account flagged on stock image platforms; using an upload ledger (upload_tracker.py) to record what has already been published and skipping completed items when rerunning can avoid duplicate submissions.

The toolkit includes `make_metadata.py` (handles three CSV formats), `upload_tracker.py` (upload ledger), `md2pdf.py` (document to PDF), and `pack_product.py` (packages the files together with a 1280×720 cover and a 600×600 thumbnail).

Using upload_tracker.py, the author implemented a three-command ledger based on the standard library (pending, done, status), with the rule being to call done at the moment a file is submitted, rather than recording it after the entire session ends.

upload_tracker.py uses a plain text file to record published assets, automatically skipping entries already marked as complete on reruns, which the author says has avoided at least two mistakes serious enough to reach the account-flagging level

make_metadata.py and upload_tracker.py use only the Python standard library and have no third-party dependencies.

upload_tracker.py maintains a plain-text ledger that records published content, marks done immediately after each successful upload, and can resume from the breakpoint when rerun after a task crash.

The upload_tracker.py script maintains a ledger of submitted assets; checking the ledger before a batch task to process only the missing parts avoids duplicate submissions to asset sites (account ban risk) and regenerating existing files (money-burning risk)

The upload tracker upload_tracker.py is a minimalist ledger using only the standard library, listing unsubmitted assets via pending items.txt, marking completion with the done command, and giving a global overview with the status command.

The author used make_metadata.py and upload_tracker.py to package 39 products, turning the metadata step from half a day of spreadsheet work per batch into three commands that finish the job.

The author used four scripts—make_metadata.py, upload_tracker.py, md2pdf.py, and pack_product.py—to package the 39 products and 856 MB of output delivered by this project on a 1 vCPU / 2 GB server.