upload_tracker.py

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.

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

The author uses upload_tracker.py as a plain-text ledger, with pending and done markers to prevent duplicate uploads, making duplicate submissions structurally impossible.

`upload_tracker.py` is a simple tracking tool built purely on the standard library, requires no installation, and avoids duplicate uploads by locally recording the status (done/pending) of each asset.

The toolkit includes four standalone scripts: make_metadata.py, upload_tracker.py, md2pdf.py, and pack_product.py, drawn from roughly 19,000 lines of pipeline code and a project backed by over 300 pages of research, now offered separately as the "AI Sticker Factory Handbook."

upload_tracker.py maintains a persistent ledger through the three commands pending, done, and status, achieving idempotency for the upload pipeline.

The tracker relies on stable filenames; if files are renamed between steps, the ledger breaks.

upload_tracker.py uses only the Python standard library with no third-party dependencies, and can run on any machine accessible via SSH.

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.

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).

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.

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.