CONTENT CREATION
Bulk-localize a product CSV into a multi-market upload file
Drop a product CSV in Dropbox to generate market-specific descriptions for every row via a Hugging Face model, then export a single enriched CSV back to Dropbox for catalog upload.
How it runs
The automated pipeline, trigger to output.
- TriggerProduct CSV dropped in DropboxDropbox
- ActionParse rows + read target markets
- ActionGenerate native copy per row per marketHugging Face
- LogicAssemble enriched multi-locale table
- OutputExport upload-ready CSV to DropboxDropbox
What it does
This workflow batch-localizes an entire product export in one pass. When you drop a product CSV into a watched Dropbox folder, the flow parses every row, and for each target market generates a native description using a hosted Hugging Face text model tuned for your tone. It writes the results into new locale columns on each row — preserving the original SKU, price, and identifiers — then exports a single enriched CSV back to a Dropbox output folder, formatted for your storefront or marketplace bulk-upload spec. A run summary records how many rows and locales were produced.
When to use it
Use it when localization happens in bulk via spreadsheets rather than per-SKU events — migrations, new-market launches, or seasonal catalog refreshes where someone exports hundreds of rows at once. It fits ops teams that prefer file-in / file-out over API plumbing, and who want a self-hosted or open model on Hugging Face for cost or data-control reasons.
How it works
- 1A Dropbox trigger fires when a CSV lands in the input folder.
- 2The flow parses the CSV into rows and reads the configured target markets.
- 3For each row and market, a Hugging Face model generates a native description.
- 4A logic step assembles the locale columns into one enriched table.
- 5The final CSV is written to the Dropbox output folder for bulk upload.
Set it up
What you configure once, before turning it on.
- 1Connect DropboxFiles and folders.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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