CONTENT CREATION
Route low-confidence Hugging Face captions to Slack for human review
Generates captions for media records with Hugging Face and auto-applies high-confidence results.
How it runs
The automated pipeline, trigger to output.
- TriggerScheduled batch of uncaptioned recordsAirtable
- ActionGenerate caption + confidence via Hugging FaceHugging Face
- LogicBranch on confidence threshold
- ActionPost low-confidence drafts to Slack for reviewSlack
- OutputWrite approved caption to AirtableAirtable
What it does
This is a human-in-the-loop captioning gate. Each image gets a Hugging Face caption plus a confidence signal. Confident captions are written straight to Airtable. Anything below the bar is posted to a Slack channel with the image preview and the draft caption, so a reviewer can confirm or correct it before it becomes canonical.
When to use it
You want the speed of automated captioning but can't ship customer-facing alt-text blindly. This routes only the uncertain cases to people, keeping reviewer load low while protecting quality.
How it works
- 1A schedule pulls a batch of uncaptioned Airtable image records.
- 2Send each image to the Hugging Face model and capture the caption and confidence.
- 3Branch on confidence: high-confidence captions write directly to Airtable and mark the record done.
- 4Low-confidence captions post to Slack with the image, the draft text, and approve/edit actions.
- 5The reviewer's confirmed or edited caption is written back to the Airtable record.
Set it up
What you configure once, before turning it on.
- 1Connect AirtableBases, tables, views, automations.
- 2Connect Hugging FaceModels, datasets, spaces — the open-source hub.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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