---
title: 'Generate image captions using Hugging Face'
description: 'Use the Hugging Face Inference API to make calls to 100,000+ Machine Learning models from Supabase Edge Functions.'
subtitle: 'Use the Hugging Face Inference API to make calls to 100,000+ Machine Learning models from Supabase Edge Functions.'
video: 'https://www.youtube.com/v/OgnYxRkxEUw'
tocVideo: 'OgnYxRkxEUw'
---

We can combine Hugging Face with [Supabase Storage](/storage) and [Database Webhooks](/docs/guides/database/webhooks) to automatically caption for any image we upload to a storage bucket.

## About Hugging Face

[Hugging Face](https://huggingface.co/) is the collaboration platform for the machine learning community.

[Huggingface.js](https://huggingface.co/docs/huggingface.js/index) provides a convenient way to make calls to 100,000+ Machine Learning models, making it easy to incorporate AI functionality into your [Supabase Edge Functions](/edge-functions).

## Setup

- Open your Supabase project dashboard or [create a new project](/dashboard/projects).
- [Create a new bucket](/dashboard/project/_/storage/buckets) called `images`.
- Generate TypeScript types from remote Database.
- Create a new Database table called `image_caption`.
  - Create `id` column of type `uuid` which references `storage.objects.id`.
  - Create a `caption` column of type `text`.
- Regenerate TypeScript types to include new `image_caption` table.
- Deploy the function to Supabase: `supabase functions deploy huggingface-image-captioning`.
- Create the Database Webhook in the [Supabase Dashboard](/dashboard/project/_/database/hooks) to trigger the `huggingface-image-captioning` function anytime a record is added to the `storage.objects` table.

## Generate TypeScript types

To generate the types.ts file for the storage and public schemas, run the following command in the terminal:

```bash
supabase gen types typescript --project-id=your-project-ref --schema=storage,public > supabase/functions/huggingface-image-captioning/types.ts
```

## Code

Find the complete code on [GitHub](https://github.com/supabase/supabase/tree/master/examples/edge-functions/supabase/functions/huggingface-image-captioning).

```ts
import { serve } from 'https://deno.land/std@0.168.0/http/server.ts'
import { HfInference } from 'https://esm.sh/@huggingface/inference@2.3.2'
import { createClient } from 'npm:@supabase/supabase-js@2'
import { Database } from './types.ts'

console.log('Hello from `huggingface-image-captioning` function!')

const hf = new HfInference(Deno.env.get('HUGGINGFACE_ACCESS_TOKEN'))

type SoRecord = Database['storage']['Tables']['objects']['Row']
interface WebhookPayload {
  type: 'INSERT' | 'UPDATE' | 'DELETE'
  table: string
  record: SoRecord
  schema: 'public'
  old_record: null | SoRecord
}

serve(async (req) => {
  const payload: WebhookPayload = await req.json()
  const soRecord = payload.record
  const supabaseAdminClient = createClient<Database>(
    // Supabase API URL - env var exported by default when deployed.
    Deno.env.get('SUPABASE_URL') ?? '',
    // Supabase API SERVICE ROLE KEY - env var exported by default when deployed.
    Deno.env.get('SUPABASE_SERVICE_ROLE_KEY') ?? ''
  )

  // Construct image url from storage
  const { data, error } = await supabaseAdminClient.storage
    .from(soRecord.bucket_id!)
    .createSignedUrl(soRecord.path_tokens!.join('/'), 60)
  if (error) throw error
  const { signedUrl } = data

  // Run image captioning with Huggingface
  const imgDesc = await hf.imageToText({
    data: await (await fetch(signedUrl)).blob(),
    model: 'nlpconnect/vit-gpt2-image-captioning',
  })

  // Store image caption in Database table
  await supabaseAdminClient
    .from('image_caption')
    .insert({ id: soRecord.id!, caption: imgDesc.generated_text })
    .throwOnError()

  return new Response('ok')
})
```
