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supabase/apps/docs/content/guides/ai/examples/huggingface-image-captioning.mdx
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---
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 { 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
}
Deno.serve(async (req) => {
const payload: WebhookPayload = await req.json()
const soRecord = payload.record
const SUPABASE_SECRET_KEYS = JSON.parse(Deno.env.get('SUPABASE_SECRET_KEYS')!)
const supabaseAdminClient = createClient<Database>(
// Supabase API URL - env var exported by default when deployed.
Deno.env.get('SUPABASE_URL') ?? '',
// Supabase API SECRET KEY - env var exported by default when deployed.
Deno.env.get(SUPABASE_SECRET_KEYS['default']) ?? ''
)
// 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')
})
```