Integrate Firebase and Hugging Face to automate workflows with scalable backend
Connect Firebase and Hugging Face nodes to in your workflow. Integrate with any tool or database and ship powerful backend logic and APIs instantly - No code required!
Getting Started
How To Connect Firebase and Hugging Face
Popular Templates With Firebase and Hugging Face
Explore our popular Google Sheets & templates below. Click. Remix. Ship!
Node stack
Supported Triggers & Actions
Firebase NODES
Firebase Auth Request
Authenticates Firebase user bearer token and add user info to context. [Full Documentation](https://docs.buildship.com/trigger-nodes/firebase-auth)
Rest API File Upload (Auth)
Create an API / HTTP endpoint to accept a file with multipart/form-data content type only from Firebase Authenticated users. Authentication is done by checking the bearer token in request header. NOTE: The trigger accepts only one file per request. [Full Documentation](https://docs.buildship.com/trigger-nodes/firebase-file-upload)
Create Firebase User
Create a new Firebase Auth User with email, full name, and password
Get User Data With Auth Token
Fetch user data from Firebase based on the auth token
Get User Data With ID
Fetch user data from Firebase Auth based given their user ID
Mark Firebase User as Verified
Mark a Firebase Auth User as verified using user id
User Exists Check
Checks if a firebase user already exists for the given email
Hugging Face NODES
Caption Image
Generate caption for the image using Hugging Face's [Salesforce/blip-image-captioning-large](https://huggingface.co/Salesforce/blip-image-captioning-large) model for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone).
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Image Classification
Get classification labels for your image using Hugging Face's [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) model which is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
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Text Summarization
Summarize long text using Hugging Face's [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) model which is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
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Text-To-Image
Generate image from text, using Hugging Face's [openskyml/dalle-3-xl](https://huggingface.co/openskyml/dalle-3-xl) test model very similar to Dall•E 3.
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Text-To-Music
Generate music from text using Hugging Face's [facebook/musicgen-small](https://huggingface.co/facebook/musicgen-small) model capable of generating high-quality music samples conditioned on text descriptions or audio prompts.
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Blog posts & Tutorials
Recommended
Reads
Below are recommneded blogs that will help in your journey
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