Integrate WhatsApp and Hugging Face to automate workflows with scalable backend
Connect WhatsApp 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 WhatsApp and Hugging Face
Popular Templates With WhatsApp and Hugging Face
Explore our popular Google Sheets & templates below. Click. Remix. Ship!
Node stack
Supported Triggers & Actions
WhatsApp NODES
WhatsApp Bot
This trigger verifies and parses the messaging event payload coming from the WhatsApp Webhook. [Full Documentation](https://docs.buildship.com/trigger-nodes/whatsapp-bot)
Attachment Auto-Responder
Automatically responds with attachments to WhatsApp messages received through a webhook.
Auto-Responder
Automatically responds to WhatsApp messages received through a webhook.
Send WhatsApp Message
Send a WhatsApp message via Facebook's Graph API
Token Verification
WhatsApp Business Account token verification.
Webhook Message Parser
Extracts information from the text message notification such as the from number, message body, etc
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
Support
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