Feature Summary
Add a Hugging Face operator to Texera so users can run pretrained models from the Hugging Face Hub directly inside workflows. This feature makes model inference a first-class workflow step, so users can apply text, image, video and audio models without writing code.
The operator would let users:
- Pick a Hugging Face task such as text generation, summarization, image classification, ASR, or VQA
- Browse/search available models for that task
- Provide the right input column or upload media when the task requires it via property panel
- Configure model-specific parameters like prompt, temperature, token limits, and output column name
- Produce a workflow output that can be chained into downstream operators
Proposed Solution or Design
The operator should work as a guided, task-aware inference component rather than a generic API wrapper. The user picks a task first, then the UI only shows the fields that matter for that task.
A simple flow would look like this:
This is a screenshot of a selected text-generation task where the user asks a question via the input operator and the selected Hugging Face model based on the models list produces the answer as workflow output.
This is a screenshot of a selected image-classification task where the user provides an image in the property panel and the chosen model outputs JSON predictions (predicted breeds with confidence).
Here are some examples of task-based flows:
- Text generation: select a prompt column, choose a model, set max tokens and temperature, get generated text in a result column
- Summarization: select a text column, choose a summarization model, emit the summary
- Image classification: upload or reference an image, choose an image model, output labels or captions
A task-aware configuration layout could be:
- Task
- Model
- Input source
- Task-specific options
- Result column
The design should include a few key behaviors:
- Model discovery and search from the Hugging Face Hub
- Backend proxying for Hugging Face API calls so the UI does not talk to Hugging Face directly
- API token support, with token fallback from environment or deployment config
- Caching of model and task metadata to reduce repeated remote calls
- Task-based validation so invalid combinations are rejected early, for example requiring an image upload for image-only tasks
Affected Area
Workflow Engine (Amber), Workflow UI, Storage / Metadata, Deployment / Infrastructure
Feature Summary
Add a Hugging Face operator to Texera so users can run pretrained models from the Hugging Face Hub directly inside workflows. This feature makes model inference a first-class workflow step, so users can apply text, image, video and audio models without writing code.
The operator would let users:
Proposed Solution or Design
The operator should work as a guided, task-aware inference component rather than a generic API wrapper. The user picks a task first, then the UI only shows the fields that matter for that task.
A simple flow would look like this:
Here are some examples of task-based flows:
A task-aware configuration layout could be:
The design should include a few key behaviors:
Affected Area
Workflow Engine (Amber), Workflow UI, Storage / Metadata, Deployment / Infrastructure