Base custom component powered by svelteflow.
Create custom workflows.
You can customise the nodes as well as the design of the workflow.
Here is a sample:
\"\"\")
# Main workflow builder section
with gr.Column(elem_classes=["workflow-section"]):
workflow_builder = WorkflowBuilder(
label="🎨 Visual Workflow Designer",
info="Drag components from the sidebar → Connect nodes by dragging from output (right) to input (left) → Click nodes to edit properties"
)
# Export section below the workflow
gr.Markdown("## 💾 Export Workflow")
with gr.Row():
with gr.Column():
export_output = gr.Code(
language="json",
label="Workflow Configuration",
lines=10
)
# Action button
with gr.Row(elem_classes=["button-row"]):
export_btn = gr.Button("💾 Export JSON", variant="primary", size="lg")
# Instructions
with gr.Accordion("📖 How to Use", open=False):
gr.Markdown(\"\"\"
### 🚀 Getting Started
1. **Add Components**: Drag items from the left sidebar onto the canvas
2. **Connect Nodes**: Drag from the blue circle on the right of a node to the left circle of another
3. **Edit Properties**: Click any node to see its editable properties on the right panel
4. **Organize**: Drag nodes around to create a clean workflow layout
5. **Delete**: Use the ✕ button on nodes or click the red circle on connections
### 🎯 Component Types
- **📥 Inputs**: Text fields, file uploads, number inputs
- **⚙️ Processing**: Language models, text processors, conditionals
- **📤 Outputs**: Text displays, file exports, charts
- **🔧 Tools**: API calls, data transformers, validators
### 💡 Pro Tips
- **Collapsible Panels**: Use the arrow buttons to hide/show sidebars
- **Live Updates**: Properties update in real-time as you edit
- **Export Options**: Get JSON config for your workflow
\"\"\")
# Event handlers
export_btn.click(
fn=export_workflow,
inputs=[workflow_builder],
outputs=[export_output]
)
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
show_error=True
)
```
""", elem_classes=["md-custom"], header_links=True)
gr.Markdown("""
## `WorkflowBuilder`
### Initialization
""", elem_classes=["md-custom"], header_links=True)
gr.ParamViewer(value=_docs["WorkflowBuilder"]["members"]["__init__"], linkify=[])
gr.Markdown("### Events")
gr.ParamViewer(value=_docs["WorkflowBuilder"]["events"], linkify=['Event'])
gr.Markdown("""
### User function
The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).
- When used as an Input, the component only impacts the input signature of the user function.
- When used as an output, the component only impacts the return signature of the user function.
The code snippet below is accurate in cases where the component is used as both an input and an output.
```python
def predict(
value: typing.Dict[str, typing.Any][str, typing.Any]
) -> typing.Dict[str, typing.Any][str, typing.Any]:
return value
```
""", elem_classes=["md-custom", "WorkflowBuilder-user-fn"], header_links=True)
demo.load(None, js=r"""function() {
const refs = {};
const user_fn_refs = {
WorkflowBuilder: [], };
requestAnimationFrame(() => {
Object.entries(user_fn_refs).forEach(([key, refs]) => {
if (refs.length > 0) {
const el = document.querySelector(`.${key}-user-fn`);
if (!el) return;
refs.forEach(ref => {
el.innerHTML = el.innerHTML.replace(
new RegExp("\\b"+ref+"\\b", "g"),
`