A basic working example demonstrating how to add a Gradio gateway to Jina `Flow`.

flow.py

import gradio as gr
from pydantic import BaseModel
from jina import Flow, Executor, requests
from docarray import DocList, BaseDoc
from fastapi import FastAPI
from jina.serve.runtimes.gateway.http.fastapi import FastAPIBaseGateway


## helper function to convert pydantic model -> gradio interface
def generate_gradio_interface(model: BaseModel):
    inputs = []

    # Process each attribute in the model
    for attr, field in model.__annotations__.items():
        input_type = field.__name__
        input_label = attr.replace("_", " ").capitalize()

        # Generate appropriate input component based on the field type
        if input_type == "str":
            # Additional options for string type
            field_info = model.__annotations__[attr]
            default = field_info.default if hasattr(field_info, "default") else None
            choices = field_info.choices if hasattr(field_info, "choices") else None

            input_component = gr.Textbox(
                label=input_label,
            )
        elif input_type == "int":
            # Additional options for integer type
            field_info = model.__annotations__[attr]
            ge = field_info.ge if hasattr(field_info, "ge") else None
            le = field_info.le if hasattr(field_info, "le") else None

            input_component = gr.Number(
                label=input_label,
                minimum=ge,
                maximum=le,
                step=1,
            )
        elif input_type == "float":
            # Additional options for float type
            field_info = model.__annotations__[attr]
            ge = field_info.ge if hasattr(field_info, "ge") else None
            le = field_info.le if hasattr(field_info, "le") else None

            input_component = gr.Number(
                label=input_label,
                minimum=ge,
                maximum=le,
                step=0.01,
            )
        elif input_type == "bool":
            # Additional options for boolean type
            field_info = model.__annotations__[attr]
            input_component = gr.Checkbox(label=input_label)
        elif input_type == "File":
            input_component = gr.File(label=input_label)
        elif input_type == "Path":
            input_component = gr.Textbox(label=input_label)
        else:
            # For unsupported types, skip the attribute
            continue

        # Add the input component to the inputs list
        inputs.append(input_component)
    return inputs
  
 
## Basic executor
class Input(BaseDoc):
    text: str

class Output(BaseDoc):
    text: str

class Capitalize(Executor):
    @requests
    def func(self, docs: DocList[Input], **kwargs) -> DocList[Output]:
        for doc in docs:
            doc.text = doc.text.title()
            
            
## Gradio Gateway
class GradioGateway(FastAPIBaseGateway):
    async def post(self, text: str):
        docs = await self.executor["capitalize_executor"].post(
            on="/",
            inputs=DocList[Input]([Input(text=text)]),
            parameters={"k": "v"},
            return_type=DocList[Output],
        )
        return docs

    @property
    def app(self):
        app = FastAPI()

        async def process(text: str):
            docs = await self.post(text)
            return docs.text[0]

        inputs = generate_gradio_interface(Input)
        outputs = generate_gradio_interface(Output)
        interface = gr.Interface(process, inputs=inputs, outputs=outputs)
        app = gr.mount_gradio_app(app, interface, path="/demo")
        return app


with Flow().config_gateway(uses=GradioGateway, protocol="http", port=59771).add(
    uses=Capitalize, name="capitalize_executor"
) as flow:
    flow.block()