Integrating gradio demo

extra-requirements.txt

@@ -36,6 +36,7 @@ docarray>=0.16.4:           core
 jina-hubble-sdk>=0.30.4:    core
 jcloud>=0.0.35:             core
 opentelemetry-api>=1.12.0:  core
+gradio:                     core
 opentelemetry-instrumentation-grpc>=0.35b0:  core 
 uvloop:                     perf,standard,devel
 prometheus_client>=0.12.0:  perf,standard,devel

jina/serve/runtimes/worker/http_fastapi_app.py

@@ -1,5 +1,7 @@
 import inspect
 from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union
+import gradio as gr
+from pydantic import BaseModel
 
 from jina import DocumentArray, Document
 from jina._docarray import docarray_v2
@@ -189,6 +191,15 @@ async def streaming_get(request: Request):
                     output_doc_list_model=output_doc_model,
                 )
 
+    def _executor_caller(*args):
+        return args
+
+    print(input_doc_model, output_doc_model)
+    inputs = generate_gradio_interface(input_doc_model)
+    outputs = generate_gradio_interface(output_doc_model)
+    interface = gr.Interface(_executor_caller, inputs, outputs)
+    app = gr.mount_gradio_app(app, interface, path="/_jina_gradio_demo")
+
     from jina.serve.runtimes.gateway.health_model import JinaHealthModel
 
     @app.get(
@@ -222,3 +233,62 @@ def _doc_to_event(doc):
         return {'event': 'update', 'data': doc.to_dict()}
     else:
         return {'event': 'update', 'data': doc.dict()}
+
+def generate_gradio_interface(model: BaseModel):
+    """helper function to convert pydantic model to gradio interface"""
+    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