app.py

import gradio as gr
import base64
from PIL import Image
import io
import json

# Add a docstring for your function; this is used to generate the MCP tool description
def process(json_input: dict) -> dict:
    """
    Processes incoming JSON data containing a base64 encoded image and robot ID.

    Args:
        json_input (dict): A dictionary with keys "image_b64" and "robot_id".

    Returns:
        dict: A response dictionary indicating success, robot ID, and image size, or an error.
    """
    try:
        data = json_input  # Already a dict from Gradio API

        img_bytes = base64.b64decode(data["image_b64"])
        img = Image.open(io.BytesIO(img_bytes))

        response = {
            "received": True,
            "robot_id": data.get("robot_id", "unknown"),
            "image_size": img.size
        }
        return response

    except Exception as e:
        return {"error": str(e)}

demo = gr.Interface(
    fn=process,
    inputs=gr.JSON(label="Input JSON from Jetson"),
    outputs=gr.JSON(label="Reply to Jetson"),
)

# Launch with mcp_server enabled
if __name__ == "__main__":
    demo.launch(mcp_server=True)