Update app.py

app.py

@@ -1,18 +1,42 @@
 import gradio as gr
+import base64
+from PIL import Image
+import io
+import json
 
-# The function now accepts a simple string (plain text)
-def process_text(input_text):
-    response = {
-        "received": True,
-        "message": f"Received: {input_text}",
-        "status": "Success"
-    }
-    return response
+# 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_text,
-    inputs=gr.Textbox(label="Input Text"), # Changed input type
-    outputs=gr.JSON(label="Reply"),
+    fn=process,
+    inputs=gr.JSON(label="Input JSON from Jetson"),
+    outputs=gr.JSON(label="Reply to Jetson"),
 )
 
-demo.launch()
+# Launch with mcp_server enabled
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)