Update app.py

app.py

@@ -3,10 +3,8 @@ import base64
 import time
 import io
 import gradio as gr
-# Replace gradio_client with fastmcp Client and transport
 from fastmcp import Client
 from fastmcp.client import StreamableHttpTransport 
-# Import asyncio to manage async calls within the stream function
 import asyncio
 from dotenv import load_dotenv
 
@@ -17,11 +15,13 @@ ROBOT_ID = os.environ.get("ROBOT_ID", "unknown")
 HF_TOKEN = os.environ.get("HF_CV_ROBOT_TOKEN")
 if not HF_TOKEN:
     print("Warning: HF_TOKEN not found. API calls may fail.")
+    # Set a placeholder string to avoid the 'None is not of type string' error
+    # The API call will fail later due to auth, but validation will pass.
+    HF_TOKEN = "missing_token_placeholder" 
 
 # The MCP URL of your remote server
 MCP_SERVER_URL = "https://oppaai-robot-mcp-server.hf.space/gradio_api/mcp/"
 SERVER_NAME = "Robot_MCP_Server"
-# The exact tool name that matches the server function:
 TOOL_NAME = "Robot_MCP_Server_robot_watch" 
 
 
@@ -30,18 +30,21 @@ HTTP_TRANSPORT = StreamableHttpTransport(url=MCP_SERVER_URL)
 MCP_CLIENT = Client(transport=HTTP_TRANSPORT, name=SERVER_NAME)
 
 
-# This function needs to be an async function because client.call_tool is async
 async def process_webcam_stream_async(image):
     """Send webcam image to HF MCP Server using MCP protocol and get result"""
     if image is None:
         return "", "", "", ""
 
+    # Check if a valid token is available before proceeding
+    if HF_TOKEN == "missing_token_placeholder":
+        return "Error: HF_TOKEN not set locally.", "", "", ""
+
     # Convert Image to base64
     buffered = io.BytesIO()
     image.save(buffered, format="JPEG")
     b64_img = base64.b64encode(buffered.getvalue()).decode("utf-8")
 
-    # Prepare payload using the keys the server expects (from the working client)
+    # Prepare payload
     payload = {
         "hf_token_input": HF_TOKEN,
         "robot_id_input": ROBOT_ID,
@@ -49,7 +52,6 @@ async def process_webcam_stream_async(image):
     }
 
     try:
-        # Use the global client instance to call the tool asynchronously
         async with MCP_CLIENT:
             response = await MCP_CLIENT.call_tool(TOOL_NAME, payload)
             
@@ -57,7 +59,6 @@ async def process_webcam_stream_async(image):
                 error_text = response.content.text if response.content else "Unknown error"
                 raise Exception(f"MCP Tool Error: {error_text}")
 
-            # Parse the JSON string response from the server's output
             import json
             response_dict = json.loads(response.content.text)
             
@@ -84,7 +85,7 @@ async def process_webcam_stream_async(image):
 
 with gr.Blocks() as demo:
     gr.Markdown("## 🎥 Robot Vision Webcam Stream (using MCP Client)")
-
+    # ... (Gradio UI layout remains the same) ...
     with gr.Row():
         webcam_input = gr.Image(
             label="Captured from Web-Cam",
@@ -97,9 +98,8 @@ with gr.Blocks() as demo:
             objects_out = gr.Textbox(label="Objects")
             environment_out = gr.Textbox(label="Environment")
 
-    # Gradio handles the local streaming loop and automatically wraps async functions
     webcam_input.stream(
-        process_webcam_stream_async, # Use the async function here
+        process_webcam_stream_async,
         inputs=[webcam_input],
         outputs=[description_out, human_out, objects_out, environment_out],
         stream_every=0.5