Update app.py

app.py

@@ -4,43 +4,60 @@ import time
 import io
 import gradio as gr
 from fastmcp import Client
-from fastmcp.client import StreamableHttpTransport 
+from fastmcp.client import StreamableHttpTransport
 import asyncio
 from dotenv import load_dotenv
-import ast # Import the Abstract Syntax Tree module
+import ast  # For safely evaluating Python literals returned from server
 
-# Load environment variables (ensure .env is set up locally)
+# -------------------------------
+# Load environment variables
+# -------------------------------
 load_dotenv()
 
-ROBOT_ID = "Robot_MCP_Client"
+ROBOT_ID = "Robot_MCP_Client"  # Local client identifier
 HF_TOKEN = os.environ.get("HF_TOKEN")
 if not HF_TOKEN:
     print("Warning: HF_TOKEN not found. API calls may fail.")
-    HF_TOKEN = "missing_token_placeholder" 
+    HF_TOKEN = "missing_token_placeholder"  # Placeholder to avoid crash
 
-# The MCP URL of your remote server
+# MCP server info
 MCP_SERVER_URL = "https://oppaai-robot-mcp-server.hf.space/gradio_api/mcp/"
 SERVER_NAME = "Robot_MCP_Server"
-TOOL_NAME = "Robot_MCP_Server_robot_watch" 
+TOOL_NAME = "Robot_MCP_Server_robot_watch"
 
-
-# Initialize the MCP client globally
+# -------------------------------
+# Initialize MCP client globally
+# -------------------------------
 HTTP_TRANSPORT = StreamableHttpTransport(url=MCP_SERVER_URL)
 MCP_CLIENT = Client(transport=HTTP_TRANSPORT, name=SERVER_NAME)
 
 
 async def process_webcam_stream_async(image):
-    """Send webcam image to HF MCP Server using MCP protocol and get result"""
+    """
+    Send webcam image to MCP server and process the response.
+
+    Args:
+        image (PIL.Image or None): Image captured from webcam or uploaded.
+
+    Returns:
+        tuple: (description, human, objects_str, environment)
+            description (str): Description of scene.
+            human (str): Human-related information.
+            objects_str (str): Comma-separated list of objects.
+            environment (str): Environment description.
+    """
     if image is None:
         return "", "", "", ""
 
     if HF_TOKEN == "missing_token_placeholder":
         return "Error: HF_TOKEN not set locally.", "", "", ""
 
+    # Convert image to Base64 string
     buffered = io.BytesIO()
     image.save(buffered, format="JPEG")
     b64_img = base64.b64encode(buffered.getvalue()).decode("utf-8")
 
+    # Prepare payload according to server's expected fields
     payload = {
         "hf_token_input": HF_TOKEN,
         "robot_id_input": ROBOT_ID,
@@ -48,62 +65,67 @@ async def process_webcam_stream_async(image):
     }
 
     try:
+        # Use async context to call MCP server tool
         async with MCP_CLIENT:
             response = await MCP_CLIENT.call_tool(TOOL_NAME, payload)
-            
+
             if response.is_error:
+                # Extract error message
                 error_text = response.content[0].text if response.content and isinstance(response.content, list) else "Unknown error"
                 raise Exception(f"MCP Tool Error: {error_text}")
 
-            # Use ast.literal_eval because your server returns Python-formatted strings (single quotes)
+            # Server may return Python-style string (single quotes)
             raw_text = response.content[0].text
             response_dict = ast.literal_eval(raw_text)
-            
-            # --- 👇 EXTRACTING EACH FIELD CORRECTLY 👇 ---
+
+            # -------------------------------
+            # Extract fields from response
+            # -------------------------------
             vlm_result = response_dict.get("result", {})
-            
+
             description_out = vlm_result.get("description", "")
             human_out = vlm_result.get("human", "")
             objects_list = vlm_result.get("objects", [])
             environment_out = vlm_result.get("environment", "")
 
-            # Format the objects list into a single string for display
+            # Convert objects list to a comma-separated string for display
             objects_str = ", ".join(objects_list) if isinstance(objects_list, list) else str(objects_list)
 
-            return (
-                description_out,
-                human_out,
-                objects_str,
-                environment_out
-            )
-            
+            return description_out, human_out, objects_str, environment_out
+
     except Exception as e:
-        print(f"Error calling remote MCP API: {e}") 
+        print(f"Error calling remote MCP API: {e}")
         import traceback
-        traceback.print_exc() # Helps debug other errors
+        traceback.print_exc()
         return f"Error: {e}", "", "", ""
 
+
+# -------------------------------
+# Gradio UI
+# -------------------------------
 with gr.Blocks() as demo:
     gr.Markdown("## 🎥 Robot Vision Webcam Stream (using MCP Client)")
     gr.Markdown("""   
     ### 🔑 Hugging Face Token Required
     To use this application, you must set a valid **Hugging Face API Token** in your local environment variables: `HF_TOKEN`.
-    **A write token is required** to upload images to the public dataset associated with this space. The resource usage for VLM inference will be tracked against *your* account.
+    **A write token is required** to upload images to the public dataset associated with this space.
+    Resource usage for VLM inference will be tracked against your account.
     """)
     with gr.Row():
+        # Webcam / upload image input
         webcam_input = gr.Image(
             label="Captured from Web-Cam",
             sources=["upload", "webcam"],
             type="pil"
         )
         with gr.Column():
-            # --- 👇 INCREASED 'lines' PARAMETER HERE 👇 ---
-            description_out = gr.Textbox(label="Description", lines=5) # Made larger
-            human_out = gr.Textbox(label="Human", lines=3)             # Made larger
-            objects_out = gr.Textbox(label="Objects", lines=2)         # Made larger
-            environment_out = gr.Textbox(label="Environment", lines=3) # Made larger
-            # --------------------------------------------------
+            # Output fields for MCP response
+            description_out = gr.Textbox(label="Description", lines=5)
+            human_out = gr.Textbox(label="Human", lines=3)
+            objects_out = gr.Textbox(label="Objects", lines=2)
+            environment_out = gr.Textbox(label="Environment", lines=3)
 
+    # Stream webcam input to server every 0.5 seconds
     webcam_input.stream(
         process_webcam_stream_async,
         inputs=[webcam_input],